CIFAR-10 Classification Pipeline with ClearML

An automated MLOps workflow for CIFAR-10 dataset classification using ClearML
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation

About The Project

This project automates the process of managing a dataset, training and evaluating a convolutional neural network (CNN) on the CIFAR-10 dataset using ClearML. It encompasses the complete MLOps lifecycle from data preprocessing to model training, evaluation, and logging of metrics and outputs for efficient tracking and analysis.

Built With

Works With

Getting Started

Option 1: Remotely Execute Steps:

Follow the instructions in this document to run steps remotely: Remote Execution

Option 2: Locally Execute Steps [NOT RECOMMENDED]:

Follow the instructions in this document to run steps locally: Local Execution

Usage

Use this pipeline to kickstart your project with basic image classification. You may experiment with different preprocessing techniques, or integrate additional steps into the pipeline as needed.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

Contact

Gitarth Vaishnav - @GitarthVaishnav

Email: [email protected] | [email protected]

Github Link: @GitarthVaishnav

Project Link: https://github.com/GitarthVaishnav/First_MLOPS_Pipeline

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