ubuntu18.04配置pytorch框架并进行fcn网络并训练 —— 深度学习（一）

ubuntu18.04配置pytorch框架并进行fcn网络并训练 —— 深度学习（一） 愚人陆陆 2023-07-20 10:30:16 877
深度学习
Ubuntu
pytorch

前言

ubuntu18.04 cpu版本 pytorch
ubuntu18.04 GPU版本

1.配置cpu环境

选择python3.6版本进行配置，利用anaconda创建python=3.6的环境fcn,参考：https://github.com/wkentaro/pytorch-fcn
https://github.com/wkentaro/pytorch-fcn

1.1 安装fcn包：

  1. #创建和激活虚拟环境
  2. conda create -n py36 python=3.6
  3. source activate py36
  5. pip install fcn
  6. #pip install --default-timeout=100 -i https://pypi.tuna.tsinghua.edu.cn/simple fcn

1.2 安装PyTorch：

进入PyTorch官网，下载cpu版本：

Start Locally | PyTorch https://pytorch.org/get-started/locally/

复制网页的命令，我的如下：

  1. conda install pytorch torchvision torchaudio cpuonly -c pytorch
  3. #或者pip
  4. pip3 install torch==1.10.2+cpu torchvision==0.11.3+cpu torchaudio==0.10.2+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html

验证安装：

  1.  clash$ conda activate py36
  2. (py36)  clash$ python
  3. Python 3.6.13 |Anaconda, Inc.| (default, Jun  4 2021, 14:25:59) 
  4. [GCC 7.5.0] on linux
  5. Type "help", "copyright", "credits" or "license" for more information.
  6. >>> import torch
  7. >>> torch.cuda.is_available()
  8. False
  9. >>>

1.3 安装pillow、scipy、tqdm

  2. pip install pillow
  3. pip install scipy
  4. pip install tqdm

1.4 验证环境配置

下载 https://github.com/wkentaro/pytorch-fcn https://github.com/wkentaro/pytorch-fcn 的代码并解压,pip install .后出现下面一堆successfully。

  1. (py36)  paper1$ cd pytorch-fcn-main/
  2. (py36)  pytorch-fcn-main$ pip install .     ######安装torchfcn
  3. Processing /home/elfoot/paper1/pytorch-fcn-main
  4.   Preparing metadata (setup.py) ... done
  5. --------------------------------
  7. Requirement already satisfied: idna<4,>=2.5 in /home/elfoot/anaconda3/envs/py36/lib/python3.6/site-packages (from requests[socks]->gdown->fcn>=6.1.5->torchfcn==1.9.7) (3.3)
  8. Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /home/elfoot/anaconda3/envs/py36/lib/python3.6/site-packages (from requests[socks]->gdown->fcn>=6.1.5->torchfcn==1.9.7) (1.7.1)
  9. Building wheels for collected packages: torchfcn
  10.   Building wheel for torchfcn (setup.py) ... done
  11.   Created wheel for torchfcn: filename=torchfcn-1.9.7-py3-none-any.whl size=137110 sha256=0e0a02e7459ab0c07e029ccefb4d80959a61ee28a9d4a052ea8574855f7c488f
  12.   Stored in directory: /home/elfoot/.cache/pip/wheels/c9/60/99/c1bd09fc67e214cb878410d34a27c1a3ac13a0e4f22bddbadf
  13. Successfully built torchfcn
  14. Installing collected packages: torchfcn
  15. Successfully installed torchfcn-1.9.7

2.利用VOC数据集训练example

  1. #!/bin/bash
  3. DIR=~/data/datasets/VOC
  5. mkdir -p $DIR
  6. cd $DIR
  8. if [ ! -e benchmark_RELEASE ]; then
  9.   wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz -O benchmark.tar
  10.   tar -xvf benchmark.tar
  11. fi
  13. if [ ! -e VOCdevkit/VOC2012 ]; then
  14.   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
  15.   tar -xvf VOCtrainval_11-May-2012.tar
  16. fi

2.1 下载数据

运行xxx/paper1/pytorch-fcn-main/examples/voc/download_dataset.sh脚本下载数据集，脚本内容如下，主要下载两个内容，并把他们放到DIR目录处：

  2. #!/bin/bash
  4. DIR=~/data/datasets/VOC
  6. mkdir -p $DIR
  7. cd $DIR
  9. if [ ! -e benchmark_RELEASE ]; then
  10.   wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz -O benchmark.tar
  11.   tar -xvf benchmark.tar
  12. fi
  14. if [ ! -e VOCdevkit/VOC2012 ]; then
  15.   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
  16.   tar -xvf VOCtrainval_11-May-2012.tar
  17. fi

关于直接在终端下载很慢，由于使用了科学上网，我直接把链接放到网页下载——贼快：

创建文件夹~/data/datasets/VOC，并把下载的文件分别解压到文件夹内：

接着如下图，分别将benchmark文件夹内的benchmark_RELEASE、VOCtrainval_11-May-2012内的VOCdevkit提到VOC目录中来。

2.2 配置git

因为xxx/pytorch-fcn-main/examples/voc/train_fcn32s.py中提到了git log以及结合报错，如下，故先配置一下git

  1. //xxx/pytorch-fcn-main/examples/voc/train_fcn32s.py截取
  2. def git_hash():
  3.     cmd = 'git log -n 1 --pretty="%h"'
  4.     ret = subprocess.check_output(shlex.split(cmd)).strip()
  5.     if isinstance(ret, bytes):
  6.         ret = ret.decode()
  7.     return ret

先在自己的github创建一个repository，其链接为：https://github.com/menghxz/fcn-pytorch-cpu.git

在~/.bashrc配置科学上网（可能需要，现在还没弄清需不需要），格式参考如下

  1. export HTTP_PROXY="http://127.0.0.1:7890"
  2. export HTTPS_PROXY="http://127.0.0.1:7890"

终端配置git：

  1. cd /home/elfoot/paper1/pytorch-fcn-main/examples/voc
  2. git init
  3. git add README.md
  4. git commit -m "first commit"
  5. git branch -M main
  6. git remote add origin https://github.com/menghxz/fcn-pytorch-cpu.git  #你的链接
  7. git push -u origin main

2.3 训练

终端进入voc目录，训练如下：

  1. cd /home/elfoot/paper1/pytorch-fcn-main/examples/voc
  2. ./train_fcn32s.py

这个过程非常慢。。。。。训练三个小时才训练到epoch1 的53%。

3 配置GPU版本

3.1 pytorch官网conda命令直接安装—失败

  1. #创建和激活虚拟环境
  2. conda create -n fcn36 python=3.6
  3. source activate fcn36
  5. pip install fcn

安装gpu版本的pytorch：

conda安装：没成功——原因是在anaconda默认的网站中没有想要的包。

  1. conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

  1. (fcn36) meng@meng:~$ conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
  2. Solving environment: failed
  4. PackagesNotFoundError: The following packages are not available from current channels:
  6.   - cudatoolkit=11.3
  7.   - libgcc-ng[version='>=9.3.0']
  8.   - __glibc[version='>=2.17']
  9.   - cudatoolkit=11.3
  10.   - libstdcxx-ng[version='>=9.3.0']
  12. Current channels:
  14.   - https://conda.anaconda.org/pytorch/linux-64
  15.   - https://conda.anaconda.org/pytorch/noarch
  16.   - https://repo.anaconda.com/pkgs/main/linux-64
  17.   - https://repo.anaconda.com/pkgs/main/noarch
  18.   - https://repo.anaconda.com/pkgs/free/linux-64
  19.   - https://repo.anaconda.com/pkgs/free/noarch
  20.   - https://repo.anaconda.com/pkgs/r/linux-64
  21.   - https://repo.anaconda.com/pkgs/r/noarch
  22.   - https://repo.anaconda.com/pkgs/pro/linux-64
  23.   - https://repo.anaconda.com/pkgs/pro/noarch
  25. To search for alternate channels that may provide the conda package you're
  26. looking for, navigate to
  27.     https://anaconda.org
  28. and use the search bar at the top of the page.

3.2 修改anaconda源为清华源—失败

直接搜索的只有condarc文件，如下，不是需要的

这因为.condarc文件是不会自动创建的。

创建.condarc文件：

  1. conda config --add channels r

修改为：清华源的anaconda部分

  1. # 编辑.condarc注释defalts
  2. channels:
  3.   - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/linux-64/
  4.   - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/linux-64/
  5. #  - defaults
  6. ssl_verify: true
  7. show_channel_urls: true

关闭科学上网；再次运行安装命令，去掉-c pytorch， 没有制定版本的包。

  1. conda install pytorch torchvision torchaudio cudatoolkit=11.3

参考链接为win10的，但可以借鉴：

Anaconda建立新的环境，出现CondaHTTPError: HTTP 000 CONNECTION FAILED for url …… 解决过程 - tianlang25 - 博客园

3.3 官网pip命令调整+取消清华源+科学上网+按提示调整——成功

取消配置的清华源：将.condarc文件清空即可

官网pip命令如下，在终端输入

  1. pip3 install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

没配置科学上网前，会一直打印输入下图的黄色字体，直到失败

配置科学上网后，输入官网给的命令，torch的版本找不到——按提示选了一个最新的版本

  1. (fcn36) meng@meng:~$ pip3 install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
  2. Looking in links: https://download.pytorch.org/whl/cu113/torch_stable.html
  3. ERROR: Could not find a version that satisfies the requirement torch==1.11.0+cu113 (from versions: 1.0.0, 1.0.1, 1.0.1.post2, 1.1.0, 1.2.0, 1.3.0, 1.3.1, 1.4.0, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.8.1, 1.9.0, 1.9.1, 1.10.0, 1.10.0+cu113, 1.10.1, 1.10.1+cu113, 1.10.2, 1.10.2+cu113)
  4. ERROR: No matching distribution found for torch==1.11.0+cu113

修改安装命令为：

  1. pip3 install torch==1.10.2+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

torch下载完后，又报错，是torchvision版本找不到

继续改

  1. pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

torchvision下载完后，torchaudio版本找不到

继续改：

  1. pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

全部安装成功

3.4 测试pytorch

4 VOC训练报错与重装cuda+cudnn

4.1 VOC数据集训练报错

  1. (fcn36) meng@meng:~/deeplearning/fcn/pytorch-fcn-main/examples/voc$ ./speedtest.py --gpu 2
  2. ==> Benchmark: gpu=2, times=1000, dynamic_input=False
  3. /home/meng/anaconda3/envs/fcn36/lib/python3.6/site-packages/chainer/_environment_check.py:75: UserWarning: 
  4. --------------------------------------------------------------------------------
  5. CuPy (cupy-cuda113) version 9.2.0 may not be compatible with this version of Chainer.
  6. Please consider installing the supported version by running:
  7.   $ pip install 'cupy-cuda113>=7.7.0,<8.0.0'
  9. See the following page for more details:
  10.   https://docs.cupy.dev/en/latest/install.html
  11. --------------------------------------------------------------------------------
  13.   requirement=requirement, help=help))
  14. ==> Testing FCN32s with Chainer
  15. Traceback (most recent call last):
  16.   File "./speedtest.py", line 110, in <module>
  17.     main()
  18.   File "./speedtest.py", line 105, in main
  19.     bench_chainer(args.gpu, args.times, args.dynamic_input)
  20.   File "./speedtest.py", line 14, in bench_chainer
  21.     chainer.cuda.get_device(gpu).use()
  22.   File "cupy/cuda/device.pyx", line 172, in cupy.cuda.device.Device.use
  23.   File "cupy/cuda/device.pyx", line 178, in cupy.cuda.device.Device.use
  24.   File "cupy_backends/cuda/api/runtime.pyx", line 485, in cupy_backends.cuda.api.runtime.setDevice
  25.   File "cupy_backends/cuda/api/runtime.pyx", line 261, in cupy_backends.cuda.api.runtime.check_status
  26. cupy_backends.cuda.api.runtime.CUDARuntimeError: cudaErrorInvalidDevice: invalid device ordinal

训练过程中显示cupy的版本不对，需要安装低版本的cupy-cuda11.3，范围为cupy-cuda11.3==7.7.0~8.0.0

4.2 查找不到低版本的cupy-cuda11.3

直接pip安装低版本的cupy-cuda11.3，终端显示找不到。

  1. (fcn36) meng@meng:~/deeplearning/fcn/pytorch-fcn-main/examples/voc$ pip install cupy-cuda113==8.0.0
  2. ERROR: Could not find a version that satisfies the requirement cupy-cuda113==8.0.0 (from versions: 9.2.0, 9.3.0, 9.4.0, 9.5.0, 9.6.0)
  3. ERROR: No matching distribution found for cupy-cuda113==8.0.0

必应搜索：cupy-cuda113下载（一定要用必应搜索，百度可能搜不到），第一条就是：

链接为：cupy-cuda113 · PyPI

进入其中查看历史版本：

发现官方没有发布低版本的，怪不得pip install不到

却发现cupy-cuda110有需要的低版本的：cupy-cuda110 · PyPI

下面的图只截取了部分：

4.3 cuda和cudnn版本选择

由4.2，选择了cuda11.0及其适配的cudnn

4.3.1 重装cuda为cuda11.0

我安装显卡驱动+cuda11.3+cudnn—-重装cuda+cudnn的部分为这篇，这里就不叙述了。

ubuntu系统(八)：ubuntu18.04双系统安装+ros安装+各种软件安装+深度学习环境配置全家桶_biter0088的博客-CSDN博客

cuda11.0下载链接：CUDA Toolkit 11.0 Download | NVIDIA Developer

4.3.2 cudnn选择

官网为：cuDNN Archive | NVIDIA Developer

选择了这个文件，下载下来的文件名称却为11.2——-自己一定要记清，省的老下载资源

  1. Fcudnn-11.2-linux-x64-v8.1.1.33.tgz

5 重新配置python环境+重新安装pytorch+重新配置fcn环境

5.1 重新配置python环境

想着上面那个fcn36就留着吧，说不定什么时候就用到cuda11.3了

创建python环境：py36cuda110:

  1. conda create -n py36cuda110 python=3.6
  2. source activate py36cuda110

5.2 重新安装pytorch

安装pytorch：

Previous PyTorch Versions | PyTorch

上面的历史版本，一直下拉，找到cuda11.0版本的命令：

  1. # CUDA 11.0
  2. pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

5.3 安装其他环境

  1. cd /home/meng/deeplearning/fcn/pytorch-fcn-main
  2. pip install .

5.4 安装cupy-cuda110-xxx

  1. pip install cupy-cuda110==7.8.0

5.5 运行测试1

  1. cd /home/meng/deeplearning/fcn/pytorch-fcn-main/examples/voc
  3. ./speedtest.py --gpu 2

报错：CuPy is not correctly installed.

  1. (py36cuda110) meng@meng:~/deeplearning/fcn/pytorch-fcn-main/examples/voc$ ./speedtest.py --gpu 2
  2. ==> Benchmark: gpu=2, times=1000, dynamic_input=False
  3. ==> Testing FCN32s with Chainer
  4. Traceback (most recent call last):
  5.   File "./speedtest.py", line 110, in <module>
  6.     main()
  7.   File "./speedtest.py", line 105, in main
  8.     bench_chainer(args.gpu, args.times, args.dynamic_input)
  9.   File "./speedtest.py", line 14, in bench_chainer
  10.     chainer.cuda.get_device(gpu).use()
  11.   File "/home/meng/anaconda3/envs/py36cuda110/lib/python3.6/site-packages/chainer/backends/cuda.py", line 354, in get_device
  12.     return _get_cuda_device(*args)
  13.   File "/home/meng/anaconda3/envs/py36cuda110/lib/python3.6/site-packages/chainer/backends/cuda.py", line 361, in _get_cuda_device
  14.     check_cuda_available()
  15.   File "/home/meng/anaconda3/envs/py36cuda110/lib/python3.6/site-packages/chainer/backends/cuda.py", line 150, in check_cuda_available
  16.     raise RuntimeError(msg)
  17. RuntimeError: CUDA environment is not correctly set up
  18. (see https://github.com/chainer/chainer#installation).CuPy is not correctly installed.
  20. If you are using wheel distribution (cupy-cudaXX), make sure that the version of CuPy you installed matches with the version of CUDA on your host.
  21. Also, confirm that only one CuPy package is installed:
  22.   $ pip freeze
  24. If you are building CuPy from source, please check your environment, uninstall CuPy and reinstall it with:
  25.   $ pip install cupy --no-cache-dir -vvvv
  27. Check the Installation Guide for details:
  28.   https://docs.cupy.dev/en/latest/install.html
  30. original error: libcublas.so.11: cannot open shared object file: No such file or directory

卸载cupy-cuda110-7.8.0

  1. pip uninstall cupy-cuda110==7.8.0

并运行：pip install cupy —no-cache-dir -vvvv

（这个命令上面报错提到的，貌似是适应性安装，然后终端输出很多东西。。。。）

终端输出的最后一些信息为：

  1.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/4a/ca/e72b3b399d7a8cb34311aa8f52924108591c013b09f0268820afb4cd96fb/pip-22.0.tar.gz#sha256=d3fa5c3e42b33de52bddce89de40268c9a263cd6ef7c94c40774808dafb32c82 (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  2.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/89/a1/2f4e58eda11e591fbfa518233378835679fc5ab766b690b3df85215014d5/pip-22.0.1-py3-none-any.whl#sha256=30739ac5fb973cfa4399b0afff0523d4fe6bed2f7a5229333f64d9c2ce0d1933 (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  3.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/63/71/5686e51f06fa59da55f7e81c3101844e57434a30f4a0d7456674d1459841/pip-22.0.1.tar.gz#sha256=7fd7a92f2fb1d2ac2ae8c72fb10b1e640560a0361ed4427453509e2bcc18605b (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  4.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/83/b5/df8640236faa5a3cb80bfafd68e9fb4b22578208b8398c032ccff803f9e0/pip-22.0.2-py3-none-any.whl#sha256=682eabc4716bfce606aca8dab488e9c7b58b0737e9001004eb858cdafcd8dbdd (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  5.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/d9/c1/146b24a7648fdf3f8b4dc6521ab0b26ac151ef903bac0b63a4e1450cb4d1/pip-22.0.2.tar.gz#sha256=27b4b70c34ec35f77947f777070d8331adbb1e444842e98e7150c288dc0caea4 (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  6.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/6a/df/a6ef77a6574781a668791419ffe366c8acd1c3cf4709d210cb53cd5ce1c2/pip-22.0.3-py3-none-any.whl#sha256=c146f331f0805c77017c6bb9740cec4a49a0d4582d0c3cc8244b057f83eca359 (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  7.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/88/d9/761f0b1e0551a3559afe4d34bd9bf68fc8de3292363b3775dda39b62ce84/pip-22.0.3.tar.gz#sha256=f29d589df8c8ab99c060e68ad294c4a9ed896624f6368c5349d70aa581b333d0 (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  8.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/4d/16/0a14ca596f30316efd412a60bdfac02a7259bf8673d4d917dc60b9a21812/pip-22.0.4-py3-none-any.whl#sha256=c6aca0f2f081363f689f041d90dab2a07a9a07fb840284db2218117a52da800b (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  9.   Link requires a different Python (3.6.13 not in: '>=3.7'): https://files.pythonhosted.org/packages/33/c9/e2164122d365d8f823213a53970fa3005eb16218edcfc56ca24cb6deba2b/pip-22.0.4.tar.gz#sha256=b3a9de2c6ef801e9247d1527a4b16f92f2cc141cd1489f3fffaf6a9e96729764 (from https://pypi.org/simple/pip/) (requires-python:>=3.7)
  10. Skipping link: not a file: https://pypi.org/simple/pip/
  11. Given no hashes to check 181 links for project 'pip': discarding no candidates
  12. Removed build tracker: '/tmp/pip-req-tracker-83poj6hz'

查看cupy-cuda110-xxx版本：居然为9.6.0

5.6 运行测试2

  1. #重新配置
  2. pip install cupy==7.8.0
  3. pip uninstall cupy==9.6.0

测试：

  1. (py36cuda110) meng@meng:~/deeplearning/fcn/pytorch-fcn-main/examples/voc$ ./speedtest.py --gpu 2
  2. ==> Benchmark: gpu=2, times=1000, dynamic_input=False
  3. ==> Testing FCN32s with Chainer
  4. Traceback (most recent call last):
  5.   File "./speedtest.py", line 110, in <module>
  6.     main()
  7.   File "./speedtest.py", line 105, in main
  8.     bench_chainer(args.gpu, args.times, args.dynamic_input)
  9.   File "./speedtest.py", line 14, in bench_chainer
  10.     chainer.cuda.get_device(gpu).use()
  11.   File "/home/meng/anaconda3/envs/py36cuda110/lib/python3.6/site-packages/chainer/backends/cuda.py", line 354, in get_device
  12.     return _get_cuda_device(*args)
  13.   File "/home/meng/anaconda3/envs/py36cuda110/lib/python3.6/site-packages/chainer/backends/cuda.py", line 361, in _get_cuda_device
  14.     check_cuda_available()
  15.   File "/home/meng/anaconda3/envs/py36cuda110/lib/python3.6/site-packages/chainer/backends/cuda.py", line 150, in check_cuda_available
  16.     raise RuntimeError(msg)
  17. RuntimeError: CUDA environment is not correctly set up
  18. (see https://github.com/chainer/chainer#installation).libcublas.so.11: cannot open shared object file: No such file or directory

目前没成功配置出GPU版本的fcn网络，大家可以给点建议不

参考链接：

Ubuntu18.04安装cpu版pytorch环境 - 简书 https://www.jianshu.com/p/43f66c69baa7https://github.com/pytorch/pytorch#installation https://github.com/pytorch/pytorch#installation

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