Skip to content
This repository was archived by the owner on Jul 9, 2025. It is now read-only.

Bump the pip group across 2 directories with 9 updates #67

Open
dependabot wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Bump the pip group across 2 directories with 9 updates #67

dependabot wants to merge 1 commit into from

Conversation

@dependabot
Copy link

@dependabot dependabot bot commented on behalf of github May 6, 2024

Bumps the pip group with 6 updates in the / directory:

Package From To
tqdm 4.46.0 4.66.3
scipy 1.4.1 1.11.1
joblib 0.14.1 1.2.0
tensorflow-gpu 1.15.2 2.12.0
tensorflow 1.15.2 2.11.1
pillow 7.1.2 10.3.0

Bumps the pip group with 8 updates in the /ldif/scripts directory:

Package From To
tqdm 4.46.1 4.66.3
scipy 1.4.1 1.11.1
joblib 0.16.0 1.2.0
tensorflow 1.15.0 2.11.1
pillow 7.1.2 10.3.0
certifi 2020.4.5.2 2023.7.22
grpcio 1.27.2 1.53.2
werkzeug 0.16.1 3.0.3

Updates tqdm from 4.46.0 to 4.66.3

Release notes

Sourced from tqdm's releases.

tqdm v4.66.3 stable

tqdm v4.66.2 stable

  • pandas: add DataFrame.progress_map (#1549)
  • notebook: fix HTML padding (#1506)
  • keras: fix resuming training when verbose>=2 (#1508)
  • fix format_num negative fractions missing leading zero (#1548)
  • fix Python 3.12 DeprecationWarning on import (#1519)
  • linting: use f-strings (#1549)
  • update tests (#1549)
  • CI: bump actions (#1549)

tqdm v4.66.1 stable

  • fix utils.envwrap types (#1493 <- #1491, #1320 <- #966, #1319)
    • e.g. cloudwatch & kubernetes workaround: export TQDM_POSITION=-1
  • drop mentions of unsupported Python versions

tqdm v4.66.0 stable

  • environment variables to override defaults (TQDM_*) (#1491 <- #1061, #950 <- #614, #1318, #619, #612, #370)
    • e.g. in CI jobs, export TQDM_MININTERVAL=5 to avoid log spam
    • add tests & docs for tqdm.utils.envwrap
  • fix & update CLI completion
  • fix & update API docs
  • minor code tidy: replace os.path => pathlib.Path
  • fix docs image hosting
  • release with CI bot account again (cli/cli#6680)

tqdm v4.65.2 stable

  • exclude examples from distributed wheel (#1492)

tqdm v4.65.1 stable

  • migrate setup.{cfg,py} => pyproject.toml (#1490)
    • fix asv benchmarks
    • update docs
  • fix snap build (#1490)
  • fix & update tests (#1490)
    • fix flaky notebook tests
    • bump pre-commit
    • bump workflow actions

tqdm v4.65.0 stable

  • add Python 3.11 and drop Python 3.6 support (#1439, #1419, #502 <- #720, #620)
  • misc code & docs tidy
  • fix & update CI workflows & tests

tqdm v4.64.1 stable

... (truncated)

Commits

Updates scipy from 1.4.1 to 1.11.1

Release notes

Sourced from scipy's releases.

SciPy 1.11.1 Release Notes

SciPy 1.11.1 is a bug-fix release with no new features compared to 1.11.0. In particular, a licensing issue discovered after the release of 1.11.0 has been addressed.

Authors

  • Name (commits)
  • h-vetinari (1)
  • Robert Kern (1)
  • Ilhan Polat (4)
  • Tyler Reddy (8)

A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete.

SciPy 1.11.0 Release Notes

SciPy 1.11.0 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 1.11.x branch, and on adding new features on the main branch.

This release requires Python 3.9+ and NumPy 1.21.6 or greater.

For running on PyPy, PyPy3 6.0+ is required.

Highlights of this release

  • Several scipy.sparse array API improvements, including sparse.sparray, a new public base class distinct from the older sparse.spmatrix class, proper 64-bit index support, and numerous deprecations paving the way to a modern sparse array experience.
  • scipy.stats added tools for survival analysis, multiple hypothesis testing, sensitivity analysis, and working with censored data.

... (truncated)

Commits
  • cfe8011 REL: 1.11.1 rel commit [wheel build]
  • 450d8aa Merge pull request #18779 from tylerjereddy/treddy_1_11_1_prep
  • 6f942e8 DOC: update 1.11.1 relnotes
  • 145cec5 MAINT: fix unuran licensing
  • 0760bab MAINT:linalg.det:Return scalars for singleton inputs (#18763)
  • a1c6f99 MAINT:linalg:Use only NumPy types in lu
  • 5cdc2fe MAINT:linalg:Remove memcpy from lu
  • d9ac3f3 FIX:linalg:Guard against possible permute_l out of bound behavior
  • 7ec5010 BUG: fix handling for factorial(..., exact=False) for 0-dim array inputs (#...
  • 90415c6 BUG: Fix work array construction for various weight shapes. (#18741)
  • Additional commits viewable in compare view

Updates joblib from 0.14.1 to 1.2.0

Changelog

Sourced from joblib's changelog.

Release 1.2.0

  • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

  • Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

  • Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

  • Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

  • Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

  • Vendor loky 3.3.0 which fixes several bugs including:

    • robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

    • avoiding leaking worker processes in case of nested loky parallel calls;

    • reliability spawn the correct number of reusable workers.

Release 1.1.1

  • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

Release 1.1.0

  • Fix byte order inconsistency issue during deserialization using joblib.load

... (truncated)

Commits
  • 5991350 Release 1.2.0
  • 3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)
  • cea26ff CI test the future loky-3.3.0 branch (#1338)
  • 8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)
  • 067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)
  • ac4ebd5 MAINT add back pytest warnings plugin (#1337)
  • a23427d Test child raises parent exits cleanly more reliable on macos (#1335)
  • ac09691 [MAINT] various test updates (#1334)
  • 4a314b1 Vendor loky 3.2.0 (#1333)
  • bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...
  • Additional commits viewable in compare view

Updates tensorflow-gpu from 1.15.2 to 2.12.0

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.12.0

Release 2.12.0

TensorFlow

Breaking Changes

  • Build, Compilation and Packaging

    • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

  • tf.function:

    • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
      • Using functools.wraps on a function with different signature
      • Using functools.partial with an invalid tf.function input
    • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
    • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
    • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
    • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

  • Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

  • Support for Python 3.11 has been added.

  • Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

  • tf.lite:

    • Add 16-bit float type support for built-in op fill.
    • Transpose now supports 6D tensors.
    • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

  • tf.experimental.dtensor:

    • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
    • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

  • tf.data:

    • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

  • tf.test:

    • Added tf.test.experimental.sync_devices, which is useful for accurately measuring performance in benchmarks.

  • tf.experimental.dtensor:

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.12.0

Breaking Changes

  • Build, Compilation and Packaging

    • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

  • tf.function:

    • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
      • Using functools.wraps on a function with different signature
      • Using functools.partial with an invalid tf.function input
    • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
    • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
    • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
    • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

  • Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

  • Support for Python 3.11 has been added.

  • Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

  • tf.lite:

    • Add 16-bit float type support for built-in op fill.
    • Transpose now supports 6D tensors.
    • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

  • tf.experimental.dtensor:

    • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
    • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

  • tf.data:

    • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
    • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

  • tf.test:

    • Added tf.test.experimental.sync_devices, which is useful for accurately measuring performance in benchmarks.

  • tf.experimental.dtensor:

    • Added experimental support to ReduceScatter fuse on GPU (NCCL).

... (truncated)

Commits
  • 0db597d Merge pull request #60051 from tensorflow/venkat2469-patch-1
  • 1a12f59 Update RELEASE.md
  • aa4d558 Merge pull request #60050 from tensorflow/venkat-patch-6
  • bd1ab8a Update the security section in RELEASE.md
  • 4905be0 Merge pull request #60049 from tensorflow/venkat-patch-5
  • 9f96caa Update setup.py on TF release branch with released version of Estimator and k...
  • e719b6b Update Relese.md (#60033)
  • 64a9d54 Merge pull request #60017 from tensorflow/joefernandez-patch-2.12-release-notes
  • 7a4ebfd Update RELEASE.md
  • e0e10a9 Merge pull request #59988 from tensorflow-jenkins/version-numbers-2.12.0-8756
  • Additional commits viewable in compare view

Updates tensorflow from 1.15.2 to 2.11.1

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.11.1

Release 2.11.1

Note: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.

  • Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourself steps. You can refer to the release notes of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.

This release also introduces several vulnerability fixes:

TensorFlow 2.11.0

Release 2.11.0

Breaking Changes

  • The tf.keras.optimizers.Optimizer base class now points to the new Keras optimizer, while the old optimizers have been moved to the tf.keras.optimizers.legacy namespace.

    If you find your workflow failing due to this change, you may be facing one of the following issues:

    • Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizer.legacy.XXX (e.g. tf.keras.optimizer.legacy.Adam).
    • TF1 compatibility. The new optimizer, tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend migrating your workflow to TF2 for stable support and new features.
    • Old optimizer API not found. The new optimizer, tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
    • Learning rate schedule access. When using a tf.keras.optimizers.schedules.LearningRateSchedule, the new optimizer's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._learning_rate.
    • If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in the Keras GitHub repo.
    • Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls the optimizer to update different parts of the model in multiple stages, please call optimizer.build(model.trainable_variables) before the training loop.
    • Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.

    The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example, tf.keras.optimizers.Adafactor) will only be implemented based on the new tf.keras.optimizers.Optimizer base class.

  • tensorflow/python/keras code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release. Please remove any import of tensorflow.python.keras and use the public API with from tensorflow import keras or import tensorflow as tf; tf.keras.

Major Features and Improvements

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.11.1

Note: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.

  • Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourself steps. You can refer to the release notes of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.

This release also introduces several vulnerability fixes:

Release 2.11.0

Breaking Changes

  • tf.keras.optimizers.Optimizer now points to the new Keras optimizer, and old optimizers have moved to the tf.keras.optimizers.legacy namespace. If you find your workflow failing due to this change, you may be facing one of the following issues:

    • Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizers.legacy.XXX (e.g. tf.keras.optimizers.legacy.Adam).
    • TF1 compatibility. The new optimizer does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend to migrate your workflow to TF2 for stable support and new features.
    • API not found. The new optimizer has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API

... (truncated)

Commits
  • a3e2c69 Merge pull request #60016 from tensorflow/fix-relnotes
  • 13b85dc Fix release notes
  • 48b18db Merge pull request #60014 from tensorflow/disable-test-that-ooms
  • eea48f5 Disable a test that results in OOM+segfault
  • a632584 Merge pull request #60000 from tensorflow/venkat-patch-3
  • 93dea7a Update RELEASE.md
  • a2ba9f1 Updating Release.md with Legal Language for Release Notes
  • fae41c7 Merge pull request #59998 from tensorflow/fix-bad-cherrypick-again
  • 2757416 Fix bad cherrypick
  • c78616f Merge pull request #59992 from tensorflow/fix-2.11-build
  • Additional commits viewable in compare view

Updates pillow from 7.1.2 to 10.3.0

Release notes

Sourced from pillow's releases.

10.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/10.3.0.html

Changes

... (truncated)

Changelog

Sourced from pillow's changelog.

10.3.0 (2024-04-01)

  • CVE-2024-28219: Use strncpy to avoid buffer overflow #7928 [radarhere, hugovk]

  • Deprecate eval(), replacing it with lambda_eval() and unsafe_eval() #7927 [radarhere, hugovk]

  • Raise ValueError if seeking to greater than offset-sized integer in TIFF #7883 [radarhere]

  • Add --report argument to __main__.py to omit supported formats #7818 [nulano, radarhere, hugovk]

  • Added RGB to I;16, I;16L, I;16B and I;16N conversion #7918, #7920 [radarhere]

  • Fix editable installation with custom build backend and configuration options #7658 [nulano, radarhere]

  • Fix putdata() for I;16N on big-endian #7209 [Yay295, hugovk, radarhere]

  • Determine MPO size from markers, not EXIF data #7884 [radarhere]

  • Improved conversion from RGB to RGBa, LA and La #7888 [radarhere]

  • Support FITS images with GZIP_1 compression #7894 [radarhere]

  • Use I;16 mode for 9-bit JPEG 2000 images #7900 [scaramallion, radarhere]

  • Raise ValueError if kmeans is negative #7891 [radarhere]

  • Remove TIFF tag OSUBFILETYPE when saving using libtiff #7893 [radarhere]

  • Raise ValueError for negative values when loading P1-P3 PPM images #7882 [radarhere]

  • Added reading of JPEG2000 palettes #7870 [radarhere]

  • Added alpha_quality argument when saving WebP images #7872 [radarhere]

... (truncated)

Commits
  • 5c89d88 10.3.0 version bump
  • 63cbfcf Update CHANGES.rst [ci skip]
  • 2776126 Merge pull request #7928 from python-pillow/lcms
  • aeb51cb Merge branch 'main' into lcms
  • 5beb0b6 Update CHANGES.rst [ci skip]
  • cac6ffa Merge pull request #7927 from python-pillow/imagemath
  • f5eeeac Name as 'options' in lambda_eval and unsafe_eval, but '_dict' in deprecated eval
  • facf3af Added release notes
  • 2a93aba Use strncpy to avoid buffer overflow
  • a670597 Update CHANGES.rst [ci skip]
  • Additional commits viewable in compare view

Updates tqdm from 4.46.1 to 4.66.3

Release notes

Sourced from tqdm's releases.

tqdm v4.66.3 stable

tqdm v4.66.2 stable

  • pandas: add DataFrame.progress_map (#1549)
  • notebook: fix HTML padding (#1506)
  • keras: fix resuming training when verbose>=2 (#1508)
  • fix format_num negative fractions missing leading zero (#1548)
  • fix Python 3.12 DeprecationWarning on import (#1519)
  • linting: use f-strings (#1549)
  • update tests (#1549)
  • CI: bump actions (#1549)

tqdm v4.66.1 stable

  • fix utils.envwrap types (#1493 <- #1491, #1320 <- #966, #1319)
    • e.g. cloudwatch & kubernetes workaround: export TQDM_POSITION=-1
  • drop mentions of unsupported Python versions

tqdm v4.66.0 stable

  • environment variables to override defaults (TQDM_*) (#1491 <- #1061, #950 <- #614, #1318, #619, #612, #370)
    • e.g. in CI jobs, export TQDM_MININTERVAL=5 to avoid log spam
    • add tests & docs for tqdm.utils.envwrap
  • fix & update CLI completion
  • fix & update API docs
  • minor code tidy: replace os.path => pathlib.Path
  • fix docs image hosting
  • release with CI bot account again (cli/cli#6680)

tqdm v4.65.2 stable

  • exclude examples from distributed wheel (#1492)

tqdm v4.65.1 stable

  • migrate setup.{cfg,py} => pyproject.toml (#1490)
    • fix asv benchmarks
    • update docs
  • fix snap build (#1490)
  • fix & update tests (#1490)
    • fix flaky notebook tests
    • bump pre-commit
    • bump workflow actions

tqdm v4.65.0 stable

  • add Python 3.11 and drop Python 3.6 support (#1439, #1419, #502 <- #720, #620)
  • misc code & docs tidy
  • fix & update CI workflows & tests

tqdm v4.64.1 stable

... (truncated)

Commits

@dependabot
Bump the pip group across 2 directories with 9 updates
339fe4f 
Bumps the pip group with 6 updates in the / directory:

| Package | From | To |
| --- | --- | --- |
| [tqdm](https://github.com/tqdm/tqdm) | `4.46.0` | `4.66.3` |
| [scipy](https://github.com/scipy/scipy) | `1.4.1` | `1.11.1` |
| [joblib](https://github.com/joblib/joblib) | `0.14.1` | `1.2.0` |
| [tensorflow-gpu](https://github.com/tensorflow/tensorflow) | `1.15.2` | `2.12.0` |
| [tensorflow](https://github.com/tensorflow/tensorflow) | `1.15.2` | `2.11.1` |
| [pillow](https://github.com/python-pillow/Pillow) | `7.1.2` | `10.3.0` |

Bumps the pip group with 8 updates in the /ldif/scripts directory:

| Package | From | To |
| --- | --- | --- |
| [tqdm](https://github.com/tqdm/tqdm) | `4.46.1` | `4.66.3` |
| [scipy](https://github.com/scipy/scipy) | `1.4.1` | `1.11.1` |
| [joblib](https://github.com/joblib/joblib) | `0.16.0` | `1.2.0` |
| [tensorflow](https://github.com/tensorflow/tensorflow) | `1.15.0` | `2.11.1` |
| [pillow](https://github.com/python-pillow/Pillow) | `7.1.2` | `10.3.0` |
| [certifi](https://github.com/certifi/python-certifi) | `2020.4.5.2` | `2023.7.22` |
| [grpcio](https://github.com/grpc/grpc) | `1.27.2` | `1.53.2` |
| [werkzeug](https://github.com/pallets/werkzeug) | `0.16.1` | `3.0.3` |



Updates `tqdm` from 4.46.0 to 4.66.3
- [Release notes](https://github.com/tqdm/tqdm/releases)
- [Commits](tqdm/tqdm@v4.46.0...v4.66.3)

Updates `scipy` from 1.4.1 to 1.11.1
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](scipy/scipy@v1.4.1...v1.11.1)

Updates `joblib` from 0.14.1 to 1.2.0
- [Release notes](https://github.com/joblib/joblib/releases)
- [Changelog](https://github.com/joblib/joblib/blob/main/CHANGES.rst)
- [Commits](joblib/joblib@0.14.1...1.2.0)

Updates `tensorflow-gpu` from 1.15.2 to 2.12.0
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@v1.15.2...v2.12.0)

Updates `tensorflow` from 1.15.2 to 2.11.1
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@v1.15.2...v2.11.1)

Updates `pillow` from 7.1.2 to 10.3.0
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](python-pillow/Pillow@7.1.2...10.3.0)

Updates `tqdm` from 4.46.1 to 4.66.3
- [Release notes](https://github.com/tqdm/tqdm/releases)
- [Commits](tqdm/tqdm@v4.46.0...v4.66.3)

Updates `scipy` from 1.4.1 to 1.11.1
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](scipy/scipy@v1.4.1...v1.11.1)

Updates `joblib` from 0.16.0 to 1.2.0
- [Release notes](https://github.com/joblib/joblib/releases)
- [Changelog](https://github.com/joblib/joblib/blob/main/CHANGES.rst)
- [Commits](joblib/joblib@0.14.1...1.2.0)

Updates `tensorflow` from 1.15.0 to 2.11.1
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@v1.15.2...v2.11.1)

Updates `pillow` from 7.1.2 to 10.3.0
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](python-pillow/Pillow@7.1.2...10.3.0)

Updates `certifi` from 2020.4.5.2 to 2023.7.22
- [Commits](certifi/python-certifi@2020.04.05.2...2023.07.22)

Updates `grpcio` from 1.27.2 to 1.53.2
- [Release notes](https://github.com/grpc/grpc/releases)
- [Changelog](https://github.com/grpc/grpc/blob/master/doc/grpc_release_schedule.md)
- [Commits](grpc/grpc@v1.27.2...v1.53.2)

Updates `werkzeug` from 0.16.1 to 3.0.3
- [Release notes](https://github.com/pallets/werkzeug/releases)
- [Changelog](https://github.com/pallets/werkzeug/blob/main/CHANGES.rst)
- [Commits](pallets/werkzeug@0.16.1...3.0.3)

---
updated-dependencies:
- dependency-name: tqdm
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: scipy
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: joblib
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: tensorflow-gpu
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: tensorflow
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: pillow
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: tqdm
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: scipy
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: joblib
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: tensorflow
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: pillow
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: certifi
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: grpcio
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: werkzeug
  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 May 6, 2024
@dependabot dependabot bot mentioned this pull request May 6, 2024
Closed
Sign up for free to subscribe to this conversation on GitHub. Already have an account? Sign in.

Reviewers

No reviews

Assignees

No one assigned

Labels

dependencies Pull requests that update a dependency file

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

0 participants