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mmcdermott / nested_ragged_tensors

Utilities for efficiently working with, saving, and loading, collections of connected nested ragged tensors in PyTorch
MIT License
7 stars 1 forks source link

Major revisions to the utility and generality of the benchmarking code. #29

Closed mmcdermott closed 1 month ago

mmcdermott commented 1 month ago

Sets up the benchmarking code to track memory usage, run only on NRT data, use a committed and run on every PR via GH actions

Summary by CodeRabbit

coderabbitai[bot] commented 1 month ago

[!CAUTION]

Review failed

The pull request is closed.

Walkthrough

The changes introduce a new benchmarking workflow in the GitHub Actions configuration, enhancing the project's performance testing capabilities. This includes the implementation of a memory tracking framework and various dataset generation tools. Additionally, several files related to performance tests have been removed or modified to streamline the testing process, while new scripts and configurations have been added to facilitate dataset creation.

Changes

Files Change Summary
.github/workflows/benchmark.yaml Added a new workflow for performance benchmarking, including job configurations for running benchmarks and storing results.
.github/workflows/tests.yaml Updated pytest command to ignore additional directories during test execution.
.gitignore Updated to ignore logs, profiling statistics, and output directories related to performance tests and benchmarks.
.pre-commit-config.yaml Modified hook configurations to exclude specific patterns and enforce removal of unused imports.
README.md Updated performance testing instructions, emphasizing external documentation for performance metrics.
benchmark/README.md Added a new README for performance benchmarking, detailing the benchmarking process and dataset generation.
benchmark/benchmarkable_dataset.py Introduced a class for benchmarking datasets, including methods for memory tracking and performance evaluation.
benchmark/nrt_dataset.py Added a new dataset class for handling nested ragged tensors, with methods for data processing and retrieval.
benchmark/run.py Implemented a benchmarking framework for evaluating dataset processing performance, including functions for summarizing output metrics.
performance_tests/configs/config.yaml Removed obsolete configuration for performance testing.
performance_tests/configs/dataset_spec/default.yaml Removed max_events_per_item configuration, allowing for more flexible dataset structures.
sample_dataset_builder/LICENSE Added an MIT License file to specify usage terms.
sample_dataset_builder/README.md Introduced documentation for the Sample Dataset Builder, detailing installation and usage instructions.
sample_dataset_builder/pyproject.toml Defined project metadata and dependencies for the Sample Dataset Builder package.
sample_dataset_builder/src/sample_dataset_builder/__main__.py Created a main script for generating datasets based on configurations.
sample_dataset_builder/src/sample_dataset_builder/dataset_generator.py Introduced classes for generating synthetic datasets with validation and error handling.

Assessment against linked issues

Objective Addressed Explanation
Incorporating memory tracking into the benchmark code. ✅

🐰 In the meadow, we hop with glee,
New benchmarks and datasets, oh so free!
With memory tracked and tests refined,
A brighter future for code we find!
Let’s celebrate with a joyful cheer,
For improvements made, we hold so dear! 🌼

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codecov-commenter commented 1 month ago

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Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 92.77%. Comparing base (d239733) to head (0f1dbb5).

:white_check_mark: All tests successful. No failed tests found.

Additional details and impacted files ```diff @@ Coverage Diff @@ ## main #29 +/- ## ======================================= Coverage 92.77% 92.77% ======================================= Files 2 2 Lines 332 332 ======================================= Hits 308 308 Misses 24 24 ```

:umbrella: View full report in Codecov by Sentry.
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