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README.md

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 # Cloud TPU performance recipes
 
-This repository provides the necessary instructions to reproduce a
-specific workload on Google Cloud TPUs. The focus is on reliably achieving
-a performance metric (e.g. throughput) that demonstrates the combined hardware
-and software stack on TPUs.
+This repository provides the instructions necessary to reproduce specific
+workload performance measurements, which are part of a confidential
+benchmarking program. This repository focuses on helping you reliably
+achieve performance metrics, for example, throughput that demonstrates
+the combined hardware and software stack on TPUs.
 
-## Organization
+**Note:** The content in this repository is not designed as a set of
+general-purpose code samples or tutorials for using Compute Engine-based products.
 
-- `./training`: instructions to reproduce the training performance of
+## 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:
+
+- **Identify your requirements:** determine the model, TPU type, workload, and
+  framework (JAX or PyTorch) you are interested in.
+- **Select a recipe:** navigate to the appropriate directory, for example,
+  `./training` or `./inference`, to find a recipe that meets your needs.
+- **Follow the procedure:** Each recipe guides you through preparing your
+  environment, running the benchmark, and analyzing the results (including detailed logs).
+
+## Repository organization
+
+- `./training`: Use these instructions to reproduce the training performance of
   popular LLMs, diffusion, and other models with PyTorch and JAX.
+- `./inference`: Use these instructions to reproduce inference performance.
+- `./microbenchmarks`: Use these instructions for low-level TPU benchmarks
+  such as matrix multiplication performance and memory bandwidth.
+- `./utils`: Find utility scripts here for your cluster and resource
+  management, for example, Ironwood for GKE TPU v7.
+
+## Repository scope
+
+This repository provides the steps that you can use to reproduce a specific
+benchmark. The actual performance measurements or 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. As such, there is no intent to maintain or
+update the reproducibility steps provided in this repository.
+
+## Resources
+
+If you are looking for general guidance on how to get started using
+Compute products, refer to the official documentation and tutorials:
 
-- `./inference`: instructions to reproduce inference performance.
+- [Official Compute Engine tutorials and samples](https://docs.cloud.google.com/compute/docs/overview)
+- [Cloud TPU documentation](https://docs.cloud.google.com/tpu/docs)
+- [AI Hypercomputer documentation](https://docs.cloud.google.com/ai-hypercomputer/docs)
 
-- `./microbenchmarks`: instructions for low-level TPU benchmarks such as
-  matrix multiplication performance and memory bandwidth.
+## Getting help
 
-- `./utils`: utility scripts for cluster and resource management (e.g., [Ironwood](utils/ironwood/README.md) for GKE TPU v7).
+If you have any questions or if you encounter any problems with this repository,
+report them through https://github.com/AI-Hypercomputer/tpu-recipes/issues.
 
 ## Contributor notes