diff --git a/CHANGELOG.rst b/CHANGELOG.rst index e7bf60e8ba..1a671a946f 100644 --- 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 index 659c242a8f..7ef4585588 100644 --- 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 `__. + +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 index db74369e73..ee9f2887c6 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -2,12 +2,12 @@ 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:: @@ -15,14 +15,15 @@ The SageMaker Python SDK consists of a few primary interfaces: 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 @@ -66,6 +76,15 @@ 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. @@ -73,13 +92,17 @@ Amazon provides implementations of some common machine learning algortithms opti .. 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 \ No newline at end of file + sagemaker.workflow.airflow diff --git a/doc/ipinsights.rst b/doc/ipinsights.rst new file mode 100644 index 0000000000..aea09bd478 --- /dev/null +++ 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 new file mode 100644 index 0000000000..022d4704a8 --- /dev/null +++ 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 index 628cfd7de8..47063f56aa 100644 --- 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 new file mode 100644 index 0000000000..05b37f0124 --- /dev/null +++ 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 index c11475671e..48c290203b 100644 --- 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 new file mode 100644 index 0000000000..1c05e41678 --- /dev/null +++ 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 index bfdd5db549..0dc65a7622 100644 --- 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 \ No newline at end of file +.. autofunction:: sagemaker.workflow.airflow.deploy_config_from_estimator