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Welcome

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Welcome to the documentation site for InTensorfier - the Automated GPU Environment Setup and Benchmarking Project.

Thank you for your interest in this project. We hope this project enhances your machine learning workflow and decision-making processes.

This project aims to simplify the process of setting up GPU environments for machine learning workloads and conducting benchmarking to compare performance.

Motivation

In the rapidly evolving field of machine learning, efficient utilization of hardware resources is crucial. This project addresses the need for automated GPU environment setup and accurate benchmarking of GPU-accelerated frameworks. By automating these processes, we aim to enhance productivity, reduce manual effort, and enable data-driven decisions when selecting frameworks and hardware.

Project Objectives

  • Automated GPU Setup: We have developed an automated process using Ansible to create consistent and reproducible GPU environments, ensuring reliable configurations.

  • Benchmarking: Our project includes benchmarking GPU-accelerated frameworks, such as TensorFlow and XGBoost, to quantify performance improvements gained from GPU utilization.

  • Documentation: Comprehensive documentation accompanies this project, guiding users through the setup process, benchmarking procedures, and result interpretation.

Acknowledgement

This documentation site is made with MkDocs and Material for MkDocs theme.

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