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46 lines
2.2 KiB
Markdown
46 lines
2.2 KiB
Markdown
# Performance
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This is a collection of tools helpful for inspecting and tracking performance of the Unstructured library.
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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.
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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.
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## Install
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Benchmarking requires no additional dependencies and should work without any initial setup.
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Profiling has a few dependencies which can be installed with:
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```bash
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pip install -r scripts/performance/requirements.txt
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npm install -g speedscope
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```
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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.
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## Run
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### Benchmark
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Export / assign desired environment variable settings:
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- DOCKER_TEST: Set to true to run benchmark inside a Docker container (default: false)
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- NUM_ITERATIONS: Number of iterations for benchmark (e.g., 100) (default: 3)
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- INSTANCE_TYPE: Type of benchmark instance (e.g., "c5.xlarge") (default: unspecified)
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- PUBLISH_RESULTS: Set to true to publish results to S3 bucket (default: false)
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-
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Usage: `./scripts/performance/benchmark.sh`
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### Profile
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Export / assign desired environment variable settings:
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- DOCKER_TEST: Set to true to run profiling inside a Docker container (default: false)
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Usage:
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**on Linux**: `./scripts/performance/profile.sh`
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**on macOS**: `sudo -E ./scripts/performance/profile.sh`; `py-spy` requires su to run on macOS
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- Run the script and choose the profiling mode: 'run' or 'view'.
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- In the 'run' mode, you can profile custom files or select existing test files.
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- In the 'view' mode, you can view previously generated profiling results.
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- The script supports time profiling with cProfile and memory profiling with memray.
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- Users can choose different visualization options such as flamegraphs, tables, trees, summaries, and statistics.
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- Test documents are synced from an S3 bucket to a local directory before running the profiles
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