A template to prototype your AI/ML projects using Jupyter Notebooks.

Jupyter notebooks simply Python development by:

• allowing the execution of individual code snippets
• allowing markdown alongside code for better documentation

You can follow the setup instructions below to clone and setup this template on your local machine and start working on the notebook.ipynb.

Looking for inspiration? Check out the examples folder in this project and/or this AI / ML manual.

Feel free to open PRs to add information / tools to benefit the entire community!

A short description of the project.

• Python 3.10
• Conda
• Jupyter notebook (or the Jupyter extension in VS Code)

1. Install Miniconda: Download and install Miniconda for your platform from https://docs.conda.io/en/latest/miniconda.html.

2. Clone the repository: Clone the repository using the following command:

   [email protected]:Ashwin27/ai-starter-kit.git

3. Create a new Conda environment: Open the terminal app and navigate to the project directory. Then, run the following command:

   chmod +x setup.sh ./setup.sh

   This will create a new environment called "myenv" with Python 3.10, activate the environment, and install the necessary packages listed in the requirements.txt file.

4. Launch Jupyter notebook: Launch Jupyter notebook by running the following command:

   jupyter notebook

   This will open Jupyter notebook in your default web browser.

5. Open the notebook: Open the notebook in Jupyter notebook and run the cells to execute the code.

• notebook.ipynb: A Jupyter notebook file containing the code for the project.
• requirements.txt: A file containing a list of packages and their versions required for the project.

Useful links on running Jupyter notebooks managed by Conda on VS Code:

1. Working with Jupyter notebooks in VS Code
2. Using Python environments with VS Code

A good alternative to running Jupyter notebooks locally is using Google Collab.

To add or remove dependencies in your Python project while keeping the environment.yml file up-to-date, you can use Conda to install or uninstall packages.

To add a new dependency to your project, you can use the following command:

conda install <package-name>

Replace <package-name> with the name of the package you want to install. If you want to install a specific version of the package, you can use the following command:

conda install <package-name>=<version>

Replace <version> with the specific version you want to install.

After installing a new package, you should update the environment.yml file to include the new package and its version. You can do this by running the following command:

conda env export --name myenv > environment.yml

This will generate a new environment.yml file that includes all the installed packages and their versions in the specified Conda environment.

To remove a dependency from your project, you can use the following command:

conda remove <package-name>

Replace <package-name> with the name of the package you want to uninstall. If you want to uninstall a specific version of the package, you can use the following command:

conda remove <package-name>=<version>

After removing a package, you should update the environment.yml file to remove the package and its version. You can do this by running the same command to export the environment as before:

conda env export --name myenv > environment.yml

This will generate a new environment.yml file that includes all the remaining installed packages and their versions in the specified Conda environment.

This project is licensed under the MIT License - see the LICENSE file for details.

• List any credits or acknowledgements here.

Note: Please make sure that the packages listed in the requirements.txt file are compatible with Python 3.10 before running the setup.sh file.