Как установить pytorch на windows 10

I was getting some kind of Rollback error on Git bash and Windows Cmd prompt so had to run Anaconda prompt as admin for:

conda install pytorch-cpu -c pytorch

and then I got another when I tried the following command on Anaconda prompt:

pip3 install torchvision

so I switched back to Windows prompt to enter it and it worked.

To test the installation, I ran this from Git Bash:

$ python reinforcement_q_learning.py

with source code that looks like (the snippet near the top anyways):

"""

import gym
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple
from itertools import count
from PIL import Image

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as T


env = gym.make('CartPole-v0').unwrapped

# set up matplotlib
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
    from IPython import display

plt.ion()

# if gpu is to be used
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


######################################################################
# Replay Memory
# -------------
#
# We'll be using experience replay memory for training our DQN. It stores
# the transitions that the agent observes, allowing us to reuse this data
# later. By sampling from it randomly, the transitions that build up a
# batch are decorrelated. It has been shown that this greatly stabilizes
# and improves the DQN training procedure.
#
# For this, we're going to need two classses:
#
# -  ``Transition`` - a named tuple representing a single transition in
#    our environment. It maps essentially maps (state, action) pairs
#    to their (next_state, reward) result, with the state being the
#    screen difference image as described later on.
# -  ``ReplayMemory`` - a cyclic buffer of bounded size that holds the
#    transitions observed recently. It also implements a ``.sample()``
#    method for selecting a random batch of transitions for training.
#

Transition = namedtuple('Transition',
                        ('state', 'action', 'next_state', 'reward'))


class ReplayMemory(object):

    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = []
        self.position = 0

    def push(self, *args):
        """Saves a transition."""
        if len(self.memory) < self.capacity:
            self.memory.append(None)
        self.memory[self.position] = Transition(*args)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size):
        return random.sample(self.memory, batch_size)

    def __len__(self):
        return len(self.memory)

############continues to line 507...

In this article, we will learn how to install Pytorch on Windows. PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.

Method 1: Using pip

Step 1: Check if python is already installed by entering the following command in the command prompt.

python –version

If this command runs successfully, and we are able to get a Python version then we are good to go or else install python by referring to How to install Python on Windows?

Step 2: Check if pip is already installed by entering the following command in the command prompt.

pip –version

If this command runs successfully, and we are able to get a pip version then we are good to go or else install pip by referring to this article Download and install pip Latest Version.

Step 3: Enter any one of the following commands (according to your system specifications) to install the latest stable release of Pytorch.

1. Compute Platform: CUDA 10.2, Nvidia Driver version should be >= 441.22

pip3 install torch==1.8.1+cu102 torchvision==0.9.1+cu102 torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

2. Compute Platform: CUDA 11.1, Nvidia Driver version should be >= 456.38

pip3 install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

3. Compute Platform: CPU

 pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

Step 4: Check if Pytorch is successfully installed by entering the following command in the command prompt.

pip3 show torch

Method 2: Using conda

Step 1: Check if conda is installed by entering the following command in Anaconda Prompt.

conda –version

If this command runs successfully, and we are able to get a conda version then we are good to go or else install Anaconda by referring to this article How to install Anaconda on windows?

Step 2: Open Anaconda Prompt in Administrator mode and enter any one of the following commands (according to your system specifications) to install the latest stable release of Pytorch.

1. Compute Platform: CUDA 10.2, Nvidia Driver version should be >= 441.22

conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch

2. Compute Platform: CUDA 11.1, Nvidia Driver version should be >= 456.38

conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge

3. Compute Platform: CPU

conda install pytorch torchvision torchaudio cpuonly -c pytorch

Step 3: Check if Pytorch is successfully installed by entering the following command in Anaconda prompt.

conda list -f pytorch

Для установки PyTorch сначала вам нужно определиться со своими предпочтениями, а затем запустить команду установки. Вы можете начать установку локально или с помощью облачного партнера. Наиболее поддерживаемая и протестированная в настоящее время версия PyTorch (1.1), которая подходит для большинства пользователей.

Если вам нужны последние сборки 1.1, но они не полностью протестированы и не поддерживаются, вам нужно выбрать «Preview (Nightly)».

Для установки необходимо, чтобы вы выполнили предварительные требования, которые подходят для вашего менеджера пакетов. Мы рекомендуем вам использовать менеджер пакетов Anaconda, потому что он устанавливает все зависимости.

Платформы, ОС, язык и другие требования
Сборка PyTorch	Suitable (1.1)	Preview
Операционная система	Linux	Mac	Windows
package	Conda	pip	LibTorch	Source
язык	Python 2.7	Python 3.5	Python 3.6	Python 3.7	С++
Cuda	9,0	10,0	None

LibTorch доступен только для C++. Теперь мы сначала устанавливаем PyTorch в Windows с помощью пакета pip, а затем используем Conda.

Чтобы установить PyTorch, вы должны сначала установить python, а затем выполнить следующие шаги.

• Шаг 1:

Сначала вам нужно войти в папку python 37, а затем в папку Scripts с помощью команды cd Scripts.

• Шаг 2:

На втором этапе вам необходимо установить pip в соответствии с требуемой версией с помощью команды pip easy_install.exe в командной строке. После завершения обработки зависимостей вы автоматически вернетесь в папку Scripts.

• Шаг 3:

Теперь нужно установить пакет numpy python для pip. Установка Numpy будет выполнена с помощью команды pip install numpy. Если у вашего python уже есть этот пакет, он покажет вам «Требование уже выполнено», в противном случае он установит пакет. Команда Pip list используется для проверки пакетов.

Когда загрузка завершена, показывается успешное сообщение и ваш курсор  возвращается в папку сценариев.

• Шаг 4:

Следующим шагом является установка pip другого пакета scipy с помощью команды pip install scipy.

После завершения загрузки ваш курсор возвращается в папку скриптов.

• Шаг 5:

Теперь проверьте все установленные пакеты, необходимые для PyTorch, с помощью команды pip list.

• Шаг 6:

Теперь вам нужно перейти на https://pytorch.org/, чтобы найти команду установки PyTorch.

---

PyTorch is a Python package that provides two high-level features:

• Tensor computation (like NumPy) with strong GPU acceleration
• Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Our trunk health (Continuous Integration signals) can be found at hud.pytorch.org.

• More About PyTorch
  • A GPU-Ready Tensor Library
  • Dynamic Neural Networks: Tape-Based Autograd
  • Python First
  • Imperative Experiences
  • Fast and Lean
  • Extensions Without Pain
• Installation
  • Binaries
    • NVIDIA Jetson Platforms
  • From Source
    • Prerequisites
    • Install Dependencies
    • Get the PyTorch Source
    • Install PyTorch
      • Adjust Build Options (Optional)
  • Docker Image
    • Using pre-built images
    • Building the image yourself
  • Building the Documentation
  • Previous Versions
• Getting Started
• Resources
• Communication
• Releases and Contributing
• The Team
• License

More About PyTorch

At a granular level, PyTorch is a library that consists of the following components:

Component	Description
torch	A Tensor library like NumPy, with strong GPU support
torch.autograd	A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch
torch.jit	A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code
torch.nn	A neural networks library deeply integrated with autograd designed for maximum flexibility
torch.multiprocessing	Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training
torch.utils	DataLoader and other utility functions for convenience

Usually, PyTorch is used either as:

• A replacement for NumPy to use the power of GPUs.
• A deep learning research platform that provides maximum flexibility and speed.

Elaborating Further:

A GPU-Ready Tensor Library

If you use NumPy, then you have used Tensors (a.k.a. ndarray).

PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the
computation by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs
such as slicing, indexing, mathematical operations, linear algebra, reductions.
And they are fast!

Dynamic Neural Networks: Tape-Based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe, and CNTK have a static view of the world.
One has to build a neural network and reuse the same structure again and again.
Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called reverse-mode auto-differentiation, which allows you to
change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes
from several research papers on this topic, as well as current and past work such as
torch-autograd,
autograd,
Chainer, etc.

While this technique is not unique to PyTorch, it’s one of the fastest implementations of it to date.
You get the best of speed and flexibility for your crazy research.

Python First

PyTorch is not a Python binding into a monolithic C++ framework.
It is built to be deeply integrated into Python.
You can use it naturally like you would use NumPy / SciPy / scikit-learn etc.
You can write your new neural network layers in Python itself, using your favorite libraries
and use packages such as Cython and Numba.
Our goal is to not reinvent the wheel where appropriate.

Imperative Experiences

PyTorch is designed to be intuitive, linear in thought, and easy to use.
When you execute a line of code, it gets executed. There isn’t an asynchronous view of the world.
When you drop into a debugger or receive error messages and stack traces, understanding them is straightforward.
The stack trace points to exactly where your code was defined.
We hope you never spend hours debugging your code because of bad stack traces or asynchronous and opaque execution engines.

Fast and Lean

PyTorch has minimal framework overhead. We integrate acceleration libraries
such as Intel MKL and NVIDIA (cuDNN, NCCL) to maximize speed.
At the core, its CPU and GPU Tensor and neural network backends
are mature and have been tested for years.

Hence, PyTorch is quite fast – whether you run small or large neural networks.

The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives.
We’ve written custom memory allocators for the GPU to make sure that
your deep learning models are maximally memory efficient.
This enables you to train bigger deep learning models than before.

Extensions Without Pain

Writing new neural network modules, or interfacing with PyTorch’s Tensor API was designed to be straightforward
and with minimal abstractions.

You can write new neural network layers in Python using the torch API
or your favorite NumPy-based libraries such as SciPy.

If you want to write your layers in C/C++, we provide a convenient extension API that is efficient and with minimal boilerplate.
No wrapper code needs to be written. You can see a tutorial here and an example here.

Installation

Binaries

Commands to install binaries via Conda or pip wheels are on our website: https://pytorch.org/get-started/locally/

NVIDIA Jetson Platforms

Python wheels for NVIDIA’s Jetson Nano, Jetson TX1/TX2, Jetson Xavier NX/AGX, and Jetson AGX Orin are provided here and the L4T container is published here

They require JetPack 4.2 and above, and @dusty-nv and @ptrblck are maintaining them.

From Source

Prerequisites

If you are installing from source, you will need:

• Python 3.7 or later (for Linux, Python 3.7.6+ or 3.8.1+ is needed)
• A C++14 compatible compiler, such as clang

We highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.

If you want to compile with CUDA support, install the following (note that CUDA is not supported on macOS)

• NVIDIA CUDA 10.2 or above
• NVIDIA cuDNN v7 or above
• Compiler compatible with CUDA

Note: You could refer to the cuDNN Support Matrix for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware

If you want to disable CUDA support, export the environment variable USE_CUDA=0.
Other potentially useful environment variables may be found in setup.py.

If you are building for NVIDIA’s Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are available here

If you want to compile with ROCm support, install

• AMD ROCm 4.0 and above installation
• ROCm is currently supported only for Linux systems.

If you want to disable ROCm support, export the environment variable USE_ROCM=0.
Other potentially useful environment variables may be found in setup.py.

Install Dependencies

Common

conda install astunparse numpy ninja pyyaml setuptools cmake cffi typing_extensions future six requests dataclasses

On Linux

conda install mkl mkl-include
# CUDA only: Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda110  # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo

On MacOS

# Add this package on intel x86 processor machines only
conda install mkl mkl-include
# Add these packages if torch.distributed is needed
conda install pkg-config libuv

On Windows

conda install mkl mkl-include
# Add these packages if torch.distributed is needed.
# Distributed package support on Windows is a prototype feature and is subject to changes.
conda install -c conda-forge libuv=1.39

Get the PyTorch Source

git clone --recursive https://github.com/pytorch/pytorch
cd pytorch
# if you are updating an existing checkout
git submodule sync
git submodule update --init --recursive --jobs 0

Install PyTorch

On Linux

If you’re compiling for AMD ROCm then first run this command:

# Only run this if you're compiling for ROCm
python tools/amd_build/build_amd.py

Install PyTorch

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
python setup.py install

Note that if you are using Anaconda, you may experience an error caused by the linker:

build/temp.linux-x86_64-3.7/torch/csrc/stub.o: file not recognized: file format not recognized
collect2: error: ld returned 1 exit status
error: command 'g++' failed with exit status 1

This is caused by ld from the Conda environment shadowing the system ld. You should use a newer version of Python that fixes this issue. The recommended Python version is 3.7.6+ and 3.8.1+.

On macOS

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install

On Windows

Choose Correct Visual Studio Version.

Sometimes there are regressions in new versions of Visual Studio, so
it’s best to use the same Visual Studio Version 16.8.5 as Pytorch CI’s.

PyTorch CI uses Visual C++ BuildTools, which come with Visual Studio Enterprise,
Professional, or Community Editions. You can also install the build tools from
https://visualstudio.microsoft.com/visual-cpp-build-tools/. The build tools do not
come with Visual Studio Code by default.

If you want to build legacy python code, please refer to Building on legacy code and CUDA

CPU-only builds

In this mode PyTorch computations will run on your CPU, not your GPU

conda activate
python setup.py install

Note on OpenMP: The desired OpenMP implementation is Intel OpenMP (iomp). In order to link against iomp, you’ll need to manually download the library and set up the building environment by tweaking CMAKE_INCLUDE_PATH and LIB. The instruction here is an example for setting up both MKL and Intel OpenMP. Without these configurations for CMake, Microsoft Visual C OpenMP runtime (vcomp) will be used.

CUDA based build

In this mode PyTorch computations will leverage your GPU via CUDA for faster number crunching

NVTX is needed to build Pytorch with CUDA.
NVTX is a part of CUDA distributive, where it is called «Nsight Compute». To install it onto an already installed CUDA run CUDA installation once again and check the corresponding checkbox.
Make sure that CUDA with Nsight Compute is installed after Visual Studio.

Currently, VS 2017 / 2019, and Ninja are supported as the generator of CMake. If ninja.exe is detected in PATH, then Ninja will be used as the default generator, otherwise, it will use VS 2017 / 2019.

If Ninja is selected as the generator, the latest MSVC will get selected as the underlying toolchain.

Additional libraries such as
Magma, oneDNN, a.k.a MKLDNN or DNNL, and Sccache are often needed. Please refer to the installation-helper to install them.

You can refer to the build_pytorch.bat script for some other environment variables configurations

cmd

:: Set the environment variables after you have downloaded and unzipped the mkl package,
:: else CMake would throw an error as `Could NOT find OpenMP`.
set CMAKE_INCLUDE_PATH={Your directory}mklinclude
set LIB={Your directory}mkllib;%LIB%

:: Read the content in the previous section carefully before you proceed.
:: [Optional] If you want to override the underlying toolset used by Ninja and Visual Studio with CUDA, please run the following script block.
:: "Visual Studio 2019 Developer Command Prompt" will be run automatically.
:: Make sure you have CMake >= 3.12 before you do this when you use the Visual Studio generator.
set CMAKE_GENERATOR_TOOLSET_VERSION=14.27
set DISTUTILS_USE_SDK=1
for /f "usebackq tokens=*" %i in (`"%ProgramFiles(x86)%Microsoft Visual StudioInstallervswhere.exe" -version [15^,17^) -products * -latest -property installationPath`) do call "%iVCAuxiliaryBuildvcvarsall.bat" x64 -vcvars_ver=%CMAKE_GENERATOR_TOOLSET_VERSION%

:: [Optional] If you want to override the CUDA host compiler
set CUDAHOSTCXX=C:Program Files (x86)Microsoft Visual Studio2019CommunityVCToolsMSVC14.27.29110binHostX64x64cl.exe

python setup.py install

Adjust Build Options (Optional)

You can adjust the configuration of cmake variables optionally (without building first), by doing
the following. For example, adjusting the pre-detected directories for CuDNN or BLAS can be done
with such a step.

On Linux

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
python setup.py build --cmake-only
ccmake build  # or cmake-gui build

On macOS

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build --cmake-only
ccmake build  # or cmake-gui build

Docker Image

Using pre-built images

You can also pull a pre-built docker image from Docker Hub and run with docker v19.03+

docker run --gpus all --rm -ti --ipc=host pytorch/pytorch:latest

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g.
for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you
should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

Building the image yourself

NOTE: Must be built with a docker version > 18.06

The Dockerfile is supplied to build images with CUDA 11.1 support and cuDNN v8.
You can pass PYTHON_VERSION=x.y make variable to specify which Python version is to be used by Miniconda, or leave it
unset to use the default.

make -f docker.Makefile
# images are tagged as docker.io/${your_docker_username}/pytorch

Building the Documentation

To build documentation in various formats, you will need Sphinx and the
readthedocs theme.

cd docs/
pip install -r requirements.txt

You can then build the documentation by running make <format> from the
docs/ folder. Run make to get a list of all available output formats.

If you get a katex error run npm install katex. If it persists, try
npm install -g katex

Note: if you installed nodejs with a different package manager (e.g.,
conda) then npm will probably install a version of katex that is not
compatible with your version of nodejs and doc builds will fail.
A combination of versions that is known to work is node@6.13.1 and
katex@0.13.18. To install the latter with npm you can run
npm install -g katex@0.13.18

Previous Versions

Installation instructions and binaries for previous PyTorch versions may be found
on our website.

Getting Started

Three-pointers to get you started:

• Tutorials: get you started with understanding and using PyTorch
• Examples: easy to understand PyTorch code across all domains
• The API Reference
• Glossary

Resources

• PyTorch.org
• PyTorch Tutorials
• PyTorch Examples
• PyTorch Models
• Intro to Deep Learning with PyTorch from Udacity
• Intro to Machine Learning with PyTorch from Udacity
• Deep Neural Networks with PyTorch from Coursera
• PyTorch Twitter
• PyTorch Blog
• PyTorch YouTube

Communication

• Forums: Discuss implementations, research, etc. https://discuss.pytorch.org
• GitHub Issues: Bug reports, feature requests, install issues, RFCs, thoughts, etc.
• Slack: The PyTorch Slack hosts a primary audience of moderate to experienced PyTorch users and developers for general chat, online discussions, collaboration, etc. If you are a beginner looking for help, the primary medium is PyTorch Forums. If you need a slack invite, please fill this form: https://goo.gl/forms/PP1AGvNHpSaJP8to1
• Newsletter: No-noise, a one-way email newsletter with important announcements about PyTorch. You can sign-up here: https://eepurl.com/cbG0rv
• Facebook Page: Important announcements about PyTorch. https://www.facebook.com/pytorch
• For brand guidelines, please visit our website at pytorch.org

Releases and Contributing

PyTorch has a 90-day release cycle (major releases). Please let us know if you encounter a bug by filing an issue.

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

If you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss the feature with us.
Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the core in a different direction than you might be aware of.

To learn more about making a contribution to Pytorch, please see our Contribution page.

The Team

PyTorch is a community-driven project with several skillful engineers and researchers contributing to it.

PyTorch is currently maintained by Adam Paszke, Sam Gross, Soumith Chintala and Gregory Chanan with major contributions coming from hundreds of talented individuals in various forms and means.
A non-exhaustive but growing list needs to mention: Trevor Killeen, Sasank Chilamkurthy, Sergey Zagoruyko, Adam Lerer, Francisco Massa, Alykhan Tejani, Luca Antiga, Alban Desmaison, Andreas Koepf, James Bradbury, Zeming Lin, Yuandong Tian, Guillaume Lample, Marat Dukhan, Natalia Gimelshein, Christian Sarofeen, Martin Raison, Edward Yang, Zachary Devito.

Note: This project is unrelated to hughperkins/pytorch with the same name. Hugh is a valuable contributor to the Torch community and has helped with many things Torch and PyTorch.

License

PyTorch has a BSD-style license, as found in the LICENSE file.

В очередной раз после переустановки Windows осознал, что надо накатить драйвера, CUDA, cuDNN, Tensorflow/Keras для обучения нейронных сетей.

Каждый раз для меня это оказывается несложной, но времязатратной операцией: найти подходящую комбинацию Tensorflow/Keras, CUDA, cuDNN и Python несложно, но вспоминаю про эти зависимости только в тот момент, когда при импорте Tensorflow вижу, что видеокарта не обнаружена и начинаю поиск нужной страницы в документации Tensorflow.

В этот раз ситуация немного усложнилась. Помимо установки Tensorflow мне потребовалось установить PyTorch. Со своими зависимостями и поддерживаемыми версиями Python, CUDA и cuDNN.

По итогам нескольких часов экспериментов решил, что надо зафиксировать все полезные ссылки в одном посте для будущего меня.

Краткий алгоритм установки Tensorflow и PyTorch

Примечание: Установить Tensorflow и PyTorch можно в одном виртуальном окружении, но в статье этого алгоритма нет.

Подготовка к установке

1. Определить какая версия Python поддерживается Tensorflow и PyTorch (на момент написания статьи мне не удалось установить PyTorch в виртуальном окружении с Python 3.9.5)
2. Для выбранной версии Python найти подходящие версии Tensorflow и PyTorch
3. Определить, какие версии CUDA поддерживают выбранные ранее версии Tensorflow и PyTorch
4. Определить поддерживаемую версию cuDNN для Tensorflow – не все поддерживаемые CUDA версии cuDNN поддерживаются Tensorflow. Для PyTorch этой особенности не заметил

Установка CUDA и cuDNN

1. Скачиваем подходящую версию CUDA и устанавливаем. Можно установить со всеми значениями по умолчанию
2. Скачиваем cuDNN, подходящую для выбранной версии Tensorflow (п.1.2). Для скачивания cuDNN потребуется регистрация на сайте NVidia. “Установка” cuDNN заключается в распакове архива и заменой существующих файлов CUDA на файлы из архива

Устанавливаем Tensorflow

1. Создаём виртуальное окружение для Tensorflow c выбранной версией Python. Назовём его, например, py38tf
2. Переключаемся в окружение py38tf и устанавливаем поддерживаемую версию Tensorflow pip install tensorflow==x.x.x
3. Проверяем поддержку GPU командой

   python -c "import tensorflow as tf; print('CUDA available' if tf.config.list_physical_devices('GPU') else 'CUDA not available')"
   

Устанавливаем PyTorch

1. Создаём виртуальное окружение для PyTorch c выбранной версией Python. Назовём его, например, py38torch
2. Переключаемся в окружение py38torch и устанавливаем поддерживаемую версию PyTorch
3. Проверяем поддержку GPU командой

python -c "import torch; print('CUDA available' if torch.cuda.is_available() else 'CUDA not available')"

В моём случае заработала комбинация:

• Python 3.8.8
• Драйвер NVidia 441.22
• CUDA 10.1
• cuDNN 7.6
• Tensorflow 2.3.0
• PyTorch 1.7.1+cu101

Tensorflow и PyTorch установлены в разных виртуальных окружениях.

Итого

Польза этой статьи будет понятна не скоро: систему переустанавливаю я не часто.

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