fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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laurenyu merged 2 commits into from
Jan 15, 2020
Merged

fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator #1230

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fix: ignore private Automatic Model Tuning hyperparameter when attach…
laurenyu Jan 13, 2020
bea6561
Merge branch 'master' into marketplace-attach
laurenyu Jan 15, 2020
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25 changes: 25 additions & 0 deletions src/sagemaker/algorithm.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
current_input_modes = current_input_modes & supported_input_modes

return current_input_modes

@classmethod
def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
"""Convert the job description to init params that can be handled by the
class constructor

Args:
job_details (dict): the returned job details from a DescribeTrainingJob
API call.
model_channel_name (str): Name of the channel where pre-trained
model data will be downloaded.

Returns:
dict: The transformed init_params
"""
init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
job_details, model_channel_name
)

# This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
# It cannot be set through instantiating an estimator.
if "_tuning_objective_metric" in init_params["hyperparameters"]:
del init_params["hyperparameters"]["_tuning_objective_metric"]

return init_params
72 changes: 72 additions & 0 deletions tests/unit/test_algorithm.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
train_instance_count=1,
sagemaker_session=session,
)


def test_algorithm_attach_from_hyperparameter_tuning():
session = Mock()
job_name = "training-job-that-is-part-of-a-tuning-job"
algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
instance_count = 1
instance_type = "ml.m4.xlarge"
train_volume_size = 30
input_mode = "File"

session.sagemaker_client.list_tags.return_value = {"Tags": []}
session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
session.sagemaker_client.describe_training_job.return_value = {
"TrainingJobName": job_name,
"TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
"TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
% job_name,
"ModelArtifacts": {
"S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
},
"TrainingJobOutput": {
"S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
},
"TrainingJobStatus": "Succeeded",
"HyperParameters": {
"_tuning_objective_metric": "validation:accuracy",
"max_leaf_nodes": 1,
"free_text_hp1": "foo",
},
"AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
"MetricDefinitions": [
{"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
],
"RoleArn": role_arn,
"InputDataConfig": [
{
"ChannelName": "training",
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
"S3DataDistributionType": "FullyReplicated",
}
},
"CompressionType": "None",
"RecordWrapperType": "None",
}
],
"OutputDataConfig": {
"KmsKeyId": "",
"S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
"RemoveJobNameFromS3OutputPath": False,
},
"ResourceConfig": {
"InstanceType": instance_type,
"InstanceCount": instance_count,
"VolumeSizeInGB": train_volume_size,
},
"StoppingCondition": {"MaxRuntimeInSeconds": 86400},
}

estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
assert estimator.algorithm_arn == algo_arn
assert estimator.role == role_arn
assert estimator.train_instance_count == instance_count
assert estimator.train_instance_type == instance_type
assert estimator.train_volume_size == train_volume_size
assert estimator.input_mode == input_mode
assert estimator.sagemaker_session == session
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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 Merged

laurenyu
merged 2 commits into

from

Jan 15, 2020
 Merged


fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
#1230
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d4d63df

fix: ignore private Automatic Model Tuning hyperparameter when attach…


laurenyu Jan 13, 2020
bea6561

Merge branch 'master' into marketplace-attach


laurenyu Jan 15, 2020

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25 changes: 25 additions & 0 deletions
src/sagemaker/algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
 current_input_modes = current_input_modes & supported_input_modes

 return current_input_modes

 @classmethod
 def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
 """Convert the job description to init params that can be handled by the
 class constructor

 Args:
 job_details (dict): the returned job details from a DescribeTrainingJob
 API call.
 model_channel_name (str): Name of the channel where pre-trained
 model data will be downloaded.

 Returns:
 dict: The transformed init_params
 """
 init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
 job_details, model_channel_name
 )

 # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
 # It cannot be set through instantiating an estimator.
 if "_tuning_objective_metric" in init_params["hyperparameters"]:
 del init_params["hyperparameters"]["_tuning_objective_metric"]

 return init_params

72 changes: 72 additions & 0 deletions
tests/unit/test_algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
 train_instance_count=1,
 sagemaker_session=session,
 )


def test_algorithm_attach_from_hyperparameter_tuning():
 session = Mock()
 job_name = "training-job-that-is-part-of-a-tuning-job"
 algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
 role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
 instance_count = 1
 instance_type = "ml.m4.xlarge"
 train_volume_size = 30
 input_mode = "File"

 session.sagemaker_client.list_tags.return_value = {"Tags": []}
 session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
 session.sagemaker_client.describe_training_job.return_value = {
 "TrainingJobName": job_name,
 "TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
 "TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
 % job_name,
 "ModelArtifacts": {
 "S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
 },
 "TrainingJobOutput": {
 "S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
 },
 "TrainingJobStatus": "Succeeded",
 "HyperParameters": {
 "_tuning_objective_metric": "validation:accuracy",
 "max_leaf_nodes": 1,
 "free_text_hp1": "foo",
 },
 "AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
 "MetricDefinitions": [
 {"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
 ],
 "RoleArn": role_arn,
 "InputDataConfig": [
 {
 "ChannelName": "training",
 "DataSource": {
 "S3DataSource": {
 "S3DataType": "S3Prefix",
 "S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
 "S3DataDistributionType": "FullyReplicated",
 }
 },
 "CompressionType": "None",
 "RecordWrapperType": "None",
 }
 ],
 "OutputDataConfig": {
 "KmsKeyId": "",
 "S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
 "RemoveJobNameFromS3OutputPath": False,
 },
 "ResourceConfig": {
 "InstanceType": instance_type,
 "InstanceCount": instance_count,
 "VolumeSizeInGB": train_volume_size,
 },
 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
 }

 estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
 assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
 assert estimator.algorithm_arn == algo_arn
 assert estimator.role == role_arn
 assert estimator.train_instance_count == instance_count
 assert estimator.train_instance_type == instance_type
 assert estimator.train_volume_size == train_volume_size
 assert estimator.input_mode == input_mode
 assert estimator.sagemaker_session == session

, '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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fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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 Merged

laurenyu
merged 2 commits into

from

Jan 15, 2020
 Merged


fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
#1230
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d4d63df

fix: ignore private Automatic Model Tuning hyperparameter when attach…


laurenyu Jan 13, 2020
bea6561

Merge branch 'master' into marketplace-attach


laurenyu Jan 15, 2020

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25 changes: 25 additions & 0 deletions
src/sagemaker/algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
 current_input_modes = current_input_modes & supported_input_modes

 return current_input_modes

 @classmethod
 def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
 """Convert the job description to init params that can be handled by the
 class constructor

 Args:
 job_details (dict): the returned job details from a DescribeTrainingJob
 API call.
 model_channel_name (str): Name of the channel where pre-trained
 model data will be downloaded.

 Returns:
 dict: The transformed init_params
 """
 init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
 job_details, model_channel_name
 )

 # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
 # It cannot be set through instantiating an estimator.
 if "_tuning_objective_metric" in init_params["hyperparameters"]:
 del init_params["hyperparameters"]["_tuning_objective_metric"]

 return init_params

72 changes: 72 additions & 0 deletions
tests/unit/test_algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
 train_instance_count=1,
 sagemaker_session=session,
 )


def test_algorithm_attach_from_hyperparameter_tuning():
 session = Mock()
 job_name = "training-job-that-is-part-of-a-tuning-job"
 algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
 role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
 instance_count = 1
 instance_type = "ml.m4.xlarge"
 train_volume_size = 30
 input_mode = "File"

 session.sagemaker_client.list_tags.return_value = {"Tags": []}
 session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
 session.sagemaker_client.describe_training_job.return_value = {
 "TrainingJobName": job_name,
 "TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
 "TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
 % job_name,
 "ModelArtifacts": {
 "S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
 },
 "TrainingJobOutput": {
 "S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
 },
 "TrainingJobStatus": "Succeeded",
 "HyperParameters": {
 "_tuning_objective_metric": "validation:accuracy",
 "max_leaf_nodes": 1,
 "free_text_hp1": "foo",
 },
 "AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
 "MetricDefinitions": [
 {"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
 ],
 "RoleArn": role_arn,
 "InputDataConfig": [
 {
 "ChannelName": "training",
 "DataSource": {
 "S3DataSource": {
 "S3DataType": "S3Prefix",
 "S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
 "S3DataDistributionType": "FullyReplicated",
 }
 },
 "CompressionType": "None",
 "RecordWrapperType": "None",
 }
 ],
 "OutputDataConfig": {
 "KmsKeyId": "",
 "S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
 "RemoveJobNameFromS3OutputPath": False,
 },
 "ResourceConfig": {
 "InstanceType": instance_type,
 "InstanceCount": instance_count,
 "VolumeSizeInGB": train_volume_size,
 },
 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
 }

 estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
 assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
 assert estimator.algorithm_arn == algo_arn
 assert estimator.role == role_arn
 assert estimator.train_instance_count == instance_count
 assert estimator.train_instance_type == instance_type
 assert estimator.train_volume_size == train_volume_size
 assert estimator.input_mode == input_mode
 assert estimator.sagemaker_session == session

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

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fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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 Merged

laurenyu
merged 2 commits into

from

Jan 15, 2020
 Merged


fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
#1230
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d4d63df

fix: ignore private Automatic Model Tuning hyperparameter when attach…


laurenyu Jan 13, 2020
bea6561

Merge branch 'master' into marketplace-attach


laurenyu Jan 15, 2020

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25 changes: 25 additions & 0 deletions
src/sagemaker/algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
 current_input_modes = current_input_modes & supported_input_modes

 return current_input_modes

 @classmethod
 def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
 """Convert the job description to init params that can be handled by the
 class constructor

 Args:
 job_details (dict): the returned job details from a DescribeTrainingJob
 API call.
 model_channel_name (str): Name of the channel where pre-trained
 model data will be downloaded.

 Returns:
 dict: The transformed init_params
 """
 init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
 job_details, model_channel_name
 )

 # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
 # It cannot be set through instantiating an estimator.
 if "_tuning_objective_metric" in init_params["hyperparameters"]:
 del init_params["hyperparameters"]["_tuning_objective_metric"]

 return init_params

72 changes: 72 additions & 0 deletions
tests/unit/test_algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
 train_instance_count=1,
 sagemaker_session=session,
 )


def test_algorithm_attach_from_hyperparameter_tuning():
 session = Mock()
 job_name = "training-job-that-is-part-of-a-tuning-job"
 algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
 role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
 instance_count = 1
 instance_type = "ml.m4.xlarge"
 train_volume_size = 30
 input_mode = "File"

 session.sagemaker_client.list_tags.return_value = {"Tags": []}
 session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
 session.sagemaker_client.describe_training_job.return_value = {
 "TrainingJobName": job_name,
 "TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
 "TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
 % job_name,
 "ModelArtifacts": {
 "S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
 },
 "TrainingJobOutput": {
 "S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
 },
 "TrainingJobStatus": "Succeeded",
 "HyperParameters": {
 "_tuning_objective_metric": "validation:accuracy",
 "max_leaf_nodes": 1,
 "free_text_hp1": "foo",
 },
 "AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
 "MetricDefinitions": [
 {"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
 ],
 "RoleArn": role_arn,
 "InputDataConfig": [
 {
 "ChannelName": "training",
 "DataSource": {
 "S3DataSource": {
 "S3DataType": "S3Prefix",
 "S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
 "S3DataDistributionType": "FullyReplicated",
 }
 },
 "CompressionType": "None",
 "RecordWrapperType": "None",
 }
 ],
 "OutputDataConfig": {
 "KmsKeyId": "",
 "S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
 "RemoveJobNameFromS3OutputPath": False,
 },
 "ResourceConfig": {
 "InstanceType": instance_type,
 "InstanceCount": instance_count,
 "VolumeSizeInGB": train_volume_size,
 },
 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
 }

 estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
 assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
 assert estimator.algorithm_arn == algo_arn
 assert estimator.role == role_arn
 assert estimator.train_instance_count == instance_count
 assert estimator.train_instance_type == instance_type
 assert estimator.train_volume_size == train_volume_size
 assert estimator.input_mode == input_mode
 assert estimator.sagemaker_session == session

, '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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fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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 Merged

laurenyu
merged 2 commits into

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Jan 15, 2020
 Merged


fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
#1230
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d4d63df

fix: ignore private Automatic Model Tuning hyperparameter when attach…


laurenyu Jan 13, 2020
bea6561

Merge branch 'master' into marketplace-attach


laurenyu Jan 15, 2020

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25 changes: 25 additions & 0 deletions
src/sagemaker/algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
 current_input_modes = current_input_modes & supported_input_modes

 return current_input_modes

 @classmethod
 def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
 """Convert the job description to init params that can be handled by the
 class constructor

 Args:
 job_details (dict): the returned job details from a DescribeTrainingJob
 API call.
 model_channel_name (str): Name of the channel where pre-trained
 model data will be downloaded.

 Returns:
 dict: The transformed init_params
 """
 init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
 job_details, model_channel_name
 )

 # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
 # It cannot be set through instantiating an estimator.
 if "_tuning_objective_metric" in init_params["hyperparameters"]:
 del init_params["hyperparameters"]["_tuning_objective_metric"]

 return init_params

72 changes: 72 additions & 0 deletions
tests/unit/test_algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
 train_instance_count=1,
 sagemaker_session=session,
 )


def test_algorithm_attach_from_hyperparameter_tuning():
 session = Mock()
 job_name = "training-job-that-is-part-of-a-tuning-job"
 algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
 role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
 instance_count = 1
 instance_type = "ml.m4.xlarge"
 train_volume_size = 30
 input_mode = "File"

 session.sagemaker_client.list_tags.return_value = {"Tags": []}
 session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
 session.sagemaker_client.describe_training_job.return_value = {
 "TrainingJobName": job_name,
 "TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
 "TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
 % job_name,
 "ModelArtifacts": {
 "S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
 },
 "TrainingJobOutput": {
 "S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
 },
 "TrainingJobStatus": "Succeeded",
 "HyperParameters": {
 "_tuning_objective_metric": "validation:accuracy",
 "max_leaf_nodes": 1,
 "free_text_hp1": "foo",
 },
 "AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
 "MetricDefinitions": [
 {"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
 ],
 "RoleArn": role_arn,
 "InputDataConfig": [
 {
 "ChannelName": "training",
 "DataSource": {
 "S3DataSource": {
 "S3DataType": "S3Prefix",
 "S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
 "S3DataDistributionType": "FullyReplicated",
 }
 },
 "CompressionType": "None",
 "RecordWrapperType": "None",
 }
 ],
 "OutputDataConfig": {
 "KmsKeyId": "",
 "S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
 "RemoveJobNameFromS3OutputPath": False,
 },
 "ResourceConfig": {
 "InstanceType": instance_type,
 "InstanceCount": instance_count,
 "VolumeSizeInGB": train_volume_size,
 },
 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
 }

 estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
 assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
 assert estimator.algorithm_arn == algo_arn
 assert estimator.role == role_arn
 assert estimator.train_instance_count == instance_count
 assert estimator.train_instance_type == instance_type
 assert estimator.train_volume_size == train_volume_size
 assert estimator.input_mode == input_mode
 assert estimator.sagemaker_session == session

, '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
fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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 Merged

laurenyu
merged 2 commits into

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Jan 15, 2020
 Merged


fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
#1230
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d4d63df

fix: ignore private Automatic Model Tuning hyperparameter when attach…


laurenyu Jan 13, 2020
bea6561

Merge branch 'master' into marketplace-attach


laurenyu Jan 15, 2020

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25 changes: 25 additions & 0 deletions
src/sagemaker/algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
 current_input_modes = current_input_modes & supported_input_modes

 return current_input_modes

 @classmethod
 def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
 """Convert the job description to init params that can be handled by the
 class constructor

 Args:
 job_details (dict): the returned job details from a DescribeTrainingJob
 API call.
 model_channel_name (str): Name of the channel where pre-trained
 model data will be downloaded.

 Returns:
 dict: The transformed init_params
 """
 init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
 job_details, model_channel_name
 )

 # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
 # It cannot be set through instantiating an estimator.
 if "_tuning_objective_metric" in init_params["hyperparameters"]:
 del init_params["hyperparameters"]["_tuning_objective_metric"]

 return init_params

72 changes: 72 additions & 0 deletions
tests/unit/test_algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
 train_instance_count=1,
 sagemaker_session=session,
 )


def test_algorithm_attach_from_hyperparameter_tuning():
 session = Mock()
 job_name = "training-job-that-is-part-of-a-tuning-job"
 algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
 role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
 instance_count = 1
 instance_type = "ml.m4.xlarge"
 train_volume_size = 30
 input_mode = "File"

 session.sagemaker_client.list_tags.return_value = {"Tags": []}
 session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
 session.sagemaker_client.describe_training_job.return_value = {
 "TrainingJobName": job_name,
 "TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
 "TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
 % job_name,
 "ModelArtifacts": {
 "S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
 },
 "TrainingJobOutput": {
 "S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
 },
 "TrainingJobStatus": "Succeeded",
 "HyperParameters": {
 "_tuning_objective_metric": "validation:accuracy",
 "max_leaf_nodes": 1,
 "free_text_hp1": "foo",
 },
 "AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
 "MetricDefinitions": [
 {"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
 ],
 "RoleArn": role_arn,
 "InputDataConfig": [
 {
 "ChannelName": "training",
 "DataSource": {
 "S3DataSource": {
 "S3DataType": "S3Prefix",
 "S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
 "S3DataDistributionType": "FullyReplicated",
 }
 },
 "CompressionType": "None",
 "RecordWrapperType": "None",
 }
 ],
 "OutputDataConfig": {
 "KmsKeyId": "",
 "S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
 "RemoveJobNameFromS3OutputPath": False,
 },
 "ResourceConfig": {
 "InstanceType": instance_type,
 "InstanceCount": instance_count,
 "VolumeSizeInGB": train_volume_size,
 },
 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
 }

 estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
 assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
 assert estimator.algorithm_arn == algo_arn
 assert estimator.role == role_arn
 assert estimator.train_instance_count == instance_count
 assert estimator.train_instance_type == instance_type
 assert estimator.train_volume_size == train_volume_size
 assert estimator.input_mode == input_mode
 assert estimator.sagemaker_session == session

, '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
fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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 Merged

laurenyu
merged 2 commits into

from

Jan 15, 2020
 Merged


fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
#1230
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d4d63df

fix: ignore private Automatic Model Tuning hyperparameter when attach…


laurenyu Jan 13, 2020
bea6561

Merge branch 'master' into marketplace-attach


laurenyu Jan 15, 2020

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25 changes: 25 additions & 0 deletions
src/sagemaker/algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
 current_input_modes = current_input_modes & supported_input_modes

 return current_input_modes

 @classmethod
 def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
 """Convert the job description to init params that can be handled by the
 class constructor

 Args:
 job_details (dict): the returned job details from a DescribeTrainingJob
 API call.
 model_channel_name (str): Name of the channel where pre-trained
 model data will be downloaded.

 Returns:
 dict: The transformed init_params
 """
 init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
 job_details, model_channel_name
 )

 # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
 # It cannot be set through instantiating an estimator.
 if "_tuning_objective_metric" in init_params["hyperparameters"]:
 del init_params["hyperparameters"]["_tuning_objective_metric"]

 return init_params

72 changes: 72 additions & 0 deletions
tests/unit/test_algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
 train_instance_count=1,
 sagemaker_session=session,
 )


def test_algorithm_attach_from_hyperparameter_tuning():
 session = Mock()
 job_name = "training-job-that-is-part-of-a-tuning-job"
 algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
 role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
 instance_count = 1
 instance_type = "ml.m4.xlarge"
 train_volume_size = 30
 input_mode = "File"

 session.sagemaker_client.list_tags.return_value = {"Tags": []}
 session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
 session.sagemaker_client.describe_training_job.return_value = {
 "TrainingJobName": job_name,
 "TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
 "TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
 % job_name,
 "ModelArtifacts": {
 "S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
 },
 "TrainingJobOutput": {
 "S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
 },
 "TrainingJobStatus": "Succeeded",
 "HyperParameters": {
 "_tuning_objective_metric": "validation:accuracy",
 "max_leaf_nodes": 1,
 "free_text_hp1": "foo",
 },
 "AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
 "MetricDefinitions": [
 {"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
 ],
 "RoleArn": role_arn,
 "InputDataConfig": [
 {
 "ChannelName": "training",
 "DataSource": {
 "S3DataSource": {
 "S3DataType": "S3Prefix",
 "S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
 "S3DataDistributionType": "FullyReplicated",
 }
 },
 "CompressionType": "None",
 "RecordWrapperType": "None",
 }
 ],
 "OutputDataConfig": {
 "KmsKeyId": "",
 "S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
 "RemoveJobNameFromS3OutputPath": False,
 },
 "ResourceConfig": {
 "InstanceType": instance_type,
 "InstanceCount": instance_count,
 "VolumeSizeInGB": train_volume_size,
 },
 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
 }

 estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
 assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
 assert estimator.algorithm_arn == algo_arn
 assert estimator.role == role_arn
 assert estimator.train_instance_count == instance_count
 assert estimator.train_instance_type == instance_type
 assert estimator.train_volume_size == train_volume_size
 assert estimator.input_mode == input_mode
 assert estimator.sagemaker_session == session

, '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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fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator#1230

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 Merged

laurenyu
merged 2 commits into

from

Jan 15, 2020
 Merged


fix: ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
#1230
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d4d63df

fix: ignore private Automatic Model Tuning hyperparameter when attach…


laurenyu Jan 13, 2020
bea6561

Merge branch 'master' into marketplace-attach


laurenyu Jan 15, 2020

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25 changes: 25 additions & 0 deletions
src/sagemaker/algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -553,3 +553,28 @@ def _algorithm_training_input_modes(self, training_channels):
 current_input_modes = current_input_modes & supported_input_modes

 return current_input_modes

 @classmethod
 def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
 """Convert the job description to init params that can be handled by the
 class constructor

 Args:
 job_details (dict): the returned job details from a DescribeTrainingJob
 API call.
 model_channel_name (str): Name of the channel where pre-trained
 model data will be downloaded.

 Returns:
 dict: The transformed init_params
 """
 init_params = super(AlgorithmEstimator, cls)._prepare_init_params_from_job_description(
 job_details, model_channel_name
 )

 # This hyperparameter is added by Amazon SageMaker Automatic Model Tuning.
 # It cannot be set through instantiating an estimator.
 if "_tuning_objective_metric" in init_params["hyperparameters"]:
 del init_params["hyperparameters"]["_tuning_objective_metric"]

 return init_params

72 changes: 72 additions & 0 deletions
tests/unit/test_algorithm.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -943,3 +943,75 @@ def test_algorithm_no_required_hyperparameters(session):
 train_instance_count=1,
 sagemaker_session=session,
 )


def test_algorithm_attach_from_hyperparameter_tuning():
 session = Mock()
 job_name = "training-job-that-is-part-of-a-tuning-job"
 algo_arn = "arn:aws:sagemaker:us-east-2:000000000000:algorithm/scikit-decision-trees"
 role_arn = "arn:aws:iam::123412341234:role/SageMakerRole"
 instance_count = 1
 instance_type = "ml.m4.xlarge"
 train_volume_size = 30
 input_mode = "File"

 session.sagemaker_client.list_tags.return_value = {"Tags": []}
 session.sagemaker_client.describe_algorithm.return_value = DESCRIBE_ALGORITHM_RESPONSE
 session.sagemaker_client.describe_training_job.return_value = {
 "TrainingJobName": job_name,
 "TrainingJobArn": "arn:aws:sagemaker:us-east-2:123412341234:training-job/%s" % job_name,
 "TuningJobArn": "arn:aws:sagemaker:us-east-2:123412341234:hyper-parameter-tuning-job/%s"
 % job_name,
 "ModelArtifacts": {
 "S3ModelArtifacts": "s3://sagemaker-us-east-2-123412341234/output/model.tar.gz"
 },
 "TrainingJobOutput": {
 "S3TrainingJobOutput": "s3://sagemaker-us-east-2-123412341234/output/output.tar.gz"
 },
 "TrainingJobStatus": "Succeeded",
 "HyperParameters": {
 "_tuning_objective_metric": "validation:accuracy",
 "max_leaf_nodes": 1,
 "free_text_hp1": "foo",
 },
 "AlgorithmSpecification": {"AlgorithmName": algo_arn, "TrainingInputMode": input_mode},
 "MetricDefinitions": [
 {"Name": "validation:accuracy", "Regex": "validation-accuracy: (\\S+)"}
 ],
 "RoleArn": role_arn,
 "InputDataConfig": [
 {
 "ChannelName": "training",
 "DataSource": {
 "S3DataSource": {
 "S3DataType": "S3Prefix",
 "S3Uri": "s3://sagemaker-us-east-2-123412341234/input/training.csv",
 "S3DataDistributionType": "FullyReplicated",
 }
 },
 "CompressionType": "None",
 "RecordWrapperType": "None",
 }
 ],
 "OutputDataConfig": {
 "KmsKeyId": "",
 "S3OutputPath": "s3://sagemaker-us-east-2-123412341234/output",
 "RemoveJobNameFromS3OutputPath": False,
 },
 "ResourceConfig": {
 "InstanceType": instance_type,
 "InstanceCount": instance_count,
 "VolumeSizeInGB": train_volume_size,
 },
 "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
 }

 estimator = AlgorithmEstimator.attach(job_name, sagemaker_session=session)
 assert estimator.hyperparameters() == {"max_leaf_nodes": 1, "free_text_hp1": "foo"}
 assert estimator.algorithm_arn == algo_arn
 assert estimator.role == role_arn
 assert estimator.train_instance_count == instance_count
 assert estimator.train_instance_type == instance_type
 assert estimator.train_volume_size == train_volume_size
 assert estimator.input_mode == input_mode
 assert estimator.sagemaker_session == session