How to install pytorch in Anaconda with conda or pip?
I am trying to install pytorch in Anaconda to work with Python 3.5 in Windows. Following the instructions in pytorch.org I introduced the following code in Anaconda:
But the following error came in:
By searching on the web I found out that it may be because of setuptools being out of date but I checked and have it updated. I also tried:
But the following error arise:
I also tried to load the pytorch’s tar.bz2 file which I download in the following website:
And then just do:
But I got the following error:
I am quite new to this programming world so I don’t really know how to dig more on the errors. Anyone knows how to get pytorch installed?
Get Started
Select preferences and run the command to install PyTorch locally, or get started quickly with one of the supported cloud platforms.
Start Locally
Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, builds that are generated nightly. Please ensure that you have met the prerequisites below (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.
NOTE: PyTorch LTS has been deprecated. For more information, see this blog.
Installing on macOS
PyTorch can be installed and used on macOS. Depending on your system and GPU capabilities, your experience with PyTorch on a Mac may vary in terms of processing time.
Prerequisites
macOS Version
PyTorch is supported on macOS 10.15 (Catalina) or above.
Python
It is recommended that you use Python 3.7 or greater, which can be installed either through the Anaconda package manager (see below), Homebrew, or the Python website.
Package Manager
To install the PyTorch binaries, you will need to use one of two supported package managers: Anaconda or pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python.
Anaconda
To install Anaconda, you can download graphical installer or use the command-line installer. If you use the command-line installer, you can right-click on the installer link, select Copy Link Address , or use the following commands on Intel Mac:
or following commands on M1 Mac:
If you installed Python via Homebrew or the Python website, pip was installed with it. If you installed Python 3.x, then you will be using the command pip3 .
Tip: If you want to use just the command pip , instead of pip3 , you can symlink pip to the pip3 binary.
Installation
Anaconda
To install PyTorch via Anaconda, use the following conda command:
To install PyTorch via pip, use one of the following two commands, depending on your Python version:
Verification
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.
The output should be something similar to:
Building from source
For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.
Prerequisites
- [Optional] Install Anaconda
- Follow the steps described here: https://github.com/pytorch/pytorch#from-source
You can verify the installation as described above.
Installing on Linux
PyTorch can be installed and used on various Linux distributions. Depending on your system and compute requirements, your experience with PyTorch on Linux may vary in terms of processing time. It is recommended, but not required, that your Linux system has an NVIDIA or AMD GPU in order to harness the full power of PyTorch’s CUDA support or ROCm support.
Prerequisites
Supported Linux Distributions
PyTorch is supported on Linux distributions that use glibc >= v2.17, which include the following:
-
, minimum version 2012-07-15 , minimum version 7.3-1611 , minimum version 8.0 , minimum version 24 , minimum version 14 , minimum version 42.1 , minimum version 2014.7 , minimum version 14.2 , minimum version 13.04
The install instructions here will generally apply to all supported Linux distributions. An example difference is that your distribution may support yum instead of apt . The specific examples shown were run on an Ubuntu 18.04 machine.
Python
Python 3.7 or greater is generally installed by default on any of our supported Linux distributions, which meets our recommendation.
Tip: By default, you will have to use the command python3 to run Python. If you want to use just the command python , instead of python3 , you can symlink python to the python3 binary.
However, if you want to install another version, there are multiple ways:
If you decide to use APT, you can run the following command to install it:
It is recommended that you use Python 3.6, 3.7 or 3.8, which can be installed via any of the mechanisms above .
If you use Anaconda to install PyTorch, it will install a sandboxed version of Python that will be used for running PyTorch applications.
Package Manager
To install the PyTorch binaries, you will need to use one of two supported package managers: Anaconda or pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python.
Anaconda
To install Anaconda, you will use the command-line installer. Right-click on the 64-bit installer link, select Copy Link Location , and then use the following commands:
You may have to open a new terminal or re-source your
/.bashrc to get access to the conda command.
While Python 3.x is installed by default on Linux, pip is not installed by default.
Tip: If you want to use just the command pip , instead of pip3 , you can symlink pip to the pip3 binary.
Installation
Anaconda
No CUDA/ROCm
To install PyTorch via Anaconda, and do not have a CUDA-capable or ROCm-capable system or do not require CUDA/ROCm (i.e. GPU support), in the above selector, choose OS: Linux, Package: Conda, Language: Python and Compute Platform: CPU. Then, run the command that is presented to you.
With CUDA
To install PyTorch via Anaconda, and you do have a CUDA-capable system, in the above selector, choose OS: Linux, Package: Conda and the CUDA version suited to your machine. Often, the latest CUDA version is better. Then, run the command that is presented to you.
With ROCm
PyTorch via Anaconda is not supported on ROCm currently. Please use pip instead.
No CUDA
To install PyTorch via pip, and do not have a CUDA-capable or ROCm-capable system or do not require CUDA/ROCm (i.e. GPU support), in the above selector, choose OS: Linux, Package: Pip, Language: Python and Compute Platform: CPU. Then, run the command that is presented to you.
With CUDA
To install PyTorch via pip, and do have a CUDA-capable system, in the above selector, choose OS: Linux, Package: Pip, Language: Python and the CUDA version suited to your machine. Often, the latest CUDA version is better. Then, run the command that is presented to you.
With ROCm
To install PyTorch via pip, and do have a ROCm-capable system, in the above selector, choose OS: Linux, Package: Pip, Language: Python and the ROCm version supported. Then, run the command that is presented to you.
Verification
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.
The output should be something similar to:
Additionally, to check if your GPU driver and CUDA/ROCm is enabled and accessible by PyTorch, run the following commands to return whether or not the GPU driver is enabled (the ROCm build of PyTorch uses the same semantics at the python API level (https://github.com/pytorch/pytorch/blob/master/docs/source/notes/hip.rst#hip-interfaces-reuse-the-cuda-interfaces), so the below commands should also work for ROCm):
Building from source
For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.
Prerequisites
- Install Anaconda or Pip
- If you need to build PyTorch with GPU support a. for NVIDIA GPUs, install CUDA, if your machine has a CUDA-enabled GPU. b. for AMD GPUs, install ROCm, if your machine has a ROCm-enabled GPU
- Follow the steps described here: https://github.com/pytorch/pytorch#from-source
You can verify the installation as described above.
Installing on Windows
PyTorch can be installed and used on various Windows distributions. Depending on your system and compute requirements, your experience with PyTorch on Windows may vary in terms of processing time. It is recommended, but not required, that your Windows system has an NVIDIA GPU in order to harness the full power of PyTorch’s CUDA support.
Prerequisites
Supported Windows Distributions
PyTorch is supported on the following Windows distributions:
-
7 and greater; Windows 10 or greater recommended. r2 and greater
The install instructions here will generally apply to all supported Windows distributions. The specific examples shown will be run on a Windows 10 Enterprise machine
Python
Currently, PyTorch on Windows only supports Python 3.7-3.9; Python 2.x is not supported.
As it is not installed by default on Windows, there are multiple ways to install Python:
If you use Anaconda to install PyTorch, it will install a sandboxed version of Python that will be used for running PyTorch applications.
If you decide to use Chocolatey, and haven’t installed Chocolatey yet, ensure that you are running your command prompt as an administrator.
For a Chocolatey-based install, run the following command in an administrative command prompt:
Package Manager
To install the PyTorch binaries, you will need to use at least one of two supported package managers: Anaconda and pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python and pip.
Anaconda
To install Anaconda, you will use the 64-bit graphical installer for PyTorch 3.x. Click on the installer link and select Run . Anaconda will download and the installer prompt will be presented to you. The default options are generally sane.
If you installed Python by any of the recommended ways above, pip will have already been installed for you.
Installation
Anaconda
To install PyTorch with Anaconda, you will need to open an Anaconda prompt via Start | Anaconda3 | Anaconda Prompt .
No CUDA
To install PyTorch via Anaconda, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Windows, Package: Conda and CUDA: None. Then, run the command that is presented to you.
With CUDA
To install PyTorch via Anaconda, and you do have a CUDA-capable system, in the above selector, choose OS: Windows, Package: Conda and the CUDA version suited to your machine. Often, the latest CUDA version is better. Then, run the command that is presented to you.
No CUDA
To install PyTorch via pip, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Windows, Package: Pip and CUDA: None. Then, run the command that is presented to you.
With CUDA
To install PyTorch via pip, and do have a CUDA-capable system, in the above selector, choose OS: Windows, Package: Pip and the CUDA version suited to your machine. Often, the latest CUDA version is better. Then, run the command that is presented to you.
Verification
To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.
From the command line, type:
then enter the following code:
The output should be something similar to:
Additionally, to check if your GPU driver and CUDA is enabled and accessible by PyTorch, run the following commands to return whether or not the CUDA driver is enabled:
Building from source
For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.
How to Install PyTorch on Windows Step by Step
This is my personal notes but hopefully it helps someone. This guide was made for Windows when PyTorch was on 0.4.1.
I have a budget gaming laptop and I wanted to see if I could leverage it for machine learning practice. I come from the Linux or MacOS world so I’m not familiar with getting things setup on windows, so getting PyTorch just installed on my laptop was a whole ordeal and took forever. Because of my unfamiliarity with working with Windows, I went down the wrong path multiple times, and so in this guide I tried to include these mishaps so they can avoided.
Note that this guide uses the Anaconda Prompt and not Command Prompt. I made the mistake of trying to figure out Python with VS Code and briefly with Command Prompt before remembering that the Anaconda installation mentioned something about Anaconda Prompt. Everything works fine in Anaconda Prompt, and there’s not much support for running things outside of the provided Anaconda tools.
The entire thing can be summarized in five points:
- Install Anaconda
- Open Anaconda Prompt (NOT Anaconda Navigator)
- conda install pytorch -c pytorch
- pip install torchvision
- Add environment to ipykernel
That’s ALL THAT YOU NEED TO KNOW. The rest of this guide helps you if you’re confused somewhere. Anyhow, lets get to installing PyTorch.
Installing PyTorch
I find minoconda3 is the easiest way to get everything installed and working for pytorch. Install the python 3.7 64 bit linux version from here:
2. Create a conda environment
It’s a good idea to use a separate environment for different projects. Create a new conda environment for deep learning stuff:
3. Activate the conda environment
4. Install python packages in conda environment
Install pytorch and its dependencies
5. Setting up CUDA
Using a GPU with pytorch requires an NVIDIA GPU and the CUDA toolkit installed. Follow instructions here to get the CUDA toolkit installed: CUDA Toolkit 10.2 Download | NVIDIA Developer
6. Configuring Jupyter
We need to link the jupyter kernelspec to the conda environment we created. The configuration for available jupyter kernels is stored in
/.local/share/jupyter/kernels . To create a new kernel for jupyter, create a directory within the kernels directory called cnn:
/.local/share/jupyter/kernels/cnn . Within this directory create a file called kernel.json containing the following (change path for your user account):
This will allow you to select the python installation created by your conda environment as a drop-down in the new notebook menu in jupyter.
7. Running jupyter lab remotely
You will need to launch jupyter on the remote server and set up ssh tunnelling on your local computer. It’s a good idea to start jupyter in a GNU screen so that you can close your ssh session and keep the notebook active.