Guide · Open source
How to use pytorch-tutorial: setup, examples and tips
Updated · written with AI from the project's own documentation and checked against our catalogue data
pytorch-tutorial is an MIT-licensed, Python repository of tutorial code for learning PyTorch, the Python library for building neural networks. It is aimed at researchers and engineers who already know Python. By the end of this guide you will know how to clone the repo, find an example for a model you care about, and run it.
PyTorch Tutorialyunjey/pytorch-tutorial · ★ 32.5k · MIT Install options & downloads Quick start
- 1Check prerequisites. Confirm you have Python 2.7 or 3.5+ and PyTorch 0.4.0+ installed, as listed in the README.
- 2Clone the repo. Run git clone https://github.com/yunjey/pytorch-tutorial.git to download the code.
- 3Change into an example folder. Run cd pytorch-tutorial/tutorials/PATH_TO_PROJECT, replacing the placeholder with a real example folder.
- 4Run main.py. Run python main.py inside that folder to execute the example.
- 5Read the model code. Open main.py to see the model, which the README says is usually under 30 lines.
What the pytorch-tutorial repository contains
The repository is organised into four groups: Basics, Intermediate, Advanced and Utilities. Basics covers PyTorch basics, linear regression, logistic regression and a feedforward neural network. Intermediate covers convolutional neural networks, deep residual networks, recurrent neural networks, bidirectional recurrent neural networks and a language model. Advanced covers generative adversarial networks, variational auto-encoders, neural style transfer and image captioning (CNN-RNN). Utilities covers TensorBoard in PyTorch.
The README states that most of the models were implemented with less than 30 lines of code, so each example is short enough to read in one sitting.
- 1. Basics: PyTorch Basics, Linear Regression, Logistic Regression, Feedforward Neural Network
- 2. Intermediate: Convolutional Neural Network, Deep Residual Network, Recurrent Neural Network, Bidirectional Recurrent Neural Network, Language Model (RNN-LM)
- 3. Advanced: Generative Adversarial Networks, Variational Auto-Encoder, Neural Style Transfer, Image Captioning (CNN-RNN)
- 4. Utilities: TensorBoard in PyTorch
Check the prerequisites before you start
The README lists Python 2.7 or 3.5+ and PyTorch 0.4.0+ as dependencies. It also recommends finishing the Official PyTorch Tutorial before starting this one, so a reader with no Python or programming background should begin there.
Note that the README's version numbers are old. The last push to the repository was in August 2023, so check whether the PyTorch APIs used in an example still run with your installed PyTorch before you rely on it.
Clone the repository and run an example
Clone the repository, change into the folder of the example you want, and run its main.py file. The README uses PATH_TO_PROJECT as a placeholder, so replace it with a real project folder such as one listed under tutorials/.
The README gives this sequence as the getting-started commands.
$ git clone https://github.com/yunjey/pytorch-tutorial.git
$ cd pytorch-tutorial/tutorials/PATH_TO_PROJECT
$ python main.pyPick the right example for your goal
If you are new to PyTorch itself, start with the Basics section and then move to the Intermediate models. If you want to see generative models, look at the Advanced section, which includes GANs and VAEs. Each entry in the README table of contents links to the specific main.py file for that model, so you can open the code before running it.
The README links some entries to specific line ranges of main.py, which point at the core model code. Reading those lines first is a quick way to see how the model is built.
Compare with other PyTorch learning resources
The README does not compare itself with other projects. Comparable resources include the Official PyTorch tutorials, pytorch-examples and d2l-en. The Official PyTorch tutorial is the one the README asks you to finish first, so it is a reasonable companion or prerequisite for this repo.
What people use it for
- Learning how a convolutional neural network is written in PyTorch using the Intermediate CNN example.
- Studying a recurrent neural network or language model in compact code.
- Seeing how a GAN or variational auto-encoder is structured in the Advanced section.
- Adding TensorBoard logging to a PyTorch project using the Utilities example.
Frequently asked questions
Is pytorch-tutorial free to use?
Yes. The repository is licensed under MIT, as stated in its listing. You can read, run and adapt the code under those terms.
What do I need to run pytorch-tutorial?
The README lists Python 2.7 or 3.5+ and PyTorch 0.4.0+. It also recommends finishing the Official PyTorch Tutorial first.
How do I install pytorch-tutorial?
There is no package installer. Clone the repository with git clone https://github.com/yunjey/pytorch-tutorial.git, then change into an example folder and run python main.py.
Is pytorch-tutorial good for beginners?
It is aimed at people who already know Python. The README points complete beginners to the Official PyTorch Tutorial first.
Does pytorch-tutorial work with the latest PyTorch?
The README only lists PyTorch 0.4.0+ and the last push was in August 2023, so newer PyTorch APIs may not match the code. Test an example before relying on it.
What are alternatives to pytorch-tutorial?
Comparable projects include the Official PyTorch tutorials, pytorch-examples and d2l-en. Use the official tutorial if you want current APIs.
New agent skills, repos and AI tools — every week
The best new skills and open-source projects, the fastest-growing repos, and one free prompt. Five minutes to read. Unsubscribe anytime.
Our verdict
pytorch-tutorial suits researchers and engineers who already know Python and want short, readable PyTorch models to run and study. If you are new to programming or need current PyTorch APIs, start with the Official PyTorch Tutorial or another maintained resource instead.
Install options & downloads