GitHub Actions Syntax

This reference guide provides comprehensive information on the GitHub Actions syntax for integrating Machine.dev GPU runners into your workflows.

Basic Workflow Structure

A GitHub Actions workflow is defined in YAML format and must include the following elements to use Machine.dev:

name: My GPU Workflow

on:
  # Trigger events (push, pull_request, workflow_dispatch, etc.)
  workflow_dispatch:

jobs:
  gpu-job:
    name: GPU-Accelerated Job
    runs-on:
      - machine       # Required to use Machine.dev
      - gpu=a10g      # Specify GPU type
      - tenancy=spot  # Specify tenancy type (spot or dedicated)

    steps:
      # Job steps
      - name: Checkout code
        uses: actions/checkout@v3

      # Add more steps...

Machine.dev Runner Labels

Required Label

runs-on:
  - machine  # Required for all Machine.dev runners

GPU Type Labels

Specify the type of GPU you want to use:

runs-on:
  - machine
  - gpu=<gpu-type>

Available GPU types:

Label	GPU Model	VRAM	CUDA Cores	Use Cases
gpu=t4	NVIDIA T4	16 GB	2,560	Training, inference, computer vision
gpu=l4	NVIDIA L4	24 GB	7,424	ML/DL training, inference, vision AI
gpu=a10g	NVIDIA A10G	24 GB	9,216	Model training, rendering, simulation
gpu=a100	NVIDIA A100	40 GB	6,912	Large model training, HPC
gpu=a100-80gb	NVIDIA A100	80 GB	6,912	Very large models, distributed training
gpu=l40s	NVIDIA L40S	48 GB	18,176	Generative AI, large models, computer vision

Tenancy Type Labels

Specify whether you want dedicated or spot instances:

runs-on:
  - machine
  - gpu=a10g
  - tenancy=<tenancy-type>

Available tenancy types:

Label	Description	Cost	Stability
tenancy=dedicated	Dedicated instances with guaranteed availability	Higher cost	Highest stability
tenancy=spot	Spot instances that may be preempted	Up to 70% lower cost	May be interrupted

Region Labels

Optionally specify the region where your runner should be provisioned:

runs-on:
  - machine
  - gpu=a10g
  - region=<region-code>

Available regions:

Label	Location	Latency (US)	Available GPUs
region=us-east	US East (Virginia)	Low	All types
region=us-west	US West (Oregon)	Medium	All types
region=eu-west	EU West (Ireland)	High	T4, L4, A10G
region=ap-southeast	Asia Pacific (Singapore)	High	T4, L4

Complete Example

Here’s a complete example that specifies all available options:

name: Complete Machine.dev Example

on:
  workflow_dispatch:
    inputs:
      model_type:
        description: 'Type of model to train'
        required: true
        default: 'small'
        type: choice
        options:
          - small
          - medium
          - large

jobs:
  train-model:
    name: Train Machine Learning Model
    runs-on:
      - machine
      - gpu=a10g
      - tenancy=spot
      - region=us-east

    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'

      - name: Install dependencies
        run: |
          pip install -r requirements.txt

      - name: Train model
        run: |
          python train.py --model-size=${{ github.event.inputs.model_type }}

      - name: Upload model artifacts
        uses: actions/upload-artifact@v3
        with:
          name: trained-model
          path: model/

Matrix Strategy for Multiple GPUs

You can use matrix strategy to run your workflow on multiple GPU types:

jobs:
  gpu-matrix:
    strategy:
      matrix:
        gpu: [t4, l4, a10g]
      fail-fast: false

    runs-on:
      - machine
      - gpu=${{ matrix.gpu }}

    steps:
      - name: Run benchmark on ${{ matrix.gpu }}
        run: |
          python benchmark.py --gpu-type=${{ matrix.gpu }}

Job Dependencies

You can coordinate between CPU and GPU jobs:

jobs:
  prepare-data:
    runs-on: ubuntu-latest
    steps:
      - name: Prepare dataset
        run: python prepare_data.py
    outputs:
      dataset_path: ${{ steps.prepare.outputs.path }}

  train-model:
    needs: prepare-data
    runs-on:
      - machine
      - gpu=a10g
    steps:
      - name: Train on prepared data
        run: |
          python train.py --data=${{ needs.prepare-data.outputs.dataset_path }}

Environment Variables

Machine.dev provides several environment variables that can be accessed in your workflow:

Variable	Description	Example Value
MACHINE_GPU_TYPE	Type of GPU	a10g
MACHINE_GPU_COUNT	Number of GPUs	1
MACHINE_TENANCY	Tenancy type	spot
MACHINE_REGION	Region code	us-east
CUDA_VISIBLE_DEVICES	CUDA devices available	0

Example usage:

steps:
  - name: Check environment
    run: |
      echo "GPU Type: $MACHINE_GPU_TYPE"
      echo "GPU Count: $MACHINE_GPU_COUNT"
      echo "Tenancy: $MACHINE_TENANCY"
      echo "Region: $MACHINE_REGION"

Error Handling for Spot Instances

When using spot instances, you should implement error handling to manage potential interruptions:

jobs:
  spot-job:
    runs-on:
      - machine
      - gpu=a10g
      - tenancy=spot

    steps:
      # Save checkpoints frequently
      - name: Train with checkpoints
        run: |
          python train.py --checkpoint-freq=5

      # Upload partial results
      - name: Upload checkpoints
        if: always()  # Run even if job is cancelled
        uses: actions/upload-artifact@v3
        with:
          name: model-checkpoints
          path: checkpoints/

Best Practices

Set Appropriate Timeouts

1. Set appropriate timeouts for your jobs:

   jobs:
     gpu-job:
       timeout-minutes: 60  # Limit to 1 hour

Use Conditional Execution

2. Use conditional execution to skip GPU tasks when appropriate:

   steps:
     - name: GPU-intensive task
       if: github.event_name == 'push' && github.ref == 'refs/heads/main'
       run: python heavy_task.py

Optimize Workflow Triggers

3. Optimize workflow triggers to avoid unnecessary runs:

   on:
     push:
       paths:
         - 'model/**'
         - 'data/**'
       branches:
         - main

Use Workflow Concurrency

4. Use workflow concurrency to avoid redundant runs:

   concurrency:
     group: ${{ github.workflow }}-${{ github.ref }}
     cancel-in-progress: true

Common Issues and Solutions

Issue	Solution
Runner not available	Try a different GPU type or region
Out of memory error	Use a GPU with more VRAM or optimize batch size
Slow performance	Check data loading bottlenecks or use a faster GPU
Spot instance terminated	Use dedicated instances for critical workloads
Long provisioning time	Pre-warm runners with scheduled workflows

Related Resources

• Machine Workflow Setup Guide
• GPU Runner Specifications
• Cost Optimization Strategies
• GitHub Actions Documentation