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https://github.com/build-on-aws/jupyter-notebook-ml-batch-amazon-eks
Container-ready Jupyter Notebook application based on a TensorFlow 2.12.0/Python 3.10 image, utilizing XGBoost model training for structured data
https://github.com/build-on-aws/jupyter-notebook-ml-batch-amazon-eks
docker jupyter-notebook python tensorflow xgboost
Last synced: 8 days ago
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Container-ready Jupyter Notebook application based on a TensorFlow 2.12.0/Python 3.10 image, utilizing XGBoost model training for structured data
- Host: GitHub
- URL: https://github.com/build-on-aws/jupyter-notebook-ml-batch-amazon-eks
- Owner: build-on-aws
- License: mit-0
- Created: 2023-10-06T21:18:17.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-10T20:38:12.000Z (about 1 year ago)
- Last Synced: 2024-12-17T17:40:55.210Z (18 days ago)
- Topics: docker, jupyter-notebook, python, tensorflow, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 219 KB
- Stars: 3
- Watchers: 6
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
## Jupyter-XGBoost Predictor Application
This application offers a streamlined Jupyter Notebook environment that's optimized for containerization. Although based on a TensorFlow 2.12.0 image with Python 3.10 support, the notebook primarily employs XGBoost for model training. It provides a comprehensive data science pipeline, featuring data manipulation with pandas and machine learning via XGBoost. Fully containerized, this Jupyter-XGBoost notebook ensures effortless deployments using Docker.
## About
* 📦 This app is purpose-built for container deployment, ensuring a uniform operating environment and hassle-free deployments via Docker.
* 🚀 This app is powered by a [Jupyter Notebook](https://jupyter.org/) environment, an open-source web application that lets you create and share documents that contain live code, equations, visualizations, and narrative text.
* ✅ The base image of this app incorporates [TensorFlow](https://www.tensorflow.org/), offering the flexibility to extend its capabilities for deep learning. TensorFlow is a leading open-source platform, renowned for its versatile machine learning toolkit and wide usage in handling unstructured data.
* 💾 This app utilizes [pandas](https://pandas.pydata.org/) for data manipulation. Pandas provides fast, flexible, and expressive data structures designed to work with structured data.
* 🗃️ This app leverages [XGBoost](https://xgboost.readthedocs.io/en/stable/) for gradient boosting. XGBoost is an optimized distributed gradient boosting library designed to be efficient, flexible, and is the most used library for structured, tabular data.
## Prerequisites
- [Install Docker](https://docs.docker.com/get-docker/)
- [Install Jupyter Notebook](https://jupyter.org/install#jupyter-notebook)
## Installation
1. Clone the repository:
```bash
git clone [email protected]:build-on-aws/jupyter-notebook-ml-batch-amazon-eks.git
cd jupyter-notebook-ml-batch-amazon-eks
```
## Quickstart
Follow these steps to initiate the notebook environment via Docker.
1. Build the Docker image in the root project directory.
```bash
docker build -t batch-ml-image .
```
2. Run the Docker container.
```bash
docker run --memory=4g --cpus=2 -p 8888:8888 batch-ml-image
```
3. Open the **Model-demo.ipynb** file to begin training the model.
## Security
See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
## License
This library is licensed under the MIT-0 License. See the LICENSE file.