https://github.com/andreasthinks/python-ml-workshop
https://github.com/andreasthinks/python-ml-workshop
Last synced: 10 months ago
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- Host: GitHub
- URL: https://github.com/andreasthinks/python-ml-workshop
- Owner: AndreasThinks
- License: mit
- Created: 2023-06-06T14:10:46.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-12T16:29:42.000Z (over 2 years ago)
- Last Synced: 2025-01-27T22:44:32.758Z (12 months ago)
- Language: Jupyter Notebook
- Size: 384 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Evidence House Machine Learning Workshop
The purpose of this tutorial is to take you through a range of core ML techniques
## Installation
### Use vscode on github (codespace)
[](https://codespaces.new/theopinard/mlflow-training/tree/tutorial?quickstart=1)
If you are using windows or do not want to install vscode on your machine, you can click on the badge above to spin up a codespace environment.
### Use vscode with dev container:
[
](
https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/theopinard/mlflow-training/
)
If you already have VS Code and Docker installed, you can click the badge above. Clicking these links will cause VS Code to automatically install the Dev Containers extension if needed, clone the source code into a container volume, and spin up a dev container for use.
### Use your IDE with a manual environemnt
If you want to do it manually you will need to create a new environemnent with python 3.10 installed (you will need to change some path on the top of the notebook).
Then you can install the requirements using pip
```bash
pip install -r requirements.txt -r requirements-dev.txt
```
You would also need to have java and pyenv installed to run the predict step in the second notebook.
To make sure your environement was set up correctly you can finally run the pipeline end to end.
```bash
python src/scripts/run_all.py local
```
## How to use this repository
We will have 2 git branchs you can switch between:
* `main` branch which will contain the code with the solution
* `tutorial` branch which contains the code with the ToDo to implement.
If you got the code from clicking on the codespace link you are already in the `tutorial` branch. Otherwise you can switch between from main to solution by running:
```bash
git checkout tutorial
```
If you are stuck you can check the solution by going to the main branch.
The tutorial consists of 2 jupyter notebooks which can be found in `src/notebook`.
You can start the jupyter server by running `jupyter lab` or use your IDE directly (pycharm pro, vscode and codespace supports jupyter notebooks).
The presentation for the workshop can be found [here](https://docs.google.com/presentation/d/1EbprRo1zkZazqYy5o7cddSlozUBO2mpnKqF8x0c0EXA/edit?usp=sharing).
## Credits
This repository was only possible thanks to others repository that as used. In particular I would like to call out:
* [mlflow-training](https://github.com/theopinard/mlflow-training)
* [python-devcontainer-template](https://github.com/godatadriven/python-devcontainer-template)
* [recipes-examples](https://github.com/mlflow/recipes-examples)