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PyPI · #1467 most downloaded on PyPI
TensorFlow Estimator.
Last release 3 years ago
no release in 18 months
Ships fairly regularly
a new release about every 5 weeks
Most releases are documented
notes for 20 of 29 stable releases
Nothing withdrawn
no release was ever pulled
8 years old
47 releases · first in 2018
This release matches TensorFlow 2.15.0. For features and changes, please see RELEASE.md
This release matches TensorFlow 2.15.0. For features and changes, please see RELEASE.md
This release matches TensorFlow 2.15.0-rc0. For features and changes, please see RELEASE.md
This release matches TensorFlow 2.15.0-rc0. For features and changes, please see RELEASE.md
One column per quarter.
This release matches TensorFlow 2.14.0. For features and changes, please see RELEASE.md
This release matches TensorFlow 2.14.0. For features and changes, please see RELEASE.md
This release matches TensorFlow 2.14.0-rc0. For features and changes, please see RELEASE.md
This release matches TensorFlow 2.14.0-rc0. For features and changes, please see RELEASE.md
This release matches TensorFlow 2.13.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.13.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.13.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.13.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.12.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.12.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.12.0-RC0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.12.0-RC0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.11.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.11.0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.11.0-RC0. For features and changes, please see RELEASE.md.
This release matches TensorFlow 2.11.0-RC0. For features and changes, please see RELEASE.md.
Nothing published for this version
Nothing published for this version
Due to security issues in TF 2.8, all boosted trees code has now been removed (after being deprecated in TF 2.8). Users should switch to TensorFlow De…
This release matches TensorFlow 2.9.0. For features and changes, please see RELEASE.md
Due to security issues in TF 2.8, all boosted trees code has now been removed (after being deprecated in TF 2.8). Users should switch to TensorFlow Decision Forests.
Nothing published for this version
This release matches TensorFlow 2.8.0
This release matches TensorFlow 2.8.0
No significant changes to add.
This release matches TensorFlow 2.8.0-RC0.
This release matches TensorFlow 2.8.0-RC0.
No significant changes to add.
This release matches TF 2.7.0 release.
This release matches TF 2.7.0 release.
Full Changelog: https://github.com/tensorflow/estimator/compare/v2.6.0...v2.7.0
This release matches TF 2.7.0 RC0 release.
This release matches TF 2.7.0 RC0 release.
Full Changelog: https://github.com/tensorflow/estimator/compare/v2.6.0...v2.7.0-rc0
This release matches TensorFlow 2.6.0 and it also includes changes needed for Keras separation to a separate repo
This release matches TensorFlow 2.6.0 and it also includes changes needed for Keras separation to a separate repo
This release matches TensorFlow 2.6.0-rc0 and it also includes changes needed for Keras separation to a separate repo
This release matches TensorFlow 2.6.0-rc0 and it also includes changes needed for Keras separation to a separate repo
This release matches TensorFlow 2.5.0
This release matches TensorFlow 2.5.0
This release matches TensorFlow 2.5.0-rc0
This release matches TensorFlow 2.5.0-rc0
This release matches TensorFlow 2.4.0
This release matches TensorFlow 2.4.0
This release matches TensorFlow 2.4.0-rc0
This release matches TensorFlow 2.4.0-rc0
This release matches TensorFlow
This release matches TensorFlow
This release matches TensorFlow 2.3.0-rc0
This release matches TensorFlow 2.3.0-rc0
Estimator SavedModels now save resource variables by default.
This release contains contributions from many people at Google.
Nothing published for this version
Nothing published for this version
Nothing published for this version
TensorFlow Estimator 2.0 has been released with a private import of a symbol from TensorFlow master. However, that symbol does not exist on TensorFlow
TensorFlow Estimator 2.0 has been released with a private import of a symbol from TensorFlow master. However, that symbol does not exist on TensorFlow 2.0, due to a race condition regarding branch cuts.
This release patches TensorFlow Estimator to resolve this symbol not found error.
This release contains contributions from many people at Google.
Both for Estimator and for main TensorFlow, tf.contrib has been deprecated, and functionality has been either migrated to the core TensorFlow API, to…
Both for Estimator and for main TensorFlow, tf.contrib has been deprecated, and functionality has been either migrated to the core TensorFlow API, to an ecosystem project such as https://www.github.com/tensorflow/addons or https://www.github.com/tensorflow/io, or removed entirely.
This release contains contributions from many people at Google.
This release is similar to 1.15.1 with the only addition of making early_stopping hook work again after TensorFlow released security patch 1.15.2
This release is similar to 1.15.1 with the only addition of making early_stopping hook work again after TensorFlow released security patch 1.15.2
This release contains contributions from many people at Google.
This release is the same as 1.15.0 but we needed a new release to update the version number inside setup.py
This release is the same as 1.15.0 but we needed a new release to update the version number inside setup.py
tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.This release contains contributions from many people at Google.
tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with m
tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.This release contains contributions from many people at Google.
Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
tf.compat.v1.estimator.inputs instead of tf.estimator.inputscontrib references with tf.estimator.experimental.* for APIs in early_stopping.py--iterations_per_loop for TPUEstimator or DistributionStrategy continues to be a challenge for our users. We propose dynamically tuning the --iterations_per_loop variable, specifically for using TPUEstimator in training mode, based on a user target TPU execution time. Users might specify a value such as: --iterations_per_loop=300s, which will result in roughly 300 seconds being spent on the TPU between host side operations.This release contains contributions from many people at Google.
Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
tf.compat.v1.estimator.inputs instead of tf.estimator.inputscontrib references with tf.estimator.experimental.* for APIs in early_stopping.py--iterations_per_loop for TPUEstimator or DistributionStrategy continues to be a challenge for our users. We propose dynamically tuning the --iterations_per_loop variable, specifically for using TPUEstimator in training mode, based on a user target TPU execution time. Users might specify a value such as: --iterations_per_loop=300s, which will result in roughly 300 seconds being spent on the TPU between host side operations.This release contains contributions from many people at Google.
Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
tf.compat.v1.estimator.inputs instead of tf.estimator.inputscontrib references with tf.estimator.experimental.* for APIs in early_stopping.py--iterations_per_loop for TPUEstimator or DistributionStrategy continues to be a challenge for our users. We propose dynamically tuning the --iterations_per_loop variable, specifically for using TPUEstimator in training mode, based on a user target TPU execution time. Users might specify a value such as: --iterations_per_loop=300s, which will result in roughly 300 seconds being spent on the TPU between host side operations.This release contains contributions from many people at Google.
Replace all occurences of tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimator
tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimatortf.contrib.estimator.DNNLinearCombinedEstimator with tf.estimator.DNNLinearCombinedEstimatortf.contrib.estimator.DNNEstimator with tf.estimator.DNNEstimatortf.contrib.estimator.LinearEstimator with tf.estimator.LinearEstimatortf.contrib.estimator.export_all_saved_models and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models.regression_head to head API for Canned Estimator V2.multi_class_head to head API for Canned Estimator V2.tf.contrib.estimator.InMemoryEvaluatorHook and tf.contrib.estimator.make_stop_at_checkpoint_step_hook with tf.estimator.experimental.InMemoryEvaluatorHook and tf.estimator.experimental.make_stop_at_checkpoint_step_hookThis release contains contributions from many people at Google.
Replace all occurences of tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimator
tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimatortf.contrib.estimator.DNNLinearCombinedEstimator with tf.estimator.DNNLinearCombinedEstimatortf.contrib.estimator.DNNEstimator with tf.estimator.DNNEstimatortf.contrib.estimator.LinearEstimator with tf.estimator.LinearEstimatortf.contrib.estimator.export_all_saved_models and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models.regression_head to head API for Canned Estimator V2.multi_class_head to head API for Canned Estimator V2.tf.contrib.estimator.InMemoryEvaluatorHook and tf.contrib.estimator.make_stop_at_checkpoint_step_hook with tf.estimator.experimental.InMemoryEvaluatorHook and tf.estimator.experimental.make_stop_at_checkpoint_step_hookThis release contains contributions from many people at Google.
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
Nothing published for this version
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