The full length Python autocompletion Video and a Twitter thread describing how it works

This is a learning/demo project to show how deep learning can be used to auto complete Python code. You can experiment with LSTM and Transformer models. We also have built a simple VSCode extension to try out the trained models.

Training model:

Evaluating trained model:

It gives quite decent results by saving above 30% key strokes in most files, and close to 50% in some. We calculated key strokes saved by making a single (best) prediction and selecting it with a single key.

The dataset we use is the python code found in repos linked in Awesome-pytorch-list. We download all the repositories as zip files, extract them, remove non python files and split them randomly to build training and validation datasets.

We train a character level model without any tokenization of the source code, since it's the simplest.

1. Clone this repo
2. Install requirements from requirements.txt
3. Run python_autocomplete/create_dataset.py.
   • It collects repos mentioned in PyTorch awesome list
   • Downloads the zip files of the repos
   • Extract the zips
   • Remove non python files
   • Collect all python code to data/train.py and, data/eval.py
4. Run python_autocomplete/train.py to train the model. Try changing hyper-parameters like model dimensions and number of layers.
5. Run evaluate.py to evaluate the model.

You can also run the training notebook on Google Colab.

1. Clone this repo

2. Install requirements from requirements.txt

3. Install npm packages

You need to have Node.JS installed

cd vscode_extension
npm install # This will install the NPM packages

4. Start the server python_autocomplete/serve.py

5. Open the extension project (folder) in VSCode

cd vscode_extension
code . # This will open vscode_extension in VSCode

If you don't have VSCode command line launcher start VSCode and open the project with File > Open

6. Run the extension from VSCode

Run > Start Debugging

This will open another VSCode editor window, with the extension

7. Create or open a python file and start editing!

Here's a sample evaluation of a trained transformer model.

Colors:

• yellow: the token predicted is wrong and the user needs to type that character.
• blue: the token predicted is correct and the user selects it with a special key press, such as TAB or ENTER.
• green: autocompleted characters based on the prediction