Revision as of 15:43, 13 November 2018

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spaCy is a Python package that provides industrial-strength natural language processing.

Installation

To see the latest version of spaCy that we have built:

[name@server ~]$ avail_wheels spacy thinc thinc_gpu_ops

For more information on listing wheels, see listing available wheels.

The preferred option is to install it using the python wheel that we compile, as follows:

1. Load a python module, either python/2.7, python/3.5, or python/3.6

2. Create and start a virtual environment.

3. Install spaCy in the virtual environment with pip install. For both GPU and CPU support:

(venv) [name@server ~] pip install spacy[cuda] --no-index

If you only need CPU support:

(venv) [name@server ~] pip install spacy --no-index

GPU version: for the moment, you need to point out where the CUDA libraries live:

(venv) [name@server ~] module load gcc/5.4.0 cuda/9
(venv) [name@server ~] export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH

Note: if you want to use thinc Pytorch wrapper, you'll also need to install torch_cpu or torch_gpu wheel.

@@ Line 22: / Line 22: @@
 :If you only need CPU support:
 :{{Command|prompt=(venv) [name@server ~]|pip install spacy --no-index}}
+'''GPU version''': for the moment, you need to point out where the CUDA libraries live:
+{{Commands
+|prompt=(venv) [name@server ~]
+|module load gcc/5.4.0 cuda/9
+|export LD_LIBRARY_PATH{{=}}$CUDA_HOME/lib64:$LD_LIBRARY_PATH
+}}
 <!--T:29-->
 '''Note''': if you want to use <tt>thinc</tt> [https://docs.computecanada.ca/wiki/PyTorch Pytorch] wrapper, you'll also need to install <tt>torch_cpu</tt> or  <tt>torch_gpu</tt> wheel.
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