{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Decompile and inspect the internal bytecode and optimization graphs generated by PyTorch's torch.compile, converting compiled code back to readable Python source and intermediate representations.","skillfed_tags":["pytorch-debugging","compiler-inspection","bytecode-decompiler"],"use_cases":["Debug why torch.compile is not optimizing a function as expected by inspecting the captured and optimized graphs.","Understand the bytecode transformations torch.compile applies to your code to identify performance bottlenecks.","Inspect forward and backward graphs generated by AOTAutograd to verify gradient computation correctness.","Step through compiled code in a debugger to trace execution flow and set breakpoints in generated functions.","Analyze compiled CPU/GPU kernels produced by Inductor to understand hardware-level optimizations."],"what_it_does":"depyf is a debugging and introspection tool for PyTorch's torch.compile compiler. It intercepts the bytecode and intermediate representations generated during compilation and writes them to disk in human-readable form, letting you see what torch.compile is actually doing to your code. The tool outputs the original source, transformed Python code, raw bytecode (loadable via dill), and the computation graphs at each optimization stage (captured graph, joint graph, forward/backward graphs, and compiled kernels).\n\nThe package is designed for machine learning researchers and practitioners who need to understand, debug, or optimize code compiled with torch.compile. It wraps your code in context managers that intercept compilation artifacts, and optionally integrates with debuggers so you can step through the compiled code. It requires PyTorch 2.2.0 or newer and depends on astor and dill for code generation and bytecode serialization.","worth_installing":"Yes, if you use torch.compile and need to understand or debug its behavior. The tool fills a genuine gap\u2014existing decompilers fail on PyTorch bytecode\u2014and is backed by the PyTorch team. The aging maintenance status (305 days since last release) is a minor concern, but the repository is not archived and remains active. Install only if you have PyTorch>=2.2.0 available."},"id":"depyf","links":{"html":"https://skillfed.io/packages/depyf","md":"https://skillfed.io/packages/depyf.md","pypi":"https://pypi.org/project/depyf/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-10-13","license_spdx":null,"license_treatment":"permissive","name":"depyf","python_support":"supports_current","summary":"Decompile python functions, from bytecode to source code!"},"popularity":{"monthly_downloads":3386060,"position":2640,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.20.0"}