Project import generated by Copybara.

Author: tf-model-analysis-team

CONTRIBUTING.md

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+# How to Contribute
+
+We'd love to accept your patches and contributions to this project. There are
+just a few small guidelines you need to follow.
+
+## Contributor License Agreement
+
+Contributions to this project must be accompanied by a Contributor License
+Agreement. You (or your employer) retain the copyright to your contribution,
+this simply gives us permission to use and redistribute your contributions as
+part of the project. Head over to <https://cla.developers.google.com/> to see
+your current agreements on file or to sign a new one.
+
+You generally only need to submit a CLA once, so if you've already submitted one
+(even if it was for a different project), you probably don't need to do it
+again.
+
+## Code reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.

LICENSE

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+   APPENDIX: How to apply the Apache License to your work.
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+      To apply the Apache License to your work, attach the following
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+   Copyright 2017, The TensorFlow Model Analysis Authors.
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MANIFEST.in

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+recursive-include static *.*

RELEASE.md

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+# Release 0.6.0
+ * Initial release of TensorFlow Model Analysis.

WORKSPACE

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+workspace(name = "org_tensorflow_model_analysis")
+
+http_archive(
+    name = "io_bazel_rules_closure",
+    sha256 = "6691c58a2cd30a86776dd9bb34898b041e37136f2dc7e24cadaeaf599c95c657",
+    strip_prefix = "rules_closure-08039ba8ca59f64248bb3b6ae016460fe9c9914f",
+    urls = [
+        "https://mirror.bazel.build/github.com/bazelbuild/rules_closure/archive/08039ba8ca59f64248bb3b6ae016460fe9c9914f.tar.gz",
+        "https://github.com/bazelbuild/rules_closure/archive/08039ba8ca59f64248bb3b6ae016460fe9c9914f.tar.gz",  # 2018-01-16
+    ],
+)
+
+load("@io_bazel_rules_closure//closure:defs.bzl", "closure_repositories")
+closure_repositories()
+
+http_archive(
+    name = "org_tensorflow_tensorboard",
+    sha256 = "a943c0242a07da4d445135ffc9a7c7cb987d9bd948ae733695bc16095dceec20",
+    strip_prefix = "tensorboard-2fdb2199553729a6c5b42b7eb0305a101b454add",
+    urls = ["https://github.com/tensorflow/tensorboard/archive/2fdb2199553729a6c5b42b7eb0305a101b454add.zip"],
+)
+
+load("@org_tensorflow_tensorboard//third_party:workspace.bzl", "tensorboard_workspace")
+tensorboard_workspace()
+
+load("//third_party:workspace.bzl", "tensorflow_model_analysis_workspace")
+# Please add all new dependencies in workspace.bzl.
+tensorflow_model_analysis_workspace()

docs/getting_started.md

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+# Getting Started with TensorFlow Model Analysis
+
+This guide introduces the basic concepts of TensorFlow Model Analysis (TFMA) and
+how to use them with some examples.
+
+## High-level Overview of TFMA
+
+At a high-level, TFMA allows you to export your model's *evaluation graph*, that
+is, the graph used for *evaluation* (as opposed to the graph used for *training*
+or *inference*) to a special SavedModel, which we call the *EvalSavedModel*.
+This *EvalSavedModel* contains additional information which allows TFMA to
+compute the same evaluation metrics defined in your model in a distributed
+manner over a large amount of data, and user-defined slices.
+
+## Instrumenting an Existing Model
+
+To use your an existing model with TFMA, you must first instrument the model to
+export the *EvalSavedModel*. You can do this by adding a call to
+`tfma.export_eval_savedmodel`, which is analogous to
+`estimator.export_savedmodel`.
+
+The following code snippet illustrates this:
+
+```
+# Define, train and export your estimator as usual
+estimator = tf.estimator.DNNClassifier(...)
+estimator.train(...)
+estimator.export_savedmodel(...)
+
+# Also export the EvalSavedModel
+tfma.export.export_eval_savedmodel(
+  estimator=estimator, export_dir_base=export_dir,
+  eval_input_receiver_fn=eval_input_receiver_fn)
+```
+
+You'll notice that you have to define an `eval_input_receiver_fn`, analogous to
+the `serving_input_receiver_fn` for `estimator.export_savedmodel`. Like
+`serving_input_receiver_fn`, `eval_input_receiver_fn` should define an input
+example placeholder, parse the features from the example, and return the parsed
+features. It should additionally parse and return the label.
+
+The following code snippet illustrates how you might define an
+`eval_input_receiver_fn`:
+
+```
+country = tf.contrib.layers.sparse_column_with_hash_buckets('country', 100)
+language = tf.contrib.layers.sparse_column_with_hash_buckets(language, 100)
+age = tf.contrib.layers.real_valued_column('age')
+label = tf.contrib.layers.real_valued_column('label')
+
+def eval_input_receiver_fn():
+  serialized_tf_example = tf.placeholder(
+    dtype=tf.string, shape=[None], name='input_example_placeholder')
+
+  # This *must* be a dictionary containing a single key 'examples', which
+  # points to the input placeholder.
+  receiver_tensors = {'examples': serialized_tf_example}
+
+  feature_spec = tf.contrib.layers.create_feature_spec_for_parsing(
+    [country, language, age, label])
+  features = tf.parse_example(serialized_tf_example, feature_spec)
+
+  return tfma.export.EvalInputReceiver(
+    features=features,
+    receiver_tensors=receiver_tensors,
+    labels=features['label'])
+```
+
+There are two things to note here:
+
+  *  `labels` can be a dictionary as well, which may be useful if you have a
+      multi-headed model.
+  *   While in most cases you will want your `eval_input_receiver_fn` to be
+      mostly the same as your `serving_input_receiver_fn`, in some cases you may
+      want to define additional features for slicing. For instance, you may want
+      to introduce an `age_category` feature which divided the `age` feature
+      into multiple buckets. You can then slice on this feature in TFMA,
+      allowing you to understand how your model's performance differs across
+      different age categories.
+
+## Using TFMA to Evaluate Your Instrumented Model
+
+TFMA allows you to perform large-scale distributed evaluation of your model by
+using [Apache Beam](http://beam.apache.org), which is a distributed processing
+framework. The evaluation results can then be visualised in a Jupyter Notebook
+using the frontend components included in TFMA.
+
+![TFMA Slicing Metrics Browser](./images/tfma-slicing-metrics-browser.png)
+
+The quickest way to try it out is to use `tfma.run_model_analysis` to perform
+the evaluation. Note that this uses Beam's local runner, so it's mainly for
+quick small-scale experimentation locally. The following code snippet shows how:
+
+```
+# Note that this code should be run in a Jupyter Notebook.
+
+# This assumes your data is a TFRecords file containing records in the format
+# your model is expecting, e.g. tf.train.Example if you're using
+# tf.parse_example in your model.
+eval_result = tfma.run_model_analysis(
+  model_location='/path/to/eval/saved/model',
+  data_location='/path/to/file/containing/tfrecords',
+  file_format='tfrecords')
+
+tfma.view.render_slicing_metrics(eval_result)
+```
+
+You can also compute metrics on slices of your data by configuring the
+`slice_spec` parameter, and add additional metrics not included in your model
+using `add_metrics_callbacks`. You can learn more by viewing the docstring for
+`run_model_analysis`.
+
+To perform distributed evaluation, you will have to construct a Beam pipeline
+with a distributed runner. This requires you to have some familiarity with
+[Apache Beam][(http://beam.apache.org).
+
+In your Beam pipeline, you can use the `tfma.EvaluateAndWriteResults` to
+perform the evaluation and write the results out. The results can later be
+loaded for visualization using `tfma.load_eval_result`. The following snippet
+illustrates this:
+
+```
+# To run the pipeline.
+with beam.Pipeline(runner=...) as p:
+  _ = (p
+       # You can change the source as appropriate, e.g. read from BigQuery.
+       | 'ReadData' >> beam.io.ReadFromTFRecord(data_location)
+       | 'EvaluateAndWriteResults' >> tfma.EvaluateAndWriteResults(
+            eval_saved_model_path='/path/to/eval/saved/model',
+            output_path='/path/to/output',
+            display_only_data_location=data_location))
+
+# To load and visualize results.
+# Note that this code should be run in a Jupyter Notebook.
+result = tfma.load_eval_result(output_path='/path/to/out')
+tfma.view.render_slicing_metrics(result)
+```