Как установить tensorflow в pycharm windows

TensorFlow is one of the most popular open-source libraries for machine learning and deep neural networks in Python—initiated by the machine learning engineers at Google!

Problem Formulation: Given a PyCharm project. How to install the TensorFlow library in your project within a virtual environment or globally?

Here’s a solution that always works:

• Open File > Settings > Project from the PyCharm menu.
• Select your current project.
• Click the Python Interpreter tab within your project tab.
• Click the small + symbol to add a new library to the project.
• Now type in the library to be installed, in your example "tensorflow" without quotes, and click Install Package.
• Wait for the installation to terminate and close all popup windows.

Here’s the installation process as a short animated video—it works analogously for TensorFlow, just type in “tensorflow” in the search field instead:

Make sure to only select the package “tensorflow” because there are many other packages that are not required but also contain the term “tensorflow” (False Positives):

Alternatively, you can run the pip install tensorflow command in your PyCharm terminal view that makes use of the PIP package manager:

$ pip install tensorflow
(venv) C:UsersxcentDesktopPythonpythonProject>pip install tensorflow
Collecting tensorflow
  Downloading tensorflow-2.6.0-cp39-cp39-win_amd64.whl (423.3 MB)
     |██████████████████              | 234.4 MB 3.3 MB/s eta 0:00:58

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Install PyCharm:

We believe PyCharm is one of the best (if not the best) IDEs for python programming. It comes with powerfull tools for code editting, navigating, refactoring, debugging and etc. The community version of this software is free and you can download it through https://www.jetbrains.com/pycharm/download/. If you are student, you also can use the professional edition using your university email (read more here).

After you download and install the PyCharm. You have to show the PyCharm that where is the location of the python file that you have installed your tensorflow environment. You can do so through the interpreter section. When you have an existing project opened (if not, create a new project), go to the setting. In project section, select the project interpreter and all local virtual environment.

Select Conda Environment and give the path to the python executable of existing environment to the interpreter.

*Note: Recall the path that you installed the Anaconda into and find the created environment in the envs folder in the Anaconda path.

Оригинал перепечатан с:https://blog.csdn.net/u012052268/article/details/74202439

оглавление

Предисловие:

В последнее время из-за необходимости использовать TensorFlow для работы могу только дополнять актуальные знания. Изначально блоггер планировал играть на Ubantu, но по некоторым причинам отказался от этой идеи и перешел на Pycharm, чтобы играть. Следующее — хорошее руководство по установке, которое я видел в процессе сбора информации, поделитесь им.

1. Установите Anaconda

Выберите соответствующую Anaconda для установки. Щелкните здесь, чтобы увидеть адрес загрузки, чтобы загрузить соответствующую системную версию Anaconda. Текущая версия официального сайта — Anaconda 4.3.1 для python3.6. Автор установил версию 4.3.0.

аналогична установке обычного программного обеспечения, просто выберите значение по умолчанию.Обратите внимание на то, чтобы добавить python3.6 в переменную окружения.

Затем устанавливается Anaconda. Мы можем использовать следующую команду, чтобы проверить, какие пакеты установлены Anaconda.
Выполнить меню «Пуск» -> Anaconda3—> Подсказка Anaconda:

conda list

Вы можете видеть, что установлены часто используемые пакеты, такие как numpy и sympy.

2. Установите Tensorflow.

В настоящее время TensorFlow поддерживает только версию Python 3.5 под Windows.

(1) Откройте приглашение Anaconda и введите образ хранилища Tsinghua, чтобы обновление было быстрее:

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes

(2) Также используйте Anaconda в Anaconda Prompt для создания среды python3.5, имя среды — tensorflow, введите следующую команду:

conda create -n tensorflow python=3.5

Запустите меню «Пуск» -> Anaconda3 -> Anaconda Navigator, щелкните «Среды» слева, вы увидите, что среда tenorflow создана.

(3) Запустите среду tenorflow в Anaconda Prompt:

activate tensorflow

Примечание: если не используется тензорный поток, закройте среду тензорного потока, команда: деактивировать

(4) Установите версию TensorFlow для процессора.

pip install --upgrade --ignore-installed tensorflow

Примечание. Метод установки версии с графическим процессором здесь не описывается. Для версии с графическим процессором необходимо установить cuda8 + cudnn5. При необходимости выполните поиск в других сообщениях блога.
Примечание: он должен быть установлен в среде только что созданного тензорного потока! ! ! !

Таким образом устанавливается версия процессора tensorflow.

(5) Тестовый тензорный поток
Запустите среду tenorflow в Anaconda Prompt и войдите в среду python.

Код теста следующий:

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

результат операции:

3. Прочие вопросы

Возможно, нас здесь не устраивает. Мы терпели неудачу при импорте tenorflow в ipython и Spyder, которые поставляются с Anaconda, подскажитеNo module named ‘tensorflow’, Как показано ниже, это потому, что мы не открывали их в среде tensorflow.

Чтобы иметь возможность использовать тензорный поток в ipython и Spyder, нам необходимо установить эти два плагина в среде тензорного потока.

Откройте Anaconda Navigator, выберите «Не установлено», найдите ipython и Spyder и установите их. Автор уже установил их здесь, поэтому они не отображаются на этой странице.

переключитесь на установленный, вы увидите, что оба они установлены, фактически, вы можете установить их в соответствии с вашими потребностями. На следующем рисунке показан установленный Spyder:

После установки подключаемого модуля нам нужно его протестировать.

Запустите среду tensorflow в Anaconda Prompt и запустите ipython, импорт tenorflow успешно завершен:

Аналогичным образом запустите среду tenorflow в Anaconda Prompt и запустите Spyder. Через некоторое время будет запущена IDE Spyder. Импорт тензорного потока также выполнен успешно:


Примечание. Вы должны запустить Spyder в среде тензорного потока, чтобы импортировать тензорный поток, не переходите в меню «Пуск», чтобы запустить Spyder, где его нельзя запустить, например:

4. Используйте тензорный поток в pycharm

Я привык использовать PyCharm для разработки, конфигурация следующая:
После создания нового проекта
File-Setting – Project Interpreter выберите интерпретатор Python в тензорном потоке,
Например, местонахождение моего переводчика:


После развертывания вы можете запустить HelloWorld

import tensorflow as tf

hello = tf.constant("Hello!TensorFlow")
sess = tf.Session()
print(sess.run(hello))

Запустить его

взлететь!

Преимущество этого метода: нет необходимости каждый раз открывать и закрывать среду.

（activate
tensorflow 、deactivate tensorflow）

Tensorflow is one of the most-used deep-learning frameworks. It’s arguably the most popular machine learning platform on the web, with a broad range of users from those just starting out, to people looking for an edge in their careers and businesses. 

Not all users know that you can install the TensorFlow GPU if your hardware supports it. We’ll discuss what Tensorflow is, how it’s used in today’s world, and how to install the latest TensorFlow version with CUDA, cudNN, and GPU support in Windows, Mac, and Linux. 

Introduction to TensorFlow

TensorFlow is an open-source software library for machine learning, created by Google. It was initially released on November 28, 2015, and it’s now used across many fields including research in the sciences and engineering.

The idea behind TensorFlow is to make it quick and simple to train deep neural networks that use a diversity of mathematical models. These networks are then able to learn from data without human intervention or supervision, making them more efficient than conventional methods. The library also offers support for processing on multiple machines simultaneously with different operating systems and GPUs.

How to Keep Track of TensorFlow/Keras Model Development with Neptune

Debug and Visualize Your TensorFlow/Keras Model: Hands-on Guide

TensorFlow applications 

TensorFlow is a library for deep learning built by Google, it’s been gaining a lot of traction ever since its introduction early last year. The main features include automatic differentiation, convolutional neural networks (CNN), and recurrent neural networks (RNN). It’s written in C++ and Python, for high performance it uses a server called a “Cloud TensorFlow” that runs on Google Cloud Platform. It doesn’t require a GPU, which is one of its main features.

The newest release of Tensorflow also supports data visualization through matplotlib. This visualization library is very popular, and it’s often used in data science coursework, as well as by artists and engineers to do data visualizations using MATLAB or Python / R / etc.

Installing the latest TensorFlow version with CUDA, cudNN, and GPU support

Let’s see how to install the latest TensorFlow version on Windows, macOS, and Linux.

Windows 

Prerequisite

• Python 3.6–3.8
• Windows 7 or later (with C++ redistributable)
• Check https://www.tensorflow.org/install/so… For latest version information

Steps 

1) Download Microsoft Visual Studio from:

https://visualstudio.microsoft.com/vs…

2) Install the NVIDIA CUDA Toolkit (https://developer.nvidia.com/cuda-too…), check the version of software and hardware requirements, we’ll be using : 

Version	Python version	Compiler	Build tools	cuDNN	CUDA
tensorflow-2.5.0	3.6-3.9	GCC 7.3.1	Bazel 3.7.2	8.1	11.2

We will install CUDA version 11.2, but make sure you install the latest or updated version (for example – 11.2.2 if it’s available).

Click on the newest version and a screen will pop up, where you can choose from a few options, so follow the below image and choose these options for Windows.

Once you choose the above options, wait for the download to complete. 

Install it with the Express (Recommended) option, it will take a while to install on your machine. 

3) Now we’ll download NVIDIA cuDNN, https://developer.nvidia.com/cudnn

Check the version code from the TensorFlow site.

Now, check versions for CUDA and cuDNN, and click download for your operating system. If you can’t find your desired version, click on cuDNN Archive and download from there. 

Once the download is complete, extract the files.

Now, copy these 3 folders (bin, include, lib). Go to C Drive>Program Files, and search for NVIDIA GPU Computing Toolkit.

Open the folder, select CUDA > Version Name, and replace (paste) those copied files. 

Now click on the bin folder and copy the path. It should look like this: C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.2bin. 

On your PC, search for Environment variables, as shown below.

Click on Environment Variables on the bottom left. Now click on the link which states PATH.

Once you click on the PATH, you will see something like this. 

Now click on New (Top Left), and paste the bin path here. Go to the CUDA folder, select libnvvm folder, and copy its path. Follow the same process and paste that path into the system path. Next, just restart your PC.

4) Installing Tensorflow

Open conda prompt. If not installed, get it here → https://www.anaconda.com/products/individual.

Now copy the below commands and paste them into the prompt (Check for the versions).

conda create --name tf2.5 python==3.8

conda activate tf2.5 (version)

pip install tensorflow (With GPU Support) //Install TensorFlow GPU command, pip install --upgrade tensorflow-gpu

You’ll see an installation screen like this.  If you see any errors, Make sure you’re using the correct version and don’t miss any steps.

We’ve installed everything, so let’s test it out in Pycharm. 

Test 

To test the whole process we’ll use Pycharm. If not installed, get the community edition → https://www.jetbrains.com/pycharm/download/#section=windows.

First, to check if TensorFlow GPU has been installed properly on your machine, run the below code:

import tensorflow as tf
tf.test.is_built_with_cuda()
tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)

It should show TRUE as output. If it’s FALSE or some error, look at the steps. 

Now let’s run some code. 

For a simple demo, we train it on the MNIST dataset of handwritten digits. We’ll see through how to create the network as well as initialize a loss function, check accuracy, and more.

Configure the env, create a new Python file, and paste the below code:

import torch
import torchvision
import torch.nn.functional as F
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch import optim
from torch import nn
from torch.utils.data import DataLoader
from tqdm import tqdm

Check the rest of the code here -> https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_simple_CNN.py.

When you run the code, look for successfully opened cuda(versioncode).

Once the training started, all the steps were successful!

MacOS

MacOS doesn’t support Nvidia GPU for the latest versions, so this will be a CPU-only installation. You can get GPU support on a Mac with some extra effort and requirements. 

Prerequisite 

• Python 3.6–3.8
• macOS 10.12.6 (Sierra) or later (no GPU support)
• Check https://www.tensorflow.org/install/so… For the latest version information

You can install the latest version available on the site, but for this tutorial, we’ll be using Python 3.8. Also, check with the TensorFlow site for version support. 

2) Prepare environment:

After installing Miniconda, open the command prompt.

conda install -y jupyter

This will take some time to install jupyter. Next, install the Mac tensorflow.yml file. You can also create a .yml file to install TensorFlow and dependencies (mentioned below).

dependencies:
    - python=3.8
    - pip>=19.0
    - jupyter
    - scikit-learn
    - scipy
    - pandas
    - pandas-datareader
    - matplotlib
    - pillow
    - tqdm
    - requests
    - h5py
    - pyyaml
    - flask
    - boto3
    - pip:
        - tensorflow==2.4
        - bayesian-optimization
        - gym
        - kaggle

Run the following command from the same directory that contains tensorflow.yml. 

conda env create -f tensorflow.yml -n tensorflow

This installation might take a few minutes. 

Activate the environment using the following command:

python -m ipykernel install --user --name tensorflow --display-name "Python 3.8 (tensorflow)"

Test 

To test the whole process, we’ll use a Jupyter notebook. Use this command to start Jupyter:

jupyter notebook

Cope the below code and run on jupyter notebook.

import sys

import tensorflow.keras
import pandas as pd
import sklearn as sk
import tensorflow as tf

print(f"Tensor Flow Version: {tf.__version__}")
print(f"Keras Version: {tensorflow.keras.__version__}")
print()
print(f"Python {sys.version}")
print(f"Pandas {pd.__version__}")
print(f"Scikit-Learn {sk.__version__}")
gpu = len(tf.config.list_physical_devices('GPU'))>0
print("GPU is", "available" if gpu else "NOT AVAILABLE")

This might take some time, but you’ll see something like this with your installed versions.

Linux 

We can install both CPU and GPU versions on Linux. 

Prerequisite 

• Python 3.6–3.8
• Ubuntu 16.04 or later
• Check https://www.tensorflow.org/install/so… for the latest version information

Steps 

1) First download and install Miniconda from https://docs.conda.io/en/latest/miniconda.html

2) To install CUDA on your machine, you will need:

• CUDA capable GPU,
• A supported version of Linux,
• NVIDIA CUDA Toolkit (https://developer.nvidia.com/cuda-downloads).

You can install CUDA by running,

$ sudo apt install nvidia-cuda-toolkit

After installing CUDA, run to verify the install:

nvcc -V

You’ll see it output something like this:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Jul_22_21:07:16_PDT_2019
Cuda compilation tools, release ‘version’

3) Now we’ll download NVIDIA cuDNN, https://developer.nvidia.com/cudnn

Check the version code from the TensorFlow site.

After downloading, extract the files:

tar -xvzf cudnn-10.1-linux-x64-'version'.tgz

Now, we’ll copy the extracted files to the CUDA installation path:

sudo cp cuda/include/cudnn.h /usr/lib/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/lib/cuda/lib64/

Setting up the file permissions of cuDNN:

$ sudo chmod a+r /usr/lib/cuda/include/cudnn.h /usr/lib/cuda/lib64/libcudnn*

4) Get the environment ready:

Export CUDA environment variables. To set them, run:

$ echo 'export LD_LIBRARY_PATH=/usr/lib/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
$ echo 'export LD_LIBRARY_PATH=/usr/lib/cuda/include:$LD_LIBRARY_PATH' >> ~/.bashrc

You can also set the environment with conda and jupyter notebook.

After installing Miniconda, open the command prompt.

conda install -y jupyter

Now,  check with TensorFlow site for version, and run the below command:

conda create --name tensorflow python=3.8

To enter the environment:

conda activate tensorflow

Let’s create Jupyter support for your new environment:

conda install nb_conda

This will take some time to get things done. 

To Install CPU only, use the following command:

conda install -c anaconda tensorflow

To Install both GPU and CPU, use the following command:

conda install -c anaconda tensorflow-gpu

To add additional libraries, update or create the ymp file in your root location, use:

conda env update --file tools.yml

Below are additional libraries you need to install (you can install them with pip).

dependencies:
    - jupyter
    - scikit-learn
    - scipy
    - pandas
    - pandas-datareader
    - matplotlib
    - pillow
    - tqdm
    - requests
    - h5py
    - pyyaml
    - flask
    - boto3
    - pip
    - pip:
        - bayesian-optimization
        - gym
        - kaggle

Test 

There are two ways you can test your GPU.

First, you can run this command:

import tensorflow as tf
tf.config.list_physical_devices("GPU")

You will see similar output, [PhysicalDevice(name=’/physical_device:GPU:0′, device_type=’GPU’)]

Second, you can also use a jupyter notebook. Use this command to start Jupyter.

jupyter notebook

Now, run the below code:

import sys

import tensorflow.keras
import pandas as pd
import sklearn as sk
import tensorflow as tf

print(f"Tensor Flow Version: {tf.__version__}")
print(f"Keras Version: {tensorflow.keras.__version__}")
print()
print(f"Python {sys.version}")
print(f"Pandas {pd.__version__}")
print(f"Scikit-Learn {sk.__version__}")
gpu = len(tf.config.list_physical_devices('GPU'))>0
print("GPU is", "available" if gpu else "NOT AVAILABLE")

You’ll see results something like this:

TensorFlow Version: 'version'
Keras Version: 'version'-tf

Python 3.8.0
Pandas 'version'
Scikit-Learn 'version'
GPU is available

So, when you see a GPU is available, you successfully installed TensorFlow on your machine. 

Conclusion

TensorFlow is a free and open-source software library for machine learning created by Google, and it’s most notably known for its GPU accelerated computation speed. We saw how to install TensorFlow on Windows, Mac, and Linux. I hope that this guide helps you get started with TensorFlow!

References and recommended reading:

• https://github.com/aladdinpersson/Machine-Learning-Collection/blob/master/ML/Pytorch/Basics/pytorch_simple_CNN.py
• https://www.youtube.com/watch?v=dj-Jntz-74g
• https://github.com/jeffheaton/t81_558_deep_learning/blob/master/install/tensorflow-install-jul-2020.ipynb
• https://www.tensorflow.org/guide/gpu
• https://www.liquidweb.com/kb/how-to-install-tensorflow-on-ubuntu-18-04/
• https://www.pyimagesearch.com/2019/12/09/how-to-install-tensorflow-2-0-on-macos/
• https://towardsdatascience.com/installing-tensorflow-gpu-in-ubuntu-20-04-4ee3ca4cb75d