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1.9 KiB
ReStructuredText
52 lines
1.9 KiB
ReStructuredText
############################################
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Using XGBoost External Memory Version (beta)
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############################################
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There is no big difference between using external memory version and in-memory version.
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The only difference is the filename format.
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The external memory version takes in the following filename format:
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.. code-block:: none
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filename#cacheprefix
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The ``filename`` is the normal path to libsvm file you want to load in, and ``cacheprefix`` is a
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path to a cache file that XGBoost will use for external memory cache.
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The following code was extracted from `demo/guide-python/external_memory.py <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/external_memory.py>`_:
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.. code-block:: python
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dtrain = xgb.DMatrix('../data/agaricus.txt.train#dtrain.cache')
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You can find that there is additional ``#dtrain.cache`` following the libsvm file, this is the name of cache file.
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For CLI version, simply add the cache suffix, e.g. ``"../data/agaricus.txt.train#dtrain.cache"``.
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****************
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Performance Note
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****************
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* the parameter ``nthread`` should be set to number of **physical** cores
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- Most modern CPUs use hyperthreading, which means a 4 core CPU may carry 8 threads
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- Set ``nthread`` to be 4 for maximum performance in such case
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*******************
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Distributed Version
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*******************
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The external memory mode naturally works on distributed version, you can simply set path like
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.. code-block:: none
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data = "hdfs://path-to-data/#dtrain.cache"
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XGBoost will cache the data to the local position. When you run on YARN, the current folder is temporal
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so that you can directly use ``dtrain.cache`` to cache to current folder.
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**********
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Usage Note
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**********
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* This is a experimental version
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* Currently only importing from libsvm format is supported
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- Contribution of ingestion from other common external memory data source is welcomed
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