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Adapt ec2-gha to Lambda Labs API
- Rename `ec2_gha` → `lambda_gha` throughout - Replace boto3 with requests for Lambda Labs REST API - Simplify action.yml inputs (no VPC, IAM, CloudWatch, security groups) - Rewrite runner.yml workflow (API key auth instead of AWS OIDC) - Update termination to call Lambda API instead of `shutdown -h now` - Remove AWS-specific demo workflows, keep minimal GPU demo - Simplify README for Lambda Labs Known TODOs: - SSH-based setup (Lambda doesn't support cloud-init userdata) - Update tests to mock requests instead of boto3/moto Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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.github/workflows/README.md

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.github/workflows/ci.yaml

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- name: Run tests
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run: |
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cd tests/
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pytest -v --cov=ec2_gha --cov-report=xml --color=yes .
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pytest -v --cov=lambda_gha --cov-report=xml --color=yes .
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- name: CodeCov
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uses: codecov/codecov-action@v4

.github/workflows/demo-cpu-sweep.yml

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.github/workflows/demo-dbg-minimal.yml

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name: Demo – minimal EC2 GPU job
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name: Demo – minimal Lambda GPU job
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on:
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workflow_dispatch:
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workflow_call: # Tested by `demos.yml`
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workflow_call:
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permissions:
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id-token: write # Required for AWS OIDC authentication
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contents: read # Required for actions/checkout; normally set by default, but must explicitly specify when defining a custom `permissions` block.
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contents: read
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jobs:
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ec2:
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# To use from another repo: Open-Athena/ec2-gha/.github/workflows/runner.yml@v2
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lambda:
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# To use from another repo: Open-Athena/lambda-gha/.github/workflows/runner.yml@main
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uses: ./.github/workflows/runner.yml
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secrets: inherit
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with:
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ec2_instance_type: g4dn.xlarge # ≈$0.56/hr GPU instance: https://instances.vantage.sh/aws/ec2/g4dn.xlarge
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ec2_image_id: ami-00096836009b16a22 # Deep Learning OSS Nvidia Driver AMI GPU PyTorch 2.4.1 (Ubuntu 22.04) 20250302
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instance_type: gpu_1x_a10 # Single A10 GPU
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region: us-south-1
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gpu-test:
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needs: ec2
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runs-on: ${{ needs.ec2.outputs.id }}
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needs: lambda
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runs-on: ${{ needs.lambda.outputs.id }}
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steps:
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- run: nvidia-smi # Verify GPU is available

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