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CloudAI

Table of Contents

  • Overview
  • Get started
    • Install CloudAI
    • Run your first experiment
    • Understand experiment configuration
  • Run experiments
    • Set up your execution environment
    • Run on Slurm
    • Run on Kubernetes
    • Compose a scenario
    • Prepare and clean up with hooks
    • Prepare model weights
    • Prepare workload prerequisites
    • Inspect commands before execution
    • Read results and generate reports
  • Optimize configurations
    • Run a configuration search
    • Choose an objective and search space
    • Plan compute usage
    • Compare trade-offs and reuse configurations
  • Automate experiments
    • Run experiments with Python
    • Handle results and failures
    • Publish results and integrate with CI
  • Workloads
    • Training
      • MegatronBridge
      • Nemo Run
      • DDLB
    • Inference
      • vLLM
      • SGLang
      • AI Dynamo
      • SRT-Slurm
    • Networking and communication
      • NCCL
      • UCC
      • OSU
      • DeepEP Benchmark
      • NIXL Bench
      • NIXL EP
      • NIXL KVBench
      • NIXL CTPerf
    • Storage
      • fio
    • Simulation and prediction
      • AIConfigurator
      • DynamoMocker
      • Chakra Replay
    • General-purpose execution
      • Bash Command
      • Slurm Container
      • Sleep
    • Legacy integrations
      • NeMo Launcher (deprecated)
      • JaxToolbox workloads (DEPRECATED)
  • Extend CloudAI
    • Architecture and extension points
    • Add a workload
    • Add a reporter
    • Add an optimization agent
    • Add an execution system or runner
    • Develop and contribute
  • Reference
    • CLI reference
    • Workload and system support
    • Systems
      • NCCL on RunAI
    • System configuration example
    • Shared system settings
    • Test definitions
    • Scenario configuration
    • Agent and search settings
    • Reports
      • Training Report Schema
    • Python API
  • Troubleshooting
  • Automate experiments

Automate experiments#

Call CloudAI from your application and use structured results to guide the next action.

  • Run experiments with Python
  • Handle results and failures
  • Publish results and integrate with CI

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Pareto Frontiers

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Run experiments with Python
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