change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e 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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change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
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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change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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6 commits
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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
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 \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
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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change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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6 commits
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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
Expand Down
4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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change: fix attach for 1P algorithm estimators#931

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Merged
ChoiByungWook merged 6 commits into from
Jul 16, 2019
Merged

change: fix attach for 1P algorithm estimators #931

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c7dae70
change: fix attach for 1P algorithm estimators
Jul 13, 2019
61299c3
Merge branch 'master' into fix_tuner_attach_1p
Jul 15, 2019
65f8947
black format, fix logic and unit tests
Jul 15, 2019
030fe1b
modify integration test
Jul 15, 2019
0262ec0
fix typo
Jul 15, 2019
71a38da
Merge branch 'master' into fix_tuner_attach_1p
Jul 16, 2019
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34 changes: 29 additions & 5 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -19,7 +19,7 @@
from enum import Enum

import sagemaker
from sagemaker.amazon.amazon_estimator import RecordSet
from sagemaker.amazon.amazon_estimator import RecordSet, AmazonAlgorithmEstimatorBase
from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa
from sagemaker.analytics import HyperparameterTuningJobAnalytics
from sagemaker.estimator import Framework
Expand DownExpand Up@@ -358,7 +358,7 @@ def attach(cls, tuning_job_name, sagemaker_session=None, job_details=None, estim
estimator_cls, job_details["TrainingJobDefinition"]
)
estimator = cls._prepare_estimator_from_job_description(
estimator_cls, job_details["TrainingJobDefinition"], sagemaker_session
estimator_cls, job_details, sagemaker_session
)
init_params = cls._prepare_init_params_from_job_description(job_details)

Expand DownExpand Up@@ -497,16 +497,25 @@ def _prepare_estimator_cls(cls, estimator_cls, training_details):
)

@classmethod
def _prepare_estimator_from_job_description(
cls, estimator_cls, training_details, sagemaker_session
):
def _prepare_estimator_from_job_description(cls, estimator_cls, job_details, sagemaker_session):
training_details = job_details["TrainingJobDefinition"]

# Swap name for static hyperparameters to what an estimator would expect
training_details["HyperParameters"] = training_details["StaticHyperParameters"]
del training_details["StaticHyperParameters"]

# Remove hyperparameter reserved by SageMaker for tuning jobs
del training_details["HyperParameters"]["_tuning_objective_metric"]

# Add missing hyperparameters defined in the hyperparameter ranges,
# as potentially required in the Amazon algorithm estimator's constructor
if issubclass(estimator_cls, AmazonAlgorithmEstimatorBase):
parameter_ranges = job_details["HyperParameterTuningJobConfig"]["ParameterRanges"]
additional_hyperparameters = cls._extract_hyperparameters_from_parameter_ranges(
parameter_ranges
)
training_details["HyperParameters"].update(additional_hyperparameters)

# Add items expected by the estimator (but aren't needed otherwise)
training_details["TrainingJobName"] = ""
if "KmsKeyId" not in training_details["OutputDataConfig"]:
Expand DownExpand Up@@ -559,6 +568,21 @@ def _prepare_parameter_ranges(cls, parameter_ranges):

return ranges

@classmethod
def _extract_hyperparameters_from_parameter_ranges(cls, parameter_ranges):
hyperparameters = {}

for parameter in parameter_ranges["CategoricalParameterRanges"]:
hyperparameters[parameter["Name"]] = parameter["Values"][0]

for parameter in parameter_ranges["ContinuousParameterRanges"]:
hyperparameters[parameter["Name"]] = float(parameter["MinValue"])

for parameter in parameter_ranges["IntegerParameterRanges"]:
hyperparameters[parameter["Name"]] = int(parameter["MinValue"])

return hyperparameters

def hyperparameter_ranges(self):
"""Return the hyperparameter ranges in a dictionary to be used as part of a request for creating a
hyperparameter tuning job.
Expand Down
11 changes: 7 additions & 4 deletions tests/integ/test_tuner.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -460,12 +460,15 @@ def test_tuning_lda(sagemaker_session):
time.sleep(15)
tuner.wait()

desc = tuner.latest_tuning_job.sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(
HyperParameterTuningJobName=latest_tuning_job_name
attached_tuner = HyperparameterTuner.attach(
tuning_job_name, sagemaker_session=sagemaker_session
)
assert desc["HyperParameterTuningJobConfig"]["TrainingJobEarlyStoppingType"] == "Auto"
assert attached_tuner.early_stopping_type == "Auto"
assert attached_tuner.estimator.alpha0 == 1.0
assert attached_tuner.estimator.num_topics == 1

best_training_job = attached_tuner.best_training_job()

best_training_job = tuner.best_training_job()
with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
predictor = tuner.deploy(1, "ml.c4.xlarge")
predict_input = np.random.rand(1, feature_num)
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4 changes: 2 additions & 2 deletions tests/unit/test_tuner.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -78,7 +78,7 @@
"IntegerParameterRanges": [
{
"MaxValue": "100",
"Name": "mini_batch_size",
"Name": "num_components",
"MinValue": "10",
"ScalingType": "Auto",
}
Expand DownExpand Up@@ -416,7 +416,7 @@ def test_attach_tuning_job_with_estimator_from_hyperparameters(sagemaker_session
assert tuner.estimator.output_kms_key == ""

assert "_tuning_objective_metric" not in tuner.estimator.hyperparameters()
assert tuner.estimator.hyperparameters()["num_components"] == "1"
assert tuner.estimator.hyperparameters()["num_components"] == "10"


def test_attach_tuning_job_with_estimator_from_hyperparameters_with_early_stopping(
Expand Down