johntorrestensor/ml_dev repository overview

This ML Development environment allows to work interactively with Jupyter Notebooks on ML Projects using VsCode and Devcontainers. Working with ETL pipelines is also possible since pyspark is also available.

Main Packages are:

• numpy
• pandas
• scikit-learn
• hyperopt
• mlflow
• xgboost
• pyarrow
• pyspark
• mlflow
• jupyterlab
• PyWavelets
• keras-tcn

Build Docker image:

sudo docker build -t ml_dev:latest .

Run interactive docker session, where "PWD" is your current working directory in the terminal:

sudo docker run -it --rm -p 8888:8888 -v "${PWD}":/home/ ml_dev:latest

Then go to your VsCode and open your working directoy, and press Crtl + Shift + p and select:

Dev containers: Attach to runnig container...

A new VsCode window will open up, now you can start working with jupyter files, python files, debuggers, etc.

For jupyter notebooks install the "Jupyter" extension on the the VsCode window.