Как в anaconda navigator добавить tensorflow
Note that you can't install TFX on machines with Apple silicon at the moment (2022-05-26)
We start with an overview of the software we will install in this tutorial: TensorFlow & TensorFlow Extended.
We use the data science platform Anaconda to install TFX
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is an end-to-end open source platform for machine learning. is an end-to-end platform for deploying production ML pipelines. When you're ready to move your models to production, use TFX to create and manage a production pipeline.
- We will use the Anaconda open source data science platform to install TensorFlow and TensorFlow Extended.
To start this tutorial, I recommend to use [Miniforge](Miniforge or Anaconda. Follow these instructions if you haven't already installed one of them on your system:
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(recommended)
Conda environments help manage dependencies and isolate projects. This is particularly useful when some packages require specific Python versions (like TensorFlow). Note that we will use pip in this environment to install packages (and not conda). Therefore, we install pip in this environment.
On Windows open the Start menu and open an Anaconda Command Prompt. On macOS or Linux open a terminal window.
We create an environment with a specific version of Python (3.9). We call the environment tf :
When conda asks you to proceed (proceed ([y]/n)? ), type y .
First of all, you have to activate the tf environment:
Now we have to use pip (pip is the package installer for Python) to install TFX. This table describes how the tfx package versions are compatible with its major dependency PyPI packages.
TensorFlow is already included in tfx.
Install additional modules:
Congratulations! You have completed the tutorial and learned how to install:
If you'd like to learn more about TensorFlow Extended, have a look at the following suggestion:
If you want to switch back to your base environment, just use:
Thank you for participating in this tutorial. If you found any issues along the way I'd appreciate it if you'd raise them by clicking the Report a mistake button at the bottom left of this site.
How to install TensorFlow and Keras using Anaconda Navigator — without the command line
Ekapope Viriyakovithya
Say no to pip install in the command line! Here’s an alternative way to install TensorFlow on your local machine in 3 steps.
Photo by Krzysztof Kowalik / Unsplash
Why am I writing this?
I played around with pip install with multiple configurations for several hours, trying to figure how to properly set my python environment for TensorFlow and Keras.
why is tensorflow so hard to install — 600k+ results 
unable to install tensorflow on windows site:stackoverflow.com — 26k+ results
Just before I gave up, I found this…
This article will walk you through the process how to install TensorFlow and Keras by using the GUI version of Anaconda. I assume you have downloaded and installed Anaconda Navigator already.
Let’s get started!
- Launch Anaconda Navigator. Go to the Environments tab and click ‘Create’.
2. Input a new environment name — I put ‘tensorflow_env’. Make sure to select Python 3.6 here! Then ‘Create’, this may take few minutes.
make sure to select Python 3.6
3. In your new ‘tensorflow_env’ environment, select ‘Not installed’, and type in ‘tensorflow’. Then, tick ‘tensorflow’ and ‘Apply’. The pop-up window will appear, go ahead and apply. This may take several minutes.
Do the same for ‘keras’.
Check your installation by importing the packages. If everything is okay, the command will return nothing. If the installation was unsuccessful, you will get an error.
no error pop up — Yeah! 
You can also try with Spyder. 
no error pop up — Yeah!
And…Ta-da! It’s done! You can follow this article to test your newly installed packages 🙂
Thank you for reading. Please give it a try, and let me know your feedback!
Consider following me on GitHub, Medium, and Twitter to get more articles and tutorials on your feed if you like what I did. 🙂
How-to setup GPU Accelerated TensorFlow & Keras on Windows 10 with Anaconda 3
In this post I share my beginners experience of how-to setup TensorFlow with Keras utilizing the GPU for computation on Windows 10. My specific goal was to find the simplest way to install these deep learning frameworks.
Environment of my setup
I use the below environment for my setup:
- Operating System: Windows 10 Home Edition
- GPU: NVIDIA GeForce GTX 970
- RAM: 16GB
- CPU: Intel i7–3770K @ 3.50GHz
Setting up Anacoda 3 as backend for TensorFlow
Anaconda is an open-source distribution of Python and R programming language packages for scientific computing with specific focus on a simple package management and deployment. It ships with Anaconda Navigator, the graphical user interface (GUI) to interact with Anaconda without using a command-line.
Download Anaconda 3 Individual Edition from here and follow the usual Windows installation steps:
I downloaded and installed Anaconda for Windows 64-bit and Python 3.7.
Setting up a TensorFlow & Keras environment with Anaconda Navigator
Start Anaconda Navigator GUI and proceed with the following steps:
- Go to the tab Environments.
- Create a new environment, I called it tf-keras-gpu-test. Make sure to select Python 3.6 here as I experienced problems with Python 3.7.
- Select Not-installed packages.
- Search for tensorflow.
- Select packages for TensorFlow and Keras. The highlighted packages selected are for TensorFlow version 2.1.0 and Keras version 2.3.1.
- Press Apply button. The installation and configuration of the selected packages will take several minutes.
The Anaconda-managed installation will fetch GPU relevant packages for GPU computation and deep learning with TensorFlow in the background, i.e. CUDA Toolkit 10.2 and NVIDIA CUDA Deep Neural Network library (cuDNN) in version 7.2.
Validation of installation
By pressing the play button highlighted besides the selected environment, we can open a Python terminal for the selected environment. By executing the following Python commands we can check the installed TensorFlow and Keras versions:
These commands in the Python terminal should bring up version 2.1.0 for TensorFlow and version 2.3.1 for Keras. To check if TensorFlow is now able to utilize CPU and/or GPU, execute the following commands:
These commands in the Python terminal should bring up both a CPU and a GPU device, if TensorFlow correctly identifies your GPU.
Installing tensorflow with anaconda in windows
I have installed Anaconda on Windows 64 bit. I have downloaded PyCharm for creating a project and in the terminal of PyCharm I have installed numpy , scipy , matplotlib using the following commands:
I am not able to install Tensorflow in the same way I installed these other packages. How should I install it?
20 Answers 20
Google has recently launched a newer version of TensorFlow r0.12 which include support of Windows both CPU and GPU version can now be installed using Python >=3.5.2 (only 64-bit) version.
For CPU only version open command prompt and enter follow command
Follow this TensorFlow on Windows for step-by-step instructions.
UPDATE
To install current latest version please run following command:
UPDATE 2020
TensorFlow 2.0 now has a single package for both CPU and GPU version, simply run
If you’re using Anaconda you can install TensorFlow GPU version and all of its dependencies (CUDA, cuDNN) by running:
To install TF on windows, follow the below-mentioned steps:
Use pip install tensorflow in place of pip install tensorflow-gpu , in case if you want to install CPU only version of TF.
Note: This installation has been tested with Anaconda Python 3.5 (64 bit). I have also tried the same installation steps with (a) Anaconda Python 3.6 (32 bit), (b) Anaconda Python 3.6 (64 bit), and (c) Anaconda Python 3.5 (32 bit), but all of them (i.e. (a), (b) and (c) ) failed.
Currently tensorflow has binaries only for Unix based OS i.e. Ubuntu Mac OS X — that’s why no mention of Windows in setup docs.
There are long discussions on Github:
- Open — Windows Support and Documentation
- Closed — How to install TensorFlow on Windows
- Closed — How to install/run/use TensorFlow on windows machines?
For now, on Windows, the easiest way to get started with TensorFlow would be to use Docker: http://tensorflow.org/get_started/os_setup.md#docker-based_installation
It should become easier to add Windows support when Bazel (the build system we are using) adds support for building on Windows, which is on the roadmap for Bazel 0.3. You can see the full Bazel roadmap here.
Or simply use a Linux VM (using VMPlayer), and the stated steps will setup it up for you.
For PyCharm — Once conda environment will be created, you’ll need to set the new interpretor (in conda environment) as the interpretor to use in PyCharm :
Now to use the conda interpreter from PyCharm go to file > settings > project > interpreter, select Add local in the project interpreter field (the little gear wheel) and browse the interpreter or past the path.
