Add missing documentation (#575)

laurenyu · web-flow · commit 63a22d6f1a00 · 2018-12-24T17:41:53.000-05:00
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -10,6 +10,9 @@ CHANGELOG
 * bug-fix: Remove ``__all__`` from ``__init__.py`` files
 * doc-fix: Add TFRecord split type to docs
 * doc-fix: Mention ``SM_HPS`` environment variable in MXNet README
+* doc-fix: Specify that Local Mode supports only framework and BYO cases
+* doc-fix: Add missing classes to API docs
+* doc-fix: Add information on necessary AWS permissions
 
 1.16.3
 ======
diff --git a/README.rst b/README.rst
@@ -69,10 +69,23 @@ Supported Operating Systems
 
 SageMaker Python SDK supports Unix/Linux and Mac.
 
-Supported Python versions
+Supported Python Versions
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-SageMaker Python SDK is tested on: \* Python 2.7 \* Python 3.5
+SageMaker Python SDK is tested on:
+
+- Python 2.7
+- Python 3.5
+
+AWS Permissions
+~~~~~~~~~~~~~~~
+
+As a managed service, Amazon SageMaker performs operations on your behalf on the AWS hardware that is managed by Amazon SageMaker.
+Amazon SageMaker can perform only operations that the user permits.
+You can read more about which permissions are necessary in the `AWS Documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html>`__.
+
+The SageMaker Python SDK should not require any additional permissions.
+However, if you are using an IAM role with a path in it, you should grant permission for ``iam:GetRole``.
 
 Licensing
 ~~~~~~~~~
@@ -203,6 +216,7 @@ Local Mode
 
 The SageMaker Python SDK supports local mode, which allows you to create estimators and deploy them to your local environment.
 This is a great way to test your deep learning scripts before running them in SageMaker's managed training or hosting environments.
+Local Mode is supported for only frameworks (e.g. TensorFlow, MXNet) and images you supply yourself.
 
 We can take the example in  `Using Estimators <#using-estimators>`__ , and use either ``local`` or ``local_gpu`` as the instance type.
 
diff --git a/doc/index.rst b/doc/index.rst
@@ -2,27 +2,28 @@ Amazon SageMaker Python SDK
 ===========================
 Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker.
 
-With the SDK, you can train and deploy models using popular deep learning frameworks: **Apache MXNet** and **TensorFlow**. You can also train and deploy models with **algorithms provided by Amazon**, these are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have **your own algorithms** built into SageMaker-compatible Docker containers, you can train and host models using these as well.
+With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.
 
-Here you'll find API docs for SageMaker Python SDK. The project home-page is in Github: https://github.com/aws/sagemaker-python-sdk, there you can find the SDK source, installation instructions and a general overview of the library there.
+Here you'll find API docs for SageMaker Python SDK. The project homepage is in Github: https://github.com/aws/sagemaker-python-sdk, where you can find the SDK source, installation instructions and a general overview of the library.
 
 Overview
-----------
+--------
 The SageMaker Python SDK consists of a few primary interfaces:
 
 .. toctree::
     :maxdepth: 2
 
     estimators
     tuner
+    model
+    pipeline
     predictors
     transformer
     session
-    model
     analytics
 
 MXNet
-----------
+-----
 A managed environment for MXNet training and hosting on Amazon SageMaker
 
 .. toctree::
@@ -39,6 +40,15 @@ A managed environment for TensorFlow training and hosting on Amazon SageMaker
 
     sagemaker.tensorflow
 
+Scikit-Learn
+------------
+A managed enrionment for Scikit-learn training and hosting on Amazon SageMaker
+
+.. toctree::
+    :maxdepth: 2
+
+    sagemaker.sklearn
+
 PyTorch
 -------
 A managed environment for PyTorch training and hosting on Amazon SageMaker
@@ -66,20 +76,33 @@ A managed environment for Reinforcement Learning training and hosting on Amazon
 
     sagemaker.rl
 
+SparkML Serving
+---------------
+A managed environment for SparkML hosting on Amazon SageMaker
+
+.. toctree::
+    :maxdepth: 2
+
+    sagemaker.sparkml
+
 SageMaker First-Party Algorithms
 --------------------------------
 Amazon provides implementations of some common machine learning algortithms optimized for GPU architecture and massive datasets.
 
 .. toctree::
     :maxdepth: 2
 
-    kmeans
-    pca
-    linear_learner
     sagemaker.amazon.amazon_estimator
     factorization_machines
+    ipinsights
+    kmeans
+    knn
     lda
+    linear_learner
     ntm
+    object2vec
+    pca
+    randomcutforest
 
 Workflows
 ---------
@@ -88,4 +111,4 @@ SageMaker APIs to export configurations for creating and managing Airflow workfl
 .. toctree::
     :maxdepth: 2
 
-    sagemaker.workflow.airflow
+    sagemaker.workflow.airflow
diff --git a/doc/ipinsights.rst b/doc/ipinsights.rst
@@ -0,0 +1,22 @@
+IP Insights
+-----------
+
+The Amazon SageMaker IP Insights algorithm.
+
+.. autoclass:: sagemaker.IPInsights
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+    :exclude-members: image, num_entity_vectors, vector_dim, batch_metrics_publish_interval, epochs, learning_rate,
+                      num_ip_encoder_layers, random_negative_sampling_rate, shuffled_negative_sampling_rate, weight_decay
+
+.. autoclass:: sagemaker.IPInsightsModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.IPInsightsPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/doc/knn.rst b/doc/knn.rst
@@ -0,0 +1,22 @@
+K-Nearest Neighbors
+-------------------
+
+The Amazon SageMaker K-Nearest Neighbors (k-NN) algorithm.
+
+.. autoclass:: sagemaker.KNN
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+    :exclude-members: image, k, sample_size, predictor_type, dimension_reduction_target, dimension_reduction_type,
+                      index_metric, index_type, faiss_index_ivf_nlists, faiss_index_pq_m
+
+.. autoclass:: sagemaker.KNNModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. autoclass:: sagemaker.KNNPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/doc/ntm.rst b/doc/ntm.rst
@@ -1,5 +1,5 @@
 NTM
---------------------
+---
 
 The Amazon SageMaker NTM algorithm.
 
diff --git a/doc/object2vec.rst b/doc/object2vec.rst
@@ -0,0 +1,21 @@
+Object2Vec
+----------
+
+The Amazon SageMaker Object2Vec algorithm.
+
+.. autoclass:: sagemaker.Object2Vec
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+    :exclude-members: image, enc_dim, mini_batch_size, epochs, early_stopping_patience, early_stopping_tolerance,
+                      dropout, weight_decay, bucket_width, num_classes, mlp_layers, mlp_dim, mlp_activation,
+                      output_layer, optimizer, learning_rate, enc0_network, enc1_network, enc0_cnn_filter_width,
+                      enc1_cnn_filter_width, enc0_max_seq_len, enc1_max_seq_len, enc0_token_embedding_dim,
+                      enc1_token_embedding_dim, enc0_vocab_size, enc1_vocab_size, enc0_layers, enc1_layers,
+                      enc0_freeze_pretrained_embedding, enc1_freeze_pretrained_embedding
+
+.. autoclass:: sagemaker.Object2VecModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/doc/randomcutforest.rst b/doc/randomcutforest.rst
@@ -1,5 +1,5 @@
 Random Cut Forest
---------------------
+-----------------
 
 The Amazon SageMaker Random Cut Forest algorithm.
 
@@ -8,7 +8,7 @@ The Amazon SageMaker Random Cut Forest algorithm.
     :undoc-members:
     :show-inheritance:
     :inherited-members:
-    :exclude-members: image, num_trees, num_samples_per_tree, eval_metrics, MINI_BATCH_SIZE
+    :exclude-members: image, num_trees, num_samples_per_tree, eval_metrics, feature_dim, MINI_BATCH_SIZE
 
 
 .. autoclass:: sagemaker.RandomCutForestModel
diff --git a/doc/sagemaker.sklearn.rst b/doc/sagemaker.sklearn.rst
@@ -0,0 +1,26 @@
+Scikit Learn
+============
+
+Scikit Learn Estimator
+----------------------
+
+.. autoclass:: sagemaker.sklearn.estimator.SKLearn
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Scikit Learn Model
+------------------
+
+.. autoclass:: sagemaker.sklearn.model.SKLearnModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Scikit Learn Predictor
+----------------------
+
+.. autoclass:: sagemaker.sklearn.model.SKLearnPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/doc/sagemaker.workflow.airflow.rst b/doc/sagemaker.workflow.airflow.rst
@@ -37,6 +37,6 @@ deploy_config
 .. autofunction:: sagemaker.workflow.airflow.deploy_config
 
 deploy_config_from_estimator
-----------------
+----------------------------
 
-.. autofunction:: sagemaker.workflow.airflow.deploy_config_from_estimator
+.. autofunction:: sagemaker.workflow.airflow.deploy_config_from_estimator

-Original file line number
+Diff line change
@@ @@ -1,5 +1,5 @@ @@
 NTM
 ---------------------
 +---
 The Amazon SageMaker NTM algorithm.