Federated learning and data analytics that just works
Table of Contents
Using the Docker images
There is a docker image for running a pod (ghcr.io/bitfount/pod:stable).
The image requires a config.yaml file to be provided to them,
by default it will try to load it from /mount/config/config.yaml inside the docker container.
You can provide this file easily by mounting/binding a volume to the container,
how you do this may vary depending on your platform/environment (Docker/docker-compose/ECS),
if you have any problems doing this then feel free to reach out to us.
Alternative you could copy a config file into a stopped container using docker cp.
If you're using a CSV data source then you'll also need to mount your data to the container, this will need to be mounted at the path specified in your config, for simplicity it's easiest put your config and your CSV in the same directory and then mount it to the container.
Once your container is running you will need to check the logs and complete the login step, allowing your container to authenticate with Bitfount. The process is the same as when running locally (e.g. the tutorials), except that we can't open the login page automatically for you.
Running the Python code
Installation
Where to get it
Binary installers for the latest released version are available at the Python Package Index (PyPI).
pip install bitfount
If you are planning on using the bitfount package with Jupyter Notebooks, we recommend you install the splinter package bitfount[tutorials] which will make sure you are running compatible jupyter dependencies.
pip install 'bitfount[tutorials]'
Installation from sources
To install bitfount from source you need to create a python virtual environment.
In the bitfount directory (same one where you found this file after cloning the git repo), execute:
uv sync
For MacOS you may also need to install libomp:
brew install libomp
Getting started (Tutorials)
In order to run the tutorials, you also need to install the tutorial requirements:
uv sync --group tutorial
To get started using the Bitfount package in a federated setting, we recommend
that you start with our tutorials. Run jupyter notebookand open up the first
tutorial in the "Connecting Data & Creating Pods folder: running_a_pod.ipynb
Federated training scripts
Some simple scripts have been provided to run a Pod or Modelling job from a config file.
⚠️ If you are running from a source install (such as from
git clone) you will need to usepython -m scripts.<script_name>rather than usebitfount <script_name>directly.
To run a pod:
bitfount run_pod --path_to_config_yaml=<CONFIG_FILE>
To run a modelling job:
bitfount run_modeller --path_to_config_yaml=<CONFIG_FILE>
License
The license for this software is available in the LICENSE file.
This can be found in the Github Repository, as well as inside the Docker image.
1 maintainer
Metadata
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Release files for bitfount 12.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| bitfount-12.0.0.tar.gz | 3.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bitfount-12.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.3 MB
Release files / bitfount-12.0.0.tar.gz
| Download URL | bitfount-12.0.0.tar.gz |
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| Size | 3.4 MB |
| Tags | Source |
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Release files / bitfount-12.0.0-py3-none-any.whl
| Download URL | bitfount-12.0.0-py3-none-any.whl |
|---|---|
| Size | 4.0 MB |
| Tags | Python 3 |
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