Deprecate enable_cloudwatch_metrics from Frameworks#292

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Merged
iquintero merged 10 commits into from
Aug 17, 2018
Merged

Deprecate enable_cloudwatch_metrics from Frameworks #292

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b339a4e
Deprecate enable_cloudwatch_metrics from Frameworks
Jul 13, 2018
c086913
Fix unit tests
Jul 14, 2018
9967d52
address PR comments
Jul 20, 2018
1b083f5
Merge branch 'master' into remove_cw_metrics_arg
Jul 20, 2018
bfaafe3
Merge branch 'master' into remove_cw_metrics_arg
andremoeller Jul 20, 2018
d6729b3
Merge branch 'master' into remove_cw_metrics_arg
andremoeller Jul 23, 2018
226720a
Merge branch 'master' into remove_cw_metrics_arg
iquintero Aug 3, 2018
370e96a
Merge branch 'master' into remove_cw_metrics_arg
laurenyu Aug 14, 2018
1d10cde
Merge branch 'master' into remove_cw_metrics_arg
laurenyu Aug 16, 2018
e5012e1
properly update changelog
Aug 16, 2018
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1 change: 1 addition & 0 deletions CHANGELOG.rst
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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down
10 changes: 7 additions & 3 deletions src/sagemaker/estimator.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
For convenience, this accepts other types for keys and values, but ``str()`` will be called
to convert them before training.
enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
training jobs. This will be ignored for now and removed in a further release.
container_log_level (int): Log level to use within the container (default: logging.INFO).
Valid values are defined in the Python logging module.
code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
super(Framework, self).__init__(**kwargs)
self.source_dir = source_dir
self.entry_point = entry_point
self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
if enable_cloudwatch_metrics:
warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
DeprecationWarning)
self.enable_cloudwatch_metrics = False
self.container_log_level = container_log_level
self._hyperparameters = hyperparameters or {}
self.code_location = code_location
Expand Down
3 changes: 0 additions & 3 deletions src/sagemaker/mxnet/README.rst
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Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
directory with any other training source code dependencies including
tne entry point file. Structure within this directory will be
preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
and hosting containers will generate Cloudwatch metrics under the
AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down
13 changes: 3 additions & 10 deletions tests/unit/test_chainer.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
base_job_name=None, use_mpi=None, num_processes=None,
process_slots_per_host=None, additional_mpi_options=None, **kwargs):
return Chainer(entry_point=SCRIPT_PATH,
framework_version=framework_version,
role=ROLE,
sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT,
train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
enable_cloudwatch_metrics=enable_cloudwatch_metrics,
base_job_name=base_job_name,
use_mpi=use_mpi,
num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
},
'hyperparameters': {
'sagemaker_program': json.dumps('dummy_script.py'),
'sagemaker_enable_cloudwatch_metrics': 'false',
'sagemaker_container_log_level': str(logging.INFO),
'sagemaker_job_name': json.dumps(JOB_NAME),
'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
framework_version=chainer_version, container_log_level=container_log_level,
py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
enable_cloudwatch_metrics=enable_cloudwatch_metrics)
py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

job_name = 'new_name'
chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
assert model.name == job_name
assert model.container_log_level == container_log_level
assert model.source_dir == source_dir
assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
custom_image = 'ubuntu:latest'
chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
image_name=custom_image, container_log_level=container_log_level,
py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
enable_cloudwatch_metrics=enable_cloudwatch_metrics)
py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
model = chainer.create_model()
Expand Down
8 changes: 2 additions & 6 deletions tests/unit/test_mxnet.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
framework_version=mxnet_version, container_log_level=container_log_level,
base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
base_job_name='job', source_dir=source_dir)

job_name = 'new_name'
mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
assert model.name == job_name
assert model.container_log_level == container_log_level
assert model.source_dir == source_dir
assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
custom_image = 'mxnet:2.0'
mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
image_name=custom_image, container_log_level=container_log_level,
base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
base_job_name='job', source_dir=source_dir)

job_name = 'new_name'
mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
assert model.name == job_name
assert model.container_log_level == container_log_level
assert model.source_dir == source_dir
assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down
11 changes: 3 additions & 8 deletions tests/unit/test_pytorch.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
base_job_name=None, **kwargs):
return PyTorch(entry_point=SCRIPT_PATH,
framework_version=framework_version,
py_version=PYTHON_VERSION,
role=ROLE,
sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT,
train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
enable_cloudwatch_metrics=enable_cloudwatch_metrics,
base_job_name=base_job_name,
**kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
framework_version=pytorch_version, container_log_level=container_log_level,
base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
base_job_name='job', source_dir=source_dir)

job_name = 'new_name'
pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
assert model.name == job_name
assert model.container_log_level == container_log_level
assert model.source_dir == source_dir
assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
image = 'pytorch:9000'
pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
container_log_level=container_log_level, image_name=image,
base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
base_job_name='job', source_dir=source_dir)

job_name = 'new_name'
pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
assert model.name == job_name
assert model.container_log_level == container_log_level
assert model.source_dir == source_dir
assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down
10 changes: 3 additions & 7 deletions tests/unit/test_tf_estimator.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
checkpoint_path=None, base_job_name=None,
training_steps=None, evaluation_steps=None, **kwargs):
return TensorFlow(entry_point=SCRIPT_PATH,
training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
train_instance_count=INSTANCE_COUNT,
train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
checkpoint_path=checkpoint_path,
enable_cloudwatch_metrics=enable_cloudwatch_metrics,
base_job_name=base_job_name,
**kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
container_log_level=container_log_level, base_job_name='job',
source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
source_dir=source_dir)

job_name = 'doing something'
tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
assert model.name == job_name
assert model.container_log_level == container_log_level
assert model.source_dir == source_dir
assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
container_log_level = '"logging.INFO"'
source_dir = 's3://mybucket/source'
enable_cloudwatch_metrics = 'true'
custom_image = 'tensorflow:1.0'
tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
train_instance_type=INSTANCE_TYPE, image_name=custom_image,
container_log_level=container_log_level, base_job_name='job',
source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
source_dir=source_dir)

job_name = 'doing something'
tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Deprecate enable_cloudwatch_metrics from Frameworks#292

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 Merged

iquintero
merged 10 commits into

from

Aug 17, 2018
 Merged


Deprecate enable_cloudwatch_metrics from Frameworks
#292
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b339a4e

Deprecate enable_cloudwatch_metrics from Frameworks

Jul 13, 2018
c086913

Fix unit tests

Jul 14, 2018
9967d52

address PR comments

Jul 20, 2018
1b083f5

Merge branch 'master' into remove_cw_metrics_arg

Jul 20, 2018
bfaafe3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 20, 2018
d6729b3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 23, 2018
226720a

Merge branch 'master' into remove_cw_metrics_arg


iquintero Aug 3, 2018
370e96a

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 14, 2018
1d10cde

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 16, 2018
e5012e1

properly update changelog

Aug 16, 2018

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1 change: 1 addition & 0 deletions
CHANGELOG.rst

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down

10 changes: 7 additions & 3 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
 For convenience, this accepts other types for keys and values, but ``str()`` will be called
 to convert them before training.
 enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
 enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
 training jobs. This will be ignored for now and removed in a further release.
 container_log_level (int): Log level to use within the container (default: logging.INFO).
 Valid values are defined in the Python logging module.
 code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 super(Framework, self).__init__(**kwargs)
 self.source_dir = source_dir
 self.entry_point = entry_point
 self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
 if enable_cloudwatch_metrics:
 warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
 DeprecationWarning)
 self.enable_cloudwatch_metrics = False
 self.container_log_level = container_log_level
 self._hyperparameters = hyperparameters or {}
 self.code_location = code_location
Expand Down

3 changes: 0 additions & 3 deletions
src/sagemaker/mxnet/README.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
 directory with any other training source code dependencies including
 tne entry point file. Structure within this directory will be
 preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
 and hosting containers will generate Cloudwatch metrics under the
 AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
 Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down

13 changes: 3 additions & 10 deletions
tests/unit/test_chainer.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
 base_job_name=None, use_mpi=None, num_processes=None,
 process_slots_per_host=None, additional_mpi_options=None, **kwargs):
 return Chainer(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 use_mpi=use_mpi,
 num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
 },
 'hyperparameters': {
 'sagemaker_program': json.dumps('dummy_script.py'),
 'sagemaker_enable_cloudwatch_metrics': 'false',
 'sagemaker_container_log_level': str(logging.INFO),
 'sagemaker_job_name': json.dumps(JOB_NAME),
 'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=chainer_version, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'ubuntu:latest'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
 model = chainer.create_model()
Expand Down

8 changes: 2 additions & 6 deletions
tests/unit/test_mxnet.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=mxnet_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'mxnet:2.0'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

11 changes: 3 additions & 8 deletions
tests/unit/test_pytorch.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
 base_job_name=None, **kwargs):
 return PyTorch(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 py_version=PYTHON_VERSION,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=pytorch_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 image = 'pytorch:9000'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 container_log_level=container_log_level, image_name=image,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

10 changes: 3 additions & 7 deletions
tests/unit/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
 checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
 checkpoint_path=None, base_job_name=None,
 training_steps=None, evaluation_steps=None, **kwargs):
 return TensorFlow(entry_point=SCRIPT_PATH,
 training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 checkpoint_path=checkpoint_path,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'tensorflow:1.0'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, image_name=custom_image,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down

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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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var __re = new RegExp('^' + ".*" + '

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 Merged

iquintero
merged 10 commits into

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Aug 17, 2018
 Merged


Deprecate enable_cloudwatch_metrics from Frameworks
#292
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b339a4e

Deprecate enable_cloudwatch_metrics from Frameworks

Jul 13, 2018
c086913

Fix unit tests

Jul 14, 2018
9967d52

address PR comments

Jul 20, 2018
1b083f5

Merge branch 'master' into remove_cw_metrics_arg

Jul 20, 2018
bfaafe3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 20, 2018
d6729b3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 23, 2018
226720a

Merge branch 'master' into remove_cw_metrics_arg


iquintero Aug 3, 2018
370e96a

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 14, 2018
1d10cde

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 16, 2018
e5012e1

properly update changelog

Aug 16, 2018

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1 change: 1 addition & 0 deletions
CHANGELOG.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down

10 changes: 7 additions & 3 deletions
src/sagemaker/estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
 For convenience, this accepts other types for keys and values, but ``str()`` will be called
 to convert them before training.
 enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
 enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
 training jobs. This will be ignored for now and removed in a further release.
 container_log_level (int): Log level to use within the container (default: logging.INFO).
 Valid values are defined in the Python logging module.
 code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 super(Framework, self).__init__(**kwargs)
 self.source_dir = source_dir
 self.entry_point = entry_point
 self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
 if enable_cloudwatch_metrics:
 warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
 DeprecationWarning)
 self.enable_cloudwatch_metrics = False
 self.container_log_level = container_log_level
 self._hyperparameters = hyperparameters or {}
 self.code_location = code_location
Expand Down

3 changes: 0 additions & 3 deletions
src/sagemaker/mxnet/README.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
 directory with any other training source code dependencies including
 tne entry point file. Structure within this directory will be
 preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
 and hosting containers will generate Cloudwatch metrics under the
 AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
 Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down

13 changes: 3 additions & 10 deletions
tests/unit/test_chainer.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
 base_job_name=None, use_mpi=None, num_processes=None,
 process_slots_per_host=None, additional_mpi_options=None, **kwargs):
 return Chainer(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 use_mpi=use_mpi,
 num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
 },
 'hyperparameters': {
 'sagemaker_program': json.dumps('dummy_script.py'),
 'sagemaker_enable_cloudwatch_metrics': 'false',
 'sagemaker_container_log_level': str(logging.INFO),
 'sagemaker_job_name': json.dumps(JOB_NAME),
 'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=chainer_version, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'ubuntu:latest'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
 model = chainer.create_model()
Expand Down

8 changes: 2 additions & 6 deletions
tests/unit/test_mxnet.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=mxnet_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'mxnet:2.0'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

11 changes: 3 additions & 8 deletions
tests/unit/test_pytorch.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
 base_job_name=None, **kwargs):
 return PyTorch(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 py_version=PYTHON_VERSION,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=pytorch_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 image = 'pytorch:9000'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 container_log_level=container_log_level, image_name=image,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

10 changes: 3 additions & 7 deletions
tests/unit/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
 checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
 checkpoint_path=None, base_job_name=None,
 training_steps=None, evaluation_steps=None, **kwargs):
 return TensorFlow(entry_point=SCRIPT_PATH,
 training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 checkpoint_path=checkpoint_path,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'tensorflow:1.0'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, image_name=custom_image,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Deprecate enable_cloudwatch_metrics from Frameworks#292

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 Merged

iquintero
merged 10 commits into

from

Aug 17, 2018
 Merged


Deprecate enable_cloudwatch_metrics from Frameworks
#292
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b339a4e

Deprecate enable_cloudwatch_metrics from Frameworks

Jul 13, 2018
c086913

Fix unit tests

Jul 14, 2018
9967d52

address PR comments

Jul 20, 2018
1b083f5

Merge branch 'master' into remove_cw_metrics_arg

Jul 20, 2018
bfaafe3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 20, 2018
d6729b3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 23, 2018
226720a

Merge branch 'master' into remove_cw_metrics_arg


iquintero Aug 3, 2018
370e96a

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 14, 2018
1d10cde

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 16, 2018
e5012e1

properly update changelog

Aug 16, 2018

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1 change: 1 addition & 0 deletions
CHANGELOG.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down

10 changes: 7 additions & 3 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
 For convenience, this accepts other types for keys and values, but ``str()`` will be called
 to convert them before training.
 enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
 enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
 training jobs. This will be ignored for now and removed in a further release.
 container_log_level (int): Log level to use within the container (default: logging.INFO).
 Valid values are defined in the Python logging module.
 code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 super(Framework, self).__init__(**kwargs)
 self.source_dir = source_dir
 self.entry_point = entry_point
 self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
 if enable_cloudwatch_metrics:
 warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
 DeprecationWarning)
 self.enable_cloudwatch_metrics = False
 self.container_log_level = container_log_level
 self._hyperparameters = hyperparameters or {}
 self.code_location = code_location
Expand Down

3 changes: 0 additions & 3 deletions
src/sagemaker/mxnet/README.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
 directory with any other training source code dependencies including
 tne entry point file. Structure within this directory will be
 preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
 and hosting containers will generate Cloudwatch metrics under the
 AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
 Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down

13 changes: 3 additions & 10 deletions
tests/unit/test_chainer.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
 base_job_name=None, use_mpi=None, num_processes=None,
 process_slots_per_host=None, additional_mpi_options=None, **kwargs):
 return Chainer(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 use_mpi=use_mpi,
 num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
 },
 'hyperparameters': {
 'sagemaker_program': json.dumps('dummy_script.py'),
 'sagemaker_enable_cloudwatch_metrics': 'false',
 'sagemaker_container_log_level': str(logging.INFO),
 'sagemaker_job_name': json.dumps(JOB_NAME),
 'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=chainer_version, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'ubuntu:latest'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
 model = chainer.create_model()
Expand Down

8 changes: 2 additions & 6 deletions
tests/unit/test_mxnet.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=mxnet_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'mxnet:2.0'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

11 changes: 3 additions & 8 deletions
tests/unit/test_pytorch.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
 base_job_name=None, **kwargs):
 return PyTorch(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 py_version=PYTHON_VERSION,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=pytorch_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 image = 'pytorch:9000'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 container_log_level=container_log_level, image_name=image,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

10 changes: 3 additions & 7 deletions
tests/unit/test_tf_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
 checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
 checkpoint_path=None, base_job_name=None,
 training_steps=None, evaluation_steps=None, **kwargs):
 return TensorFlow(entry_point=SCRIPT_PATH,
 training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 checkpoint_path=checkpoint_path,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'tensorflow:1.0'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, image_name=custom_image,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Deprecate enable_cloudwatch_metrics from Frameworks#292

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 Merged

iquintero
merged 10 commits into

from

Aug 17, 2018
 Merged


Deprecate enable_cloudwatch_metrics from Frameworks
#292
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 Changes from all commits
Commits

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Select commit
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b339a4e

Deprecate enable_cloudwatch_metrics from Frameworks

Jul 13, 2018
c086913

Fix unit tests

Jul 14, 2018
9967d52

address PR comments

Jul 20, 2018
1b083f5

Merge branch 'master' into remove_cw_metrics_arg

Jul 20, 2018
bfaafe3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 20, 2018
d6729b3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 23, 2018
226720a

Merge branch 'master' into remove_cw_metrics_arg


iquintero Aug 3, 2018
370e96a

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 14, 2018
1d10cde

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 16, 2018
e5012e1

properly update changelog

Aug 16, 2018

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1 change: 1 addition & 0 deletions
CHANGELOG.rst

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down

10 changes: 7 additions & 3 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
 For convenience, this accepts other types for keys and values, but ``str()`` will be called
 to convert them before training.
 enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
 enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
 training jobs. This will be ignored for now and removed in a further release.
 container_log_level (int): Log level to use within the container (default: logging.INFO).
 Valid values are defined in the Python logging module.
 code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 super(Framework, self).__init__(**kwargs)
 self.source_dir = source_dir
 self.entry_point = entry_point
 self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
 if enable_cloudwatch_metrics:
 warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
 DeprecationWarning)
 self.enable_cloudwatch_metrics = False
 self.container_log_level = container_log_level
 self._hyperparameters = hyperparameters or {}
 self.code_location = code_location
Expand Down

3 changes: 0 additions & 3 deletions
src/sagemaker/mxnet/README.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
 directory with any other training source code dependencies including
 tne entry point file. Structure within this directory will be
 preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
 and hosting containers will generate Cloudwatch metrics under the
 AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
 Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down

13 changes: 3 additions & 10 deletions
tests/unit/test_chainer.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
 base_job_name=None, use_mpi=None, num_processes=None,
 process_slots_per_host=None, additional_mpi_options=None, **kwargs):
 return Chainer(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 use_mpi=use_mpi,
 num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
 },
 'hyperparameters': {
 'sagemaker_program': json.dumps('dummy_script.py'),
 'sagemaker_enable_cloudwatch_metrics': 'false',
 'sagemaker_container_log_level': str(logging.INFO),
 'sagemaker_job_name': json.dumps(JOB_NAME),
 'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=chainer_version, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'ubuntu:latest'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
 model = chainer.create_model()
Expand Down

8 changes: 2 additions & 6 deletions
tests/unit/test_mxnet.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=mxnet_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'mxnet:2.0'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

11 changes: 3 additions & 8 deletions
tests/unit/test_pytorch.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
 base_job_name=None, **kwargs):
 return PyTorch(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 py_version=PYTHON_VERSION,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=pytorch_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 image = 'pytorch:9000'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 container_log_level=container_log_level, image_name=image,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

10 changes: 3 additions & 7 deletions
tests/unit/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
 checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
 checkpoint_path=None, base_job_name=None,
 training_steps=None, evaluation_steps=None, **kwargs):
 return TensorFlow(entry_point=SCRIPT_PATH,
 training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 checkpoint_path=checkpoint_path,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'tensorflow:1.0'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, image_name=custom_image,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 Merged

iquintero
merged 10 commits into

from

Aug 17, 2018
 Merged


Deprecate enable_cloudwatch_metrics from Frameworks
#292
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b339a4e

Deprecate enable_cloudwatch_metrics from Frameworks

Jul 13, 2018
c086913

Fix unit tests

Jul 14, 2018
9967d52

address PR comments

Jul 20, 2018
1b083f5

Merge branch 'master' into remove_cw_metrics_arg

Jul 20, 2018
bfaafe3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 20, 2018
d6729b3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 23, 2018
226720a

Merge branch 'master' into remove_cw_metrics_arg


iquintero Aug 3, 2018
370e96a

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 14, 2018
1d10cde

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 16, 2018
e5012e1

properly update changelog

Aug 16, 2018

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1 change: 1 addition & 0 deletions
CHANGELOG.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down

10 changes: 7 additions & 3 deletions
src/sagemaker/estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
 For convenience, this accepts other types for keys and values, but ``str()`` will be called
 to convert them before training.
 enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
 enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
 training jobs. This will be ignored for now and removed in a further release.
 container_log_level (int): Log level to use within the container (default: logging.INFO).
 Valid values are defined in the Python logging module.
 code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 super(Framework, self).__init__(**kwargs)
 self.source_dir = source_dir
 self.entry_point = entry_point
 self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
 if enable_cloudwatch_metrics:
 warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
 DeprecationWarning)
 self.enable_cloudwatch_metrics = False
 self.container_log_level = container_log_level
 self._hyperparameters = hyperparameters or {}
 self.code_location = code_location
Expand Down

3 changes: 0 additions & 3 deletions
src/sagemaker/mxnet/README.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
 directory with any other training source code dependencies including
 tne entry point file. Structure within this directory will be
 preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
 and hosting containers will generate Cloudwatch metrics under the
 AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
 Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down

13 changes: 3 additions & 10 deletions
tests/unit/test_chainer.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
 base_job_name=None, use_mpi=None, num_processes=None,
 process_slots_per_host=None, additional_mpi_options=None, **kwargs):
 return Chainer(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 use_mpi=use_mpi,
 num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
 },
 'hyperparameters': {
 'sagemaker_program': json.dumps('dummy_script.py'),
 'sagemaker_enable_cloudwatch_metrics': 'false',
 'sagemaker_container_log_level': str(logging.INFO),
 'sagemaker_job_name': json.dumps(JOB_NAME),
 'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=chainer_version, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'ubuntu:latest'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
 model = chainer.create_model()
Expand Down

8 changes: 2 additions & 6 deletions
tests/unit/test_mxnet.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=mxnet_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'mxnet:2.0'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

11 changes: 3 additions & 8 deletions
tests/unit/test_pytorch.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
 base_job_name=None, **kwargs):
 return PyTorch(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 py_version=PYTHON_VERSION,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=pytorch_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 image = 'pytorch:9000'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 container_log_level=container_log_level, image_name=image,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

10 changes: 3 additions & 7 deletions
tests/unit/test_tf_estimator.py

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
 checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
 checkpoint_path=None, base_job_name=None,
 training_steps=None, evaluation_steps=None, **kwargs):
 return TensorFlow(entry_point=SCRIPT_PATH,
 training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 checkpoint_path=checkpoint_path,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'tensorflow:1.0'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, image_name=custom_image,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 Merged

iquintero
merged 10 commits into

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Aug 17, 2018
 Merged


Deprecate enable_cloudwatch_metrics from Frameworks
#292
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b339a4e

Deprecate enable_cloudwatch_metrics from Frameworks

Jul 13, 2018
c086913

Fix unit tests

Jul 14, 2018
9967d52

address PR comments

Jul 20, 2018
1b083f5

Merge branch 'master' into remove_cw_metrics_arg

Jul 20, 2018
bfaafe3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 20, 2018
d6729b3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 23, 2018
226720a

Merge branch 'master' into remove_cw_metrics_arg


iquintero Aug 3, 2018
370e96a

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 14, 2018
1d10cde

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 16, 2018
e5012e1

properly update changelog

Aug 16, 2018

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1 change: 1 addition & 0 deletions
CHANGELOG.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down

10 changes: 7 additions & 3 deletions
src/sagemaker/estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
 For convenience, this accepts other types for keys and values, but ``str()`` will be called
 to convert them before training.
 enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
 enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
 training jobs. This will be ignored for now and removed in a further release.
 container_log_level (int): Log level to use within the container (default: logging.INFO).
 Valid values are defined in the Python logging module.
 code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 super(Framework, self).__init__(**kwargs)
 self.source_dir = source_dir
 self.entry_point = entry_point
 self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
 if enable_cloudwatch_metrics:
 warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
 DeprecationWarning)
 self.enable_cloudwatch_metrics = False
 self.container_log_level = container_log_level
 self._hyperparameters = hyperparameters or {}
 self.code_location = code_location
Expand Down

3 changes: 0 additions & 3 deletions
src/sagemaker/mxnet/README.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
 directory with any other training source code dependencies including
 tne entry point file. Structure within this directory will be
 preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
 and hosting containers will generate Cloudwatch metrics under the
 AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
 Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down

13 changes: 3 additions & 10 deletions
tests/unit/test_chainer.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
 base_job_name=None, use_mpi=None, num_processes=None,
 process_slots_per_host=None, additional_mpi_options=None, **kwargs):
 return Chainer(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 use_mpi=use_mpi,
 num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
 },
 'hyperparameters': {
 'sagemaker_program': json.dumps('dummy_script.py'),
 'sagemaker_enable_cloudwatch_metrics': 'false',
 'sagemaker_container_log_level': str(logging.INFO),
 'sagemaker_job_name': json.dumps(JOB_NAME),
 'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=chainer_version, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'ubuntu:latest'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
 model = chainer.create_model()
Expand Down

8 changes: 2 additions & 6 deletions
tests/unit/test_mxnet.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=mxnet_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'mxnet:2.0'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

11 changes: 3 additions & 8 deletions
tests/unit/test_pytorch.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
 base_job_name=None, **kwargs):
 return PyTorch(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 py_version=PYTHON_VERSION,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=pytorch_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 image = 'pytorch:9000'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 container_log_level=container_log_level, image_name=image,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

10 changes: 3 additions & 7 deletions
tests/unit/test_tf_estimator.py

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
 checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
 checkpoint_path=None, base_job_name=None,
 training_steps=None, evaluation_steps=None, **kwargs):
 return TensorFlow(entry_point=SCRIPT_PATH,
 training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 checkpoint_path=checkpoint_path,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'tensorflow:1.0'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, image_name=custom_image,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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 Merged

iquintero
merged 10 commits into

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Aug 17, 2018
 Merged


Deprecate enable_cloudwatch_metrics from Frameworks
#292
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b339a4e

Deprecate enable_cloudwatch_metrics from Frameworks

Jul 13, 2018
c086913

Fix unit tests

Jul 14, 2018
9967d52

address PR comments

Jul 20, 2018
1b083f5

Merge branch 'master' into remove_cw_metrics_arg

Jul 20, 2018
bfaafe3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 20, 2018
d6729b3

Merge branch 'master' into remove_cw_metrics_arg


andremoeller Jul 23, 2018
226720a

Merge branch 'master' into remove_cw_metrics_arg


iquintero Aug 3, 2018
370e96a

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 14, 2018
1d10cde

Merge branch 'master' into remove_cw_metrics_arg


laurenyu Aug 16, 2018
e5012e1

properly update changelog

Aug 16, 2018

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1 change: 1 addition & 0 deletions
CHANGELOG.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -6,6 +6,7 @@ CHANGELOG
========

* bug-fix: Estimators: Fix serialization of single records
* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.

1.9.0
=====
Expand Down

10 changes: 7 additions & 3 deletions
src/sagemaker/estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import json
import logging
import os
import warnings
from abc import ABCMeta
from abc import abstractmethod
from six import with_metaclass
Expand DownExpand Up@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
 For convenience, this accepts other types for keys and values, but ``str()`` will be called
 to convert them before training.
 enable_cloudwatch_metrics (bool): Whether training and hosting containers will
generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
 enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
 training jobs. This will be ignored for now and removed in a further release.
 container_log_level (int): Log level to use within the container (default: logging.INFO).
 Valid values are defined in the Python logging module.
 code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
Expand All@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
 super(Framework, self).__init__(**kwargs)
 self.source_dir = source_dir
 self.entry_point = entry_point
 self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
 if enable_cloudwatch_metrics:
 warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
 DeprecationWarning)
 self.enable_cloudwatch_metrics = False
 self.container_log_level = container_log_level
 self._hyperparameters = hyperparameters or {}
 self.code_location = code_location
Expand Down

3 changes: 0 additions & 3 deletions
src/sagemaker/mxnet/README.rst

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Original file line numberDiff line numberDiff line change
Expand Up@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
 directory with any other training source code dependencies including
 tne entry point file. Structure within this directory will be
 preserved when training on SageMaker.
- ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
 and hosting containers will generate Cloudwatch metrics under the
 AWS/SageMakerContainer namespace.
- ``container_log_level (int):`` Log level to use within the container.
 Valid values are defined in the Python logging module.
- ``code_location (str):`` Optional. Name of the S3 bucket where your
Expand Down

13 changes: 3 additions & 10 deletions
tests/unit/test_chainer.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):


def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
 base_job_name=None, use_mpi=None, num_processes=None,
 process_slots_per_host=None, additional_mpi_options=None, **kwargs):
 return Chainer(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 use_mpi=use_mpi,
 num_processes=num_processes,
Expand DownExpand Up@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
 },
 'hyperparameters': {
 'sagemaker_program': json.dumps('dummy_script.py'),
 'sagemaker_enable_cloudwatch_metrics': 'false',
 'sagemaker_container_log_level': str(logging.INFO),
 'sagemaker_job_name': json.dumps(JOB_NAME),
 'sagemaker_submit_directory':
Expand DownExpand Up@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
def test_create_model(sagemaker_session, chainer_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=chainer_version, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'ubuntu:latest'
 chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)

 chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
 model = chainer.create_model()
Expand Down

8 changes: 2 additions & 6 deletions
tests/unit/test_mxnet.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -101,11 +101,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, mxnet_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=mxnet_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'mxnet:2.0'
 mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 image_name=custom_image, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 mx.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

11 changes: 3 additions & 8 deletions
tests/unit/test_pytorch.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):


def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
 base_job_name=None, **kwargs):
 return PyTorch(entry_point=SCRIPT_PATH,
 framework_version=framework_version,
 py_version=PYTHON_VERSION,
 role=ROLE,
 sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -119,11 +118,10 @@ def _create_train_job(version):
def test_create_model(sagemaker_session, pytorch_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 framework_version=pytorch_version, container_log_level=container_log_level,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 image = 'pytorch:9000'
 pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 container_log_level=container_log_level, image_name=image,
 base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 base_job_name='job', source_dir=source_dir)

 job_name = 'new_name'
 pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
Expand All@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


@patch('time.strftime', return_value=TIMESTAMP)
Expand Down

10 changes: 3 additions & 7 deletions
tests/unit/test_tf_estimator.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -107,7 +107,7 @@ def _create_train_job(tf_version):


def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
 checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
 checkpoint_path=None, base_job_name=None,
 training_steps=None, evaluation_steps=None, **kwargs):
 return TensorFlow(entry_point=SCRIPT_PATH,
 training_steps=training_steps,
Expand All@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
 train_instance_count=INSTANCE_COUNT,
 train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
 checkpoint_path=checkpoint_path,
 enable_cloudwatch_metrics=enable_cloudwatch_metrics,
 base_job_name=base_job_name,
 **kwargs)

Expand DownExpand Up@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
def test_create_model(sagemaker_session, tf_version):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand All@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
 assert model.name == job_name
 assert model.container_log_level == container_log_level
 assert model.source_dir == source_dir
 assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics


def test_create_model_with_optional_params(sagemaker_session):
Expand All@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
def test_create_model_with_custom_image(sagemaker_session):
 container_log_level = '"logging.INFO"'
 source_dir = 's3://mybucket/source'
 enable_cloudwatch_metrics = 'true'
 custom_image = 'tensorflow:1.0'
 tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
 training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
 train_instance_type=INSTANCE_TYPE, image_name=custom_image,
 container_log_level=container_log_level, base_job_name='job',
 source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
 source_dir=source_dir)

 job_name = 'doing something'
 tf.fit(inputs='s3://mybucket/train', job_name=job_name)
Expand Down