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Published 20 hours ago • verified publisher icon Rose-STL-Lab

Bump the pip group with 15 updates

Open dependabot[bot] opened this issue 1 year ago • 1 comments
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Bumps the pip group with 15 updates:

Package From To
torch 1.11.0 1.13.1
pytorch-lightning 1.5.10 1.6.0
scipy 1.7.3 1.10.0
aiohttp 3.8.1 3.9.4
certifi 2021.10.8 2023.7.22
grpcio 1.44.0 1.53.2
idna 3.3 3.7
jinja2 3.0.3 3.1.3
numpy 1.21.5 1.22.0
oauthlib 3.2.0 3.2.2
protobuf 3.19.4 3.19.5
requests 2.27.1 2.31.0
setuptools 59.5.0 65.5.1
urllib3 1.26.8 1.26.18
werkzeug 2.0.3 2.3.8

Updates torch from 1.11.0 to 1.13.1

Release notes

Sourced from torch's releases.

PyTorch 1.13.1 Release, small bug fix release

This release is meant to fix the following issues (regressions / silent correctness):

  • RuntimeError by torch.nn.modules.activation.MultiheadAttention with bias=False and batch_first=True #88669
  • Installation via pip on Amazon Linux 2, regression #88869
  • Installation using poetry on Mac M1, failure #88049
  • Missing masked tensor documentation #89734
  • torch.jit.annotations.parse_type_line is not safe (command injection) #88868
  • Use the Python frame safely in _pythonCallstack #88993
  • Double-backward with full_backward_hook causes RuntimeError #88312
  • Fix logical error in get_default_qat_qconfig #88876
  • Fix cuda/cpu check on NoneType and unit test #88854 and #88970
  • Onnx ATen Fallback for BUILD_CAFFE2=0 for ONNX-only ops #88504
  • Onnx operator_export_type on the new registry #87735
  • torchrun AttributeError caused by file_based_local_timer on Windows #85427

The release tracker should contain all relevant pull requests related to this release as well as links to related issues

PyTorch 1.13: beta versions of functorch and improved support for Apple’s new M1 chips are now available

Pytorch 1.13 Release Notes

  • Highlights
  • Backwards Incompatible Changes
  • New Features
  • Improvements
  • Performance
  • Documentation
  • Developers

Highlights

We are excited to announce the release of PyTorch 1.13! This includes stable versions of BetterTransformer. We deprecated CUDA 10.2 and 11.3 and completed migration of CUDA 11.6 and 11.7. Beta includes improved support for Apple M1 chips and functorch, a library that offers composable vmap (vectorization) and autodiff transforms, being included in-tree with the PyTorch release. This release is composed of over 3,749 commits and 467 contributors since 1.12.1. We want to sincerely thank our dedicated community for your contributions.

Summary:

  • The BetterTransformer feature set supports fastpath execution for common Transformer models during Inference out-of-the-box, without the need to modify the model. Additional improvements include accelerated add+matmul linear algebra kernels for sizes commonly used in Transformer models and Nested Tensors is now enabled by default.

  • Timely deprecating older CUDA versions allows us to proceed with introducing the latest CUDA version as they are introduced by Nvidia®, and hence allows support for C++17 in PyTorch and new NVIDIA Open GPU Kernel Modules.

  • Previously, functorch was released out-of-tree in a separate package. After installing PyTorch, a user will be able to import functorch and use functorch without needing to install another package.

  • PyTorch is offering native builds for Apple® silicon machines that use Apple's new M1 chip as a beta feature, providing improved support across PyTorch's APIs.

Stable Beta Prototype
Better TransformerCUDA 10.2 and 11.3 CI/CD Deprecation Enable Intel® VTune™ Profiler's Instrumentation and Tracing Technology APIsExtend NNC to support channels last and bf16Functorch now in PyTorch Core LibraryBeta Support for M1 devices Arm® Compute Library backend support for AWS Graviton CUDA Sanitizer

You can check the blogpost that shows the new features here.

Backwards Incompatible changes

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits

Updates pytorch-lightning from 1.5.10 to 1.6.0

Release notes

Sourced from pytorch-lightning's releases.

PyTorch Lightning 1.6: Support Intel's Habana Accelerator, New efficient DDP strategy (Bagua), Manual Fault-tolerance, Stability and Reliability.

The core team is excited to announce the PyTorch Lightning 1.6 release ⚡

Highlights

PyTorch Lightning 1.6 is the work of 99 contributors who have worked on features, bug-fixes, and documentation for a total of over 750 commits since 1.5. This is our most active release yet. Here are some highlights:

Introducing Intel's Habana Accelerator

Lightning 1.6 now supports the Habana® framework, which includes Gaudi® AI training processors. Their heterogeneous architecture includes a cluster of fully programmable Tensor Processing Cores (TPC) along with its associated development tools and libraries and a configurable Matrix Math engine.

You can leverage the Habana hardware to accelerate your Deep Learning training workloads simply by passing:

trainer = pl.Trainer(accelerator="hpu")

single Gaudi training


trainer = pl.Trainer(accelerator="hpu", devices=1)


distributed training with 8 Gaudi


trainer = pl.Trainer(accelerator="hpu", devices=8)

The Bagua Strategy

The Bagua Strategy is a deep learning acceleration framework that supports multiple, advanced distributed training algorithms with state-of-the-art system relaxation techniques. Enabling Bagua, which can be considerably faster than vanilla PyTorch DDP, is as simple as:

trainer = pl.Trainer(strategy="bagua")

or to choose a custom algorithm


trainer = pl.Trainer(strategy=BaguaStrategy(algorithm="gradient_allreduce")  # default

Towards stable Accelerator, Strategy, and Plugin APIs

The Accelerator, Strategy, and Plugin APIs are a core part of PyTorch Lightning. They're where all the distributed boilerplate lives, and we're constantly working to improve both them and the overall PyTorch Lightning platform experience.

In this release, we've made some large changes to achieve that goal. Not to worry, though! The only users affected by these changes are those who use custom implementations of Accelerator and Strategy (TrainingTypePlugin) as well as certain Plugins. In particular, we want to highlight the following changes:

  • All TrainingTypePlugins have been renamed to Strategy (#11120). Strategy is a more appropriate name because it encompasses more than simply training communcation. This change is now aligned with the changes we implemented in 1.5, which introduced the new strategy and devices flags to the Trainer.

... (truncated)

Changelog

Sourced from pytorch-lightning's changelog.

[1.6.0] - 2022-03-29

Added

  • Allow logging to an existing run ID in MLflow with MLFlowLogger (#12290)
  • Enable gradient accumulation using Horovod's backward_passes_per_step (#11911)
  • Add new DETAIL log level to provide useful logs for improving monitoring and debugging of batch jobs (#11008)
  • Added a flag SLURMEnvironment(auto_requeue=True|False) to control whether Lightning handles the requeuing (#10601)
  • Fault Tolerant Manual
    • Add _Stateful protocol to detect if classes are stateful (#10646)
    • Add _FaultTolerantMode enum used to track different supported fault tolerant modes (#10645)
    • Add a _rotate_worker_indices utility to reload the state according the latest worker (#10647)
    • Add stateful workers (#10674)
    • Add an utility to collect the states across processes (#10639)
    • Add logic to reload the states across data loading components (#10699)
    • Cleanup some fault tolerant utilities (#10703)
    • Enable Fault Tolerant Manual Training (#10707)
    • Broadcast the _terminate_gracefully to all processes and add support for DDP (#10638)
  • Added support for re-instantiation of custom (subclasses of) DataLoaders returned in the *_dataloader() methods, i.e., automatic replacement of samplers now works with custom types of DataLoader (#10680)
  • Added a function to validate if fault tolerant training is supported. (#10465)
  • Added a private callback to manage the creation and deletion of fault-tolerance checkpoints (#11862)
  • Show a better error message when a custom DataLoader implementation is not well implemented and we need to reconstruct it (#10719)
  • Show a better error message when frozen dataclass is used as a batch (#10927)
  • Save the Loop's state by default in the checkpoint (#10784)
  • Added Loop.replace to easily switch one loop for another (#10324)
  • Added support for --lr_scheduler=ReduceLROnPlateau to the LightningCLI (#10860)
  • Added LightningCLI.configure_optimizers to override the configure_optimizers return value (#10860)
  • Added LightningCLI(auto_registry) flag to register all subclasses of the registerable components automatically (#12108)
  • Added a warning that shows when max_epochs in the Trainer is not set (#10700)
  • Added support for returning a single Callback from LightningModule.configure_callbacks without wrapping it into a list (#11060)
  • Added console_kwargs for RichProgressBar to initialize inner Console (#10875)
  • Added support for shorthand notation to instantiate loggers with the LightningCLI (#11533)
  • Added a LOGGER_REGISTRY instance to register custom loggers to the LightningCLI (#11533)
  • Added info message when the Trainer arguments limit_*_batches, overfit_batches, or val_check_interval are set to 1 or 1.0 (#11950)
  • Added a PrecisionPlugin.teardown method (#10990)
  • Added LightningModule.lr_scheduler_step (#10249)
  • Added support for no pre-fetching to DataFetcher (#11606)
  • Added support for optimizer step progress tracking with manual optimization (#11848)
  • Return the output of the optimizer.step. This can be useful for LightningLite users, manual optimization users, or users overriding LightningModule.optimizer_step (#11711)
  • Teardown the active loop and strategy on exception (#11620)
  • Added a MisconfigurationException if user provided opt_idx in scheduler config doesn't match with actual optimizer index of its respective optimizer (#11247)
  • Added a loggers property to Trainer which returns a list of loggers provided by the user (#11683)
  • Added a loggers property to LightningModule which retrieves the loggers property from Trainer (#11683)
  • Added support for DDP when using a CombinedLoader for the training data (#11648)
  • Added a warning when using DistributedSampler during validation/testing (#11479)
  • Added support for Bagua training strategy (#11146)
  • Added support for manually returning a poptorch.DataLoader in a *_dataloader hook (#12116)
  • Added rank_zero module to centralize utilities (#11747)
  • Added a _Stateful support for LightningDataModule (#11637)
  • Added _Stateful support for PrecisionPlugin (#11638)

... (truncated)

Commits

Updates scipy from 1.7.3 to 1.10.0

Release notes

Sourced from scipy's releases.

SciPy 1.10.0 Release Notes

SciPy 1.10.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Before upgrading, we recommend that users check that their own code does not use deprecated SciPy functionality (to do so, run your code with python -Wd and check for DeprecationWarning s). Our development attention will now shift to bug-fix releases on the 1.10.x branch, and on adding new features on the main branch.

This release requires Python 3.8+ and NumPy 1.19.5 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

  • A new dedicated datasets submodule (scipy.datasets) has been added, and is now preferred over usage of scipy.misc for dataset retrieval.
  • A new scipy.interpolate.make_smoothing_spline function was added. This function constructs a smoothing cubic spline from noisy data, using the generalized cross-validation (GCV) criterion to find the tradeoff between smoothness and proximity to data points.
  • scipy.stats has three new distributions, two new hypothesis tests, three new sample statistics, a class for greater control over calculations involving covariance matrices, and many other enhancements.

New features

scipy.datasets introduction

  • A new dedicated datasets submodule has been added. The submodules is meant for datasets that are relevant to other SciPy submodules ands content (tutorials, examples, tests), as well as contain a curated set of datasets that are of wider interest. As of this release, all the datasets from scipy.misc have been added to scipy.datasets (and deprecated in scipy.misc).
  • The submodule is based on Pooch (a new optional dependency for SciPy), a Python package to simplify fetching data files. This move will, in a subsequent release, facilitate SciPy to trim down the sdist/wheel sizes, by decoupling the data files and moving them out of the SciPy repository, hosting them externally and

... (truncated)

Commits
  • dde5059 REL: 1.10.0 final [wheel build]
  • 7856f28 Merge pull request #17696 from tylerjereddy/treddy_110_final_prep
  • 205b624 DOC: add missing author
  • 1ab9f1b DOC: update 1.10.0 relnotes
  • ac2f45f MAINT: integrate._qmc_quad: mark as private with preceding underscore
  • 3e0ae1a REV: integrate.qmc_quad: delay release to SciPy 1.11.0
  • 34cdf05 MAINT: FFT pybind11 fixups
  • 843500a Merge pull request #17689 from mdhaber/gh17686
  • 089924b REL: integrate.qmc_quad: remove from release notes
  • 3e47110 REL: 1.10.0rc3 unreleased
  • Additional commits viewable in compare view

Updates aiohttp from 3.8.1 to 3.9.4

Release notes

Sourced from aiohttp's releases.

3.9.4

Bug fixes

  • The asynchronous internals now set the underlying causes when assigning exceptions to the future objects -- by :user:webknjaz.

    Related issues and pull requests on GitHub: #8089.

  • Treated values of Accept-Encoding header as case-insensitive when checking for gzip files -- by :user:steverep.

    Related issues and pull requests on GitHub: #8104.

  • Improved the DNS resolution performance on cache hit -- by :user:bdraco.

    This is achieved by avoiding an :mod:asyncio task creation in this case.

    Related issues and pull requests on GitHub: #8163.

  • Changed the type annotations to allow dict on :meth:aiohttp.MultipartWriter.append, :meth:aiohttp.MultipartWriter.append_json and :meth:aiohttp.MultipartWriter.append_form -- by :user:cakemanny

    Related issues and pull requests on GitHub: #7741.

  • Ensure websocket transport is closed when client does not close it -- by :user:bdraco.

    The transport could remain open if the client did not close it. This change ensures the transport is closed when the client does not close it.

... (truncated)

Changelog

Sourced from aiohttp's changelog.

3.9.4 (2024-04-11)

Bug fixes

  • The asynchronous internals now set the underlying causes when assigning exceptions to the future objects -- by :user:webknjaz.

    Related issues and pull requests on GitHub: :issue:8089.

  • Treated values of Accept-Encoding header as case-insensitive when checking for gzip files -- by :user:steverep.

    Related issues and pull requests on GitHub: :issue:8104.

  • Improved the DNS resolution performance on cache hit -- by :user:bdraco.

    This is achieved by avoiding an :mod:asyncio task creation in this case.

    Related issues and pull requests on GitHub: :issue:8163.

  • Changed the type annotations to allow dict on :meth:aiohttp.MultipartWriter.append, :meth:aiohttp.MultipartWriter.append_json and :meth:aiohttp.MultipartWriter.append_form -- by :user:cakemanny

    Related issues and pull requests on GitHub: :issue:7741.

  • Ensure websocket transport is closed when client does not close it -- by :user:bdraco.

    The transport could remain open if the client did not close it. This change ensures the transport is closed when the client does not close it.

... (truncated)

Commits

Updates certifi from 2021.10.8 to 2023.7.22

Commits

Updates grpcio from 1.44.0 to 1.53.2

Release notes

Sourced from grpcio's releases.

Release v1.53.2

This is release gRPC Core 1.53.2 (glockenspiel).

For gRPC documentation, see grpc.io. For previous releases, see Releases.

This release contains refinements, improvements, and bug fixes.

Core

Release v1.53.1

This is release gRPC Core 1.53.1 (glockenspiel).

For gRPC documentation, see grpc.io. For previous releases, see Releases.

This release contains refinements, improvements, and bug fixes.

Release v1.53.0

This is release 1.53.0 (glockenspiel) of gRPC Core.

For gRPC documentation, see grpc.io. For previous releases, see Releases.

This release contains refinements, improvements, and bug fixes, with highlights listed below.

Core

  • xDS: fix crash when removing the last endpoint from the last locality in weighted_target. (#32592)
  • filter stack: pass peer name up via recv_initial_metadata batch. (#31933)
  • [EventEngine] Add advice against blocking work in callbacks. (#32397)
  • [http2] Dont drop connections on metadata limit exceeded. (#32309)
  • xDS: reject aggregate cluster with empty cluster list. (#32238)
  • Fix Python epoll1 Fork Support. (#32196)
  • server: introduce ServerMetricRecorder API and move per-call reporting from a C++ interceptor to a C-core filter. (#32106)
  • [EventEngine] Add invalid handle types to the public API. (#32202)
  • [EventEngine] Refactoring the EventEngine Test Suite: Part 1. (#32127)
  • xDS: fix WeightedClusters total weight handling. (#32134)

C++

  • Update minimum MSVC version to 2019. (#32615)
  • Use CMake variables for paths in pkg-config files. (#31671)

... (truncated)

Changelog

Sourced from grpcio's changelog.

gRPC Release Schedule

Below is the release schedule for gRPC Java, Go and Core and its dependent languages C++, C#, Objective-C, PHP, Python and Ruby.

Releases are scheduled every six weeks on Tuesdays on a best effort basis. In some unavoidable situations a release may be delayed or released early or a language may skip a release altogether and do the next release to catch up with other languages. See the past releases in the links above. A six-week cycle gives us a good balance between delivering new features/fixes quickly and keeping the release overhead low.

The gRPC release support policy can be found here.

Releases are cut from release branches. For Core and Java repos, the release branch is cut two weeks before the scheduled release date. For Go, the branch is cut just before the release. An RC (release candidate) is published for Core and its dependent languages just after the branch cut. This RC is later promoted to release version if no further changes are made to the release branch. We do our best to keep head of master branch stable at all times regardless of release schedule. Daily build packages from master branch for C#, PHP, Python, Ruby and Protoc plugins are published on packages.grpc.io. If you depend on gRPC in production we recommend to set up your CI system to test the RCs and, if possible, the daily builds.

Names of gRPC releases are here.

Release Scheduled Branch Cut Scheduled Release Date
v1.17.0 Nov 19, 2018 Dec 4, 2018
v1.18.0 Jan 2, 2019 Jan 15, 2019
v1.19.0 Feb 12, 2019 Feb 26, 2019
v1.20.0 Mar 26, 2019 Apr 9, 2019
v1.21.0 May 7, 2019 May 21, 2019
v1.22.0 Jun 18, 2019 Jul 2, 2019
v1.23.0 Jul 30, 2019 Aug 13, 2019
v1.24.0 Sept 10, 2019 Sept 24, 2019
v1.25.0 Oct 22, 2019 Nov 5, 2019
v1.26.0 Dec 3, 2019 Dec 17, 2019
v1.27.0 Jan 14, 2020 Jan 28, 2020
v1.28.0 Feb 25, 2020 Mar 10, 2020
v1.29.0 Apr 7, 2020 Apr 21, 2020
v1.30.0 May 19, 2020 Jun 2, 2020
v1.31.0 Jul 14, 2020 Jul 28, 2020
v1.32.0 Aug 25, 2020 Sep 8, 2020
v1.33.0 Oct 6, 2020 Oct 20, 2020
v1.34.0 Nov 17, 2020 Dec 1, 2020
v1.35.0 Dec 29, 2020 Jan 12, 2021
v1.36.0 Feb 9, 2021 Feb 23, 2021
v1.37.0 Mar 23, 2021 Apr 6, 2021
v1.38.0 May 4, 2021 May 18, 2021
v1.39.0 Jun 15, 2021 Jun 29, 2021
v1.40.0 Jul 27, 2021 Aug 10, 2021
v1.41.0 Sep 7, 2021 Sep 21, 2021
v1.42.0 Oct 19, 2021 Nov 2, 2021
v1.43.0 Nov 30, 2021 Dec 14, 2021
Commits
  • afb307f [v1.53.x][Interop] Backport Python image update (#33864)
  • 7a9373b [Backport] [dependency] Restrict cython to less than 3.X (#33770)
  • fdb64a6 [v1.53][Build] Update Phusion baseimage (#33767) (#33836)
  • cdf4186 [PSM Interop] Legacy tests: fix xDS test client build (v1.53.x backport) (#33...
  • ce5b93a [PSM Interop] Legacy test builds always pull the driver from master (v1.53.x ...
  • b24b6ea [release] Bump release version to 1.53.2 (#33709)
  • 1e86ca5 [backport][iomgr][EventEngine] Improve server handling of file descriptor exh...
  • aff3066 [PSM interop] Don't fail url_map target if sub-target already failed (v1.53.x...
  • 539d75c [PSM interop] Don't fail target if sub-target already failed (#33222) (v1.53....
  • 3e79c88 [Release] Bump version to 1.53.1 (on v1.53.x branch) (#33047)
  • Additional commits viewable in compare view

Updates idna from 3.3 to 3.7

Release notes

Sourced from idna's releases.

v3.7

What's Changed

  • Fix issue where specially crafted inputs to encode() could take exceptionally long amount of time to process. [CVE-2024-3651]

Thanks to Guido Vranken for reporting the issue.

Full Changelog: https://github.com/kjd/idna/compare/v3.6...v3.7

Changelog

Sourced from idna's changelog.

3.7 (2024-04-11) ++++++++++++++++

  • Fix issue where specially crafted inputs to encode() could take exceptionally long amount of time to process. [CVE-2024-3651]

Thanks to Guido Vranken for reporting the issue.

3.6 (2023-11-25) ++++++++++++++++

  • Fix regression to include tests in source distribution.

3.5 (2023-11-24) ++++++++++++++++

  • Update to Unicode 15.1.0
  • String codec name is now "idna2008" as overriding the system codec "idna" was not working.
  • Fix typing error for codec encoding
  • "setup.cfg" has been added for this release due to some downstream lack of adherence to PEP 517. Should be removed in a future release so please prepare accordingly.
  • Removed reliance on a symlink for the "idna-data" tool to comport with PEP 517 and the Python Packaging User Guide for sdist archives.
  • Added security reporting protocol for project

Thanks Jon Ribbens, Diogo Teles Sant'Anna, Wu Tingfeng for contributions to this release.

3.4 (2022-09-14) ++++++++++++++++

  • Update to Unicode 15.0.0
  • Migrate to pyproject.toml for build information (PEP 621)
  • Correct another instance where generic exception was raised instead of IDNAError for malformed input
  • Source distribution uses zeroized file ownership for improved reproducibility

Thanks to Seth Michael Larson for contributions to this release.

Commits
  • 1d365e1 Release v3.7
  • c1b3154 Merge pull request #172 from kjd/optimize-contextj
  • 0394ec7 Merge branch 'master' into optimize-contextj
  • cd58a23 Merge pull request #152 from elliotwutingfeng/dev
  • 5beb28b More efficient resolution of joiner contexts
  • 1b12148 Update ossf/scorecard-action to v2.3.1
  • d516b87 Update Github actions/checkout to v4
  • c095c75 Merge branch 'master' into dev
  • 60a0a4c Fix typo in GitHub Actions workflow key
  • 5918a0e Merge branch 'master' into dev
  • Additional commits viewable in compare view

Updates jinja2 from 3.0.3 to 3.1.3

Release notes

Sourced from jinja2's releases.

3.1.3

This is a fix release for the 3.1.x feature branch.

3.1.2

This is a fix release for the 3.1.0 feature release.

3.1.1

3.1.0

This is a feature release, which includes new features and removes previously deprecated features. The 3.1.x branch is now the supported bugfix branch, the 3.0.x branch has become a tag marking the end of support for that branch. We encourage everyone to upgrade, and to use a tool such as pip-tools to pin all dependencies and control upgrades. We also encourage upgrading to MarkupSafe 2.1.1, the latest version at this time.

Changelog

Sourced from jinja2's changelog.

Version 3.1.3

Released 2024-01-10

  • Fix compiler error when checking if required blocks in parent templates are empty. :pr:1858
  • xmlattr filter does not allow keys with spaces. GHSA-h5c8-rqwp-cp95
  • Make error messages stemming from invalid nesting of {% trans %} blocks more helpful. :pr:1918

Version 3.1.2

Released 2022-04-28

  • Add parameters to Environment.overlay to match __init__. :issue:1645
  • Handle race condition in FileSystemBytecodeCache. :issue:1654

Version 3.1.1

Released 2022-03-25

  • The template filename on Windows uses the primary path separator. :issue:1637

Version 3.1.0

Released 2022-03-24

  • Drop support for Python 3.6. :pr:1534

  • Remove previously deprecated code. :pr:1544

    • WithExtension and AutoEscapeExtension are built-in now.
    • contextfilter and contextfunction are replaced by pass_context. evalcontextfilter and evalcontextfunction are replaced by pass_eval_context. environmentfilter and environmentfunction are replaced by pass_environment.
    • Markup and escape should be imported from MarkupSafe.
    • Compiled templates from very old Jinja versions may need to be recompiled.
    • Legacy resolve mode for Context subclasses is no longer supported. Override resolve_or_missing instead of

... (truncated)

Commits

dependabot[bot] avatar Apr 22 '24 00:04 dependabot[bot]

Codecov Report

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

Project coverage is 35.64%. Comparing base (93597ca) to head (c706241).

Additional details and impacted files 
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##             main     #325   +/-   ##
=======================================
  Coverage   35.64%   35.64%           
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  Files           8        8           
  Lines         491      491           
=======================================
  Hits          175      175           
  Misses        316      316

:umbrella: View full report in Codecov by Sentry.
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codecov[bot] avatar Apr 22 '24 04:04 codecov[bot]