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To avoid outage on HUD, I plan to migrate perf stats to dynamoDB as follows: 1. Upload perf stats to both Rockset and dynamoDB 2. Copy all the existing content from Rockset to dynamoDB 3. Create new Rockset tables to map to dynamoDB 4. Switch HUD to use the new Rockset tables (temporarily) 5. Delete the existing tables This depends on https://github.com/pytorch-labs/pytorch-gha-infra/pull/422 ### Testing ``` python3 -m tools.stats.upload_dynamo_perf_stats --workflow-run-id 9770217910 --workflow-run-attempt 1 --repo "pytorch/pytorch" --head-branch "gh/shunting314/162/head" --rockset-collection torch_dynamo_perf_stats --rockset-workspace inductor --dynamodb-table torchci-dynamo-perf-stats --match-filename "^inductor_" ... Writing 1607 documents to DynamoDB torchci-dynamo-perf-stats ``` And confirm the same number of documents is on the table  Pull Request resolved: https://github.com/pytorch/pytorch/pull/129544 Approved by: https://github.com/clee2000
This folder contains a number of scripts which are used as
part of the PyTorch build process. This directory also doubles
as a Python module hierarchy (thus the __init__.py).
Overview
Modern infrastructure:
- autograd - Code generation for autograd. This includes definitions of all our derivatives.
- jit - Code generation for JIT
- shared - Generic infrastructure that scripts in
tools may find useful.
- module_loader.py - Makes it easier to import arbitrary Python files in a script, without having to add them to the PYTHONPATH first.
Build system pieces:
- setup_helpers - Helper code for searching for third-party dependencies on the user system.
- build_pytorch_libs.py - cross-platform script that builds all of the constituent libraries of PyTorch, but not the PyTorch Python extension itself.
- build_libtorch.py - Script for building libtorch, a standalone C++ library without Python support. This build script is tested in CI.
Developer tools which you might find useful:
- git_add_generated_dirs.sh and git_reset_generated_dirs.sh - Use this to force add generated files to your Git index, so that you can conveniently run diffs on them when working on code-generation. (See also generated_dirs.txt which specifies the list of directories with generated files.)
Important if you want to run on AMD GPU:
- amd_build - HIPify scripts, for transpiling CUDA
into AMD HIP. Right now, PyTorch and Caffe2 share logic for how to
do this transpilation, but have separate entry-points for transpiling
either PyTorch or Caffe2 code.
- build_amd.py - Top-level entry point for HIPifying our codebase.
Tools which are only situationally useful:
- docker - Dockerfile for running (but not developing) PyTorch, using the official conda binary distribution. Context: https://github.com/pytorch/pytorch/issues/1619
- download_mnist.py - Download the MNIST dataset; this is necessary if you want to run the C++ API tests.