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Pale-Blue-Dot-97 / Minerva

Minerva project includes the minerva package that aids in the fitting and testing of neural network models. Includes pre and post-processing of land cover data. Designed for use with torchgeo datasets.
MIT License
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Bump torch from 2.4.1 to 2.5.1 in /requirements #557

Closed dependabot[bot] closed 1 day ago

dependabot[bot] commented 2 weeks ago

Bumps torch from 2.4.1 to 2.5.1.

Release notes

Sourced from torch's releases.

PyTorch 2.5.1: bug fix release

This release is meant to fix the following regressions:

Besides the regression fixes, the release includes several documentation updates.

See release tracker pytorch/pytorch#132400 for additional information.

PyTorch 2.5.0 Release, SDPA CuDNN backend, Flex Attention

PyTorch 2.5 Release Notes

  • Highlights
  • Backwards Incompatible Change
  • Deprecations
  • New Features
  • Improvements
  • Bug fixes
  • Performance
  • Documentation
  • Developers
  • Security

Highlights

We are excited to announce the release of PyTorch® 2.5! This release features a new CuDNN backend for SDPA, enabling speedups by default for users of SDPA on H100s or newer GPUs. As well, regional compilation of torch.compile offers a way to reduce the cold start up time for torch.compile by allowing users to compile a repeated nn.Module (e.g. a transformer layer in LLM) without recompilations. Finally, TorchInductor CPP backend offers solid performance speedup with numerous enhancements like FP16 support, CPP wrapper, AOT-Inductor mode, and max-autotune mode. This release is composed of 4095 commits from 504 contributors since PyTorch 2.4. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.5. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page. As well, please check out our new ecosystem projects releases with TorchRec and TorchFix.

Beta Prototype
CuDNN backend for SDPA FlexAttention
torch.compile regional compilation without recompilations Compiled Autograd
TorchDynamo added support for exception handling & MutableMapping types Flight Recorder
TorchInductor CPU backend optimization Max-autotune Support on CPU with GEMM Template
TorchInductor on Windows
FP16 support on CPU path for both eager mode and TorchInductor CPP backend
Autoload Device Extension
Enhanced Intel GPU support

*To see a full list of public feature submissions click here.

BETA FEATURES

[Beta] CuDNN backend for SDPA

The cuDNN "Fused Flash Attention" backend was landed for torch.nn.functional.scaled_dot_product_attention. On NVIDIA H100 GPUs this can provide up to 75% speed-up over FlashAttentionV2. This speedup is enabled by default for all users of SDPA on H100 or newer GPUs.

[Beta] torch.compile regional compilation without recompilations

Regional compilation without recompilations, via torch._dynamo.config.inline_inbuilt_nn_modules which default to True in 2.5+. This option allows users to compile a repeated nn.Module (e.g. a transformer layer in LLM) without recompilations. Compared to compiling the full model, this option can result in smaller compilation latencies with 1%-5% performance degradation compared to full model compilation.

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codecov[bot] commented 1 week ago

Codecov Report

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

Project coverage is 90.70%. Comparing base (860c081) to head (9f14a4d). Report is 4 commits behind head on main.

Additional details and impacted files ```diff @@ Coverage Diff @@ ## main #557 +/- ## ======================================= Coverage 90.70% 90.70% ======================================= Files 42 42 Lines 4993 4993 ======================================= Hits 4529 4529 Misses 464 464 ```

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