mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
09a5b075fe
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/75601 User annotations was previously pushed down to the GPU timelines but was disabled during a refactoring some time back. This patch re-enables it in PyTorch Profiler. Test Plan: CI Tests Reviewed By: chaekit Differential Revision: D34591916 Pulled By: aaronenyeshi fbshipit-source-id: 3f4d5327b391725f4ce4e3eb16740bac2cd1c618 (cherry picked from commit 4bc07174dfef8fb2ffbefba224773a4618ed203a) |
||
|---|---|---|
| .. | ||
| cpp | ||
| distributed | ||
| fastrnns | ||
| framework_overhead_benchmark | ||
| functional_autograd_benchmark | ||
| fuser | ||
| instruction_counts | ||
| operator_benchmark | ||
| overrides_benchmark | ||
| profiler_benchmark | ||
| record_function_benchmark | ||
| serialization | ||
| sparse | ||
| static_runtime | ||
| tensorexpr | ||
| compare-fastrnn-results.py | ||
| compare.sh | ||
| README.md | ||
| upload_scribe.py | ||
PyTorch Benchmarks
This folder contains scripts that produce reproducible timings of various PyTorch features.
It also provides mechanisms to compare PyTorch with other frameworks.
Setup environment
Make sure you're on a machine with CUDA, torchvision, and pytorch installed. Install in the following order:
# Install torchvision. It comes with the pytorch stable release binary
conda install pytorch torchvision -c pytorch
# Install the latest pytorch master from source.
# It should supersede the installation from the release binary.
cd $PYTORCH_HOME
python setup.py build develop
# Check the pytorch installation version
python -c "import torch; print(torch.__version__)"
Benchmark List
Please refer to each subfolder to discover each benchmark suite