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YeonwooSung / MLOps

Miscellaneous codes and writings for MLOps
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build(deps): bump the pip group across 5 directories with 2 updates #113

Closed dependabot[bot] closed 4 months ago

dependabot[bot] commented 4 months ago

Bumps the pip group with 1 update in the /LLM/llama_index/samples/mixtral_ollama directory: torch. Bumps the pip group with 1 update in the /ml-serving/custom-serving/fastapi/ray/ray_distilbert directory: torch. Bumps the pip group with 1 update in the /ml-serving/custom-serving/fastapi/ray/ray_stablediffusion directory: torch. Bumps the pip group with 1 update in the /ml-serving/custom-serving/fastapi/ray/ray_yolov5s directory: torch. Bumps the pip group with 2 updates in the /recommender-systems/FinalMLP directory: scikit-learn and torch.

Updates torch from 2.2.0 to 2.4.0

Release notes

Sourced from torch's releases.

PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. 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 2.4.

Tracked Regressions

Subproc exception with torch.compile and onnxruntime-training

There is a reported issue when using torch.compile if onnxruntime-training lib is installed. The issue will be fixed in v2.4.1. It can be solved locally by setting the environment variable TORCHINDUCTOR_WORKER_START=fork before executing the script.

cu118 wheels will not work with pre-cuda12 drivers

It was also reported that the new version of triton uses cuda features that are not compatible with pre-cuda12 drivers. In this case, the workaround is to set TRITON_PTXAS_PATH manually as follows (adapt the code according to the local installation path):

TRITON_PTXAS_PATH=/usr/local/lib/python3.10/site-packages/torch/bin/ptxas  python script.py

Backwards Incompatible Change

Python frontend

Default TreadPool size to number of physical cores (#125963)

Changed the default number of threads used for intra-op parallelism from the number of logical cores to the number of

... (truncated)

Commits


Updates torch from 2.2.0 to 2.4.0

Release notes

Sourced from torch's releases.

PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. 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 2.4.

Tracked Regressions

Subproc exception with torch.compile and onnxruntime-training

There is a reported issue when using torch.compile if onnxruntime-training lib is installed. The issue will be fixed in v2.4.1. It can be solved locally by setting the environment variable TORCHINDUCTOR_WORKER_START=fork before executing the script.

cu118 wheels will not work with pre-cuda12 drivers

It was also reported that the new version of triton uses cuda features that are not compatible with pre-cuda12 drivers. In this case, the workaround is to set TRITON_PTXAS_PATH manually as follows (adapt the code according to the local installation path):

TRITON_PTXAS_PATH=/usr/local/lib/python3.10/site-packages/torch/bin/ptxas  python script.py

Backwards Incompatible Change

Python frontend

Default TreadPool size to number of physical cores (#125963)

Changed the default number of threads used for intra-op parallelism from the number of logical cores to the number of

... (truncated)

Commits


Updates torch from 2.2.0 to 2.4.0

Release notes

Sourced from torch's releases.

PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. 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 2.4.

Tracked Regressions

Subproc exception with torch.compile and onnxruntime-training

There is a reported issue when using torch.compile if onnxruntime-training lib is installed. The issue will be fixed in v2.4.1. It can be solved locally by setting the environment variable TORCHINDUCTOR_WORKER_START=fork before executing the script.

cu118 wheels will not work with pre-cuda12 drivers

It was also reported that the new version of triton uses cuda features that are not compatible with pre-cuda12 drivers. In this case, the workaround is to set TRITON_PTXAS_PATH manually as follows (adapt the code according to the local installation path):

TRITON_PTXAS_PATH=/usr/local/lib/python3.10/site-packages/torch/bin/ptxas  python script.py

Backwards Incompatible Change

Python frontend

Default TreadPool size to number of physical cores (#125963)

Changed the default number of threads used for intra-op parallelism from the number of logical cores to the number of

... (truncated)

Commits


Updates torch from 2.2.0 to 2.4.0

Release notes

Sourced from torch's releases.

PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. 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 2.4.

Tracked Regressions

Subproc exception with torch.compile and onnxruntime-training

There is a reported issue when using torch.compile if onnxruntime-training lib is installed. The issue will be fixed in v2.4.1. It can be solved locally by setting the environment variable TORCHINDUCTOR_WORKER_START=fork before executing the script.

cu118 wheels will not work with pre-cuda12 drivers

It was also reported that the new version of triton uses cuda features that are not compatible with pre-cuda12 drivers. In this case, the workaround is to set TRITON_PTXAS_PATH manually as follows (adapt the code according to the local installation path):

TRITON_PTXAS_PATH=/usr/local/lib/python3.10/site-packages/torch/bin/ptxas  python script.py

Backwards Incompatible Change

Python frontend

Default TreadPool size to number of physical cores (#125963)

Changed the default number of threads used for intra-op parallelism from the number of logical cores to the number of

... (truncated)

Commits


Updates scikit-learn from 1.5.0 to 1.5.1

Release notes

Sourced from scikit-learn's releases.

Scikit-learn 1.5.1

We're happy to announce the 1.5.1 release.

This release contains fixes for a few regressions introduced in 1.5.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-1

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Thanks to everyone who contributed to this release !

Commits
  • 70fdc84 Bump version [cd build]
  • b0ec847 DOC Add missing PR number in changelog entry (#29384)
  • 8179b8f DOC Set 1.5.1 release month (#29377)
  • 851c0d6 FIX: accuracy and zero_loss support for multilabel with Array API (#29336)
  • 99d8a32 FIX zero_one_loss breaks with multilabel and Array API (#29269)
  • 059070b ENH Add Array API compatibility to cosine_similarity (#29014)
  • ada571f Fix a regression in GridSearchCV for parameter grids that have arrays of diff...
  • fb26476 :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot:...
  • 5a74cc0 Fix performance regression in ColumnTransformer (#29330)
  • c3d69b2 MAINT Pin the ruff version on CI linters (#29359)
  • Additional commits viewable in compare view


Updates torch from 1.13.1 to 2.2.0

Release notes

Sourced from torch's releases.

PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. 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 2.4.

Tracked Regressions

Subproc exception with torch.compile and onnxruntime-training

There is a reported issue when using torch.compile if onnxruntime-training lib is installed. The issue will be fixed in v2.4.1. It can be solved locally by setting the environment variable TORCHINDUCTOR_WORKER_START=fork before executing the script.

cu118 wheels will not work with pre-cuda12 drivers

It was also reported that the new version of triton uses cuda features that are not compatible with pre-cuda12 drivers. In this case, the workaround is to set TRITON_PTXAS_PATH manually as follows (adapt the code according to the local installation path):

TRITON_PTXAS_PATH=/usr/local/lib/python3.10/site-packages/torch/bin/ptxas  python script.py

Backwards Incompatible Change

Python frontend

Default TreadPool size to number of physical cores (#125963)

Changed the default number of threads used for intra-op parallelism from the number of logical cores to the number of

... (truncated)

Commits


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