65 lines
2.5 KiB
ReStructuredText
65 lines
2.5 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 `URI <https://en.wikipedia.org/wiki/Uniform_Resource_Identifier>`_ 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 format file you want to load in, and
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``cacheprefix`` is a path to a cache file that XGBoost will use for caching preprocessed
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data in binary form.
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.. note:: External memory is also available with GPU algorithms (i.e. when ``tree_method`` is set to ``gpu_hist``)
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To provide a simple example for illustration, extracting the code from
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`demo/guide-python/external_memory.py <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/external_memory.py>`_. If
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you have a dataset stored in a file similar to ``agaricus.txt.train`` with libSVM format, the external memory support can be enabled by:
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.. code-block:: python
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dtrain = DMatrix('../data/agaricus.txt.train#dtrain.cache')
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XGBoost will first load ``agaricus.txt.train`` in, preprocess it, then write to a new file named
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``dtrain.cache`` as an on disk cache for storing preprocessed data in a internal binary format. For
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more notes about text input formats, see :doc:`/tutorials/input_format`.
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.. code-block:: python
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dtrain = xgb.DMatrix('../data/agaricus.txt.train#dtrain.cache')
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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 an experimental version
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* Currently only importing from libsvm format is supported
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* OSX is not tested.
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- Contribution of ingestion from other common external memory data source is welcomed
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