Internal change

PiperOrigin-RevId: 456569753

docs/README.md

+# Public docs for TensorFlow Models
+
+This directory contains the top-level public documentation for
+[TensorFlow Models](https://github.com/tensorflow/models)
+
+This directory is mirrored to https://tensorflow.org/tfmodels, and is mainly
+concerned with documenting the tools provided in the `tensorflow_models` pip
+package (including `orbit`).
+
+Api-reference pages are
+[available on the site](https://www.tensorflow.org/api_docs/more).
+
+The
+[Official Models](https://github.com/tensorflow/models/blob/master/official/projects)
+and [Research Models](https://github.com/tensorflow/models/blob/master/research)
+directories are not described in detail here, refer to the individual project
+directories for more information.

docs/index.md

+# Model Garden overview
+
+The TensorFlow Model Garden provides implementations of many state-of-the-art
+machine learning (ML) models for vision and natural language processing (NLP),
+as well as workflow tools to let you quickly configure and run those models on
+standard datasets. Whether you are looking to benchmark performance for a
+well-known model, verify the results of recently released research, or extend
+existing models, the Model Garden can help you drive your ML research and
+applications forward.
+
+The Model Garden includes the following resources for machine learning
+developers:
+
+-   [**Official models**](#official) for vision and NLP, maintained by Google
+    engineers
+-   [**Research models**](#research) published as part of ML research papers
+-   [**Training experiment framework**](#training_framework) for fast,
+    declarative training configuration of official models
+-   [**Specialized ML operations**](#ops) for vision and natural language
+    processing (NLP)
+-   [**Model training loop**](#orbit) management with Orbit
+
+These resources are built to be used with the TensorFlow Core framework and
+integrate with your existing TensorFlow development projects. Model
+Garden resources are also provided under an [open
+source](https://github.com/tensorflow/models/blob/master/LICENSE) license, so
+you can freely extend and distribute the models and tools.
+
+Practical ML models are computationally intensive to train and run, and may
+require accelerators such as Graphical Processing Units (GPUs) and Tensor
+Processing Units (TPUs). Most of the models in Model Garden were trained on
+large datasets using TPUs. However, you can also train and run these models on
+GPU and CPU processors.
+
+## Model Garden models
+
+The machine learning models in the Model Garden include full code so you can
+test, train, or re-train them for research and experimentation. The Model Garden
+includes two primary categories of models: *official models* and *research
+models*.
+
+### Official models {:#official}
+
+The [Official Models](https://github.com/tensorflow/models/tree/master/official)
+repository is a collection of state-of-the-art models, with a focus on
+vision and natural language processing (NLP).
+These models are implemented using current TensorFlow 2.x high-level
+APIs. Model libraries in this repository are optimized for fast performance and
+actively maintained by Google engineers. The official models include additional
+metadata you can use to quickly configure experiments using the Model Garden
+[training experiment framework](#training_framework).
+
+### Research models {:#research}
+
+The [Research Models](https://github.com/tensorflow/models/tree/master/research)
+repository is a collection of models published as code resources for research
+papers. These models are implemented using both TensorFlow 1.x and 2.x. Model
+libraries in the research folder are supported by the code owners and the
+research community.
+
+## Training experiment framework {:#training_framework}
+
+The Model Garden training experiment framework lets you quickly assemble and run
+training experiments using its official models and standard datasets. The
+training framework uses additional metadata included with the Model Garden's
+official models to allow you to configure models quickly using a declarative
+programming model. You can define a training experiment using Python commands in
+the
+[TensorFlow Model library](https://www.tensorflow.org/api_docs/python/tfm/core)
+or configure training using a YAML configuration file, like this
+[example](https://github.com/tensorflow/models/blob/master/official/vision/configs/experiments/image_classification/imagenet_resnet50_tpu.yaml).
+
+The training framework uses
+[`tfm.core.base_trainer.ExperimentConfig`](https://www.tensorflow.org/api_docs/python/tfm/core/base_trainer/ExperimentConfig)
+as the configuration object, which contains the following top-level
+configuration objects:
+
+-   [`runtime`](https://www.tensorflow.org/api_docs/python/tfm/core/base_task/RuntimeConfig):
+    Defines the processing hardware, distribution strategy, and other
+    performance optimizations
+-   [`task`](https://www.tensorflow.org/api_docs/python/tfm/core/config_definitions/TaskConfig):
+    Defines the model, training data, losses, and initialization
+-   [`trainer`](https://www.tensorflow.org/api_docs/python/tfm/core/base_trainer/TrainerConfig):
+    Defines the optimizer, training loops, evaluation loops, summaries, and
+    checkpoints
+
+For a complete example using the Model Garden training experiment framework, see
+the [Image classification with Model Garden](vision/image_classification.ipynb)
+tutorial. For information on the training experiment framework, check out the
+[TensorFlow Models API documentation](https://tensorflow.org/api_docs/python/tfm/core).
+If you are looking for a solution to manage training loops for your model
+training experiments, check out [Orbit](#orbit).
+
+## Specialized ML operations {:#ops}
+
+The Model Garden contains many vision and NLP operations specifically designed
+to execute state-of-the-art models that run efficiently on GPUs and TPUs. Review
+the TensorFlow Models Vision library API docs for a list of specialized
+[vision operations](https://www.tensorflow.org/api_docs/python/tfm/vision).
+Review the TensorFlow Models NLP Library API docs for a list of
+[NLP operations](https://www.tensorflow.org/api_docs/python/tfm/nlp). These
+libraries also include additional utility functions used for vision and NLP data
+processing, training, and model execution.
+
+## Training loops with Orbit {:#orbit}
+
+The Orbit tool is a flexible, lightweight library designed to make it easier to
+write custom training loops in TensorFlow 2.x, and works well with the Model
+Garden [training experiment framework](#training_framework). Orbit handles
+common model training tasks such as saving checkpoints, running model
+evaluations, and setting up summary writing. It seamlessly integrates with
+`tf.distribute` and supports running on different device types, including CPU,
+GPU, and TPU hardware. The Orbit tool is also [open
+source](https://github.com/tensorflow/models/blob/master/orbit/LICENSE), so you
+can extend and adapt to your model training needs.
+
+You generally train TensorFlow models by writing a
+[custom training loop](https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch),
+or using the high-level Keras
+[Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)
+function. For simple models, you can define and manage a custom training loop
+with low-level TensorFlow methods such as `tf.GradientTape` or `tf.function`.
+Alternatively, you can use the high-level Keras `Model.fit`.
+
+However, if your model is complex and your training loop requires more flexible
+control or customization, then you should use Orbit. You can define most of your
+training loop by the `orbit.AbstractTrainer` or `orbit.StandardTrainer` class.
+Learn more about the Orbit tool in the
+[Orbit API documentation](https://www.tensorflow.org/api_docs/python/orbit).
+
+Note: You can customize how the Keras API executes training. Mainly you must
+override the `Model.train_step` method or use `keras.callbacks` like
+`callbacks.ModelCheckpoint` or `callbacks.TensorBoard`. For more information
+about modifying the behavior of `train_step`, check out the
+[Customize what happens in Model.fit](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit)
+page.

official/colab/nlp/customize_encoder.ipynb → docs/nlp/customize_encoder.ipynb

@@ -48,16 +48,16 @@
       "source": [
         "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/customize_encoder\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/tfmodels/nlp/customize_encoder\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/docs/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/docs/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
+        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/docs/nlp/customize_encoder.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "\u003c/table\u003e"
       ]
@@ -191,7 +191,7 @@
         "id": "Qe2UWI6_tsHo"
       },
       "source": [
-        "`canonical_classifier_model` can be trained using the training data. For details about how to train the model, please see the colab [fine_tuning_bert.ipynb](https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb). We skip the code that trains the model here.\n",
+        "`canonical_classifier_model` can be trained using the training data. For details about how to train the model, please see the [Fine tuning bert](https://www.tensorflow.org/text/tutorials/fine_tune_bert) notebook. We skip the code that trains the model here.\n",
         "\n",
         "After training, we can apply the model to do prediction.\n"
       ]

official/colab/decoding_api_in_tf_nlp.ipynb → docs/nlp/decoding_api.ipynb

@@ -48,16 +48,16 @@
       "source": [
         "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/tutorials/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/tfmodels/nlp/decoding_api\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/docs/nlp/decoding_api.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/docs/nlp/decoding_api.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/decoding_api_in_tf_nlp.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
+        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/docs/nlp/decoding_api.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "\u003c/table\u003e"
       ]
@@ -226,10 +226,10 @@
       "outputs": [],
       "source": [
         "params = {\n",
-        "    'num_heads': 2\n",
-        "    'num_layers': 2\n",
-        "    'batch_size': 2\n",
-        "    'n_dims': 256\n",
+        "    'num_heads': 2,\n",
+        "    'num_layers': 2,\n",
+        "    'batch_size': 2,\n",
+        "    'n_dims': 256,\n",
         "    'max_decode_length': 4}"
       ]
     },

official/colab/nlp/nlp_modeling_library_intro.ipynb → docs/nlp/index.ipynb

@@ -48,16 +48,16 @@
       "source": [
         "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/official_models/nlp/nlp_modeling_library_intro\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/tfmodels/nlp\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/docs/nlp/index.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
+        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/docs/nlp/index.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "  \u003ctd\u003e\n",
-        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/official/colab/nlp/nlp_modeling_library_intro.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
+        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/models/docs/nlp/index.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
         "  \u003c/td\u003e\n",
         "\u003c/table\u003e"
       ]
@@ -538,7 +538,7 @@
         "id": "mzBqOylZo3og"
       },
       "source": [
-        "With the `loss`, you can optimize the model. Please see [run_classifier.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_classifier.py) or the colab [fine_tuning_bert.ipynb](https://github.com/tensorflow/models/blob/master/official/colab/fine_tuning_bert.ipynb) for the full example."
+        "With the `loss`, you can optimize the model. Please see [run_classifier.py](https://github.com/tensorflow/models/blob/master/official/nlp/bert/run_classifier.py) or the [Fine tune_bert](https://www.tensorflow.org/text/tutorials/fine_tune_bert) notebook for the full example."
       ]
     }
   ],

official/colab/README.md

+# Moved
+
+These files have moved to:
+https://github.com/tensorflow/models/blob/master/docs
\ No newline at end of file

official/nlp/modeling/README.md

@@ -20,7 +20,7 @@ examples.
 * [`losses`](losses) contains common loss computation used in NLP tasks.
 
 Please see the colab
-[nlp_modeling_library_intro.ipynb](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb)
+[NLP modeling library intro.ipynb](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/docs/nlp/index.ipynb)
 for how to build transformer-based NLP models using above primitives.
 
 Besides the pre-defined primitives, it also provides scaffold classes to allow
@@ -43,7 +43,7 @@ custom hidden layer (which will replace the Transformer instantiation in the
 encoder).
 
 Please see the colab
-[customize_encoder.ipynb](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb)
+[customize_encoder.ipynb](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/docs/nlp/customize_encoder.ipynb)
 for how to use scaffold classes to build noval achitectures.
 
 BERT and ALBERT models in this repo are implemented using this library.