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Bump the pip group across 1 directory with 4 updates#2
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Bumps the pip group with 4 updates in the / directory: deepspeed, gradio, lightning and onnx.

Updates deepspeed from 0.14.2 to 0.15.1

Release notes

Sourced from deepspeed's releases.

v0.15.1 Patch release

What's Changed

New Contributors

Full Changelog: deepspeedai/DeepSpeed@v0.15.0...v0.15.1

DeepSpeed v0.15.0

What's Changed

... (truncated)

Commits
  • 10ba3dd Handle an edge case where CUDA_HOME is not defined on ROCm systems (#6488)
  • 662a421 Safe usage of popen (#6490)
  • ddd3571 Add default value to "checkpoint_folder" in "load_state_dict" of bf16_optimiz...
  • 5d1a30c DS_BUILD_OPS should build only compatible ops (#6489)
  • ddeb0c1 Fix patch for parameter partitioning in zero.Init() (#6388)
  • 9d17116 print warning if actual triton cache dir is on NFS, not just for default (#6487)
  • 5df12a4 DeepNVMe tutorial (#6449)
  • cfc6ed3 bf16_optimizer: fixes to different grad acc dtype (#6485)
  • 9b7fc54 Add workflow to build DS without torch to better test before releases (#6450)
  • 89c4d9f TestLowCpuMemUsage UT get device by device_name (#6397)
  • Additional commits viewable in compare view

Updates gradio from 4.32.2 to 5.5.0

Changelog

Sourced from gradio's changelog.

5.5.0

Features

Fixes

5.4.0

Features

Fixes

... (truncated)

Commits
  • b5eaba1 chore: update versions (#9874)
  • fa5d433 Do not load code in gr.NO_RELOAD in the reload mode watch thread (#9886)
  • b6725cf Lite auto-load imported modules with pyodide.loadPackagesFromImports (#9726)
  • e10bbd2 Fix live interfaces for audio/image streaming (#9883)
  • dcfa7ad Enforce meta key present during preprocess in FileData payloads (#9898)
  • 7d77024 Fix dataframe height increasing on scroll (#9892)
  • 4d90883 Allows selection of directories in File Explorer (#9835)
  • 6c8a064 Ensure non-form elements are correctly positioned when scale is applied (#9882)
  • a1582a6 Lite worker refactoring (#9424)
  • f109497 Fix frontend errors on ApiDocs and RecordingSnippet (#9786)
  • Additional commits viewable in compare view

Updates lightning from 2.2.4 to 2.3.3

Release notes

Sourced from lightning's releases.

Patch release v2.3.3

This release removes the code from the main lightning package that was reported in CVE-2024-5980.

Patch release v2.3.2

Includes a minor bugfix that avoids a conflict with the entrypoint command with another package #20041.

Patch release v2.3.1

Includes minor bugfixes and stability improvements.

Full Changelog: Lightning-AI/pytorch-lightning@2.3.0...2.3.1

Lightning v2.3: Tensor Parallelism and 2D Parallelism

Lightning AI is excited to announce the release of Lightning 2.3 ⚡

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

This release introduces experimental support for Tensor Parallelism and 2D Parallelism, PyTorch 2.3 support, and several bugfixes and stability improvements.

Highlights

Tensor Parallelism (beta)

Tensor parallelism (TP) is a technique that splits up the computation of selected layers across GPUs to save memory and speed up distributed models. To enable TP as well as other forms of parallelism, we introduce a ModelParallelStrategy for both Lightning Trainer and Fabric. Under the hood, TP is enabled through new experimental PyTorch APIs like DTensor and torch.distributed.tensor.parallel.

PyTorch Lightning

Enabling TP in a model with PyTorch Lightning requires you to implement the LightningModule.configure_model() method where you convert selected layers of a model to paralellized layers. This is an advanced feature, because it requires a deep understanding of the model architecture. Open the tutorial Studio to learn the basics of Tensor Parallelism.

 

... (truncated)

Commits

Updates onnx from 1.16.0 to 1.17.0

Release notes

Sourced from onnx's releases.

v1.17.0

ONNX v1.17.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

Key Updates

ai.onnx Opset 22

Python Changes

  • Support for numpy >= 2.0

Bug fixes and infrastructure improvements

  • Fix Check URLs errors 5972
  • Use CMAKE_PREFIX_PATH in finding libprotobuf 5975
  • Bump main VERSION_NUMBER to 1.17.0 5968
  • Fix source and pip tar.gz builds on s390x systems 5984
  • Fix unique_name 5992
  • Fix SegFault bug in shape inference 5990
  • Fix onnx.compose when connecting subgraphs 5991
  • Fix conversion from split 11 to split 18 6020
  • Update error messages for NegativeLogLikelihoodLoss inference function 6021
  • Generalize input/output number check in shape inference 6005
  • Replace rank inference with shape inference for Einsum op 6010
  • build from source instruction with latest cmake change 6038
  • Handle OneHot's depth value during shape inference 5963
  • Not to install cmake in pyproject.toml on Windows 6045
  • fix a skipped shape infer code 6049
  • Include the ".onnxtext" extension in supported serialization format 6051
  • Allow ReferenceEvaluator to return intermediate results 6066
  • Fix 1 typo in numpy_helper.py 6041
  • Remove benchmarking code 6076
  • Prevent crash on import after GCC 8 builds 6048
  • Check graph outputs are defined 6083
  • Enable additional ruff rules 6032
  • Add missing shape inference check for DequantizeLinear 6080
  • Add bfloat16 to all relevant ops 6099
  • fix(ci): install python dependencies with --only-binary :all: in manylinux 6120
  • fix: install google-re2 with --only-binary option 6129
  • Specify axis parameter for DequantizeLinear when input rank is 1 6095
  • Pin onnxruntime to 1.17.3 for release CIs 6143
  • Fix INT4 TensorProto byte size is 5x larger than expected with negative values 6161
  • Mitigate tarball directory traversal risks 6164
  • Fix reference implementation for ScatterND with 4D tensors 6174
  • Addition of group > 1 in test and in backend for ConvTranspose 6175
  • Support for bfloat16 for binary, unary operators in reference implementation 6166
  • Refactor windows workflow to work on standard windows 6190
  • Fix a few crashes while running shape inference 6195
  • Update onnx to work with numpy>=2.0 6196
  • Use sets to improve performance of dfs search 6213

... (truncated)

Commits

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@dependabot
Bump the pip group across 1 directory with 4 updates
ddbe922 
Bumps the pip group with 4 updates in the / directory: [deepspeed](https://github.com/microsoft/DeepSpeed), [gradio](https://github.com/gradio-app/gradio), [lightning](https://github.com/Lightning-AI/lightning) and [onnx](https://github.com/onnx/onnx).


Updates `deepspeed` from 0.14.2 to 0.15.1
- [Release notes](https://github.com/microsoft/DeepSpeed/releases)
- [Commits](deepspeedai/DeepSpeed@v0.14.2...v0.15.1)

Updates `gradio` from 4.32.2 to 5.5.0
- [Release notes](https://github.com/gradio-app/gradio/releases)
- [Changelog](https://github.com/gradio-app/gradio/blob/main/CHANGELOG.md)
- [Commits](https://github.com/gradio-app/gradio/compare/gradio@4.32.2...gradio@5.5.0)

Updates `lightning` from 2.2.4 to 2.3.3
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](Lightning-AI/pytorch-lightning@2.2.4...2.3.3)

Updates `onnx` from 1.16.0 to 1.17.0
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](onnx/onnx@v1.16.0...v1.17.0)

---
updated-dependencies:
- dependency-name: deepspeed
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: gradio
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: lightning
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: onnx
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Dec 17, 2024
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