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Distributed Workloads

Examples

  • Fine-Tune LLMs with Ray and DeepSpeed on OpenShift AI
  • Fine-Tune Stable Diffusion with DreamBooth and Ray Train
  • Hyperparameters Optimization with Ray Tune on OpenShift AI

Integration Tests

Prerequisites

  • Admin access to an OpenShift cluster (CRC is fine)

  • Installed OpenDataHub or RHOAI, enabled all Distributed Workload components

  • Installed Go 1.21

Common environment variables

  • CODEFLARE_TEST_OUTPUT_DIR - Output directory for test logs

  • CODEFLARE_TEST_TIMEOUT_SHORT - Timeout duration for short tasks

  • CODEFLARE_TEST_TIMEOUT_MEDIUM - Timeout duration for medium tasks

  • CODEFLARE_TEST_TIMEOUT_LONG - Timeout duration for long tasks

  • CODEFLARE_TEST_RAY_IMAGE (Optional) - Ray image used for raycluster configuration

  • MINIO_CLI_IMAGE (Optional) - Minio CLI image used for uploading/downloading data from/into s3 bucket

    NOTE: quay.io/modh/ray:2.35.0-py311-cu121 is the default image used for creating a RayCluster resource. If you have your own custom ray image which suits your purposes, specify it in CODEFLARE_TEST_RAY_IMAGE environment variable.

Environment variables for Training operator test suite

  • FMS_HF_TUNING_IMAGE - Image tag used in PyTorchJob CR for model training

Environment variables for Training operator GPU test suite

  • TEST_NAMESPACE_NAME (Optional) - Existing namespace where will the Training operator GPU tests be executed
  • HF_TOKEN - HuggingFace token used to pull models which has limited access
  • GPTQ_MODEL_PVC_NAME - Name of PersistenceVolumeClaim containing downloaded GPTQ models

To upload trained model into S3 compatible storage, use the environment variables mentioned below :

  • AWS_DEFAULT_ENDPOINT - Storage bucket endpoint to upload trained dataset to, if set then test will upload model into s3 bucket
  • AWS_ACCESS_KEY_ID - Storage bucket access key
  • AWS_SECRET_ACCESS_KEY - Storage bucket secret key
  • AWS_STORAGE_BUCKET - Storage bucket name
  • AWS_STORAGE_BUCKET_MODEL_PATH (Optional) - Path in the storage bucket where trained model will be stored to

Environment variables for ODH integration test suite

  • ODH_NAMESPACE - Namespace where ODH components are installed to
  • NOTEBOOK_USER_NAME - Username of user used for running Workbench
  • NOTEBOOK_USER_TOKEN - Login token of user used for running Workbench
  • NOTEBOOK_IMAGE - Image used for running Workbench

To download MNIST training script datasets from S3 compatible storage, use the environment variables mentioned below :

  • AWS_DEFAULT_ENDPOINT - Storage bucket endpoint from which to download MNIST datasets
  • AWS_ACCESS_KEY_ID - Storage bucket access key
  • AWS_SECRET_ACCESS_KEY - Storage bucket secret key
  • AWS_STORAGE_BUCKET - Storage bucket name
  • AWS_STORAGE_BUCKET_MNIST_DIR - Storage bucket directory from which to download MNIST datasets.

Running Tests

Execute tests like standard Go unit tests.

go test -timeout 60m ./tests/kfto/