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chore(deps): bump the pip group across 2 directories with 3 updates#3
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Bumps the pip group with 1 update in the /deploy/solar_renderer/reactor directory: torch.
Bumps the pip group with 3 updates in the /deploy/solar_wm directory: torch, accelerate and pillow.

Updates torch from 2.8.0 to 2.13.0

Release notes

Sourced from torch's releases.

PyTorch 2.13.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Tracked Regressions

ROCm wheels break torch.compile on CPU in environments without a GPU

Running a torch==2.13.0+rocm7.2 wheel in an environment where no GPU is available (torch.cuda.is_available() is False) breaks torch.compile on the CPU path: the first compile raises RuntimeError: Can't detect vectorized ISA for CPU (#189194). This is a regression from torch==2.12.1+rocm7.2, which compiles CPU code fine (detecting e.g. VecAVX2) in the same setup. The 2.13 ROCm wheel appears to rely on something present in the ROCm builder image to detect the CPU vectorized ISA, so it works when run on a ROCm image but fails on a plain CPU-only image.

Workaround: run the +rocm wheel on a ROCm image, or install a standard CPU/CUDA build for GPU-less environments.

Backwards Incompatible Changes

  • Stop building CPython 3.13t (free-threaded) binaries (#182951)

    Upstream pypa/manylinux removed CPython 3.13t (free-threaded) on 2026-05-07, because 3.13t was experimental and has been superseded by the now-non-experimental CPython 3.14t. As a result, PyTorch 2.13 no longer ships cp313t wheels (Linux, Triton, and related artifacts). Users on the free-threaded interpreter should move to Python 3.14t.

    PyTorch 2.12:

    # cp313t (free-threaded 3.13) wheels were available
python3.13t -m pip install torch

    PyTorch 2.13:

... (truncated)

Commits
  • cf30153 [release/2.13] Strip +PTX from CUDA arch list on release/RC builds (#188914) ...
  • 3e3e24b [release/2.13] Restrict cuda-bindings to Python < 3.15 for CUDA 12.9 builds (...
  • 7986b06 [release/2.13] Bump binary build timeout 280 -> 400 minutes (#188551)
  • 0bdbc26 [release/2.13] Add CUDA 12.9 to TORCH_CUDA_ARCH_LIST tables (#188443)
  • 9cabb45 [release/2.13] Update manywheel docker image pin to 78e737ad (#188409)
  • 78e737a [release/2.13] Revert "Tighten generalized scatter graph target (#184075)" (#...
  • 0bb9b5b [release/2.13] Revert "dynamo: round-trip torch.cuda.stream ctx mgr across gr...
  • aaac2bf [release/2.13] Revert "[Reland] Port D104346887/PR 182675 for index_add fast ...
  • 9330813 Fix build_with_debinfo.py broken by CONFIGURE_DEPENDS globbing (#188192)
  • 4e077a7 Remove setuptools upper bound (#188190)
  • Additional commits viewable in compare view

Updates torch from 2.10.0 to 2.13.0

Release notes

Sourced from torch's releases.

PyTorch 2.13.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Tracked Regressions

ROCm wheels break torch.compile on CPU in environments without a GPU

Running a torch==2.13.0+rocm7.2 wheel in an environment where no GPU is available (torch.cuda.is_available() is False) breaks torch.compile on the CPU path: the first compile raises RuntimeError: Can't detect vectorized ISA for CPU (#189194). This is a regression from torch==2.12.1+rocm7.2, which compiles CPU code fine (detecting e.g. VecAVX2) in the same setup. The 2.13 ROCm wheel appears to rely on something present in the ROCm builder image to detect the CPU vectorized ISA, so it works when run on a ROCm image but fails on a plain CPU-only image.

Workaround: run the +rocm wheel on a ROCm image, or install a standard CPU/CUDA build for GPU-less environments.

Backwards Incompatible Changes

  • Stop building CPython 3.13t (free-threaded) binaries (#182951)

    Upstream pypa/manylinux removed CPython 3.13t (free-threaded) on 2026-05-07, because 3.13t was experimental and has been superseded by the now-non-experimental CPython 3.14t. As a result, PyTorch 2.13 no longer ships cp313t wheels (Linux, Triton, and related artifacts). Users on the free-threaded interpreter should move to Python 3.14t.

    PyTorch 2.12:

    # cp313t (free-threaded 3.13) wheels were available
python3.13t -m pip install torch

    PyTorch 2.13:

... (truncated)

Commits
  • cf30153 [release/2.13] Strip +PTX from CUDA arch list on release/RC builds (#188914) ...
  • 3e3e24b [release/2.13] Restrict cuda-bindings to Python < 3.15 for CUDA 12.9 builds (...
  • 7986b06 [release/2.13] Bump binary build timeout 280 -> 400 minutes (#188551)
  • 0bdbc26 [release/2.13] Add CUDA 12.9 to TORCH_CUDA_ARCH_LIST tables (#188443)
  • 9cabb45 [release/2.13] Update manywheel docker image pin to 78e737ad (#188409)
  • 78e737a [release/2.13] Revert "Tighten generalized scatter graph target (#184075)" (#...
  • 0bb9b5b [release/2.13] Revert "dynamo: round-trip torch.cuda.stream ctx mgr across gr...
  • aaac2bf [release/2.13] Revert "[Reland] Port D104346887/PR 182675 for index_add fast ...
  • 9330813 Fix build_with_debinfo.py broken by CONFIGURE_DEPENDS globbing (#188192)
  • 4e077a7 Remove setuptools upper bound (#188190)
  • Additional commits viewable in compare view

Updates accelerate from 1.14.0 to 1.15.0

Release notes

Sourced from accelerate's releases.

v1.15.0: FSDP2 activation memory, dtensor improvements

FSDP2

A large batch of FSDP2 work this release: two fixes that cut activation memory at long sequence lengths, tied-embedding support on torch >= 2.13, and a round of checkpointing correctness and scale fixes.

Activation checkpointing was wrapping each child of the matched layer (self_attn, mlp, the norms) instead of the layer itself, so every inter-child activation stayed saved for backward. It now wraps the layer.

There's also a new FSDP2-only activation_checkpointing_offload, which moves the remaining per-layer checkpoint inputs to pinned CPU memory. Gradients are exactly those of plain activation checkpointing:

# fsdp2.yaml
fsdp_config:
  fsdp_version: 2
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_activation_checkpointing: true
  fsdp_activation_checkpointing_offload: true
accelerate launch --config_file fsdp2.yaml train.py
  • FSDP2 activation checkpointing: wrap the matched transformer layer itself, not each of its children by @​qgallouedec in #4172
  • Add FSDP2 activation_checkpointing_offload: offload checkpointed layer inputs to pinned CPU memory by @​qgallouedec in #4175
  • Fix FSDP2 tied-embedding models on torch >= 2.13: put the output embedding in the same fully_shard group by @​qgallouedec in #4171
  • Fix FSDP2/PEFT/FULL_STATE_DICT dropping every rank's adapter shard except rank 0 by @​AmineDiro in #4206
  • FSDP2: per-rank torch.save/load for SHARDED_STATE_DICT to fix 2800+ NPU checkpoint timeout by @​gygdh-001 in #4105
  • Fix FSDP sharded checkpoint path resolution by @​HaomingSong in #4119
  • Raise a clear error when FSDP is enabled on a mesh with no shard dimension by @​qgallouedec in #4180

DTensor

Two fixes for DTensor-sharded models, which you hit with FSDP2, tensor parallelism, or any N-D parallelism setup: gradient clipping no longer fails on the foreach op when plain tensors and DTensors are mixed, and prepare_model leaves an already-sharded model where it is:

Offloading & Quantization

An entire model can now be dispatched to disk, including tied weights — useful for tools like llm-compressor that compress large models on machines that can't hold them:

Trackers

Custom trackers can be registered by name and then selected from log_with= like any built-in one:

from accelerate import Accelerator
from accelerate.tracking import register_tracker_class
</tr></table>

... (truncated)

Commits
  • 6afc1e5 Release: v1.15.0
  • 636a934 Fix nightly CI: tensor parallel size detection and DeepSpeed warmup steps (#4...
  • 385d9fb docs: fix three dead links left by the docs restructure (#4230)
  • c424d0b docs: fix garbled sentence in CONTRIBUTING.md (#4231)
  • 0f7e35f Clip grad norm support for dtensors (#4219)
  • b2ac509 Fix FSDP2/ PEFT/FULL_STATE_DICT dropping every rank's adapter shard except ...
  • 33e8bc4 Fix load_accelerator_state only restoring one RNG backend (#4217)
  • 50de3ad Treat MPS out-of-memory errors as OOM in find_executable_batch_size (#4227)
  • ab5fe8e feat: add the neuron device branch in state (#4218)
  • 9baf95b Fix MLFLOW_NESTED_RUN never being able to turn nesting off (#4223)
  • Additional commits viewable in compare view

Updates pillow from 11.3.0 to 13.0.0

Release notes

Sourced from pillow's releases.

13.0.0

https://pillow.readthedocs.io/en/stable/releasenotes/13.0.0.html

Removals

Deprecations

Documentation

Dependencies

... (truncated)

Commits
  • 293218a 13.0.0 version bump
  • 262e8e4 Update release notes for Pillow 13.0.0 (#10223)
  • af56ace Validate TIFF tag values against libtiff's own field definition before TIFFSe...
  • 570df03 Validate TIFF tag values against libtiff's own field definition before TIFFSe...
  • 53318b5 Take libtiff scanline geometry tags from the image, not tiffinfo/exif (#10226)
  • 0a87417 Bound retained TIFF tag data by stream size (#10225)
  • 11582e5 Fix heap buffer overflow in _tiffWriteProc when saving TIFF to BytesIO (#10224)
  • 8501adc Fix test
  • ec5f1cb Take libtiff scanline geometry tags from the image, not tiffinfo/exif
  • 4b63dff Share TIFF tag data budget across frames
  • Additional commits viewable in compare view

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@dependabot
chore(deps): bump the pip group across 2 directories with 3 updates
74c3f2d 
Bumps the pip group with 1 update in the /deploy/solar_renderer/reactor directory: [torch](https://github.com/pytorch/pytorch).
Bumps the pip group with 3 updates in the /deploy/solar_wm directory: [torch](https://github.com/pytorch/pytorch), [accelerate](https://github.com/huggingface/accelerate) and [pillow](https://github.com/python-pillow/Pillow).


Updates `torch` from 2.8.0 to 2.13.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.8.0...v2.13.0)

Updates `torch` from 2.10.0 to 2.13.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.8.0...v2.13.0)

Updates `accelerate` from 1.14.0 to 1.15.0
- [Release notes](https://github.com/huggingface/accelerate/releases)
- [Commits](huggingface/accelerate@v1.14.0...v1.15.0)

Updates `pillow` from 11.3.0 to 13.0.0
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](python-pillow/Pillow@11.3.0...13.0.0)

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.13.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: torch
  dependency-version: 2.13.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: accelerate
  dependency-version: 1.15.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: pillow
  dependency-version: 13.0.0
  dependency-type: direct:production
  dependency-group: pip
...

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@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Nov 2, 2026
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