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Bump the requirements-txt group across 2 directories with 3 updates #620

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Bumps the requirements-txt group with 3 updates in the /demo-notebooks/guided-demos directory: pytorch-lightning, torchmetrics and torchvision.
Bumps the requirements-txt group with 3 updates in the /tests/e2e directory: pytorch-lightning, torchmetrics and torchvision.

Updates pytorch-lightning from 1.9.5 to 2.3.3

Release notes

Sourced from pytorch-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 torchmetrics from 0.9.1 to 1.4.0.post0

Release notes

Sourced from torchmetrics's releases.

Minor dependency correction

Full Changelog: Lightning-AI/torchmetrics@v1.4.0...v1.4.0.post0

Metrics for segmentation

In Torchmetrics v1.4, we are happy to introduce a new domain of metrics to the library: segmentation metrics. Segmentation metrics are used to evaluate how well segmentation algorithms are performing, e.g., algorithms that take in an image and pixel-by-pixel decide what kind of object it is. These kind of algorithms are necessary in applications such as self driven cars. Segmentations are closely related to classification metrics, but for now, in Torchmetrics, expect the input to be formatted differently; see the documentation for more info. For now, MeanIoU and GeneralizedDiceScore have been added to the subpackage, with many more to follow in upcoming releases of Torchmetrics. We are happy to receive any feedback on metrics to add in the future or the user interface for the new segmentation metrics.

Torchmetrics v1.3 adds new metrics to the classification and image subpackage and has multiple bug fixes and other quality-of-life improvements. We refer to the changelog for the complete list of changes.

[1.4.0] - 2024-05-03

Added

  • Added SensitivityAtSpecificity metric to classification subpackage (#2217)
  • Added QualityWithNoReference metric to image subpackage (#2288)
  • Added a new segmentation metric:
  • Added support for calculating segmentation quality and recognition quality in PanopticQuality metric (#2381)
  • Added pretty-errors for improving error prints (#2431)
  • Added support for torch.float weighted networks for FID and KID calculations (#2483)
  • Added zero_division argument to selected classification metrics (#2198)

Changed

  • Made __getattr__ and __setattr__ of ClasswiseWrapper more general (#2424)

Fixed

  • Fix getitem for metric collection when prefix/postfix is set (#2430)
  • Fixed axis names with Precision-Recall curve (#2462)
  • Fixed list synchronization with partly empty lists (#2468)
  • Fixed memory leak in metrics using list states (#2492)
  • Fixed bug in computation of ERGAS metric (#2498)
  • Fixed BootStrapper wrapper not working with kwargs provided argument (#2503)
  • Fixed warnings being suppressed in MeanAveragePrecision when requested (#2501)
  • Fixed corner-case in binary_average_precision when only negative samples are provided (#2507)

Key Contributors

@​baskrahmer, @​Borda, @​ChristophReich1996, @​daniel-code, @​furkan-celik, @​i-aki-y, @​jlcsilva, @​NielsRogge, @​oguz-hanoglu, @​SkafteNicki, @​ywchan2005

New Contributors

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Note: we move fast, but still we preserve 0.1 version (one feature release) back compatibility.

[UnReleased] - 2022-MM-DD

Added

  • Added a new audio metric DNSMOS (#2525)

  • Added MetricInputTransformer wrapper (#2392)

  • Added input_format argument to segmentation metrics (#2572)

  • Added better error messages for intersection detection metrics for wrong user input (#2577)

  • Added multi-output support for MAE metric (#2605)

Changed

  • Calculate text color of ConfusionMatrix plot based on luminance

Removed

Fixed

  • Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2571)

  • Fixed class order of panoptic_quality(..., return_per_class=True) output (#2548)

  • Fixed BootstrapWrapper not being reset correctly (#2574)

... (truncated)

Commits
  • 3f11239 correction release 1.4.0.post
  • dd5b67e build(deps): update matplotlib requirement from <3.8.0,>=3.3.0 to >=3.3.0,<3....
  • 8deefe0 build(deps): update regex requirement from <=2024.4.16,>=2021.9.24 to >=2021....
  • 5d707c0 build(deps): bump pytest-xdist from 3.5.0 to 3.6.1 in /requirements (#2537)
  • 68f21b7 build(deps): update numpy requirement from <1.25.0 to <1.27.0 in /requirement...
  • 1e54cdd build(deps): update scikit-image requirement from ~=0.21 to ~=0.22 in /requir...
  • 71c088f build(deps): bump coverage from 7.5.0 to 7.5.1 in /requirements (#2529)
  • e5a00a2 build(deps): update pytorch-lightning requirement from <2.0.0,>=1.9.0 to >=1....
  • df45079 ci/test: patch unstable test_bleu_score_functional (#2533)
  • d44b729 req: move pretty-errors and option for debug extras (#2527)
  • Additional commits viewable in compare view

Updates torchvision from 0.12.0 to 0.19.0

Release notes

Sourced from torchvision's releases.

Torchvision 0.19 release

Highlights

Encoding / Decoding images

Torchvision is extending its encoding/decoding capabilities. For this version, we added a GIF decoder which is available as torchvision.io.decode_gif(raw_tensor), torchvision.io.decode_image(raw_tensor), and torchvision.io.read_image(path_to_image).

We also added support for jpeg GPU encoding in torchvision.io.encode_jpeg(). This is 10X faster than the existing CPU jpeg encoder.

Read more on the docs!

Stay tuned for more improvements coming in the next versions. We plan to improve jpeg GPU decoding, and add more image decoders (webp in particular).

Resizing according to the longest edge of an image

It is now possible to resize images by setting torchvision.transforms.v2.Resize(max_size=N): this will resize the longest edge of the image exactly to max_size, making sure the image dimension don't exceed this value. Read more on the docs!

Detailed changes

Bug Fixes

[datasets] SBDataset: Only download noval file when image_set='train_noval' (#8475) [datasets] Update the download url in class EMNIST (#8350) [io] Fix compilation error when there is no libjpeg (#8342) [reference scripts] Fix use of cutmix_alpha in classification training references (#8448) [utils] Allow K=1 in draw_keypoints (#8439)

New Features

[io] Add decoder for GIF images (decode_gif(), decode_image(),read_image()) (#8406, #8419) [transforms] Add GaussianNoise transform (#8381)

Improvements

[transforms] Allow v2 Resize to resize longer edge exactly to max_size (#8459) [transforms] Add min_area parameter to SanitizeBoundingBox (#7735) [transforms] Make adjust_hue() work with numpy 2.0 (#8463) [transforms] Enable one-hot-encoded labels in MixUp and CutMix (#8427) [transforms] Create kernel on-device for transforms.functional.gaussian_blur (#8426) [io] Adding GPU acceleration to encode_jpeg (10X faster than CPU encoder) (#8391) [io] read_video: accept BytesIO objects on pyav backend (#8442) [io] Add compatibility with FFMPEG 7.0 (#8408) [datasets] Add extra to install gdown (#8430) [datasets] Support encoded RLE format in for COCO segmentations (#8387) [datasets] Added binary cat vs dog classification target type to Oxford pet dataset (#8388) [datasets] Return labels for FER2013 if possible (#8452) [ops] Force use of torch.compile on deterministic roi_align implementation (#8436) [utils] add float support to utils.draw_bounding_boxes() (#8328)

... (truncated)

Commits

Updates pytorch-lightning from 1.9.5 to 2.3.3

Release notes

Sourced from pytorch-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 torchmetrics from 0.9.1 to 1.4.0.post0

Release notes

Sourced from torchmetrics's releases.

Minor dependency correction

Full Changelog: Lightning-AI/torchmetrics@v1.4.0...v1.4.0.post0

Metrics for segmentation

In Torchmetrics v1.4, we are happy to introduce a new domain of metrics to the library: segmentation metrics. Segmentation metrics are used to evaluate how well segmentation algorithms are performing, e.g., algorithms that take in an image and pixel-by-pixel decide what kind of object it is. These kind of algorithms are necessary in applications such as self driven cars. Segmentations are closely related to classification metrics, but for now, in Torchmetrics, expect the input to be formatted differently; see the documentation for more info. For now, MeanIoU and GeneralizedDiceScore have been added to the subpackage, with many more to follow in upcoming releases of Torchmetrics. We are happy to receive any feedback on metrics to add in the future or the user interface for the new segmentation metrics.

Torchmetrics v1.3 adds new metrics to the classification and image subpackage and has multiple bug fixes and other quality-of-life improvements. We refer to the changelog for the complete list of changes.

[1.4.0] - 2024-05-03

Added

  • Added SensitivityAtSpecificity metric to classification subpackage (#2217)
  • Added QualityWithNoReference metric to image subpackage (#2288)
  • Added a new segmentation metric:
  • Added support for calculating segmentation quality and recognition quality in PanopticQuality metric (#2381)
  • Added pretty-errors for improving error prints (#2431)
  • Added support for torch.float weighted networks for FID and KID calculations (#2483)
  • Added zero_division argument to selected classification metrics (#2198)

Changed

  • Made __getattr__ and __setattr__ of ClasswiseWrapper more general (#2424)

Fixed

  • Fix getitem for metric collection when prefix/postfix is set (#2430)
  • Fixed axis names with Precision-Recall curve (#2462)
  • Fixed list synchronization with partly empty lists (#2468)
  • Fixed memory leak in metrics using list states (#2492)
  • Fixed bug in computation of ERGAS metric (#2498)
  • Fixed BootStrapper wrapper not working with kwargs provided argument (#2503)
  • Fixed warnings being suppressed in MeanAveragePrecision when requested (#2501)
  • Fixed corner-case in binary_average_precision when only negative samples are provided (#2507)

Key Contributors

@​baskrahmer, @​Borda, @​ChristophReich1996, @​daniel-code, @​furkan-celik, @​i-aki-y, @​jlcsilva, @​NielsRogge, @​oguz-hanoglu, @​SkafteNicki, @​ywchan2005

New Contributors

... (truncated)

Changelog

Sourced from torchmetrics's changelog.

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Note: we move fast, but still we preserve 0.1 version (one feature release) back compatibility.

[UnReleased] - 2022-MM-DD

Added

  • Added a new audio metric DNSMOS (#2525)

  • Added MetricInputTransformer wrapper (#2392)

  • Added input_format argument to segmentation metrics (#2572)

  • Added better error messages for intersection detection metrics for wrong user input (#2577)

  • Added multi-output support for MAE metric (#2605)

Changed

  • Calculate text color of ConfusionMatrix plot based on luminance

Removed

Fixed

  • Fixed bug in MetricCollection when using compute groups and compute is called more than once (#2571)

  • Fixed class order of panoptic_quality(..., return_per_class=True) output (#2548)

  • Fixed BootstrapWrapper not being reset correctly (#2574)

... (truncated)

Commits
  • 3f11239 correction release 1.4.0.post
  • dd5b67e build(deps): update matplotlib requirement from <3.8.0,>=3.3.0 to >=3.3.0,<3....
  • 8deefe0 build(deps): update regex requirement from <=2024.4.16,>=2021.9.24 to >=2021....
  • 5d707c0 build(deps): bump pytest-xdist from 3.5.0 to 3.6.1 in /requirements (#2537)
  • 68f21b7 build(deps): update numpy requirement from <1.25.0 to <1.27.0 in /requirement...
  • 1e54cdd build(deps): update scikit-image requirement from ~=0.21 to ~=0.22 in /requir...
  • 71c088f build(deps): bump coverage from 7.5.0 to 7.5.1 in /requirements (#2529)
  • e5a00a2 build(deps): update pytorch-lightning requirement from <2.0.0,>=1.9.0 to >=1....
  • df45079 ci/test: patch unstable test_bleu_score_functional (#2533)
  • d44b729 req: move pretty-errors and option for debug extras (#2527)
  • Additional commits viewable in compare view

Updates torchvision from 0.12.0 to 0.19.0

Release notes

Sourced from torchvision's releases.

Torchvision 0.19 release

Highlights

Encoding / Decoding images

Torchvision is extending its encoding/decoding capabilities. For this version, we added a GIF decoder which is available as torchvision.io.decode_gif(raw_tensor), torchvision.io.decode_image(raw_tensor), and torchvision.io.read_image(path_to_image).

We also added support for jpeg GPU encoding in torchvision.io.encode_jpeg(). This is 10X faster than the existing CPU jpeg encoder.

Read more on the docs!

Stay tuned for more improvements coming in the next versions. We plan to improve jpeg GPU decoding, and add more image decoders (webp in particular).

Resizing according to the longest edge of an image

It is now possible to resize images by setting torchvision.transforms.v2.Resize(max_size=N): this will resize the longest edge of the image exactly to max_size, making sure the image dimension don't exceed this value. Read more on the docs!

Detailed changes

Bug Fixes

[datasets] SBDataset: Only download noval file when image_set='train_noval' (#8475) [datasets] Update the download url in class EMNIST (#8350) [io] Fix compilation error when there is no libjpeg (#8342) [reference scripts] Fix use of cutmix_alpha in classification training references (#8448) [utils] Allow K=1 in draw_keypoints (#8439)

New Features

[io] Add decoder for GIF images (decode_gif(), decode_image(),read_image()) (#8406, #8419) [transforms] Add GaussianNoise transform (#8381)

Improvements

[transforms] Allow v2 Resize to resize longer edge exactly to max_size (#8459) [transforms] Add min_area parameter to SanitizeBoundingBox (#7735) [transforms] Make adjust_hue() work with numpy 2.0 (#8463) [transforms] Enable one-hot-encoded labels in MixUp and CutMix (#8427) [transforms] Create kernel on-device for transforms.functional.gaussian_blur (#8426) [io] Adding GPU acceleration to encode_jpeg (10X faster than CPU encoder) (#8391) [io] read_video: accept BytesIO objects on pyav backend (#8442) [io] Add compatibility with FFMPEG 7.0 (#8408) [datasets] Add extra to install gdown (#8430) [datasets] Support encoded RLE format in for COCO segmentations (#8387) [datasets] Added binary cat vs dog classification target type to Oxford pet dataset (#8388) [datasets] Return labels for FER2013 if possible (#8452) [ops] Force use of torch.compile on deterministic roi_align implementation (#8436) [utils] add float support to utils.draw_bounding_boxes() (#8328)

... (truncated)

Commits

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@dependabot
Bump the requirements-txt group across 2 directories with 3 updates
795c43a 
Bumps the requirements-txt group with 3 updates in the /demo-notebooks/guided-demos directory: [pytorch-lightning](https://github.com/Lightning-AI/lightning), [torchmetrics](https://github.com/Lightning-AI/torchmetrics) and [torchvision](https://github.com/pytorch/vision).
Bumps the requirements-txt group with 3 updates in the /tests/e2e directory: [pytorch-lightning](https://github.com/Lightning-AI/lightning), [torchmetrics](https://github.com/Lightning-AI/torchmetrics) and [torchvision](https://github.com/pytorch/vision).


Updates `pytorch-lightning` from 1.9.5 to 2.3.3
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](Lightning-AI/pytorch-lightning@1.9.5...2.3.3)

Updates `torchmetrics` from 0.9.1 to 1.4.0.post0
- [Release notes](https://github.com/Lightning-AI/torchmetrics/releases)
- [Changelog](https://github.com/Lightning-AI/torchmetrics/blob/master/CHANGELOG.md)
- [Commits](Lightning-AI/torchmetrics@v0.9.1...v1.4.0.post0)

Updates `torchvision` from 0.12.0 to 0.19.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.12.0...0.19.0)

Updates `pytorch-lightning` from 1.9.5 to 2.3.3
- [Release notes](https://github.com/Lightning-AI/lightning/releases)
- [Commits](Lightning-AI/pytorch-lightning@1.9.5...2.3.3)

Updates `torchmetrics` from 0.9.1 to 1.4.0.post0
- [Release notes](https://github.com/Lightning-AI/torchmetrics/releases)
- [Changelog](https://github.com/Lightning-AI/torchmetrics/blob/master/CHANGELOG.md)
- [Commits](Lightning-AI/torchmetrics@v0.9.1...v1.4.0.post0)

Updates `torchvision` from 0.12.0 to 0.19.0
- [Release notes](https://github.com/pytorch/vision/releases)
- [Commits](pytorch/vision@v0.12.0...0.19.0)

---
updated-dependencies:
- dependency-name: pytorch-lightning
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchmetrics
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchvision
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: requirements-txt
- dependency-name: pytorch-lightning
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchmetrics
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: requirements-txt
- dependency-name: torchvision
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: requirements-txt
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added the test-guided-notebooks Run PR check to verify Guided notebooks label Aug 2, 2024
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dependabot bot commented on behalf of github Aug 5, 2024

Superseded by #634.

@dependabot dependabot bot closed this Aug 5, 2024
@dependabot dependabot bot deleted the dependabot/pip/demo-notebooks/guided-demos/requirements-txt-d7158fd327 branch August 5, 2024 22:37
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