feature: add wait argument to estimator deploy#842

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Merged
laurenyu merged 2 commits into from
Jun 11, 2019
Merged

feature: add wait argument to estimator deploy #842

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494e2c1
feature: add wait argument to estimator deploy
imujjwal96 Jun 8, 2019
0428569
Merge branch 'master' into issue_731
laurenyu Jun 10, 2019
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6 changes: 4 additions & 2 deletions src/sagemaker/estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
return estimator

def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
use_compiled_model=False, update_endpoint=False, **kwargs):
use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
"""Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
>>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
/api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
wait (bool): Whether the call should wait until the deployment of model completes (default: True).

**kwargs: Passed to invocation of ``create_model()``. Implementations may customize
``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
accelerator_type=accelerator_type,
endpoint_name=endpoint_name,
update_endpoint=update_endpoint,
tags=self.tags)
tags=self.tags,
wait=wait)

@property
def model_data(self):
Expand Down
5 changes: 3 additions & 2 deletions src/sagemaker/model.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
return self

def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
update_endpoint=False, tags=None, kms_key=None):
update_endpoint=False, tags=None, kms_key=None, wait=True):
"""Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
storage volume attached to the instance hosting the endpoint.
wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

Returns:
callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
else:
self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
tags, kms_key)
tags, kms_key, wait)

if self.predictor_cls:
return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down
6 changes: 4 additions & 2 deletions src/sagemaker/pipeline.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

return sagemaker.pipeline_container_def(self.models, instance_type)

def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
"""Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
endpoint_name (str): The name of the endpoint to create (default: None).
If not specified, a unique endpoint name will be created.
tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
wait (bool): Whether the call should wait until the deployment of model completes (default: True).

Returns:
callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
self.endpoint_name = endpoint_name or self.name
self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
wait=wait)
if self.predictor_cls:
return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down
6 changes: 4 additions & 2 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

return tuner

def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
**kwargs):
"""Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
the name of the training job is used.
wait (bool): Whether the call should wait until the deployment of model completes (default: True).
**kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
sagemaker_session=self.estimator.sagemaker_session)
return best_estimator.deploy(initial_instance_count, instance_type,
accelerator_type=accelerator_type,
endpoint_name=endpoint_name, **kwargs)
endpoint_name=endpoint_name, wait=wait, **kwargs)

def stop_tuning_job(self):
"""Stop latest running hyperparameter tuning job.
Expand Down
3 changes: 2 additions & 1 deletion tests/unit/test_estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
variant,
tags,
None)
None,
True)

sagemaker_session.create_model.assert_called_with(
ANY,
Expand Down
15 changes: 10 additions & 5 deletions tests/unit/test_model.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
'InitialInstanceCount': 1,
'VariantName': 'AllTraffic'}],
None,
None)
None,
True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
'InitialInstanceCount': 55,
'VariantName': 'AllTraffic'}],
None,
None)
None,
True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
'InitialInstanceCount': 1,
'VariantName': 'AllTraffic'}],
tags,
None)
None,
True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
'VariantName': 'AllTraffic',
'AcceleratorType': ACCELERATOR_TYPE}],
None,
None)
None,
True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
'InitialInstanceCount': 1,
'VariantName': 'AllTraffic'}],
None,
key)
key,
True)


@patch('sagemaker.session.Session')
Expand Down
9 changes: 6 additions & 3 deletions tests/unit/test_pipeline_model.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
'InstanceType': INSTANCE_TYPE,
'InitialInstanceCount': 1,
'VariantName': 'AllTraffic'}],
None)
None,
wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
'InstanceType': INSTANCE_TYPE,
'InitialInstanceCount': 1,
'VariantName': 'AllTraffic'}],
None)
None,
wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
'InstanceType': INSTANCE_TYPE,
'InitialInstanceCount': 1,
'VariantName': 'AllTraffic'}],
tags)
tags,
wait=True)


def test_delete_model_without_deploy(sagemaker_session):
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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feature: add wait argument to estimator deploy#842

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 Merged

laurenyu
merged 2 commits into

from

Jun 11, 2019
 Merged


feature: add wait argument to estimator deploy
#842
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494e2c1

feature: add wait argument to estimator deploy


imujjwal96 Jun 8, 2019
0428569

Merge branch 'master' into issue_731


laurenyu Jun 10, 2019

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6 changes: 4 additions & 2 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
 return estimator

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 use_compiled_model=False, update_endpoint=False, **kwargs):
 use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

 More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
 For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
 /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 update_endpoint=update_endpoint,
 tags=self.tags)
 tags=self.tags,
 wait=wait)

 @property
 def model_data(self):
Expand Down

5 changes: 3 additions & 2 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
 return self

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 update_endpoint=False, tags=None, kms_key=None):
 update_endpoint=False, tags=None, kms_key=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
 storage volume attached to the instance hosting the endpoint.
 wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
 else:
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
 tags, kms_key)
 tags, kms_key, wait)

 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/pipeline.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

 return sagemaker.pipeline_container_def(self.models, instance_type)

 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 endpoint_name (str): The name of the endpoint to create (default: None).
 If not specified, a unique endpoint name will be created.
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

 production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
 self.endpoint_name = endpoint_name or self.name
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
 wait=wait)
 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

 return tuner

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
 **kwargs):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
 ``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
 endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
 the name of the training job is used.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
 the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 sagemaker_session=self.estimator.sagemaker_session)
 return best_estimator.deploy(initial_instance_count, instance_type,
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name, **kwargs)
 endpoint_name=endpoint_name, wait=wait, **kwargs)

 def stop_tuning_job(self):
 """Stop latest running hyperparameter tuning job.
Expand Down

3 changes: 2 additions & 1 deletion
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
 sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
 variant,
 tags,
 None)
 None,
 True)

 sagemaker_session.create_model.assert_called_with(
 ANY,
Expand Down

15 changes: 10 additions & 5 deletions
tests/unit/test_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
 'InitialInstanceCount': 55,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
 'VariantName': 'AllTraffic',
 'AcceleratorType': ACCELERATOR_TYPE}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 key)
 key,
 True)


@patch('sagemaker.session.Session')
Expand Down

9 changes: 6 additions & 3 deletions
tests/unit/test_pipeline_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags)
 tags,
 wait=True)


def test_delete_model_without_deploy(sagemaker_session):
Expand Down

, 'i');
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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
feature: add wait argument to estimator deploy#842

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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 Merged

laurenyu
merged 2 commits into

from

Jun 11, 2019
 Merged


feature: add wait argument to estimator deploy
#842
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494e2c1

feature: add wait argument to estimator deploy


imujjwal96 Jun 8, 2019
0428569

Merge branch 'master' into issue_731


laurenyu Jun 10, 2019

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6 changes: 4 additions & 2 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
 return estimator

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 use_compiled_model=False, update_endpoint=False, **kwargs):
 use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

 More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
 For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
 /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 update_endpoint=update_endpoint,
 tags=self.tags)
 tags=self.tags,
 wait=wait)

 @property
 def model_data(self):
Expand Down

5 changes: 3 additions & 2 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
 return self

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 update_endpoint=False, tags=None, kms_key=None):
 update_endpoint=False, tags=None, kms_key=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
 storage volume attached to the instance hosting the endpoint.
 wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
 else:
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
 tags, kms_key)
 tags, kms_key, wait)

 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/pipeline.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

 return sagemaker.pipeline_container_def(self.models, instance_type)

 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 endpoint_name (str): The name of the endpoint to create (default: None).
 If not specified, a unique endpoint name will be created.
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

 production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
 self.endpoint_name = endpoint_name or self.name
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
 wait=wait)
 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

 return tuner

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
 **kwargs):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
 ``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
 endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
 the name of the training job is used.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
 the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 sagemaker_session=self.estimator.sagemaker_session)
 return best_estimator.deploy(initial_instance_count, instance_type,
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name, **kwargs)
 endpoint_name=endpoint_name, wait=wait, **kwargs)

 def stop_tuning_job(self):
 """Stop latest running hyperparameter tuning job.
Expand Down

3 changes: 2 additions & 1 deletion
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
 sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
 variant,
 tags,
 None)
 None,
 True)

 sagemaker_session.create_model.assert_called_with(
 ANY,
Expand Down

15 changes: 10 additions & 5 deletions
tests/unit/test_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
 'InitialInstanceCount': 55,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
 'VariantName': 'AllTraffic',
 'AcceleratorType': ACCELERATOR_TYPE}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 key)
 key,
 True)


@patch('sagemaker.session.Session')
Expand Down

9 changes: 6 additions & 3 deletions
tests/unit/test_pipeline_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags)
 tags,
 wait=True)


def test_delete_model_without_deploy(sagemaker_session):
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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 Merged

laurenyu
merged 2 commits into

from

Jun 11, 2019
 Merged


feature: add wait argument to estimator deploy
#842
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494e2c1

feature: add wait argument to estimator deploy


imujjwal96 Jun 8, 2019
0428569

Merge branch 'master' into issue_731


laurenyu Jun 10, 2019

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6 changes: 4 additions & 2 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
 return estimator

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 use_compiled_model=False, update_endpoint=False, **kwargs):
 use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

 More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
 For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
 /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 update_endpoint=update_endpoint,
 tags=self.tags)
 tags=self.tags,
 wait=wait)

 @property
 def model_data(self):
Expand Down

5 changes: 3 additions & 2 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
 return self

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 update_endpoint=False, tags=None, kms_key=None):
 update_endpoint=False, tags=None, kms_key=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
 storage volume attached to the instance hosting the endpoint.
 wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
 else:
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
 tags, kms_key)
 tags, kms_key, wait)

 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/pipeline.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

 return sagemaker.pipeline_container_def(self.models, instance_type)

 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 endpoint_name (str): The name of the endpoint to create (default: None).
 If not specified, a unique endpoint name will be created.
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

 production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
 self.endpoint_name = endpoint_name or self.name
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
 wait=wait)
 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

 return tuner

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
 **kwargs):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
 ``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
 endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
 the name of the training job is used.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
 the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 sagemaker_session=self.estimator.sagemaker_session)
 return best_estimator.deploy(initial_instance_count, instance_type,
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name, **kwargs)
 endpoint_name=endpoint_name, wait=wait, **kwargs)

 def stop_tuning_job(self):
 """Stop latest running hyperparameter tuning job.
Expand Down

3 changes: 2 additions & 1 deletion
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
 sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
 variant,
 tags,
 None)
 None,
 True)

 sagemaker_session.create_model.assert_called_with(
 ANY,
Expand Down

15 changes: 10 additions & 5 deletions
tests/unit/test_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
 'InitialInstanceCount': 55,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
 'VariantName': 'AllTraffic',
 'AcceleratorType': ACCELERATOR_TYPE}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 key)
 key,
 True)


@patch('sagemaker.session.Session')
Expand Down

9 changes: 6 additions & 3 deletions
tests/unit/test_pipeline_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags)
 tags,
 wait=True)


def test_delete_model_without_deploy(sagemaker_session):
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
feature: add wait argument to estimator deploy#842

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

 Sign up for GitHub

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privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

laurenyu
merged 2 commits into

from

Jun 11, 2019
 Merged


feature: add wait argument to estimator deploy
#842
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494e2c1

feature: add wait argument to estimator deploy


imujjwal96 Jun 8, 2019
0428569

Merge branch 'master' into issue_731


laurenyu Jun 10, 2019

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6 changes: 4 additions & 2 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
 return estimator

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 use_compiled_model=False, update_endpoint=False, **kwargs):
 use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

 More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
 For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
 /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 update_endpoint=update_endpoint,
 tags=self.tags)
 tags=self.tags,
 wait=wait)

 @property
 def model_data(self):
Expand Down

5 changes: 3 additions & 2 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
 return self

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 update_endpoint=False, tags=None, kms_key=None):
 update_endpoint=False, tags=None, kms_key=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
 storage volume attached to the instance hosting the endpoint.
 wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
 else:
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
 tags, kms_key)
 tags, kms_key, wait)

 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/pipeline.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

 return sagemaker.pipeline_container_def(self.models, instance_type)

 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 endpoint_name (str): The name of the endpoint to create (default: None).
 If not specified, a unique endpoint name will be created.
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

 production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
 self.endpoint_name = endpoint_name or self.name
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
 wait=wait)
 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

 return tuner

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
 **kwargs):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
 ``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
 endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
 the name of the training job is used.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
 the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 sagemaker_session=self.estimator.sagemaker_session)
 return best_estimator.deploy(initial_instance_count, instance_type,
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name, **kwargs)
 endpoint_name=endpoint_name, wait=wait, **kwargs)

 def stop_tuning_job(self):
 """Stop latest running hyperparameter tuning job.
Expand Down

3 changes: 2 additions & 1 deletion
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
 sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
 variant,
 tags,
 None)
 None,
 True)

 sagemaker_session.create_model.assert_called_with(
 ANY,
Expand Down

15 changes: 10 additions & 5 deletions
tests/unit/test_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
 'InitialInstanceCount': 55,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
 'VariantName': 'AllTraffic',
 'AcceleratorType': ACCELERATOR_TYPE}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 key)
 key,
 True)


@patch('sagemaker.session.Session')
Expand Down

9 changes: 6 additions & 3 deletions
tests/unit/test_pipeline_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags)
 tags,
 wait=True)


def test_delete_model_without_deploy(sagemaker_session):
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('^' + ".*" + '

Skip to content
feature: add wait argument to estimator deploy#842

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 Merged

laurenyu
merged 2 commits into

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Jun 11, 2019
 Merged


feature: add wait argument to estimator deploy
#842
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494e2c1

feature: add wait argument to estimator deploy


imujjwal96 Jun 8, 2019
0428569

Merge branch 'master' into issue_731


laurenyu Jun 10, 2019

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6 changes: 4 additions & 2 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
 return estimator

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 use_compiled_model=False, update_endpoint=False, **kwargs):
 use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

 More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
 For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
 /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 update_endpoint=update_endpoint,
 tags=self.tags)
 tags=self.tags,
 wait=wait)

 @property
 def model_data(self):
Expand Down

5 changes: 3 additions & 2 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
 return self

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 update_endpoint=False, tags=None, kms_key=None):
 update_endpoint=False, tags=None, kms_key=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
 storage volume attached to the instance hosting the endpoint.
 wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
 else:
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
 tags, kms_key)
 tags, kms_key, wait)

 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/pipeline.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

 return sagemaker.pipeline_container_def(self.models, instance_type)

 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 endpoint_name (str): The name of the endpoint to create (default: None).
 If not specified, a unique endpoint name will be created.
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

 production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
 self.endpoint_name = endpoint_name or self.name
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
 wait=wait)
 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

 return tuner

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
 **kwargs):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
 ``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
 endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
 the name of the training job is used.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
 the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 sagemaker_session=self.estimator.sagemaker_session)
 return best_estimator.deploy(initial_instance_count, instance_type,
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name, **kwargs)
 endpoint_name=endpoint_name, wait=wait, **kwargs)

 def stop_tuning_job(self):
 """Stop latest running hyperparameter tuning job.
Expand Down

3 changes: 2 additions & 1 deletion
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
 sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
 variant,
 tags,
 None)
 None,
 True)

 sagemaker_session.create_model.assert_called_with(
 ANY,
Expand Down

15 changes: 10 additions & 5 deletions
tests/unit/test_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
 'InitialInstanceCount': 55,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
 'VariantName': 'AllTraffic',
 'AcceleratorType': ACCELERATOR_TYPE}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 key)
 key,
 True)


@patch('sagemaker.session.Session')
Expand Down

9 changes: 6 additions & 3 deletions
tests/unit/test_pipeline_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags)
 tags,
 wait=True)


def test_delete_model_without_deploy(sagemaker_session):
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('^' + ".*" + '

Skip to content
feature: add wait argument to estimator deploy#842

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

laurenyu
merged 2 commits into

from

Jun 11, 2019
 Merged


feature: add wait argument to estimator deploy
#842
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494e2c1

feature: add wait argument to estimator deploy


imujjwal96 Jun 8, 2019
0428569

Merge branch 'master' into issue_731


laurenyu Jun 10, 2019

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6 changes: 4 additions & 2 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
 return estimator

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 use_compiled_model=False, update_endpoint=False, **kwargs):
 use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

 More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
 For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
 /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 update_endpoint=update_endpoint,
 tags=self.tags)
 tags=self.tags,
 wait=wait)

 @property
 def model_data(self):
Expand Down

5 changes: 3 additions & 2 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
 return self

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 update_endpoint=False, tags=None, kms_key=None):
 update_endpoint=False, tags=None, kms_key=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
 storage volume attached to the instance hosting the endpoint.
 wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
 else:
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
 tags, kms_key)
 tags, kms_key, wait)

 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/pipeline.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

 return sagemaker.pipeline_container_def(self.models, instance_type)

 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 endpoint_name (str): The name of the endpoint to create (default: None).
 If not specified, a unique endpoint name will be created.
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

 production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
 self.endpoint_name = endpoint_name or self.name
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
 wait=wait)
 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

 return tuner

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
 **kwargs):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
 ``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
 endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
 the name of the training job is used.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
 the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 sagemaker_session=self.estimator.sagemaker_session)
 return best_estimator.deploy(initial_instance_count, instance_type,
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name, **kwargs)
 endpoint_name=endpoint_name, wait=wait, **kwargs)

 def stop_tuning_job(self):
 """Stop latest running hyperparameter tuning job.
Expand Down

3 changes: 2 additions & 1 deletion
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
 sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
 variant,
 tags,
 None)
 None,
 True)

 sagemaker_session.create_model.assert_called_with(
 ANY,
Expand Down

15 changes: 10 additions & 5 deletions
tests/unit/test_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
 'InitialInstanceCount': 55,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
 'VariantName': 'AllTraffic',
 'AcceleratorType': ACCELERATOR_TYPE}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 key)
 key,
 True)


@patch('sagemaker.session.Session')
Expand Down

9 changes: 6 additions & 3 deletions
tests/unit/test_pipeline_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags)
 tags,
 wait=True)


def test_delete_model_without_deploy(sagemaker_session):
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

laurenyu
merged 2 commits into

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Jun 11, 2019
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feature: add wait argument to estimator deploy
#842
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494e2c1

feature: add wait argument to estimator deploy


imujjwal96 Jun 8, 2019
0428569

Merge branch 'master' into issue_731


laurenyu Jun 10, 2019

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6 changes: 4 additions & 2 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -331,7 +331,7 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name='m
 return estimator

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 use_compiled_model=False, update_endpoint=False, **kwargs):
 use_compiled_model=False, update_endpoint=False, wait=True, **kwargs):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.

 More information:
Expand All@@ -355,6 +355,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 >>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
 For more information about tags, see https://boto3.amazonaws.com/v1/documentation\
 /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 **kwargs: Passed to invocation of ``create_model()``. Implementations may customize
 ``create_model()`` to accept ``**kwargs`` to customize model creation during deploy.
Expand All@@ -381,7 +382,8 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 update_endpoint=update_endpoint,
 tags=self.tags)
 tags=self.tags,
 wait=wait)

 @property
 def model_data(self):
Expand Down

5 changes: 3 additions & 2 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -228,7 +228,7 @@ def compile(self, target_instance_family, input_shape, output_path, role,
 return self

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None,
 update_endpoint=False, tags=None, kms_key=None):
 update_endpoint=False, tags=None, kms_key=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand DownExpand Up@@ -256,6 +256,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 kms_key (str): The ARN of the KMS key that is used to encrypt the data on the
 storage volume attached to the instance hosting the endpoint.
 wait (bool): Whether the call should wait until the deployment of this model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand DownExpand Up@@ -296,7 +297,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 self.sagemaker_session.update_endpoint(self.endpoint_name, endpoint_config_name)
 else:
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant],
 tags, kms_key)
 tags, kms_key, wait)

 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/pipeline.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,7 @@ def pipeline_container_def(self, instance_type):

 return sagemaker.pipeline_container_def(self.models, instance_type)

 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None):
 def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags=None, wait=True):
 """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.

 Create a SageMaker ``Model`` and ``EndpointConfig``, and deploy an ``Endpoint`` from this ``Model``.
Expand All@@ -86,6 +86,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
 endpoint_name (str): The name of the endpoint to create (default: None).
 If not specified, a unique endpoint name will be created.
 tags(List[dict[str, str]]): The list of tags to attach to this specific endpoint.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).

 Returns:
 callable[string, sagemaker.session.Session] or None: Invocation of ``self.predictor_cls`` on
Expand All@@ -101,7 +102,8 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags

 production_variant = sagemaker.production_variant(self.name, instance_type, initial_instance_count)
 self.endpoint_name = endpoint_name or self.name
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags)
 self.sagemaker_session.endpoint_from_production_variants(self.endpoint_name, [production_variant], tags,
 wait=wait)
 if self.predictor_cls:
 return self.predictor_cls(self.endpoint_name, self.sagemaker_session)

Expand Down

6 changes: 4 additions & 2 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -326,7 +326,8 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim

 return tuner

 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, **kwargs):
 def deploy(self, initial_instance_count, instance_type, accelerator_type=None, endpoint_name=None, wait=True,
 **kwargs):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
 ``sagemaker.RealTimePredictor`` object.

Expand All@@ -342,6 +343,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html
 endpoint_name (str): Name to use for creating an Amazon SageMaker endpoint. If not specified,
 the name of the training job is used.
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 **kwargs: Other arguments needed for deployment. Please refer to the ``create_model()`` method of
 the associated estimator to see what other arguments are needed.

Expand All@@ -354,7 +356,7 @@ def deploy(self, initial_instance_count, instance_type, accelerator_type=None, e
 sagemaker_session=self.estimator.sagemaker_session)
 return best_estimator.deploy(initial_instance_count, instance_type,
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name, **kwargs)
 endpoint_name=endpoint_name, wait=wait, **kwargs)

 def stop_tuning_job(self):
 """Stop latest running hyperparameter tuning job.
Expand Down

3 changes: 2 additions & 1 deletion
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -922,7 +922,8 @@ def test_fit_deploy_keep_tags(sagemaker_session):
 sagemaker_session.endpoint_from_production_variants.assert_called_with(job_name,
 variant,
 tags,
 None)
 None,
 True)

 sagemaker_session.create_model.assert_called_with(
 ANY,
Expand Down

15 changes: 10 additions & 5 deletions
tests/unit/test_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -166,7 +166,8 @@ def test_deploy(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -182,7 +183,8 @@ def test_deploy_endpoint_name(sagemaker_session, tmpdir):
 'InitialInstanceCount': 55,
 'VariantName': 'AllTraffic'}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -199,7 +201,8 @@ def test_deploy_tags(sagemaker_session, tmpdir):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -217,7 +220,8 @@ def test_deploy_accelerator_type(tfo, time, sagemaker_session):
 'VariantName': 'AllTraffic',
 'AcceleratorType': ACCELERATOR_TYPE}],
 None,
 None)
 None,
 True)


@patch('sagemaker.fw_utils.tar_and_upload_dir', MagicMock())
Expand All@@ -235,7 +239,8 @@ def test_deploy_kms_key(tfo, time, sagemaker_session):
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None,
 key)
 key,
 True)


@patch('sagemaker.session.Session')
Expand Down

9 changes: 6 additions & 3 deletions
tests/unit/test_pipeline_model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -102,7 +102,8 @@ def test_deploy(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand All@@ -119,7 +120,8 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 None)
 None,
 wait=True)


@patch('tarfile.open')
Expand DownExpand Up@@ -178,7 +180,8 @@ def test_deploy_tags(tfo, time, sagemaker_session):
 'InstanceType': INSTANCE_TYPE,
 'InitialInstanceCount': 1,
 'VariantName': 'AllTraffic'}],
 tags)
 tags,
 wait=True)


def test_delete_model_without_deploy(sagemaker_session):
Expand Down