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Add codepath for computing buckets without int conversion #113

Add codepath for computing buckets without int conversion

Add codepath for computing buckets without int conversion #113

Workflow file for this run

name: "GPU CI/CD"
on:
push:
branches:
- main
pull_request:
branches:
# We can run gpuCI on any PR targeting these branches
- 'main'
- '[rv][0-9].[0-9].[0-9]'
- '[rv][0-9].[0-9].[0-9]rc[0-9]'
# PR has to be labeled with "gpuCI" label
# If new commits are added, the "gpuCI" label has to be removed and re-added to rerun gpuCI
types: [ labeled ]
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
# First, we build and push a NeMo-Curator container
build-container:
# "build-container" job is run if the "gpuci" label is added to the PR
if: ${{ github.event.label.name == 'gpuci' || github.ref == 'refs/heads/main' }}
uses: NVIDIA/NeMo-FW-CI-templates/.github/workflows/_build_container.yml@v0.11.0
with:
image-name: nemo_curator_container
dockerfile: Dockerfile
image-label: nemo-curator
build-args: |
IMAGE_LABEL=nemo-curator
REPO_URL=https://github.com/${{ github.repository }}.git
CURATOR_COMMIT=${{ github.sha }}
prune-filter-timerange: 24h
# Then, we run our PyTests in the container we just built
run-gpu-tests:
needs: build-container
# This is the tag on our Azure runner found in Actions -> Runners -> Self-hosted runners
# It has 2 A100 GPUs
runs-on: self-hosted-azure
# "run-gpu-tests" job is run if the "gpuci" label is added to the PR
if: ${{ github.event.label.name == 'gpuci' || github.ref == 'refs/heads/main' }}
steps:
# If something went wrong during the last cleanup, this step ensures any existing container is removed
- name: Remove existing container if it exists
run: |
if [ "$(docker ps -aq -f name=nemo-curator-container)" ]; then
docker rm -f nemo-curator-container
fi
# This runs the container which was pushed by build-container, which we call "nemo-curator-container"
# `--gpus all` ensures that all of the GPUs from our self-hosted-azure runner are available in the container
# We use "github.run_id" to identify the PR with the commits we want to run the PyTests with
# `bash -c "sleep infinity"` keeps the container running indefinitely without exiting
- name: Run Docker container
run: |
docker run --gpus all --name nemo-curator-container -d nemoci.azurecr.io/nemo_curator_container:${{ github.run_id }} bash -c "sleep infinity"
# Expect `whoami` to be "azureuser"
# Expect `nvidia-smi` to show our 2 A100 GPUs
- name: Check GPUs
run: |
whoami
docker exec nemo-curator-container nvidia-smi
# In the virtual environment (called "curator") we created in the container,
# list all of our packages. Useful for debugging
- name: Verify installations
run: |
docker exec nemo-curator-container pip list
# In the virtual environment (called "curator") we created in the container,
# run our PyTests marked with `@pytest.mark.gpu`
# We specify the `rootdir` to help locate the "pyproject.toml" file (which is in the root directory of the repository),
# and then the directory where the PyTests are located
- name: Run PyTests with GPU mark
run: |
docker exec nemo-curator-container pytest -m gpu --rootdir /opt/NeMo-Curator /opt/NeMo-Curator/tests
# After running `docker stop`, the container remains in an exited state
# It is still present on our system and could be restarted with `docker start`
# Thus, we use `docker rm` to permanently removed it from the system
- name: Cleanup
if: always()
run: |
docker stop nemo-curator-container && docker rm nemo-curator-container