Data analysis via machine learning is becoming increasingly important in the modern world. PyTorch is a machine learning Python library, developed by the Facebook AI research group, that acts as a high-level interface for developers to create applications like natural language processors. In this tutorial, we are going to cover how to install PyTorch via Anaconda and PIP.
- These instructions are being performed on an Ubuntu VPS server as the root user
- This installation does not include support for GPU acceleration.
- Python 3.5 or higher is required.
Install via Anaconda
Step 1: Install Anaconda
Anaconda is a popular package management system for data scientists working with Python. The reason for this is because it comes pre-baked with full data science packages. This means developers have less work to do in the software dependency department. It is the recommended package management interface for PyTorch.
First, as a best-practice, ensure all packages are up to date:
root@ubuntu1604:~# apt-get update -y
After this, we need to download and run the bash installation script for Anaconda.
root@ubuntu1604:~# curl -O https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
Once the installation script is finished downloading, run it, and follow the prompts. We are going to use the defaults.
root@ubuntu1604:~# bash Miniconda3-latest-Linux-x86_64.sh
To start using Anaconda, we will need to refresh the terminal:
root@ubuntu1604:~# source ~/.bashrc
(Optional). You’ll notice “(base)” now precedes your normal command prompt. This means Anaconda’s base environment is active. By default, it is auto-activated each time you enter a new shell session. To turn this behavior off, we can run this command:
(base) root@ubuntu1604:~# conda config --set auto_activate_base false
For the purposes of this tutorial, we want to allow Anaconda’s base environment to continue to be active.
Step 2: Install PyTorch
Now that we have Anaconda installed and activated, it’s time to install PyTorch.
(base) root@ubuntu1604:~# conda install pytorch torchvision cpuonly -c pytorch
You’ll notice a prompt during installation, enter “y” to finish the installation.
Proceed ([y]/n)? y
Verify PyTorch Is Installed
Finally, it’s time to verify that PyTorch is installed and available to use. To do so, we need to drop into a Python repl (a read–eval–print loop)
(base) root@ubuntu1604:~# Python Python 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0] :: Anaconda, Inc. on linux Type "help", "copyright", "credits" or "license" for more information. >>>
Once we are in the repl, we can paste in the following snippet
import torch x = torch.rand(3, 7) print(x)
The output should look something like this:
>>> import torch >>> >>> x = torch.rand(3, 7) >>> print(x) tensor([[0.2833, 0.7547, 0.1444, 0.6286, 0.4838, 0.1576, 0.3350], [0.8544, 0.5095, 0.0808, 0.4367, 0.4174, 0.4669, 0.6155], [0.4436, 0.7904, 0.8911, 0.2073, 0.5987, 0.3607, 0.4366]]) >>>
To exit the Python shell, hold the ctrl key and press the D key (Ctrl+D).
Now that we are back at the command prompt, we can deactivate the Anaconda base environment if we use
(base) root@ubuntu1604:~# conda deactivate root@ubuntu1604:~#
Install via PIP
Step 1: Install python3-venv
If you don’t need all of the additional packages that come along with Anaconda, you can install PyTorch using Pip, the Python Package manager, in a virtual Python environment. To ensure that the installation of PyTorch and it’s dependencies has no adverse effect on your system’s Python installation, it’s advisable to install it in a virtual Python environment.
First, we need to install the python3-venv package to make it possible to create a virtual Python environment.
root@ubuntu1604:~# apt-get install -y python3-venv
Step 2: Prepare the Environment
To start, make a directory to house your project and change into it using the cd command:
root@ubuntu1604:~# mkdir pytorch_awesome root@ubuntu1604:~# cd pytorch_awesome root@ubuntu1604:~/pytorch_awesome#
Now it’s time to create the virtual Python environment where we will install PyTorch
root@ubuntu1604:~/pytorch_awesome# python3 -m venv pytorch-awesome
.Next, we need to activate the virtual Python environment we just created.
root@ubuntu1604:~/pytorch_awesome# source pytorch-awesome/bin/activate (pytorch-awesome) root@ubuntu1604:~/pytorch_awesome#
We can now see “(pytorch-awesome)” precedes our normal shell prompt because our newly created virtual Python environment is activated.
Step 3: Install PyTorch
While the new Python virtual environment is active, we can install PyTorch.
(pytorch-awesome) root@ubuntu1604:~/pytorch_awesome# pip install torch==1.3.0+cpu torchvision==0.4.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
We can test PyTorch is properly installed the same way we did with the Anaconda installation. First drop into a Python shell
(pytorch-awesome) root@ubuntu1604:~/pytorch_awesome# Python Python 3.5.2 (default, Oct 8 2019, 13:06:37) [GCC 5.4.0 20160609] on linux Type "help", "copyright", "credits" or "license" for more information. >>>
Now copy and paste this snippet into the Python shell and hit enter:
import torch x = torch.rand(3, 7) print(x)
The output will look something like this:
>>> import torch >>> >>> x = torch.rand(3, 7) >>> print(x) tensor([[0.0416, 0.9980, 0.2793, 0.8503, 0.0285, 0.8286, 0.8091], [0.8038, 0.7944, 0.0110, 0.1239, 0.0611, 0.9727, 0.6899], [0.3036, 0.0378, 0.1660, 0.7076, 0.5073, 0.0686, 0.9490]]) >>>
To exit the Python shell, hold the ctrl key and press the D key (Ctrk+D).
Once we are back at our command prompt, we can deactivate the Python virtual environment.
(pytorch-awesome) root@ubuntu1604:~/pytorch_awesome# deactivate root@ubuntu1604:~/pytorch_awesome#
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