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battery-dev

Note: this is not finished, there isn't really a model here. The fundamental issue is the low amount of data. I think an interesting direction to go is to implement a RNN that from a history of charge/discharge profiles predicts the next charge/discharge profile, though defining what is input data and what is target data in that case becomes very muddy.

Project repository for battery dev team code-better Please see 'notebooks/results' for an overview of the model (quite simple) and approach. Here, we have tried to use best-practices to ensure model reproducibility, and hope others will show interest in these tools for their own projects.

Architecture Choices

The tools used to ensure model reproducibility include

  • Github: needs no introduction, you're here
  • Docker: allows no ambiguity and easy setup for environment dependencies
  • Jupyter Lab: a flexible and highly adopted entrypoint for writing & debugging python code
  • Data Version Control (DVC): fully defined workflows and easy data sharing when combined with the cloud

Setup

NOTE: alternate option - it's just as easy if not easier to use a conda environment. Just run:

conda create -n batterydev python=3.9
pip install -r requirements.txt

I use conda b/c I prefer to use VSCode compared to Jupyter Lab, and VSCode runs Jupyter notebooks inside the IDE.

We're using Docker to ensure we all have identical environments to work in. To get started, first install Docker for your operating system: https://docs.docker.com/get-docker/.

Then it's as easy as:

docker compose build
docker compose up

And you'll have the correctly configured environment up and running on your computer! When it launches, jupyter will recommend 3 urls to you to link into it:

jupyter    |     To access the server, open this file in a browser:
jupyter    |         file:///root/.local/share/jupyter/runtime/jpserver-7-open.html
jupyter    |     Or copy and paste one of these URLs:
jupyter    |         http://04cde2523688:8888/lab?token=b30eb555d814d414311c7e4a5c9806ca0572c9e445857b30
jupyter    |      or http://127.0.0.1:8888/lab?token=b30eb555d814d414311c7e4a5c9806ca0572c9e445857b30

Pick the last one that begins in http://127.0.0.1:8888. The other two won't work, as those can only be reached from a browser running inside the container.

Stop the server by issuing the keyboard interrupt command for your OS in the terminal.

dvc setup

I've hosted the dvc remote cache on a folder in my google drive. I believe it should be sufficient based on gdrive limits. When you first run dvc pull or dvc push you will be prompted to login. Just paste the auth code back into the terminal like dvc asks and you should be good to go!

When you launch for the first time, to get data, simply run dvc pull.

daemon mode

If you would like the jupyter container to run in the background, and not have it occupy your terminal with logs:

docker compose up -d

Do your stuff, then when you're done and want to turn it off:

cd path/to/battery-dev # if you navigated around with your terminal
docker compose down

Repo Guide

All code in the top-level directory is configuration, container setup, or the container executable.

  • /data
    • contains data csvs used by the models
    • entire dir tracked by dvc
  • /exploration
    • notebooks for playing around with data
  • /stages
    • contains source code for dvc stages
  • dvc.lock
    • lockfile for dvc-tracked files
  • dvc.yaml
    • dvc pipeline definition file
  • params.yaml
    • dvc pipeline parameters file

Running DVC

  • To pull all data from the cloud: dvc pull
  • To track and commit a new data file: dvc add; dvc commit
  • To run a pipeline, automatically committing data changes dvc repro
  • To push data to the cloud: dvc push

Architecture Choices

  • Docker: allows no ambiguity and easy setup for environment dependencies
  • Jupyter Lab: a flexible and highly adopted entrypoint for writing & debugging python code
  • Data Version Control (DVC): fully defined workflows and easy data sharing when combined with the cloud
  • Plotly: you can make plots with huge numbers of data points that have pan & zoom. Enough said.
  • PyTorch: This was an opportunity to learn it