feature: Add Granular Target Description support for compilation (Neo)#1752

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Merged
laurenyu merged 1 commit into from
Jul 28, 2020
Merged

feature: Add Granular Target Description support for compilation (Neo) #1752

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23 changes: 23 additions & 0 deletions src/sagemaker/estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
framework_version=None,
compile_max_run=15 * 60,
tags=None,
target_platform_os=None,
target_platform_arch=None,
target_platform_accelerator=None,
compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.
**kwargs
):
"""Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
tags (list[dict]): List of tags for labeling a compilation job. For
more, see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
target_platform_os (str): Target Platform OS, for example: 'LINUX'.
For allowed strings see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
It can be used instead of target_instance_family.
target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
For allowed strings see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
It can be used instead of target_instance_family.
target_platform_accelerator (str, optional): Target Platform Accelerator,
for example: 'NVIDIA'. For allowed strings see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
It can be used instead of target_instance_family.
compiler_options (dict, optional): Additional parameters for compiler.
Compiler Options are TargetPlatform / target_instance_family specific. See
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
**kwargs: Passed to invocation of ``create_model()``.
Implementations may customize ``create_model()`` to accept
``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
compile_max_run,
framework=framework,
framework_version=framework_version,
target_platform_os=target_platform_os,
target_platform_arch=target_platform_arch,
target_platform_accelerator=target_platform_accelerator,
compiler_options=compiler_options,
)
return self._compiled_models[target_instance_family]

Expand Down
108 changes: 85 additions & 23 deletions src/sagemaker/model.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
job_name,
framework,
tags,
target_platform_os=None,
target_platform_arch=None,
target_platform_accelerator=None,
compiler_options=None,
):
"""
Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
job_name:
framework:
tags:
target_platform_os:
target_platform_arch:
target_platform_accelerator:
compiler_options:
"""
input_model_config = {
"S3Uri": self.model_data,
"DataInputConfig": input_shape
if not isinstance(input_shape, dict)
else json.dumps(input_shape),
"DataInputConfig": json.dumps(input_shape)
if isinstance(input_shape, dict)
else input_shape,
"Framework": framework,
}
role = self.sagemaker_session.expand_role(role)
output_model_config = {
"TargetDevice": target_instance_type,
"S3OutputLocation": output_path,
}

if target_instance_type is not None:
output_model_config["TargetDevice"] = target_instance_type
else:
if target_platform_os is None and target_platform_arch is None:
raise ValueError(
"target_instance_type or (target_platform_os and target_platform_arch) "
"should be provided"
)
target_platform = {
"Os": target_platform_os,
"Arch": target_platform_arch,
}
if target_platform_accelerator is not None:
target_platform["Accelerator"] = target_platform_accelerator
output_model_config["TargetPlatform"] = target_platform

if compiler_options is not None:
output_model_config["CompilerOptions"] = (
json.dumps(compiler_options)
if isinstance(compiler_options, dict)
else compiler_options
)

return {
"input_model_config": input_model_config,
"output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
compile_max_run=5 * 60,
framework=None,
framework_version=None,
target_platform_os=None,
target_platform_arch=None,
target_platform_accelerator=None,
compiler_options=None,
):
"""Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
run your model after compilation, for example: ml_c5. For allowed
strings see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
Alternatively, you can select an OS, Architecture and Accelerator using
``target_platform_os``, ``target_platform_arch``,
and ``target_platform_accelerator``.
input_shape (dict): Specifies the name and shape of the expected
inputs for your trained model in json dictionary form, for
example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
'onnx', 'xgboost'
framework_version (str):
target_platform_os (str): Target Platform OS, for example: 'LINUX'.
For allowed strings see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
It can be used instead of target_instance_family.
target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
For allowed strings see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
It can be used instead of target_instance_family.
target_platform_accelerator (str, optional): Target Platform Accelerator,
for example: 'NVIDIA'. For allowed strings see
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
It can be used instead of target_instance_family.
compiler_options (dict, optional): Additional parameters for compiler.
Compiler Options are TargetPlatform / target_instance_family specific. See
https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

Returns:
sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
job_name,
framework,
tags,
target_platform_os,
target_platform_arch,
target_platform_accelerator,
compiler_options,
)
self.sagemaker_session.compile_model(**config)
job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
if target_instance_family.startswith("ml_"):
self.image = self._neo_image(
self.sagemaker_session.boto_region_name,
target_instance_family,
framework,
framework_version,
)
self._is_compiled_model = True
elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
self.image = self._inferentia_image(
self.sagemaker_session.boto_region_name,
target_instance_family,
framework,
framework_version,
)
self._is_compiled_model = True
if target_instance_family is not None:
if target_instance_family.startswith("ml_"):
self.image = self._neo_image(
self.sagemaker_session.boto_region_name,
target_instance_family,
framework,
framework_version,
)
self._is_compiled_model = True
elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
self.image = self._inferentia_image(
self.sagemaker_session.boto_region_name,
target_instance_family,
framework,
framework_version,
)
self._is_compiled_model = True
else:
LOGGER.warning(
"The instance type %s is not supported for deployment via SageMaker."
"Please deploy the model manually.",
target_instance_family,
)
else:
LOGGER.warning(
"The instance type %s is not supported to deploy via SageMaker,"
"please deploy the model manually.",
target_instance_family,
"Devices described by Target Platform OS, Architecture and Accelerator are not"
"supported for deployment via SageMaker. Please deploy the model manually."
)
return self

Expand Down
39 changes: 39 additions & 0 deletions tests/unit/sagemaker/model/test_neo.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
sagemaker_session.wait_for_compilation_job = Mock(
return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
)
model = _create_model(sagemaker_session)
model.compile(
target_instance_family=None,
input_shape={"data": [1, 3, 1024, 1024]},

@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}

^ suggesting to use single-quote (based on what we discussed earlier)

@apivovarovapivovarovJul 28, 2020•
edited
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This is not JSON-like String which we use in Console UI or AWS CLI.
This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".
Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.

output_path="s3://output",
role="role",
framework="tensorflow",
job_name="compile-model",
target_platform_os="LINUX",
target_platform_arch="ARM64",
target_platform_accelerator="NVIDIA",
compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
)
assert model._is_compiled_model is False
Comment thread
laurenyu marked this conversation as resolved.


def test_compile_model_android_armv7(sagemaker_session):
sagemaker_session.wait_for_compilation_job = Mock(
return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
)
model = _create_model(sagemaker_session)
model.compile(
target_instance_family=None,
input_shape={"data": [1, 3, 1024, 1024]},

@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}

^ suggesting to use single-quote (based on what we discussed earlier)

@apivovarovapivovarovJul 28, 2020•
edited
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because of black-check python code style check we have to use double-quotes here

output_path="s3://output",
role="role",
framework="tensorflow",
job_name="compile-model",
target_platform_os="ANDROID",
target_platform_arch="ARM_EABI",
compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
)
assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
sagemaker_session.wait_for_compilation_job = Mock(
return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
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 Granular Target Description support for compilation (Neo)#1752

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Sign in
to your account

 Merged

laurenyu
merged 1 commit into

from

Jul 28, 2020
 Merged


feature: Add Granular Target Description support for compilation (Neo)
#1752
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23 changes: 23 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
 framework_version=None,
 compile_max_run=15 * 60,
 tags=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.

 **kwargs
 ):
 """Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
 tags (list[dict]): List of tags for labeling a compilation job. For
 more, see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
 **kwargs: Passed to invocation of ``create_model()``.
 Implementations may customize ``create_model()`` to accept
 ``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
 compile_max_run,
 framework=framework,
 framework_version=framework_version,
 target_platform_os=target_platform_os,
 target_platform_arch=target_platform_arch,
 target_platform_accelerator=target_platform_accelerator,
 compiler_options=compiler_options,
 )
 return self._compiled_models[target_instance_family]

Expand Down

108 changes: 85 additions & 23 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
 job_name,
 framework,
 tags,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """
 Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
 job_name:
 framework:
 tags:
 target_platform_os:
 target_platform_arch:
 target_platform_accelerator:
 compiler_options:
 """
 input_model_config = {
 "S3Uri": self.model_data,
 "DataInputConfig": input_shape
 if not isinstance(input_shape, dict)
 else json.dumps(input_shape),
 "DataInputConfig": json.dumps(input_shape)
 if isinstance(input_shape, dict)
 else input_shape,
 "Framework": framework,
 }
 role = self.sagemaker_session.expand_role(role)
 output_model_config = {
 "TargetDevice": target_instance_type,
 "S3OutputLocation": output_path,
 }

 if target_instance_type is not None:
 output_model_config["TargetDevice"] = target_instance_type
 else:
 if target_platform_os is None and target_platform_arch is None:
 raise ValueError(
 "target_instance_type or (target_platform_os and target_platform_arch) "
 "should be provided"
 )
 target_platform = {
 "Os": target_platform_os,
 "Arch": target_platform_arch,
 }
 if target_platform_accelerator is not None:
 target_platform["Accelerator"] = target_platform_accelerator
 output_model_config["TargetPlatform"] = target_platform

 if compiler_options is not None:
 output_model_config["CompilerOptions"] = (
 json.dumps(compiler_options)
 if isinstance(compiler_options, dict)
 else compiler_options
 )

 return {
 "input_model_config": input_model_config,
 "output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
 compile_max_run=5 * 60,
 framework=None,
 framework_version=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
 run your model after compilation, for example: ml_c5. For allowed
 strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 Alternatively, you can select an OS, Architecture and Accelerator using
 ``target_platform_os``, ``target_platform_arch``,
 and ``target_platform_accelerator``.
 input_shape (dict): Specifies the name and shape of the expected
 inputs for your trained model in json dictionary form, for
 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
 model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
 'onnx', 'xgboost'
 framework_version (str):
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

 Returns:
 sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
 job_name,
 framework,
 tags,
 target_platform_os,
 target_platform_arch,
 target_platform_accelerator,
 compiler_options,
 )
 self.sagemaker_session.compile_model(**config)
 job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
 self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 if target_instance_family is not None:
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 else:
 LOGGER.warning(
 "The instance type %s is not supported for deployment via SageMaker."
 "Please deploy the model manually.",
 target_instance_family,
 )
 else:
 LOGGER.warning(
 "The instance type %s is not supported to deploy via SageMaker,"
 "please deploy the model manually.",
 target_instance_family,
 "Devices described by Target Platform OS, Architecture and Accelerator are not"
 "supported for deployment via SageMaker. Please deploy the model manually."
 )
 return self

Expand Down

39 changes: 39 additions & 0 deletions
tests/unit/sagemaker/model/test_neo.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
 assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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The reason will be displayed to describe this comment to others. Learn more.

This is not JSON-like String which we use in Console UI or AWS CLI.

This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".

Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="LINUX",
 target_platform_arch="ARM64",
 target_platform_accelerator="NVIDIA",
 compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
 )
 assert model._is_compiled_model is False
Comment thread
laurenyu marked this conversation as resolved.



def test_compile_model_android_armv7(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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because of black-check python code style check we have to use double-quotes here
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="ANDROID",
 target_platform_arch="ARM_EABI",
 compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
 )
 assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
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('^' + ".*" + '

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feature: Add Granular Target Description support for compilation (Neo)#1752

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laurenyu
merged 1 commit into

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Jul 28, 2020
 Merged


feature: Add Granular Target Description support for compilation (Neo)
#1752
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23 changes: 23 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
 framework_version=None,
 compile_max_run=15 * 60,
 tags=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.

 **kwargs
 ):
 """Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
 tags (list[dict]): List of tags for labeling a compilation job. For
 more, see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
 **kwargs: Passed to invocation of ``create_model()``.
 Implementations may customize ``create_model()`` to accept
 ``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
 compile_max_run,
 framework=framework,
 framework_version=framework_version,
 target_platform_os=target_platform_os,
 target_platform_arch=target_platform_arch,
 target_platform_accelerator=target_platform_accelerator,
 compiler_options=compiler_options,
 )
 return self._compiled_models[target_instance_family]

Expand Down

108 changes: 85 additions & 23 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
 job_name,
 framework,
 tags,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """
 Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
 job_name:
 framework:
 tags:
 target_platform_os:
 target_platform_arch:
 target_platform_accelerator:
 compiler_options:
 """
 input_model_config = {
 "S3Uri": self.model_data,
 "DataInputConfig": input_shape
 if not isinstance(input_shape, dict)
 else json.dumps(input_shape),
 "DataInputConfig": json.dumps(input_shape)
 if isinstance(input_shape, dict)
 else input_shape,
 "Framework": framework,
 }
 role = self.sagemaker_session.expand_role(role)
 output_model_config = {
 "TargetDevice": target_instance_type,
 "S3OutputLocation": output_path,
 }

 if target_instance_type is not None:
 output_model_config["TargetDevice"] = target_instance_type
 else:
 if target_platform_os is None and target_platform_arch is None:
 raise ValueError(
 "target_instance_type or (target_platform_os and target_platform_arch) "
 "should be provided"
 )
 target_platform = {
 "Os": target_platform_os,
 "Arch": target_platform_arch,
 }
 if target_platform_accelerator is not None:
 target_platform["Accelerator"] = target_platform_accelerator
 output_model_config["TargetPlatform"] = target_platform

 if compiler_options is not None:
 output_model_config["CompilerOptions"] = (
 json.dumps(compiler_options)
 if isinstance(compiler_options, dict)
 else compiler_options
 )

 return {
 "input_model_config": input_model_config,
 "output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
 compile_max_run=5 * 60,
 framework=None,
 framework_version=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
 run your model after compilation, for example: ml_c5. For allowed
 strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 Alternatively, you can select an OS, Architecture and Accelerator using
 ``target_platform_os``, ``target_platform_arch``,
 and ``target_platform_accelerator``.
 input_shape (dict): Specifies the name and shape of the expected
 inputs for your trained model in json dictionary form, for
 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
 model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
 'onnx', 'xgboost'
 framework_version (str):
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

 Returns:
 sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
 job_name,
 framework,
 tags,
 target_platform_os,
 target_platform_arch,
 target_platform_accelerator,
 compiler_options,
 )
 self.sagemaker_session.compile_model(**config)
 job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
 self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 if target_instance_family is not None:
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 else:
 LOGGER.warning(
 "The instance type %s is not supported for deployment via SageMaker."
 "Please deploy the model manually.",
 target_instance_family,
 )
 else:
 LOGGER.warning(
 "The instance type %s is not supported to deploy via SageMaker,"
 "please deploy the model manually.",
 target_instance_family,
 "Devices described by Target Platform OS, Architecture and Accelerator are not"
 "supported for deployment via SageMaker. Please deploy the model manually."
 )
 return self

Expand Down

39 changes: 39 additions & 0 deletions
tests/unit/sagemaker/model/test_neo.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
 assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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This is not JSON-like String which we use in Console UI or AWS CLI.

This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".

Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="LINUX",
 target_platform_arch="ARM64",
 target_platform_accelerator="NVIDIA",
 compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
 )
 assert model._is_compiled_model is False
Comment thread
laurenyu marked this conversation as resolved.



def test_compile_model_android_armv7(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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because of black-check python code style check we have to use double-quotes here
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="ANDROID",
 target_platform_arch="ARM_EABI",
 compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
 )
 assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
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('^' + ".*" + '

Skip to content
feature: Add Granular Target Description support for compilation (Neo)#1752

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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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Sign in
to your account

 Merged

laurenyu
merged 1 commit into

from

Jul 28, 2020
 Merged


feature: Add Granular Target Description support for compilation (Neo)
#1752
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23 changes: 23 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
 framework_version=None,
 compile_max_run=15 * 60,
 tags=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.

 **kwargs
 ):
 """Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
 tags (list[dict]): List of tags for labeling a compilation job. For
 more, see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
 **kwargs: Passed to invocation of ``create_model()``.
 Implementations may customize ``create_model()`` to accept
 ``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
 compile_max_run,
 framework=framework,
 framework_version=framework_version,
 target_platform_os=target_platform_os,
 target_platform_arch=target_platform_arch,
 target_platform_accelerator=target_platform_accelerator,
 compiler_options=compiler_options,
 )
 return self._compiled_models[target_instance_family]

Expand Down

108 changes: 85 additions & 23 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
 job_name,
 framework,
 tags,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """
 Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
 job_name:
 framework:
 tags:
 target_platform_os:
 target_platform_arch:
 target_platform_accelerator:
 compiler_options:
 """
 input_model_config = {
 "S3Uri": self.model_data,
 "DataInputConfig": input_shape
 if not isinstance(input_shape, dict)
 else json.dumps(input_shape),
 "DataInputConfig": json.dumps(input_shape)
 if isinstance(input_shape, dict)
 else input_shape,
 "Framework": framework,
 }
 role = self.sagemaker_session.expand_role(role)
 output_model_config = {
 "TargetDevice": target_instance_type,
 "S3OutputLocation": output_path,
 }

 if target_instance_type is not None:
 output_model_config["TargetDevice"] = target_instance_type
 else:
 if target_platform_os is None and target_platform_arch is None:
 raise ValueError(
 "target_instance_type or (target_platform_os and target_platform_arch) "
 "should be provided"
 )
 target_platform = {
 "Os": target_platform_os,
 "Arch": target_platform_arch,
 }
 if target_platform_accelerator is not None:
 target_platform["Accelerator"] = target_platform_accelerator
 output_model_config["TargetPlatform"] = target_platform

 if compiler_options is not None:
 output_model_config["CompilerOptions"] = (
 json.dumps(compiler_options)
 if isinstance(compiler_options, dict)
 else compiler_options
 )

 return {
 "input_model_config": input_model_config,
 "output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
 compile_max_run=5 * 60,
 framework=None,
 framework_version=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
 run your model after compilation, for example: ml_c5. For allowed
 strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 Alternatively, you can select an OS, Architecture and Accelerator using
 ``target_platform_os``, ``target_platform_arch``,
 and ``target_platform_accelerator``.
 input_shape (dict): Specifies the name and shape of the expected
 inputs for your trained model in json dictionary form, for
 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
 model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
 'onnx', 'xgboost'
 framework_version (str):
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

 Returns:
 sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
 job_name,
 framework,
 tags,
 target_platform_os,
 target_platform_arch,
 target_platform_accelerator,
 compiler_options,
 )
 self.sagemaker_session.compile_model(**config)
 job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
 self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 if target_instance_family is not None:
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 else:
 LOGGER.warning(
 "The instance type %s is not supported for deployment via SageMaker."
 "Please deploy the model manually.",
 target_instance_family,
 )
 else:
 LOGGER.warning(
 "The instance type %s is not supported to deploy via SageMaker,"
 "please deploy the model manually.",
 target_instance_family,
 "Devices described by Target Platform OS, Architecture and Accelerator are not"
 "supported for deployment via SageMaker. Please deploy the model manually."
 )
 return self

Expand Down

39 changes: 39 additions & 0 deletions
tests/unit/sagemaker/model/test_neo.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
 assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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ContributorAuthor
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The reason will be displayed to describe this comment to others. Learn more.

This is not JSON-like String which we use in Console UI or AWS CLI.

This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".

Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="LINUX",
 target_platform_arch="ARM64",
 target_platform_accelerator="NVIDIA",
 compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
 )
 assert model._is_compiled_model is False
Comment thread
laurenyu marked this conversation as resolved.



def test_compile_model_android_armv7(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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because of black-check python code style check we have to use double-quotes here
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="ANDROID",
 target_platform_arch="ARM_EABI",
 compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
 )
 assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
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" + '

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feature: Add Granular Target Description support for compilation (Neo)#1752

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 Merged

laurenyu
merged 1 commit into

from

Jul 28, 2020
 Merged


feature: Add Granular Target Description support for compilation (Neo)
#1752
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23 changes: 23 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
 framework_version=None,
 compile_max_run=15 * 60,
 tags=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.

 **kwargs
 ):
 """Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
 tags (list[dict]): List of tags for labeling a compilation job. For
 more, see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
 **kwargs: Passed to invocation of ``create_model()``.
 Implementations may customize ``create_model()`` to accept
 ``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
 compile_max_run,
 framework=framework,
 framework_version=framework_version,
 target_platform_os=target_platform_os,
 target_platform_arch=target_platform_arch,
 target_platform_accelerator=target_platform_accelerator,
 compiler_options=compiler_options,
 )
 return self._compiled_models[target_instance_family]

Expand Down

108 changes: 85 additions & 23 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
 job_name,
 framework,
 tags,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """
 Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
 job_name:
 framework:
 tags:
 target_platform_os:
 target_platform_arch:
 target_platform_accelerator:
 compiler_options:
 """
 input_model_config = {
 "S3Uri": self.model_data,
 "DataInputConfig": input_shape
 if not isinstance(input_shape, dict)
 else json.dumps(input_shape),
 "DataInputConfig": json.dumps(input_shape)
 if isinstance(input_shape, dict)
 else input_shape,
 "Framework": framework,
 }
 role = self.sagemaker_session.expand_role(role)
 output_model_config = {
 "TargetDevice": target_instance_type,
 "S3OutputLocation": output_path,
 }

 if target_instance_type is not None:
 output_model_config["TargetDevice"] = target_instance_type
 else:
 if target_platform_os is None and target_platform_arch is None:
 raise ValueError(
 "target_instance_type or (target_platform_os and target_platform_arch) "
 "should be provided"
 )
 target_platform = {
 "Os": target_platform_os,
 "Arch": target_platform_arch,
 }
 if target_platform_accelerator is not None:
 target_platform["Accelerator"] = target_platform_accelerator
 output_model_config["TargetPlatform"] = target_platform

 if compiler_options is not None:
 output_model_config["CompilerOptions"] = (
 json.dumps(compiler_options)
 if isinstance(compiler_options, dict)
 else compiler_options
 )

 return {
 "input_model_config": input_model_config,
 "output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
 compile_max_run=5 * 60,
 framework=None,
 framework_version=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
 run your model after compilation, for example: ml_c5. For allowed
 strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 Alternatively, you can select an OS, Architecture and Accelerator using
 ``target_platform_os``, ``target_platform_arch``,
 and ``target_platform_accelerator``.
 input_shape (dict): Specifies the name and shape of the expected
 inputs for your trained model in json dictionary form, for
 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
 model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
 'onnx', 'xgboost'
 framework_version (str):
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

 Returns:
 sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
 job_name,
 framework,
 tags,
 target_platform_os,
 target_platform_arch,
 target_platform_accelerator,
 compiler_options,
 )
 self.sagemaker_session.compile_model(**config)
 job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
 self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 if target_instance_family is not None:
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 else:
 LOGGER.warning(
 "The instance type %s is not supported for deployment via SageMaker."
 "Please deploy the model manually.",
 target_instance_family,
 )
 else:
 LOGGER.warning(
 "The instance type %s is not supported to deploy via SageMaker,"
 "please deploy the model manually.",
 target_instance_family,
 "Devices described by Target Platform OS, Architecture and Accelerator are not"
 "supported for deployment via SageMaker. Please deploy the model manually."
 )
 return self

Expand Down

39 changes: 39 additions & 0 deletions
tests/unit/sagemaker/model/test_neo.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
 assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

Loading

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Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This is not JSON-like String which we use in Console UI or AWS CLI.

This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".

Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="LINUX",
 target_platform_arch="ARM64",
 target_platform_accelerator="NVIDIA",
 compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
 )
 assert model._is_compiled_model is False
Comment thread
laurenyu marked this conversation as resolved.



def test_compile_model_android_armv7(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

Loading

Copy link


Copy Markdown

ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

because of black-check python code style check we have to use double-quotes here
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="ANDROID",
 target_platform_arch="ARM_EABI",
 compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
 )
 assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
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 Granular Target Description support for compilation (Neo)#1752

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.

 Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and
privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
Sign in
to your account

 Merged

laurenyu
merged 1 commit into

from

Jul 28, 2020
 Merged


feature: Add Granular Target Description support for compilation (Neo)
#1752
Show file tree
Hide file tree
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Commits

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1 commit


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Failed to load files. 
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 Diff view


23 changes: 23 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
 framework_version=None,
 compile_max_run=15 * 60,
 tags=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.

 **kwargs
 ):
 """Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
 tags (list[dict]): List of tags for labeling a compilation job. For
 more, see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
 **kwargs: Passed to invocation of ``create_model()``.
 Implementations may customize ``create_model()`` to accept
 ``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
 compile_max_run,
 framework=framework,
 framework_version=framework_version,
 target_platform_os=target_platform_os,
 target_platform_arch=target_platform_arch,
 target_platform_accelerator=target_platform_accelerator,
 compiler_options=compiler_options,
 )
 return self._compiled_models[target_instance_family]

Expand Down

108 changes: 85 additions & 23 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
 job_name,
 framework,
 tags,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """
 Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
 job_name:
 framework:
 tags:
 target_platform_os:
 target_platform_arch:
 target_platform_accelerator:
 compiler_options:
 """
 input_model_config = {
 "S3Uri": self.model_data,
 "DataInputConfig": input_shape
 if not isinstance(input_shape, dict)
 else json.dumps(input_shape),
 "DataInputConfig": json.dumps(input_shape)
 if isinstance(input_shape, dict)
 else input_shape,
 "Framework": framework,
 }
 role = self.sagemaker_session.expand_role(role)
 output_model_config = {
 "TargetDevice": target_instance_type,
 "S3OutputLocation": output_path,
 }

 if target_instance_type is not None:
 output_model_config["TargetDevice"] = target_instance_type
 else:
 if target_platform_os is None and target_platform_arch is None:
 raise ValueError(
 "target_instance_type or (target_platform_os and target_platform_arch) "
 "should be provided"
 )
 target_platform = {
 "Os": target_platform_os,
 "Arch": target_platform_arch,
 }
 if target_platform_accelerator is not None:
 target_platform["Accelerator"] = target_platform_accelerator
 output_model_config["TargetPlatform"] = target_platform

 if compiler_options is not None:
 output_model_config["CompilerOptions"] = (
 json.dumps(compiler_options)
 if isinstance(compiler_options, dict)
 else compiler_options
 )

 return {
 "input_model_config": input_model_config,
 "output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
 compile_max_run=5 * 60,
 framework=None,
 framework_version=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
 run your model after compilation, for example: ml_c5. For allowed
 strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 Alternatively, you can select an OS, Architecture and Accelerator using
 ``target_platform_os``, ``target_platform_arch``,
 and ``target_platform_accelerator``.
 input_shape (dict): Specifies the name and shape of the expected
 inputs for your trained model in json dictionary form, for
 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
 model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
 'onnx', 'xgboost'
 framework_version (str):
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

 Returns:
 sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
 job_name,
 framework,
 tags,
 target_platform_os,
 target_platform_arch,
 target_platform_accelerator,
 compiler_options,
 )
 self.sagemaker_session.compile_model(**config)
 job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
 self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 if target_instance_family is not None:
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 else:
 LOGGER.warning(
 "The instance type %s is not supported for deployment via SageMaker."
 "Please deploy the model manually.",
 target_instance_family,
 )
 else:
 LOGGER.warning(
 "The instance type %s is not supported to deploy via SageMaker,"
 "please deploy the model manually.",
 target_instance_family,
 "Devices described by Target Platform OS, Architecture and Accelerator are not"
 "supported for deployment via SageMaker. Please deploy the model manually."
 )
 return self

Expand Down

39 changes: 39 additions & 0 deletions
tests/unit/sagemaker/model/test_neo.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
 assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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This is not JSON-like String which we use in Console UI or AWS CLI.

This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".

Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="LINUX",
 target_platform_arch="ARM64",
 target_platform_accelerator="NVIDIA",
 compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
 )
 assert model._is_compiled_model is False
Comment thread
laurenyu marked this conversation as resolved.



def test_compile_model_android_armv7(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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because of black-check python code style check we have to use double-quotes here
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="ANDROID",
 target_platform_arch="ARM_EABI",
 compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
 )
 assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
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 Granular Target Description support for compilation (Neo)#1752

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 Merged

laurenyu
merged 1 commit into

from

Jul 28, 2020
 Merged


feature: Add Granular Target Description support for compilation (Neo)
#1752
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23 changes: 23 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
 framework_version=None,
 compile_max_run=15 * 60,
 tags=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.

 **kwargs
 ):
 """Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
 tags (list[dict]): List of tags for labeling a compilation job. For
 more, see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
 **kwargs: Passed to invocation of ``create_model()``.
 Implementations may customize ``create_model()`` to accept
 ``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
 compile_max_run,
 framework=framework,
 framework_version=framework_version,
 target_platform_os=target_platform_os,
 target_platform_arch=target_platform_arch,
 target_platform_accelerator=target_platform_accelerator,
 compiler_options=compiler_options,
 )
 return self._compiled_models[target_instance_family]

Expand Down

108 changes: 85 additions & 23 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
 job_name,
 framework,
 tags,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """
 Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
 job_name:
 framework:
 tags:
 target_platform_os:
 target_platform_arch:
 target_platform_accelerator:
 compiler_options:
 """
 input_model_config = {
 "S3Uri": self.model_data,
 "DataInputConfig": input_shape
 if not isinstance(input_shape, dict)
 else json.dumps(input_shape),
 "DataInputConfig": json.dumps(input_shape)
 if isinstance(input_shape, dict)
 else input_shape,
 "Framework": framework,
 }
 role = self.sagemaker_session.expand_role(role)
 output_model_config = {
 "TargetDevice": target_instance_type,
 "S3OutputLocation": output_path,
 }

 if target_instance_type is not None:
 output_model_config["TargetDevice"] = target_instance_type
 else:
 if target_platform_os is None and target_platform_arch is None:
 raise ValueError(
 "target_instance_type or (target_platform_os and target_platform_arch) "
 "should be provided"
 )
 target_platform = {
 "Os": target_platform_os,
 "Arch": target_platform_arch,
 }
 if target_platform_accelerator is not None:
 target_platform["Accelerator"] = target_platform_accelerator
 output_model_config["TargetPlatform"] = target_platform

 if compiler_options is not None:
 output_model_config["CompilerOptions"] = (
 json.dumps(compiler_options)
 if isinstance(compiler_options, dict)
 else compiler_options
 )

 return {
 "input_model_config": input_model_config,
 "output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
 compile_max_run=5 * 60,
 framework=None,
 framework_version=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
 run your model after compilation, for example: ml_c5. For allowed
 strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 Alternatively, you can select an OS, Architecture and Accelerator using
 ``target_platform_os``, ``target_platform_arch``,
 and ``target_platform_accelerator``.
 input_shape (dict): Specifies the name and shape of the expected
 inputs for your trained model in json dictionary form, for
 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
 model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
 'onnx', 'xgboost'
 framework_version (str):
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

 Returns:
 sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
 job_name,
 framework,
 tags,
 target_platform_os,
 target_platform_arch,
 target_platform_accelerator,
 compiler_options,
 )
 self.sagemaker_session.compile_model(**config)
 job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
 self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 if target_instance_family is not None:
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 else:
 LOGGER.warning(
 "The instance type %s is not supported for deployment via SageMaker."
 "Please deploy the model manually.",
 target_instance_family,
 )
 else:
 LOGGER.warning(
 "The instance type %s is not supported to deploy via SageMaker,"
 "please deploy the model manually.",
 target_instance_family,
 "Devices described by Target Platform OS, Architecture and Accelerator are not"
 "supported for deployment via SageMaker. Please deploy the model manually."
 )
 return self

Expand Down

39 changes: 39 additions & 0 deletions
tests/unit/sagemaker/model/test_neo.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
 assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

Loading

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ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This is not JSON-like String which we use in Console UI or AWS CLI.

This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".

Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="LINUX",
 target_platform_arch="ARM64",
 target_platform_accelerator="NVIDIA",
 compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
 )
 assert model._is_compiled_model is False
Comment thread
laurenyu marked this conversation as resolved.



def test_compile_model_android_armv7(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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ContributorAuthor
Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

because of black-check python code style check we have to use double-quotes here
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="ANDROID",
 target_platform_arch="ARM_EABI",
 compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
 )
 assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
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); }
})();
})();

Skip to content
feature: Add Granular Target Description support for compilation (Neo)#1752

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.

 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 1 commit into

from

Jul 28, 2020
 Merged


feature: Add Granular Target Description support for compilation (Neo)
#1752
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23 changes: 23 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -512,6 +512,10 @@ def compile_model(
 framework_version=None,
 compile_max_run=15 * 60,
 tags=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
Comment thread
laurenyu marked this conversation as resolved.

 **kwargs
 ):
 """Compile a Neo model using the input model.
Expand All@@ -536,6 +540,21 @@ def compile_model(
 tags (list[dict]): List of tags for labeling a compilation job. For
 more, see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.
 **kwargs: Passed to invocation of ``create_model()``.
 Implementations may customize ``create_model()`` to accept
 ``**kwargs`` to customize model creation during deploy. For
Expand DownExpand Up@@ -565,6 +584,10 @@ def compile_model(
 compile_max_run,
 framework=framework,
 framework_version=framework_version,
 target_platform_os=target_platform_os,
 target_platform_arch=target_platform_arch,
 target_platform_accelerator=target_platform_accelerator,
 compiler_options=compiler_options,
 )
 return self._compiled_models[target_instance_family]

Expand Down

108 changes: 85 additions & 23 deletions
src/sagemaker/model.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -215,6 +215,10 @@ def _compilation_job_config(
 job_name,
 framework,
 tags,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """
 Args:
Expand All@@ -226,20 +230,46 @@ def _compilation_job_config(
 job_name:
 framework:
 tags:
 target_platform_os:
 target_platform_arch:
 target_platform_accelerator:
 compiler_options:
 """
 input_model_config = {
 "S3Uri": self.model_data,
 "DataInputConfig": input_shape
 if not isinstance(input_shape, dict)
 else json.dumps(input_shape),
 "DataInputConfig": json.dumps(input_shape)
 if isinstance(input_shape, dict)
 else input_shape,
 "Framework": framework,
 }
 role = self.sagemaker_session.expand_role(role)
 output_model_config = {
 "TargetDevice": target_instance_type,
 "S3OutputLocation": output_path,
 }

 if target_instance_type is not None:
 output_model_config["TargetDevice"] = target_instance_type
 else:
 if target_platform_os is None and target_platform_arch is None:
 raise ValueError(
 "target_instance_type or (target_platform_os and target_platform_arch) "
 "should be provided"
 )
 target_platform = {
 "Os": target_platform_os,
 "Arch": target_platform_arch,
 }
 if target_platform_accelerator is not None:
 target_platform["Accelerator"] = target_platform_accelerator
 output_model_config["TargetPlatform"] = target_platform

 if compiler_options is not None:
 output_model_config["CompilerOptions"] = (
 json.dumps(compiler_options)
 if isinstance(compiler_options, dict)
 else compiler_options
 )

 return {
 "input_model_config": input_model_config,
 "output_model_config": output_model_config,
Expand DownExpand Up@@ -320,6 +350,10 @@ def compile(
 compile_max_run=5 * 60,
 framework=None,
 framework_version=None,
 target_platform_os=None,
 target_platform_arch=None,
 target_platform_accelerator=None,
 compiler_options=None,
 ):
 """Compile this ``Model`` with SageMaker Neo.

Expand All@@ -328,6 +362,9 @@ def compile(
 run your model after compilation, for example: ml_c5. For allowed
 strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 Alternatively, you can select an OS, Architecture and Accelerator using
 ``target_platform_os``, ``target_platform_arch``,
 and ``target_platform_accelerator``.
 input_shape (dict): Specifies the name and shape of the expected
 inputs for your trained model in json dictionary form, for
 example: {'data': [1,3,1024,1024]}, or {'var1': [1,1,28,28],
Expand All@@ -345,6 +382,21 @@ def compile(
 model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch',
 'onnx', 'xgboost'
 framework_version (str):
 target_platform_os (str): Target Platform OS, for example: 'LINUX'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_arch (str): Target Platform Architecture, for example: 'X86_64'.
 For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 target_platform_accelerator (str, optional): Target Platform Accelerator,
 for example: 'NVIDIA'. For allowed strings see
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.
 It can be used instead of target_instance_family.
 compiler_options (dict, optional): Additional parameters for compiler.
 Compiler Options are TargetPlatform / target_instance_family specific. See
 https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

 Returns:
 sagemaker.model.Model: A SageMaker ``Model`` object. See
Expand DownExpand Up@@ -375,31 +427,41 @@ def compile(
 job_name,
 framework,
 tags,
 target_platform_os,
 target_platform_arch,
 target_platform_accelerator,
 compiler_options,
 )
 self.sagemaker_session.compile_model(**config)
 job_status = self.sagemaker_session.wait_for_compilation_job(job_name)
 self.model_data = job_status["ModelArtifacts"]["S3ModelArtifacts"]
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 if target_instance_family is not None:
 if target_instance_family.startswith("ml_"):
 self.image = self._neo_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 elif target_instance_family.startswith(INFERENTIA_INSTANCE_PREFIX):
 self.image = self._inferentia_image(
 self.sagemaker_session.boto_region_name,
 target_instance_family,
 framework,
 framework_version,
 )
 self._is_compiled_model = True
 else:
 LOGGER.warning(
 "The instance type %s is not supported for deployment via SageMaker."
 "Please deploy the model manually.",
 target_instance_family,
 )
 else:
 LOGGER.warning(
 "The instance type %s is not supported to deploy via SageMaker,"
 "please deploy the model manually.",
 target_instance_family,
 "Devices described by Target Platform OS, Architecture and Accelerator are not"
 "supported for deployment via SageMaker. Please deploy the model manually."
 )
 return self

Expand Down

39 changes: 39 additions & 0 deletions
tests/unit/sagemaker/model/test_neo.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -95,6 +95,45 @@ def test_compile_model_for_edge_device_tflite(sagemaker_session):
 assert model._is_compiled_model is False


def test_compile_model_linux_arm64_nvidia(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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This is not JSON-like String which we use in Console UI or AWS CLI.

This is Python code. In Python SDK we use Dictionary {...} instead of JSON-string "{...}".

Technically we can use single quotes here. However, This project automatic build runs Python code style check (black-check) and it requires all strings to use double-quoutes for Strings in Python code.
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="LINUX",
 target_platform_arch="ARM64",
 target_platform_accelerator="NVIDIA",
 compiler_options={"gpu-code": "sm_72", "trt-ver": "6.0.1", "cuda-ver": "10.1"},
 )
 assert model._is_compiled_model is False
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laurenyu marked this conversation as resolved.



def test_compile_model_android_armv7(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
 )
 model = _create_model(sagemaker_session)
 model.compile(
 target_instance_family=None,
 input_shape={"data": [1, 3, 1024, 1024]},
@mchoi8739mchoi8739Jul 27, 2020

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input_shape={'data': [1, 3, 1024, 1024]}
^ suggesting to use single-quote (based on what we discussed earlier)
@apivovarovapivovarovJul 28, 2020•

edited

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because of black-check python code style check we have to use double-quotes here
 output_path="s3://output",
 role="role",
 framework="tensorflow",
 job_name="compile-model",
 target_platform_os="ANDROID",
 target_platform_arch="ARM_EABI",
 compiler_options={"ANDROID_PLATFORM": 25, "mattr": ["+neon"]},
 )
 assert model._is_compiled_model is False


def test_compile_model_for_cloud(sagemaker_session):
 sagemaker_session.wait_for_compilation_job = Mock(
 return_value=DESCRIBE_COMPILATION_JOB_RESPONSE
Expand Down