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Remove cuda extras for non-linux arm in install widget (#10796)
* Remove cuda extras for non-linux arm platforms in install widget * Extend cuda versions install widget * Update GPU install docs to clarify cuda
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@ -129,15 +129,14 @@ machine learning library, [Thinc](https://thinc.ai). For GPU support, we've been
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grateful to use the work of Chainer's [CuPy](https://cupy.chainer.org) module,
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which provides a numpy-compatible interface for GPU arrays.
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spaCy can be installed on GPU by specifying `spacy[cuda]`, `spacy[cuda90]`,
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`spacy[cuda91]`, `spacy[cuda92]`, `spacy[cuda100]`, `spacy[cuda101]`,
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`spacy[cuda102]`, `spacy[cuda110]`, `spacy[cuda111]` or `spacy[cuda112]`. If you
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know your cuda version, using the more explicit specifier allows cupy to be
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installed via wheel, saving some compilation time. The specifiers should install
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spaCy can be installed for a CUDA-compatible GPU by specifying `spacy[cuda]`,
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`spacy[cuda102]`, `spacy[cuda112]`, `spacy[cuda113]`, etc. If you know your
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CUDA version, using the more explicit specifier allows CuPy to be installed via
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wheel, saving some compilation time. The specifiers should install
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[`cupy`](https://cupy.chainer.org).
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```bash
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$ pip install -U %%SPACY_PKG_NAME[cuda92]%%SPACY_PKG_FLAGS
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$ pip install -U %%SPACY_PKG_NAME[cuda113]%%SPACY_PKG_FLAGS
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```
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Once you have a GPU-enabled installation, the best way to activate it is to call
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@ -23,6 +23,8 @@ const CUDA = {
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'11.2': 'cuda112',
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'11.3': 'cuda113',
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'11.4': 'cuda114',
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'11.5': 'cuda115',
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'11.6': 'cuda116',
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}
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const LANG_EXTRAS = ['ja'] // only for languages with models
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@ -48,7 +50,7 @@ const QuickstartInstall = ({ id, title }) => {
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const modelExtras = train ? selectedModels.filter(m => LANG_EXTRAS.includes(m)) : []
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const apple = os === 'mac' && platform === 'arm'
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const pipExtras = [
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hardware === 'gpu' && cuda,
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(hardware === 'gpu' && (platform !== 'arm' || os === 'linux')) && cuda,
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train && 'transformers',
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train && 'lookups',
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apple && 'apple',
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