Skip to content

Commit 3c3bfb8

Browse files
nadiayanadiaya
authored
Merge branch 'master' into model_client_config
2 parents 7b2dc1a + 18af12b commit 3c3bfb8

File tree

132 files changed

+3278
-2030
lines changed

Some content is hidden

Large Commits have some content hidden by default. Use the searchbox below for content that may be hidden.

132 files changed

+3278
-2030
lines changed

‎CHANGELOG.md

+121
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,126 @@
11
# Changelog
22

3+
## v1.60.2 (2020-05-29)
4+
5+
### Bug Fixes and Other Changes
6+
7+
* [doc] Added Amazon Components for Kubeflow Pipelines
8+
9+
## v1.60.1.post0 (2020-05-28)
10+
11+
### Documentation Changes
12+
13+
* clarify that entry_point must be in the root of source_dir (if applicable)
14+
15+
## v1.60.1 (2020-05-27)
16+
17+
### Bug Fixes and Other Changes
18+
19+
* refactor the navigation
20+
21+
### Documentation Changes
22+
23+
* fix undoc directive; removes extra tabs
24+
25+
## v1.60.0.post0 (2020-05-26)
26+
27+
### Documentation Changes
28+
29+
* remove some duplicated documentation from main README
30+
* fix TF requirements.txt documentation
31+
32+
## v1.60.0 (2020-05-25)
33+
34+
### Features
35+
36+
* support TensorFlow training 2.2
37+
38+
### Bug Fixes and Other Changes
39+
40+
* blacklist unknown xgboost image versions
41+
* use format strings instead of os.path.join for S3 URI in S3Downloader
42+
43+
### Documentation Changes
44+
45+
* consolidate framework version and image information
46+
47+
## v1.59.0 (2020-05-21)
48+
49+
### Features
50+
51+
* MXNet elastic inference support
52+
53+
### Bug Fixes and Other Changes
54+
55+
* add Batch Transform data processing options to Airflow config
56+
* add v2 warning messages
57+
* don't try to use local output path for KMS key in Local Mode
58+
59+
### Documentation Changes
60+
61+
* add instructions for how to enable 'local code' for Local Mode
62+
63+
## v1.58.4 (2020-05-20)
64+
65+
### Bug Fixes and Other Changes
66+
67+
* update AutoML default max_candidate value to use the service default
68+
* add describe_transform_job in session class
69+
70+
### Documentation Changes
71+
72+
* clarify support for requirements.txt in Tensorflow docs
73+
74+
### Testing and Release Infrastructure
75+
76+
* wait for DisassociateTrialComponent to take effect in experiment integ test cleanup
77+
78+
## v1.58.3 (2020-05-19)
79+
80+
### Bug Fixes and Other Changes
81+
82+
* update DatasetFormat key name for sagemakerCaptureJson
83+
84+
### Documentation Changes
85+
86+
* update Processing job max_runtime_in_seconds docstring
87+
88+
## v1.58.2.post0 (2020-05-18)
89+
90+
### Documentation Changes
91+
92+
* specify S3 source_dir needs to point to a tar file
93+
* update PyTorch BYOM topic
94+
95+
## v1.58.2 (2020-05-13)
96+
97+
### Bug Fixes and Other Changes
98+
99+
* address flake8 error
100+
101+
## v1.58.1 (2020-05-11)
102+
103+
### Bug Fixes and Other Changes
104+
105+
* upgrade boto3 to 1.13.6
106+
107+
## v1.58.0 (2020-05-08)
108+
109+
### Features
110+
111+
* support inter container traffic encryption for processing jobs
112+
113+
### Documentation Changes
114+
115+
* add note that v2.0.0 plans have been posted
116+
117+
## v1.57.0 (2020-05-07)
118+
119+
### Features
120+
121+
* add tensorflow training 1.15.2 py37 support
122+
* PyTorch 1.5.0 support
123+
3124
## v1.56.3 (2020-05-06)
4125

5126
### Bug Fixes and Other Changes

‎README.rst

+31-211
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,8 @@
1+
+-------------------------------------------------------------------------------------------------+
2+
| **NOTE**: We are working on v2.0.0. See https://github.com/aws/sagemaker-python-sdk/issues/1459 |
3+
| for more info on our plans and to leave feedback! |
4+
+-------------------------------------------------------------------------------------------------+
5+
16
.. image:: https://github.com/aws/sagemaker-python-sdk/raw/master/branding/icon/sagemaker-banner.png
27
:height: 100px
38
:alt: SageMaker
@@ -29,36 +34,36 @@ You can also train and deploy models with **Amazon algorithms**,
2934
which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training.
3035
If you have **your own algorithms** built into SageMaker compatible Docker containers, you can train and host models using these as well.
3136

32-
For detailed API reference please go to: `Read the Docs <https://sagemaker.readthedocs.io>`_
37+
For detailed documentation, including the API reference, see `Read the Docs <https://sagemaker.readthedocs.io>`_.
3338

3439
Table of Contents
3540
-----------------
3641

37-
1. `Installing SageMaker Python SDK <#installing-the-sagemaker-python-sdk>`__
38-
2. `Using the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html>`__
39-
3. `MXNet SageMaker Estimators <#mxnet-sagemaker-estimators>`__
40-
4. `TensorFlow SageMaker Estimators <#tensorflow-sagemaker-estimators>`__
41-
5. `Chainer SageMaker Estimators <#chainer-sagemaker-estimators>`__
42-
6. `PyTorch SageMaker Estimators <#pytorch-sagemaker-estimators>`__
43-
7. `Scikit-learn SageMaker Estimators <#scikit-learn-sagemaker-estimators>`__
44-
8. `XGBoost SageMaker Estimators <#xgboost-sagemaker-estimators>`__
45-
9. `SageMaker Reinforcement Learning Estimators <#sagemaker-reinforcement-learning-estimators>`__
46-
10. `SageMaker SparkML Serving <#sagemaker-sparkml-serving>`__
47-
11. `AWS SageMaker Estimators <#aws-sagemaker-estimators>`__
48-
12. `Using SageMaker AlgorithmEstimators <https://sagemaker.readthedocs.io/en/stable/overview.html#using-sagemaker-algorithmestimators>`__
49-
13. `Consuming SageMaker Model Packages <https://sagemaker.readthedocs.io/en/stable/overview.html#consuming-sagemaker-model-packages>`__
50-
14. `BYO Docker Containers with SageMaker Estimators <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-docker-containers-with-sagemaker-estimators>`__
51-
15. `SageMaker Automatic Model Tuning <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-automatic-model-tuning>`__
52-
16. `SageMaker Batch Transform <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-batch-transform>`__
53-
17. `Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>`__
54-
18. `BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>`__
55-
19. `Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>`__
56-
20. `Amazon SageMaker Operators for Kubernetes <#amazon-sagemaker-operators-for-kubernetes>`__
57-
21. `Amazon SageMaker Operators in Apache Airflow <#sagemaker-workflow>`__
58-
22. `SageMaker Autopilot <#sagemaker-autopilot>`__
59-
23. `Model Monitoring <#amazon-sagemaker-model-monitoring>`__
60-
24. `SageMaker Debugger <#amazon-sagemaker-debugger>`__
61-
25. `SageMaker Processing <#amazon-sagemaker-processing>`__
42+
#. `Installing SageMaker Python SDK <#installing-the-sagemaker-python-sdk>`__
43+
#. `Using the SageMaker Python SDK <https://sagemaker.readthedocs.io/en/stable/overview.html>`__
44+
#. `Using MXNet <https://sagemaker.readthedocs.io/en/stable/using_mxnet.html>`__
45+
#. `Using TensorFlow <https://sagemaker.readthedocs.io/en/stable/using_tf.html>`__
46+
#. `Using Chainer <https://sagemaker.readthedocs.io/en/stable/using_chainer.html>`__
47+
#. `Using PyTorch <https://sagemaker.readthedocs.io/en/stable/using_pytorch.html>`__
48+
#. `Using Scikit-learn <https://sagemaker.readthedocs.io/en/stable/using_sklearn.html>`__
49+
#. `Using XGBoost <https://sagemaker.readthedocs.io/en/stable/using_xgboost.html>`__
50+
#. `SageMaker Reinforcement Learning Estimators <https://sagemaker.readthedocs.io/en/stable/using_rl.html>`__
51+
#. `SageMaker SparkML Serving <#sagemaker-sparkml-serving>`__
52+
#. `Amazon SageMaker Built-in Algorithm Estimators <src/sagemaker/amazon/README.rst>`__
53+
#. `Using SageMaker AlgorithmEstimators <https://sagemaker.readthedocs.io/en/stable/overview.html#using-sagemaker-algorithmestimators>`__
54+
#. `Consuming SageMaker Model Packages <https://sagemaker.readthedocs.io/en/stable/overview.html#consuming-sagemaker-model-packages>`__
55+
#. `BYO Docker Containers with SageMaker Estimators <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-docker-containers-with-sagemaker-estimators>`__
56+
#. `SageMaker Automatic Model Tuning <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-automatic-model-tuning>`__
57+
#. `SageMaker Batch Transform <https://sagemaker.readthedocs.io/en/stable/overview.html#sagemaker-batch-transform>`__
58+
#. `Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>`__
59+
#. `BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>`__
60+
#. `Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>`__
61+
#. `Amazon SageMaker Operators for Kubernetes <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_operators_for_kubernetes.html>`__
62+
#. `Amazon SageMaker Operators in Apache Airflow <https://sagemaker.readthedocs.io/en/stable/using_workflow.html>`__
63+
#. `SageMaker Autopilot <src/sagemaker/automl/README.rst>`__
64+
#. `Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>`__
65+
#. `SageMaker Debugger <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html>`__
66+
#. `SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>`__
6267

6368

6469
Installing the SageMaker Python SDK
@@ -192,120 +197,6 @@ Preview the site with a Python web server:
192197

193198
View the website by visiting http://localhost:8000
194199

195-
196-
MXNet SageMaker Estimators
197-
--------------------------
198-
199-
By using MXNet SageMaker Estimators, you can train and host MXNet models on Amazon SageMaker.
200-
201-
Supported versions of MXNet: ``0.12.1``, ``1.0.0``, ``1.1.0``, ``1.2.1``, ``1.3.0``, ``1.4.0``, ``1.4.1``, ``1.6.0``.
202-
203-
Supported versions of MXNet for Elastic Inference: ``1.3.0``, ``1.4.0``, ``1.4.1``.
204-
205-
We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
206-
207-
For more information, see `Using MXNet with the SageMaker Python SDK`_.
208-
209-
.. _Using MXNet with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_mxnet.html
210-
211-
212-
TensorFlow SageMaker Estimators
213-
-------------------------------
214-
215-
By using TensorFlow SageMaker Estimators, you can train and host TensorFlow models on Amazon SageMaker.
216-
217-
Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``, ``1.10.0``, ``1.11.0``, ``1.12.0``, ``1.13.1``, ``1.14.0``, ``1.15.0``, ``1.15.2``, ``2.0.0``, ``2.0.1``, ``2.1.0``.
218-
219-
Supported versions of TensorFlow for Elastic Inference: ``1.11.0``, ``1.12.0``, ``1.13.1``, ``1.14.0``, ``1.15.0``, ``2.0.0``.
220-
221-
We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
222-
223-
For more information, see `Using TensorFlow with the SageMaker Python SDK`_.
224-
225-
.. _Using TensorFlow with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_tf.html
226-
227-
228-
Chainer SageMaker Estimators
229-
----------------------------
230-
231-
By using Chainer SageMaker Estimators, you can train and host Chainer models on Amazon SageMaker.
232-
233-
Supported versions of Chainer: ``4.0.0``, ``4.1.0``, ``5.0.0``.
234-
235-
We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
236-
237-
For more information about Chainer, see https://github.com/chainer/chainer.
238-
239-
For more information about Chainer SageMaker Estimators, see `Using Chainer with the SageMaker Python SDK`_.
240-
241-
.. _Using Chainer with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_chainer.html
242-
243-
244-
PyTorch SageMaker Estimators
245-
----------------------------
246-
247-
With PyTorch SageMaker Estimators, you can train and host PyTorch models on Amazon SageMaker.
248-
249-
Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``, ``1.2.0``, ``1.3.1``, ``1.4.0``, ``1.5.0``.
250-
251-
Supported versions of PyTorch for Elastic Inference: ``1.3.1``.
252-
253-
We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
254-
255-
For more information about PyTorch, see https://github.com/pytorch/pytorch.
256-
257-
For more information about PyTorch SageMaker Estimators, see `Using PyTorch with the SageMaker Python SDK`_.
258-
259-
.. _Using PyTorch with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_pytorch.html
260-
261-
262-
Scikit-learn SageMaker Estimators
263-
---------------------------------
264-
265-
With Scikit-learn SageMaker Estimators, you can train and host Scikit-learn models on Amazon SageMaker.
266-
267-
Supported versions of Scikit-learn: ``0.20.0``.
268-
269-
We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
270-
271-
For more information about Scikit-learn, see https://scikit-learn.org/stable/
272-
273-
For more information about Scikit-learn SageMaker Estimators, see `Using Scikit-learn with the SageMaker Python SDK`_.
274-
275-
.. _Using Scikit-learn with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_sklearn.html
276-
277-
XGBoost SageMaker Estimators
278-
----------------------------
279-
280-
With XGBoost SageMaker Estimators, you can train and host XGBoost models on Amazon SageMaker.
281-
282-
Supported versions of XGBoost: ``0.90-1``.
283-
284-
We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
285-
286-
For more information about XGBoost, see https://xgboost.readthedocs.io/en/latest/
287-
288-
For more information about XGBoost SageMaker Estimators, see `Using XGBoost with the SageMaker Python SDK`_.
289-
290-
.. _Using XGBoost with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_xgboost.html
291-
292-
293-
SageMaker Reinforcement Learning Estimators
294-
-------------------------------------------
295-
296-
With Reinforcement Learning (RL) Estimators, you can use reinforcement learning to train models on Amazon SageMaker.
297-
298-
Supported versions of Coach: ``0.10.1``, ``0.11.1`` with TensorFlow, ``0.11.0`` with TensorFlow or MXNet.
299-
For more information about Coach, see https://github.com/NervanaSystems/coach
300-
301-
Supported versions of Ray: ``0.5.3``, ``0.6.5`` with TensorFlow.
302-
For more information about Ray, see https://github.com/ray-project/ray
303-
304-
For more information about SageMaker RL Estimators, see `SageMaker Reinforcement Learning Estimators`_.
305-
306-
.. _SageMaker Reinforcement Learning Estimators: src/sagemaker/rl/README.rst
307-
308-
309200
SageMaker SparkML Serving
310201
-------------------------
311202

@@ -338,74 +229,3 @@ For more information about the different ``content-type`` and ``Accept`` formats
338229
``schema`` that SageMaker SparkML Serving recognizes, please see `SageMaker SparkML Serving Container`_.
339230

340231
.. _SageMaker SparkML Serving Container: https://github.com/aws/sagemaker-sparkml-serving-container
341-
342-
AWS SageMaker Estimators
343-
------------------------
344-
Amazon SageMaker provides several built-in machine learning algorithms that you can use to solve a variety of problems.
345-
346-
The full list of algorithms is available at: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
347-
348-
The SageMaker Python SDK includes estimator wrappers for the AWS K-means, Principal Components Analysis (PCA), Linear Learner, Factorization Machines,
349-
Latent Dirichlet Allocation (LDA), Neural Topic Model (NTM), Random Cut Forest, k-nearest neighbors (k-NN), Object2Vec, and IP Insights algorithms.
350-
351-
For more information, see `AWS SageMaker Estimators and Models`_.
352-
353-
.. _AWS SageMaker Estimators and Models: src/sagemaker/amazon/README.rst
354-
355-
Amazon SageMaker Operators for Kubernetes
356-
-----------------------------------------
357-
358-
You can use Amazon SageMaker Operators for Kubernetes to optimize hyperparameters for a given model, run batch transform jobs over existing models, and set up inference endpoints.
359-
360-
For more information, see `Amazon SageMaker Operators for Kubernetes`_.
361-
362-
.. _Amazon SageMaker Operators for Kubernetes: https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_operators_for_kubernetes.html
363-
364-
Amazon SageMaker Operators in Apache Airflow
365-
--------------------------------------------
366-
367-
You can use Apache Airflow to author, schedule and monitor SageMaker workflow.
368-
369-
For more information, see `Amazon SageMaker Operators in Apache Airflow`_.
370-
371-
.. _Amazon SageMaker Operators in Apache Airflow: https://sagemaker.readthedocs.io/en/stable/using_workflow.html
372-
373-
SageMaker Autopilot
374-
-------------------
375-
376-
Amazon SageMaker Autopilot is an automated machine learning solution (commonly referred to as "AutoML") for tabular
377-
datasets. It automatically trains and tunes the best machine learning models for classification or regression based
378-
on your data, and hosts a series of models on an Inference Pipeline.
379-
380-
For more information about SageMaker Autopilot, see `SageMaker Autopilot`_.
381-
382-
.. _SageMaker Autopilot: src/sagemaker/automl/README.rst
383-
384-
Amazon SageMaker Model Monitoring
385-
---------------------------------
386-
387-
You can use Amazon SageMaker Model Monitoring to automatically detect concept drift by monitoring your machine learning models.
388-
389-
For more information, see `Amazon SageMaker Model Monitoring`_.
390-
391-
.. _Amazon SageMaker Model Monitoring: https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html
392-
393-
Amazon SageMaker Debugger
394-
-------------------------
395-
396-
You can use Amazon SageMaker Debugger to automatically detect anomalies while training your machine learning models.
397-
398-
For more information, see `Amazon SageMaker Debugger`_.
399-
400-
.. _Amazon SageMaker Debugger: https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html
401-
402-
403-
Amazon SageMaker Processing
404-
---------------------------------
405-
406-
You can use Amazon SageMaker Processing to perform data processing tasks such as data pre- and post-processing, feature engineering, data validation, and model evaluation
407-
408-
409-
For more information, see `Amazon SageMaker Processing`_.
410-
411-
.. _Amazon SageMaker Processing: https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html

‎VERSION

+1-1
Original file line numberDiff line numberDiff line change
@@ -1 +1 @@
1-
1.56.4.dev0
1+
1.60.3.dev0

0 commit comments

Comments
 (0)