This repository contains lecture notes and materials for the tutorial presented at PyCon Sweden 2022
In this tutorial we will dive into the main features and abstractions provided by the PyTorch framework to define complex data
pipelines for machine learning. We will explore multiple examples using torch.utils.data and the latest torchdata with insights and
best practice for reproducible data and evaluation pipelines.
Clone the current repository, in order to get the course materials. To do so, once connected to your remote machine (via SSH), execute the following instructions:
cd $HOME # This will make sure you'll be in your HOME folder
git clone https://github.com/leriomaggio/pytorch-data-pipelines-tutorialNote: This will create a new folder named pytorch-data-pipelines-tutorial. Move into this folder by typing:
cd pytorch-data-pipelines-tutorialWell done! Now you should do be in the right location. Bear with me another few seconds, following instructions reported below 🙏
To execute the notebooks in this repository, it is necessary to set up the environment.
Please refer to the Get-Ready.ipynb notebook for a step-by-step guide on how to setup the environment, and check that all is working, and ready to go.
Note: You could run this notebook directly in VSCode, or in your existing Jupyter notebook/lab environment:
jupyter notebook Get-Ready.ipynbAuthor: Valerio Maggio (@leriomaggio), Senior Research Associate, University of Bristol.
All the Code material is distributed under the terms of the Apache License. See LICENSE file for additional details.
All the instructional materials in this repository are free to use, and made available under the [Creative Commons Attribution license][https://creativecommons.org/licenses/by/4.0/]. The following is a human-readable summary of (and not a substitute for) the full legal text of the CC BY 4.0 license.
You are free:
- to Share---copy and redistribute the material in any medium or format
- to Adapt---remix, transform, and build upon the material
for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution---You must give appropriate credit (mentioning that your work is derived from work that is Copyright © Software Carpentry and, where practical, linking to http://software-carpentry.org/), provide a [link to the license][cc-by-human], and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
No additional restrictions---You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
For any questions or doubts, feel free to open an issue in the repository, or drop me an email @ vmaggio_at_anaconda_dot_com