Explain how to use Dask to parallelize grid search across GPUs #241
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docs/user/parallelism.rst
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| Skorch supports model parallelism via `Dask | ||
| <https://dask.pydata.org>`_. In this section we'll describe how to | ||
| use Dask to efficiently distribute a grid search or a randomized | ||
| search on hyper paramerers across multiple GPUs and potentially |
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paramerers parameters
docs/user/parallelism.rst
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| CUDA_VISIBLE_DEVICES=1 dask-worker 127.0.0.1:8786 --nthreads 1 | ||
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| Now, instead of importing ``GridSearchCV`` or ``RandomizedSearchCV`` | ||
| from scikit-learn, import from ``dask_searchcv`` instead. And set up |
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"instead ... instead." Remove the 2nd "instead"
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addresses #95 |
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Updated to use joblib's |
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Thanks, great work. |
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Based on #237
Work in progress, but please give me feedback.
Also explains how to use Palladium to run grid search in parallel. This feature currently requires ottogroup/palladium#87