Implement Memory-mapped MLModel #275
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OctoberChang
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| @@ -1168,3 +1168,36 @@ def test_mmap(tmpdir): | |||
| py_pred = py_model.predict(Xt, **kwargs).todense() | |||
| mmap_pred = mmap_model.predict(Xt, **kwargs).todense() | |||
| assert mmap_pred == approx(py_pred, abs=1e-6), f"post_processor:{pp}" | |||
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| def test_mmap_mlmodel(tmpdir): | |||
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I think this test should be put in https://github.com/amzn/pecos/blob/mainline/test/pecos/xmc/test_xmc.py since the functionalities added are for the xmc/base.py class.
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Fixed. See latest Revision.
weiliw-amz
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pecos/core/libpecos.cpp
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| // ==== C Interface of XMC Models ==== | ||
| // ==== C Interface of MLModels ==== | ||
| // Only implemented for w_matrix_t = pecos::csc_t | ||
| //typedef pecos::bin_search_chunked_matrix_t MLMODEL_MAT_T; |
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Remove this unused comment.
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Fixed. See latest Revision.
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Issue #, if available:
Description of changes:
Implement Memory-mapped MLModel class for both C and Python Interface.
Usage
User needs to have a MLModel saved on disk (in original .npz format),
and manually compile into mmap format by calling compile_mmap_model:
Then user can load the memory-mapped model and do inference:
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.