Support of Horovod and TF 1.12 for TensorFlow Script Mode. TFS 1.12 support

CHANGELOG.rst

@@ -2,11 +2,13 @@
 CHANGELOG
 =========
 
-1.17.3.dev
-==========
+
+1.17.3
+======
 
 * bug-fix: Handle StopIteration in CloudWatch Logs retrieval
 * feature: Update EI TensorFlow latest version to 1.12
+* feature: Support for Horovod
 
 1.17.2
 ======
@@ -19,10 +21,15 @@ CHANGELOG
 * enhancement: Workflow: Specify tasks from which training/tuning operator to transform/deploy in related operators
 * feature: Supporting inter-container traffic encryption flag
 
+
 1.17.0
 ======
 
+
 * bug-fix: Workflow: Revert appending Airflow retry id to default job name
+* feature: support for Tensorflow 1.12
+* feature: support for Tensorflow Serving 1.12
+* bug-fix: Revert appending Airflow retry id to default job name
 * bug-fix: Session: don't allow get_execution_role() to return an ARN that's not a role but has "role" in the name
 * bug-fix: Remove ``__all__`` from ``__init__.py`` files
 * doc-fix: Add TFRecord split type to docs
@@ -35,6 +42,7 @@ CHANGELOG
 * enhancement: Add Model.transformer()
 * bug-fix: HyperparameterTuner: make ``include_cls_metadata`` default to ``False`` for everything except Frameworks
 
+
 1.16.3
 ======
 

src/sagemaker/__init__.py

@@ -39,4 +39,4 @@
 from sagemaker.session import s3_input  # noqa: F401
 from sagemaker.session import get_execution_role  # noqa: F401
 
-__version__ = '1.17.2'
+__version__ = '1.17.3'

src/sagemaker/estimator.py

@@ -749,6 +749,9 @@ class Framework(EstimatorBase):
 
     __framework_name__ = None
     LAUNCH_PS_ENV_NAME = 'sagemaker_parameter_server_enabled'
+    LAUNCH_MPI_ENV_NAME = 'sagemaker_mpi_enabled'
+    MPI_NUM_PROCESSES_PER_HOST = 'sagemaker_mpi_num_of_processes_per_host'
+    MPI_CUSTOM_MPI_OPTIONS = 'sagemaker_mpi_custom_mpi_options'
 
     def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cloudwatch_metrics=False,
                  container_log_level=logging.INFO, code_location=None, image_name=None, dependencies=None, **kwargs):

src/sagemaker/tensorflow/README.rst

@@ -207,6 +207,67 @@ After attaching, the estimator can be deployed as usual.
 
     tf_estimator = TensorFlow.attach(training_job_name=training_job_name)
 
+Distributed Training
+''''''''''''''''''''
+
+To run your training job with multiple instances in a distributed fashion, set ``train_instance_count``
+to a number larger than 1. We support two different types of distributed training, parameter server and Horovod.
+The ``distributions`` parameter is used to configure which distributed training strategy to use.
+
+Training with parameter servers
+"""""""""""""""""""""""""""""""
+
+If you specify parameter_server as the value of the distributions parameter, the container launches a parameter server
+thread on each instance in the training cluster, and then executes your training code. You can find more information on
+TensorFlow distributed training at `TensorFlow docs <https://www.tensorflow.org/deploy/distributed>`__.
+To enable parameter server training:
+
+.. code:: python
+
+  from sagemaker.tensorflow import TensorFlow
+
+  tf_estimator = TensorFlow(entry_point='tf-train.py', role='SageMakerRole',
+                            train_instance_count=2, train_instance_type='ml.p2.xlarge',
+                            framework_version='1.11', py_version='py3',
+                            distributions={'parameter_server': {'enabled': True}})
+  tf_estimator.fit('s3://bucket/path/to/training/data')
+
+Training with Horovod
+"""""""""""""""""""""
+
+Horovod is a distributed training framework based on MPI. You can find more details at `Horovod README <https://github.com/uber/horovod>`__.
+
+The container sets up the MPI environment and executes the ``mpirun`` command enabling you to run any Horovod
+training script with Script Mode.
+
+Training with ``MPI`` is configured by specifying following fields in ``distributions``:
+
+- ``enabled (bool)``: If set to ``True``, the MPI setup is performed and ``mpirun`` command is executed.
+- ``processes_per_host (int)``: Number of processes MPI should launch on each host. Note, this should not be
+  greater than the available slots on the selected instance type. This flag should be set for the multi-cpu/gpu
+  training.
+- ``custom_mpi_options (str)``:  Any `mpirun` flag(s) can be passed in this field that will be added to the `mpirun`
+  command executed by SageMaker to launch distributed horovod training.
+
+
+In the below example we create an estimator to launch Horovod distributed training with 2 processes on one host:
+
+.. code:: python
+
+    from sagemaker.tensorflow import TensorFlow
+
+    tf_estimator = TensorFlow(entry_point='tf-train.py', role='SageMakerRole',
+                              train_instance_count=1, train_instance_type='ml.p2.xlarge',
+                              framework_version='1.12', py_version='py3',
+                              distributions={
+                                  'mpi': {
+                                      'enabled': True,
+                                      'processes_per_host': 2,
+                                      'custom_mpi_options': '--NCCL_DEBUG INFO'
+                                  }
+                              })
+    tf_estimator.fit('s3://bucket/path/to/training/data')
+
 sagemaker.tensorflow.TensorFlow class
 '''''''''''''''''''''''''''''''''''''
 
@@ -277,11 +338,10 @@ Optional:
 - ``model_dir (str)`` Location where model data, checkpoint data, and TensorBoard checkpoints should be saved during training.
   If not specified a S3 location will be generated under the training job's default bucket. And ``model_dir`` will be
   passed in your training script as one of the command line arguments.
-- ``distributions (dict)`` Configure your distrubtion strategy with this argument. For launching parameter server for
-  for distributed training, you must set ``distributions`` to ``{'parameter_server': {'enabled': True}}``
+- ``distributions (dict)`` Configure your distribution strategy with this argument.
 
 Training with Pipe Mode using PipeModeDataset
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Amazon SageMaker allows users to create training jobs using Pipe input mode.
 With Pipe input mode, your dataset is streamed directly to your training instances instead of being downloaded first.
@@ -327,9 +387,9 @@ To run training job with Pipe input mode, pass in ``input_mode='Pipe'`` to your
     from sagemaker.tensorflow import TensorFlow
 
     tf_estimator = TensorFlow(entry_point='tf-train-with-pipemodedataset.py', role='SageMakerRole',
-                            training_steps=10000, evaluation_steps=100,
-                            train_instance_count=1, train_instance_type='ml.p2.xlarge',
-                            framework_version='1.10.0', input_mode='Pipe')
+                              training_steps=10000, evaluation_steps=100,
+                              train_instance_count=1, train_instance_type='ml.p2.xlarge',
+                              framework_version='1.10.0', input_mode='Pipe')
 
     tf_estimator.fit('s3://bucket/path/to/training/data')
 

src/sagemaker/tensorflow/estimator.py

@@ -201,14 +201,20 @@ def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=N
             script_mode (bool): If set to True will the estimator will use the Script Mode containers (default: False).
                 This will be ignored if py_version is set to 'py3'.
             distributions (dict): A dictionary with information on how to run distributed training
-                (default: None). Currently we only support distributed training with parameter servers. To enable it
-                use the following setup:
-                    {
+                (default: None). Currently we support distributed training with parameter servers and MPI. To enable
+                parameter server use the following setup:
                         'parameter_server':
                         {
                             'enabled': True
                         }
                     }
+                To enable MPI:
+                    {
+                        'mpi':
+                        {
+                            'enabled': True
+                        }
+                    }
             **kwargs: Additional kwargs passed to the Framework constructor.
         """
         if framework_version is None:
@@ -420,13 +426,24 @@ def hyperparameters(self):
         hyperparameters = super(TensorFlow, self).hyperparameters()
 
         self.checkpoint_path = self.checkpoint_path or self._default_s3_path('checkpoints')
+        mpi_enabled = False
 
         if self._script_mode_enabled():
-            self.model_dir = self.model_dir or self._default_s3_path('model')
-            additional_hyperparameters = {'model_dir': self.model_dir}
+            additional_hyperparameters = {}
+
             if 'parameter_server' in self.distributions:
-                enabled = self.distributions['parameter_server'].get('enabled', False)
-                additional_hyperparameters[self.LAUNCH_PS_ENV_NAME] = enabled
+                ps_enabled = self.distributions['parameter_server'].get('enabled', False)
+                additional_hyperparameters[self.LAUNCH_PS_ENV_NAME] = ps_enabled
+
+            if 'mpi' in self.distributions:
+                mpi_dict = self.distributions['mpi']
+                mpi_enabled = mpi_dict.get('enabled', False)
+                additional_hyperparameters[self.LAUNCH_MPI_ENV_NAME] = mpi_enabled
+                additional_hyperparameters[self.MPI_NUM_PROCESSES_PER_HOST] = mpi_dict.get('processes_per_host', 1)
+                additional_hyperparameters[self.MPI_CUSTOM_MPI_OPTIONS] = mpi_dict.get('custom_mpi_options', '')
+
+            self.model_dir = self.model_dir or self._default_s3_path('model', mpi=mpi_enabled)
+            additional_hyperparameters['model_dir'] = self.model_dir
         else:
             additional_hyperparameters = {'checkpoint_path': self.checkpoint_path,
                                           'training_steps': self.training_steps,
@@ -436,10 +453,12 @@ def hyperparameters(self):
         hyperparameters.update(Framework._json_encode_hyperparameters(additional_hyperparameters))
         return hyperparameters
 
-    def _default_s3_path(self, directory):
+    def _default_s3_path(self, directory, mpi=False):
         local_code = get_config_value('local.local_code', self.sagemaker_session.config)
         if self.sagemaker_session.local_mode and local_code:
             return '/opt/ml/shared/{}'.format(directory)
+        elif mpi:
+            return '/opt/ml/model'
         else:
             return os.path.join(self.output_path, self._current_job_name, directory)
 

tests/data/horovod/launcher.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+
+python benchmarks/scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --num_batches=500 --model vgg16 --variable_update horovod --horovod_device gpu --use_fp16 --summary_verbosity 1 --save_summaries_steps 10 --train_dir /opt/ml/model --eval_dir /opt/ml/model --batch_size 32

tests/data/horovod/test_hvd_basic.py

@@ -0,0 +1,11 @@
+import json
+import os
+import horovod.tensorflow as hvd
+
+hvd.init()
+
+with open(os.path.join('/opt/ml/model/rank-%s' % hvd.rank()), 'w+') as f:
+    basic_info = {'rank': hvd.rank(), 'size': hvd.size()}
+
+    print(basic_info)
+    json.dump(basic_info, f)

tests/integ/test_horovod.py

@@ -0,0 +1,103 @@
+# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+from __future__ import absolute_import
+
+import json
+import os
+import tarfile
+from six.moves.urllib.parse import urlparse
+
+import boto3
+import pytest
+
+import tests.integ as integ
+from sagemaker.tensorflow import TensorFlow
+from tests.integ import timeout
+
+horovod_dir = os.path.join(os.path.dirname(__file__), '..', 'data', 'horovod')
+
+
+@pytest.mark.parametrize('instance_type', ['ml.c5.xlarge', 'ml.p3.2xlarge'])
+def test_horovod(sagemaker_session, instance_type, tmpdir):
+
+    estimator = TensorFlow(entry_point=os.path.join(horovod_dir, 'test_hvd_basic.py'),
+                           role='SageMakerRole',
+                           train_instance_count=2,
+                           train_instance_type=instance_type,
+                           sagemaker_session=sagemaker_session,
+                           py_version=integ.PYTHON_VERSION,
+                           script_mode=True,
+                           framework_version='1.12',
+                           distributions={'mpi': {'enabled': True}},
+                           base_job_name='test-tf-horovod')
+
+    with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        estimator.fit()
+
+        tmp = str(tmpdir)
+        extract_files_from_s3(estimator.model_data, tmp)
+
+        for rank in range(2):
+            assert read_json('rank-%s' % rank, tmp)['rank'] == rank
+
+
+@pytest.mark.parametrize('instances, processes', [
+    [1, 2],
+    (2, 1),
+    (2, 2)])
+def test_horovod_local_mode(instances, processes, tmpdir):
+    output_path = 'file://%s' % tmpdir
+
+    estimator = TensorFlow(entry_point=os.path.join(horovod_dir, 'test_hvd_basic.py'),
+                           role='SageMakerRole',
+                           train_instance_count=2,
+                           train_instance_type='local',
+                           py_version=integ.PYTHON_VERSION,
+                           script_mode=True,
+                           output_path=output_path,
+                           framework_version='1.12',
+                           distributions={'mpi': {'enabled': True,
+                                                  'processes_per_host': processes}},
+                           base_job_name='test-tf-horovod')
+
+    with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        estimator.fit()
+
+        tmp = str(tmpdir)
+        extract_files(output_path.replace('file://', ''), tmp)
+
+        size = instances * processes
+
+        for rank in range(size):
+            assert read_json('rank-%s' % rank, tmp)['rank'] == rank
+
+
+def extract_files(output_path, tmpdir):
+    with tarfile.open(os.path.join(output_path, 'model.tar.gz')) as tar:
+        tar.extractall(tmpdir)
+
+
+def read_json(file, tmp):
+    with open(os.path.join(tmp, file)) as f:
+        return json.load(f)
+
+
+def extract_files_from_s3(s3_url, tmpdir):
+    parsed_url = urlparse(s3_url)
+    s3 = boto3.resource('s3')
+
+    model = os.path.join(tmpdir, 'model')
+    s3.Bucket(parsed_url.netloc).download_file(parsed_url.path.lstrip('/'), model)
+
+    with tarfile.open(model, 'r') as tar_file:
+        tar_file.extractall(tmpdir)

tests/integ/test_tf_script_mode.py

@@ -23,7 +23,8 @@
 
 RESOURCE_PATH = os.path.join(os.path.dirname(__file__), '..', 'data', 'tensorflow_mnist')
 SCRIPT = os.path.join(RESOURCE_PATH, 'mnist.py')
-DISTRIBUTION_ENABLED = {'parameter_server': {'enabled': True}}
+PARAMETER_SERVER_DISTRIBUTION = {'parameter_server': {'enabled': True}}
+MPI_DISTRIBUTION = {'mpi': {'enabled': True}}
 
 
 @pytest.fixture(scope='session', params=['ml.c5.xlarge', 'ml.p2.xlarge'])
@@ -62,7 +63,7 @@ def test_mnist_distributed(sagemaker_session, instance_type):
                            py_version=integ.PYTHON_VERSION,
                            script_mode=True,
                            framework_version='1.11',
-                           distributions=DISTRIBUTION_ENABLED,
+                           distributions=PARAMETER_SERVER_DISTRIBUTION,
                            base_job_name='test-tf-sm-mnist')
     inputs = estimator.sagemaker_session.upload_data(
         path=os.path.join(RESOURCE_PATH, 'data'),