groqflow 4.2.0

GroqFlow toolchain library

GroqFlow 🚀

GroqFlow™ is the easiest way to get started with Groq's technology. GroqFlow provides an automated tool flow for compiling machine learning and linear algebra workloads into Groq programs and executing those programs on GroqChip™ processors.

We recommend that your system meets the following hardware requirements:

• To build models: 32GB or more of RAM.
• To run models: 8 GroqChip processors is recommended, especially for larger models.

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Installation Guide

Sign-up on support.groq.com to download and install GroqWare™ Suite version >=0.9.2.1.

For installation instructions, please have a look at our Install Guide.

Getting Started

To Groq a PyTorch model, simply provide your model and inputs to the groqit() function. Once groqit() has built your model, you can execute your Groq model the same way you execute a PyTorch model.

groqit() also works with ONNX files and provides many customization options. You can find more information about how to use groqit() in our User Guide.

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Navigating GroqFlow

• demo_helpers: Scripts used for GroqFlow demos and proof points.

• docs: All information you'd need to be successful with GroqFlow.

• examples: Includes various GroqFlow examples.

• groqflow: The source code for the groqflow package.

• proof_points: Machine learning proof points using GroqFlow.

• readme.md: This readme.

• setup.py: GroqFlow setup script for installation.

Contributors

GroqFlow development is primarily conducted within Groq's internal repo and is periodically synced to GitHub. This approach means that developer contributions are not immediately obvious in the commit log.

This project follows the all-contributors specification. Contributions of any kind are welcome!