Comments (1)
Test workflow: https://github.com/pytorch/benchmark/actions/runs/9454579616
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Related Issues (20)
- V2 Performance Signal Detected by TorchBench CI on '2.4.0.dev20240604+cu121'
- V3 Performance Signal Detected by TorchBench Userbenchmark "torch-nightly" on '2.4.0.dev20240610+cu121' HOT 1
- V3 Performance Signal Detected by TorchBench Userbenchmark "torch-nightly" on '2.5.0.dev20240613+cu124'
- Add option to skip model for install
- fixture 'compiler' not found while executing "pytest test_bench.py" HOT 2
- V3 Performance Signal Detected by TorchBench Userbenchmark "torch-nightly" on '2.5.0.dev20240619+cu124'
- Add jax pallas kernel examples for tritonbench
- Add thunderkittens pytorch custom ops to tritonbench HOT 1
- V3 Performance Signal Detected by TorchBench Userbenchmark "torch-nightly" on '2.5.0.dev20240623+cu124'
- sam_fast will re-install torch HOT 5
- `DALLE2_pytorch` moved to `canary_models` due to NumPy 2.0 upgrade. HOT 1
- nightly docker does not correctly install fbgemm HOT 1
- V3 Performance Signal Detected by TorchBench Userbenchmark "torch-nightly" on '2.5.0.dev20240627+cu124'
- Use ARC Kubernetes mode to run benchmark HOT 2
- Use AWS to store the GCP base docker
- Use the default NVIDIA driver version in the `test-infra` for K8s cluster
- Refactor the t4 and a100 nightly workflows
- [BE] Improve tritonbench handling of built-in metrics
- Flop counter crashes with multiple compiled benchmarks HOT 3
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