Enable the C wrapper to extract model to memory and initialize model from memory #1189
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@eisber I thought you had looked into RAM-based model storage in the past? What's the status? |
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You’ll have to implement something like https://github.com/JohnLangford/vowpal_wabbit/blob/master/cs/cli/clr_io_memory.h
save_load_header takes in an io_buf: https://github.com/JohnLangford/vowpal_wabbit/blob/master/vowpalwabbit/parse_regressor.h#L19
…-Markus
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Yes, the PR includes |
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I'm hazy on an important detail here. @eisber is this functionality already there? If not, why is there an interface? |
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@JohnLangford it's not there yet. It's only in the C# side. I think this is good. |
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Merged in, thanks. |
JohnLangford
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May 3, 2017
* Add prerequisites: zlib-devel (or zlib1g-dev) (#1226) * Added zlib-devel (or zlib1g-dev) to Prerequisites * Update README.md * do not use deprecated sklearn module (#1223) * Enable the C wrapper to extract model to memory and initialize model from memory (#1189) * extract model to memory and initialize model from memory * add get_confidence C wrapper function * JNI concurrent interface extensions (#1215) * replaced NativeUtils with Java's own library loader; Added MulticlassMultilabel Interface to JNI; generated OS dependant lib files; modified pom to include generated lib file in java library path; removed unused variable in multiclassleaner; * Since an instance of vw model is not thread safe for multiple predictions in parallel, we introduce a concurrent learner (for multilabel and multiline multiclass learners) that works with a pool of learners to get high throughput for predictions in an online setting. * Learner pool now creates vw instances of the same model using seed_vw_model method instead of initialize. Former method reuses shared variables from the seed learner instance and hence has much less memory footprint in comparison to latter which allocates new memory for all new instances created. * Fixed space alignment issue and changed getLearner api to return Optional to stop any unwanted NPEs. * Removed TODO as we now use seed_vw_model to instantiate multiple learner instances of the same model. * remove java8 components * added method to return Future in abstract concurrent predictor; added method to create concurrent predictor using thread pool and predictor pool of same size in factory; * tweaks
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This workaround allows library users to load and store the model directly without using the file system.