Rflow-tfx

Latest version: v1.1.18

Safety actively analyzes 666166 Python packages for vulnerabilities to keep your Python projects secure.

Scan your dependencies

Page 5 of 9

0.30.1

Major Features and Improvements

* TFX CLI now supports
[Vertex Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
use it with `--engine=vertex` flag.

Breaking Changes

For Pipeline Authors

* N/A

For Component Authors

* N/A

Deprecations

* N/A

Bug Fixes and Other Changes

* Fix resolver artifact filter in TFX -> KFP IR compiler with OP filter syntax.
* Forces keyword arguments for AirflowComponent to make it compatible with
Apache Airflow 2.1.0 and later.

Documentation Updates

* N/A

0.30.0

Major Features and Improvements

* Upgraded TFX to KFP compiler to use KFP IR schema version 2.0.0.
* InfraValidator can now produce a [SavedModel with warmup requests](
https://www.tensorflow.org/tfx/serving/saved_model_warmup). This feature is
enabled by setting `RequestSpec.make_warmup = True`. The SavedModel will be
stored in the InfraBlessing artifact (`blessing` output of InfraValidator).
* Pusher's `model` input is now optional, and `infra_blessing` can be used
instead to push the SavedModel with warmup requests, produced by an
InfraValidator. Note that InfraValidator does not always create a SavedModel,
and the producer InfraValidator must be configured with
`RequestSpec.make_warmup = True` in order to be pushed by a Pusher.
* Support is added for the JSON_VALUE artifact property type, allowing storage
of JSON-compatible objects as artifact metadata.
* Support is added for the KFP v2 artifact metadata field when executing using
the KFP v2 container entrypoint.
* InfraValidator for Kubernetes now can override Pod manifest to customize
annotations and environment variables.
* Allow Beam pipeline args to be extended by specifying
`beam_pipeline_args` per component.
* Support string RuntimeParameters on Airflow.
* User code specified through the `module_file` argument for the Evaluator,
Transform, Trainer and Tuner components is now packaged as a pip wheel for
execution. For Evaluator and Transform, these wheel packages are now
installed on remote Apache Beam workers.

Breaking Changes

For Pipeline Authors

* CLI usage with kubeflow changed significantly. You MUST use the new:
* `--build-image` to build a container image when
updating a pipeline with kubeflow engine.
* `--build-target-image` flag in CLI is changed to `--build-image` without
any container image argument. TFX will auto detect the image specified in
the KubeflowDagRunnerConfig class instance. For example,
python
tfx pipeline create --pipeline-path=runner.py --endpoint=xxx --build-image
tfx pipeline update --pipeline-path=runner.py --endpoint=xxx --build-image

* `--package-path` and `--skaffold_cmd` flags were deleted. The compiled path
can be specified when creating a KubeflowDagRunner class instance. TFX CLI
doesn't depend on skaffold any more and use Docker SDK directly.
* Specify the container image for KubeflowDagRunner in the
KubeflowDagRunnerConfig directly instead of reading it from an environment
variable. CLI will not set `KUBEFLOW_TFX_IMAGE` environment variable any
more. See
[example](https://github.com/tensorflow/tfx/blob/c315e7cf75822088e974e15b43c96fab86746733/tfx/experimental/templates/taxi/kubeflow_runner.py#L63).
* Default orchestration engine of CLI was changed to `local` orchestrator from
`beam` orchestrator. You can still use `beam` orchestrator with
`--engine=beam` flag.
* Trainer now uses GenericExecutor as default. To use the previous Estimator
based Trainer, please set custom_executor_spec to trainer.executor.Executor.
* Changed the pattern spec supported for QueryBasedDriver:
* span_begin_timestamp: Start of span interval, Timestamp in seconds.
* span_end_timestamp: End of span interval, Timestamp in seconds.
* span_yyyymmdd_utc: STRING with format, e.g., '20180114', corresponding
to the span interval begin in UTC.
* Removed the already deprecated compile() method on Kubeflow V2 Dag Runner.
* Removed project_id argument from KubeflowV2DagRunnerConfig which is not used
and meaningless if not used with GCP.
* Removed config from LocalDagRunner's constructor, and dropped pipeline proto
support from LocalDagRunner's run function.
* Removed input parameter in ExampleGen constructor and external_input in
dsl_utils, which were called as deprecated in TFX 0.23.
* Changed the storage type of `span` and `version` custom property in Examples
artifact from string to int.
* `ResolverStrategy.resolve_artifacts()` method signature has changed to take
`ml_metadata.MetadataStore` object as the first argument.
* Artifacts param is deprecated/ignored in Channel constructor.
* Removed matching_channel_name from Channel's constructor.
* Deleted all usages of instance_name, which was deprecated in version 0.25.0.
Please use .with_id() method of components.
* Removed output channel overwrite functionality from all official components.
* Transform will use the native TF2 implementation of tf.transform unless TF2
behaviors are explicitly disabled. The previous behaviour can still be
obtained by setting `force_tf_compat_v1=True`.

For Component Authors

* N/A

Deprecations

* RuntimeParameter usage for `module_file` and user-defined function paths is
marked experimental.
* `LatestArtifactsResolver`, `LatestBlessedModelResolver`, `SpansResolver`
are renamed to `LatestArtifactStrategy`, `LatestBlessedModelStrategy`,
`SpanRangeStrategy` respectively.

Bug Fixes and Other Changes

* GCP compute project in BigQuery Pusher executor can be specified.
* New extra dependencies for convenience.
- tfx[airflow] installs all Apache Airflow orchestrator dependencies.
- tfx[kfp] installs all Kubeflow Pipelines orchestrator dependencies.
- tfx[tf-ranking] installs packages for TensorFlow Ranking.
NOTE: TensorFlow Ranking only compatible with TF >= 2.0.
* Depends on `google-cloud-bigquery>=1.28.0,<3`. (This was already installed
as a transitive dependency from the first release of TFX.)
* Depends on `google-cloud-aiplatform>=0.5.0,<0.8`.
* Depends on `ml-metadata>=0.30.0,<0.31.0`.
* Depends on `portpicker>=1.3.1,<2`.
* Depends on `struct2tensor>=0.30.0,<0.31.0`.
* Depends on `tensorflow-data-validation>=0.30.0,<0.31.0`.
* Depends on `tensorflow-model-analysis>=0.30.0,<0.31.0`.
* Depends on `tensorflow-transform>=0.30.0,<0.31.0`.
* Depends on `tfx-bsl>=0.30.0,<0.31.0`.

Documentation Updates

* N/A

0.29.0

Major Features and Improvements

* Added a simple query based driver that supports Span spec and static_range.
* Added e2e rolling window example/test for Span Resolver.
* Performance improvement in Transform by avoiding excess encodings and
decodings when it materializes transformed examples or generates statistics
(both enabled by default).
* Added an accessor (`.data_view_decode_fn`) to the decoder function wrapped in
the DataView in Trainer `FnArgs.data_accessor`.
* Expanded the penguin example pipeline with instructions for using
[JAX/Flax](https://github.com/google/flax) in addition to
TensorFlow/Keras to write and train the model. The support for JAX/Flax in
TFX is still experimental.
* Updated CloudTuner KFP e2e example running on Google Cloud Platform with
distributed tuning and GPU distributed training for each trial.

Breaking Changes

* Starting in this version, following artifacts will be stored in new format,
but artifacts produced by older versions can be read in a backwards
compatible way:
* Change split sub-folder format to 'Split-<split_name>', this applies to
all artifacts that contain splits. Old format '<split_name>' can still
be loaded by TFX.
* Change Model artifact's sub-folder name to 'Format-TFMA' for eval model
and 'Format-Serving' for serving model. Old Model artifact format
('eval_model_dir'/'serving_model_dir') can still be loaded by TFX.
* Change ExampleStatistics artifact payload to binary proto
FeatureStats.pb file. Old payload format (tfrecord stats_tfrecord file)
can still be loaded by TFX.
* Change ExampleAnomalies artifact payload to binary proto SchemaDiff.pb
file. Old payload format (text proto anomalies.pbtxt file) is deprecated
as TFX doesn't have downstream components that take ExampleAnomalies
artifact.


For Pipeline Authors

* CLI requires Apache Airflow 1.10.14 or later. If you are using an older
version of airflow, you can still copy runner definition to the DAG
directory manually and run using airflow UIs.

For Component Authors

* N/A

Deprecations

* Deprecated input/output compatibility aliases for Transform and
StatisticsGen.

Bug Fixes and Other Changes

* The `tfx_version` custom property of output artifacts is now set by the
default publisher to the TFX SDK version.
* Depends on `absl-py>=0.9,<0.13`.
* Depends on `kfp-pipeline-spec>=0.1.7,<0.2`.
* Depends on `ml-metadata>=0.29.0,<0.30.0`.
* Depends on `packaging>=20,<21`.
* Depends on `struct2tensor>=0.29.0,<0.30.0`.
* Depends on `tensorflow-data-validation>=0.29.0,<0.30.0`.
* Depends on `tensorflow-model-analysis>=0.29.0,<0.30.0`.
* Depends on `tensorflow-transform>=0.29.0,<0.30.0`.
* Depends on `tfx-bsl>=0.29.0,<0.30.0`.

Documentation Updates

* Simplified Apache Spark and Flink example deployment scripts by using Beam's
SparkRunner and FlinkRunner classes.
* Upgraded example Apache Flink deployment to Flink 1.12.1.
* Upgraded example Apache Spark deployment to Spark 2.4.7.
* Added the "TFX Python function component" notebook tutorial.

0.28.0

Major Features and Improvements

* Publically released TFX docker image in [tensorflow/tfx](
https://hub.docker.com/r/tensorflow/tfx) will use GPU
compatible based TensorFlow images from [Deep Learning Containers](
https://cloud.google.com/ai-platform/deep-learning-containers). This allow
these images to be used with GPU out of box.
* Added an example pipeline for a ranking model (using
[tensorflow_ranking](https://github.com/tensorflow/ranking))
at `tfx/examples/ranking`. More documentation will be available in future
releases.
* Added a [spans_resolver](
https://github.com/tensorflow/tfx/blob/master/tfx/dsl/experimental/spans_resolver.py)
that can resolve spans based on range_config.

Breaking Changes

For Pipeline Authors

* Custom arg key in `google_cloud_ai_platform.tuner.executor` is renamed to
`ai_platform_tuning_args` from `ai_platform_training_args`, to better
distinguish usage with Trainer.

For component authors

* N/A

Deprecations

* Deprecated input/output compatibility aliases for Transform and SchemaGen.

Bug Fixes and Other Changes

* Change Bigquery ML Pusher to publish the model to the user specified project
instead of the default project from run time context.
* Depends on `apache-beam[gcp]>=2.28,<3`.
* Depends on `ml-metadata>=0.28.0,<0.29.0`.
* Depends on `kfp-pipeline-spec>=0.1.6,<0.2`.
* Depends on `struct2tensor>=0.28.0,<0.29.0`.
* Depends on `tensorflow-data-validation>=0.28.0,<0.29.0`.
* Depends on `tensorflow-model-analysis>=0.28.0,<0.29.0`.
* Depends on `tensorflow-transform>=0.28.0,<0.29.0`.
* Depends on `tfx-bsl>=0.28.1,<0.29.0`.

Documentation Updates

* Published a [migration instruction](
https://github.com/tensorflow/tfx/blob/master/tfx/orchestration/launcher/README.md)
for legacy custom launcher developers.

0.27.0

Major Features and Improvements

* Updated the `tfx.components.evaluator.Evaluator` component to support
[TFMA's "model-agnostic" evaluation](https://www.tensorflow.org/tfx/model_analysis/faq#how_do_i_setup_tfma_to_work_with_pre-calculated_ie_model-agnostic_predictions_tfrecord_and_tfexample).
The `model` channel is now optional when constructing the component, which
is useful when the `examples` channel provides tf.Examples containing both
the labels and pre-computed model predictions, i.e. "model-agnostic"
evaluation.
* Supports different types of quantizations on TFLite conversion using
TFLITE_REWRITER by setting `quantization_optimizations`,
`quantization_supported_types` and `quantization_enable_full_integer`. Flag
definitions can be found here: [Post-traning
quantization](https://www.tensorflow.org/lite/performance/post_training_quantization).
* Added automatic population of `tfdv.StatsOptions.vocab_paths` when computing
statistics within the Transform component.

Breaking changes

For pipeline authors

* `enable_quantization` from TFLITE_REWRITER is removed and setting
quantization_optimizations = [tf.lite.Optimize.DEFAULT] will perform the
same type of quantization, dynamic range quantization. Users of the
TFLITE_REWRITER who do not enable quantization should be uneffected.
* Default value for `infer_feature_shape` for SchemaGen changed from `False`
to `True`, as indicated in previous release log. The inferred schema might
change if you do not specify `infer_feature_shape`. It might leads to
changes of the type of input features in Transform and Trainer code.

For component authors

* N/A

Deprecations

* Pipeline information is not be stored on the local filesystem anymore using
Kubeflow Pipelines orchestration with CLI. Instead, CLI will always use the
latest version of the pipeline in the Kubeflow Pipeline cluster. All
operations will be executed based on the information on the Kubeflow
Pipeline cluster. There might be some left files on
`${HOME}/tfx/kubeflow` or `${HOME}/kubeflow` but those will not be used
any more.
* The `tfx.components.common_nodes.importer_node.ImporterNode` class has been
moved to `tfx.dsl.components.common.importer.Importer`, with its
old module path kept as a deprecated alias, which will be removed in a
future version.
* The `tfx.components.common_nodes.resolver_node.ResolverNode` class has been
moved to `tfx.dsl.components.common.resolver.Resolver`, with its
old module path kept as a deprecated alias, which will be removed in a
future version.
* The `tfx.dsl.resolvers.BaseResolver` class has been
moved to `tfx.dsl.components.common.resolver.ResolverStrategy`, with its
old module path kept as a deprecated alias, which will be removed in a
future version.
* Deprecated input/output compatibility aliases for ExampleValidator,
Evaluator, Trainer and Pusher.

Bug fixes and other changes

* Add error condition checks to BulkInferrer's `output_example_spec`.
Previously, when the `output_example_spec` did not include the correct spec
definitions, the BulkInferrer would fail silently and output examples
without predictions.
* InfraValidator supports using alternative TensorFlow Serving image in case
deployed environment cannot reach the public internet (nor the docker hub).
Such alternative image should behave the same as official
`tensorflow/serving` image such as the same model volume path, serving port,
etc.
* Executor in `tfx.extensions.google_cloud_ai_platform.pusher.executor`
supported regional endpoint and machine_type.
* Starting from this version, proto files which are used to generate
component-level configs are included in the `tfx` package directly.
* The `tfx.dsl.io.fileio.NotFoundError` exception unifies handling of not-
found errors across different filesystem plugin backends.
* Fixes the serialization of zero-valued default when using `RuntimeParameter`
on Kubeflow.
* Depends on `apache-beam[gcp]>=2.27,<3`.
* Depends on `ml-metadata>=0.27.0,<0.28.0`.
* Depends on `numpy>=1.16,<1.20`.
* Depends on `pyarrow>=1,<3`.
* Depends on `kfp-pipeline-spec>=0.1.5,<0.2` in test and image.
* Depends on `tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,<3`.
* Depends on `tensorflow-data-validation>=0.27.0,<0.28.0`.
* Depends on `tensorflow-model-analysis>=0.27.0,<0.28.0`.
* Depends on `tensorflow-serving-api>=1.15,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,<3`.
* Depends on `tensorflow-transform>=0.27.0,<0.28.0`.
* Depends on `tfx-bsl>=0.27.0,<0.28.0`.

Documentation updates

* N/A

0.26.4

* This a bug fix only version.

Major Features and Improvements

* N/A

Breaking changes

For pipeline authors

* N/A

For component authors

* N/A

Deprecations

* N/A

Bug fixes and other changes

* Depends on `apache-beam[gcp]>=2.25,!=2.26,<2.29`.
* Depends on `tensorflow-data-validation>=0.26.1,<0.27`.

Documentation updates

* N/A

Page 5 of 9

© 2024 Safety CLI Cybersecurity Inc. All Rights Reserved.