documentation: updating SageMaker ReadTheDocs

doc/algorithms/audio/audioembedding_tensorflow.rst

@@ -0,0 +1,9 @@
+##################################
+Audio Embedding - Tensorflow
+##################################
+
+This is an audio embedding algorithm which supports many pre-trained models available in Tensorflow Hub. The following
+`sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_image_classification/Amazon_JumpStart_Image_Classification.ipynb>`__
+demonstrates how to use the Sagemaker Python SDK for Image Classification for using these algorithms.
+
+For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`

doc/algorithms/audio/index.rst

@@ -0,0 +1,10 @@
+######################
+Audio
+######################
+
+Amazon SageMaker provides audio processing algorithms that are used for embedding.
+
+.. toctree::
+    :maxdepth: 2
+
+    audioembedding_tensorflow

doc/algorithms/index.rst

@@ -17,3 +17,4 @@ Built-in algorithms are offered in 2 modes:
     time_series/index
     unsupervised/index
     vision/index
+    audio/index

doc/algorithms/text/index.rst

@@ -15,8 +15,11 @@ Amazon SageMaker provides algorithms that are tailored to the analysis of textua
     text_classification_tensorflow
     sentence_pair_classification_tensorflow
     sentence_pair_classification_hugging_face
-    question_answering_pytorch
+    question_answering_hugging_face
     named_entity_recognition_hugging_face
     text_summarization_hugging_face
     text_generation_hugging_face
     machine_translation_hugging_face
+    text_classification_hugging_face
+    txt2img_hugging_face
+    zstc_hugging_face

doc/algorithms/text/question_answering_pytorch.rst → doc/algorithms/text/question_answering_hugging_face.rst

@@ -1,5 +1,5 @@
 #####################################
-Question Answering - PyTorch
+Question Answering - HuggingFace
 #####################################
 
 This is a supervised question answering algorithm which supports fine-tuning of many pre-trained models available in Hugging Face. The following

doc/algorithms/text/text_classification_hugging_face.rst

@@ -0,0 +1,9 @@
+##################################
+Text Classification - HuggingFace
+##################################
+
+This is a text classification algorithm which supports many pre-trained models available in HuggingFace Hub. The following
+`sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_classification/Amazon_JumpStart_Text_Classification.ipynb>`__
+demonstrates how to use the Sagemaker Python SDK for Text Classification for using these algorithms.
+
+For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`

doc/algorithms/text/txt2img_hugging_face.rst

@@ -0,0 +1,9 @@
+##################################
+Text to Image - HuggingFace
+##################################
+
+This is a text to image algorithm which supports many pre-trained models available in HuggingFace Hub. The following
+`sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_classification/Amazon_JumpStart_Text_Classification.ipynb>`__
+demonstrates how to use the Sagemaker Python SDK for Text Classification for using these algorithms.
+
+For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`

doc/algorithms/text/zstc_hugging_face.rst

@@ -0,0 +1,9 @@
+################################################
+Zero-Shot Text Classification - HuggingFace
+################################################
+
+This is a zero-shot text classification algorithm which supports many pre-trained models available in HuggingFace Hub. The following
+`sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_classification/Amazon_JumpStart_Text_Classification.ipynb>`__
+demonstrates how to use the Sagemaker Python SDK for Text Classification for using these algorithms.
+
+For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`

doc/algorithms/vision/image_classification_tensorflow.rst

@@ -6,4 +6,4 @@ This is a supervised image clasification algorithm which supports fine-tuning of
 `sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_image_classification/Amazon_JumpStart_Image_Classification.ipynb>`__
 demonstrates how to use the Sagemaker Python SDK for Image Classification for using these algorithms.
 
-For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`
+For detailed documentation please refer `Use Amazon SageMaker Built-in Algorithms or Pre-trained Models <https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html>`__

doc/algorithms/vision/index.rst

@@ -18,4 +18,6 @@ Amazon SageMaker provides image processing algorithms that are used for image cl
     object_detection_mxnet
     semantic_segmentation_mxnet_gluoncv
     semantic_segmentation_mxnet
-    text_embedding_tensorflow_mxnet
+    text_embedding_tensorflow
+    text_embedding_mxnet
+    upscaling_hugging_face

doc/algorithms/vision/object_detection_tensorflow.rst

@@ -6,4 +6,4 @@ This is a supervised object detection algorithm which supports fine-tuning of ma
 `sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_object_detection/Amazon_JumpStart_Object_Detection.ipynb>`__
 demonstrates how to use the Sagemaker Python SDK for Object Detection for using these algorithms.
 
-For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`
+For detailed documentation please refer `Use Amazon SageMaker Built-in Algorithms or Pre-trained Models <https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html>`__

doc/algorithms/vision/text_embedding_tensorflow_mxnet.rst → doc/algorithms/vision/text_embedding_mxnet.rst

@@ -1,8 +1,8 @@
 ####################################
-Text Embedding - TensorFlow, MxNet
+Text Embedding - MxNet
 ####################################
 
-This is a supervised text embedding algorithm which supports many pre-trained models available in MXNet and Tensorflow Hub. The following
+This is a supervised text embedding algorithm which supports many pre-trained models available in MXNet Hub. The following
 `sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_embedding/Amazon_JumpStart_Text_Embedding.ipynb>`__
 demonstrates how to use the Sagemaker Python SDK for Text Embedding for using these algorithms.
 

doc/algorithms/vision/text_embedding_tensorflow.rst

@@ -0,0 +1,9 @@
+####################################
+Text Embedding - TensorFlow
+####################################
+
+This is a supervised text embedding algorithm which supports many pre-trained models available in Tensorflow Hub. The following
+`sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_embedding/Amazon_JumpStart_Text_Embedding.ipynb>`__
+demonstrates how to use the Sagemaker Python SDK for Text Embedding for using these algorithms.
+
+For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`

doc/algorithms/vision/upscaling_hugging_face.rst

@@ -0,0 +1,9 @@
+##################################
+Image Upscaling - HuggingFace
+##################################
+
+This is a image upscaling algorithm which supports many pre-trained models available in HuggingFace Hub. The following
+`sample notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_image_classification/Amazon_JumpStart_Image_Classification.ipynb>`__
+demonstrates how to use the Sagemaker Python SDK for Image Classification for using these algorithms.
+
+For detailed documentation please refer :ref:`Use Built-in Algorithms with Pre-trained Models in SageMaker Python SDK <built-in-algos>`

doc/doc_utils/jumpstart_doc_utils.py

@@ -31,6 +31,10 @@ class Tasks(str, Enum):
     TEXT_GENERATION = "textgeneration"
     IC_EMBEDDING = "icembedding"
     TC_EMBEDDING = "tcembedding"
+    AUDIO_EMBEDDING = "audioembedding"
+    TXT2IMG = "txt2img"
+    UPSCALING = "upscaling"
+    ZSTC = "zstc"
     NER = "ner"
     SUMMARIZATION = "summarization"
     TRANSLATION = "translation"
@@ -116,14 +120,16 @@ class Frameworks(str, Enum):
     (Tasks.IS, Frameworks.GLUONCV): "algorithms/vision/instance_segmentation_mxnet.rst",
     (Tasks.OD, Frameworks.GLUONCV): "algorithms/vision/object_detection_mxnet.rst",
     (Tasks.OD, Frameworks.PYTORCH): "algorithms/vision/object_detection_pytorch.rst",
+    (Tasks.OD1, Frameworks.PYTORCH): "algorithms/vision/object_detection_pytorch.rst",
     (Tasks.OD, Frameworks.TENSORFLOW): "algorithms/vision/object_detection_tensorflow.rst",
+    (Tasks.OD1, Frameworks.TENSORFLOW): "algorithms/vision/object_detection_tensorflow.rst",
     (Tasks.SEMSEG, Frameworks.GLUONCV): "algorithms/vision/semantic_segmentation_mxnet.rst",
     (
         Tasks.TRANSLATION,
         Frameworks.HUGGINGFACE,
     ): "algorithms/text/machine_translation_hugging_face.rst",
-    (Tasks.NER, Frameworks.GLUONCV): "algorithms/text/named_entity_recognition_hugging_face.rst",
-    (Tasks.EQA, Frameworks.PYTORCH): "algorithms/text/question_answering_pytorch.rst",
+    (Tasks.NER, Frameworks.HUGGINGFACE): "algorithms/text/named_entity_recognition_hugging_face.rst",
+    (Tasks.EQA, Frameworks.HUGGINGFACE): "algorithms/text/question_answering_hugging_face.rst",
     (
         Tasks.SPC,
         Frameworks.HUGGINGFACE,
@@ -136,11 +142,11 @@ class Frameworks(str, Enum):
     (
         Tasks.TC_EMBEDDING,
         Frameworks.GLUONCV,
-    ): "algorithms/vision/text_embedding_tensorflow_mxnet.rst",
+    ): "algorithms/vision/text_embedding_mxnet.rst",
     (
         Tasks.TC_EMBEDDING,
         Frameworks.TENSORFLOW,
-    ): "algorithms/vision/text_embedding_tensorflow_mxnet.rst",
+    ): "algorithms/vision/text_embedding_tensorflow.rst",
     (
         Tasks.TEXT_GENERATION,
         Frameworks.HUGGINGFACE,
@@ -149,8 +155,60 @@ class Frameworks(str, Enum):
         Tasks.SUMMARIZATION,
         Frameworks.HUGGINGFACE,
     ): "algorithms/text/text_summarization_hugging_face.rst",
+    (
+        Tasks.ZSTC,
+        Frameworks.HUGGINGFACE,
+    ): "algorithms/text/zstc_hugging_face.rst",
+    (
+        Tasks.TXT2IMG,
+        Frameworks.HUGGINGFACE,
+    ): "algorithms/text/txt2img_hugging_face.rst",
+    (
+        Tasks.UPSCALING,
+        Frameworks.HUGGINGFACE,
+    ): "algorithms/vision/upscaling_hugging_face.rst",
+    (
+        Tasks.AUDIO_EMBEDDING,
+        Frameworks.TENSORFLOW,
+    ): "algorithms/audio/audioembedding_tensorflow.rst",
 }
 
+SAMPLE_NOTEBOOK_MODALITY_MAP = {
+    (Tasks.IC): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_image_classification/Amazon_JumpStart_Image_Classification.ipynb",
+    (Tasks.IC_EMBEDDING): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_image_embedding/Amazon_JumpStart_Image_Embedding.ipynb",
+    (Tasks.IS): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_instance_segmentation/Amazon_JumpStart_Instance_Segmentation.ipynb",
+    (Tasks.OD): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_object_detection/Amazon_JumpStart_Object_Detection.ipynb",
+    (Tasks.OD1): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_object_detection/Amazon_JumpStart_Object_Detection.ipynb",
+    (Tasks.SEMSEG): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_semantic_segmentation/Amazon_JumpStart_Semantic_Segmentation.ipynb",
+    (Tasks.TRANSLATION): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_machine_translation/Amazon_JumpStart_Machine_Translation.ipynb",
+    (Tasks.NER): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_named_entity_recognition/Amazon_JumpStart_Named_Entity_Recognition.ipynb",
+    (Tasks.EQA): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_question_answering/Amazon_JumpStart_Question_Answering.ipynb",
+    (Tasks.SPC): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_sentence_pair_classification/Amazon_JumpStart_Sentence_Pair_Classification.ipynb",
+    (Tasks.TC): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_classification/Amazon_JumpStart_Text_Classification.ipynb",
+    (Tasks.TC_EMBEDDING): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_embedding/Amazon_JumpStart_Text_Embedding.ipynb",
+    (Tasks.TEXT_GENERATION): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_generation/Amazon_JumpStart_Text_Generation.ipynb",
+    (Tasks.SUMMARIZATION): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_summarization/Amazon_JumpStart_Text_Summarization.ipynb",
+    # TODO: Find notebook link for  this task
+    (Tasks.ZSTC): "https://www.amazon.com/",
+    (Tasks.TXT2IMG): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart_text_to_image/Amazon_JumpStart_Text_To_Image.ipynb",
+    # TODO: Find notebook link for  this task
+    (Tasks.UPSCALING): "https://www.amazon.com/",
+    # TODO: Find notebook link for  this task
+    (Tasks.AUDIO_EMBEDDING): "https://www.amazon.com/",
+} 
+
+SAMPLE_TENSORFLOW_NOTEBOOK_MODALITY_MAP = {
+    (Tasks.IC): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/image_classification_tensorflow/Amazon_TensorFlow_Image_Classification.ipynb",
+    (Tasks.OD): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/object_detection_tensorflow/Amazon_Tensorflow_Object_Detection.ipynb",
+    (Tasks.OD1): "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/object_detection_tensorflow/Amazon_Tensorflow_Object_Detection.ipynb",
+}
+
+def get_sample_notebook_url(task, framework):
+    if (framework == Frameworks.TENSORFLOW and task in SAMPLE_TENSORFLOW_NOTEBOOK_MODALITY_MAP):
+        return SAMPLE_TENSORFLOW_NOTEBOOK_MODALITY_MAP[task]
+    else:
+        return SAMPLE_NOTEBOOK_MODALITY_MAP[task]
+
 
 def get_jumpstart_sdk_manifest():
     url = "{}/{}".format(JUMPSTART_BUCKET_BASE_URL, SDK_MANIFEST_FILE)
@@ -170,13 +228,12 @@ def get_model_task(id):
     task_short = id.split("-")[1]
     return TASK_MAP[task_short] if task_short in TASK_MAP else "Source"
 
-
 def get_string_model_task(id):
     return id.split("-")[1]
 
 
 def get_model_source(url):
-    if "tfhub" in url:
+    if "tfhub" in url or "tensorflow" in url:
         return "Tensorflow Hub"
     if "pytorch" in url:
         return "Pytorch Hub"
@@ -208,7 +265,6 @@ def create_jumpstart_model_table():
                 sdk_manifest_top_versions_for_models[model["model_id"]]["version"]
             ) < Version(model["version"]):
                 sdk_manifest_top_versions_for_models[model["model_id"]] = model
-
     file_content_intro = []
 
     file_content_intro.append(".. _all-pretrained-models:\n\n")
@@ -274,15 +330,16 @@ def create_jumpstart_model_table():
             ):
                 file_content_single_entry.append("\n")
                 file_content_single_entry.append(".. list-table:: Available Models\n")
-                file_content_single_entry.append("   :widths: 50 20 20 20 20\n")
+                file_content_single_entry.append("   :widths: 50 20 20 20 20 20\n")
                 file_content_single_entry.append("   :header-rows: 1\n")
                 file_content_single_entry.append("   :class: datatable\n")
                 file_content_single_entry.append("\n")
                 file_content_single_entry.append("   * - Model ID\n")
                 file_content_single_entry.append("     - Fine Tunable?\n")
                 file_content_single_entry.append("     - Latest Version\n")
                 file_content_single_entry.append("     - Min SDK Version\n")
-                file_content_single_entry.append("     - Source\n")
+                file_content_single_entry.append("     - Sample Notebook\n")
+                file_content_single_entry.append("     - Original Source\n")
 
                 dynamic_table_files.append(
                     MODALITY_MAP[(string_model_task, TO_FRAMEWORK[model_source])]
@@ -292,6 +349,9 @@ def create_jumpstart_model_table():
             file_content_single_entry.append("     - {}\n".format(model_spec["training_supported"]))
             file_content_single_entry.append("     - {}\n".format(model["version"]))
             file_content_single_entry.append("     - {}\n".format(model["min_version"]))
+            file_content_single_entry.append(
+                "     - `{} <{}>`__\n".format("Sample Notebook", get_sample_notebook_url(string_model_task, TO_FRAMEWORK[model_source]))
+            )
             file_content_single_entry.append(
                 "     - `{} <{}>`__\n".format(model_source, model_spec["url"])
             )