KO
|
EN
gitlite — search
Search
#typescript
#ai-agents
#ai
#dsh-plugin
#deepseek-harness
#open-source
#claude-code
#codex
#cli
#developer-tools
#react
#windows
tpu-recipes
★ 121
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
hazardous
:
Competing Risks and Survival Analysis
rag-with-langchain-colbert-and-ragatouille
:
Build a Streamlit Chatbot using Langchain, ColBERT, Ragatouille, and ChromaDB
ai-sdk-js
:
SAP Cloud SDK for AI is the official Software Development Kit (SDK) for SAP AI Core, SAP Generative AI Hub, and Orchestration Service.
sbom-everywhere
:
Improve Software Bill of Materials (SBOM) tooling and training to encourage adoption
LiveResearchBench
:
A live benchmark and evaluation framework for open-ended deep research in the wild.
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
tpu-recipes
?
Download (.md)
# Cloud TPU performance benchmark recipes This repository contains recipes that provide instructions to reproduce specific workload performance measurements, which are part of a confidential benchmarking program. These recipes focus on helping you reliably achieve performance metrics, such as throughput, that demonstrate the combined hardware and software stack on TPUs. **Note:** The recipes in this repository are not designed as general-purpose code samples or tutorials for using Compute Engine-based products. ## Intended audience This content is for you if you are a customer or partner who needs to: - Validate hardware performance with your suppliers. - Inform purchasing decisions using the benchmarking data. - Reproduce optimal performance scenarios before you customize workflows for your own requirements. ## How to use these recipes To reproduce a benchmark, follow these steps: 1.**Identify your requirements:** determine the model, TPU version, workload, and framework (JAX or PyTorch) that you are interested in. 2.**Select a recipe:** navigate to the appropriate directory, such as `./training` or `./inference`, to find a recipe that meets your needs. 3.**Follow the procedure:** each recipe guides you through preparing your environment, running the benchmark, and analyzing the results (including detailed logs). You can automate your infrastructure setup using Cluster Toolkit. For more information, see [Automated TPU environment deployment with Cluster Toolkit](https://cloud.google.com/cluster-toolkit/docs/deploy/gke/gke-tpu-overview). ## Repository organization - `./training`: This directory contains recipes with instructions to reproduce the training performance of popular models, using PyTorch and JAX on specific TPU versions. - `./inference`: This directory contains recipes that provide instructions and configurations to reproduce inference performance of models running on specific TPU versions. - `./microbenchmarks`: This directory contains instructions for running low-level performance tests on TPUs, specifically focusing on matrix multiplication performance and memory bandwidth. - `./utils`: This directory contains utility scripts for cluster and resource management for TPU7x (Ironwood) in GKE. For fully automated, production-ready cluster deployment, we recommend using the [Automated TPU environment deployment with Cluster Toolkit](https://cloud.google.com/cluster-toolkit/docs/deploy/gke/gke-tpu-7x). ## Repository scope This repository provides the steps that you can use to reproduce a specific benchmark. The actual performance measurements and the complete, confidential benchmark report are not included. ## Methodology Performance benchmarks measure the performance of various workloads on the platform. These benchmarks are primarily used to validate performance with hardware suppliers and to provide you with data for purchasing decisions. ## Maintenance policy Benchmark data is considered a point-in-time measurement and completed benchmarks are not repeated. We maintain and update the recipes in this repository on a best-effort basis. ## Resources For general guidance on using Google Cloud compute products, see the official documentation and tutorials: - [Compute Engine overview](https://docs.cloud.google.com/compute/docs/overview) - [Compute Engine samples](https://docs.cloud.google.com/compute/docs/samples) - [Cloud TPU documentation](https://docs.cloud.google.com/tpu/docs) - [AI Hypercomputer documentation](https://docs.cloud.google.com/ai-hypercomputer/docs) - [Automated TPU environment deployment with Cluster Toolkit](https://cloud.google.com/cluster-toolkit/docs/deploy/gke/gke-tpu-overview) ## Report issues If you have questions or encounter problems with this repository, report them through [GitHub Issues](https://github.com/AI-Hypercomputer/tpu-recipes/issues) or reach out to your Google Cloud account team for assistance. ## Contributor notes Note: 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).
