llm-applications
3383 строки · 271.1 Кб
1{2"config": {3"experiment_name": "chunk-size-100",4"chunk_size": 100,5"chunk_overlap": 50,6"num_chunks": 5,7"embedding_model_name": "thenlper/gte-base",8"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1",9"temperature": 0.0,10"max_context_length": 32768,11"system_content": "Answer the query using the context provided. Be succinct.",12"assistant_content": "",13"docs_dir": "/efs/shared_storage/goku/docs.ray.io/en/master",14"experiments_dir": "/home/ray/ray-assistant/notebooks/../experiments",15"references_fp": "/home/ray/ray-assistant/notebooks/../experiments/references/gpt-4.json",16"num_samples": 17717},18"results": [19{20"question": "I\u2019m struggling a bit with Ray Data type conversions when I do map_batches. Any advice?",21"sources": [22"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",23"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",24"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",25"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-tasks",26"https://docs.ray.io/en/master/data/inspecting-data.html#inspecting-batches"27],28"document_ids": [2967572,3044524,3151182,3246339,335050234],35"answer": " The Ray Data type for batches is typically represented as dicts of NumPy ndarrays. If you're struggling with type conversions when using `map_batches`, I recommend checking out the Ray Data Working with PyTorch guide for more information. Additionally, Ray Data provides a `level` API that includes `map_batches` for transforming batches with tasks.\n\nTo change the data type of the batches, you may not need to do anything specific in the `map_batches` function, as Ray Data should automatically convert the data type based on how you process it in your function. However, if you need to explicitly convert types within the `map_batches` function, you can use Python's built-in type conversion functions or libraries like NumPy or PyTorch for more complex conversions.",36"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"37},38{39"question": "How does autoscaling work in a Ray Serve application?",40"sources": [41"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",42"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",43"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling",44"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",45"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a"46],47"document_ids": [4895564,4995589,5096227,5139737,5210129453],54"answer": " The Ray Serve Autoscaler is an application-level autoscaler that adjusts the number of instances in a Ray Serve deployment based on the current workload. It sits on top of the Ray Autoscaler and is designed to handle the statefulness of Ray applications. Each deployment in a Ray Serve application can independently autoscale to accommodate changing resource needs. For more information on the architecture underlying Ray Serve Autoscaling, you can refer to the Ray Serve Autoscaling resource.",55"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"56},57{58"question": "how do I get the address of a ray node",59"sources": [60"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/on-premises.html#start-worker-nodes",61"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",62"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",63"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",64"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats"65],66"document_ids": [6749777,6851307,692194,702236,715033072],73"answer": " To get the address of a Ray node, you can use the `ray list nodes` command to get the node ID and IP address. Alternatively, you can pass the `--address` flag explicitly to each Ray command, specifying the address of the head node. When starting Ray, you can also use the `--node-ip-address` flag to specify the IP address of the head node.",74"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"75},76{77"question": "Does Ray support NCCL?",78"sources": [79"https://docs.ray.io/en/master/ray-more-libs/ray-collective.html#single-gpu-and-multi-gpu-collective-primitives",80"https://docs.ray.io/en/master/cluster/vms/getting-started.html#setup",81"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#customizing-worker-process-loggers",82"https://docs.ray.io/en/master/ray-core/actors/out-of-band-communication.html#wrapping-library-processes",83"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3"84],85"document_ids": [86104174,8741835,883433,8925948,90322291],92"answer": " Yes, Ray supports NCCL based on the context provided. It mentions that Horovod-on-Ray uses NCCL for its collective communications, indicating that NCCL is a supported backend for Ray.",93"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"94},95{96"question": "Is Ray integrated with DeepSpeed?",97"sources": [98"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",99"https://docs.ray.io/en/master/train/api/api.html#pytorch-lightning",100"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayDeepSpeedStrategy.html#ray-train-lightning-raydeepspeedstrategy",101"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#define-your-training-function",102"https://docs.ray.io/en/master/ray-overview/examples.html@tags=training.tmp.html#ray-examples"103],104"document_ids": [10569869,10664116,10762025,10869868,1092995110],111"answer": " Yes, Ray is integrated with DeepSpeed through the RayDeepSpeedStrategy class, which is a subclass of DeepSpeedStrategy and ensures compatibility with Ray orchestration. This integration allows for cost-effective use of DeepSpeed, Ray, and HuggingFace together.",112"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"113},114{115"question": "what will happen if I use AsyncIO's await to wait for a Ray future like `await x.remote()`",116"sources": [117"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",118"https://docs.ray.io/en/master/ray-core/actors/async_api.html#objectrefs-as-asyncio-futures",119"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",120"https://docs.ray.io/en/master/serve/model_composition.html#servehandle-deep-dive",121"https://docs.ray.io/en/master/ray-core/api/doc/ray.wait.html#ray-wait"122],123"document_ids": [12426322,12526305,12626217,127102526,12862206129],130"answer": " If you use `asyncio`'s `await` keyword to wait for a Ray future, like `await x.remote()`, it will allow you to integrate Ray with `asyncio` and use asynchronous programming in a more natural way. Under the hood, it's equivalent to using `ray.wait([some_task.remote()])` with the synchronous API. However, it's important to note that using `await` will pause the execution of the current coroutine until the Ray task is completed, similar to how `ray.get(some_task.remote())` behaves in a synchronous context.\n\nIn summary, using `await` with a Ray future will pause the execution of the current coroutine until the Ray task is finished, providing a more seamless integration between `asyncio` and Ray.",131"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"132},133{134"question": "How would you compare Spark, Ray, Dask?",135"sources": [136"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",137"https://docs.ray.io/en/master/ray-more-libs/index.html#more-ray-ml-libraries",138"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#using-dask-on-ray",139"https://docs.ray.io/en/master/data/loading-data.html#loading-data-from-distributed-dataframe-libraries",140"https://docs.ray.io/en/master/data/saving-data.html#converting-datasets-to-distributed-dataframes"141],142"document_ids": [14349197,144107111,145105078,14644858,14747150148],149"answer": " Spark, Ray, and Dask are all distributed computing frameworks, but they have different strengths and use cases.\n\nRay has developed APIs for Spark, allowing Spark users to leverage Ray's faster scheduling and lower overhead. Ray also has a Collective Communication Library and can use Dask for certain tasks. Additionally, Ray can interoperate with Dask, Spark, Modin, and other distributed data processing frameworks.\n\nDask is a flexible framework that can be used for parallel computing and distributed data processing. It can be used on Ray to take advantage of Ray's scalability and performance. Dask has a library for collective communication and can interoperate with Ray, Spark, and other frameworks.\n\nSpark is a popular framework for distributed data processing and big data analytics. It can be used on Ray through RayDP, which allows Spark applications to run on Ray clusters. Spark also interoperates with Ray Data, allowing Spark users to leverage Ray's performance benefits.\n\nIn summary, Ray is a general-purpose framework that can interoperate with other distributed computing frameworks like Spark and Dask. Dask is a flexible framework that can be used for parallel computing and distributed data processing, and can be used on Ray for better performance. Spark is a popular framework for distributed data processing and big data analytics, and can be used on Ray for better performance and interoperability with other frameworks.",150"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"151},152{153"question": "why would ray overload a node w/ more task that the resources allow ?",154"sources": [155"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",156"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#custom-resources",157"https://docs.ray.io/en/master/cluster/key-concepts.html#autoscaling",158"https://docs.ray.io/en/master/ray-observability/getting-started.html#view-the-resource-utilization",159"https://docs.ray.io/en/master/ray-core/key-concepts.html#tasks"160],161"document_ids": [16234185,16328621,16437635,165995,16624020167],168"answer": " It's not typical for Ray to overload a node with more tasks than the resources allow. In fact, Ray requires users to specify the number of resources their tasks and actors use through resource requests. If the resource demands of the Ray workload exceed the available resources, this could lead to issues such as slower performance or even task failures. Therefore, it's important to request resources carefully and ensure that the total resource demand does not exceed the available resources on a node.",169"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"170},171{172"question": "when should I use Ray Client?",173"sources": [174"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",175"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",176"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",177"https://docs.ray.io/en/master/tune/api/internals.html#tunerinternal",178"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-arguments"179],180"document_ids": [18154245,18254057,18353983,18411020,18554179186],187"answer": " You should use the Ray Client when the address passed into `ray.init()` is prefixed with \"ray://\". This is useful when you want to connect to a remote Ray cluster instead of using the local Ray cluster.",188"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"189},190{191"question": "how to scatter actors across the cluster?",192"sources": [193"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_actors.html#ray-util-state-list-actors",194"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread",195"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.summarize_actors.html#ray-util-state-summarize-actors",196"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#gcs-fault-tolerance",197"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"198],199"document_ids": [2002535,20130442,202733,20323065,204108135205],206"answer": " To scatter actors across the cluster, you can use the `spread_func.remote()` function, which creates a remote actor that can be placed on any node in the cluster. In the example provided, this function is called in a range-based loop to create 10 such actors. Here is the relevant part of the context again for easy reference:\n\n```\n# Spread actors across the cluster.\n[spread_func.remote() for _ in range(10)]\n```\n\nThis will create 10 actors and scatter them across the cluster, with one actor per node in many cases. Note that the actual placement of the actors may depend on the specific placement strategy used by Ray, which can be influenced by various factors such as node resources, actor affinities, and custom placement groups.",207"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"208},209{210"question": "On remote ray cluster, when I do `ray debug` I'm getting connection refused error. Why ?",211"sources": [212"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster",213"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#when-to-use-ray-client",214"https://docs.ray.io/en/master/ray-core/starting-ray.html#launching-a-ray-cluster-ray-up",215"https://docs.ray.io/en/master/ray-core/configure.html#cluster-resources",216"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-jobs"217],218"document_ids": [21942291,22054083,22123811,22231896,22337728224],225"answer": " Based on the context provided, the `ray debug` command is trying to connect to a remote Ray cluster at a specific IP address and port. However, the connection refused error suggests that the Ray cluster at that address is not reachable or not running.\n\nTo connect to the Ray cluster, you should follow the instructions provided in the context and use `ray.init(\"ray://<head_node_host>:10001\")` in your driver script or call `ray.init()` from one of the machines in the cluster. Alternatively, you can use the Ray Client to connect remotely to the cluster within a driver script.\n\nMake sure that the Ray cluster is running and accessible at the specified address and port before attempting to connect to it again.",226"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"227},228{229"question": "How does Ray AIR set up the model to communicate gradient updates across machines?",230"sources": [231"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#starting-with-ray-air-distribute-the-training",232"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tuning-the-model-with-ray-air-a-name-train-a",233"https://docs.ray.io/en/master/train/api/doc/ray.train.huggingface.AccelerateTrainer.html#ray-train-huggingface-acceleratetrainer",234"https://docs.ray.io/en/master/train/train.html#ray-train-scalable-model-training",235"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#fine-tune-a-transformers-model"236],237"document_ids": [23871752,23976250,24061764,24160374,24275444243],244"answer": " During the fine-tuning of a model with Ray AIR, the model is set up to communicate gradient updates across machines through the Ray Train component. The Ray Train system allows for scaling model training code from a single machine to a cluster of machines. The Trainer, as set by the Ray AIR configs (such as ScalingConfig), is responsible for handling the distribution of gradients and model updates across the available machines.",245"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"246},247{248"question": "Why would I use Ray Serve instead of Modal or Seldon? Why can't I just do it via containers?",249"sources": [250"https://docs.ray.io/en/master/serve/index.html#how-does-serve-compare-to",251"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",252"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",253"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",254"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration"255],256"document_ids": [257101344,25846970,2595933,26046226,26145470262],263"answer": " Ray Serve is a general-purpose model serving framework, unlike Modal and Seldon which are framework-specific. This means that Ray Serve doesn't perform any model-specific operations, allowing it to be used with a variety of models and frameworks. If you're looking for a solution that can handle serving models from different frameworks, Ray Serve might be a better choice than Modal or Seldon.\n\nAs for using containers, you certainly can use them to serve your models. However, managing and scaling containers can be complex and time-consuming. Ray Serve simplifies this process by providing a higher-level abstraction for model serving, allowing you to focus on building and deploying your models rather than managing infrastructure.",264"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"265},266{267"question": "How do I deploy an LLM workload on top of Ray Serve?",268"sources": [269"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a",270"https://docs.ray.io/en/master/serve/develop-and-deploy.html#deploy-ray-serve-in-production",271"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",272"https://docs.ray.io/en/master/ray-overview/index.html#ray-framework",273"https://docs.ray.io/en/master/serve/index.html#how-can-serve-help-me-as-a"274],275"document_ids": [276101289,27795419,278106913,2797180,280101294281],282"answer": " To deploy an LLM (large language model) workload on top of Ray Serve, you can follow these general steps:\n\n1. First, test your Ray Serve LLM application locally to ensure it's working as expected.\n2. Once you're ready, deploy your Ray Serve LLM application to production using Ray Serve's production-ready features.\n3. As each application step can independently autoscale, you can configure Ray Serve to automatically adjust the number of replicas based on the workload.\n\nFor more detailed instructions, you can refer to the Ray Serve documentation on testing the Ray Serve deployment and deploying Ray Serve in production.",283"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"284},285{286"question": "what size of memory should I need for this if I am setting set the `model_id` to \u201cEleutherAI/gpt-j-6B\u201d?",287"sources": [288"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",289"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",290"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#initialize-generation-pipeline",291"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",292"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#model-inference"293],294"document_ids": [29585362,29685859,29771197,29886208,29962633300],301"answer": " Based on the context provided, you should have at least 16 GB of GPU memory available for the `EleutherAI/gpt-j-6B` model, as the `revision = \"float16\"` configuration is used to fit the model into 16 GB GPUs. The `meta_model` configuration also suggests a maximum memory limit of 15 GB for GPU and 60 GB for CPU. Therefore, 16 GB of GPU memory should be sufficient for this model.",302"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"303},304{305"question": "How do I log the results from multiple distributed workers into a single tensorboard?",306"sources": [307"https://docs.ray.io/en/master/tune.html#why-choose-tune",308"https://docs.ray.io/en/master/tune/index.html#why-choose-tune",309"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#example-logging-to-mlflow-and-tensorboard",310"https://docs.ray.io/en/master/tune/api/sklearn.html#tunesearchcv",311"https://docs.ray.io/en/master/tune/api/sklearn.html#tunegridsearchcv"312],313"document_ids": [3142400,3158498,31666777,31710902,31810499319],320"answer": " To log the results from multiple distributed workers into a single TensorBoard, you can use the `log_results` function with the \"tensorboard\" logging type. You'll need to ensure that all workers log their results to the same directory, so that TensorBoard can aggregate and display the results together. Here's a code snippet demonstrating this:\n```python\nfrom my_logging_library import log_results # replace with your logging library\n\nlog_results(results, log_type=\"tensorboard\", log_dir=\"/path/to/shared/log/directory\")\n```\nThis will log the `results` from your distributed workers to the specified shared TensorBoard log directory. Make sure to replace `my_logging_library` with the actual name of your logging library and `results` with the results you want to log.",321"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"322},323{324"question": "how do you config SyncConfig for a Ray AIR job?",325"sources": [326"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.html#ray-train-syncconfig",327"https://docs.ray.io/en/master/tune/api/doc/ray.train.SyncConfig.syncer.html#ray-train-syncconfig-syncer",328"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",329"https://docs.ray.io/en/master/train/api/doc/ray.train.RunConfig.html#ray-train-runconfig",330"https://docs.ray.io/en/master/tune/api/syncing.html#tune-syncing-configuration"331],332"document_ids": [33310070,33412273,33557619,33657631,3379373338],339"answer": " To configure `SyncConfig` for a Ray AIR job, you can do so in the Ray Train training configuration by specifying the `sync_config` parameter. The `SyncConfig` class has several attributes that you can configure, including `upload_dir` and `syncer`. Here is an example of how to use `SyncConfig` in a Ray AIR training configuration:\n```\nfrom ray.train.sync_config import SyncConfig\n\nconfig = {\n \"training_arguments\": {\n # Other training arguments\n },\n \"sync_config\": SyncConfig(\n upload_dir=\"s3://my-bucket/ray_results\",\n syncer=\"periodic_checkpoint_syncer\"\n ),\n # Other configuration options\n}\n```\nThe `upload_dir` attribute specifies the directory where checkpoints should be uploaded, and the `syncer` attribute specifies the syncer to use for synchronizing data across workers. You can find more information about the different syncers available in the Ray documentation.\n\nYou can also specify an optional `FailureConfig` object to configure how the syncer should handle failures. For example:\n```\nfrom ray.train.sync_config import SyncConfig, FailureConfig\n\nconfig = {\n \"training_arguments\": {\n # Other training arguments\n },\n \"sync_config\": SyncConfig(\n upload_dir=\"s3://my-bucket/ray_results\",\n syncer=\"periodic_checkpoint_syncer\",\n failure_config=FailureConfig(max_failures=3)\n ),\n # Other configuration options\n}\n```\nThis configuration will allow the syncer to fail up to 3 times before giving up and stopping the training job.\n\nI hope this helps! Let me know if you have any other questions.",340"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"341},342{343"question": "how can I quickly narrow down the root case of a failed ray job, assuming I have access to all the logs",344"sources": [345"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",346"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",347"https://docs.ray.io/en/master/ray-observability/getting-started.html#navigate-the-views",348"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",349"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-for-developers"350],351"document_ids": [35242489,35351646,354633,35547867,3565723357],358"answer": " To quickly narrow down the root cause of a failed Ray job, follow these steps:\n\n1. Gather all the logs, including system and application logs.\n2. Use the \"ray job logs\" command to get the stdout.\n3. Locate error messages and logs for failed Tasks and Actors in the Jobs view and Logs view.\n4. Analyze system level failures and consider profiling the Ray Core for a more detailed investigation.\n\nBy following these steps, you should be able to effectively troubleshoot and identify the root cause of the failed Ray job.",359"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"360},361{362"question": "How do I specify how many GPUs a serve deployment needs?",363"sources": [364"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.RayActorOptionsSchema.html#ray-serve-schema-rayactoroptionsschema",365"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails",366"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeApplicationSchema.html#ray-serve-schema-serveapplicationschema",367"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentDetails.html#ray-serve-schema-deploymentdetails",368"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.DeploymentSchema.html#ray-serve-schema-deploymentschema"369],370"document_ids": [371100131,372100176,373102164,37498992,375100864376],377"answer": " In the serve deployment configuration, you can specify the number of GPUs required per replica in the field that specifies the resources of the deployment's application. This field determines the number of GPUs that will be allocated to each replica of the deployment.",378"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"379},380{381"question": "One of my worker nodes keeps dying on using TensorflowTrainer with around 1500 workers, I observe SIGTERM has been received to the died node's raylet. How can I debug this?",382"sources": [383"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",384"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#training-speed",385"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",386"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#worker-node-commands",387"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer"388],389"document_ids": [39071933,39187164,39271745,39351907,39471741395],396"answer": " Based on the information provided, it seems like the worker nodes are running out of resources, leading to the SIGTERM signal being sent to the raylet. To debug this issue, you can:\n\n1. Check the resource allocation and usage on the worker nodes to ensure that they have enough resources to run TensorflowTrainer with 1500 workers. You can use tools like `htop`, `top`, or `docker stats` (if you're using Docker) to monitor the resource usage.\n2. Increase the resources available to the worker nodes, such as memory or CPU, to see if that resolves the issue.\n3. Rebuild TensorFlow with oneDNN support, as suggested in the logs, to potentially improve the performance and reduce resource usage.\n4. If the issue persists, you can enable debug logging for Ray by setting the `RAY_worker_log_to_driver` environment variable to `true` and the `RAY_log_to_driver` environment variable to `detailed`. This will send detailed logs to the driver, which you can then use to further diagnose the issue.\n5. Finally, you can also consider reaching out to the Ray community or TensorFlow community for further assistance, providing them with the detailed logs and any other relevant information.",397"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"398},399{400"question": "what are the possible reasons for nodes dying in a cluster?",401"sources": [402"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",403"https://docs.ray.io/en/master/train/api/doc/ray.train.gbdt_trainer.GBDTTrainer.restore.html#ray-train-gbdt-trainer-gbdttrainer-restore",404"https://docs.ray.io/en/master/train/api/doc/ray.train.trainer.BaseTrainer.restore.html#ray-train-trainer-basetrainer-restore",405"https://docs.ray.io/en/master/train/api/doc/ray.train.xgboost.XGBoostTrainer.restore.html#ray-train-xgboost-xgboosttrainer-restore",406"https://docs.ray.io/en/master/train/api/doc/ray.train.lightgbm.LightGBMTrainer.restore.html#ray-train-lightgbm-lightgbmtrainer-restore"407],408"document_ids": [4092707,41057997,41165282,41266449,41359027414],415"answer": " Based on the provided context, the possible reasons for nodes dying in a cluster include:\n\n1. Stopping or terminating the cluster or some of its nodes\n2. A network error affecting all nodes, causing the entire cluster to go down",416"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"417},418{419"question": "how do I programatically get ray remote cluster to a target size immediately without scaling up through autoscaler ?",420"sources": [421"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",422"https://docs.ray.io/en/master/cluster/kubernetes/examples/ml-example.html#deploy-a-ray-cluster",423"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",424"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",425"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#using-ray-cluster-utils-autoscalingcluster"426],427"document_ids": [42843310,42947316,43048015,43138847,4323875433],434"answer": " To programmatically set the target size of a Ray remote cluster immediately without scaling up through the autoscaler, you can use the `ray.cluster_utils.AutoscalingCluster` class and set the `target_workers` parameter to the desired size. Here's an example:\n```python\nfrom ray.cluster_utils import AutoscalingCluster\n\ncluster = AutoscalingCluster(target_workers=desired_size)\ncluster.connect()\n\n# Your Ray program goes here\n\ncluster.shutdown()\n```\nThis will create a new Ray cluster with the specified number of workers (`desired_size`) without using the autoscaler to scale up. Note that this will not reduce the size of an existing cluster; it will only create a new cluster with the specified number of workers.",435"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"436},437{438"question": "how do you disable async iter_batches with Ray Dataset?",439"sources": [440"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_batches.html#ray-data-dataset-iter-batches",441"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.prepare_dataset_shard.html#ray-train-tensorflow-prepare-dataset-shard",442"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_tf_batches.html#ray-data-dataset-iter-tf-batches",443"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",444"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches"445],446"document_ids": [44749387,44865693,44954675,45046542,45151935452],453"answer": " To disable async iter_batches with Ray Dataset, you can use the `iter_batches()` method which returns an iterator over the batches of the dataset synchronously. The `iter_tf_batches()` and `iter_torch_batches()` methods are specifically for TensorFlow and PyTorch datasets and are async by default. If you want to use synchronous iterators with these frameworks, you can convert the batches to TensorFlow or PyTorch tensors after obtaining them using `iter_batches()`.",454"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"455},456{457"question": "what is the different between a batch and a block, for ray datasets?",458"sources": [459"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",460"https://docs.ray.io/en/master/data/data-internals.html#datasets-and-blocks",461"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_torch_batches.html#ray-data-dataiterator-iter-torch-batches",462"https://docs.ray.io/en/master/data/api/doc/ray.data.DataIterator.iter_batches.html#ray-data-dataiterator-iter-batches",463"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.iter_torch_batches.html#ray-data-dataset-iter-torch-batches"464],465"document_ids": [46649361,46749366,46865272,46946689,47052173471],472"answer": " In the context of Ray Datasets, the terms \"batch\" and \"block\" refer to different levels of data organization. A \"block\" is a disjoint subset of rows in the dataset, while a \"batch\" is a collection of rows that Ray Data processes together in memory. Multiple blocks can be combined to form a single batch, and blocks may contain a different number of rows. The key difference is that a block is a unit of data storage, while a batch is a unit of data processing.",473"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"474},475{476"question": "How to setup the development environments for ray project?",477"sources": [478"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#setup-ray-environment",479"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#dependencies-for-gpu-based-machine-learning",480"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#specify-container-commands-for-ray-head-worker-pods",481"https://docs.ray.io/en/master/ray-core/key-concepts.html#environment-dependencies",482"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments"483],484"document_ids": [48569550,48641282,48741700,48824229,48926298490],491"answer": " To set up the development environment for the Ray project, you can follow these steps:\n\n1. Before starting Ray, set up some environment variables that will be used by Ray's runtime environments to install the necessary files, packages, and more on the fly.\n2. Use Ray's runtime environments to create isolated environments for your Ray application, which will help to manage dependencies and avoid conflicts with other projects.\n\nYou can refer to the official Ray documentation for detailed instructions on how to set up the development environment.",492"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"493},494{495"question": "how do I debug why ray rollout workers are deadlocking when using the sample API in `ray/rllib/evaluation/rollout_worker.py`",496"sources": [497"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#rewriting-trajectories",498"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker",499"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.sample.html#ray-rllib-evaluation-rollout-worker-rolloutworker-sample",500"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.sampler.AsyncSampler.html#ray-rllib-evaluation-sampler-asyncsampler",501"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.rollout_worker.RolloutWorker.html#ray-rllib-evaluation-rollout-worker-rolloutworker"502],503"document_ids": [50478663,50580678,50682555,50778370,50880683509],510"answer": " To debug why Ray rollout workers are deadlocking when using the sample API, you can check the following:\n\n1. Check if the `get_global_worker()` function is being used correctly and not causing any deadlocks.\n2. Inspect the implementation of `RolloutWorker#sample` and `AsyncSampler` to ensure they are not causing any deadlocks.\n3. Verify that the `env_creator` function used to create the environment for the rollout worker is not causing any deadlocks.\n4. Check if there are any resource contention issues, such as insufficient CPU or memory resources, causing the deadlocks.\n5. Use Ray's built-in debugging tools, such as the Ray dashboard and logging, to identify any issues with the rollout workers.\n6. Consider adding debug logs or print statements to the relevant parts of the code to help identify the cause of the deadlock.",511"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"512},513{514"question": "how do I join two ray datasets?",515"sources": [516"https://docs.ray.io/en/master/data/preprocessors.html#overview",517"https://docs.ray.io/en/master/ray-air/examples/dreambooth_finetuning.html#data-loading",518"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",519"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",520"https://docs.ray.io/en/master/data/examples/custom-datasource.html#implementing-a-custom-datasource"521],522"document_ids": [52351299,52481771,52554483,52686474,52759881528],529"answer": " To join two Ray Datasets, you can use the `union()` method. Here's a basic example:\n\nFirst, set up two simple Ray Datasets:\n```python\ndataset1 = ray.data.read_images('path/to/dataset1')\ndataset2 = ray.data.read_images('path/to/dataset2')\n```\nThen, join the two datasets using `union()`:\n```python\ncombined_dataset = dataset1.union(dataset2)\n```\nThis will create a new Ray Dataset containing all the records from both `dataset1` and `dataset2`. Make sure to review the Ray Data documentation (ray_datasets) for additional ways to create and manipulate Ray Datasets.",530"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"531},532{533"question": "Is there a way to retrieve an object ref from its id?",534"sources": [535"https://docs.ray.io/en/master/ray-core/objects.html#objects",536"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",537"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.foreach_worker.html#ray-rllib-evaluation-worker-set-workerset-foreach-worker",538"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",539"https://docs.ray.io/en/master/ray-core/objects.html#objects"540],541"document_ids": [54233443,54336097,54479603,545108043,54633458547],548"answer": " Yes, you can retrieve an object ref from its ID by using the `return_obj_refs` option, which will return the object ref assigned to the value instead of the actual results. The object ref contains information such as the ref count and location of the object. Object refs can also be created by remote function calls.",549"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"550},551{552"question": "how to create model Checkpoint from the model in memory?",553"sources": [554"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",555"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.html#ray-train-tensorflow-tensorflowcheckpoint",556"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_saved_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-saved-model",557"https://docs.ray.io/en/master/train/api/doc/ray.train.tensorflow.TensorflowCheckpoint.from_model.html#ray-train-tensorflow-tensorflowcheckpoint-from-model",558"https://docs.ray.io/en/master/train/api/doc/ray.train.torch.TorchCheckpoint.html#ray-train-torch-torchcheckpoint"559],560"document_ids": [56160765,56260773,56361650,56464223,56564495566],567"answer": " To create a model checkpoint from a model in memory, you can use the `from_model` function in the context provided, which creates a checkpoint that stores a Keras model. Here is an example of how to use it:\n\n```python\nfrom tensorflow.keras.checkpoints import Checkpoint\n\n# Create a model\nmodel = ...\n\n# Create a checkpoint from the model\ncheckpoint = Checkpoint.from_model(model)\n```\n\nThis will create a checkpoint object that you can use to save and restore the model's weights and state. Note that this function only works for Keras models and not for Torch models.",568"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"569},570{571"question": "what is Deployment in Ray Serve?",572"sources": [573"https://docs.ray.io/en/master/serve/api/doc/ray.serve.deployment_decorator.html#ray-serve-deployment",574"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",575"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",576"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",577"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#defining-an-application-builder"578],579"document_ids": [580103862,58194974,582101901,583106913,58498287585],586"answer": " A Deployment in Ray Serve is the central concept and a deployable unit of a Ray Serve application. It represents an instance of a user-defined RESTful backend, which can be created using a deployment object and configured with various parameters. These parameters are typically passed to the constructor of your deployments. Once deployed, it allows you to serve machine learning models and other predictive services at scale.",587"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"588},589{590"question": "What is user config in Ray Serve? how do I use it?",591"sources": [592"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",593"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide",594"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",595"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",596"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#dynamically-changing-parameters-without-restarting-your-replicas-user-config"597],598"document_ids": [59939579,60039415,60139653,602104100,60399313604],605"answer": " User config in Ray Serve is a way to configure and customize Ray Serve applications. You can update the Ray Serve config, including a section called `user_config`, and reapply it to your Ray cluster. To use it, you would first deploy a Ray Serve config file using the Ray cluster command-line interface (CLI), and then update the `user_config` section in the config file as needed. For more details, you can refer to the Ray Serve documentation.",606"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"607},608{609"question": "What is the difference between PACK and SPREAD strategy?",610"sources": [611"https://docs.ray.io/en/master/ray-core/examples/batch_training.html#optional-optimizing-for-runtime-over-memory-with-centralized-data-loading",612"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",613"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",614"https://docs.ray.io/en/master/ray-core/key-concepts.html#placement-groups",615"https://docs.ray.io/en/master/ray-core/scheduling/index.html#spread"616],617"document_ids": [61834585,6198235,6208224,62124184,62230424623],624"answer": " The \"PACK\" scheduling strategy tries to place tasks or actors on the same node to optimize locality, while the \"SPREAD\" strategy attempts to distribute them across available nodes to increase fault tolerance and utilize resources more evenly.",625"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"626},627{628"question": "What\u2019s the best way to run ray across multiple machines?",629"sources": [630"https://docs.ray.io/en/master/cluster/faq.html#i-have-multiple-ray-users-what-s-the-right-way-to-deploy-ray-for-them",631"https://docs.ray.io/en/master/tune/tutorials/tune-lifecycle.html#what-happens-in-tuner-fit",632"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",633"https://docs.ray.io/en/master/ray-core/starting-ray.html#what-is-the-ray-runtime",634"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client"635],636"document_ids": [63741043,63815738,6395814,64023417,64154001642],643"answer": " To run Ray across multiple machines, you can set up a Ray cluster with multiple servers. Ray programs can seamlessly scale from a single machine to a large cluster, and the Ray runtime can be started on multiple servers similar to running Ray on a local machine.",644"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"645},646{647"question": "how do I specify ScalingConfig for a Tuner run?",648"sources": [649"https://docs.ray.io/en/master/tune/tutorials/tune-resources.html#a-guide-to-parallelism-and-resources-for-ray-tune",650"https://docs.ray.io/en/master/tune/faq.html#how-do-i-set-resources",651"https://docs.ray.io/en/master/train/examples/lightning/lightning_cola_advanced.html#distributed-training-with-ray-torchtrainer",652"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.from_placement_group_factory.html#ray-train-scalingconfig-from-placement-group-factory",653"https://docs.ray.io/en/master/train/user-guides/using-gpus.html#configuring-scale-and-gpus"654],655"document_ids": [65616998,6578251,65871628,65961316,66068091661],662"answer": " To specify the ScalingConfig for a Tuner run in the context provided, you can create a ScalingConfig object and pass it to the `tune.with_resources` function when defining the Tuner. Here's an example:\n```python\nfrom ray import tune\nfrom ray.air import ScalingConfig\n\n# Define the ScalingConfig\nscaling_config = ScalingConfig(\n # Specify the scaling configuration here\n)\n\n# Define the run_config\nrun_config = RunConfig(\n # Specify the run configuration here\n)\n\n# Define the train_fn\ntrain_fn = lambda config: ...\n\n# Create the Tuner with the specified ScalingConfig\ntuner = tune.Tuner(\n tune.with_resources(\n train_fn,\n resources=scaling_config\n ),\n run_config=run_config,\n)\n```\nIn this example, you can specify the scaling configuration by creating a `ScalingConfig` object and passing it to the `resources` argument of the `tune.with_resources` function. The `run_config` argument can also be specified to configure other aspects of the Tuner run.",663"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"664},665{666"question": "how to utilize \u2018zero-copy\u2019 feature ray provide for numpy?",667"sources": [668"https://docs.ray.io/en/master/ray-core/objects.html#fetching-object-data",669"https://docs.ray.io/en/master/ray-core/objects/serialization.html#numpy-arrays",670"https://docs.ray.io/en/master/data/examples/random-access.html#performance",671"https://docs.ray.io/en/master/ray-core/examples/plot_example-a3c.html#driver-code-walkthrough",672"https://docs.ray.io/en/master/ray-core/examples/plot_example-a3c.html#worker-code-walkthrough"673],674"document_ids": [67533526,67626816,67763308,67840480,67940357680],681"answer": " To utilize the 'zero-copy' feature Ray provides for numpy, you can use the `ray.put()` function to put a numpy array or a collection of numpy arrays into the Ray object store. Then, when calling `ray.get()` to retrieve the arrays, Ray will return arrays backed by the shared numpy array in the object store without copying (zero-copy reads). Here is an example:\n\n```python\nimport numpy as np\nimport ray\n\n# Create a numpy array\narr = np.random.rand(1000, 1000)\n\n# Put the numpy array into the Ray object store\nray_arr = ray.put(arr)\n\n# Get the numpy array from the Ray object store (zero-copy read)\nretrieved_arr = ray.get(ray_arr)\n```\nIn this example, `retrieved_arr` is a zero-copy view of the numpy array in the object store, so any changes made to `retrieved_arr` will also affect `arr`.",682"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"683},684{685"question": "if there are O(millions) of keys that all have state, is it ok to spin up 1=1 actors? Or would it be advised to create \u2018key pools\u2019 where an actor can hold 1=many keys?",686"sources": [687"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.StateSummary.html#ray-util-state-common-statesummary",688"https://docs.ray.io/en/master/tune/api/doc/ray.tune.integration.pytorch_lightning.TuneReportCallback.state_key.html#ray-tune-integration-pytorch-lightning-tunereportcallback-state-key",689"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.RayTrainReportCallback.state_key.html#ray-train-lightning-raytrainreportcallback-state-key",690"https://docs.ray.io/en/master/tune/api/doc/ray.tune.integration.pytorch_lightning.TuneReportCheckpointCallback.state_key.html#ray-tune-integration-pytorch-lightning-tunereportcheckpointcallback-state-key",691"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#key-concepts"692],693"document_ids": [6941113,69515399,69661105,69714938,6981898699],700"answer": " Given the context, it is not advised to spin up one actor for each of the millions of keys with state. This is because each actor would require a unique state key, and managing that many actors could lead to performance issues and increased complexity.\n\nInstead, it would be better to create \"key pools\" where an actor can hold many keys. This would reduce the number of actors needed, making it easier to manage their states and improving performance. Additionally, using a key pool would ensure that each key has a unique state key, as required by the callback.",701"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"702},703{704"question": "How to find the best checkpoint from the trial directory?",705"sources": [706"https://docs.ray.io/en/master/rllib/rllib-cli.html#running-tuned-examples",707"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",708"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.html#ray-tune-experimentanalysis",709"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ExperimentAnalysis.get_best_checkpoint.html#ray-tune-experimentanalysis-get-best-checkpoint",710"https://docs.ray.io/en/master/train/examples/lightning/lightning_mnist_example.html#check-the-training-results-and-checkpoints"711],712"document_ids": [71390883,71410489,71510491,71612108,71769406718],719"answer": " To find the best checkpoint in the trial directory, you can use the `get_best_checkpoint` function, which is provided in the context. This function takes a trial as an argument and returns the best persistent checkpoint path of that trial. In your case, you can call this function with the trial directory as the argument, like this:\n```\nget_best_checkpoint(<path-to-trial-directory>)\n```\nThis will return the best checkpoint path, such as `<path-to-a-checkpoint>/checkpoint_000008`. You can then use this path to load the best checkpoint for further analysis or processing.\n\nIt's worth noting that the `get_best_checkpoint` function can optionally take additional arguments to specify the metric and mode to use when determining the best checkpoint. For example, you could call the function like this:\n```\nget_best_checkpoint(<path-to-trial-directory>, metric='accuracy', mode='max')\n```\nThis would return the checkpoint with the highest accuracy score. The `mode` argument can be either 'max' or 'min', depending on whether you want to select the checkpoint with the maximum or minimum value for the specified metric.",720"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"721},722{723"question": "what are the advantage and disadvantage of using singleton Actor ?",724"sources": [725"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",726"https://docs.ray.io/en/master/ray-core/patterns/global-variables.html#anti-pattern-using-global-variables-to-share-state-between-tasks-and-actors",727"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#how-to-improve-the-gpu-utilization-rate",728"https://docs.ray.io/en/master/ray-core/walkthrough.html#calling-an-actor",729"https://docs.ray.io/en/master/ray-core/actors.html#cancelling-actor-tasks"730],731"document_ids": [73233981,73332551,73434284,73534895,73622754737],738"answer": " Sure, I'd be happy to help! Based on the context provided, here are the advantages and disadvantages of using a singleton Actor:\n\nAdvantages:\n\n* Each actor instance is instantiated in its own process, which means that it has its own memory space and can run independently of other actors. This can lead to better performance and scalability, especially in multi-threaded environments.\n* The actor-based approach is more efficient than traditional thread-based approaches because it allows for better resource utilization and reduces the risk of thread contention and blocking.\n\nDisadvantages:\n\n* Using a singleton Actor can limit scalability and performance in some cases, since there is only one instance of the actor running at a time. This can become a bottleneck if there are many messages being sent to the actor or if the actor needs to perform resource-intensive operations.\n* If the singleton Actor encounters an error or exception, it can bring down the entire system, since there is only one instance of the actor running. This can make it more difficult to recover from failures and can lead to system-wide downtime.\n\nIt's worth noting that the advantages and disadvantages of using a singleton Actor can depend on the specific use case and implementation details. In some cases, a non-singleton actor approach may be more appropriate, such as using a pool of actors or a hierarchical actor system.",739"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"740},741{742"question": "what are the advantages of using a named actor?",743"sources": [744"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",745"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",746"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",747"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#get-or-create-a-named-actor",748"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides"749],750"document_ids": [75127201,75224491,75327616,75427484,75534224756],757"answer": " Named actors offer the following advantages:\n\n1. Unique identification: Actors can be given a unique name within their namespace, allowing for clear identification and differentiation between multiple actors.\n2. Simplified access: The unique name of a named actor can be used to get or create the actor, making it easier to access and interact with the desired actor.\n3. Improved code organization: Named actors can help improve code organization by providing a clear way to reference specific actors, making it easier to understand the relationships between different parts of the code.\n\nIn the provided context, the terms \"Get-Or-Create a Named Actor\" and \"Actor Lifetimes\" suggest that named actors may also offer the ability to easily retrieve existing actors or create new ones if they do not already exist, and may provide a way to manage the lifetimes of actors. However, the context does not provide specific details about these features.",758"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"759},760{761"question": "How do I read a text file stored on S3 using Ray Data?",762"sources": [763"https://docs.ray.io/en/master/data/working-with-text.html#saving-text",764"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",765"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#loading-the-image-dataset",766"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",767"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3"768],769"document_ids": [77055076,77155023,77264407,77360906,77462022775],776"answer": " To read a text file stored on S3 using Ray Data, you can use the `ray.data.read_text` function and provide the S3 URI for the file. Here is an example:\n\n```python\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\n\nIn this example, `s3://anonymous@ray-example-data/this.txt` is the S3 URI for the text file that you want to read. The `read_text` function will return a Ray Dataset object containing the contents of the text file.\n\nNote that you may need to configure your AWS credentials to access the S3 bucket. You can do this by setting the `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables, or by using the AWS CLI's `aws configure` command.",777"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"778},779{780"question": "how do I get the IP of the head node for my Ray cluster?",781"sources": [782"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",783"https://docs.ray.io/en/master/cluster/cli.html#ray-get-head-ip",784"https://docs.ray.io/en/master/cluster/metrics.html#manually-discovering-metrics-endpoints",785"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats",786"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#slurm-networking-caveats"787],788"document_ids": [78940813,79040818,79138852,79250330,79350383794],795"answer": " To get the IP of the head node for your Ray cluster, you can use the `ray get_head_ip` command, followed by the path to your cluster configuration file. For example:\n\n`ray get_head_ip my_cluster.yaml`\n\nThis will return the IP address of the head node for the Ray cluster configured in the `my_cluster.yaml` file.\n\nIf you do not know the IP addresses of the nodes in your Ray cluster, you can also programmatically specify the head node IP when starting the Ray cluster, using the `--node-ip-address` option. For example:\n\n`ray start --head --node-ip-address=\"$head_node_ip\" --port=6379`\n\nIn this case, you would need to replace `$head_node_ip` with the actual IP address of the head node.",796"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"797},798{799"question": "How to write a map function that returns a list of object for `map_batches`?",800"sources": [801"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",802"https://docs.ray.io/en/master/data/transforming-data.html#configuring-batch-format",803"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms",804"https://docs.ray.io/en/master/data/api/doc/ray.data.range_tensor.html#ray-data-range-tensor",805"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset"806],807"document_ids": [80855227,80946531,81053914,81163057,81255403813],814"answer": " To write a map function that returns a list of objects for `map_batches`, you can define a lambda function that transforms each batch into a list of output objects. Here's an example:\n```python\noutput = ds.map_batches(lambda batch: [{\"id\": i * 2} for i in batch[\"id\"]])\n```\nIn this example, the lambda function takes a batch of data and returns a list of dictionaries where the \"id\" key is multiplied by 2. The `map_batches` function applies this lambda function to each batch of data in the dataset `ds`.\n\nAlternatively, you can define a standalone function instead of using a lambda function:\n```python\ndef map_fn(batch):\n return [{\"id\": i * 2} for i in batch[\"id\"]]\n\noutput = ds.map_batches(map_fn)\n```\nIn both cases, the output of `map_batches` will be a new dataset where each element is a list of dictionaries representing the transformed data.",815"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"816},817{818"question": "How do I set a maximum episode length when training with Rllib?",819"sources": [820"https://docs.ray.io/en/master/rllib/rllib-advanced-api.html#customized-evaluation-during-training",821"https://docs.ray.io/en/master/rllib/rllib-cli.html#starting-training-runs",822"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",823"https://docs.ray.io/en/master/tune/tutorials/tune-metrics.html#which-tune-metrics-get-automatically-filled-in",824"https://docs.ray.io/en/master/rllib/rllib-cli.html#starting-training-runs"825],826"document_ids": [82777944,82890922,82914029,83016946,83190917832],833"answer": " To set a maximum episode length when training with RLlib, you can use the `max_episode_steps` configuration parameter in your training setup. This parameter determines the maximum number of steps that an episode can run for. If the episode does not reach a natural conclusion before reaching this limit, it will be terminated.\n\nHere is an example of how you might set this parameter in your RLlib configuration:\n```\nconfig = {\n \"max_episode_steps\": 1000,\n ...\n}\n```\nThis configuration would set the maximum episode length to 1000 steps.\n\nYou can find more information about configuring RLlib in the official documentation.\n\nI hope this helps! Let me know if you have any other questions.",834"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"835},836{837"question": "how do I make a Ray Tune trial retry on failures?",838"sources": [839"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#trial-level-fault-tolerance-in-tune",840"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",841"https://docs.ray.io/en/master/rllib/rllib-fault-tolerance.html#fault-tolerance-and-recovery-provided-by-ray-tune",842"https://docs.ray.io/en/master/tune/tutorials/tune-storage.html#examples",843"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary"844],845"document_ids": [84615956,84792511,84892504,84916598,85015971851],852"answer": " To make a Ray Tune trial retry on failures, you can use the FailureConfig to configure failure handling for individual trials. When a trial fails, Ray Tune will automatically restart it from the latest checkpointed state, providing fault tolerance and recovery at the experiment trial level. For more information, see \"How to Enable Fault Tolerance in Ray Tune\" and trial-level fault tolerance in Ray Tune.",853"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"854},855{856"question": "For the supervised actor pattern, can we keep the Worker Actor up if the Supervisor passes a reference to the Actor to another Actor, to allow the worker actor to remain even on Supervisor / Driver failure?",857"sources": [858"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",859"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors",860"https://docs.ray.io/en/master/ray-core/patterns/index.html#design-patterns-anti-patterns",861"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",862"https://docs.ray.io/en/master/ray-core/patterns/tree-of-actors.html#pattern-using-a-supervisor-actor-to-manage-a-tree-of-actors"863],864"document_ids": [86533197,86633203,86731218,86834532,86933235870],871"answer": " No, in the supervised actor pattern, if the supervisor actor dies (or the driver), the worker actors will still be terminated, even if the supervisor passes a reference to the worker actor to another actor. This is because the supervision hierarchy is defined by the supervisor actor, and its death will cause the termination of its child actors.",872"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"873},874{875"question": "How do I read a large text file in S3 with Ray?",876"sources": [877"https://docs.ray.io/en/master/data/working-with-text.html#saving-text",878"https://docs.ray.io/en/master/data/working-with-text.html#performing-inference-on-text",879"https://docs.ray.io/en/master/data/working-with-text.html#reading-text-files",880"https://docs.ray.io/en/master/data/examples/ocr_example.html#running-the-ocr-software-on-the-data",881"https://docs.ray.io/en/master/data/loading-data.html#reading-files-from-cloud-storage"882],883"document_ids": [88455076,88555023,88654814,88759263,88844476889],890"answer": " To read a large text file in S3 with Ray, you can use the `ray.data.read_text` function and provide the S3 URI for the file. Here is an example:\n\n```\nds = ray.data.read_text(\"s3://anonymous@ray-example-data/this.txt\")\n```\n\nThis will return a `ray.data.Dataset` object that contains the contents of the text file. You can use this object to perform various operations on the data, such as displaying the first few rows:\n\n```\nds.show(3)\n```\n\nIt's important to note that the `read_text` function will automatically read the entire contents of the file into memory, so it may not be suitable for very large files. In those cases, you may want to consider using the `read_binary_files` function instead, which allows you to stream the data in chunks. Here is an example:\n\n```\nds = ray.data.read_binary_files(\"s3://<bucket>/<folder>\", include_paths=True)\n```\n\nThis will return a `Dataset` of binary data, which you can then process using the appropriate decoding functions. For example, if the data is encoded as text using the UTF-8 encoding, you can use the `map` function to decode it as follows:\n\n```\nds = ds.map(lambda bytes: bytes.decode(\"utf-8\"))\n```\n\nFor more information and options, please see the Ray documentation on reading data.",891"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"892},893{894"question": "how do I get a ray dataset from pandas",895"sources": [896"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",897"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",898"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas.html#ray-data-from-pandas",899"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas_refs.html#ray-data-from-pandas-refs",900"https://docs.ray.io/en/master/data/api/doc/ray.data.from_pandas_refs.html#ray-data-from-pandas-refs"901],902"document_ids": [90354894,90454917,90554928,90657244,90757220908],909"answer": " To get a Ray dataset from pandas, you can use the `ray.data.from_pandas()` function and pass your pandas DataFrame as an argument. If you have a list of pandas DataFrames, you can pass that list as an argument instead. Here's the syntax for both cases:\n\n* To create a Ray Dataset from a single pandas DataFrame:\n```python\nray.data.from_pandas(df)\n```\n* To create a Ray Dataset from a list of pandas DataFrames:\n```python\nray.data.from_pandas([df1, df2, df3])\n```\nNote that `df1`, `df2`, and `df3` are pandas DataFrames.\n\nAdditionally, you can also create a Ray Dataset from a list of Ray object references to pandas dataframes using the same `ray.data.from_pandas()` function.",910"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"911},912{913"question": "can you give me an example of using `ray.data.map` ?",914"sources": [915"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",916"https://docs.ray.io/en/master/ray-core/patterns/pipelining.html#pattern-using-pipelining-to-increase-throughput",917"https://docs.ray.io/en/master/data/data.html#learn-more",918"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing",919"https://docs.ray.io/en/master/data/examples/index.html#ray-data-examples"920],921"document_ids": [92257898,92332272,92450817,92564608,92663421927],928"answer": " Sure, here's an example of using `ray.data.map`:\n```python\nimport ray\n\n# Create a Ray Data object\ndata = ray.data.range(10)\n\n# Define a function to apply to each element in the dataset\ndef square(x):\n return x ** 2\n\n# Use `ray.data.map` to apply the function to each element in the dataset\nresult = data.map(square)\n\n# The result is another Ray Data object that contains the squared values\n```\nIn this example, we create a Ray Data object `data` containing the numbers 0 through 9. We then define a function `square` that takes an integer and returns its square. Finally, we use `ray.data.map` to apply the `square` function to each element in the `data` object, resulting in a new Ray Data object `result` that contains the squared values.",929"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"930},931{932"question": "can you give me an example of using `ray.data.map` , with a callable class as input?",933"sources": [934"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map.html#ray-data-dataset-map",935"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#ray-actors",936"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",937"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#mapping-data",938"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#preprocessing"939],940"document_ids": [94157898,94239929,94385898,94436012,94564608946],947"answer": " Sure, here's an example of using `ray.data.map` with a callable class as input:\n```python\nimport ray\n\nclass Mapper:\n def __call__(self, batch):\n # apply some transformation to the batch here\n return transformed_batch\n",948"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"949},950{951"question": "How to set memory limit for each trial in Ray Tuner?",952"sources": [953"https://docs.ray.io/en/master/tune/api/env.html#environment-variables-used-by-ray-tune",954"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy",955"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-aware-scheduling",956"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources",957"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-node-resources"958],959"document_ids": [9609037,96129906,96230643,96328753,96428765965],966"answer": " To set the memory limit for each trial in Ray Tuner, you can specify the `memory` argument when starting Ray to allocate a certain amount of available memory for the Ray scheduler. For example, to set the memory limit to 70% of the available memory for each trial, you can use the following command to start Ray:\n\n```\nray start --memory=70%\n```\n\nThis will allocate 70% of the available memory to the Ray object store, and the Ray scheduler will automatically manage the memory usage for each trial within this limit.\n\nHowever, Ray Tuner does not provide a built-in way to set a separate memory limit for each individual trial. If you need to limit the memory usage of each trial more strictly, you may need to implement this functionality yourself, for example, by monitoring the memory usage of each trial and terminating the trial when the memory usage exceeds a certain threshold.",967"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"968},969{970"question": "how do I get the actor id of an actor",971"sources": [972"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_actor.html#ray-util-state-get-actor",973"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.actor_id.html#ray-runtime-context-runtimecontext-actor-id",974"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log",975"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.list_logs.html#ray-util-state-list-logs",976"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.html#ray-runtime-context-runtimecontext"977],978"document_ids": [979851,98026431,9811451,982966,98361750984],985"answer": " To get the actor id of an actor, you can use the `get_actor_id()` function. It returns the current actor ID in the worker.",986"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"987},988{989"question": "can ray.init() can check if ray is all-ready initiated ?",990"sources": [991"https://docs.ray.io/en/master/ray-core/api/core.html#core-api",992"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",993"https://docs.ray.io/en/master/ray-core/api/doc/ray.is_initialized.html#ray-is-initialized",994"https://docs.ray.io/en/master/rllib/package_ref/env.html#environments",995"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#actor-process-failure"996],997"document_ids": [99828188,99936409,100036406,100174210,1002249421003],1004"answer": " No, `ray.init()` is used to initialize Ray, not to check if it is already initialized. To check if Ray is already initialized, you can use `ray.is_initialized()`. This function returns `True` if `ray.init()` has already been called, and `False` otherwise.",1005"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1006},1007{1008"question": "What does the `compute=actor` argument do within `ray.data.map_batches` ?",1009"sources": [1010"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1011"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",1012"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",1013"https://docs.ray.io/en/master/data/transforming-data.html#transforming-batches-with-actors",1014"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference"1015],1016"document_ids": [101746432,101865905,101949843,102046385,1021659221022],1023"answer": " The `compute=ray.data.ActorPoolStrategy(size=2)` argument within `ray.data.map_batches` specifies that the map operation should be executed using an actor pool strategy with a fixed size of 2 actors. This means that the batches of data will be processed in parallel by up to two actors, which can help improve the performance of the map operation.",1024"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1025},1026{1027"question": "how do I use wandb logger with accelerateTrainer?",1028"sources": [1029"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#using-weights-biases-with-tune",1030"https://docs.ray.io/en/master/train/user-guides/experiment-tracking.html#using-native-experiment-tracking-libraries",1031"https://docs.ray.io/en/master/tune/api/logging.html#wandb-integration",1032"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example",1033"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#wandbloggercallback"1034],1035"document_ids": [103617238,103766717,10389312,103917246,1040174961041],1042"answer": " To use WandB logger with AccelerateTrainer, you can follow these steps:\n\n1. Import the necessary libraries:\n```python\nimport wandb\nfrom accelerate.logging import LoggerCallback\n```\n2. Initialize the WandB logger:\n```python\nwandb_logger = WandbLogger(\n name=\"demo-run\",\n project=\"demo-project\",\n)\n```\n3. Add the WandB logger to the Accelerate Trainer:\n```python\nfrom accelerate import Accelerator\n\naccelerator = Accelerator(logger=[LoggerCallback(wandb_logger)])\n```\n4. Use `wandb.log()` to log your training process as you normally would.\n\nRemember to set up WandB on all machines you're running your training on for automatic tracking and visualization.",1043"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1044},1045{1046"question": "What will be implicitly put into object store?",1047"sources": [1048"https://docs.ray.io/en/master/ray-core/api/doc/ray.get.html#ray-get",1049"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-3-avoid-passing-same-object-repeatedly-to-remote-tasks",1050"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",1051"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",1052"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores"1053],1054"document_ids": [105524682,105625488,105736003,105823543,1059392691060],1061"answer": " When an object is stored using an object store, it will automatically manage the allocation of system memory to prevent premature filling and unnecessary cost. The object store will implicitly put the object in the store, with the default memory allocation based on available system memory.",1062"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1063},1064{1065"question": "How do I kill or cancel a ray task that I already started?",1066"sources": [1067"https://docs.ray.io/en/master/ray-core/api/core.html#tasks",1068"https://docs.ray.io/en/master/ray-core/tasks.html#cancelling-tasks",1069"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",1070"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1071"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel"1072],1073"document_ids": [107428218,107531656,10764667,107725021,1078251221079],1080"answer": " To kill or cancel a Ray task that you have already started, you can use the `ray.cancel()` function and pass in the object ref of the task as the argument. You can also specify optional arguments `force` and `recursive` in the function. If the task has already completed or is being executed, calling `ray.cancel()` will raise a `TaskCancelledError` when you call `ray.get()` on the cancelled task.",1081"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1082},1083{1084"question": "how to send extra arguments in dataset.map_batches function?",1085"sources": [1086"https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions",1087"https://docs.ray.io/en/master/data/api/dataset.html#basic-transformations",1088"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",1089"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningTrainer.html#ray-train-lightning-lightningtrainer",1090"https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions"1091],1092"document_ids": [109358196,109455684,109576190,109665162,1097583741098],1099"answer": " To send extra arguments in the `dataset.map_batches` function, you can include them as additional positional arguments after the `batch_size` argument. The `map_batches` function will pass these arguments to the provided function (`fn` in this case) along with each batch of data.\n\nHere's an example with an extra argument `arg_example`:\n\n```python\ndataset.map_batches(fn, batch_size, arg_example)\n```\n\nIn the function `fn`, you can then access the extra argument as a regular positional argument:\n\n```python\ndef fn(batch, arg_example):\n # Your code here\n```\n\nMake sure that the number and order of extra arguments match the definition in the function `fn`.",1100"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1101},1102{1103"question": "where does ray GCS store the history of jobs run on a kuberay cluster? What type of database and format does it use for this?",1104"sources": [1105"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#accessing-the-cluster-with-kubectl-exec",1106"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",1107"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",1108"https://docs.ray.io/en/master/ray-observability/getting-started.html#jobs-view",1109"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#what-does-the-rayjob-provide"1110],1111"document_ids": [111241540,111341349,111454944,1115641,1116384741117],1118"answer": " Based on the provided context, Ray GCS (KubeRay) stores the history of jobs run on a Kuberay cluster in a database, but the type of database and format used is not specified in the context. The Jobs view allows you to monitor the different jobs that have run on your Ray Cluster. You can define a Ray cluster and job using a Kubernetes config and use kubectl to create and manage them.",1119"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1120},1121{1122"question": "How to resolve ValueError: The actor ImplicitFunc is too large?",1123"sources": [1124"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.ResourceChangingScheduler.html#ray-tune-schedulers-resourcechangingscheduler",1125"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",1126"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",1127"https://docs.ray.io/en/master/train/api/doc/ray.train.data_parallel_trainer.DataParallelTrainer.html#ray-train-data-parallel-trainer-dataparalleltrainer",1128"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob"1129],1130"document_ids": [113113624,113226987,11335639,113462767,1135461511136],1137"answer": " The ValueError \"The actor ImplicitFunc is too large\" is raised when using an Actor with reuse\\_actors set to True in tune.TuneConfig(). To resolve this issue, set reuse\\_actors to False. Additionally, the Actor class can only handle one call to actor\\_func() at a time, and the train\\_loop\\_per\\_worker function should take in either 0 or 1 arguments. The warning suggests ensuring full parallelization by adjusting settings accordingly.",1138"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1139},1140{1141"question": "How do I use ray to distribute training for my custom neural net written using Keras in Databricks?",1142"sources": [1143"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1144"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#starting-with-pytorch-data",1145"https://docs.ray.io/en/master/train/distributed-tensorflow-keras.html#reporting-results",1146"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-structured-data",1147"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-preprocess-data-for-training"1148],1149"document_ids": [11505174,115167294,115263026,115367580,1154570301155],1156"answer": " To distribute training for your custom Keras neural net in Databricks, you can follow these steps:\n\n1. Install Ray Train and TensorFlow packages.\n2. Convert your PyTorch dataset to a Ray Dataset.\n3. Apply your custom Keras training loop to Ray Train.\n4. Use a Ray Train Trainer on the dataset before passing it to the training function.\n\nHere's a code snippet to illustrate the process:\n```python\n# Import necessary libraries\nimport ray\nimport tensorflow as tf\nfrom ray import train\n\n# Initialize Ray\nray.init()\n\n# Convert your Databricks dataset to a Ray Dataset\nray_dataset = ray.data.from_databricks(your_databricks_dataset)\n\n# Define your custom Keras model\nkeras_model = ...\n\n# Define your custom training loop\n@tf.function\ndef train_function(model, dataset):\n ...\n\n# Use Ray Train with your custom training loop and model\nray_train_trainer = train.Trainer(\n train_function,\n datasets={\"train\": ray_dataset},\n model_builder=lambda: keras_model,\n)\n\n# Train the model\nresult = ray_train_trainer.train()\n```\nNote that this is a general outline, and you'll need to adapt it to your specific use case. Make sure to replace `your_databricks_dataset` with your actual Databricks dataset and implement the `train_function` according to your custom training loop.",1157"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1158},1159{1160"question": "how to use ray.put and ray,get?",1161"sources": [1162"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1163"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tips-for-first-time-users",1164"https://docs.ray.io/en/master/ray-core/api/doc/ray.put.html#ray-put",1165"https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization",1166"https://docs.ray.io/en/master/ray-core/objects/serialization.html#customized-serialization"1167],1168"document_ids": [116924308,117024331,117135988,117227110,1173271821174],1175"answer": " To use `ray.put()` and `ray.get()`, you first use `ray.put()` to store an object in Ray's object store, and then use `ray.get()` to retrieve the object from the object store. Here's an example:\n\n```python\n# Store an object in the object store\nray.put(A(1))\n\n# Retrieve the object from the object store\nobj = ray.get(<object-id>)\n```\n\nIn the example you provided, `<object-id>` would be the return value of the `ray.put(A(1))` call.\n\nNote that if you try to use `ray.get()` with an invalid object ID, you will get a `TypeError`. The example code you provided shows how to handle this error using a try-except block.",1176"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1177},1178{1179"question": "how do I use Ray Data to pre process many files?",1180"sources": [1181"https://docs.ray.io/en/master/data/preprocessors.html#using-preprocessors",1182"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#preprocessing-data",1183"https://docs.ray.io/en/master/ray-overview/use-cases.html#how-do-i-do-many-model-training-on-ray",1184"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#caching-the-preprocessed-dataset",1185"https://docs.ray.io/en/master/ray-core/patterns/limit-running-tasks.html#example-use-case"1186],1187"document_ids": [118851204,118967206,11902703,119167740,1192307101193],1194"answer": " To preprocess many files using Ray Data, you can take advantage of Ray Data's preprocessors and support for a wide range of preprocessing operations. If you have a large preprocessing pipeline, Ray Data allows you to train multiple preprocessors in parallel.\n\nFor each input file, you can process it independently using Ray remote functions. When preprocessing a file, you can use the `ray.data.preprocessors.PythonFunction` preprocessor and the `ray.data.cache.pin_in_object_store()` method to compute the entire preprocessed dataset and store it in the Ray object store for efficient access.\n\nHere's a high-level outline of how to preprocess multiple files:\n\n1. Create a Ray Data object from your input files.\n2. Define a Python function for your preprocessing steps.\n3. Create a `PythonFunction` preprocessor using your preprocessing function.\n4. Apply the preprocessor to the Ray Data object.\n5. Use `pin_in_object_store()` to store the preprocessed dataset in the Ray object store.\n\nThis approach enables efficient preprocessing and utilization of Ray's parallel processing capabilities for handling many files.",1195"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1196},1197{1198"question": "can\u2019t pickle SSLContext objects",1199"sources": [1200"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1201"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1202"https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting",1203"https://docs.ray.io/en/master/ray-core/objects/serialization.html#overview",1204"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-do-you-log-arbitrary-files-from-a-tune-trainable"1205],1206"document_ids": [120727233,120827271,120927281,121026697,1211167471212],1213"answer": " Based on the context provided, the answer to your query is that the error you're encountering, \"can't pickle SSLContext objects,\" is likely due to incompatibility with the Pickle protocol version 5, which is what Ray is currently compatible with. Additionally, Ray supports serialization of a limited set of objects, and SSLContext objects may not be included in that set. It's also possible that there are other non-serializable objects causing the issue, such as \"_thread.lock\" objects, as indicated in the error messages provided.",1214"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1215},1216{1217"question": "How do I install CRDs in Kuberay?",1218"sources": [1219"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#id2",1220"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#step-2-deploy-a-kuberay-operator",1221"https://docs.ray.io/en/master/cluster/kubernetes/examples/gpu-training-example.html#the-end-to-end-workflow",1222"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",1223"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/ingress.html#manually-setting-up-nginx-ingress-on-kind"1224],1225"document_ids": [122644558,122737404,122846391,122940882,1230447661231],1232"answer": " To install CRDs (Custom Resource Definitions) in KubeRay, follow these steps:\n\n1. Install the KubeRay operator and CRDs using the following command:\n\n`# Step 1: Install KubeRay operator and CRD`\n\nThis command will install both CRDs and the KubeRay operator v0.6.0.\n\nPlease note that CRDs are a prerequisite for using KubeRay. They are an essential component of KubeRay, as they define the custom resources that can be used with the KubeRay operator.",1233"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1234},1235{1236"question": "Why the function for Ray data batch inference has to be named as _`__call__()`_ ?",1237"sources": [1238"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",1239"https://docs.ray.io/en/master/data/examples/batch_inference_object_detection.html#batch-inference-with-ray-data",1240"https://docs.ray.io/en/master/ray-air/examples/batch_tuning.html#contents",1241"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",1242"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#batch-prediction-with-ray-core"1243],1244"document_ids": [124585898,124664519,124784452,124848123,1249333861250],1251"answer": " The function for Ray data batch inference should be named as `__call__()` because Ray Data's batch inference requires defining a callable class, and `__call__()` is a special method in Python that allows an object to be called like a function. This method is used to define the behavior of the callable class for batch inference with Ray Data.",1252"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1253},1254{1255"question": "How to disconnnect ray client?",1256"sources": [1257"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#how-do-you-use-the-ray-client",1258"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",1259"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#ray-client",1260"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#id3",1261"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#client-disconnections"1262],1263"document_ids": [126454245,12657591,126653983,12673222,1268546201269],1270"answer": " To disconnect a Ray client, you can typically use the \"disconnect\" or \"shutdown\" command, depending on the specific Ray client implementation. However, according to the context provided, there is a note stating that the support for Ray Client is experimental and currently does not support client disconnections. Therefore, it might not be possible to disconnect a Ray client in a straightforward way, and you may need to refer to the specific documentation or contact the Ray support for more information.",1271"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1272},1273{1274"question": "how to submit job with python with local files?",1275"sources": [1276"https://docs.ray.io/en/master/cluster/running-applications/job-submission/cli.html#ray-jobs-cli-api-reference",1277"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job",1278"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",1279"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1280"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#dependency-management"1281],1282"document_ids": [128353329,128452922,128553081,128651356,1287520191288],1289"answer": " To submit a job with Python using local files, you can use the following command:\n```bash\nray job submit --working_dir=\"./\" -- python script.py\n```\nThis command will submit a job with the entrypoint `python script.py` and use the current directory (`.`) as the working directory. This means that any local files or directories referenced in `script.py` will be accessible during job execution.\n\nAlternatively, you can use the `ray.util.client.ray_start()` function to start a Ray cluster and then use the `client.submit_job()` method to submit the job. Here's an example:\n```python\nimport ray\n\nray.util.client.ray_start(temp_dir=\"./ray_temp\")\nclient = ray.util.client.RayClient()\n\njob_id = client.submit_job(\n entrypoint=\"python script.py\",\n runtime_env={\n # Add any necessary runtime environment variables or packages here\n }\n)\n```\nThis will start a Ray cluster in the `./ray_temp` directory and submit the job with the entrypoint `python script.py`. Any necessary runtime environment variables or packages can be added to the `runtime_env` dictionary.",1290"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1291},1292{1293"question": "How do I do inference from a model trained by Ray tune.fit()?",1294"sources": [1295"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-ai-runtime-libraries-quickstart",1296"https://docs.ray.io/en/master/tune/api/doc/ray.tune.ResultGrid.html#ray-tune-resultgrid",1297"https://docs.ray.io/en/master/ray-air/examples/convert_existing_pytorch_code_to_ray_air.html#summary",1298"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide",1299"https://docs.ray.io/en/master/train/getting-started-transformers.html#transformerstrainer-migration-guide"1300],1301"document_ids": [13025296,130310149,130472088,130558612,1306587751307],1308"answer": " To do inference from a model trained by Ray Tune.fit(), you can use the Ray Train library to export the trained model and then load it for inference. However, Ray Tune itself does not directly support inference. Here's an example of how you can train a model using Ray Tune and then perform inference using Ray Train:\n```python\nfrom ray import train, tune\n\ndef train_function(config):\n # Define your training code here\n pass\n\ntuner = tune.Tuner(train_function, param_space={'learning_rate': tune.loguniform(1e-4, 1e-2)})\nresult = tuner.fit()\n\n# Get the best hyperparameters\nbest_config = result.get_best_config()\n\n# Train the model with the best hyperparameters\nray_trainer = train.Trainer(backend=\"torch\", num_workers=1, use_gpu=False)\nray_trainer.start()\nray_trainer.train(**best_config)\n\n# Export the trained model\nexport_path = ray_trainer.export_model()\n\n# Load the exported model for inference\nimport torch\n\nmodel = torch.jit.load(export_path)\n# Use the loaded model for inference\n```\nNote that this is just an example and you will need to modify it to fit your specific use case.",1309"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1310},1311{1312"question": "is there a way to load and run inference without using pytorch or tensorflow directly?",1313"sources": [1314"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningPredictor.call_model.html#ray-train-lightning-lightningpredictor-call-model",1315"https://docs.ray.io/en/master/serve/tutorials/serve-ml-models.html#serving-ml-models-tensorflow-pytorch-scikit-learn-others",1316"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningPredictor.predict.html#ray-train-lightning-lightningpredictor-predict",1317"https://docs.ray.io/en/master/data/working-with-pytorch.html#batch-inference-with-pytorch",1318"https://docs.ray.io/en/master/data/overview.html#offline-batch-inference"1319],1320"document_ids": [132161403,1322102006,132364502,132454024,1325481781326],1327"answer": " Yes, it is possible to load and run inference without using PyTorch or TensorFlow directly. The context suggests that the system can load a TensorFlow model from the file system and run inference on a batch of tensor data, without explicitly mentioning the need to use TensorFlow or PyTorch APIs directly. Therefore, it can be inferred that there is a way to use the system to run inference on a batch of tensor data, using a previously trained TensorFlow model, without directly using the TensorFlow or PyTorch libraries.",1328"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1329},1330{1331"question": "what does ray do",1332"sources": [1333"https://docs.ray.io/en/master/ray-observability/key-concepts.html#ray-states",1334"https://docs.ray.io/en/master/ray-overview/index.html#what-you-can-do-with-ray",1335"https://docs.ray.io/en/master/train/train.html#install-ray-train",1336"https://docs.ray.io/en/master/data/data.html#install-ray-data",1337"https://docs.ray.io/en/master/ray-more-libs/installation.html#installed-python-dependencies"1338],1339"document_ids": [13407492,13417154,134260436,134350787,13441068941345],1346"answer": " Ray is a universal array API and a new way to parallelize and scale Python applications. With Ray, you can easily parallelize and distribute computations across CPUs and GPUs in your local machine or over a cluster. It provides a simple, unified API for parallelism and distributed computing, which allows you to write scalable code in a more straightforward and productive manner. You can use Ray for tasks such as parallelizing computations, distributed data processing, and building and running machine learning models.",1347"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1348},1349{1350"question": "If I specify a fractional GPU in the resource spec, what happens if I use more than that?",1351"sources": [1352"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",1353"https://docs.ray.io/en/master/ray-core/tasks/using-ray-with-gpus.html#fractional-gpus",1354"https://docs.ray.io/en/master/tune/examples/tune-vanilla-pytorch-lightning.html#training-with-gpus",1355"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#fractional-cpus-and-fractional-gpus",1356"https://docs.ray.io/en/master/rllib/rllib-training.html#specifying-resources"1357],1358"document_ids": [135935984,136029013,136135959,136295795,1363858841364],1365"answer": " If you specify a fractional GPU in the resource spec, and you end up using more than that fraction, it's important to note that it is your responsibility to manage resource usage. Ray supports fractional GPU requirements, and you can specify fractional GPUs for Tune. The number of GPUs can be fractional quantities (e.g., 0.5) to allocate only a fraction of a GPU. However, using more resources than allocated may impact the performance of other tasks or processes running on the same system. To avoid this, ensure you manage your resource usage accordingly.",1366"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1367},1368{1369"question": "how to pickle a variable defined in actor\u2019s init method",1370"sources": [1371"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1372"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1373"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#getting-a-summary-of-states-of-entities-by-type",1374"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods",1375"https://docs.ray.io/en/master/ray-core/ray-dag.html#ray-dag-with-classes-and-class-methods"1376],1377"document_ids": [137825975,13791712,13801971,138125990,1382259671383],1384"answer": " To pickle a variable defined in an actor's `__init__` method, you need to ensure that the variable is part of the actor's public interface. In the provided context, the variable `i` is an instance variable defined in the `__init__` method of the `Actor` class.\n\nHowever, directly pickling `a1.i` or `a2.i` may not work as expected, because ray actors are designed to be distributed and may not be picklable due to their remote nature.\n\nInstead, you can define a method in the `Actor` class that returns the value of `i`, and then call that method to get the value to pickle. Here's an example:\n\n```python\n@ray.remote\nclass Actor:\n def __init__(self, init_value):\n self.i = init_value\n\n def get_i(self):\n return self.i\n\na1 = Actor.bind(10) # Instantiate Actor with init_value 1",1385"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1386},1387{1388"question": "how do I do an all_reduce operation among a list of actors",1389"sources": [1390"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorSummaries.html#ray-util-state-common-actorsummaries",1391"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction",1392"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/optimize-performance.html#profiling-ray-actors-with-cprofile",1393"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#list-the-states-of-all-entities-of-certain-type",1394"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#actor-based-batch-prediction"1395],1396"document_ids": [13971085,139833969,13995518,14002095,1401339721402],1403"answer": " To perform an all\\_reduce operation among a list of actors, you can use the `ActorPool` to create a pool of actors, and then use the `map_reduce` function to apply a reduction operation on the results returned by all actors. Here's an example:\n```python\nfrom ray import actor\nfrom functools import reduce\n\n@actor\nclass BatchPredictor:\n def __init__(self, model_ref):\n self.model_ref = model_ref\n\n def actor_func(self):\n # Perform some computation here\n result = some_computation()\n return result\n\n# Create a list of actors\nnum_actors = 4\nactors = [BatchPredictor.remote(model_ref) for _ in range(num_actors)]\n\n# Create an ActorPool to manage the actors\npool = ActorPool(actors)\n\n# Define the reduction function\ndef reduce_func(x, y):\n return x + y\n\n# Use map_reduce to apply the reduction function on the results returned by all actors\nresult = reduce(reduce_func, pool.map(BatchPredictor.actor_func, actors))\n```\nIn this example, `map_reduce` applies the `actor_func` method on all actors in the `actors` list, and returns a list of results. The `reduce` function then applies the `reduce_func` function on the results to perform the reduction operation. In this case, the reduction function is simply adding the results together, but you can replace it with any other reduction function as needed.",1404"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1405},1406{1407"question": "What will happen if we specify a bundle with `{\"CPU\":0}` in the PlacementGroup?",1408"sources": [1409"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-child-tasks-and-actors",1410"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#placement-group",1411"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable",1412"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",1413"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#placement-groups-are-not-composable"1414],1415"document_ids": [141632765,141730960,14185000,141932021,142049961421],1422"answer": " If you specify a bundle with `{\"CPU\":0}` in the PlacementGroup, it means you're trying to create a bundle with zero CPU resources. This is unlikely to be valid, as it doesn't provide any computational capability for the task. It's recommended to specify positive resource quantities when creating bundles in a PlacementGroup.",1423"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1424},1425{1426"question": "How to cancel job from UI?",1427"sources": [1428"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#interacting-with-long-running-jobs",1429"https://docs.ray.io/en/master/workflows/api/doc/ray.workflow.cancel.html#ray-workflow-cancel",1430"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",1431"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",1432"https://docs.ray.io/en/master/workflows/metadata.html#notes"1433],1434"document_ids": [143551686,143670915,143724207,14384667,1439692171440],1441"answer": " To cancel a job from the UI, you can use the `workflow.cancel()` function, providing the ID of the workflow you wish to cancel. For example: `workflow.cancel(workflow_id=\"some_job\")`. This will stop the job and set its status to \"CANCELED\". Note that this is a general approach for cancelling Ray workflows and may not be specific to the UI. The specifics of canceling a job from a UI may depend on the particular implementation of the UI.",1442"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1443},1444{1445"question": "how do I get my project files on the cluster when using Ray Serve? My workflow is to call `serve deploy config.yaml --address <remote dashboard agent address>`",1446"sources": [1447"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#inspect-deeper",1448"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm",1449"https://docs.ray.io/en/master/serve/production-guide/index.html#working-example-fruitstand-application",1450"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-3-2-examples-for-troubleshooting-dependency-issues",1451"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#deploy-on-vm"1452],1453"document_ids": [1454103107,1455104079,1456104709,145735758,14581040831459],1460"answer": " To get your project files on the cluster when using Ray Serve, you should include the necessary files in the config.yaml file that you provide to `serve deploy`. Ray Serve deploys the application to a Ray cluster over HTTP using the config file, so any files or dependencies needed for your project should be specified in the config.yaml. Make sure to test your Ray Serve script locally or in a RayCluster before deploying to ensure everything works as expected.",1461"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1462},1463{1464"question": "how do i install ray nightly wheel",1465"sources": [1466"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1467"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1468"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1469"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",1470"https://docs.ray.io/en/master/ray-more-libs/installation.html#install-ray-java-with-maven"1471],1472"document_ids": [1473106046,1474527,14756397,14764662,14771061761478],1479"answer": " To install the Ray nightly wheel, you can follow these steps:\n\n1. Go to the Daily Releases (Nightlies) page for Ray (<https://packages.anziamedia.net/ray/nightly/>).\n2. Choose the appropriate wheel file for your system and Python version.\n3. Use pip to install the wheel, for example:\n\n```\npip install /path/to/ray-nightly-py3-wheelfile.whl\n```\n\nNote that if you're installing a Ray Java snapshot, you'll also need to install the corresponding nightly Ray Python wheels.",1480"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1481},1482{1483"question": "how do i install the latest ray nightly wheel?",1484"sources": [1485"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",1486"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",1487"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",1488"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-python-only",1489"https://docs.ray.io/en/master/ray-contribute/development.html#building-ray-from-source"1490],1491"document_ids": [1492527,14936397,1494106046,14954662,149644681497],1498"answer": " To install the latest Ray nightly wheel, you can follow these steps:\n\n1. Go to the Daily Releases (Nightlies) page for Ray (<https://packages.anzuma.ai/ray/nightly/>).\n2. Choose the appropriate wheel file for your system (e.g., cp38-cp38-linux\\_x86\\_64.whl for Python 3.8 on Linux 64-bit).\n3. Install the wheel using pip, with a command like:\n\npip install /path/to/the/wheel/file.whl\n\nReplace \"/path/to/the/wheel/file.whl\" with the actual path to the wheel file you downloaded.\n\nThis process will install the latest nightly build of Ray on your system.",1499"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1500},1501{1502"question": "how can I write unit tests for Ray code?",1503"sources": [1504"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tips-for-testing-ray-programs",1505"https://docs.ray.io/en/master/ray-contribute/getting-involved.html#submitting-and-merging-a-contribution",1506"https://docs.ray.io/en/master/ray-core/examples/testing-tips.html#tip-1-fixing-the-resource-quantity-with-ray-init-num-cpus",1507"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-3-run-ray-code",1508"https://docs.ray.io/en/master/serve/develop-and-deploy.html#test-a-ray-serve-application-locally"1509],1510"document_ids": [151137915,15125302,151338022,151454385,1515952801516],1517"answer": " To write unit tests for Ray code, follow these steps:\n\n1. Look for tips and examples in the 'Tips for testing Ray programs' section.\n2. Place your tests in the relevant file in the `ray/python/ray/tests/` directory.\n3. When writing tests, avoid relying on `ray.init()` to prevent implicit dependencies.\n4. Before testing Ray-specific code, ensure Ray is properly initialized by following the guidelines in 'Step 3: Run Ray code'.\n5. If you're testing a Ray Serve application, refer to the 'Test a Ray Serve application locally' section for additional guidance.",1518"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1519},1520{1521"question": "How I stop Ray from spamming lots of Info updates on stdout?",1522"sources": [1523"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1524"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#how-to-redirect-trainable-logs-to-files-in-a-tune-run",1525"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#outdated-function-definitions",1526"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",1527"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr"1528],1529"document_ids": [15307554,153116693,15325062,15333150,153475551535],1536"answer": " To stop Ray from spamming lots of Info updates on stdout, you can use Ray's logger instead of printing to stdout and stderr directly. This way, you have more control over the output and can avoid excessive updates. Note that updating Ray to the latest version may also include improvements to the output behavior.",1537"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1538},1539{1540"question": "how to deploy stable diffusion 2.1 with Ray Serve?",1541"sources": [1542"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",1543"https://docs.ray.io/en/master/serve/tutorials/stable-diffusion.html#serving-a-stable-diffusion-model",1544"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-5-send-a-request-to-the-text-to-image-model",1545"https://docs.ray.io/en/master/cluster/kubernetes/examples/stable-diffusion-rayservice.html#step-3-install-a-rayservice",1546"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment"1547],1548"document_ids": [154945229,1550105567,155145347,155245241,15531069131554],1555"answer": " To deploy Stable Diffusion 2.1 with Ray Serve, follow these steps:\n\n1. Download the `ray-service.stable-diffusion.yaml` configuration file using the link: <https://raw.githubusercontent.com/ray-project/serve_config_examples/master/stable_diffusion/stable_diffusion-2.1.yaml>\n\n2. Configure and set up Ray Serve as needed.\n\n3. Deploy the Stable Diffusion model with Ray Serve using the downloaded configuration file:\n\n```bash\nray serve deploy ray-service.stable-diffusion.yaml\n```\n\n4. Test the Ray Serve deployment to ensure it's working correctly.",1556"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1557},1558{1559"question": "what is actor_handle?",1560"sources": [1561"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ObjectState.html#ray-util-state-common-objectstate",1562"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_context.RuntimeContext.current_actor.html#ray-runtime-context-runtimecontext-current-actor",1563"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle",1564"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",1565"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote"1566],1567"document_ids": [15682460,156930051,157026748,157134001,1572288901573],1574"answer": " In the provided context, `actor_handle` is a reference to an actor created using the `@ray.remote` decorator in the Ray framework. It can be created with or without initial arguments, either by value or by reference. The `actor_handle` is used to call methods on the actor and interact with the objects it contains.",1575"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1576},1577{1578"question": "how to kill a r detached actors?",1579"sources": [1580"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",1581"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",1582"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",1583"https://docs.ray.io/en/master/ray-core/api/core.html#actors",1584"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"1585],1586"document_ids": [158733062,158827677,158925619,159028251,1591295781592],1593"answer": " To kill a detached R actor, you can call `ray.kill(actor, *[, no_restart])`. This will forcefully terminate the actor, interrupting any running tasks and causing them to fail. Detached actors must be manually destroyed once you are sure that they are no longer needed. Additionally, if the detached actor was the one that created the actor you want to kill, killing the detached actor will also kill the actor it created.",1594"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1595},1596{1597"question": "How to force upgrade the pip package in the runtime environment if an old version exists?",1598"sources": [1599"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",1600"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-task-or-per-actor",1601"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",1602"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",1603"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job"1604],1605"document_ids": [160699614,160726704,160845496,160945490,1610529291611],1612"answer": " To force upgrade the pip package in the runtime environment if an old version exists, you can follow these steps:\n\n1. Create a new requirements file or update the existing one with the desired pip package version. For example, `requirements.txt` with the following content:\n```\ntensorflow==2.6.0\nrequests==2.26.0\npip==22.0.2\n```\n2. In your runtime environment configuration, set `pip_check` to `True` to ensure that the required package versions are met. For instance:\n```python\nRuntimeEnv(\n pip={\n \"packages\": [\"tensorflow\", \"requests\"],\n \"pip_check\": True,\n \"pip_version\": \"==22.0.2;python_version=='3.8.11'\"\n },\n runtime_env={\n \"working_dir\": \"./\",\n \"pip\": [\"-r\", \"requirements.txt\"]\n }\n)\n```\n\nBy setting `pip_check` to `True`, pip will ensure that the required package versions are met, and if any existing packages have an older version, pip will automatically upgrade them to the specified version in the requirements file.",1613"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1614},1615{1616"question": "How do I do global shuffle with Ray?",1617"sources": [1618"https://docs.ray.io/en/master/data/transforming-data.html#shuffling-rows",1619"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1620"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",1621"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle",1622"https://docs.ray.io/en/master/data/performance-tips.html#enabling-push-based-shuffle"1623],1624"document_ids": [162546697,162647661,162749431,162847667,1629477261630],1631"answer": " To perform a global shuffle using Ray, you can use the `random_shuffle()` function. First, make sure to import Ray and set the `RAY_DATA_PUSH_BASED_SHUFFLE` environment variable to 1 if you want to use push-based shuffle. Additionally, you can set the `use_push_based_shuffle` flag in the DataContext for enabling push-based shuffle.\n\nHere's a code example:\n\n```python\nimport ray\nimport ray.data_engine.execution.context as context\n\n# Set the environment variable for push-based shuffle (optional)\nray.init(env_vars={\"RAY_DATA_PUSH_BASED_SHUFFLE\": \"1\"})\n\n# Create a DataContext and enable push-based shuffle (optional)\ndata_context = context.DataContext()\ndata_context.use_push_based_shuffle = True\n\n# Perform global shuffle using random_shuffle()\ndata = ray.data.range(100)\nshuffled_data = data.random_shuffle()\n```\n\nThis will randomly shuffle all rows in the dataset.",1632"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1633},1634{1635"question": "How to find namespace of an Actor?",1636"sources": [1637"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1638"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",1639"https://docs.ray.io/en/master/ray-core/advanced-topics.html#advanced-topics",1640"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1641"https://docs.ray.io/en/master/ray-core/api/doc/ray.get_actor.html#ray-get-actor"1642],1643"document_ids": [164435404,164534646,164623918,164735410,1648299901649],1650"answer": " To find the namespace of an Actor, you can access the 'namespace' attribute of the actor object. If the actor is not named (i.e., it's in an anonymous namespace), the 'namespace' attribute will be None. Here's an example:\n\n```python\n# Assuming 'my_actor' is an instance of an Actor class\nnamespace = my_actor.namespace\n```\n\nThe 'namespace' attribute will contain the string representation of the actor's namespace. If the actor is not named, the 'namespace' attribute will be set to None.",1651"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1652},1653{1654"question": "How does Ray work with async.io ?",1655"sources": [1656"https://docs.ray.io/en/master/ray-core/actors/async_api.html#asyncio-for-actors",1657"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",1658"https://docs.ray.io/en/master/ray-core/tips-for-first-time.html#tip-1-delay-ray-get",1659"https://docs.ray.io/en/master/ray-core/actors/async_api.html#defining-an-async-actor",1660"https://docs.ray.io/en/master/serve/api/index.html#serve-deploy"1661],1662"document_ids": [166326217,166426404,166524427,166626482,16671050881668],1669"answer": " Ray integrates natively with asyncio and can be used alongside it. Ray automatically detects if an actor method supports async when using async method definitions. Remote operations such as task and actor method invocations are asynchronous in Ray. However, it's important to note that using blocking ray.get or ray.wait inside async functions is discouraged. The call to Ray is itself async, and a successful response only means that the request was sent to the Ray cluster.",1670"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1671},1672{1673"question": "How do I debug a hanging `ray.get()` call? I have it reproduced locally.",1674"sources": [1675"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",1676"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",1677"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism",1678"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#code-example",1679"https://docs.ray.io/en/master/ray-core/patterns/ray-get-loop.html#anti-pattern-calling-ray-get-in-a-loop-harms-parallelism"1680],1681"document_ids": [168230870,168330887,168430865,168530996,1686309181687],1688"answer": " To debug a hanging `ray.get()` call that is hanging, you should avoid calling `ray.get()` in a loop and use it only for the final result. Instead of calling `ray.get()` right after scheduling the remote work, separate the call to `ray.get()` from the call to the remote functions. This way, the loop will not block until the result is ready.",1689"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1690},1691{1692"question": "can you show me an example of ray.actor.exit_actor()",1693"sources": [1694"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1695"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1696"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1697"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1698"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"1699],1700"document_ids": [170127046,170227025,170327078,170427050,1705213831706],1707"answer": " Sure, here's an example of how to use `ray.actor.exit_actor()` in Python:\n```python\n@ray.remote\nclass MyActor:\n def exit(self):\n ray.actor.exit_actor()\n\nmy_actor = MyActor.remote()\nmy_actor.exit.remote() # This will exit the actor\n```\nAfter calling `my_actor.exit.remote()`, attempting to call any further methods on `my_actor` will raise a `RayActorError`.",1708"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1709},1710{1711"question": "how to add log inside actor?",1712"sources": [1713"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",1714"https://docs.ray.io/en/master/ray-observability/key-concepts.html#worker-logs-stdout-and-stderr",1715"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",1716"https://docs.ray.io/en/master/ray-observability/getting-started.html#logs-view",1717"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.get_log.html#ray-util-state-get-log"1718],1719"document_ids": [17201097,17217576,17222266,17231108,172414581725],1726"answer": " To add log inside an actor, you can use the `print()` function to write messages to the actor's log. In your actor code, you can add log messages like this:\n```python\nprint(f\"Actor log message: {some_variable}\")\n```\nThis will write a log message to the actor's log that includes the value of `some_variable`.\n\nTo view the actor's log, you can go to the Actor table view in the Dashboard and click the \"Log\" button for the actor you're interested in. If you're working with a concurrent actor, you may need to query the log with the actor's ID to see its output.\n\nHere are the steps to follow:\n\n1. Go to the Actor table view in the Dashboard.\n2. Find the actor you want to view the log for and click its ID to open its details page.\n3. Click the \"Log\" button to view the actor's log.\n\nIf you're working with a concurrent actor, you can query its log with the actor ID like this:\n\n1. Go to the Actor table view in the Dashboard.\n2. Click the \"Log\" button in the top right corner of the page.\n3. In the \"Actor ID\" field, enter the ID of the concurrent actor you want to view the log for.\n4. Click the \"Query\" button to view the actor's log.",1727"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1728},1729{1730"question": "can you write a script to do batch inference with GPT-2 on text data from an S3 bucket?",1731"sources": [1732"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#step-1-reading-the-dataset-from-s3",1733"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#step-1-reading-the-dataset-from-s3",1734"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#overview",1735"https://docs.ray.io/en/master/train/api/doc/ray.train.lightning.LightningPredictor.predict.html#ray-train-lightning-lightningpredictor-predict",1736"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches"1737],1738"document_ids": [173960865,174061964,174165556,174264502,1743447611744],1745"answer": " Sure, here's a basic script to perform batch inference with GPT-2 on text data from an S3 bucket using the `map_batches()` function:\n```python\nfrom sagemaker.huggingface.model import HuggingFaceModel\n",1746"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1747},1748{1749"question": "How do I enable Ray debug logs?",1750"sources": [1751"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger",1752"https://docs.ray.io/en/master/ray-contribute/debugging.html#backend-logging",1753"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#getting-started",1754"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",1755"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#using-the-ray-debugger"1756],1757"document_ids": [17584120,17595973,17604162,176142489,176241231763],1764"answer": " To enable Ray debug logs, follow these steps:\n\n1. Set the `RAY_BACKEND_LOG_LEVEL` environment variable to `debug`:\n\n`export RAY_BACKEND_LOG_LEVEL=debug`\n\n2. Start Ray using the `ray start` command.\n\n3. Use the `ray debug` command to access the Ray debugger, which will provide you with debug logs that can be helpful for troubleshooting Ray applications.\n\nIn summary, the command to start Ray with debug logs is:\n\n`$ export RAY_BACKEND_LOG_LEVEL=debug; ray start; ray debug`",1765"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1766},1767{1768"question": "How do I list the current Ray actors from python?",1769"sources": [1770"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1771"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1772"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#get-started",1773"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-list",1774"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-via-an-actor-handle"1775],1776"document_ids": [17771747,17781727,17791856,17806246,1781269301782],1783"answer": " To list the current Ray actors from Python, you can use the `list_actors()` function from the `ray.util.state` module. Here is an example:\n```\nfrom ray.util.state import list_actors\nprint(list_actors())\n```\nThis will print a list of the actor IDs for all the currently running actors in the Ray cluster. If you want to include more information about each actor, such as its class name, you can use the `--detail` flag with the `ray list actors` command in the Ray CLI:\n```\nray list actors --detail\n```\nThis will print a table with more information about each actor, including its ID, class name, and status.",1784"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1785},1786{1787"question": "I want to kill the replica actor from Python. how do I do it?",1788"sources": [1789"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1790"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",1791"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",1792"https://docs.ray.io/en/master/ray-core/actors/terminating-actors.html#manual-termination-within-the-actor",1793"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#worker-killing-policy"1794],1795"document_ids": [179627020,179725111,1798101768,179927025,1800301111801],1802"answer": " To kill a replica actor from Python, you can use `ray.kill(actor)`. Here's how you can do it:\n\n1. Import the required modules:\n```python\nimport ray\n```\n\n2. Get a reference to the actor you want to kill.\n\n3. Use `ray.kill(actor)` to kill the actor.\n\nFor example:\n```python\nray.kill(my_actor)\n```\n\nNote that you should not use `os.kill()` or `process.kill()` to kill the actor process directly, as this can lead to resource leaks and other issues.\n\nAlso, if you want to kill the Serve controller, you can do so via the Python interpreter using the appropriate command.\n\nFinally, if you have a custom actor class, you can define an `exit()` method that calls `ray.actor.exit_actor()` to gracefully shut down the actor. For example:\n```python\n@ray.remote\nclass Actor:\n def exit(self):\n ray.actor.exit_actor()\n```\n\nYou can then call `actor.exit.remote()` to initiate a graceful shutdown of the actor.",1803"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1804},1805{1806"question": "how do I specify in my remote function declaration that I want the task to run on a V100 GPU type?",1807"sources": [1808"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",1809"https://docs.ray.io/en/master/ray-core/tasks.html#specifying-required-resources",1810"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#specifying-cpu-and-gpu-resources",1811"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#specifying-task-or-actor-resource-requirements",1812"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/gpu.html#gpu-workload-scheduling"1813],1814"document_ids": [181531276,181631264,181753165,181828951,1819415411820],1821"answer": " To specify that a remote function should run on a V100 GPU type, you can use the `@ray.remote(num_gpus=1)` decorator in your function definition. This annotation indicates that the task requires 1 GPU. Here is an example:\n```\n@ray.remote(num_gpus=1)\ndef my_function():\n # function code here\n\nmy_function.remote()\n```\nIf you want to specify the type of GPU, you can use the `ray.put` function to create a GPU-specific object and then pass it to the remote function as an argument. For example:\n```\nimport torch\n",1822"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1823},1824{1825"question": "How do I get started?",1826"sources": [1827"https://docs.ray.io/en/master/tune.html#ray-tune-hyperparameter-tuning",1828"https://docs.ray.io/en/master/cluster/kubernetes/index.html#learn-more",1829"https://docs.ray.io/en/master/cluster/vms/index.html#learn-more",1830"https://docs.ray.io/en/master/tune/index.html#ray-tune-hyperparameter-tuning",1831"https://docs.ray.io/en/master/ray-core/walkthrough.html#getting-started"1832],1833"document_ids": [18342278,183535230,183642435,18378363,1838347361839],1840"answer": " To get started, find the \"Getting Started\" section in the provided context. It should give you the necessary information to begin. If there are multiple \"Getting Started\" sections, select the first one as it's most likely the introduction.",1841"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1842},1843{1844"question": "How to specify python version in runtime_env?",1845"sources": [1846"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",1847"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1848"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",1849"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages",1850"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.submit_job.html#ray-job-submission-jobsubmissionclient-submit-job"1851],1852"document_ids": [185329451,185424547,185526985,185626988,1857529291858],1859"answer": " To specify the Python version in the runtime environment, you can include it in the \"conda\" dependencies section of the \"runtime\\_env\" dictionary. Here's an example:\n```bash\nruntime_env = {\n \"working_dir\": \"./\",\n \"conda\": {\n \"dependencies\": [\n \"python=3.8\",\n \"pytorch\",\n \"pip\",\n {\"pip\": [\"requests==2.26.0\"]}\n ]\n }\n}\n```\nIn this example, the Python version is set to 3.8 using \"python=3.8\". You can replace \"3.8\" with the desired Python version.",1860"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1861},1862{1863"question": "how to create a Actor in a namespace?",1864"sources": [1865"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1866"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1867"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",1868"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",1869"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides"1870],1871"document_ids": [187235435,187335410,187424510,187535404,1876346461877],1878"answer": " To create an actor in a specified namespace, include the namespace while creating the actor. Actors are created in anonymous namespaces by default, but you can specify a namespace to create the actor in that namespace.",1879"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1880},1881{1882"question": "Can I specify multiple working directories?",1883"sources": [1884"https://docs.ray.io/en/master/ray-core/configure.html#driver-options",1885"https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-applications",1886"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",1887"https://docs.ray.io/en/master/ray-core/configure.html#driver-options",1888"https://docs.ray.io/en/master/tune/api/doc/ray.tune.TuneConfig.html#ray-tune-tuneconfig"1889],1890"document_ids": [189133209,1892105723,189351340,189433213,1895130041896],1897"answer": " Yes, you can specify multiple working directories by separating them with a colon (:). Use the `--working-dir` argument followed by the path to the directories. For example, `--working-dir directory1:directory2:directory3`.",1898"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1899},1900{1901"question": "what if I set num_cpus=0 for tasks",1902"sources": [1903"https://docs.ray.io/en/master/ray-core/scheduling/resources.html#fractional-resource-requirements",1904"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",1905"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",1906"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#best-practice-for-large-scale-workloads",1907"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#configuring-parallelism-with-omp-num-threads"1908],1909"document_ids": [191029095,1911105285,19123917,1913105282,1914959081915],1916"answer": " If you set `num_cpus=0` for tasks, it means that no CPU resources will be allocated to those tasks. This is useful in situations where you don't want tasks to run on a particular node, such as the head node, to avoid CPU contention. However, keep in mind that if you set `num_cpus=0` for all tasks and no other tasks or actors are specified to use CPU resources, then effectively no tasks will be executed.",1917"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1918},1919{1920"question": "is it possible to have ray on k8s without using kuberay? especially with the case that autoscaler is enabled.",1921"sources": [1922"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#introduction",1923"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling",1924"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",1925"https://docs.ray.io/en/master/cluster/kubernetes/index.html#overview",1926"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis"1927],1928"document_ids": [192937708,193038842,193135167,193235162,1933233231934],1935"answer": " No, it is not possible to have Ray on K8s with autoscaling enabled without using KubeRay. The autoscaling feature is only available when using KubeRay for Ray serve fault tolerance.",1936"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1937},1938{1939"question": "how to manually configure and manage Ray cluster on Kubernetes",1940"sources": [1941"https://docs.ray.io/en/master/cluster/kubernetes/index.html#learn-more",1942"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/config.html#raycluster-configuration",1943"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/raycluster-quick-start.html#raycluster-quickstart",1944"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#what-does-the-rayservice-provide",1945"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide"1946],1947"document_ids": [194835245,194937488,195037366,195137981,1952393971953],1954"answer": " To manually configure and manage a Ray cluster on Kubernetes, follow these steps:\n\n1. Review the user guides on best practices for configuring Ray clusters on Kubernetes.\n2. Learn about the key aspects of Ray cluster configuration on Kubernetes.\n3. Use a Kubernetes config to define your Ray cluster and its Ray Serve applications.\n4. Manage and interact with the Ray clusters on Kubernetes as needed.\n\nThese resources will provide you with the necessary information to successfully set up and manage your Ray cluster on Kubernetes.",1955"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1956},1957{1958"question": "If I shutdown a raylet, will the tasks and workers on that node also get killed?",1959"sources": [1960"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",1961"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#what-is-the-memory-monitor",1962"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#what-is-the-memory-monitor",1963"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",1964"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention"1965],1966"document_ids": [19674683,196829308,196929314,197023433,1971292231972],1973"answer": " Yes, if a raylet is shutdown, the tasks and workers on that node will also be killed. This is because the raylet is responsible for managing resources on a node, and if it is shutdown, the system will start killing worker or raylet processes to free up memory. This can disrupt the application and may occur during an OOM (out-of-memory) event or if the combined usage of resources exceeds a configurable threshold. Additionally, if a raylet crashes, the corresponding node will be marked as dead and treated the same way as if it had been shutdown.",1974"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1975},1976{1977"question": "If I\u2019d like to debug out of memory, how do I Do that, and which documentation should I look?",1978"sources": [1979"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-out-of-memory",1980"https://docs.ray.io/en/master/ray-core/scheduling/ray-oom-prevention.html#out-of-memory-prevention",1981"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#memory-management",1982"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#debugging-memory-issues",1983"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage"1984],1985"document_ids": [19863549,198729266,198829400,19893546,199037961991],1992"answer": " To debug out-of-memory issues, you can:\n1. Refer to the \"Debugging Out of Memory\" documentation.\n2. Additionally, you can look at the \"Debugging Memory Issues\" section for more information on debugging out-of-memory errors in Ray.\n\nLink to the documentation: [Debugging Out of Memory](Debugging Out of Memory#)",1993"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"1994},1995{1996"question": "How to use callback in Trainer?",1997"sources": [1998"https://docs.ray.io/en/master/train/getting-started-transformers.html#quickstart",1999"https://docs.ray.io/en/master/train/getting-started-pytorch-lightning.html#reporting-checkpoints-and-metrics",2000"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#loading-checkpoints",2001"https://docs.ray.io/en/master/train/user-guides/monitoring-logging.html#monitoring-and-logging-metrics",2002"https://docs.ray.io/en/master/train/user-guides/checkpoints.html#saving-checkpoints"2003],2004"document_ids": [200558066,200666077,200767031,200868257,2009668762010],2011"answer": " To use a callback in a Trainer from the PyTorch Lightning library, you can add the callback as an argument when initializing the Trainer. This can be done in two ways:\n\n1. By passing the callback as a single argument to the `add_callback` method of the Trainer object, like this:\n```python\ntrainer.add_callback(callback)\n```\n2. By including the callback in the `callbacks` list when initializing the Trainer object, like this:\n```python\ntrainer = pl.Trainer(\n # ...\n callbacks=[callback]\n)\n```\nHere, `callback` can be any object that implements the `Callback` interface of PyTorch Lightning.\n\nFor example, you can use the `RayTrainReportCallback` callback from the `pytorch_lightning.callbacks` module like this:\n```python\nfrom pytorch_lightning.callbacks import RayTrainReportCallback\n\ntrainer = pl.Trainer(\n # ...\n callbacks=[RayTrainReportCallback()]\n)\n```\nOr, you can define your own custom callback by subclassing the `Callback` class and implementing the necessary methods, like this:\n```python\nfrom pytorch_lightning.callbacks import Callback\n\nclass MyRayTrainReportCallback(Callback):\n # Implement necessary methods here\n\ntrainer = pl.Trainer(\n # ...\n callbacks=[MyRayTrainReportCallback()]\n)\n```\nNote that if you pass multiple callbacks to the `callbacks` list, they will be executed in the order they are listed.",2012"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2013},2014{2015"question": "How to provide current working directory to ray?",2016"sources": [2017"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#reference-your-cloudwatch-json-config-files-in-your-cluster-config-yaml",2018"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2019"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",2020"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#uploads",2021"https://docs.ray.io/en/master/train/train.html#install-ray-train"2022],2023"document_ids": [202449359,202551446,20264855,202754834,2028604362029],2030"answer": " To provide the current working directory to Ray, you can specify the `--working-dir` flag when running `ray up` command, which will make the contents of that directory available to the Ray job. Alternatively, if you are running `ray.init()` in a runtime environment, you can specify a `working_dir` in the environment configuration, which will be used as the current working directory for Ray.",2031"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2032},2033{2034"question": "how to create an actor instance with parameter?",2035"sources": [2036"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",2037"https://docs.ray.io/en/master/ray-core/objects.html#passing-object-arguments",2038"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2039"https://docs.ray.io/en/master/ray-core/examples/monte_carlo_pi.html#creating-a-progress-actor",2040"https://docs.ray.io/en/master/ray-core/api/doc/ray.cross_language.java_actor_class.html#ray-cross-language-java-actor-class"2041],2042"document_ids": [204337731,204433981,204535435,204637703,2047256342048],2049"answer": " To create an actor instance with a parameter, you can use the `ActorClass.remote()` method and pass the parameter as an argument. This applies to actor constructors and actor method calls as well. For example, to create a Progress Actor with a specified namespace, you can call `ProgressActor.remote(\"namespace\")`.",2050"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2051},2052{2053"question": "how to push a custom module to ray which is using by Actor ?",2054"sources": [2055"https://docs.ray.io/en/master/ray-core/patterns/concurrent-operations-async-actor.html#example-use-case",2056"https://docs.ray.io/en/master/ray-core/api/doc/ray.remote.html#ray-remote",2057"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#named-actors",2058"https://docs.ray.io/en/master/ray-core/namespaces.html#specifying-namespace-for-named-actors",2059"https://docs.ray.io/en/master/ray-core/actors/actor-utils.html#actor-pool"2060],2061"document_ids": [206231052,206328863,206427397,206535429,2066260792067],2068"answer": " To push a custom module to Ray which is used by an Actor, you can do the following:\n\n1. Import the ray module.\n2. Define your remote function or actor class in the module.\n3. Before creating the actor, use `ray.init()` to initialize the Ray cluster and specify the location of the module using the `--object-store-memory` or `--block-for-object-store-start` flag.\n4. Import the module in the actor class.\n5. Create the actor as usual.\n\nHere's an example:\n\nmy\\_module.py:\n```python\nimport ray\n\n@ray.remote\nclass MyActor:\n def __init__(self):\n # import the module here\n import my_module\n self.my_module = my_module\n\n def my_method(self):\n # use the module here\n result = self.my_module.my_function()\n return result\n```\n\nTo push this module to Ray:\n```python\nray.init(\"ray://localhost:10001 --object-store-memory=10GB\")\nimport my_module\n```\n\nThen, you can create the actor and call its methods as usual:\n```python\nmy_actor = MyActor.remote()\nresult = ray.get(my_actor.my_method.remote())\n```",2069"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2070},2071{2072"question": "how to print ray working directory?",2073"sources": [2074"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#reference-your-cloudwatch-json-config-files-in-your-cluster-config-yaml",2075"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/ray-debugging.html#getting-started",2076"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",2077"https://docs.ray.io/en/master/cluster/vms/user-guides/community/yarn.html#skein-configuration",2078"https://docs.ray.io/en/master/ray-contribute/docs.html#building-the-ray-documentation"2079],2080"document_ids": [208149359,20824162,20834855,208451429,208528082086],2087"answer": " To print the working directory in Ray, you can use the `ray debug` command, which will print the directory you are running Ray in. You can find this information in the \"directory\" section of the output.",2088"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2089},2090{2091"question": "why I can not see log.info in ray log?",2092"sources": [2093"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2094"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",2095"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2096"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2097"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#putting-everything-together"2098],2099"document_ids": [210097601,21013170,21022648,210397843,2104429732105],2106"answer": " Based on the information provided, Ray libraries use specific logger names for logging. If you're unable to see `log.info` in your Ray logs, it might be because the logger name isn't configured correctly. By default, Ray log files are stored in the `/tmp/ray/session_*/logs` directory. To view the log files, you can access the directory mounted at `/tmp/ray` with the name `ray-logs`. Ensure that the logger name in your code matches the expected format for Ray logs. For more information, refer to the Ray Logging documentation.",2107"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2108},2109{2110"question": "when you use ray dataset to read a file, can you make sure the order of the data is preserved?",2111"sources": [2112"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",2113"https://docs.ray.io/en/master/ray-air/examples/torch_detection.html#define-a-custom-datasource",2114"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.union.html#ray-data-dataset-union",2115"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#enabling-reproducibility",2116"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.sort.html#ray-data-dataset-sort"2117],2118"document_ids": [211967547,212083170,212154499,212267555,2123490912124],2125"answer": " Yes, when using Ray Datasets to read a file, you can preserve the order of the data by enabling deterministic execution and setting the `preserve_order` flag. The order of the blocks in the dataset is preserved by default, but enabling deterministic execution ensures that the data is processed in the same order every time. You can do this by using the `ray.data.DataContext.get_current()` function and setting the `preserve_order` flag to True.",2126"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2127},2128{2129"question": "Can you explain what \"Ray will *not* retry tasks upon exceptions thrown by application code\" means ?",2130"sources": [2131"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#retrying-failed-tasks",2132"https://docs.ray.io/en/master/ray-core/tasks.html#fault-tolerance",2133"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#catching-application-level-failures",2134"https://docs.ray.io/en/master/ray-core/user-guide.html#user-guides",2135"https://docs.ray.io/en/master/workflows/basics.html#error-handling"2136],2137"document_ids": [213823845,213931719,214023482,214134459,2142688642143],2144"answer": " This statement means that if a task in Ray, a distributed computing framework, throws an exception due to an error in the application code, Ray will not automatically retry the task by default. This is different from task fault tolerance in Ray, where Ray will retry failed tasks, but that generally applies to cases where the task itself is valid and the failure is due to external reasons like node failures.",2145"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2146},2147{2148"question": "how do I specify the log directory when starting Ray?",2149"sources": [2150"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2151"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#ray-log-directory",2152"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#ray-log-directory",2153"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#using-rays-logger",2154"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory"2155],2156"document_ids": [21572666,215842571,215947907,21603170,216126632162],2163"answer": " To specify the log directory when starting Ray, you can include the `--log-directory` flag followed by the desired directory path when calling `ray start` or `ray.init()`. For example:\n\n```\nray start --log-directory /path/to/log/directory\n```\n\nor\n\n```\nimport ray\nray.init(log_directory=\"/path/to/log/directory\")\n```\n\nThis will set the log directory for Ray to the specified path. If you are using Ray libraries, you can specify the appropriate logger name to direct logs to this directory.",2164"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2165},2166{2167"question": "how to launch a ray cluster with 10 nodes, without setting the min worker as 10",2168"sources": [2169"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#running-a-basic-example",2170"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#cluster-yaml-configuration-options",2171"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2172"https://docs.ray.io/en/master/cluster/key-concepts.html#key-concepts",2173"https://docs.ray.io/en/master/cluster/key-concepts.html#ray-cluster"2174],2175"document_ids": [217649015,217743103,217847213,217937459,2180374972181],2182"answer": " To launch a Ray cluster with 10 nodes, you can use the `setup_ray_cluster` function and specify the number of worker nodes as 10. However, you don't need to set the minimum worker nodes (`min_worker_nodes`) as 10. Here's an example command:\n```css\nsetup_ray_cluster(num_worker_nodes=10)\n```\nAfter defining the cluster configuration, you can use the Ray CLI to launch the cluster.\n\nNote that the `ray_worker_small` configuration includes a parameter for `min_worker_nodes`, but this is optional and not required to set to 10 in this case.\n\nSo, to summarize, you can launch a Ray cluster with 10 nodes without setting the minimum worker nodes to 10.",2183"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2184},2185{2186"question": "how to use ray api to scale up a cluster",2187"sources": [2188"https://docs.ray.io/en/master/ray-references/api.html#api-references",2189"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api",2190"https://docs.ray.io/en/master/cluster/running-applications/autoscaling/reference.html#ray-autoscaler-sdk-request-resources",2191"https://docs.ray.io/en/master/serve/key-concepts.html#deployment",2192"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#kuberay-autoscaling"2193],2194"document_ids": [2195107747,219639647,219743310,219894979,2199388472200],2201"answer": " To use the Ray API to scale up a cluster, you can use the Ray Cluster Management API or the Ray Jobs CLI API Reference for programmatic cluster scaling. Within a Ray program, you can command the autoscaler to scale the cluster up to a desired size using the Ray autoscaler, which is a Ray cluster process that automatically scales a cluster up and down based on the workload. This can help handle incoming requests and can be scaled up to run across a Ray cluster.",2202"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2203},2204{2205"question": "we plan to use Ray cloud launcher to start a cluster in AWS. How can we specify a subnet in the deployment file?",2206"sources": [2207"https://docs.ray.io/en/master/data/overview.html#why-choose-ray-data",2208"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#launching-ray-clusters-on-aws",2209"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#fault-tolerant-fairseq-training",2210"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/index.html#table-of-contents",2211"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-2-check-ports"2212],2213"document_ids": [221448062,221548181,221635473,221750428,2218543132219],2220"answer": " To specify a subnet when using Ray cloud launcher to start a cluster in AWS, you can include the `subnet_id` parameter in the deployment file. Here's an example:\n```yaml\ncluster_name: my-ray-cluster\nprovider:\n type: aws\n region: us-west-2\n availability_zone: us-west-2a\n subnet_id: subnet-12345678\n security_group_ids:\n - sg-12345678\nhead:\n InstanceType: m5.xlarge\n InstanceCount: 1\nworker:\n InstanceType: m5.xlarge\n InstanceCount: 2\n```\nNote that you also need to install the AWS CLI and configure your AWS credentials. You can follow the instructions in the Ray documentation for launching Ray clusters on AWS to get started.",2221"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2222},2223{2224"question": "where I can find HTTP server error code log for Ray serve",2225"sources": [2226"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-5-verify-the-status-of-the-serve-applications",2227"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#step-5-verify-the-status-of-the-serve-applications",2228"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-api-server",2229"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",2230"https://docs.ray.io/en/master/serve/tutorials/index.html#examples"2231],2232"document_ids": [223340061,223438271,223596303,223635428,22371079812238],2239"answer": " To find the HTTP server error code log for Ray serve, you can check the Serve page in the Ray dashboard at <http://localhost:8265/#/serve>. The Ray Serve API Server logs can be accessed here, including the HTTP error codes. You can also refer to the Ray Serve Logging and Ray Logging documentation for more details.",2240"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2241},2242{2243"question": "I am running ray cluster on amazon and I have troubles displaying the dashboard. When a I tunnel the dashboard port from the headnode to my machine, the dashboard opens, and then it disappears (internal refresh fails). Is it a known problem? What am I doing wrong?",2244"sources": [2245"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",2246"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#viewing-ray-dashboard-in-browsers",2247"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#accessing-the-cluster-with-kubectl-exec",2248"https://docs.ray.io/en/master/ray-observability/reference/cli.html#state",2249"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#changing-the-ray-dashboard-port"2250],2251"document_ids": [2252607,225336704,225441519,22556145,2256366252257],2258"answer": " Yes, this is a known issue with Ray clusters running on Amazon. The dashboard's internal refresh mechanism may fail if the dashboard port is not publicly accessible.\n\nTo resolve this issue, you can try the following:\n\n1. Ensure that the Ray Dashboard is included when starting the Ray cluster, which is the default configuration.\n2. Customize the dashboard port to a publicly accessible range if it's not already.\n3. Once the Ray cluster is up and running, use SSH tunneling to access the dashboard port from your local machine.\n\nIf you have already tried these steps and the dashboard still disappears, it may be due to a network issue or a limitation of the Amazon environment. In this case, you can try accessing the dashboard using a node within the same VPC as the head node or by using a jump host.\n\nIt's also worth checking the Ray cluster logs for any error messages related to the dashboard.",2259"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2260},2261{2262"question": "In the Ray cluster launcher YAML, does `max_workers` include the head node, or only worker nodes?",2263"sources": [2264"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#running-a-basic-example",2265"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#object-stores",2266"https://docs.ray.io/en/master/cluster/vms/index.html#overview",2267"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#creating-a-long-running-ray-cluster-on-spark-cluster",2268"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary"2269],2270"document_ids": [227149015,227239289,227342400,227449146,22751080042276],2277"answer": " In the Ray cluster launcher YAML, `max_workers` refers to the number of worker nodes and does not include the head node. This is because the head node is a single node that runs the driver process, while worker nodes are multiple nodes that run worker processes. So, `max_workers` specifically controls the number of worker nodes in the Ray cluster.",2278"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2279},2280{2281"question": "How to update files in working directory ?",2282"sources": [2283"https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-applications",2284"https://docs.ray.io/en/master/serve/api/index.html#serve-run",2285"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",2286"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",2287"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-4-incorrect-import-path"2288],2289"document_ids": [2290105723,2291105270,229239681,229339704,2294360462295],2296"answer": " To update files in the working directory, you can follow these steps:\n\n1. Navigate to the working directory using the `working_dir` command followed by the path to the directory.\n2. Once you are in the working directory, you can modify the files as needed.\n3. The changes you make to the files in the working directory will be automatically detected by the system, since it is listening for changes to files in the working directory.\n\nSo, in summary, to update files in the working directory, you need to navigate to the working directory and make changes to the files. The system will automatically detect these changes.",2297"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2298},2299{2300"question": "How I can update working directory file when ray allready initiated ?",2301"sources": [2302"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",2303"https://docs.ray.io/en/master/cluster/vms/user-guides/launching-clusters/aws.html#reference-your-cloudwatch-json-config-files-in-your-cluster-config-yaml",2304"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2305"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job",2306"https://docs.ray.io/en/master/cluster/metrics.html#auto-discovering-metrics-endpoints"2307],2308"document_ids": [23094855,231049359,23112666,231251446,2313387232314],2315"answer": " To update the working directory file when Ray has already been initiated, you can use the `ray.put()` and `ray.get()` functions to add and retrieve data to/from the object store. However, Ray does not automatically update a specific file in the working directory.\n\nIf you want to use a specific file in the working directory, you should specify the directory when calling `ray start` or `ray.init()` using the `--working-dir` or `temp_dir` argument. Ray will then make the contents of that directory available to the Ray job. Ray periodically updates a file in the working directory with information about the Ray cluster.\n\nSo, to update the file, you can write to it before calling `ray.init()` or `ray start`, and then read from it after Ray has been initialized. If you need to update the file while Ray is running, you can write to it in your Ray application code and then read from it as needed. Just keep in mind that Ray does not automatically update the file for you.",2316"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2317},2318{2319"question": "how can I force ray head node to use custom pem file to ssh worker node?",2320"sources": [2321"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",2322"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#step-1-set-up-your-ray-cluster",2323"https://docs.ray.io/en/master/cluster/key-concepts.html#worker-node",2324"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#starting-the-ray-worker-nodes",2325"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#running-jobs-interactively"2326],2327"document_ids": [232850118,232954258,233037592,233150129,2332550492333],2334"answer": " To force the Ray head node to use a custom PEM file to SSH into the worker node, you can specify the `--redis-address` and `--object-store-address` flags when starting the Ray head node, pointing to the worker node's address and using the custom PEM file for SSH. Here's an example command:\n```css\nray start --head --redis-address=<worker_node_address>:6379 --object-store-address=<worker_node_address>:12379 --block --port=10001 --logging-level=INFO --num-cpus=4 --num-gpus=1 --machine-type=<machine_type> --resources='{\"cpu\":4, \"gpu\":1}' --verbose --temp-dir=/tmp/ray --metrics-agent-port=6441 --auth=<auth_key> --redis-password=<redis_password> --block --machine-overrides='{\"<worker_node_address>\": {\"instance-type\": \"<instance_type>\", \"ssh-user\": \"<ssh_user>\", \"ssh-private-key-file\": \"/path/to/custom/pem/file.pem\"}}'\n```\nReplace `<worker_node_address>` with the worker node's address, `<machine_type>` with the machine type, `<ssh_user>` with the SSH user, and `/path/to/custom/pem/file.pem` with the path to your custom PEM file.\n\nNote that this command assumes that you have already set up passwordless SSH between the head node and worker node using the custom PEM file. If you haven't done so, you'll need to set that up first before using this command.",2335"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2336},2337{2338"question": "what doess the GCS server do, and why is my GCS server taking up so much memory on the head node?",2339"sources": [2340"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts",2341"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error",2342"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2343"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",2344"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-component-logs"2345],2346"document_ids": [234729422,23483901,2349108003,235041374,235127822352],2353"answer": " The GCS (Global Control Store) server in Ray runs on the head node and is responsible for managing Ray cluster metadata, including the list of nodes and actors. It's a stateless server, meaning it doesn't permanently store data. The reason your GCS server might be taking up a lot of memory on the head node is that the head node has more memory-demanding system components, such as GCS or the dashboard. The memory usage of GCS depends on the size of your cluster and the number of nodes and actors in it.",2354"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2355},2356{2357"question": "when starting cluster with ray up, there are few nodes \"pending\" for a long time. how can I debug this?",2358"sources": [2359"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#system-metrics",2360"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",2361"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-cli.html#updating-an-existing-cluster-ray-up",2362"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#updating-the-raycluster-config",2363"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#what-does-the-rayservice-provide"2364],2365"document_ids": [23665949,236717105,236842597,2369104182,2370394522371],2372"answer": " If you encounter pending nodes that take a long time to start when using \"ray up\" to start a cluster, you can debug this by checking the Ray service status. When Ray is starting a cluster, it first prepares a pending cluster and waits for it to be ready. To get more information about the status of the Ray service and the pending cluster, you can check the logs and resources allocated to the pending nodes.\n\nYou can also try restarting the Ray cluster using \"ray up\" command if it seems to be in a bad state. If the issue persists, you can check if there are any resource constraints or network issues that might be causing the delay in starting the pending nodes.",2373"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2374},2375{2376"question": "how to install Ray 2.5.1 from github or wheel?",2377"sources": [2378"https://docs.ray.io/en/master/installation.html#daily-releases-nightlies",2379"https://docs.ray.io/en/master/ray-more-libs/installation.html#daily-releases-nightlies",2380"https://docs.ray.io/en/master/ray-overview/installation.html#daily-releases-nightlies",2381"https://docs.ray.io/en/master/ray-more-libs/installation.html#from-wheels",2382"https://docs.ray.io/en/master/installation.html#from-wheels"2383],2384"document_ids": [2385534,2386106061,23876404,2388105990,23895062390],2391"answer": " To install Ray 2.5.1 specifically from a wheel, you would need to find a wheel file for that version with the extension .whl, and use the following command:\n\n`pip install -U LINK_TO_RAY_2.5.1_WHEEL.whl`\n\nReplace \"LINK\\_TO\\_RAY\\_2.5.1\\_WHEEL.whl\" with the actual link to the Ray 2.5.1 wheel file.\n\nAlternatively, if you want to install the latest version of Ray from a wheel, you can do so with the following command:\n\n`pip install -U ray`\n\nThis will install the latest version of Ray available on PyPI. Note that this may not be Ray 2.5.1 if a newer version has been released.",2392"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2393},2394{2395"question": "How do I use `worker_setup_hook` in a runtime env to set do some setup on worker node creation?",2396"sources": [2397"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2398"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#worker-setup-commands",2399"https://docs.ray.io/en/master/ray-core/api/doc/ray.runtime_env.RuntimeEnv.html#ray-runtime-env-runtimeenv",2400"https://docs.ray.io/en/master/rllib/package_ref/doc/ray.rllib.evaluation.worker_set.WorkerSet.__init__.html#ray-rllib-evaluation-worker-set-workerset-init",2401"https://docs.ray.io/en/master/cluster/vms/getting-started.html#launch-a-cluster-on-a-cloud-provider"2402],2403"document_ids": [240445711,240544083,240645293,240783760,2408421942409],2410"answer": " To use `worker_setup_hook` in a runtime environment to set up on worker node creation, you can do the following:\n\n1. Define a function that contains the setup commands you want to execute.\n2. Pass this function as a value for the `worker_setup_hook` parameter in the worker node configuration.\n\nHere's an example configuration:\n\n```python\nworker_node_config = {\n \"worker\": {\n \"node_config\": {\n \"worker_setup_hook\": my_setup_function,\n \"worker_process_setup_hook\": True,\n # other configuration options...\n }\n }\n}\n```\n\nIn this example, `my_setup_function` is the function that contains the setup commands you want to execute.\n\nNote that `worker_process_setup_hook` is set to `True` to indicate that the hook should be executed for each worker process.\n\nAlso, keep in mind that this feature is experimental, so it may not be fully stable or feature-complete.",2411"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2412},2413{2414"question": "how to use Ray dataset on aws",2415"sources": [2416"https://docs.ray.io/en/master/data/examples/pytorch_resnet_batch_prediction.html#image-classification-batch-inference-with-pytorch",2417"https://docs.ray.io/en/master/data/examples/huggingface_vit_batch_prediction.html#image-classification-batch-inference-with-huggingface-vision-transformer",2418"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",2419"https://docs.ray.io/en/master/data/working-with-pytorch.html#custom-pytorch-datasets",2420"https://docs.ray.io/en/master/data/examples/custom-datasource.html#implementing-a-custom-datasource"2421],2422"document_ids": [242361910,242460813,242586474,242654496,2427598812428],2429"answer": " To use a Ray dataset on AWS, you can follow these steps:\n\n1. Install and set up Ray on your AWS instance.\n2. Use the `ImageDataset` class from `ray_datasets` to load the Imagenette dataset from an S3 bucket. Here's an example:\n```python\nfrom ray_datasets import ImageDataset\n\nbucket_name = \"ray-example-data\"\ndir_path = \"batoidea/JPEGImages/\"\ndataset = ImageDataset(bucket_name=bucket_name, dir_path=dir_path)\n```\nThis will create a Ray dataset by loading the Imagenette dataset from the specified S3 bucket and directory path.\n\nNote that Ray Data supports multiple ways to create a dataset, so you can also use other methods to load data from AWS if needed.",2430"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2431},2432{2433"question": "How do I avoid my dataset shuffling during a ray.data.map_batches?",2434"sources": [2435"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2436"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.random_shuffle.html#ray-data-dataset-random-shuffle",2437"https://docs.ray.io/en/master/data/examples/nyc_taxi_basic_processing.html#parallel-batch-inference",2438"https://docs.ray.io/en/master/data/data-internals.html#shuffling-data",2439"https://docs.ray.io/en/master/data/iterating-over-data.html#iterating-over-batches-with-shuffling"2440],2441"document_ids": [244244524,244356549,244465895,244549431,2446490482447],2448"answer": " To avoid your dataset from shuffling during a `ray.data.map_batches()` call, you don't need to do anything specific since `map_batches()` does not shuffle the data by default. The shuffling behavior is only present in `random_shuffle()`, `sort()`, and `groupby()` methods in Ray Data. Therefore, your dataset will not be shuffled during `map_batches()` execution.",2449"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2450},2451{2452"question": "Is the order of the input data preserved after a map_batches operation?",2453"sources": [2454"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.take_batch.html#ray-data-dataset-take-batch",2455"https://docs.ray.io/en/master/data/performance-tips.html#batching-transforms",2456"https://docs.ray.io/en/master/data/preprocessors.html#implementing-custom-preprocessors",2457"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.map_batches.html#ray-data-dataset-map-batches",2458"https://docs.ray.io/en/master/data/examples/batch_training.html#define-training-functions"2459],2460"document_ids": [246155026,246247220,246351967,246444524,2465581962466],2467"answer": " Yes, the order of the input data is preserved after a map_batches operation. This method is used for directly transforming your dataset and is faster than map(), preserving the original order of the data.",2468"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2469},2470{2471"question": "ray serve returns generic internal service error when there is an internal failure, how do I get it to emit more detailed errors or logs?",2472"sources": [2473"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#rayservice-troubleshooting",2474"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayservice-quick-start.html#next-steps",2475"https://docs.ray.io/en/master/serve/architecture.html#fault-tolerance",2476"https://docs.ray.io/en/master/ray-core/fault-tolerance.html#fault-tolerance",2477"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray"2478],2479"document_ids": [248035320,248138409,248296157,248330564,248446492485],2486"answer": " According to the context provided, you can refer to the RayService troubleshooting guide for assistance with issues, including internal failures. To get more detailed errors or logs for application-level failures in Ray Serve, Ray provides mechanisms to catch errors. In Ray, there are three types of failures, and you can find more details in the exception APIs. However, the text does not explicitly mention how to emit more detailed errors or logs for internal failures. It is recommended to check the official Ray Serve documentation or contact Ray support for further assistance.",2487"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2488},2489{2490"question": "how do i track an uncaught exception in ray serve",2491"sources": [2492"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RayActorError.html#ray-exceptions-rayactorerror",2493"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",2494"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror",2495"https://docs.ray.io/en/master/ray-core/api/index.html#ray-core-api",2496"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.RaySystemError.html#ray-exceptions-raysystemerror"2497],2498"document_ids": [249961641,250021352,250144616,250221806,2503446222504],2505"answer": " To track an uncaught exception in Ray Serve, you can use the `on_predicate_error` callback in the `RayServeHandler` class. This callback allows you to define a function that will be called whenever an uncaught exception occurs in a Ray Serve deployment. Here's an example of how to use it:\n```python\nfrom ray import serve\n\n@serve.deployment\nclass MyService:\n def __init__(self):\n self.exception_count = 0\n\n def __call__(self, request):\n try:\n # Your code here\n pass\n except Exception as e:\n self.exception_count += 1\n raise e\n\nmy_service = MyService.bind()\n\n@my_service.on_predicate_error\ndef track_exception(exception, request_context):\n print(f\"An exception occurred: {exception}\")\n print(f\"Request context: {request_context}\")\n```\nIn this example, whenever an uncaught exception occurs in the `__call__` method of `MyService`, the `track_exception` function will be called with the exception and request context as arguments. You can modify this function to track the exceptions in your preferred way, such as logging them or sending them to an error tracking service.\n\nNote that Ray Serve also provides built-in metrics for tracking exceptions, which you can access using the Ray Serve dashboard or the Ray Serve API. These metrics include the number of exceptions and the response time for requests that result in exceptions.",2506"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2507},2508{2509"question": "where do I view logs using python logger emitted by my ray serve endpoint in the ray cluster",2510"sources": [2511"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",2512"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",2513"https://docs.ray.io/en/master/serve/monitoring.html#ray-logging",2514"https://docs.ray.io/en/master/serve/monitoring.html#filtering-logs-with-loki",2515"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs"2516],2517"document_ids": [251835428,251935402,252097614,252198166,252275862523],2524"answer": " To view logs from the Python logger emitted by your Ray Serserve endpoint in the Ray cluster, you can check the Ray Serve logs directly by accessing the log files on the pods. You can also use the Ray logging documentation for more details. The logger in your Python code should use the name \"ray.serve\", like this:\n\n```python\nimport logging\nlogger = logging.getLogger(\"ray.serve\")\n```\n\nAdditionally, you can use the Ray CLI command `ray job logs` or the Python SDK method `JobSubmissionClient.get_logs()` to view the logs.",2525"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2526},2527{2528"question": "where can I see logs for a failed ray serve deployment",2529"sources": [2530"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#monitoring",2531"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",2532"https://docs.ray.io/en/master/serve/tutorials/java.html#testing-the-ray-serve-deployment",2533"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#method-3-check-logs-of-ray-pods",2534"https://docs.ray.io/en/master/serve/tutorials/aws-neuron-core-inference.html#serving-an-inference-model-on-aws-neuroncores-using-fast-api-experimental"2535],2536"document_ids": [2537104338,253835402,2539106913,254035428,25411054932542],2543"answer": " You can see logs for a failed Ray Serve deployment in the log files located at /tmp/ray/session\\_latest/logs/serve/ in the pods. You can also check the Ray Serve logs directly on the pods for more details.",2544"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2545},2546{2547"question": "How to take a subset of a Ray Dataset?",2548"sources": [2549"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",2550"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#preprocessing-the-data-with-ray-air-a-name-preprocess-a",2551"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.write_sql.html#ray-data-dataset-write-sql",2552"https://docs.ray.io/en/master/data/api/doc/ray.data.Dataset.html#ray-data-dataset",2553"https://docs.ray.io/en/master/train/user-guides/data-loading-preprocessing.html#quickstart"2554],2555"document_ids": [255686474,255776242,255864696,255955083,2560670652561],2562"answer": " To take a subset of a Ray Dataset, you can use the `take` or `sample` methods provided by the Ray Datasets API. Here's an example:\n```python\nsubset_size = 1000\nsubset_dataset = dataset.take(subset_size)\n# or\nsubset_dataset = dataset.sample(subset_size)\n```\nIn this example, `subset_dataset` will contain a subset of `dataset` with the specified size. The `take` method returns the first `n` rows of the dataset, while the `sample` method returns a random subset of the dataset with the specified size.\n\nNote that the above example assumes that `dataset` is a Ray Dataset object, and `subset_size` is the number of rows you want to include in the subset. You can adjust the `subset_size` variable to take a larger or smaller subset as needed.",2563"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2564},2565{2566"question": "How do I load all checkpoints from trials of a Tune experiment launched with `tune.run`? I ran my initial experiment with cloud checkpointing, so I\u2019d need to download all the checkpoints to analyze them.",2567"sources": [2568"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",2569"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#summary",2570"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#example-for-using-tune-with-spot-instances-aws",2571"https://docs.ray.io/en/master/tune/tutorials/tune-fault-tolerance.html#summary",2572"https://docs.ray.io/en/master/tune/tutorials/tune-trial-checkpoints.html#how-to-save-and-load-trial-checkpoints"2573],2574"document_ids": [257515691,257615688,257717147,257815974,2579155822580],2581"answer": " To load all checkpoints from trials of a Tune experiment launched with `tune.run`, you can use the `ray.tune.experiment.load_checkpoints` function. Since you've used cloud checkpointing, you'll first need to download the checkpoints to your local machine.\n\nHere's a step-by-step process:\n\n1. Download the checkpoints from the cloud storage to your local machine.\n2. Load the checkpoints using `ray.tune.experiment.load_checkpoints`:\n\n```python\ncheckpoints = ray.tune.experiment.load_checkpoints('path/to/checkpoint/directory')\n```\n\n3. Analyze or restore the checkpoints as needed.\n\nFor more information on saving and loading trial checkpoints, refer to the Tune documentation: [Running Distributed Experiments with Ray Tune](https://docs.ray.io/en/latest/tune/tutorials/distributed.html#save-and-load-trial-checkpoints) and [Extract checkpoints from Tune experiment results](https://docs.ray.io/en/latest/tune/tutorials/results.html#extract-checkpoints-from-tune-experiment-results).",2582"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2583},2584{2585"question": "How can I kill a \"detached\" Actor ?",2586"sources": [2587"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#advanced-detached-placement-group",2588"https://docs.ray.io/en/master/ray-core/actors/named-actors.html#actor-lifetimes",2589"https://docs.ray.io/en/master/ray-core/fault_tolerance/actors.html#force-killing-a-misbehaving-actor",2590"https://docs.ray.io/en/master/ray-core/api/doc/ray.job_config.JobConfig.html#ray-job-config-jobconfig",2591"https://docs.ray.io/en/master/ray-core/api/doc/ray.kill.html#ray-kill"2592],2593"document_ids": [259433062,259527677,259625619,259761507,2598296062599],2600"answer": " To kill a \"detached\" Actor, you must manually destroy it once you are sure it is no longer needed. You can do this by calling the terminate() method on the Actor object. Detached actors are not linked to any particular handle or context, so you cannot obtain a handle to it via getHandle() or similar methods. If the detached actor was created from another actor, it will be killed when that actor is killed.",2601"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2602},2603{2604"question": "How do I set env variables in ray init? Let\u2019 say it\u2019s export foo=\u201cfoo\u201d",2605"sources": [2606"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/general-debugging.html#environment-variables-are-not-passed-from-the-driver-process-to-worker-processes",2607"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-local-files",2608"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#configuring-parallelism-with-omp-num-threads",2609"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/pod-command.html#specify-container-commands-for-ray-head-worker-pods",2610"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example"2611],2612"document_ids": [26134889,261426787,261595925,261641700,2617173132618],2619"answer": " To set environment variables in `ray.init()`, you can include the `env_vars` parameter in the `runtime_env` argument. For example, to set the `foo` environment variable to `\"foo\"`, you can use the following code:\n\n```python\nray.init(runtime_env={\"env_vars\": {\"foo\": \"foo\"}})\n```\n\nAlternatively, you can set environment variables before starting Ray, as shown in the example:\n\n```python\nimport os\nos.environ[\"FOO\"] = \"bar\"\nray.init()\n```\n\nYou can also set environment variables within your function or class definition, like so:\n\n```python\n@ray.remote\ndef my_function():\n os.environ[\"FOO\"] = \"bar\"\n # rest of function code\n```\n\nNote that environment variables set within a function or class definition will only be available within that function or class.",2620"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2621},2622{2623"question": "What is the rest api for getting the head node id?",2624"sources": [2625"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api",2626"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2627"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",2628"https://docs.ray.io/en/master/cluster/vms/user-guides/community/slurm.html#obtain-the-head-ip-address",2629"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobSubmissionClient.html#ray-job-submission-jobsubmissionclient"2630],2631"document_ids": [263252233,2633108037,26341704,263549977,2636431212637],2638"answer": " Based on the context provided, the REST API for getting the head node ID is not explicitly stated. However, it is mentioned that you can obtain the head node's information including its node\\_id, node\\_name, is\\_head\\_node, node\\_ip, and state. It is also mentioned that you can obtain the head IP address of the dashboard server on the head node. Therefore, you can use the following REST API endpoint to get information about the head node:\n\n`http://<head-node-ip>:8265/nodes/<node_id>`\n\nReplace `<head-node-ip>` with the IP address of the head node and `<node_id>` with the ID of the head node. You can check the `is_head_node` field in the response to confirm that it is the head node.",2639"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2640},2641{2642"question": "how to rerun a canceled ray task",2643"sources": [2644"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2645"https://docs.ray.io/en/master/ray-core/api/doc/ray.cancel.html#ray-cancel",2646"https://docs.ray.io/en/master/ray-core/fault_tolerance/tasks.html#cancelling-misbehaving-tasks",2647"https://docs.ray.io/en/master/ray-core/api/core.html#tasks",2648"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions"2649],2650"document_ids": [265125021,265225122,265324235,265428218,2655213642656],2657"answer": " To rerun a canceled Ray task, you need to create a new task with the same function and arguments as the canceled task and then run it again using `ray.put()` or `ray.task.remote()`. Ray will not automatically retry tasks that have been canceled. If you try to call `ray.get()` on a cancelled task, it will raise a `TaskCancelledError`.",2658"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2659},2660{2661"question": "How do I set the max parallel concurrent scheduled tasks in map_batches?",2662"sources": [2663"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",2664"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution",2665"https://docs.ray.io/en/master/serve/api/doc/ray.serve.batch.html#ray-serve-batch",2666"https://docs.ray.io/en/master/ray-core/api/doc/ray.actor.ActorClass.options.html#ray-actor-actorclass-options",2667"https://docs.ray.io/en/master/data/data-internals.html#streaming-execution"2668],2669"document_ids": [267049835,267149843,267298642,267324468,2674498462675],2676"answer": " To set the maximum number of parallel concurrent scheduled tasks in `map_batches()`, you can use the `max_concurrency` argument. Here is an example:\n```python\n.map_batches(sleep, max_concurrency=2, compute=ray.data.ActorPoolStrategy(min_size=2, max_size=4))\n```\nIn this example, `max_concurrency=2` sets the maximum number of concurrent calls to `sleep` to 2. The `compute` argument specifies the strategy for creating and managing Ray actors for parallel processing.\n\nNote that `max_concurrency` limits the number of concurrent calls to the function passed to `map_batches()`, and not the number of Ray tasks or actors. Therefore, it's important to choose an appropriate value for `max_concurrency` based on the resources available and the requirements of your function.",2677"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2678},2679{2680"question": "How do I get the number of cpus from ray cluster?",2681"sources": [2682"https://docs.ray.io/en/master/ray-more-libs/actors.html#faq-actors-workers-and-resources",2683"https://docs.ray.io/en/master/rllib/actors.html#faq-actors-workers-and-resources",2684"https://docs.ray.io/en/master/ray-core/actors.html#faq-actors-workers-and-resources",2685"https://docs.ray.io/en/master/ray-core/examples/plot_example-lm.html#training",2686"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-head-node"2687],2688"document_ids": [2689107531,269093154,269122949,269235839,2693485832694],2695"answer": " To get the number of CPUs in a Ray cluster, you can use the following code:\n```python\nn_cpus = int(ray.cluster_resources()[\"CPU\"])\n```\nIn your specific case, with `num_cpus=2` and a total of 16 CPUs in the cluster, `ray.cluster_resources()[\"CPU\"]` would return the string '16', so you would need to convert it to an integer using the `int()` function.\n\nNote that for Ray clusters deployed using KubeRay, you should set `rayStartParams: {\"num-cpus\": \"0\"}` to allow Ray to automatically determine the number of CPUs to use.",2696"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2697},2698{2699"question": "How to use the exclude option to the runtime_env",2700"sources": [2701"https://docs.ray.io/en/master/ray-air/examples/stablediffusion_batch_prediction.html#stable-diffusion-batch-prediction-with-ray-air",2702"https://docs.ray.io/en/master/ray-air/examples/gptj_batch_prediction.html#gpt-j-6b-batch-prediction-with-ray-air",2703"https://docs.ray.io/en/master/ray-air/examples/gptj_serving.html#gpt-j-6b-serving-with-ray-air",2704"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#set-up-ray-a-name-setup-a",2705"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#specifying-a-runtime-environment-per-job"2706],2707"document_ids": [270881351,270985385,271085880,271186224,2712264732713],2714"answer": " To use the `exclude` option for `runtime_env`, you would include it in the `runtime_env` argument as follows:\n\n`runtime_env=runtime_env(exclude=[\"package1\", \"package2\"])`\n\nThis will exclude the specified packages from the runtime environment. However, keep in mind that if you have all the necessary packages already installed on each system, you can omit the `runtime_env` argument altogether.",2715"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2716},2717{2718"question": "show a map batch example with batch_format",2719"sources": [2720"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2721"https://docs.ray.io/en/master/data/examples/batch_training.html#transforming-a-dataset-in-parallel-using-custom-functions-a-class-anchor-id-transform-ds-a",2722"https://docs.ray.io/en/master/train/examples/lightning/vicuna_13b_lightning_deepspeed_finetune.html#load-and-preprocess-datasets",2723"https://docs.ray.io/en/master/ray-air/examples/gptj_deepspeed_fine_tuning.html#loading-the-dataset-a-name-load-a",2724"https://docs.ray.io/en/master/data/working-with-pytorch.html#built-in-pytorch-transforms"2725],2726"document_ids": [2727107830,272857766,272969703,273086561,2731539142732],2733"answer": " Here's an example of using `batch_format` in the `map_batches()` method:\n\n```python\ndef tokenize(batch):\n # tokenization code here\n",2734"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2735},2736{2737"question": "how to find local ray address",2738"sources": [2739"https://docs.ray.io/en/master/tune/tutorials/tune-distributed.html#running-a-distributed-tune-experiment",2740"https://docs.ray.io/en/master/ray-air/examples/huggingface_text_classification.html#set-up-ray-a-name-setup-a",2741"https://docs.ray.io/en/master/data/examples/batch_training.html#walkthrough",2742"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2743"https://docs.ray.io/en/master/tune/examples/tune-wandb.html#running-a-weights-biases-example"2744],2745"document_ids": [274617107,274775512,274857081,274951797,2750173172751],2752"answer": " To find the local Ray address, you can check the environment variable `RAY_ADDRESS`. If it's not set, you can use the address 'localhost:6379' as the default local Ray address. The Ray Dashboard can be viewed in your local browser at http://127.0.0.1:8265 or http://127.0.0.1:8266.",2753"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2754},2755{2756"question": "How to start ray cluster on multiple node via CLI?",2757"sources": [2758"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#using-ray-cli-tools-from-outside-the-cluster",2759"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",2760"https://docs.ray.io/en/master/cluster/running-applications/job-submission/ray-client.html#connect-to-multiple-ray-clusters-experimental",2761"https://docs.ray.io/en/master/ray-core/configure.html#configuring-ray",2762"https://docs.ray.io/en/master/ray-core/configure.html#java-applications"2763],2764"document_ids": [27652437,276654535,276754540,276831771,2769329062770],2771"answer": " To start a Ray cluster on multiple nodes via the command-line interface (CLI), follow these steps:\n\n1. First, you need to start Ray on the command line for each node in the cluster.\n2. Next, use the Ray CLI commands on one of the nodes in the Ray Cluster. You can use the following commands as examples:\n * cli1 = ray.init(\"ray://<head\\_node\\_host\\_cluster\\_1>:10001\", allow\\_multiple=True)\n * cli2 = ray.init(\"ray://<head\\_node\\_host\\_cluster\\_2>:10001\", allow\\_multiple=True)\n\nReplace `<head_node_host_cluster_1>` and `<head_node_host_cluster_2>` with the hostnames of the head nodes for each cluster.",2772"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2773},2774{2775"question": "my ray tuner shows \"running\" but CPU usage is almost 0%. why ?",2776"sources": [2777"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",2778"https://docs.ray.io/en/master/ray-contribute/profiling.html#getting-a-stack-trace-of-ray-c-processes",2779"https://docs.ray.io/en/master/data/data-internals.html#ray-data-and-tune",2780"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources",2781"https://docs.ray.io/en/master/ray-core/scheduling/placement-group.html#schedule-tasks-and-actors-to-placement-groups-use-reserved-resources"2782],2783"document_ids": [278449578,27855770,278649570,278731832,2788318262789],2790"answer": " The Ray tuner showing \"running\" but with almost 0% CPU usage could be because the Ray actors and tasks only require 1 CPU when scheduled, but once created, they occupy 0 CPU. This means that even though the Ray process is running, it may not be using a significant amount of CPU resources. Additionally, Tune tries to fully utilize cluster CPUs by default, which could prevent the Ray process from using all available CPUs. It's important to ensure there are enough free CPUs for Ray Data to run on, and there may be enough free CPUs in this case, leading to low CPU usage.",2791"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2792},2793{2794"question": "should the Ray head node and all workers have the same object store memory size allocated?",2795"sources": [2796"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#ray-on-spark-apis",2797"https://docs.ray.io/en/master/ray-core/objects/serialization.html#serialization",2798"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#find-per-task-and-actor-memory-usage",2799"https://docs.ray.io/en/master/cluster/vms/user-guides/community/spark.html#running-a-basic-example",2800"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-memory.html#head-node-out-of-memory-error"2801],2802"document_ids": [280349461,280426648,28053817,280649003,280739072808],2809"answer": " Yes, the Ray head node and all workers should have the same object store memory size allocated, which is ideally 30% of the host memory. This is because Ray processes do not share memory space, and data transferred between workers and nodes will need to be stored in the Ray object store. Equal memory allocation ensures efficient communication and data handling across all nodes, including the head node.",2810"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2811},2812{2813"question": "In Ray Serve, how to specify whether to set up an httpproxy on each node, or just the head node?",2814"sources": [2815"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#load-balancer",2816"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2817"https://docs.ray.io/en/master/ray-references/glossary.html#ray-glossary",2818"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",2819"https://docs.ray.io/en/master/cluster/running-applications/job-submission/rest.html#ray-jobs-rest-api"2820],2821"document_ids": [2822104281,2823104435,2824108000,28257591,2826522282827],2828"answer": " In Ray Serve, an HTTP proxy is set up on every node, and it's not necessary to specify it for the head node only. The HTTP proxy allows you to use `/-/routes` as the endpoint for each node. The Ray head node is used to run Ray Serve commands like `serve start` and `serve run`, and it's where the job server runs, accepting HTTP calls and direct requests.",2829"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2830},2831{2832"question": "Want to embed Grafana into the Ray Dashboard, given that I am using KubeRay\n\nGiven the context that Prometheus and Grafana are not running on my Head node, and that I am using KubeRay, how should I be setting the following variables?\n\u2022 `RAY_GRAFANA_HOST`\n\u2022 `RAY_PROMETHEUS_HOST`\nAnd is there a way to set them more intelligently, given that head node IP is changing every time we reconfigure our cluster?",2833"sources": [2834"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location",2835"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",2836"https://docs.ray.io/en/master/ray-observability/getting-started.html#set-up-dashboard",2837"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#embed-grafana-visualizations-into-ray-dashboard",2838"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#alternate-grafana-host-location"2839],2840"document_ids": [284137299,2842616,2843618,284437073,2845373022846],2847"answer": " To embed Grafana into the Ray Dashboard when using KubeRay, and with Prometheus and Grafana not running on your Head node, you should:\n\n1. Set up Prometheus and Grafana and integrate them with the Ray Dashboard in the same Kubernetes cluster.\n2. Since the head node IP is changing every time you reconfigure your cluster, you should set `RAY_GRAFANA_HOST` to the external URL of the Grafana ingress.\n3. `RAY_PROMETHEUS_HOST` should be set to the external URL of the Prometheus ingress.\n\nYou can set these environment variables in your Ray configuration or when starting Ray processes.",2848"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2849},2850{2851"question": "How the GCS determines which Kubernetes pod to kill when using KubeRay autoscaling?",2852"sources": [2853"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling",2854"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#fine-grained-control-of-scale-down",2855"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#advanced-deploying-a-static-ray-cluster-without-kuberay",2856"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/configuring-autoscaling.html#architecture-one-ray-autoscaler-per-ray-cluster",2857"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#autoscaling"2858],2859"document_ids": [2860104237,286139752,286240939,286339810,28641042122865],2866"answer": " When using KubeRay autoscaling in a GKE cluster, if the resources are overutilized, the Horizontal Pod Autoscaler (HPA) in the Kubernetes control plane determines which pods to kill based on predefined metrics. Once the HPA makes a decision, the KubeRay operator deletes the specified pod. To enable autoscaling in a KubeRay Cluster, you need to set enableInTreeAutoscaling to True.",2867"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2868},2869{2870"question": "How can I set the `request_timeout_s` in `http_options` section of a Ray Serve YAML config file?",2871"sources": [2872"https://docs.ray.io/en/master/serve/advanced-guides/performance.html#set-an-end-to-end-request-timeout",2873"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/rayservice.html#step-3-install-a-rayservice",2874"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2875"https://docs.ray.io/en/master/serve/production-guide/config.html#serve-config-files-serve-build",2876"https://docs.ray.io/en/master/cluster/vms/getting-started.html#running-applications-on-a-ray-cluster"2877],2878"document_ids": [287997868,288039579,288199538,288299473,2883423182884],2885"answer": " To set the `request_timeout_s` in the `http_options` section of a Ray Serve YAML config file, you should follow these steps:\n\n1. Open the Ray Serve config file (serveConfigV2) in a text editor. This file is typically embedded in the RayService YAML file.\n2. Locate the `http_options` field in the config file.\n3. Add the `request_timeout_s` field to the `http_options` section, and set its value to the desired timeout duration in seconds.\n\nHere's an example of what the YAML config file might look like with the `request_timeout_s` field set to 10 seconds:\n```yaml\nhttp_options:\n request_timeout_s: 10\n```\n4. Save the changes to the config file and restart the Ray Serve application for the changes to take effect.\n\nNote that the YAML file format requires proper indentation and use of whitespace to define nested fields. Be sure to follow the YAML syntax rules when editing the config file.",2886"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2887},2888{2889"question": "How do I make the GPU available on my M1 laptop to ray?",2890"sources": [2891"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2892"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-cluster-quickstart",2893"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2894"https://docs.ray.io/en/master/cluster/vms/references/ray-cluster-configuration.html#full-configuration",2895"https://docs.ray.io/en/master/train/getting-started-pytorch-lightning.html#getting-started-with-pytorch-lightning"2896],2897"document_ids": [289845447,28995920,290046204,290146952,2902659602903],2904"answer": " Unfortunately, the M1 chip on your laptop is an ARM-based processor, and the current Ray GPU support only extends to NVIDIA GPUs, which use a different architecture. Therefore, you cannot make the M1 GPU available to Ray. You can still use Ray on your M1 laptop, but it will be limited to using the CPU only. You can specify the CPU-only image by using \"rayproject/ray-ml:latest-cpu\" in your configuration.",2905"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2906},2907{2908"question": "How can I add a timeout for the Ray job?",2909"sources": [2910"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-tasks",2911"https://docs.ray.io/en/master/ray-core/api/exceptions.html#exceptions",2912"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-actors",2913"https://docs.ray.io/en/master/ray-observability/reference/cli.html#ray-summary-objects",2914"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#callbacks"2915],2916"document_ids": [29176168,291821462,29196188,29206227,29211057112922],2923"answer": " To add a timeout for a Ray job, you can use the `--timeout` option followed by the desired number of seconds. This option can be used with the `ray summary tasks`, `ray summary actors`, and `ray summary objects` commands. Here is an example of how to use it:\n\n```\nray summary tasks --timeout 60\n```\n\nThis command will show the summary of Ray tasks with a timeout of 60 seconds. If the task does not complete within this time, Ray will throw a `GetTimeoutError`.\n\nYou can also set a timeout at the start of a Ray task by using the `timer()` function and storing the start time. You can then check the elapsed time in your task code and raise a timeout error if necessary. Here is an example:\n\n```\nimport time\nfrom ray import timers\n\nstart_time = timers.timer()\n\n# Task code here\n\nelapsed_time = time.time() - start_time.time()\nif elapsed_time > TIMEOUT:\n raise GetTimeoutError(\"Task timed out\")\n```\n\nThis will raise a `GetTimeoutError` if the task takes longer than the specified `TIMEOUT` value.",2924"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2925},2926{2927"question": "how do I set custom /tmp directory for remote cluster?",2928"sources": [2929"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#using-a-remote-cluster",2930"https://docs.ray.io/en/master/serve/advanced-guides/deploy-vm.html#using-a-remote-cluster",2931"https://docs.ray.io/en/master/serve/production-guide/handling-dependencies.html#add-a-runtime-environment",2932"https://docs.ray.io/en/master/cluster/vms/user-guides/community/index.html#using-a-custom-cloud-or-cluster-manager",2933"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#submitting-a-job"2934],2935"document_ids": [293651717,2937104228,293899265,293952032,2940513502941],2942"answer": " To set a custom /tmp directory for a remote cluster, you would need to check the Cluster Configuration for more details on how to configure the remote cluster. When using a remote cluster, it is required to upload the working directory to the cluster. You may also consider using a runtime environment with a remote URI that hosts the custom directory.",2943"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2944},2945{2946"question": "if I set --temp-dir to a different directory than /tmp, will ray object spill to the custom directory ?",2947"sources": [2948"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2949"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",2950"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#logging-directory",2951"https://docs.ray.io/en/master/ray-contribute/fake-autoscaler.html#setting-up-in-a-docker-in-docker-dind-environment",2952"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling"2953],2954"document_ids": [29552660,29564379,29572663,29584401,2959274292960],2961"answer": " Yes, if you set the --temp-dir or the RAY\\_TEMPDIR environment variable to a different directory than /tmp, Ray object spill will occur in the custom directory.",2962"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2963},2964{2965"question": "can you give me an example for *`--runtime-env-json`*",2966"sources": [2967"https://docs.ray.io/en/master/serve/advanced-guides/dev-workflow.html#testing-on-a-remote-cluster",2968"https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-applications",2969"https://docs.ray.io/en/master/serve/api/index.html#put-api-serve-applications",2970"https://docs.ray.io/en/master/cluster/running-applications/job-submission/doc/ray.job_submission.JobInfo.to_json.html#ray-job-submission-jobinfo-to-json",2971"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeDeploySchema.html#ray-serve-schema-servedeployschema"2972],2973"document_ids": [2974105736,2975105764,2976105719,297741712,2978977342979],2980"answer": " Sure, here's an example of using the `--runtime-env-json` argument:\n\n`--runtime-env-json '{\"runtime_env\": {\"title\": \"Runtime Env\"}}'`\n\nThis argument sets the runtime environment with a title of \"Runtime Env\". Note that the value is a JSON-serialized string, as mentioned in the context.",2981"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"2982},2983{2984"question": "What should be the value of `maxConcurrentReplicas` if autoscaling configuration is specified?",2985"sources": [2986"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",2987"https://docs.ray.io/en/master/serve/tutorials/text-classification.html#serving-a-distilbert-model",2988"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#configuring-the-autoscaler",2989"https://docs.ray.io/en/master/cluster/vms/user-guides/configuring-autoscaling.html#configuring-autoscaling",2990"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments"2991],2992"document_ids": [299398948,2994106408,299548613,299648001,2997988082998],2999"answer": " Based on the context provided, `maxConcurrentReplicas` should be set to the value specified in the `max_replicas` field of the autoscaling configuration. For example, in the first config, it should be 5, and in the second config, it should be 2.",3000"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3001},3002{3003"question": "Yes what should be the value of `max_concurrent_queries` when `target_num_ongoing_requests_per_replica` is specified?",3004"sources": [3005"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling",3006"https://docs.ray.io/en/master/serve/architecture.html#lifetime-of-a-request",3007"https://docs.ray.io/en/master/serve/configure-serve-deployment.html#configure-ray-serve-deployments",3008"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3009"https://docs.ray.io/en/master/serve/architecture.html#ray-serve-autoscaling"3010],3011"document_ids": [301295518,301396110,301498799,301595640,3016962743017],3018"answer": " Based on the context provided, `max_concurrent_queries` should be set to a value that is higher than `target_num_ongoing_requests_per_replica`. This is because `max_concurrent_queries` is the maximum number of queries that can be sent to a replica, while `target_num_ongoing_requests_per_replica` is the expected number of ongoing requests per replica. If more than `max_concurrent_queries` requests are outstanding at each replica, queries will be sent to replicas in a round-robin fashion. Therefore, setting `max_concurrent_queries` to a value higher than `target_num_ongoing_requests_per_replica` ensures that queries can be distributed evenly across replicas while avoiding overloading any single replica.",3019"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3020},3021{3022"question": "what is a `smoothing_factor`",3023"sources": [3024"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3025"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3026"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters",3027"https://docs.ray.io/en/master/tune/api/doc/ray.tune.schedulers.PopulationBasedTraining.html#ray-tune-schedulers-populationbasedtraining",3028"https://docs.ray.io/en/master/serve/scaling-and-resource-allocation.html#autoscaling-config-parameters"3029],3030"document_ids": [303195736,303295713,303395727,303414046,3035957233036],3037"answer": " A `smoothing_factor` is a multiplicative factor that can speed up or slow down the process of scaling up or down resources in a system. It can be thought of as a \"gain\" factor that determines how quickly the system responds to changes in resource demand. For example, increasing the `upscale_smoothing_factor` will cause the system to scale up its resources more quickly in response to increased demand. The `downscale_smoothing_factor` determines how quickly the system will scale down its resources when demand decreases. The default value for both factors is typically 1.0.",3038"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3039},3040{3041"question": "What is the reason actors change their state to unhealthy?",3042"sources": [3043"https://docs.ray.io/en/master/serve/production-guide/best-practices.html#inspect-an-application-with-serve-config-and-serve-status",3044"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.ActorState.html#ray-util-state-common-actorstate",3045"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeInstanceDetails.html#ray-serve-schema-serveinstancedetails",3046"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ServeStatusSchema.html#ray-serve-schema-servestatusschema",3047"https://docs.ray.io/en/master/serve/api/doc/ray.serve.schema.ApplicationDetails.html#ray-serve-schema-applicationdetails"3048],3049"document_ids": [305098938,30511694,3052102882,305399537,30541000823055],3056"answer": " Actors in this context can change to an \"UNHEALTHY\" state if their deployment fails to update or if the update is successful but the actor becomes unhealthy for some reason. This state can also occur when an actor is in the process of being deleted.",3057"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3058},3059{3060"question": "How do I access logs for a dead node?",3061"sources": [3062"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc",3063"https://docs.ray.io/en/master/cluster/vms/user-guides/logging.html#log-persistence",3064"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/logging.html#log-persistence",3065"https://docs.ray.io/en/master/ray-observability/reference/cli.html#log",3066"https://docs.ray.io/en/master/ray-observability/user-guides/cli-sdk.html#fetch-the-logs-of-a-particular-entity-task-actor-etc"3067],3068"document_ids": [30692163,307047874,307142534,30726357,307321603074],3075"answer": " Based on the context provided, it is not possible to access logs for a dead node. The State API allows you to access Ray logs, but only for alive nodes. If a node terminates unexpectedly, you can access system logs for the alive nodes in the cluster to help diagnose the issue.",3076"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3077},3078{3079"question": "What are the reasons for a node to change it\u2019s status to dead?",3080"sources": [3081"https://docs.ray.io/en/master/ray-observability/reference/doc/ray.util.state.common.NodeState.html#ray-util-state-common-nodestate",3082"https://docs.ray.io/en/master/ray-core/fault_tolerance/nodes.html#raylet-failure",3083"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.TaskUnschedulableError.html#ray-exceptions-taskunschedulableerror",3084"https://docs.ray.io/en/master/ray-core/api/doc/ray.exceptions.ActorUnschedulableError.html#ray-exceptions-actorunschedulableerror",3085"https://docs.ray.io/en/master/ray-core/fault_tolerance/objects.html#recovering-from-owner-failure"3086],3087"document_ids": [30881735,308923436,309024068,309126547,3092245543093],3094"answer": " A node can change its status to dead due to node failure or worker process failure, which can be caused by hardware or software issues. Additionally, if a node specified through NodeAffinitySchedulingStrategy is unable to run workloads, it may also be marked as dead. The owner of an object can also die due to node or worker process failure, resulting in the node being treated as dead.",3095"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3096},3097{3098"question": "What are the reasons for spikes in node CPU utilization",3099"sources": [3100"https://docs.ray.io/en/master/cluster/faq.html#i-am-having-problems-getting-my-slurm-cluster-to-work",3101"https://docs.ray.io/en/master/cluster/kubernetes/benchmarks/memory-scalability-benchmark.html#experiment-results-kuberay-v0-6-0",3102"https://docs.ray.io/en/master/ray-core/examples/batch_prediction.html#how-to-improve-the-gpu-utilization-rate",3103"https://docs.ray.io/en/master/tune/tutorials/tune-output.html#where-to-find-log-to-file-files",3104"https://docs.ray.io/en/master/train/api/doc/ray.train.ScalingConfig.html#ray-train-scalingconfig"3105],3106"document_ids": [310741195,310839324,310934295,311016726,3111662343112],3113"answer": " The spikes in node CPU utilization can be due to:\n\n1. Unexpected resource allocation, particularly when there are too many CPUs per node.\n2. Scheduler issues or node failures.\n3. The node spending more time on the actual workload, which may limit memory usage.\n4. Experimental: The `_max_cpu_fraction_per_node` setting can also impact CPU utilization, as it limits the maximum fraction of CPUs that can be used by a node.",3114"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3115},3116{3117"question": "What AWS machine type is recommended to deploy a RayService on EKS?",3118"sources": [3119"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#deploy-on-kubernetes",3120"https://docs.ray.io/en/master/serve/production-guide/index.html#production-guide",3121"https://docs.ray.io/en/master/cluster/running-applications/job-submission/quickstart.html#setup",3122"https://docs.ray.io/en/master/cluster/running-applications/job-submission/sdk.html#setup",3123"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/experimental.html#experimental-features"3124],3125"document_ids": [3126103124,3127104369,312851195,312952487,3130444153131],3132"answer": " The recommended machine type to deploy a RayService on EKS (Elastic Kubernetes Service) would be on AWS VMs, following the guidelines for deploying Ray on VMs and Kubernetes, as stated in the context. However, the specific machine type is not mentioned in the provided information. It's essential to consider factors like the workload, number of nodes, and budget when choosing the machine type.",3133"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3134},3135{3136"question": "Is there a way to configure the session name generated by ray?",3137"sources": [3138"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3139"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging",3140"https://docs.ray.io/en/master/tune/examples/pbt_visualization/pbt_visualization.html#configure-pbt-and-tuner",3141"https://docs.ray.io/en/master/ray-core/starting-ray.html#starting-ray-on-a-single-machine",3142"https://docs.ray.io/en/master/ray-core/configure.html#logging-and-debugging"3143],3144"document_ids": [314531978,314632029,314721933,314823643,3149320113150],3151"answer": " Yes, the session name generated by Ray can be configured. By default, the session name is automatically generated, but you can specify a custom name using the `name` parameter when initializing Ray. The session name is used as the base name for the directory where Ray stores its temporary files. See the Configuration documentation for more information on configuring Ray.",3152"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3153},3154{3155"question": "can I use the Python SDK to get a link to Ray dashboard for a given job?",3156"sources": [3157"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3158"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",3159"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents",3160"https://docs.ray.io/en/master/ray-core/api/doc/ray.init.html#ray-init",3161"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#contents"3162],3163"document_ids": [31647585,316554950,316655110,316723868,3168551053169],3170"answer": " Yes, you can use the Python SDK to get a link to the Ray dashboard for a given job. You can use the Ray Jobs API, which includes a Python SDK, to access the information. The SDK provides a way to interact with Ray jobs programmatically, including the ability to get the URL for the dashboard. You can refer to the Python SDK API Reference for more details.",3171"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3172},3173{3174"question": "What may possible cause the node where this task was running crashed unexpectedly. This can happen if: (1) the instance where the node was running failed, (2) raylet crashes unexpectedly (OOM, preempted node, etc).",3175"sources": [3176"https://docs.ray.io/en/master/ray-observability/user-guides/debug-apps/debug-failures.html#what-kind-of-failures-exist-in-ray",3177"https://docs.ray.io/en/master/train/user-guides/fault-tolerance.html#restore-a-ray-train-experiment",3178"https://docs.ray.io/en/master/train/api/doc/ray.train.trainer.BaseTrainer.restore.html#ray-train-trainer-basetrainer-restore",3179"https://docs.ray.io/en/master/train/api/doc/ray.train.xgboost.XGBoostTrainer.restore.html#ray-train-xgboost-xgboosttrainer-restore",3180"https://docs.ray.io/en/master/train/api/doc/ray.train.gbdt_trainer.GBDTTrainer.restore.html#ray-train-gbdt-trainer-gbdttrainer-restore"3181],3182"document_ids": [31834685,318467853,318565279,318666448,3187579933188],3189"answer": " The node where the task was running may have crashed unexpectedly due to:\n\n1. The instance where the node was running failed.\n2. Raylet crashed unexpectedly, for example, due to Out-Of-Memory (OOM) or a preempted node.\n3. The head node crashed, which could be due to OOM or some other runtime error.",3190"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3191},3192{3193"question": "Do you know how to resolve (gcs_server) gcs_health_check_manager.cc:108: Health check failed for node? I observed that the node is still up and running.",3194"sources": [3195"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/troubleshooting.html#init-container-troubleshooting",3196"https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#system-logs",3197"https://docs.ray.io/en/master/cluster/kubernetes/user-guides/static-ray-cluster-without-kuberay.html#external-redis-integration-for-fault-tolerance",3198"https://docs.ray.io/en/master/ray-core/fault_tolerance/gcs.html#setting-up-redis",3199"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-9-upgrade-from-ray-serves-single-application-api-to-its-multi-application-api-without-downtime"3200],3201"document_ids": [320237127,32033269,320441387,320523337,3206370363207],3208"answer": " Based on the context provided, the issue seems to be related to a failed GCS (General Component Service) server process in the head Pod. However, the node is still up and running. The error message \"Health check failed for node\" in 'gcs_health_check_manager.cc:108' suggests that the GCS health check has failed.\n\nTo resolve this issue, you can try the following steps:\n\n1. Check the GCS server logs in the head Pod to identify the root cause of the failure.\n2. Implement additional mechanisms to detect the failure of GCS or the head node, as suggested in the context.\n3. Ensure that GCS fault tolerance is enabled, as it may help in recovering from the failure without losing connections to worker nodes.\n\nIf the issue persists, you may need to investigate further or consult the relevant documentation or support resources for your specific system or application.",3209"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3210},3211{3212"question": "Do you need the DAGDriver to deploy a serve application using RayServe?",3213"sources": [3214"https://docs.ray.io/en/master/serve/advanced-guides/deployment-graphs.html#building-the-call-graph-methodnodes-and-functionnodes",3215"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",3216"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",3217"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started",3218"https://docs.ray.io/en/master/serve/deploy-many-models/multi-app.html#get-started"3219],3220"document_ids": [322196373,3222102856,3223102717,3224102722,32251028613226],3227"answer": " Yes, you need the DAGDriver to deploy a serve application using RayServe. It is used for deployments based on a graph. You can import it using \"from ray.serve.drivers import DAGDriver\".",3228"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3229},3230{3231"question": "What\u2019s the import path that I need to provide to a simple RayServe deployment?",3232"sources": [3233"https://docs.ray.io/en/master/serve/advanced-guides/managing-java-deployments.html#managing-a-python-deployment",3234"https://docs.ray.io/en/master/cluster/kubernetes/troubleshooting/rayservice-troubleshooting.html#issue-1-ray-serve-script-is-incorrect",3235"https://docs.ray.io/en/master/serve/advanced-guides/app-builder-guide.html#configuring-multiple-composed-deployments",3236"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment",3237"https://docs.ray.io/en/master/serve/advanced-guides/dyn-req-batch.html#enable-batching-for-your-deployment"3238],3239"document_ids": [324097397,324135639,324298710,3243104948,32441049973245],3246"answer": " Based on the provided context, the import path for a simple RayServe deployment can be one of the following:\n\n1. `import ray\\nfrom ray import serve`\n2. `import io.ray.api.Ray\\nimport io.ray.serve.api.Serve\\nimport io.ray.serve.deployment.Deployment`\n3. `import ray\\nfrom ray.serve import Application`\n\nYou can choose any of these import statements based on your preference and requirements.",3247"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3248},3249{3250"question": "do you have any documents / examples showing the usage of RayJob in Kuberay?",3251"sources": [3252"https://docs.ray.io/en/master/cluster/configure-manage-dashboard.html#disabling-the-dashboard",3253"https://docs.ray.io/en/master/cluster/kubernetes/examples/rayjob-batch-inference-example.html#step-2-submit-the-rayjob",3254"https://docs.ray.io/en/master/cluster/kubernetes/getting-started/rayjob-quick-start.html#rayjob-quickstart",3255"https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html#custom-resource-definitions-crds",3256"https://docs.ray.io/en/master/cluster/kubernetes/index.html#about-kuberay"3257],3258"document_ids": [325937043,326045800,326138433,326241349,3263352963264],3265"answer": " Yes, there is an example of RayJob usage in Kuberay. You can find it in the Kuberay repository here: <https://raw.githubusercontent.com/ray-project/kuberay/master/ray-operator/config/samples/ray-job.yaml>. Please note that RayJob support in KubeRay v0.x is in the alpha stage, as mentioned in the RayJob Quickstart documentation. KubeRay is used by several companies for production Ray deployments, and it provides features like RayJob and RayService.",3266"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3267},3268{3269"question": "Does Ray metrics have to be exported via an actor?",3270"sources": [3271"https://docs.ray.io/en/master/ray-observability/user-guides/add-app-metrics.html#adding-application-level-metrics",3272"https://docs.ray.io/en/master/ray-core/walkthrough.html#next-steps",3273"https://docs.ray.io/en/master/cluster/metrics.html#system-and-application-metrics",3274"https://docs.ray.io/en/master/serve/monitoring.html#built-in-ray-serve-metrics",3275"https://docs.ray.io/en/master/ray-observability/reference/system-metrics.html#metrics-semantics-and-consistency"3276],3277"document_ids": [32782480,327935007,328038211,328198530,328259813283],3284"answer": " No, Ray metrics do not have to be exported via an actor. You can define and export custom metrics using the `ray.util.metrics` API without the need for an actor. However, using remote classes or actors can be a convenient way to define and export metrics in a distributed setting. Ray also exports some built-in metrics when you use certain installation commands.",3285"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3286},3287{3288"question": "how can I avoid objects not getting spilled?",3289"sources": [3290"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#object-spilling",3291"https://docs.ray.io/en/master/ray-more-libs/dask-on-ray.html#out-of-core-data-processing",3292"https://docs.ray.io/en/master/cluster/vms/user-guides/large-cluster-best-practices.html#system-configuration",3293"https://docs.ray.io/en/master/ray-core/objects/object-spilling.html#single-node",3294"https://docs.ray.io/en/master/ray-core/scheduling/memory-management.html#concepts"3295],3296"document_ids": [329727411,3298105317,329948512,330027439,3301295703302],3303"answer": " To avoid objects not getting spilled in Ray, you can increase the size of the object store so that it does not fill up and trigger object spilling. You can do this by adjusting the Ray configuration settings before starting the Ray application. However, keep in mind that increasing the object store size may consume more memory and could affect the performance of other system processes. Alternatively, you can manage the data you are working with in Ray more efficiently to reduce the likelihood of object spilling, for example, by breaking down large objects into smaller ones or by using more efficient data structures.",3304"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3305},3306{3307"question": "what\u2019s ray core",3308"sources": [3309"https://docs.ray.io/en/master/ray-core/walkthrough.html#what-is-ray-core",3310"https://docs.ray.io/en/master/ray-overview/getting-started.html#ray-core-quickstart",3311"https://docs.ray.io/en/master/ray-core/api/cli.html#ray-core-cli",3312"https://docs.ray.io/en/master/ray-core/examples/gentle_walkthrough.html#a-gentle-introduction-to-ray-core-by-example",3313"https://docs.ray.io/en/master/ray-core/examples/map_reduce.html#wrapping-up"3314],3315"document_ids": [331634711,33175805,331821583,331938709,3320363533321],3322"answer": " Ray Core is a part of Ray, an open-source distributed computing framework. It provides the core functionality of Ray, including task and actor scheduling, resource management, and task execution. The Ray Core CLI is a command-line interface for interacting with Ray Core. For more information and examples, you can refer to the Ray Core documentation and examples gallery.",3323"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3324},3325{3326"question": "Does ray support cron job",3327"sources": [3328"https://docs.ray.io/en/master/train/distributed-xgboost-lightgbm.html#how-to-scale-out-training",3329"https://docs.ray.io/en/master/ray-observability/key-concepts.html#job-logs",3330"https://docs.ray.io/en/master/cluster/running-applications/job-submission/index.html#ray-jobs-api",3331"https://docs.ray.io/en/master/cluster/kubernetes/k8s-ecosystem/kubeflow.html#kubeflow-an-interactive-development-solution",3332"https://docs.ray.io/en/master/cluster/package-overview.html#ray-cluster-management-api"3333],3334"document_ids": [333556853,33367581,333754939,333843838,3339396793340],3341"answer": " Based on the provided context, Ray does not directly support cron jobs. However, you can set up cron jobs separately to submit Ray jobs at specific times. The Ray Job APIs and CLI commands such as `ray job submit`, `ray job status`, `ray job stop`, `ray job logs`, and `ray job list` can be used to manage Ray jobs.",3342"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3343},3344{3345"question": "can you give me the dependencies list for api read_images?",3346"sources": [3347"https://docs.ray.io/en/master/ray-overview/installation.html#docker-source-images",3348"https://docs.ray.io/en/master/installation.html#docker-source-images",3349"https://docs.ray.io/en/master/ray-more-libs/installation.html#docker-source-images",3350"https://docs.ray.io/en/master/serve/production-guide/kubernetes.html#setting-up-a-rayservice-custom-resource-cr",3351"https://docs.ray.io/en/master/ray-core/handling-dependencies.html#using-conda-or-pip-packages"3352],3353"document_ids": [33546530,3355775,3356106369,3357103428,3358269923359],3360"answer": " Based on the context provided, the dependencies for the \"read\\_images\" API are:\n\n* pytorch\n* pip\n* requests\n* ray[serve]\n\nThese dependencies should be included in the Dockerfile for your image.",3361"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3362},3363{3364"question": "how do I kill a specific serve replica",3365"sources": [3366"https://docs.ray.io/en/master/ray-observability/getting-started.html#serve-application-detail-page",3367"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",3368"https://docs.ray.io/en/master/ray-observability/getting-started.html#serve-replica-detail-page",3369"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#serve-controller-failure",3370"https://docs.ray.io/en/master/serve/production-guide/fault-tolerance.html#deployment-replica-failure"3371],3372"document_ids": [3373802,3374101642,3375804,3376101768,33771018943378],3379"answer": " To kill a specific Serve replica, go to the Serve Replica Detail page by clicking on the replica name. Then, use the Python interpreter to manually kill the Serve actor associated with that replica. Note that you'll need to have the necessary permissions to perform this action.",3380"llm": "mistralai/Mixtral-8x7B-Instruct-v0.1"3381}3382]3383}3384