Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and "torch" it! At the end of it, you should be able to understand PyTorch's key components and how to assemble them together into a working model.
We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming.
Open it in Google Colab PyTorch101_Colab.ipynb.
If you'd rather use a local environment, please follow these steps (assuming you use Anaconda):
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Install GraphViz: https://www.graphviz.org/
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Create a conda environment:
conda create -n pytorch101 pip conda python==3.8.5 -
Activate the conda environment:
conda activate pytorch101 -
Install PyTorch: https://pytorch.org/get-started/locally/
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Install other packages:
conda install scikit-learn==0.23.2 matplotlib==3.3.2 jupyter==1.0.0 ipywidgets==7.5.1 plotly==4.14.3 -c anaconda -
Install torchviz:
pip install torchviz -
Clone this repo:
git clone https://github.com/dvgodoy/PyTorch101_ODSC_Europe2022.git -
Start Jupyter:
jupyter notebook