fix model storage issue #2
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name: CI-CD-Compelete-Deployment | ||
on: | ||
workflow_dispatch: | ||
jobs: | ||
build-and-push-ecr-image: | ||
name: Build and push ECR image | ||
runs-on: ubuntu-latest | ||
outputs: | ||
commit_id: ${{ steps.get_commit_id.outputs.commit_id }} | ||
steps: | ||
- name: Checkout Code | ||
uses: actions/checkout@v3 | ||
- name: Install Utilities | ||
run: | | ||
sudo apt-get update | ||
sudo apt-get install -y jq unzip | ||
- name: Configure AWS credentials | ||
uses: aws-actions/configure-aws-credentials@v1 | ||
with: | ||
aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }} | ||
aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | ||
aws-region: ${{ secrets.AWS_REGION }} | ||
- name: Login to Amazon ECR | ||
id: login-ecr | ||
uses: aws-actions/amazon-ecr-login@v1 | ||
- name: Get latest commit ID | ||
id: get_commit_id | ||
run: | | ||
latest_commit=$(git rev-parse HEAD) | ||
echo "::set-output name=commit_id::$latest_commit" | ||
- name: Display the commit ID | ||
run: | | ||
echo "Latest commit ID is: ${{ steps.get_commit_id.outputs.commit_id }}" | ||
- name: Build, tag, and push image to Amazon ECR | ||
id: build-image | ||
env: | ||
ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }} | ||
ECR_REPOSITORY: ${{ secrets.ECR_REPOSITORY_NAME }} | ||
IMAGE_TAG: latest | ||
run: | | ||
# Build a docker container and | ||
# push it to ECR so that it can | ||
# be deployed to ECS. | ||
docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG . | ||
docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG | ||
echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG" | ||
launch-runner: | ||
runs-on: ubuntu-latest | ||
needs: build-and-push-ecr-image | ||
outputs: | ||
commit_id: ${{ steps.get_commit_id_runner.outputs.commit_id }} | ||
steps: | ||
- uses: actions/checkout@v3 | ||
- uses: iterative/setup-cml@v2 | ||
- name: Display the commit ID | ||
run: | | ||
echo "Latest commit ID is: ${{ needs.build-and-push-ecr-image.outputs.commit_id }}" | ||
- name: Get latest commit ID | ||
id: get_commit_id_runner | ||
run: | | ||
echo "::set-output name=commit_id::${{ needs.build-and-push-ecr-image.outputs.commit_id }}" | ||
- name: Deploy runner on AWS EC2 | ||
env: | ||
REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }} | ||
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} | ||
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | ||
run: | | ||
cml runner launch \ | ||
--cloud=aws \ | ||
--name=session-08 \ | ||
--cloud-region=ap-south-1 \ | ||
--cloud-type=g4dn.xlarge \ | ||
--cloud-hdd-size=64 \ | ||
--cloud-spot \ | ||
--single \ | ||
--labels=cml-gpu \ | ||
--idle-timeout=100 | ||
train-and-deploy: | ||
runs-on: [self-hosted, cml-gpu] | ||
needs: launch-runner | ||
outputs: | ||
commit_id: ${{ steps.get_commit_id_ec2.outputs.commit_id }} | ||
timeout-minutes: 20 | ||
# container: | ||
# image: docker://pytorch/pytorch:2.4.0-cuda12.4-cudnn9-runtime | ||
# options: --gpus all | ||
# runs-on: ubuntu-latest | ||
steps: | ||
- name: Display CUDA Version | ||
run: | | ||
echo "CUDA Version:" | ||
nvcc --version || true | ||
- name: Display cuDNN Version | ||
run: | | ||
echo "cuDNN Version:" | ||
cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 || true | ||
- name: Verify EC2 Instance | ||
run: | | ||
echo "Checking instance information..." | ||
# Check if we're on EC2 | ||
TOKEN=$(curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600") | ||
curl -H "X-aws-ec2-metadata-token: $TOKEN" http://169.254.169.254/latest/meta-data/instance-type | ||
echo "Checking system resources..." | ||
lscpu | ||
free -h | ||
df -h | ||
nvidia-smi # This will show GPU if available | ||
echo "Checking environment..." | ||
env | grep AWS || true | ||
hostname | ||
whoami | ||
pwd | ||
# Install the AWS CLI if not already available | ||
if ! command -v aws &> /dev/null; then | ||
apt-get update | ||
apt-get install -y awscli | ||
fi | ||
# Get ECR login command and execute it | ||
$(aws ecr get-login --no-include-email --region ap-south-1) | ||
aws ecr get-login-password --region ap-south-1 | docker login --username AWS --password-stdin 306093656765.dkr.ecr.ap-south-1.amazonaws.com | ||
- name: Set up AWS CLI credentials | ||
env: | ||
AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} | ||
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} | ||
AWS_DEFAULT_REGION: ap-south-1 # Change to your desired region | ||
run: | | ||
# Create the AWS config and credentials files | ||
mkdir -p ~/.aws | ||
echo "[default]" > ~/.aws/config | ||
echo "region=${AWS_DEFAULT_REGION}" >> ~/.aws/config | ||
echo "[default]" > ~/.aws/credentials | ||
echo "aws_access_key_id=${AWS_ACCESS_KEY_ID}" >> ~/.aws/credentials | ||
echo "aws_secret_access_key=${AWS_SECRET_ACCESS_KEY}" >> ~/.aws/credentials | ||
- name: Test AWS CLI | ||
run: | | ||
# Now you can run any AWS CLI command | ||
aws s3 ls # Example command to list S3 buckets | ||
- name: Pull Docker image from ECR | ||
run: | | ||
docker pull ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest | ||
ls -a | ||
- name: Run DVC commands in container | ||
run: | | ||
mkdir -p model_storage | ||
mkdir -p gradio_demo | ||
docker run --gpus=all \ | ||
--name session-10-container \ | ||
-v "$(pwd)/model_storage:/workspace/model_storage" \ | ||
-e AWS_ACCESS_KEY_ID=${{ secrets.AWS_ACCESS_KEY_ID }} \ | ||
-e AWS_SECRET_ACCESS_KEY=${{ secrets.AWS_SECRET_ACCESS_KEY }} \ | ||
-e AWS_DEFAULT_REGION=${{ secrets.AWS_REGION }} \ | ||
${{ secrets.AWS_ECR_LOGIN_URI }}/${{ secrets.ECR_REPOSITORY_NAME }}:latest \ | ||
/bin/bash -c " | ||
dvc pull -r myremote && \ | ||
mkdir -p model_storage && \ | ||
dvc repro -f | ||
" | ||
# # Wait a moment to ensure the container has started | ||
# sleep 5 | ||
ls model_storage/ | ||
docker cp session-10-container:/workspace/gradio_demo/. ./gradio_demo/ | ||
ls ./gradio_demo/ | ||
# # Print logs from the container | ||
# docker logs $CONTAINER_ID | ||
# # Stop the container after retrieving logs | ||
# docker stop $CONTAINER_ID | ||
- name: List files in folder | ||
run: | | ||
ls -l ./ | ||
- name: Install jq | ||
run: | | ||
sudo apt-get update | ||
sudo apt-get install -y jq | ||
- name: List files in folder | ||
run: | | ||
ls -l ./model_storage | ||
- name: Read best checkpoint file name | ||
id: read_checkpoint | ||
run: | | ||
checkpoint_file=$(head -n 1 ./model_storage/best_model_checkpoint.txt) | ||
echo "CHECKPOINT_FILE=$checkpoint_file" >> $GITHUB_ENV | ||
- name: Upload checkpoint to S3 | ||
run: | | ||
checkpoint_path="${{ env.CHECKPOINT_FILE }}" # Use the checkpoint path from the file | ||
bucket_name="mybucket-emlo-mumbai" # Change to your S3 bucket name | ||
s3_key="session-08-checkpoint/${{ needs.launch-runner.outputs.commit_id }}/$(basename "$checkpoint_path")" # Define S3 key | ||
echo "Uploading $checkpoint_path to s3://$bucket_name/$s3_key" | ||
aws s3 cp "$checkpoint_path" "s3://$bucket_name/$s3_key" | ||
# Deployment on Huggingface | ||
- name: Install Gradio | ||
run: | | ||
cd gradio_demo/ | ||
python -m pip install -r requirements.txt | ||
- name: Log in to Hugging Face | ||
run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")' | ||
- name: Deploy to Spaces | ||
run: gradio deploy | ||
- name: Clean previous images and containers | ||
run: | | ||
docker system prune -f |