75 lines
2.6 KiB
ReStructuredText
75 lines
2.6 KiB
ReStructuredText
#####################################
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Using XGBoost External Memory Version
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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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To load from csv files, use the following syntax:
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.. code-block:: none
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filename.csv?format=csv&label_column=0#cacheprefix
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where ``label_column`` should point to the csv column acting as the label.
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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 an internal binary format. For
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more notes about text input formats, see :doc:`/tutorials/input_format`.
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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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GPU Version
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***********
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External memory is fully supported in GPU algorithms (i.e. when ``tree_method`` is set to ``gpu_hist``).
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If you are still getting out-of-memory errors after enabling external memory, try subsampling the
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data to further reduce GPU memory usage:
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.. code-block:: python
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param = {
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...
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'subsample': 0.1,
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'sampling_method': 'gradient_based',
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}
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For more information, see `this paper <https://arxiv.org/abs/2005.09148>`_.
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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 temporary
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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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Limitations
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***********
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* The ``hist`` tree method hasn't been tested thoroughly with external memory support (see
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`this issue <https://github.com/dmlc/xgboost/issues/4093>`_).
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* OSX is not tested.
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