CI: Fix GPU workflow dependencies and increase timeouts - #179
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April 20, 2026 23:58
justinchuby
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Apr 21, 2026
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tadani3
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April 21, 2026 00:24
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Pull request overview
Updates CI dependency installation to ensure GPU workflows use CUDA-capable packages and extends GPU test step timeouts, aligning with the repo’s ONNX Runtime inference-based test suite.
Changes:
- Bump
onnxruntime-easyminimum version to allow workflows to choose CPU vs GPU ONNX Runtime explicitly. - Explicitly install
onnxruntime(CPU) oronnxruntime-gpu(GPU) across workflows instead of relying on transitive installs. - Increase GPU test step timeouts to 60 minutes in golden/generation workflows and the main GPU golden comparison job.
Reviewed changes
Copilot reviewed 7 out of 7 changed files in this pull request and generated 4 comments.
Show a summary per file
| File | Description |
|---|---|
| pyproject.toml | Bumps onnxruntime-easy minimum version in the testing extra. |
| .github/workflows/main.yml | Installs onnxruntime for CPU test jobs, fixes GPU job to install CUDA torch + onnxruntime-gpu, and increases golden step timeout. |
| .github/workflows/gpu_l4_golden_parity.yml | Installs onnxruntime-gpu and increases test step timeout to 60 minutes. |
| .github/workflows/gpu_l5_generation_e2e.yml | Installs onnxruntime-gpu and increases test step timeout to 60 minutes. |
| .github/workflows/validation_examples_gpu.yml | Installs onnxruntime-gpu for GPU example validation runs. |
| .github/workflows/golden_regen.yml | Conditionally installs onnxruntime vs onnxruntime-gpu based on inputs.device. |
| .github/workflows/pages.yml | Installs onnxruntime for docs build environment consistency. |
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| testing = [ | ||
| "onnxruntime-easy", | ||
| "onnxruntime-easy>=0.1.1", | ||
| "torch", | ||
| "transformers>=5.0", | ||
| "safetensors", |
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| --junitxml=junit-l4.xml \ | ||
| --cov=src --cov-report=xml --cov-branch \ | ||
| --tb=short | ||
| timeout-minutes: 30 | ||
| timeout-minutes: 60 |
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| --junitxml=junit-l5.xml \ | ||
| --cov=src --cov-report=xml --cov-branch \ | ||
| --tb=short | ||
| timeout-minutes: 30 | ||
| timeout-minutes: 60 |
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| - name: Install onnxruntime (CPU) | ||
| if: inputs.device != 'cuda' | ||
| run: pip install onnxruntime | ||
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| - name: Install onnxruntime (GPU) | ||
| if: inputs.device == 'cuda' | ||
| run: pip install onnxruntime-gpu | ||
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CI: Fix GPU workflow dependencies and increase timeouts
Summary
Ensures all GPU CI jobs use CUDA-enabled dependencies (
torchwith CUDA index,onnxruntime-gpu) and increases test step timeouts to 60 minutes across GPU workflows.Changes
1. Bump
onnxruntime-easyminimum versiononnxruntime-easy→onnxruntime-easy>=0.1.1>=0.1.1no longer bundles a specificonnxruntimevariant, allowing explicit control over CPU vs GPU installs without conflicts.2. Explicit
onnxruntime/onnxruntime-gpuinstallsSince
onnxruntime-easy>=0.1.1no longer pulls inonnxruntimeitself, every workflow now explicitly installs the correct variant:smoke-testsonnxruntimesynthetic-parityonnxruntimemulti-pythononnxruntimegolden-comparisononnxruntime-gpuintegration-fastonnxruntime-gpugolden-testsonnxruntime-gpugeneration-testsonnxruntime-gpurun-exampleonnxruntime-gpuregenerateonnxruntimeoronnxruntime-gpubased oninputs.device)buildonnxruntime3. Fix PyTorch install on GPU runner
golden-comparison): Changed frompip install torch --index-url .../cputopip install torch --index-url .../cu124. This job runs on an A10 GPU but was previously installing CPU-only PyTorch.4. Increase GPU test step timeouts to 60 minutes
golden-comparison(step)Job-level timeouts on the dedicated GPU workflows were already 60 minutes; only the step-level timeouts were lagging behind.
Testing
No code changes — CI configuration only. Changes can be verified by observing the next workflow runs install the correct packages.