Установить torch python anaconda

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How we will able install pytorch, opencv, numpy etc, in anaconda platform

borhanreo/PyTorch_Install

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README.md

Install pytorch on Ubuntu with Anaconda environment

Step 1.1 Need to install anaconda

step 1.2 Run the Anaconda Script

$ cd Downloads $ bash Anaconda3-5.3.0-Linux-x86_64.sh 

Output Welcome to Anaconda3-5.3.0 In order to continue the installation process, please review the license agreement. Please, press ENTER to continue >>> . Do you approve the license terms? [yes|no] 

Step 1.3 Complete Installation Process

Output Anaconda3 will now be installed into this location: /home/borhan/anaconda3 - Press ENTER to confirm the location - Press CTRL-C to abort the installation - Or specify a different location below [/home/borhan/anaconda3] >>> 

Output . installation finished. Do you wish the installer to prepend the Anaconda3 install location to PATH in your /home/borhan/.bashrc ? [yes|no] [no] >>> 

Step 1.5 Activate Installation

Step 1.6 Test Installation

Some cases need to restart $ conda list
You will ses some output

# packages in environment at /home/borhan/anaconda3: # # Name Version Build Channel _ipyw_jlab_nb_ext_conf 0.1.0 py36he11e457_0 alabaster 0.7.10 py36h306e16b_0 anaconda 5.2.0 py36_3 

Step 1.7 Set Up Anaconda Environments

 $ conda create --name pyTorch python=3 

Step 1.8 Activate Anaconda Environments

Step 1.9 checking the python version

(pyTorch) borhan@ubuntu:~$ python --version 

Step 1.10 Inspect all of the environments you have set up with this command:

# conda environments: # base /home/borhan/anaconda3 pyTorch * /home/borhan/anaconda3/envs/pyTorch 

$ conda install pytorch-cpu torchvision-cpu -c pytorch 

With CUDA 9.0

$ conda install pytorch torchvision -c pytorch 

Take little time (depend on internet speed) for install many library. you can go for tea break

$ python3 import torch print(torch.__version__) 

$ conda install -c conda-forge opencv $ conda install -c conda-forge/label/broken opencv 

$ python3 import cv2 print(cv2.__version__) 

Step 3.1 Install TensorFlow

$ conda install -c intel tensorflow 

$ python3 import tensorflow print(tensorflow.__version__) 

Last step test full install (pytorch, OpenCV and tensorflow)

import torch from __future__ import print_function import cv2 import tensorflow print("Torch version: ",torch.__version__) print("OpenCV version",cv2.__version__) print("TensorFlow version",tensorflow.__version__) print(torch.rand(5,4)) torch.cuda.is_available() 

Torch version: 0.4.1 OpenCV version 3.4.2 TensorFlow version 1.10.0 tensor([[0.2159, 0.6831, 0.0893, 0.7964], [0.3756, 0.5890, 0.7941, 0.3761], [0.8911, 0.6263, 0.1474, 0.4778], [0.4070, 0.9507, 0.7599, 0.9507], [0.3709, 0.5526, 0.6739, 0.8450]]) False 

List all env

This topic only for NVIDA GPU user only

if cuda is installed with your conda ??

import torch print(torch.version.cuda) 

open the additional-drivers app, search for it in the dash, or in software and updates select NVIDIA binary driver Run a command

$ sudo apt install nvidia-cuda-toolkit $ sudo reboot 

Test Nvidia gpu CUDA

import torch print(torch.cuda.isavailable()) 

conda info —envs conda activate pyTorch

$ conda install —yes —file requirements.txt

About

How we will able install pytorch, opencv, numpy etc, in anaconda platform

Источник

Установка и настройка PyTorch на компьютере

На предыдущем этапе работы с этим учебником мы обсуждали основы PyTorch и предварительные требования, которые нужно выполнить, чтобы иметь возможность использовать эту библиотеку при создании модели машинного обучения. Выполняя действия этой статьи, мы установим ее на вашем компьютере.

Получение PyTorch

Во-первых, необходимо настроить среду Python.

Мы рекомендуем настроить виртуальную среду Python в Windows, используя в качестве диспетчера пакетов платформу Anaconda. Далее в командах установки предполагается, что вы используете среду Anaconda.

Не забудьте установить Python 3.x. В настоящее время PyTorch в Windows поддерживает только Python 3.x; Python 2.x не поддерживается.

После завершения установки проверьте версии Anaconda и Python.

1. Откройте диспетчер Anaconda, перейдя к разделу «Start (Запуск) — Anaconda3 — Anaconda PowerShell Prompt», и протестируйте свои версии:

Проверить версию Python можно с помощью следующей команды: python –-version

Проверить версию Anaconda можно с помощью следующей команды: conda –-version

Теперь вы можете установить пакет PyTorch из двоичных файлов с помощью Conda.

Выберите соответствующие сведения об установке PyTorch:

• Сборка PyTorch — стабильная.
• Ваша ОС — Windows
• Пакет — Conda
• Язык — Python
• Платформа вычислений — ЦП или выберите свою версию Cuda. Выполняя действия в этом учебнике, вы будете обучать и выводить модель на ЦП, но вы также можете использовать графический процессор Nvidia.

conda install pytorch torchvision torchaudio cpuonly -c pytorch 

Давайте проверим установку PyTorch, выполнив пример кода PyTorch для создания случайно инициализированного тензора.

Затем введите следующий код:

import torch x = torch.rand(2, 3) print(x) 

В качестве вывода должен отображатся случайный тензор 5×3. Числа будут отличаться, однако результат должен выглядеть примерно так, как показано ниже.

Next Steps

Теперь, установив PyTorch, мы можем перейти к настройке данных для нашей модели.

Источник

Install and configure PyTorch on your machine.

In the previous stage of this tutorial, we discussed the basics of PyTorch and the prerequisites of using it to create a machine learning model. Here, we’ll install it on your machine.

Get PyTorch

First, you’ll need to setup a Python environment.

We recommend setting up a virtual Python environment inside Windows, using Anaconda as a package manager. The rest of this setup assumes you use an Anaconda environment.

Be aware to install Python 3.x. Currently, PyTorch on Windows only supports Python 3.x; Python 2.x is not supported.

After the installation is complete, verify your Anaconda and Python versions.

You can check your Python version by running the following command: python –-version

You can check your Anaconda version by running the following command: conda –-version

Now, you can install PyTorch package from binaries via Conda.

Select the relevant PyTorch installation details:

• PyTorch build – stable.
• Your OS – Windows
• Package – Conda
• Language – Python
• Compute Platform – CPU, or choose your version of Cuda. In this tutorial, you will train and inference model on CPU, but you could use a Nvidia GPU as well.

conda install pytorch torchvision torchaudio cpuonly -c pytorch 

Let’s verify PyTorch installation by running sample PyTorch code to construct a randomly initialized tensor.

Next, enter the following code:

import torch x = torch.rand(2, 3) print(x) 

The output should be a random 5×3 tensor. The numbers will be different, but it should look similar to the below.

Interested in learning more? Visit the PyTorch official website

Next Steps

Now that we’ve installed PyTorch, we’re ready to set up the data for our model.

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Источник

How to Install PyTorch in Anaconda with Conda or Pip

PyTorch is an opensource machine learning framework that allows developers to build and train neural networks It is widely used in the data science community due to its flexibility and ease of use PyTorch can be installed using Anaconda a popular distribution of the Python programming language that is widely used in data science

PyTorch is an open-source machine learning framework that allows developers to build and train neural networks. It is widely used in the data science community due to its flexibility and ease of use. PyTorch can be installed using Anaconda, a popular distribution of the Python programming language that is widely used in data science.

In this blog post, we will explore two methods for installing PyTorch in Anaconda: using Conda and using Pip. We will discuss the advantages and disadvantages of each method, as well as the steps required to install PyTorch using each method.

Need a straightforward way to install PyTorch in Anaconda? Saturn Cloud provides an easy-to-use environment to handle your data science installations. Join for free today.

Method 1: Installing PyTorch with Conda

Conda is a package manager that is widely used in the data science community. It allows developers to easily install and manage packages, dependencies, and environments. Installing PyTorch with Conda is straightforward and can be done in a few simple steps.

1. Open the Anaconda Prompt or Terminal.
2. Create a new conda environment for PyTorch using the following command:

conda create --name pytorch_env 

conda activate pytorch_env 

conda install pytorch torchvision torchaudio -c [pytorch](https://saturncloud.io/glossary/pytorch) 

import torch print(torch.__version__) 

Method 2: Installing PyTorch with Pip

Pip is another popular package manager that is widely used in the Python community. It allows developers to easily install and manage Python packages and dependencies. Installing PyTorch with Pip is also straightforward and can be done in a few simple steps.

1. Open the Anaconda Prompt or Terminal.
2. Create a new conda environment for PyTorch using the following command:

conda create --name pytorch_env 

conda activate pytorch_env 

pip install torch torchvision torchaudio 

import torch print(torch.__version__) 

Need a straightforward way to install PyTorch in Anaconda? Saturn Cloud provides an easy-to-use environment to handle your data science installations. Join for free today.

Conclusion

In this blog post, we have explored two methods for installing PyTorch in Anaconda: using Conda and using Pip. Both methods are straightforward and can be done in a few simple steps. While Conda is the recommended method for installing PyTorch, Pip is also a viable option. The choice of method depends on your specific needs and preferences.

Regardless of the method you choose, it is important to verify the installation by running a simple Python script that imports PyTorch. This will ensure that PyTorch is installed correctly and that you can start building and training neural networks using this powerful machine learning framework.

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