automate generating cuda/cpu metrics from DPP#156
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Signed-off-by: Joshua Rosenkranz <jmrosenk@us.ibm.com>
Signed-off-by: Joshua Rosenkranz <jmrosenk@us.ibm.com>
Signed-off-by: Joshua Rosenkranz <jmrosenk@us.ibm.com>
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Signed-off-by: Joshua Rosenkranz <jmrosenk@us.ibm.com>
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Signed-off-by: Joshua Rosenkranz <jmrosenk@us.ibm.com>
Signed-off-by: Sukriti-Sharma4 <sukriti.sharma4@ibm.com>
Ssukriti
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Jan 7, 2026
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| parser.add_argument( | ||
| "--cross_entropy_threshold", | ||
| "--default_cross_entropy_threshold", |
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lets not change the argument name, as this is a breaking change and too many teams have adopted DPP. We dont have documentation yet . I am reverting it back as its not a necessary change
Ssukriti
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Jan 9, 2026
| distributed_kwargs = {} | ||
| if USE_DISTRIBUTED: | ||
| if generate_metrics: | ||
| torch.cuda.set_device(local_rank) |
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I ran into a blocker testing this PR as the machines in our cluster do not have GPUs. I think to generate the metrics file for every new model, it will be a challenge from CI/CD perspective as we do not have access to GPU machines. So need to think about this a bit
Signed-off-by: Sukriti-Sharma4 <sukriti.sharma4@ibm.com>
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This PR adds a method to generate metrics from DPP which will fully match the testing workflow. It also includes parameters to take in the generated metrics file and produce the proper assertions per test run.
To run this with generate_metrics requires the following PR foundation-model-stack/foundation-model-stack#481