Motivation and Objectives

Motivation Behind the Project

The project is driven by the need to streamline the process of setting up GPU environments for machine learning workloads. By automating the setup and benchmarking, we aim to enhance efficiency, reduce manual configuration effort, and facilitate accurate performance comparisons.

Project Objectives

By achieving the following objectives, the project aims to simplify GPU environment setup, enhance performance analysis, and contribute to more informed decision-making in selecting the optimal tools for machine learning workloads:

1. Automated GPU Setup

Develop an automated process using Ansible to set up GPU environments for machine learning tasks. This ensures consistent configurations and minimizes human error.

2. Ease of Management

Create an environment that's easy to manage and maintain. By using Conda, we establish isolated environments with dependencies, simplifying software management.

3. Benchmarking GPU Accelerated Frameworks

Perform benchmarking on GPU-accelerated frameworks like TensorFlow and XGBoost. Compare execution time and resource utilization with and without GPU acceleration.

4. Documentation

Develop comprehensive documentation detailing the automated setup process, benchmarking methodologies, and results interpretation. This ensures transparency and facilitates future improvements.

5. Scalability

Design the project to be scalable, allowing it to handle various workloads and adapt to evolving hardware and software environments.