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| author | Jen Herting <jen@herting.cc> | 2024-10-18 14:55:27 -0400 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-10-18 20:55:27 +0200 |
| commit | 957c0cc9dad60b6e6a3ae3ace37c4513f800700c (patch) | |
| tree | 47af2f2ab998a0d46cd9b7b19b835c752e713286 /var | |
| parent | 99e4d6b4468f9ad52ed9aa41102316230854b5c8 (diff) | |
| download | spack-957c0cc9dad60b6e6a3ae3ace37c4513f800700c.tar.gz spack-957c0cc9dad60b6e6a3ae3ace37c4513f800700c.tar.bz2 spack-957c0cc9dad60b6e6a3ae3ace37c4513f800700c.tar.xz spack-957c0cc9dad60b6e6a3ae3ace37c4513f800700c.zip | |
py-clip-anytorch: new package (#47050)
* py-clip-anytorch: new package
* py-clip-anytorch: ran black
py-langchain-core: ran black
py-pydantic: ran black
py-dalle2-pytorch: ran black
* [py-clip-anytorch] fixed license(checked_by)
* Apply suggestion from Wouter on fixing CI
Co-authored-by: Wouter Deconinck <wdconinc@gmail.com>
---------
Co-authored-by: Alex C Leute <acl2809@rit.edu>
Co-authored-by: Bernhard Kaindl <bernhardkaindl7@gmail.com>
Co-authored-by: Wouter Deconinck <wdconinc@gmail.com>
Diffstat (limited to 'var')
| -rw-r--r-- | var/spack/repos/builtin/packages/py-clip-anytorch/package.py | 33 |
1 files changed, 33 insertions, 0 deletions
diff --git a/var/spack/repos/builtin/packages/py-clip-anytorch/package.py b/var/spack/repos/builtin/packages/py-clip-anytorch/package.py new file mode 100644 index 0000000000..283adbfeea --- /dev/null +++ b/var/spack/repos/builtin/packages/py-clip-anytorch/package.py @@ -0,0 +1,33 @@ +# Copyright 2013-2024 Lawrence Livermore National Security, LLC and other +# Spack Project Developers. See the top-level COPYRIGHT file for details. +# +# SPDX-License-Identifier: (Apache-2.0 OR MIT) + + +from spack.package import * + + +class PyClipAnytorch(PythonPackage): + """CLIP (Contrastive Language-Image Pre-Training) is a neural network + trained on a variety of (image, text) pairs. It can be instructed in + natural language to predict the most relevant text snippet, given an image, + without directly optimizing for the task, similarly to the zero-shot + capabilities of GPT-2 and 3. We found CLIP matches the performance of the + original ResNet50 on ImageNet "zero-shot" without using any of the original + 1.28M labeled examples, overcoming several major challenges in computer + vision.""" + + homepage = "https://github.com/rom1504/CLIP" + # PyPI source is missing requirements.txt + url = "https://github.com/rom1504/CLIP/archive/refs/tags/2.6.0.tar.gz" + + license("MIT", checked_by="qwertos") + + version("2.6.0", sha256="1ac1f6ca47dfb5d4e55be8f45cc2f3bdf6415b91973a04b4529e812a8ae29bea") + + depends_on("py-setuptools", type="build") + depends_on("py-ftfy", type=("build", "run")) + depends_on("py-regex", type=("build", "run")) + depends_on("py-tqdm", type=("build", "run")) + depends_on("py-torch", type=("build", "run")) + depends_on("py-torchvision", type=("build", "run")) |