feature: allow custom model name during deploy#792

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Merged
chuyang-deng merged 4 commits into from
Jul 8, 2019
Merged

feature: allow custom model name during deploy #792

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9b316d3
feature: allow custom model name during deploy
imujjwal96 May 10, 2019
3fcc5d7
black check
imujjwal96 Jul 5, 2019
4b09f17
Merge branch 'master' into MLFW-2229
chuyang-deng Jul 8, 2019
9337c4a
Merge branch 'master' into MLFW-2229
chuyang-deng Jul 8, 2019
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7 changes: 6 additions & 1 deletion src/sagemaker/estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
use_compiled_model=False,
update_endpoint=False,
wait=True,
model_name=None,
**kwargs
):
"""Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
corresponding to the previous EndpointConfig. Default: False
wait (bool): Whether the call should wait until the deployment of model completes (default: True).
model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
the training job is used.
tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
>>> 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@@ -429,6 +432,7 @@ def deploy(
"""
self._ensure_latest_training_job()
endpoint_name = endpoint_name or self.latest_training_job.name
model_name = model_name or self.latest_training_job.name
self.deploy_instance_type = instance_type
if use_compiled_model:
family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
model = self._compiled_models[family]
else:
model = self.create_model(**kwargs)
model.name = model_name
return model.deploy(
instance_type=instance_type,
initial_instance_count=initial_instance_count,
Expand Down
4 changes: 4 additions & 0 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
accelerator_type=None,
endpoint_name=None,
wait=True,
model_name=None,
**kwargs
):
"""Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
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).
model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
the training job is used.
**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@@ -410,6 +413,7 @@ def deploy(
accelerator_type=accelerator_type,
endpoint_name=endpoint_name,
wait=wait,
model_name=model_name,
**kwargs
)

Expand Down
14 changes: 13 additions & 1 deletion tests/integ/test_tf_script_mode.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
training_job_name = estimator.latest_training_job.name
time.sleep(20)
endpoint_name = training_job_name
model_name = "model-name-1"
_assert_training_job_tags_match(
sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
)
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
training_job_name=training_job_name, sagemaker_session=sagemaker_session
)
predictor = estimator.deploy(
initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
initial_instance_count=1,
instance_type="ml.c4.xlarge",
endpoint_name=endpoint_name,
model_name=model_name,
)

result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
_assert_model_tags_match(
sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
)
_assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
TrainingJobName=training_job_name
)
_assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
endpoint_config_description = sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_config_name
)
assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]
17 changes: 14 additions & 3 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
time.sleep(15)
tuner.wait()

endpoint_name = tuning_job_name
model_name = "model-name-1"
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert attached_tuner.early_stopping_type == "Auto"

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
predictor = attached_tuner.deploy(
1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
)
data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
output = predictor.predict(data)

assert output.shape == (batch_size, 10)
_assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
for row in data:
js["instances"].append({"features": row.tolist()})
return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
endpoint_config_description = sagemaker_client.describe_endpoint_config(
EndpointConfigName=endpoint_config_name
)
assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]
37 changes: 37 additions & 0 deletions tests/unit/test_estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
estimator = Estimator(
IMAGE_NAME,
ROLE,
INSTANCE_COUNT,
INSTANCE_TYPE,
output_path=OUTPUT_PATH,
sagemaker_session=sagemaker_session,
)
estimator.set_hyperparameters(**HYPERPARAMS)
estimator.fit({"train": "s3://bucket/training-prefix"})
model_name = "model-name"
estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

sagemaker_session.create_model.assert_called_once()
args, kwargs = sagemaker_session.create_model.call_args
assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
estimator = Estimator(
IMAGE_NAME,
ROLE,
INSTANCE_COUNT,
INSTANCE_TYPE,
output_path=OUTPUT_PATH,
sagemaker_session=sagemaker_session,
)
estimator.set_hyperparameters(**HYPERPARAMS)
estimator.fit({"train": "s3://bucket/training-prefix"})
estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

sagemaker_session.create_model.assert_called_once()
args, kwargs = sagemaker_session.create_model.call_args
assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down
3 changes: 2 additions & 1 deletion tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

tuner.estimator.sagemaker_session.create_model.assert_called_once()
args = tuner.estimator.sagemaker_session.create_model.call_args[0]
assert args[0].startswith(IMAGE_NAME)

assert args[0] == "neo"
assert args[1] == ROLE
assert args[2]["Image"] == IMAGE_NAME
assert args[2]["ModelDataUrl"] == MODEL_DATA
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: allow custom model name during deploy#792

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 Merged

chuyang-deng
merged 4 commits into

from

Jul 8, 2019
 Merged


feature: allow custom model name during deploy
#792
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9b316d3

feature: allow custom model name during deploy


imujjwal96 May 10, 2019
3fcc5d7

black check


imujjwal96 Jul 5, 2019
4b09f17

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019
9337c4a

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019

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7 changes: 6 additions & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
 use_compiled_model=False,
 update_endpoint=False,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
 update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
 If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
 corresponding to the previous EndpointConfig. Default: False
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
 >>> 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@@ -429,6 +432,7 @@ def deploy(
 """
 self._ensure_latest_training_job()
 endpoint_name = endpoint_name or self.latest_training_job.name
 model_name = model_name or self.latest_training_job.name
 self.deploy_instance_type = instance_type
 if use_compiled_model:
 family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
 model = self._compiled_models[family]
 else:
 model = self.create_model(**kwargs)
 model.name = model_name
 return model.deploy(
 instance_type=instance_type,
 initial_instance_count=initial_instance_count,
Expand Down

4 changes: 4 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
 accelerator_type=None,
 endpoint_name=None,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
 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).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 **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@@ -410,6 +413,7 @@ def deploy(
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 wait=wait,
 model_name=model_name,
 **kwargs
 )

Expand Down

14 changes: 13 additions & 1 deletion
tests/integ/test_tf_script_mode.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
 training_job_name = estimator.latest_training_job.name
 time.sleep(20)
 endpoint_name = training_job_name
 model_name = "model-name-1"
 _assert_training_job_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
 predictor = estimator.deploy(
 initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
 initial_instance_count=1,
 instance_type="ml.c4.xlarge",
 endpoint_name=endpoint_name,
 model_name=model_name,
 )

 result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
 _assert_model_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
 TrainingJobName=training_job_name
 )
 _assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

17 changes: 14 additions & 3 deletions
tests/integ/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
 time.sleep(15)
 tuner.wait()

 endpoint_name = tuning_job_name
 model_name = "model-name-1"
 attached_tuner = HyperparameterTuner.attach(
 tuning_job_name, sagemaker_session=sagemaker_session
 )
 assert attached_tuner.early_stopping_type == "Auto"

 best_training_job = tuner.best_training_job()
 with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
 predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
 with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 predictor = attached_tuner.deploy(
 1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
 )
 data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
 predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 output = predictor.predict(data)

 assert output.shape == (batch_size, 10)
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
 for row in data:
 js["instances"].append({"features": row.tolist()})
 return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

37 changes: 37 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
 sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 model_name = "model-name"
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

 tuner.estimator.sagemaker_session.create_model.assert_called_once()
 args = tuner.estimator.sagemaker_session.create_model.call_args[0]
 assert args[0].startswith(IMAGE_NAME)

 assert args[0] == "neo"
 assert args[1] == ROLE
 assert args[2]["Image"] == IMAGE_NAME
 assert args[2]["ModelDataUrl"] == MODEL_DATA
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: allow custom model name during deploy#792

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 Merged

chuyang-deng
merged 4 commits into

from

Jul 8, 2019
 Merged


feature: allow custom model name during deploy
#792
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9b316d3

feature: allow custom model name during deploy


imujjwal96 May 10, 2019
3fcc5d7

black check


imujjwal96 Jul 5, 2019
4b09f17

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019
9337c4a

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019

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7 changes: 6 additions & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
 use_compiled_model=False,
 update_endpoint=False,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
 update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
 If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
 corresponding to the previous EndpointConfig. Default: False
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
 >>> 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@@ -429,6 +432,7 @@ def deploy(
 """
 self._ensure_latest_training_job()
 endpoint_name = endpoint_name or self.latest_training_job.name
 model_name = model_name or self.latest_training_job.name
 self.deploy_instance_type = instance_type
 if use_compiled_model:
 family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
 model = self._compiled_models[family]
 else:
 model = self.create_model(**kwargs)
 model.name = model_name
 return model.deploy(
 instance_type=instance_type,
 initial_instance_count=initial_instance_count,
Expand Down

4 changes: 4 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
 accelerator_type=None,
 endpoint_name=None,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
 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).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 **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@@ -410,6 +413,7 @@ def deploy(
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 wait=wait,
 model_name=model_name,
 **kwargs
 )

Expand Down

14 changes: 13 additions & 1 deletion
tests/integ/test_tf_script_mode.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
 training_job_name = estimator.latest_training_job.name
 time.sleep(20)
 endpoint_name = training_job_name
 model_name = "model-name-1"
 _assert_training_job_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
 predictor = estimator.deploy(
 initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
 initial_instance_count=1,
 instance_type="ml.c4.xlarge",
 endpoint_name=endpoint_name,
 model_name=model_name,
 )

 result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
 _assert_model_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
 TrainingJobName=training_job_name
 )
 _assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

17 changes: 14 additions & 3 deletions
tests/integ/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
 time.sleep(15)
 tuner.wait()

 endpoint_name = tuning_job_name
 model_name = "model-name-1"
 attached_tuner = HyperparameterTuner.attach(
 tuning_job_name, sagemaker_session=sagemaker_session
 )
 assert attached_tuner.early_stopping_type == "Auto"

 best_training_job = tuner.best_training_job()
 with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
 predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
 with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 predictor = attached_tuner.deploy(
 1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
 )
 data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
 predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 output = predictor.predict(data)

 assert output.shape == (batch_size, 10)
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
 for row in data:
 js["instances"].append({"features": row.tolist()})
 return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

37 changes: 37 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
 sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 model_name = "model-name"
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

 tuner.estimator.sagemaker_session.create_model.assert_called_once()
 args = tuner.estimator.sagemaker_session.create_model.call_args[0]
 assert args[0].startswith(IMAGE_NAME)

 assert args[0] == "neo"
 assert args[1] == ROLE
 assert args[2]["Image"] == IMAGE_NAME
 assert args[2]["ModelDataUrl"] == MODEL_DATA
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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feature: allow custom model name during deploy#792

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 Merged

chuyang-deng
merged 4 commits into

from

Jul 8, 2019
 Merged


feature: allow custom model name during deploy
#792
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9b316d3

feature: allow custom model name during deploy


imujjwal96 May 10, 2019
3fcc5d7

black check


imujjwal96 Jul 5, 2019
4b09f17

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019
9337c4a

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019

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7 changes: 6 additions & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
 use_compiled_model=False,
 update_endpoint=False,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
 update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
 If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
 corresponding to the previous EndpointConfig. Default: False
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
 >>> 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@@ -429,6 +432,7 @@ def deploy(
 """
 self._ensure_latest_training_job()
 endpoint_name = endpoint_name or self.latest_training_job.name
 model_name = model_name or self.latest_training_job.name
 self.deploy_instance_type = instance_type
 if use_compiled_model:
 family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
 model = self._compiled_models[family]
 else:
 model = self.create_model(**kwargs)
 model.name = model_name
 return model.deploy(
 instance_type=instance_type,
 initial_instance_count=initial_instance_count,
Expand Down

4 changes: 4 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
 accelerator_type=None,
 endpoint_name=None,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
 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).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 **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@@ -410,6 +413,7 @@ def deploy(
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 wait=wait,
 model_name=model_name,
 **kwargs
 )

Expand Down

14 changes: 13 additions & 1 deletion
tests/integ/test_tf_script_mode.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
 training_job_name = estimator.latest_training_job.name
 time.sleep(20)
 endpoint_name = training_job_name
 model_name = "model-name-1"
 _assert_training_job_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
 predictor = estimator.deploy(
 initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
 initial_instance_count=1,
 instance_type="ml.c4.xlarge",
 endpoint_name=endpoint_name,
 model_name=model_name,
 )

 result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
 _assert_model_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
 TrainingJobName=training_job_name
 )
 _assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

17 changes: 14 additions & 3 deletions
tests/integ/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
 time.sleep(15)
 tuner.wait()

 endpoint_name = tuning_job_name
 model_name = "model-name-1"
 attached_tuner = HyperparameterTuner.attach(
 tuning_job_name, sagemaker_session=sagemaker_session
 )
 assert attached_tuner.early_stopping_type == "Auto"

 best_training_job = tuner.best_training_job()
 with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
 predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
 with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 predictor = attached_tuner.deploy(
 1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
 )
 data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
 predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 output = predictor.predict(data)

 assert output.shape == (batch_size, 10)
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
 for row in data:
 js["instances"].append({"features": row.tolist()})
 return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

37 changes: 37 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
 sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 model_name = "model-name"
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

 tuner.estimator.sagemaker_session.create_model.assert_called_once()
 args = tuner.estimator.sagemaker_session.create_model.call_args[0]
 assert args[0].startswith(IMAGE_NAME)

 assert args[0] == "neo"
 assert args[1] == ROLE
 assert args[2]["Image"] == IMAGE_NAME
 assert args[2]["ModelDataUrl"] == MODEL_DATA
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: allow custom model name during deploy#792

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

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to your account

 Merged

chuyang-deng
merged 4 commits into

from

Jul 8, 2019
 Merged


feature: allow custom model name during deploy
#792
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9b316d3

feature: allow custom model name during deploy


imujjwal96 May 10, 2019
3fcc5d7

black check


imujjwal96 Jul 5, 2019
4b09f17

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019
9337c4a

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019

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7 changes: 6 additions & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
 use_compiled_model=False,
 update_endpoint=False,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
 update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
 If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
 corresponding to the previous EndpointConfig. Default: False
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
 >>> 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@@ -429,6 +432,7 @@ def deploy(
 """
 self._ensure_latest_training_job()
 endpoint_name = endpoint_name or self.latest_training_job.name
 model_name = model_name or self.latest_training_job.name
 self.deploy_instance_type = instance_type
 if use_compiled_model:
 family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
 model = self._compiled_models[family]
 else:
 model = self.create_model(**kwargs)
 model.name = model_name
 return model.deploy(
 instance_type=instance_type,
 initial_instance_count=initial_instance_count,
Expand Down

4 changes: 4 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
 accelerator_type=None,
 endpoint_name=None,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
 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).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 **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@@ -410,6 +413,7 @@ def deploy(
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 wait=wait,
 model_name=model_name,
 **kwargs
 )

Expand Down

14 changes: 13 additions & 1 deletion
tests/integ/test_tf_script_mode.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
 training_job_name = estimator.latest_training_job.name
 time.sleep(20)
 endpoint_name = training_job_name
 model_name = "model-name-1"
 _assert_training_job_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
 predictor = estimator.deploy(
 initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
 initial_instance_count=1,
 instance_type="ml.c4.xlarge",
 endpoint_name=endpoint_name,
 model_name=model_name,
 )

 result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
 _assert_model_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
 TrainingJobName=training_job_name
 )
 _assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

17 changes: 14 additions & 3 deletions
tests/integ/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
 time.sleep(15)
 tuner.wait()

 endpoint_name = tuning_job_name
 model_name = "model-name-1"
 attached_tuner = HyperparameterTuner.attach(
 tuning_job_name, sagemaker_session=sagemaker_session
 )
 assert attached_tuner.early_stopping_type == "Auto"

 best_training_job = tuner.best_training_job()
 with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
 predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
 with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 predictor = attached_tuner.deploy(
 1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
 )
 data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
 predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 output = predictor.predict(data)

 assert output.shape == (batch_size, 10)
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
 for row in data:
 js["instances"].append({"features": row.tolist()})
 return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

37 changes: 37 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
 sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 model_name = "model-name"
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

 tuner.estimator.sagemaker_session.create_model.assert_called_once()
 args = tuner.estimator.sagemaker_session.create_model.call_args[0]
 assert args[0].startswith(IMAGE_NAME)

 assert args[0] == "neo"
 assert args[1] == ROLE
 assert args[2]["Image"] == IMAGE_NAME
 assert args[2]["ModelDataUrl"] == MODEL_DATA
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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feature: allow custom model name during deploy#792

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chuyang-deng
merged 4 commits into

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Jul 8, 2019
 Merged


feature: allow custom model name during deploy
#792
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9b316d3

feature: allow custom model name during deploy


imujjwal96 May 10, 2019
3fcc5d7

black check


imujjwal96 Jul 5, 2019
4b09f17

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019
9337c4a

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019

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7 changes: 6 additions & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
 use_compiled_model=False,
 update_endpoint=False,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
 update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
 If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
 corresponding to the previous EndpointConfig. Default: False
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
 >>> 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@@ -429,6 +432,7 @@ def deploy(
 """
 self._ensure_latest_training_job()
 endpoint_name = endpoint_name or self.latest_training_job.name
 model_name = model_name or self.latest_training_job.name
 self.deploy_instance_type = instance_type
 if use_compiled_model:
 family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
 model = self._compiled_models[family]
 else:
 model = self.create_model(**kwargs)
 model.name = model_name
 return model.deploy(
 instance_type=instance_type,
 initial_instance_count=initial_instance_count,
Expand Down

4 changes: 4 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
 accelerator_type=None,
 endpoint_name=None,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
 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).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 **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@@ -410,6 +413,7 @@ def deploy(
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 wait=wait,
 model_name=model_name,
 **kwargs
 )

Expand Down

14 changes: 13 additions & 1 deletion
tests/integ/test_tf_script_mode.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
 training_job_name = estimator.latest_training_job.name
 time.sleep(20)
 endpoint_name = training_job_name
 model_name = "model-name-1"
 _assert_training_job_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
 predictor = estimator.deploy(
 initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
 initial_instance_count=1,
 instance_type="ml.c4.xlarge",
 endpoint_name=endpoint_name,
 model_name=model_name,
 )

 result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
 _assert_model_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
 TrainingJobName=training_job_name
 )
 _assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

17 changes: 14 additions & 3 deletions
tests/integ/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
 time.sleep(15)
 tuner.wait()

 endpoint_name = tuning_job_name
 model_name = "model-name-1"
 attached_tuner = HyperparameterTuner.attach(
 tuning_job_name, sagemaker_session=sagemaker_session
 )
 assert attached_tuner.early_stopping_type == "Auto"

 best_training_job = tuner.best_training_job()
 with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
 predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
 with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 predictor = attached_tuner.deploy(
 1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
 )
 data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
 predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 output = predictor.predict(data)

 assert output.shape == (batch_size, 10)
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
 for row in data:
 js["instances"].append({"features": row.tolist()})
 return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

37 changes: 37 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
 sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 model_name = "model-name"
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

 tuner.estimator.sagemaker_session.create_model.assert_called_once()
 args = tuner.estimator.sagemaker_session.create_model.call_args[0]
 assert args[0].startswith(IMAGE_NAME)

 assert args[0] == "neo"
 assert args[1] == ROLE
 assert args[2]["Image"] == IMAGE_NAME
 assert args[2]["ModelDataUrl"] == MODEL_DATA
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: allow custom model name during deploy#792

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.

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to your account

 Merged

chuyang-deng
merged 4 commits into

from

Jul 8, 2019
 Merged


feature: allow custom model name during deploy
#792
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9b316d3

feature: allow custom model name during deploy


imujjwal96 May 10, 2019
3fcc5d7

black check


imujjwal96 Jul 5, 2019
4b09f17

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019
9337c4a

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019

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7 changes: 6 additions & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
 use_compiled_model=False,
 update_endpoint=False,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
 update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
 If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
 corresponding to the previous EndpointConfig. Default: False
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
 >>> 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@@ -429,6 +432,7 @@ def deploy(
 """
 self._ensure_latest_training_job()
 endpoint_name = endpoint_name or self.latest_training_job.name
 model_name = model_name or self.latest_training_job.name
 self.deploy_instance_type = instance_type
 if use_compiled_model:
 family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
 model = self._compiled_models[family]
 else:
 model = self.create_model(**kwargs)
 model.name = model_name
 return model.deploy(
 instance_type=instance_type,
 initial_instance_count=initial_instance_count,
Expand Down

4 changes: 4 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
 accelerator_type=None,
 endpoint_name=None,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
 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).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 **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@@ -410,6 +413,7 @@ def deploy(
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 wait=wait,
 model_name=model_name,
 **kwargs
 )

Expand Down

14 changes: 13 additions & 1 deletion
tests/integ/test_tf_script_mode.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
 training_job_name = estimator.latest_training_job.name
 time.sleep(20)
 endpoint_name = training_job_name
 model_name = "model-name-1"
 _assert_training_job_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
 predictor = estimator.deploy(
 initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
 initial_instance_count=1,
 instance_type="ml.c4.xlarge",
 endpoint_name=endpoint_name,
 model_name=model_name,
 )

 result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
 _assert_model_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
 TrainingJobName=training_job_name
 )
 _assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

17 changes: 14 additions & 3 deletions
tests/integ/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
 time.sleep(15)
 tuner.wait()

 endpoint_name = tuning_job_name
 model_name = "model-name-1"
 attached_tuner = HyperparameterTuner.attach(
 tuning_job_name, sagemaker_session=sagemaker_session
 )
 assert attached_tuner.early_stopping_type == "Auto"

 best_training_job = tuner.best_training_job()
 with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
 predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
 with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 predictor = attached_tuner.deploy(
 1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
 )
 data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
 predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 output = predictor.predict(data)

 assert output.shape == (batch_size, 10)
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
 for row in data:
 js["instances"].append({"features": row.tolist()})
 return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

37 changes: 37 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
 sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 model_name = "model-name"
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

 tuner.estimator.sagemaker_session.create_model.assert_called_once()
 args = tuner.estimator.sagemaker_session.create_model.call_args[0]
 assert args[0].startswith(IMAGE_NAME)

 assert args[0] == "neo"
 assert args[1] == ROLE
 assert args[2]["Image"] == IMAGE_NAME
 assert args[2]["ModelDataUrl"] == MODEL_DATA
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

chuyang-deng
merged 4 commits into

from

Jul 8, 2019
 Merged


feature: allow custom model name during deploy
#792
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9b316d3

feature: allow custom model name during deploy


imujjwal96 May 10, 2019
3fcc5d7

black check


imujjwal96 Jul 5, 2019
4b09f17

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019
9337c4a

Merge branch 'master' into MLFW-2229


chuyang-deng Jul 8, 2019

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7 changes: 6 additions & 1 deletion
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -392,6 +392,7 @@ def deploy(
 use_compiled_model=False,
 update_endpoint=False,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
Expand All@@ -413,11 +414,13 @@ def deploy(
 update_endpoint (bool): Flag to update the model in an existing Amazon SageMaker endpoint.
 If True, this will deploy a new EndpointConfig to an already existing endpoint and delete resources
 corresponding to the previous EndpointConfig. Default: False
 wait (bool): Whether the call should wait until the deployment of model completes (default: True).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 tags(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example:
 >>> 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@@ -429,6 +432,7 @@ def deploy(
 """
 self._ensure_latest_training_job()
 endpoint_name = endpoint_name or self.latest_training_job.name
 model_name = model_name or self.latest_training_job.name
 self.deploy_instance_type = instance_type
 if use_compiled_model:
 family = "_".join(instance_type.split(".")[:-1])
Expand All@@ -440,6 +444,7 @@ def deploy(
 model = self._compiled_models[family]
 else:
 model = self.create_model(**kwargs)
 model.name = model_name
 return model.deploy(
 instance_type=instance_type,
 initial_instance_count=initial_instance_count,
Expand Down

4 changes: 4 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -375,6 +375,7 @@ def deploy(
 accelerator_type=None,
 endpoint_name=None,
 wait=True,
 model_name=None,
 **kwargs
 ):
 """Deploy the best trained or user specified model to an Amazon SageMaker endpoint and return a
Expand All@@ -393,6 +394,8 @@ def deploy(
 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).
 model_name (str): Name to use for creating an Amazon SageMaker model. If not specified, the name of
 the training job is used.
 **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@@ -410,6 +413,7 @@ def deploy(
 accelerator_type=accelerator_type,
 endpoint_name=endpoint_name,
 wait=wait,
 model_name=model_name,
 **kwargs
 )

Expand Down

14 changes: 13 additions & 1 deletion
tests/integ/test_tf_script_mode.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -159,6 +159,7 @@ def test_mnist_async(sagemaker_session):
 training_job_name = estimator.latest_training_job.name
 time.sleep(20)
 endpoint_name = training_job_name
 model_name = "model-name-1"
 _assert_training_job_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
Expand All@@ -167,7 +168,10 @@ def test_mnist_async(sagemaker_session):
 training_job_name=training_job_name, sagemaker_session=sagemaker_session
 )
 predictor = estimator.deploy(
 initial_instance_count=1, instance_type="ml.c4.xlarge", endpoint_name=endpoint_name
 initial_instance_count=1,
 instance_type="ml.c4.xlarge",
 endpoint_name=endpoint_name,
 model_name=model_name,
 )

 result = predictor.predict(np.zeros(784))
Expand All@@ -176,6 +180,7 @@ def test_mnist_async(sagemaker_session):
 _assert_model_tags_match(
 sagemaker_session.sagemaker_client, estimator.latest_training_job.name, TAGS
 )
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


def test_deploy_with_input_handlers(sagemaker_session, instance_type):
Expand DownExpand Up@@ -241,3 +246,10 @@ def _assert_training_job_tags_match(sagemaker_client, training_job_name, tags):
 TrainingJobName=training_job_name
 )
 _assert_tags_match(sagemaker_client, training_job_description["TrainingJobArn"], tags)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

17 changes: 14 additions & 3 deletions
tests/integ/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -843,14 +843,17 @@ def test_attach_tuning_pytorch(sagemaker_session):
 time.sleep(15)
 tuner.wait()

 endpoint_name = tuning_job_name
 model_name = "model-name-1"
 attached_tuner = HyperparameterTuner.attach(
 tuning_job_name, sagemaker_session=sagemaker_session
 )
 assert attached_tuner.early_stopping_type == "Auto"

 best_training_job = tuner.best_training_job()
 with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
 predictor = attached_tuner.deploy(1, "ml.c4.xlarge")
 with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
 predictor = attached_tuner.deploy(
 1, "ml.c4.xlarge", endpoint_name=endpoint_name, model_name=model_name
 )
 data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
 predictor.predict(data)

Expand All@@ -859,6 +862,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 output = predictor.predict(data)

 assert output.shape == (batch_size, 10)
 _assert_model_name_match(sagemaker_session.sagemaker_client, endpoint_name, model_name)


@pytest.mark.canary_quick
Expand DownExpand Up@@ -941,3 +945,10 @@ def _fm_serializer(data):
 for row in data:
 js["instances"].append({"features": row.tolist()})
 return json.dumps(js)


def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):
 endpoint_config_description = sagemaker_client.describe_endpoint_config(
 EndpointConfigName=endpoint_config_name
 )
 assert model_name == endpoint_config_description["ProductionVariants"][0]["ModelName"]

37 changes: 37 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -1711,6 +1711,43 @@ def test_deploy_with_update_endpoint(sagemaker_session):
 sagemaker_session.create_endpoint.assert_not_called()


def test_deploy_with_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 model_name = "model-name"
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE, model_name=model_name)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0] == model_name


def test_deploy_with_no_model_name(sagemaker_session):
 estimator = Estimator(
 IMAGE_NAME,
 ROLE,
 INSTANCE_COUNT,
 INSTANCE_TYPE,
 output_path=OUTPUT_PATH,
 sagemaker_session=sagemaker_session,
 )
 estimator.set_hyperparameters(**HYPERPARAMS)
 estimator.fit({"train": "s3://bucket/training-prefix"})
 estimator.deploy(INSTANCE_COUNT, INSTANCE_TYPE)

 sagemaker_session.create_model.assert_called_once()
 args, kwargs = sagemaker_session.create_model.call_args
 assert args[0].startswith(IMAGE_NAME)


@patch("sagemaker.estimator.LocalSession")
@patch("sagemaker.estimator.Session")
def test_local_mode(session_class, local_session_class):
Expand Down

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

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -645,7 +645,8 @@ def test_deploy_default(tuner):

 tuner.estimator.sagemaker_session.create_model.assert_called_once()
 args = tuner.estimator.sagemaker_session.create_model.call_args[0]
 assert args[0].startswith(IMAGE_NAME)

 assert args[0] == "neo"
 assert args[1] == ROLE
 assert args[2]["Image"] == IMAGE_NAME
 assert args[2]["ModelDataUrl"] == MODEL_DATA
Expand Down