mirror of
https://github.com/vale981/ray
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a12f5ff5d6
Adding a FAQ page. Currently has some basic questions that have come up in the past. Explaining how to use Matplotlib due to threading in the distributed training function.
154 lines
4.9 KiB
YAML
154 lines
4.9 KiB
YAML
format: jb-book
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root: index
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parts:
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- caption: Overview
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chapters:
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- file: ray-overview/index
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- file: ray-overview/installation
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- file: ray-overview/ray-libraries
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- caption: Ray ML
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chapters:
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- file: data/dataset
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title: Ray Data
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sections:
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- file: data/getting-started
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- file: data/key-concepts
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- file: data/user-guide
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- file: data/examples/big_data_ingestion
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- file: data/package-ref
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- file: data/integrations
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- file: train/train
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title: Ray Train
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sections:
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- file: train/user_guide
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- file: train/examples
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- file: train/faq
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- file: train/architecture
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- file: train/migration-guide
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- file: raysgd/raysgd
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title: "RaySGD v1: Distributed Training Wrappers"
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- file: train/api
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- file: tune/index
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title: Ray Tune
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sections:
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- file: tune/getting-started
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- file: tune/key-concepts
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- file: tune/tutorials/overview
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sections:
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- file: tune/tutorials/tune-lifecycle
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title: "How Tune Works"
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- file: tune/tutorials/tune-stopping
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title: "How to Stop and Resume"
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- file: tune/tutorials/tune-metrics
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title: "Using Callbacks and Metrics"
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- file: tune/tutorials/tune-distributed
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title: "Distributed Tuning"
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- file: tune/tutorials/tune-output
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title: "Logging Tune Runs"
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- file: tune/tutorials/tune-resources
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title: "Managing Resources"
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- file: tune/tutorials/tune-checkpoints
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title: "Working with Checkpoints"
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- file: tune/tutorials/tune-search-spaces
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title: "Using Search Spaces"
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- file: tune/tutorials/tune-advanced-tutorial
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title: "Understanding PBT"
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- file: tune/tutorials/tune-scalability
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title: "Scalability Benchmarks"
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- file: tune/examples/index
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- file: tune/faq
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- file: tune/api_docs/overview.rst
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- file: serve/index
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title: Ray Serve
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sections:
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- file: serve/end_to_end_tutorial
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- file: serve/core-apis
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- file: serve/http-servehandle
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- file: serve/deployment
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- file: serve/ml-models
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- file: serve/deployment-graph
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- file: serve/performance
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- file: serve/architecture
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- file: serve/tutorials/index
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- file: serve/faq
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- file: serve/package-ref
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- file: rllib/index
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title: Ray RLlib
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sections:
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- file: rllib/core-concepts
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- file: rllib/rllib-training
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- file: rllib/rllib-env
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- file: rllib/rllib-algorithms
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- file: rllib/user-guides
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sections:
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- file: rllib/rllib-models
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- file: rllib/rllib-concepts
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- file: rllib/rllib-sample-collection
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- file: rllib/rllib-offline
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- file: rllib/rllib-dev
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- file: rllib/rllib-examples
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- file: rllib/package_ref/index
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- file: workflows/concepts
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title: Ray Workflows
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sections:
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- file: workflows/basics
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- file: workflows/management
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- file: workflows/actors
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- file: workflows/metadata
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- file: workflows/events
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- file: workflows/comparison
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- file: workflows/advanced
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- file: workflows/package-ref
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- file: ray-more-libs/index
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title: More Ray ML Libraries
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- caption: Ray Core
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chapters:
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- file: ray-core/walkthrough
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title: Getting Started
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- file: ray-core/key-concepts
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title: "Key Concepts"
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- file: ray-core/user-guide
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title: "User Guides"
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- file: ray-core/examples/overview
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title: "Examples"
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sections:
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- file: ray-core/examples/plot_example-a3c
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- file: ray-core/examples/plot_example-lm
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- file: ray-core/examples/plot_hyperparameter
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- file: ray-core/examples/plot_lbfgs
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- file: ray-core/examples/plot_parameter_server
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- file: ray-core/examples/plot_pong_example
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- file: ray-core/package-ref
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- caption: Ray Clusters
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chapters:
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- file: cluster/quickstart
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- file: cluster/user-guide
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- file: cluster/cloud
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- file: cluster/deploy
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- file: cluster/api
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- caption: References
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chapters:
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- file: ray-references/api
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- caption: Developer Guides
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chapters:
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- file: ray-contribute/getting-involved
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sections:
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- file: ray-contribute/development
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- file: ray-contribute/docs
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- file: ray-contribute/fake-autoscaler
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- file: ray-core/examples/testing-tips
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- file: ray-core/configure
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- file: ray-observability/index
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- file: ray-contribute/whitepaper
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# TODO: Add examples section
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