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pytorch0.4.1_cuda11

Pytorch 0.4.1 with CUDA 11 and cudnn 8.2

https://github.com/garry1ng/pytorch0.4.1_cuda11

Science Score: 18.0%

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    Low similarity (7.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Pytorch 0.4.1 with CUDA 11 and cudnn 8.2

Basic Info
  • Host: GitHub
  • Owner: garry1ng
  • License: other
  • Language: C++
  • Default Branch: master
  • Size: 59.6 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created about 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme Contributing License Citation Codeowners

README.md

Pytorch0.4.1 with CUDA 11 and cudnn 8.2

This is a repo for setup Pytorch0.4.1 with CUDA 11 and cudnn 8.2

  1. Download CUDA 11.1 and cudnn 8.0.5 from Nvidia. I use cuda_11.1.0_455.23.05_linux.run and cudnn-11.1-linux-x64-v8.0.5.39.tgz. Set up your CUDAHOME and LDLIBRARY_PATH in your .bashrc.

  2. Install dependencies from conda.

bash conda create -n "pytorch0.4" python=3.7 conda activate "pytorch0.4" conda install numpy pyyaml mkl=2021.4.0 mkl-include setuptools cmake cffi typing

  1. Recursively Clone Pytorch0.4.1 from https://github.com/garry1ng/pytorch0.4.1_cuda11.git. Make sure the submodules except nervanagpu are also included.

```bash git clone https://github.com/garry1ng/pytorch0.4.1cuda11.git cd pytorch0.4.1cuda11/

git submodule deinit third_party/nervanagpu/

git rm third_party/nervanagpu

git submodule sync git submodule update --init --recursive ```

  1. Install PyTorch. bash python setup.py install

  2. run test_pytorch0.4_cu11_cudnn.py to check if it is correctly installed. You should have some output like this:

Note: the patch may be outdated if the third party libraries update (very unlikely though). Locating these files can save time.

  • thirdparty/nccl/src/commoncoll.h
  • third_party/gloo/cmake/Cuda.cmake

Owner

  • Name: Jiaying Lin
  • Login: garry1ng
  • Kind: user
  • Location: 幻想郷

I make computers understand and create pixels and points.

Citation (CITATION) 

@inproceedings{paszke2017automatic,
  title={Automatic differentiation in PyTorch},
  author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  booktitle={NIPS-W},
  year={2017}
}

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caffe2/contrib/docker-ubuntu-14.04/Dockerfile docker
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docker/caffe2/jenkins/centos-cuda/Dockerfile docker
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docker/caffe2/jenkins/ubuntu/Dockerfile docker
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docker/caffe2/jenkins/ubuntu-cuda/Dockerfile docker
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docker/caffe2/jenkins/ubuntu-rocm/Dockerfile docker
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docker/caffe2/ubuntu-14.04-cpu-all-options/Dockerfile docker
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docker/caffe2/ubuntu-14.04-cpu-minimal/Dockerfile docker
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docker/caffe2/ubuntu-16.04-cpu-all-options/Dockerfile docker
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docker/caffe2/ubuntu-16.04-cpu-minimal/Dockerfile docker
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docker/caffe2/ubuntu-16.04-cuda8-cudnn6-all-options/Dockerfile docker
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docker/caffe2/ubuntu-16.04-cuda8-cudnn7-all-options/Dockerfile docker
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docker/caffe2/ubuntu-16.04-gpu-tutorial/Dockerfile docker
  • caffe2ai/caffe2 latest build
docker/pytorch/Dockerfile docker
  • nvidia/cuda 9.0-cudnn7-devel-ubuntu16.04 build
caffe2/requirements.txt pypi
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  • numpy *
docs/requirements.txt pypi
  • sphinx *
requirements.txt pypi
  • pyyaml *
  • typing *
setup.py pypi
test/cpp_extensions/setup.py pypi