Bump tensorflow from 1.6.0 to 2.5.0 #44

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opened 2025-10-14 17:16:46 -06:00 by navan · 0 comments

navan commented 2025-10-14 17:16:46 -06:00

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Originally created by @dependabot[bot] on 5/21/2021

Bumps tensorflow from 1.6.0 to 2.5.0.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.5.0

Release 2.5.0

Major Features and Improvements

• Support for Python3.9 has been added.
• tf.data:
  • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
  • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
  • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
  • Options returned by tf.data.Dataset.options() are no longer mutable.
  • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
• tf.lite
  • Enabled the new MLIR-based quantization backend by default
    • The new backend is used for 8 bits full integer post-training quantization
    • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)
    • Set experimental_new_quantizer in tf.lite.TFLiteConverter to False to disable this change
• tf.keras
  • tf.keras.metrics.AUC now support logit predictions.
  • Enabled a new supported input type in Model.fit, tf.keras.utils.experimental.DatasetCreator, which takes a callable, dataset_fn. DatasetCreator is intended to work across all tf.distribute strategies, and is the only input type supported for Parameter Server strategy.
• tf.distribute
  • tf.distribute.experimental.ParameterServerStrategy now supports training with Keras Model.fit when used with DatasetCreator.
  • Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.
• TPU embedding support
  • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
• PluggableDevice
  • Third-party devices can now connect to TensorFlow as plug-ins through StreamExecutor C API. and PluggableDevice interface.
    • Add custom ops and kernels through kernel and op registration C API.
    • Register custom graph optimization passes with graph optimization C API.
• oneAPI Deep Neural Network Library (oneDNN) CPU performance optimizations from Intel-optimized TensorFlow are now available in the official x86-64 Linux and Windows builds.
  • They are off by default. Enable them by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.
  • We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.
• TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0

Breaking Changes

• The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

Bug Fixes and Other Changes

• tf.keras:
  • Preprocessing layers API consistency changes:
    • StringLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization.
    • IntegerLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization. Renamed max_values, oov_value and mask_value to max_tokens, oov_token and mask_token to align with StringLookup and TextVectorization.
    • TextVectorization default for pad_to_max_tokens switched to False.
    • CategoryEncoding no longer supports adapt, IntegerLookup now supports equivalent functionality. max_tokens argument renamed to num_tokens.
    • Discretization added num_bins argument for learning bins boundaries through calling adapt on a dataset. Renamed bins argument to bin_boundaries for specifying bins without adapt.
  • Improvements to model saving/loading:
    • model.load_weights now accepts paths to saved models.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.5.0

Breaking Changes

• The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

Known Caveats

Major Features and Improvements

• TPU embedding support

  • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.

• tf.keras.metrics.AUC now support logit predictions.

• Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.

• tf.data:

  • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
  • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
  • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
  • Options returned by tf.data.Dataset.options() are no longer mutable.
  • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().

• tf.lite

  • Enabled the new MLIR-based quantization backend by default
    • The new backend is used for 8 bits full integer post-training quantization
    • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)

... (truncated)

Commits

• a4dfb8d Merge pull request #49124 from tensorflow/mm-cherrypick-tf-data-segfault-fix-...
• 2107b1d Merge pull request #49116 from tensorflow-jenkins/version-numbers-2.5.0-17609
• 16b8139 Update snapshot_dataset_op.cc
• 86a0d86 Merge pull request #49126 from geetachavan1/cherrypicks_X9ZNY
• 9436ae6 Merge pull request #49128 from geetachavan1/cherrypicks_D73J5
• 6b2bf99 Validate that a and b are proper sparse tensors
• c03ad1a Ensure validation sticks in banded_triangular_solve_op
• 12a6ead Merge pull request #49120 from geetachavan1/cherrypicks_KJ5M9
• b67f5b8 Merge pull request #49118 from geetachavan1/cherrypicks_BIDTR
• a13c0ad [tf.data][cherrypick] Fix snapshot segfault when using repeat and prefecth
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

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You can disable automated security fix PRs for this repo from the Security Alerts page.

*Originally created by @dependabot[bot] on 5/21/2021* Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 1.6.0 to 2.5.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/releases">tensorflow's releases</a>.</em></p> <blockquote> <h2>TensorFlow 2.5.0</h2> <h1>Release 2.5.0</h1> <h2>Major Features and Improvements</h2> <ul> <li>Support for Python3.9 has been added.</li> <li><code>tf.data</code>: <ul> <li><code>tf.data</code> service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.</li> <li>tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing <code>compression=None</code> to <code>tf.data.experimental.service.distribute(...)</code>.</li> <li><code>tf.data.Dataset.batch()</code> now supports <code>num_parallel_calls</code> and <code>deterministic</code> arguments. <code>num_parallel_calls</code> is used to indicate that multiple input batches should be computed in parallel. With <code>num_parallel_calls</code> set, <code>deterministic</code> is used to indicate that outputs can be obtained in the non-deterministic order.</li> <li>Options returned by <code>tf.data.Dataset.options()</code> are no longer mutable.</li> <li>tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as <code>map</code>. The debug mode can be enabled through <code>tf.data.experimental.enable_debug_mode()</code>.</li> </ul> </li> <li><code>tf.lite</code> <ul> <li>Enabled the new MLIR-based quantization backend by default <ul> <li>The new backend is used for 8 bits full integer post-training quantization</li> <li>The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)</li> <li>Set <code>experimental_new_quantizer</code> in tf.lite.TFLiteConverter to False to disable this change</li> </ul> </li> </ul> </li> <li><code>tf.keras</code> <ul> <li><code>tf.keras.metrics.AUC</code> now support logit predictions.</li> <li>Enabled a new supported input type in <code>Model.fit</code>, <code>tf.keras.utils.experimental.DatasetCreator</code>, which takes a callable, <code>dataset_fn</code>. <code>DatasetCreator</code> is intended to work across all <code>tf.distribute</code> strategies, and is the only input type supported for Parameter Server strategy.</li> </ul> </li> <li><code>tf.distribute</code> <ul> <li><code>tf.distribute.experimental.ParameterServerStrategy</code> now supports training with Keras <code>Model.fit</code> when used with <code>DatasetCreator</code>.</li> <li>Creating <code>tf.random.Generator</code> under <code>tf.distribute.Strategy</code> scopes is now allowed (except for <code>tf.distribute.experimental.CentralStorageStrategy</code> and <code>tf.distribute.experimental.ParameterServerStrategy</code>). Different replicas will get different random-number streams.</li> </ul> </li> <li>TPU embedding support <ul> <li>Added <code>profile_data_directory</code> to <code>EmbeddingConfigSpec</code> in <code>_tpu_estimator_embedding.py</code>. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.</li> </ul> </li> <li>PluggableDevice <ul> <li>Third-party devices can now connect to TensorFlow as plug-ins through <a href="https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api.md">StreamExecutor C API</a>. and <a href="https://github.com/tensorflow/community/blob/master/rfcs/20200624-pluggable-device-for-tensorflow.md">PluggableDevice</a> interface. <ul> <li>Add custom ops and kernels through <a href="https://github.com/tensorflow/community/blob/master/rfcs/20190814-kernel-and-op-registration.md">kernel and op registration C API</a>.</li> <li>Register custom graph optimization passes with <a href="https://github.com/tensorflow/community/blob/master/rfcs/20201027-modular-tensorflow-graph-c-api.md">graph optimization C API</a>.</li> </ul> </li> </ul> </li> <li><a href="https://github.com/oneapi-src/oneDNN">oneAPI Deep Neural Network Library (oneDNN)</a> CPU performance optimizations from <a href="https://software.intel.com/content/www/us/en/develop/articles/intel-optimization-for-tensorflow-installation-guide.html">Intel-optimized TensorFlow</a> are now available in the official x86-64 Linux and Windows builds. <ul> <li>They are off by default. Enable them by setting the environment variable <code>TF_ENABLE_ONEDNN_OPTS=1</code>.</li> <li>We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.</li> </ul> </li> <li>TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0</li> </ul> <h2>Breaking Changes</h2> <ul> <li>The <code>TF_CPP_MIN_VLOG_LEVEL</code> environment variable has been renamed to to <code>TF_CPP_MAX_VLOG_LEVEL</code> which correctly describes its effect.</li> </ul> <h2>Bug Fixes and Other Changes</h2> <ul> <li><code>tf.keras</code>: <ul> <li>Preprocessing layers API consistency changes: <ul> <li><code>StringLookup</code> added <code>output_mode</code>, <code>sparse</code>, and <code>pad_to_max_tokens</code> arguments with same semantics as <code>TextVectorization</code>.</li> <li><code>IntegerLookup</code> added <code>output_mode</code>, <code>sparse</code>, and <code>pad_to_max_tokens</code> arguments with same semantics as <code>TextVectorization</code>. Renamed <code>max_values</code>, <code>oov_value</code> and <code>mask_value</code> to <code>max_tokens</code>, <code>oov_token</code> and <code>mask_token</code> to align with <code>StringLookup</code> and <code>TextVectorization</code>.</li> <li><code>TextVectorization</code> default for <code>pad_to_max_tokens</code> switched to False.</li> <li><code>CategoryEncoding</code> no longer supports <code>adapt</code>, <code>IntegerLookup</code> now supports equivalent functionality. <code>max_tokens</code> argument renamed to <code>num_tokens</code>.</li> <li><code>Discretization</code> added <code>num_bins</code> argument for learning bins boundaries through calling <code>adapt</code> on a dataset. Renamed <code>bins</code> argument to <code>bin_boundaries</code> for specifying bins without <code>adapt</code>.</li> </ul> </li> <li>Improvements to model saving/loading: <ul> <li><code>model.load_weights</code> now accepts paths to saved models.</li> </ul> </li> </ul> </li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md">tensorflow's changelog</a>.</em></p> <blockquote> <h1>Release 2.5.0</h1> <!-- raw HTML omitted --> <h2>Breaking Changes</h2> <ul> <li> <!-- raw HTML omitted --> </li> <li>The <code>TF_CPP_MIN_VLOG_LEVEL</code> environment variable has been renamed to to <code>TF_CPP_MAX_VLOG_LEVEL</code> which correctly describes its effect.</li> </ul> <h2>Known Caveats</h2> <ul> <li><!-- raw HTML omitted --></li> <li><!-- raw HTML omitted --></li> <li><!-- raw HTML omitted --></li> </ul> <h2>Major Features and Improvements</h2> <ul> <li> <p><!-- raw HTML omitted --></p> </li> <li> <p><!-- raw HTML omitted --></p> </li> <li> <p>TPU embedding support</p> <ul> <li>Added <code>profile_data_directory</code> to <code>EmbeddingConfigSpec</code> in <code>_tpu_estimator_embedding.py</code>. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.</li> </ul> </li> <li> <p><code>tf.keras.metrics.AUC</code> now support logit predictions.</p> </li> <li> <p>Creating <code>tf.random.Generator</code> under <code>tf.distribute.Strategy</code> scopes is now allowed (except for <code>tf.distribute.experimental.CentralStorageStrategy</code> and <code>tf.distribute.experimental.ParameterServerStrategy</code>). Different replicas will get different random-number streams.</p> </li> <li> <p><code>tf.data</code>:</p> <ul> <li>tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.</li> <li>tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing <code>compression=None</code> to <code>tf.data.experimental.service.distribute(...)</code>.</li> <li><code>tf.data.Dataset.batch()</code> now supports <code>num_parallel_calls</code> and <code>deterministic</code> arguments. <code>num_parallel_calls</code> is used to indicate that multiple input batches should be computed in parallel. With <code>num_parallel_calls</code> set, <code>deterministic</code> is used to indicate that outputs can be obtained in the non-deterministic order.</li> <li>Options returned by <code>tf.data.Dataset.options()</code> are no longer mutable.</li> <li>tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as <code>map</code>. The debug mode can be enabled through <code>tf.data.experimental.enable_debug_mode()</code>.</li> </ul> </li> <li> <p><code>tf.lite</code></p> <ul> <li>Enabled the new MLIR-based quantization backend by default <ul> <li>The new backend is used for 8 bits full integer post-training quantization</li> <li>The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)</li> </ul> </li> </ul> </li> </ul> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="https://github.com/tensorflow/tensorflow/commit/a4dfb8d1a71385bd6d122e4f27f86dcebb96712d"><code>a4dfb8d</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49124">#49124</a> from tensorflow/mm-cherrypick-tf-data-segfault-fix-...</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/2107b1dc414edb3fc78e632bca4f4936171093b2"><code>2107b1d</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49116">#49116</a> from tensorflow-jenkins/version-numbers-2.5.0-17609</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/16b813906fcb46306aef29a04ddd0cbdb4e77918"><code>16b8139</code></a> Update snapshot_dataset_op.cc</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/86a0d86cb5da6a28b78ea7f886ec2831d23f6d6b"><code>86a0d86</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49126">#49126</a> from geetachavan1/cherrypicks_X9ZNY</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/9436ae693ef66a9efb7e7e7888134173d9a0821d"><code>9436ae6</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49128">#49128</a> from geetachavan1/cherrypicks_D73J5</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/6b2bf99cd9336026689579b683a709c5efcb4ae9"><code>6b2bf99</code></a> Validate that a and b are proper sparse tensors</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/c03ad1a46d5b3f23df67dad03185a0ee16020c96"><code>c03ad1a</code></a> Ensure validation sticks in banded_triangular_solve_op</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/12a6ead7ac968c402feb85ce0a8069ccbc6bf735"><code>12a6ead</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49120">#49120</a> from geetachavan1/cherrypicks_KJ5M9</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/b67f5b8a0a098c34c71c679aa46480035c46886e"><code>b67f5b8</code></a> Merge pull request <a href="https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/49118">#49118</a> from geetachavan1/cherrypicks_BIDTR</li> <li><a href="https://github.com/tensorflow/tensorflow/commit/a13c0ade86295bd3a8356b4b8cc980cf0c5e70e0"><code>a13c0ad</code></a> [tf.data][cherrypick] Fix snapshot segfault when using repeat and prefecth</li> <li>Additional commits viewable in <a href="https://github.com/tensorflow/tensorflow/compare/v1.6.0...v2.5.0">compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`. [//]: # (dependabot-automerge-start) [//]: # (dependabot-automerge-end) --- <details> <summary>Dependabot commands and options</summary> <br /> You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) - `@dependabot use these labels` will set the current labels as the default for future PRs for this repo and language - `@dependabot use these reviewers` will set the current reviewers as the default for future PRs for this repo and language - `@dependabot use these assignees` will set the current assignees as the default for future PRs for this repo and language - `@dependabot use this milestone` will set the current milestone as the default for future PRs for this repo and language You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/sjvasquez/handwriting-synthesis/network/alerts). </details>

navan 2025-10-14 17:16:48 -06:00

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Reference: github/handwriting-synthesis#44

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