Release 0.11.0

November 21, 2018 · View on GitHub

Major Features and Improvements

We now support unsupervised models which have model_fns that do not take a labels argument.
Improved performance by using make_callable instead of repeated session.run calls.
Improved performance by better choice of default "combine" batch size.
We now support passing in custom extractors in the model_eval_lib API.
Added support for models which have multiple examples per raw input (e.g. input is a compressed example which expands to multiple examples when parsed by the model). For such models, you must specify an example_ref parameter to your EvalInputReceiver. This 1-D integer Tensor should be batch aligned with features, predictions and labels and each element in it is an index in the raw input tensor to identify which input each feature / prediction / label came from. See eval_saved_model/example_trainers/fake_multi_examples_per_input_estimator.py for an example.
Added support for metrics with string value_ops.
Added support for metrics whose value_ops return multidimensional arrays.
We now support including your serving graph in the EvalSavedModel. You can do this by passing a serving_input_receiver_fn to export_eval_savedmodel or any of the TFMA Exporters.

Depends on apache-beam[gcp]>=2.8,<3.
Depends on tensorflow-transform>=0.11,<1.
Requires pre-installed TensorFlow >=1.11,<2.
Factor our utility functions for adding sliceable "meta-features" to FPL.
Added public API docs
Add an extractor to add sliceable "meta-features" to FPL.
Potentially improved performance by fanning out large slices.
Add support for assets_extra in tfma.exporter.FinalExporter.
Add a light-weight library that includes only the export-related modules for TFMA for use in your Trainer. See docstring in tensorflow_model_analysis/export_only/__init__.py for usage.
Update EvalInputReceiver so the TFMA collections written to the graph only contain the results of the last call if multiple calls to EvalInputReceiver are made.
We now finalize the graph after it's loaded and post-export metrics are added, potentially improving performance.
Fix a bug in post-export PrecisionRecallAtK where labels with only 1 dimension were not correctly handled.
Fix an issue where we were not correctly wrapping SparseTensors for features and labels in tf.identity, which could cause TFMA to encounter TensorFlow issue #17568 if there were control dependencies on these features or labels.
We now correctly preserve dtypes when splitting and concatenating SparseTensors internally. The failure to do so previously could result in unexpectedly large memory usage if string values were involved due to the inefficient pickling of NumPy string arrays with a large number of elements.

Requires pre-installed TensorFlow >=1.11,<2.
We now require that EvalInputReceiver, export_eval_savedmodel, make_export_strategy, make_final_exporter, FinalExporter and LatestExporter be called with keyword arguments only.
Removed check_metric_compatibility from EvalSavedModel.
We now enforce that the receiver_tensors dictionary for EvalInputReceiver contains exactly one key named examples.
Post-export metrics have now been moved up one level to tfma.post_export_metrics. They should now be accessed via tfma.post_export_metrics.auc instead of tfma.post_export_metrics.post_export_metrics.auc as they were before.
Separated extraction from evaluation. EvaluteAndWriteResults is now called ExtractEvaluateAndWriteResults.
Added EvalSharedModel type to encapsulate model_path and add_metrics_callbacks along with a handle to a shared model instance.

Improved performance especially when slicing across many features and/or feature values.

Depends on apache-beam[gcp]>=2.6,<3.
Updated ExampleCount to use the batch dimension as the example count. It also now tries a few fallbacks if none of the standard keys are found in the predictions dictionary: the first key in sorted order in the predictions dictionary, or failing that, the first key in sorted order in the labels dictionary, or failing that, it defaults to zero.
Fix bug where we were mutating an element in a DoFn - this is prohibited in the Beam model and can cause subtle bugs.
Fix bug where we were creating a separate Shared handle for each stage in Evaluate, resulting in no sharing of the model across stages.

Add a TFMA unit test library for unit testing your the exported model and associated metrics computations.
Add tfma.export.make_export_strategy which is analogous to tf.contrib.learn.make_export_strategy.
Add tfma.exporter.FinalExporter and tfma.exporter.LatestExporter which are analogous to tf.estimator.FinalExporter and tf.estimator.LastExporter.
Add tfma.export.build_parsing_eval_input_receiver_fn which is analogous to tf.estimator.export.build_parsing_serving_input_receiver_fn.
Add integration testing for DNN-based estimators.
Add new post export metrics:
- AUC (tfma.post_export_metrics.post_export_metrics.auc)
- Precision/Recall at K (tfma.post_export_metrics.post_export_metrics.precision_recall_at_k)
- Confusion matrix at thresholds (tfma.post_export_metrics.post_export_metrics.confusion_matrix_at_thresholds)

Peak memory usage for large DataFlow jobs should be lower with a fix in when we compact batches of metrics during the combine phase of metrics computation.
Remove batch size override in chicago_taxi example.
Added dependency on protobuf>=3.6.0<4 for protocol buffers.
Updated SparseTensor code to work with SparseTensors of any dimension. Previously on SparseTensors with dimension 2 (batch_size x values) were supported in the features dictionary.
Updated code to work with SparseTensors and dense Tensors of variable lengths across batches.

EvalSavedModels produced by TFMA 0.6.0 will not be compatible with later versions due to the following changes:
- EvalSavedModels are now written out with a custom "eval_saved_model" tag, as opposed to the "serving" tag before.
- EvalSavedModels now include version metadata about the TFMA version that they were exported with.
Metrics and plot outputs now are converted into proto and serialized. Metrics and plots produced by TFMA 0.6.0 will not be compatible with later versions.
Requires pre-installed TensorFlow >=1.9,<2.
TFMA now uses the TensorFlow Estimator functionality for exporting models of different modes behind the scenes. There are no user-facing changes API-wise, but EvalSavedModels produced by earlier versions of TFMA will not be compatible with this version of TFMA.
tf.contrib.learn Estimators are no longer supported by TFMA. Only tf.estimator Estimators are supported.
Metrics and plot outputs now include version metadata about the TFMA version that they were exported with. Metrics and plots produced by earlier versions of TFMA will not be compatible with this version of TFMA.