Comments (1)
The Dockerfiles and run_and_time.sh scripts are exactly what is used to produce reference numbers. My understanding is that exact version of python can vary by benchmark; but will be either python 2.7 or a recent stable python 3 versions, both CPython.
We'll take a look how to better document this, thanks for pointing this out.
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Related Issues (20)
- Alternative method for downloading Llama2 70b HOT 2
- Gradient clipping not working for llama2_70b_lora benchmark HOT 1
- MLPerf library version for 4.0 Submission
- Llama2 - LoRA Reference Implementation HOT 3
- Data download for Stable Diffusion fails HOT 4
- DLRM criteo day23 MD5 varify faild HOT 1
- Hardware Configuration HOT 1
- llama3 support? HOT 1
- Invalid `local_replica_id` with `MultiWorkerMirroredStrategy` HOT 1
- `IndexError` in `cross_device_ops` with `MultiWorkerMirroredStrategy` HOT 1
- Scope of ML based benchmarks in MLPerf.
- Bus error (core dumped) in graph_neural_network
- Problem downloading S3 bucket
- Running udnet3d on multiple GPUS HOT 1
- Stable Diffusion Dataset HOT 10
- Stable Diffusion RCP request for Global Batch Size = 4096 HOT 1
- recommendation_v2/torchrec_dlrm Fatal Python error: Segmentation fault HOT 1
- TorchRec DLRM No such file or directory: 'sbatch' HOT 1
- TorchRec DLRM Failed to initialize NumPy: _ARRAY_API not found
- llama2-lora model/dataset download link doesn't exist HOT 1
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