Distributed Workloads

Examples

• Fine-Tune Llama 2 Models with Ray and DeepSpeed 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

  NOTE: quay.io/rhoai/ray:2.23.0-py39-cu121 is the default community 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 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/