User Guides

• Running Basic Experiments
  • Running Independent Tune Trials in Parallel
  • How does Tune compare to using Ray Core (ray.remote)?
• Logging and Outputs in Tune
  • How to configure logging in Tune?
  • How to log your Tune runs to TensorBoard?
  • How to control console output with Tune?
  • How to redirect Trainable logs to files in a Tune run?
  • How do you log arbitrary files from a Tune Trainable?
  • How to Build Custom Tune Loggers?
• Setting Trial Resources
  • How to leverage GPUs in Tune?
  • How to run distributed training with Tune?
  • How to limit concurrency in Tune?
• Using Search Spaces
  • How to use Custom and Conditional Search Spaces in Tune?
• How to Define Stopping Criteria for a Ray Tune Experiment
  • Stop a Tune experiment manually
  • Stop using metric-based criteria
  • Stop trials after a certain amount of time
  • Stop on trial failures
  • Early stopping with Tune schedulers
  • Summary
• How to Save and Load Trial Checkpoints
  • Function API Checkpointing
  • Class API Checkpointing
  • Configurations
  • Summary
  • Appendix: Types of data stored by Tune
• How to Configure Persistent Storage in Ray Tune
  • Storage Options in Tune
  • Examples
  • Advanced configuration
• How to Enable Fault Tolerance in Ray Tune
  • Experiment-level Fault Tolerance in Tune
  • Trial-level Fault Tolerance in Tune
  • Summary
• Using Callbacks and Metrics
  • How to work with Callbacks in Ray Tune?
  • How to use log metrics in Tune?
• Getting Data in and out of Tune
  • Getting data into Tune
  • Getting data out of Ray Tune
• Analyzing Tune Experiment Results
  • Loading experiment results from an directory
  • Experiment-level Analysis: Working with ResultGrid
  • Trial-level Analysis: Working with an individual Result
  • Plotting metrics
  • Accessing checkpoints and loading for test inference
  • Summary
• A Guide to Population Based Training with Tune
  • Function API with Population Based Training
  • Replaying a PBT run
  • Example: DCGAN with PBT
  • Visualization
  • Summary
• Deploying Tune in the Cloud
  • Summary
  • Example: Distributed Tune on AWS VMs
  • Running a Distributed Tune Experiment
  • Storage Options in a Distributed Tune Run
  • Tune Runs on preemptible instances
  • Fault Tolerance of Tune Runs
  • Common Tune Commands
  • Troubleshooting
• Tune Architecture
  • What happens in Tuner.fit?
  • Lifecycle of a Tune Trial
  • Tune’s Architecture
• Scalability Benchmarks
  • Result throughput
  • Network overhead in Ray Tune