SpaCy: Difference between revisions

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= Installation = <!--T:26-->
= Installation = <!--T:26-->


==Latest available wheels==
==Latest available wheels== <!--T:31-->


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To see the latest version of <tt>spaCy</tt> that we have built:
To see the latest version of <tt>spaCy</tt> that we have built:
{{Command|avail_wheels spacy thinc thinc_gpu_ops}}
{{Command|avail_wheels spacy thinc thinc_gpu_ops}}
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'''GPU version''': At the present time, in order to use the GPU version you need to add the CUDA libraries to LD_LIBRARY_PATH:
'''GPU version''': At the present time, in order to use the GPU version you need to add the CUDA libraries to <tt>LD_LIBRARY_PATH</tt>:
{{Commands
{{Commands
|prompt=(venv) [name@server ~]
|prompt=(venv) [name@server ~]
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If you want to use the <tt>thinc</tt> wrapper for [https://docs.computecanada.ca/wiki/PyTorch Pytorch], you'll also need to install <tt>torch_cpu</tt> or  <tt>torch_gpu</tt> from a wheel.
If you want to use the [https://docs.computecanada.ca/wiki/PyTorch Pytorch] wrapper with <tt>thinc</tt>, you'll also need to install the <tt>torch_cpu</tt> or  <tt>torch_gpu</tt> wheel.


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Latest revision as of 13:02, 19 July 2022

Other languages:

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

Installation

Latest available wheels

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

Question.png
[name@server ~]$ avail_wheels spacy thinc thinc_gpu_ops

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

Pre-build

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

1. Load python 3.6 module: python/3.6
2. Create and activate a virtual environment.
3. Install spaCy in the virtual environment with pip install. For both GPU and CPU support:
Question.png
(venv) [name@server ~] pip install spacy[cuda] --no-index
If you only need CPU support:
Question.png
(venv) [name@server ~] pip install spacy --no-index

GPU version: At the present time, in order to use the GPU version you need to add the CUDA libraries to LD_LIBRARY_PATH:

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


If you want to use the Pytorch wrapper with thinc, you'll also need to install the torch_cpu or torch_gpu wheel.