AI-Hypercomputer/accelerator-agents

Dernière activité source enregistrée: 14 octobre 2026 à 04:20

Catalogue SkillsMP mis à jour: 5 novembre 2026 à 19:57

skills collectés: 1

Étoiles GitHub: 99
Forks GitHub: 23

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# Accelerator Agents **Accelerator Agents** is a collection of AI-powered tools designed to accelerate machine learning development on Google Cloud TPUs. This repository hosts agents that assist with code migration, kernel optimization, and performance tuning, enabling developers to leverage the full power of TPUs with greater velocity. > **Disclaimer:** This is not an officially supported Google product. This > project is not eligible for the [Google Open Source Software Vulnerability > Rewards Program](https://bughunters.google.com/open-source-security). ## Overview As machine learning models grow in complexity, optimizing them for specific hardware accelerators like TPUs becomes increasingly challenging. This project aims to provide a suite of "Agents"—specialized AI tools powered by Gemini—to automate and assist with these complex tasks. The project includes two primary agents: ### 1. MaxCode The **MaxCode** agent facilitates the conversion of existing PyTorch models and codebases into JAX. It is designed to help users migrate their workloads to run efficiently on TPUs, leveraging high-performance frameworks like [MaxText](https://github.com/AI-Hypercomputer/maxtext). *Note: MaxCode is under active development, and we are continuously working to improve migration quality and expand model coverage.* **Features:** * **Automated Conversion:** Converts functional code blocks and model layers from PyTorch to JAX. * **MaxText Integration:** Generates JAX code compatible with the MaxText framework for immediate training and inference on TPUs. * **Human-in-the-Loop:** Designed to draft initial implementations that developers can review and refine. ### 2. MaxKernel The **MaxKernel** agent is a specialized tool for high-performance kernel development on TPUs. It assists engineers in writing, optimizing, and debugging custom kernels, specifically focusing on **Pallas** (JAX's kernel language). **Features:** * **Kernel Writing:** Drafts Pallas kernels from scratch or based on JAX reference implementations. * **CUDA to Pallas Conversion:** Assists in porting custom CUDA/GPU kernels to run optimally on TPUs. * **Optimization & Profiling:** Provides profiling insights and optimization suggestions to improve kernel performance (MFU). * **Test Harness Generation:** Automatically generates boilerplate code for correctness testing and compilation checks. ## Getting Started ### Prerequisites * A Google Cloud VM. A CPU-only VM is sufficient for MaxCode, while a TPU VM is recommended for MaxKernel. * Python 3.11+ * Access to Gemini API (for agent reasoning capabilities). ### Installation Clone the repository: ```bash git clone https://github.com/AI-Hypercomputer/accelerator-agents.git cd accelerator-agents``` *(Note: Specific installation instructions for each agent can be found in their respective subdirectories.)* ### Contributing We welcome contributions! Please see [CONTRIBUTING.md](docs/contributing.md) for details on how to submit pull requests, report issues, and contribute to the project. ### License This project is licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for the full license text.

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Orchestrates Pallas TPU kernel development and optimization. Converts JAX, PyTorch or CUDA into a Pallas TPU kernel and runs a self-refinement loop (plan -> implement -> compile -> test -> autotune -> profile) by dispatching the maxkernel-worker subagent once…

Langue du texte source : anglais

2026-10-14

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