Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Project description
Whitebox - E2E machine learning monitoring
Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes
Documentation: https://whitebox-ai.github.io/whitebox
Source Code: https://github.com/whitebox-ai/whitebox
Roadmap: https://github.com/whitebox-ai/whitebox/milestones
Issue tracking https://github.com/orgs/whitebox-ai/projects/1/views/3
Discord: https://discord.gg/bkAcsx4V
Whitebox is an open source E2E ML monitoring platform with edge capabilities that plays nicely with kubernetes.
The key features are:
- Classification models metrics
- Regression models metrics
- Data / model drift monitoring
- Alerts
Design guidelines:
- Easy: Very easy to set up and get started with.
- Intuitive: Designed to be intuitive and easy to use.
- Pythonic SDK: Pythonic SDK for building your own monitoring infrastructure.
- Robust: Get production-ready MLOps system.
- Kubernetes: Get production-ready code. With automatic interactive documentation.
Installation
Install the server using docker compose
. See the docs for more info.
Install the SDK with pip
:
pip install whitebox-sdk
How to use
After you are done installing the server and the SDK, you can start using it.
After you get the API key, all you have to do is create an instance of the Whitebox class adding your host and API key as parameters:
from whitebox import Whitebox
wb = Whitebox(host="127.0.0.1:8000", api_key="some_api_key")
Now you're ready to start using Whitebox! Read the documentation to learn more about the SDK.
Set up locally for development
Whitebox supports Postgres and SQLite. You can use either one of them.
If you want to use SQLite, you need to set up a SQLite database and set the DATABASE_URL
environment variable to the database URL.
If you want to use Postgres, you don't need to do anything. Just have a Postgres database running and set the DATABASE_URL
environment variable to the database URL.
Install packages:
python -m venv .venv
pip install -r requirements.txt
pre-commit install
Run the server:
ENV=dev uvicorn whitebox.main:app --reload
Quick way to start a postgres database:
docker compose up postgres -d
Tests:
- Run:
ENV=test pytest
orENV=test pytest -s
to preserve logs. - Watch:
ENV=test ptw
- Run test coverage
ENV=test coverage run -m pytest
- Look at coverage report:
coverage report
orcoverage html
to generate an html. To view it in your browser open thehtmlcov/index.html
file.
Docs
Documentation is hosted bby GitHub here: https://whitebox-ai.github.io/whitebox
mkdocs serve -f docs/mkdocs/mkdocs.yml -a localhost:8001
Deploy Whitebox
Using docker
Whitebox uses postgres as its database. They need to run in the same docker network. An example docker-compose file is located in the examples
folder. Make sure you replace the SECRET_KEY with one of your own. Look below for more info.
docker-compose -f examples/docker-compose/docker-compose.yml up
If you just need to run Whitebox, make sure you set the DATABASE_URL
in the environment.
docker run -dp 8000:8000 sqdhub/whitebox:main -e DATABASE_URL=postgresql://user:password@host:port/db_name
To save the api key encrypted in the database, provide a SECRET_KEY variable in the environment that is consisted of a 16 bytes string.
python -c "from secrets import token_hex; print(token_hex(16))"
Save this token somewhere safe.
The api key can be retrieved directly from the postgres database:
API_KEY=$(docker exec <postgres_container_id> /bin/sh -c "psql -U postgres -c \"SELECT api_key FROM users WHERE username='admin';\" -tA")
echo $API_KEY
If you've set the SECRET_KEY
in the environment get the decrypted key using:
docker exec <whitebox_container_id> /usr/local/bin/python scripts/decrypt_api_key.py $API_KEY
Using Helm
You can also install Whitebox server and all of its dependencies in your k8s cluster using helm
helm repo add squaredev https://chartmuseum.squaredev.io/
helm repo update
helm install whitebox squaredev/whitebox
Contributing
We happily welcome contributions to Whitebox. You can start by opening a new issue!
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