v2.0.0

• initial release compatible with TensorFlow 2.x.
• API changes:
  • removed TFNode.start_cluster_server, which is not required for tf.keras and tf.estimator.
  • removed TFNode.export_saved_model, which can be replaced by TF native APIs now.
  • added TFNodeContext.num_workers to count master, chief, and worker nodes.
• Spark ML Pipeline API changes:
  • Scala API has been removed for now, since the Java library for TensorFlow 2.0 is not available yet.
  • removed InputMode.TENSORFLOW support for ML Pipelines, since the input data is always a Spark DataFrame for this API.
  • added HasMasterNode and HasGraceSecs params.
  • removed optional export_fn argument for Spark ML TFEstimator (use TF export APIs instead).
  • added standard default values for signature_def_key and tag_set for Spark ML TFModel.
  • modified inferencing code in TFModel for TF2.x APIs.
• older TF 1.x examples have been replaced with TF 2.x compatible examples.

v1.4.4

• last expected release compatible with TensorFlow 1.x (aside from any critical fixes), since the master branch will be moving to TF 2.0 compatibility.
• handle multiple outputs with signaturedef (thanks to @markromedia).
• handle exceptions after data feeding.
• moved API docs to sphinx_rtd_theme.
• updated to Spark 2.4.4.

v1.4.3

• removed tensorflow as a dependency, in order to support other variants like tensorflow-gpu or tf-nightly.
• allow use of evaluator node type in cluster (thanks to @bbshetty)
• refactored cluster template generation.
• updated wide-deep example to use models/official code.
• restore termination of feed in mnist/spark example.
• updated sample notebook instructions.
• updated to use Spark 2.3.3.

v1.4.2

• Set TF_CONFIG for "chief" clusters (required by DistributionStrategy APIs)
• Fix GPU allocation for multi-gpu nodes
• Updated examples for MNIST
• Updated Hadoop and Spark dependency versions

v1.4.1

• Added util.single_node_env(), which can be used to initialize the environment (HDFS compatibility + GPU allocation) for running a single-node instance of TensorFlow on the Spark driver.
• Added an example of parallelized inferencing from a pre-trained SavedModel.

v1.4.0

• More deterministic GPU allocation for multi-GPU nodes.
• Added timeout argument to TFCluster.shutdown() (default is 3 days). This is intended to shutdown the Spark application in the event that any of the TF nodes hang for any reason. Set to -1 to disable timeout.
• Added ability to start reservation server on a specific port (contributed by @AvihayTsayeg).
• Updated pipeline API for latest TF APIs (contributed by @AvihayTsayeg)
• Added unit test for tf.SparseTensor support.
• Updated examples to latest TF APIs (including workaround for tensorflow/tensorflow#21745).
• Updated Spark version dependency for Scala Inferencing API.
• Added __version__ to module.

v1.3.4

• Travis CI integration for Python documentation and Scala Inferencing API builds.
• Added sys.path to tensorboard search path.

v1.3.3

• Only set TF_CONFIG environment variable if cluster_spec has a "master", i.e. when using tf.estimator.
• Updated mnist/keras/mnist_mlp_estimator.py with example of distributed/parallel inferencing via estimator.predict.
• Added optional feed_timeout argument to TFCluster.train() for InputMode.SPARK.
• Added optional grace_secs argument to TFCluster.shutdown().
• Workaround for firewall proxy issue with get_ip_address (contributed by @viplav).
• Add support for all Hadoop-compatible File System schemes (contributed by @vishnu2kmohan).
• Added error messages to assert statements.
• Initial Travis CI integration.

v1.3.2

• add grace period to TFCluster.shutdown()
• add wide & deep example (contributed by @crafet)
• update mnist/pipeline examples to tf.data, add instructions, and misc code cleanup (from @yileic)
• parameterize versions in pom.xml and code cleanup (from @tmielika)
• update Scala Inferencing pom.xml to latest tensorflow-hadoop artifact (contributed by @psuszyns)

v1.3.1

• Add keras/estimator example
• Update original keras example to latest tf.keras apis
• Update Scala Inferencing pom.xml to latest TF java version
• Allow PS to use CPU on TF-GPU builds (contributed by @dratini6)
• More pep8
• More py2/py3 compat