Add support for Chainer 5.0

CHANGELOG.rst

@@ -10,6 +10,7 @@ CHANGELOG
 * feature: Add APIs to export Airflow training and tuning config
 * doc-fix: Fix typos in tensorflow serving documentation
 * doc-fix: Add estimator base classes to API docs
+* feature: Add support for Chainer 5.0
 
 1.14.2
 ======

README.rst

@@ -358,7 +358,7 @@ Chainer SageMaker Estimators
 
 By using Chainer SageMaker ``Estimators``, you can train and host Chainer models on Amazon SageMaker.
 
-Supported versions of Chainer: ``4.0.0``, ``4.1.0``.
+Supported versions of Chainer: ``4.0.0``, ``4.1.0``, ``5.0.0``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 

src/sagemaker/chainer/README.rst

@@ -4,7 +4,7 @@ Chainer SageMaker Estimators and Models
 
 With Chainer Estimators, you can train and host Chainer models on Amazon SageMaker.
 
-Supported versions of Chainer: ``4.0.0``, ``4.1.0``
+Supported versions of Chainer: ``4.0.0``, ``4.1.0``, ``5.0.0``
 
 You can visit the Chainer repository at https://github.com/chainer/chainer.
 
@@ -32,7 +32,7 @@ Suppose that you already have an Chainer training script called
                                 role='SageMakerRole',
                                 train_instance_type='ml.p3.2xlarge',
                                 train_instance_count=1,
-                                framework_version='4.1.0')
+                                framework_version='5.0.0')
     chainer_estimator.fit('s3://bucket/path/to/training/data')
 
 Where the S3 URL is a path to your training data, within Amazon S3. The constructor keyword arguments define how
@@ -111,7 +111,7 @@ directories ('train' and 'test').
     chainer_estimator = Chainer('chainer-train.py',
                                 train_instance_type='ml.p3.2xlarge',
                                 train_instance_count=1,
-                                framework_version='4.1.0',
+                                framework_version='5.0.0',
                                 hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1})
     chainer_estimator.fit({'train': 's3://my-data-bucket/path/to/my/training/data',
                            'test': 's3://my-data-bucket/path/to/my/test/data'})
@@ -285,7 +285,7 @@ operation.
     chainer_estimator = Chainer(entry_point='train_and_deploy.py',
                                 train_instance_type='ml.p3.2xlarge',
                                 train_instance_count=1,
-                                framework_version='4.1.0')
+                                framework_version='5.0.0')
     chainer_estimator.fit('s3://my_bucket/my_training_data/')
 
     # Deploy my estimator to a SageMaker Endpoint and get a Predictor

src/sagemaker/chainer/defaults.py

@@ -12,4 +12,4 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
-CHAINER_VERSION = '4.1.0'
+CHAINER_VERSION = '5.0.0'

tests/conftest.py

@@ -96,7 +96,7 @@ def pytorch_version(request):
     return request.param
 
 
-@pytest.fixture(scope='module', params=['4.0', '4.0.0', '4.1', '4.1.0'])
+@pytest.fixture(scope='module', params=['4.0', '4.0.0', '4.1', '4.1.0', '5.0', '5.0.0'])
 def chainer_version(request):
     return request.param