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Corrected docs to run JPQD in DDP mode #315
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@vuiseng9 @yujiepan-work @AlexKoff88 please take a look |
LGTM |
Thanks @ljaljushkin ! It would be good to update the example tests too (https://github.com/huggingface/optimum-intel/blob/main/tests/openvino/test_training_examples.py ), so that the test tests what we show in the docs (probably good to test both methods for at least one example). We can also do that in a separate PR. |
The documentation is not available anymore as the PR was closed or merged. |
@@ -92,4 +92,4 @@ python run_audio_classification.py \ | |||
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This script should take about 3 hours on a single V100 GPU and produce a quantized Wav2Vec2-base model with ~80% structured sparsity in its linear layers. The model accuracy should converge to about 97.5%. | |||
This script should take about 3 hours on a single V100 GPU and produce a quantized Wav2Vec2-base model with ~80% structured sparsity in its linear layers. The model accuracy should converge to about 97.5%. For launching the script on multiple GPUs specify `--nproc-per-node=<number of GPU>`. Note, that different batch size and other hyperparameters might be required to achieve the same results as on a single GPU. |
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@vuiseng9 @yujiepan-work @AlexKoff88 mentioned necessity for hyperparameters tuning in case of multiple GPU
Thanks @helena-intel! It makes sense. I've corrected tests here: #319 |
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LGTM, thanks @ljaljushkin
What does this PR do?
Corrects command line for launching JPQD. Currently, it launches training in DataParallel (DP) mode, which is slower than Distributed Data Parallel (DDP) and may have some issue with movement pruning openvinotoolkit/nncf#1582
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