Neural Network Programming - Deep Learning with PyTorch

with deeplizard.

PyTorch Install - Quick and Easy

September 7, 2018 by

Blog

Getting ready to install PyTorch

Welcome back to this series on neural network programming with PyTorch. In this episode, we are going to cover the needed prerequisites for installing PyTorch. Without further ado, let's get started.

pytorch logo

Installing PyTorch with Anaconda and Conda

Getting started with PyTorch is very easy. The recommended best option is to use the Anaconda Python package manager.

With Anaconda, it's easy to get and manage Python, Jupyter Notebook, and other commonly used packages for scientific computing and data science, like PyTorch!

Let’s go over the steps:

  1. Download and install Anaconda (Go with the latest Python version).
  2. Go to the Getting Started section on the PyTorch website.
  3. Specify the appropriate configuration options for your particular environment. For example:
    • OS: Windows
    • Package Manager: conda
    • Python: 3.7
    • CUDA: 9.0
  4. Run the presented command in the terminal to install PyTorch.

For the example configuration we specified in step (3), we have the following command:

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

Notice that we are installing both PyTorch and torchvision. Also, there is no need to install CUDA separately. The needed CUDA software comes installed with PyTorch if a CUDA version is selected in step (3). All we need to do is select a version of CUDA if we have a supported Nvidia GPU on our system.

conda list torch
# packages in environment at C:\Users\deeplizard\Anaconda3:
#
# Name         Version    Build                    Channel
pytorch        1.1.0      py3.6_cuda90_cudnn7_1    pytorch
torchvision    0.3.0      py36_cu90_1              pytorch

Jupyter Notebook and VS Code (optional)

In this series, we’ll be using the following software for writing, debugging our code:

Once you have Visual Studio Code installed, you’ll also want to install the Python plugin. This is done from inside VS Code, in the plugins section.

We'll be using VS Code primarily for debugging our code. VS code makes debugging our code and inspecting our objects pretty easy. It's also useful for exploring the PyTorch source code. The navigation features for source code are pretty robust.

We won't use VS code until part two of the series, and most of our time will be spent inside Jupyter notebook. We automatically get Jupyter Notebook with the Anaconda installation. Neither of these tools are necessary, but they do make our lives as developers a lot easier.

Verify the PyTorch install

To verify our PyTorch installation is all set and that we are ready to code, we'll do this in a notebook. To organize the various parts of our project, we will create a folder called PyTorch and put everything in this folder.

Steps to verify the install:

  1. To use PyTorch we import torch.
  2. To check the version, we use torch.__version__

Now, to verify our GPU capabilities, we use torch.cuda.is_available() and check the cuda version.

> torch.cuda.is_available()
True

> torch.version.cuda
'9.0'

If your torch.cuda.is_available() call returns false, it may be because you don’t have a supported Nvidia GPU installed on your system. However, don’t worry, a GPU is not required to use PyTorch or to follow this series.

nvidia logo

We can obtain quite good results in a reasonable amount of time even without having a GPU. If you’re interested in checking whether your Nvidia GPU supports CUDA, you can check for it here.

Wrapping up

In the next post, we’ll learn more about CUDA, GPUs, and importantly, why we even use GPUs in deep learning in the first place.

Let me know if you are all set, and I’ll see you in the next one!

Description

Getting started with PyTorch is very easy. The recommended best option is to use the Anaconda Python package manager. With Anaconda, it's easy to get and manage Python, Jupyter Notebook, and other commonly used packages for scientific computing and data science, like PyTorch! Let's do it! 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👀 OUR VLOG: 🔗 https://www.youtube.com/channel/UC9cBIteC3u7Ee6bzeOcl_Og 👉 Check out the blog post and other resources for this video: 🔗 https://deeplizard.com/learn/video/UWlFM0R_x6I 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 https://www.patreon.com/posts/27743395 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 https://deeplizard.com/hivemind 🤜 Support collective intelligence, create a quiz question for this video: 🔗 https://deeplizard.com/create-quiz-question 🚀 Boost collective intelligence by sharing this video on social media! ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: yasser Prash 👀 Follow deeplizard: Our vlog: https://www.youtube.com/channel/UC9cBIteC3u7Ee6bzeOcl_Og Twitter: https://twitter.com/deeplizard Facebook: https://www.facebook.com/Deeplizard-145413762948316 Patreon: https://www.patreon.com/deeplizard YouTube: https://www.youtube.com/deeplizard Instagram: https://www.instagram.com/deeplizard/ 🎓 Other deeplizard courses: Reinforcement Learning - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xoWNVdDudn51XM8lOuZ_Njv NN Programming - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG DL Fundamentals - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU Keras - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL TensorFlow.js - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xr83l8w44N_g3pygvajLrJ- Data Science - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xrth-Cqs_R9- Trading - https://deeplizard.com/learn/playlist/PLZbbT5o_s2xr17PqeytCKiCD-TJj89rII 🛒 Check out products deeplizard recommends on Amazon: 🔗 https://www.amazon.com/shop/deeplizard 📕 Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard’s link: 🔗 https://amzn.to/2yoqWRn 🎵 deeplizard uses music by Kevin MacLeod 🔗 https://www.youtube.com/channel/UCSZXFhRIx6b0dFX3xS8L1yQ 🔗 http://incompetech.com/ ❤️ Please use the knowledge gained from deeplizard content for good, not evil.