b54994ae95
Reviewers: I recommend reviewing commit-by-commit or just looking at the final version of `partition/docx.py` as View File. This refactor solves a few problems but mostly lays the groundwork to allow us to refine further aspects such as page-break detection, list-item detection, and moving python-docx internals upstream to that library so our work doesn't depend on that domain-knowledge.
Performance
This is a collection of tools helpful for inspecting and tracking performance of the Unstructured library.
The benchmarking script allows a user to track performance time to partitioning results against a fixed set of test documents and store those results with indication of architecture, instance type, and git hash, in S3.
The profiling script allows a user to inspect how time time and memory are spent across called functions when performing partitioning on a given document.
Install
Benchmarking requires no additional dependencies and should work without any initial setup. Profiling has a few dependencies which can be installed with:
pip install -r scripts/performance/requirements.txt
npm install -g speedscope
The second dependency speedscope provides a tool to view profiling results from py-spy locally. Alternatively you can also drop the profile result *.speedscope into https://www.speedscope.app/ to view the results online.
Run
Benchmark
Export / assign desired environment variable settings:
- DOCKER_TEST: Set to true to run benchmark inside a Docker container (default: false)
- NUM_ITERATIONS: Number of iterations for benchmark (e.g., 100) (default: 3)
- INSTANCE_TYPE: Type of benchmark instance (e.g., "c5.xlarge") (default: unspecified)
- PUBLISH_RESULTS: Set to true to publish results to S3 bucket (default: false)
Usage: ./scripts/performance/benchmark.sh
Profile
Export / assign desired environment variable settings:
- DOCKER_TEST: Set to true to run profiling inside a Docker container (default: false)
Usage:
on Linux: ./scripts/performance/profile.sh
on macOS: sudo -E ./scripts/performance/profile.sh; py-spy requires su to run on macOS
- Run the script and choose the profiling mode: 'run' or 'view'.
- In the 'run' mode, you can profile custom files or select existing test files.
- In the 'view' mode, you can view previously generated profiling results.
- The script supports time profiling with cProfile and memory profiling with memray.
- Users can choose different visualization options such as flamegraphs, tables, trees, summaries, and statistics.
- Test documents are synced from an S3 bucket to a local directory before running the profiles