fix: update TrainingInputMode with s3_input InputMode#776

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Merged
laurenyu merged 4 commits into from
May 8, 2019
Merged

fix: update TrainingInputMode with s3_input InputMode #776

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80573a6
fix: update TrainingInputMode with s3_input InputMode
imujjwal96 May 6, 2019
6fad54b
Add unit test for s3_input InputMode
imujjwal96 May 6, 2019
ae7a7e9
fix: Update HPO TrainingInputMode with s3_input InputMode
imujjwal96 May 8, 2019
1ea93f6
fix: export notebook test environment variables
imujjwal96 May 8, 2019
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6 changes: 6 additions & 0 deletions src/sagemaker/estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
train_args['tags'] = estimator.tags
train_args['metric_definitions'] = estimator.metric_definitions

if isinstance(inputs, s3_input):
if 'InputMode' in inputs.config:
logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
.format(inputs.config['InputMode']))
train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.

if estimator.enable_network_isolation():
train_args['enable_network_isolation'] = True

Expand Down
8 changes: 8 additions & 0 deletions src/sagemaker/tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
tuner_args['warm_start_config'] = warm_start_config_req
tuner_args['early_stopping_type'] = tuner.early_stopping_type

if isinstance(inputs, s3_input):
if 'InputMode' in inputs.config:
logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
.format(inputs.config['InputMode']))
tuner_args['input_mode'] = inputs.config['InputMode']

if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
else:
Expand Down
4 changes: 2 additions & 2 deletions tests/scripts/run-notebook-test.sh
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down
11 changes: 11 additions & 0 deletions tests/unit/test_estimator.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
expected_input_mode = 'Pipe'
fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
enable_cloudwatch_metrics=True)
fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down
26 changes: 26 additions & 0 deletions tests/unit/test_tuner.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
expected_input_mode = 'Pipe'

script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
mxnet = MXNet(entry_point=script_path,
role=ROLE,
framework_version=FRAMEWORK_VERSION,
train_instance_count=TRAIN_INSTANCE_COUNT,
train_instance_type=TRAIN_INSTANCE_TYPE,
sagemaker_session=sagemaker_session)
tuner.estimator = mxnet

tags = [{'Name': 'some-tag-without-a-value'}]
tuner.tags = tags

hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
tuner._hyperparameter_ranges = hyperparameter_ranges

tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
fix: update TrainingInputMode with s3_input InputMode#776

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to your account

 Merged

laurenyu
merged 4 commits into

from

May 8, 2019
 Merged


fix: update TrainingInputMode with s3_input InputMode
#776
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4 commits


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80573a6

fix: update TrainingInputMode with s3_input InputMode


imujjwal96 May 6, 2019
6fad54b

Add unit test for s3_input InputMode


imujjwal96 May 6, 2019
ae7a7e9

fix: Update HPO TrainingInputMode with s3_input InputMode


imujjwal96 May 8, 2019
1ea93f6

fix: export notebook test environment variables


imujjwal96 May 8, 2019

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6 changes: 6 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
 train_args['tags'] = estimator.tags
 train_args['metric_definitions'] = estimator.metric_definitions

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.


 if estimator.enable_network_isolation():
 train_args['enable_network_isolation'] = True

Expand Down

8 changes: 8 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
 IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
 tuner_args['warm_start_config'] = warm_start_config_req
 tuner_args['early_stopping_type'] = tuner.early_stopping_type

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 tuner_args['input_mode'] = inputs.config['InputMode']

 if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
 tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
 else:
Expand Down

4 changes: 2 additions & 2 deletions
tests/scripts/run-notebook-test.sh

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down

11 changes: 11 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
 assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
 expected_input_mode = 'Pipe'
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 enable_cloudwatch_metrics=True)
 fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down

26 changes: 26 additions & 0 deletions
tests/unit/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
 create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
 WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
 assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
 expected_input_mode = 'Pipe'

 script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
 mxnet = MXNet(entry_point=script_path,
 role=ROLE,
 framework_version=FRAMEWORK_VERSION,
 train_instance_count=TRAIN_INSTANCE_COUNT,
 train_instance_type=TRAIN_INSTANCE_TYPE,
 sagemaker_session=sagemaker_session)
 tuner.estimator = mxnet

 tags = [{'Name': 'some-tag-without-a-value'}]
 tuner.tags = tags

 hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
 'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
 tuner._hyperparameter_ranges = hyperparameter_ranges

 tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
 pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
 base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
fix: update TrainingInputMode with s3_input InputMode#776

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

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

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Sign in
to your account

 Merged

laurenyu
merged 4 commits into

from

May 8, 2019
 Merged


fix: update TrainingInputMode with s3_input InputMode
#776
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80573a6

fix: update TrainingInputMode with s3_input InputMode


imujjwal96 May 6, 2019
6fad54b

Add unit test for s3_input InputMode


imujjwal96 May 6, 2019
ae7a7e9

fix: Update HPO TrainingInputMode with s3_input InputMode


imujjwal96 May 8, 2019
1ea93f6

fix: export notebook test environment variables


imujjwal96 May 8, 2019

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6 changes: 6 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
 train_args['tags'] = estimator.tags
 train_args['metric_definitions'] = estimator.metric_definitions

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.


 if estimator.enable_network_isolation():
 train_args['enable_network_isolation'] = True

Expand Down

8 changes: 8 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
 IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
 tuner_args['warm_start_config'] = warm_start_config_req
 tuner_args['early_stopping_type'] = tuner.early_stopping_type

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 tuner_args['input_mode'] = inputs.config['InputMode']

 if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
 tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
 else:
Expand Down

4 changes: 2 additions & 2 deletions
tests/scripts/run-notebook-test.sh

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down

11 changes: 11 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
 assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
 expected_input_mode = 'Pipe'
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 enable_cloudwatch_metrics=True)
 fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down

26 changes: 26 additions & 0 deletions
tests/unit/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
 create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
 WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
 assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
 expected_input_mode = 'Pipe'

 script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
 mxnet = MXNet(entry_point=script_path,
 role=ROLE,
 framework_version=FRAMEWORK_VERSION,
 train_instance_count=TRAIN_INSTANCE_COUNT,
 train_instance_type=TRAIN_INSTANCE_TYPE,
 sagemaker_session=sagemaker_session)
 tuner.estimator = mxnet

 tags = [{'Name': 'some-tag-without-a-value'}]
 tuner.tags = tags

 hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
 'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
 tuner._hyperparameter_ranges = hyperparameter_ranges

 tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
 pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
 base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down

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

Skip to content
fix: update TrainingInputMode with s3_input InputMode#776

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 Merged

laurenyu
merged 4 commits into

from

May 8, 2019
 Merged


fix: update TrainingInputMode with s3_input InputMode
#776
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80573a6

fix: update TrainingInputMode with s3_input InputMode


imujjwal96 May 6, 2019
6fad54b

Add unit test for s3_input InputMode


imujjwal96 May 6, 2019
ae7a7e9

fix: Update HPO TrainingInputMode with s3_input InputMode


imujjwal96 May 8, 2019
1ea93f6

fix: export notebook test environment variables


imujjwal96 May 8, 2019

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6 changes: 6 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
 train_args['tags'] = estimator.tags
 train_args['metric_definitions'] = estimator.metric_definitions

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.


 if estimator.enable_network_isolation():
 train_args['enable_network_isolation'] = True

Expand Down

8 changes: 8 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
 IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
 tuner_args['warm_start_config'] = warm_start_config_req
 tuner_args['early_stopping_type'] = tuner.early_stopping_type

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 tuner_args['input_mode'] = inputs.config['InputMode']

 if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
 tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
 else:
Expand Down

4 changes: 2 additions & 2 deletions
tests/scripts/run-notebook-test.sh

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down

11 changes: 11 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
 assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
 expected_input_mode = 'Pipe'
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 enable_cloudwatch_metrics=True)
 fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down

26 changes: 26 additions & 0 deletions
tests/unit/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
 create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
 WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
 assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
 expected_input_mode = 'Pipe'

 script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
 mxnet = MXNet(entry_point=script_path,
 role=ROLE,
 framework_version=FRAMEWORK_VERSION,
 train_instance_count=TRAIN_INSTANCE_COUNT,
 train_instance_type=TRAIN_INSTANCE_TYPE,
 sagemaker_session=sagemaker_session)
 tuner.estimator = mxnet

 tags = [{'Name': 'some-tag-without-a-value'}]
 tuner.tags = tags

 hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
 'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
 tuner._hyperparameter_ranges = hyperparameter_ranges

 tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
 pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
 base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
fix: update TrainingInputMode with s3_input InputMode#776

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

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

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to your account

 Merged

laurenyu
merged 4 commits into

from

May 8, 2019
 Merged


fix: update TrainingInputMode with s3_input InputMode
#776
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80573a6

fix: update TrainingInputMode with s3_input InputMode


imujjwal96 May 6, 2019
6fad54b

Add unit test for s3_input InputMode


imujjwal96 May 6, 2019
ae7a7e9

fix: Update HPO TrainingInputMode with s3_input InputMode


imujjwal96 May 8, 2019
1ea93f6

fix: export notebook test environment variables


imujjwal96 May 8, 2019

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6 changes: 6 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
 train_args['tags'] = estimator.tags
 train_args['metric_definitions'] = estimator.metric_definitions

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.


 if estimator.enable_network_isolation():
 train_args['enable_network_isolation'] = True

Expand Down

8 changes: 8 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
 IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
 tuner_args['warm_start_config'] = warm_start_config_req
 tuner_args['early_stopping_type'] = tuner.early_stopping_type

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 tuner_args['input_mode'] = inputs.config['InputMode']

 if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
 tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
 else:
Expand Down

4 changes: 2 additions & 2 deletions
tests/scripts/run-notebook-test.sh

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down

11 changes: 11 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
 assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
 expected_input_mode = 'Pipe'
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 enable_cloudwatch_metrics=True)
 fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down

26 changes: 26 additions & 0 deletions
tests/unit/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
 create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
 WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
 assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
 expected_input_mode = 'Pipe'

 script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
 mxnet = MXNet(entry_point=script_path,
 role=ROLE,
 framework_version=FRAMEWORK_VERSION,
 train_instance_count=TRAIN_INSTANCE_COUNT,
 train_instance_type=TRAIN_INSTANCE_TYPE,
 sagemaker_session=sagemaker_session)
 tuner.estimator = mxnet

 tags = [{'Name': 'some-tag-without-a-value'}]
 tuner.tags = tags

 hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
 'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
 tuner._hyperparameter_ranges = hyperparameter_ranges

 tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
 pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
 base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down

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

Skip to content
fix: update TrainingInputMode with s3_input InputMode#776

New issue

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

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

Already on GitHub?
Sign in
to your account

 Merged

laurenyu
merged 4 commits into

from

May 8, 2019
 Merged


fix: update TrainingInputMode with s3_input InputMode
#776
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4 commits


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80573a6

fix: update TrainingInputMode with s3_input InputMode


imujjwal96 May 6, 2019
6fad54b

Add unit test for s3_input InputMode


imujjwal96 May 6, 2019
ae7a7e9

fix: Update HPO TrainingInputMode with s3_input InputMode


imujjwal96 May 8, 2019
1ea93f6

fix: export notebook test environment variables


imujjwal96 May 8, 2019

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6 changes: 6 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
 train_args['tags'] = estimator.tags
 train_args['metric_definitions'] = estimator.metric_definitions

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.


 if estimator.enable_network_isolation():
 train_args['enable_network_isolation'] = True

Expand Down

8 changes: 8 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
 IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
 tuner_args['warm_start_config'] = warm_start_config_req
 tuner_args['early_stopping_type'] = tuner.early_stopping_type

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 tuner_args['input_mode'] = inputs.config['InputMode']

 if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
 tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
 else:
Expand Down

4 changes: 2 additions & 2 deletions
tests/scripts/run-notebook-test.sh

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down

11 changes: 11 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
 assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
 expected_input_mode = 'Pipe'
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 enable_cloudwatch_metrics=True)
 fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down

26 changes: 26 additions & 0 deletions
tests/unit/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
 create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
 WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
 assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
 expected_input_mode = 'Pipe'

 script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
 mxnet = MXNet(entry_point=script_path,
 role=ROLE,
 framework_version=FRAMEWORK_VERSION,
 train_instance_count=TRAIN_INSTANCE_COUNT,
 train_instance_type=TRAIN_INSTANCE_TYPE,
 sagemaker_session=sagemaker_session)
 tuner.estimator = mxnet

 tags = [{'Name': 'some-tag-without-a-value'}]
 tuner.tags = tags

 hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
 'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
 tuner._hyperparameter_ranges = hyperparameter_ranges

 tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
 pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
 base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down

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

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fix: update TrainingInputMode with s3_input InputMode#776

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 Merged

laurenyu
merged 4 commits into

from

May 8, 2019
 Merged


fix: update TrainingInputMode with s3_input InputMode
#776
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80573a6

fix: update TrainingInputMode with s3_input InputMode


imujjwal96 May 6, 2019
6fad54b

Add unit test for s3_input InputMode


imujjwal96 May 6, 2019
ae7a7e9

fix: Update HPO TrainingInputMode with s3_input InputMode


imujjwal96 May 8, 2019
1ea93f6

fix: export notebook test environment variables


imujjwal96 May 8, 2019

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6 changes: 6 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
 train_args['tags'] = estimator.tags
 train_args['metric_definitions'] = estimator.metric_definitions

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.


 if estimator.enable_network_isolation():
 train_args['enable_network_isolation'] = True

Expand Down

8 changes: 8 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
 IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
 tuner_args['warm_start_config'] = warm_start_config_req
 tuner_args['early_stopping_type'] = tuner.early_stopping_type

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 tuner_args['input_mode'] = inputs.config['InputMode']

 if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
 tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
 else:
Expand Down

4 changes: 2 additions & 2 deletions
tests/scripts/run-notebook-test.sh

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down

11 changes: 11 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
 assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
 expected_input_mode = 'Pipe'
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 enable_cloudwatch_metrics=True)
 fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down

26 changes: 26 additions & 0 deletions
tests/unit/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
 create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
 WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
 assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
 expected_input_mode = 'Pipe'

 script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
 mxnet = MXNet(entry_point=script_path,
 role=ROLE,
 framework_version=FRAMEWORK_VERSION,
 train_instance_count=TRAIN_INSTANCE_COUNT,
 train_instance_type=TRAIN_INSTANCE_TYPE,
 sagemaker_session=sagemaker_session)
 tuner.estimator = mxnet

 tags = [{'Name': 'some-tag-without-a-value'}]
 tuner.tags = tags

 hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
 'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
 tuner._hyperparameter_ranges = hyperparameter_ranges

 tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
 pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
 base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down

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

Skip to content
fix: update TrainingInputMode with s3_input InputMode#776

New issue

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

 Sign up for GitHub

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

Already on GitHub?
Sign in
to your account

 Merged

laurenyu
merged 4 commits into

from

May 8, 2019
 Merged


fix: update TrainingInputMode with s3_input InputMode
#776
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80573a6

fix: update TrainingInputMode with s3_input InputMode


imujjwal96 May 6, 2019
6fad54b

Add unit test for s3_input InputMode


imujjwal96 May 6, 2019
ae7a7e9

fix: Update HPO TrainingInputMode with s3_input InputMode


imujjwal96 May 8, 2019
1ea93f6

fix: export notebook test environment variables


imujjwal96 May 8, 2019

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6 changes: 6 additions & 0 deletions
src/sagemaker/estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -569,6 +569,12 @@ def start_new(cls, estimator, inputs):
 train_args['tags'] = estimator.tags
 train_args['metric_definitions'] = estimator.metric_definitions

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 train_args['input_mode'] = inputs.config['InputMode']
Comment thread
laurenyu marked this conversation as resolved.


 if estimator.enable_network_isolation():
 train_args['enable_network_isolation'] = True

Expand Down

8 changes: 8 additions & 0 deletions
src/sagemaker/tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -15,6 +15,7 @@
import importlib
import inspect
import json
import logging
from enum import Enum

import sagemaker
Expand All@@ -26,6 +27,7 @@
from sagemaker.parameter import (CategoricalParameter, ContinuousParameter,
 IntegerParameter, ParameterRange)
from sagemaker.session import Session
from sagemaker.session import s3_input
from sagemaker.utils import base_name_from_image, name_from_base, to_str

AMAZON_ESTIMATOR_MODULE = 'sagemaker'
Expand DownExpand Up@@ -640,6 +642,12 @@ def start_new(cls, tuner, inputs):
 tuner_args['warm_start_config'] = warm_start_config_req
 tuner_args['early_stopping_type'] = tuner.early_stopping_type

 if isinstance(inputs, s3_input):
 if 'InputMode' in inputs.config:
 logging.debug('Selecting s3_input\'s input_mode ({}) for TrainingInputMode.'
 .format(inputs.config['InputMode']))
 tuner_args['input_mode'] = inputs.config['InputMode']

 if isinstance(tuner.estimator, sagemaker.algorithm.AlgorithmEstimator):
 tuner_args['algorithm_arn'] = tuner.estimator.algorithm_arn
 else:
Expand Down

4 changes: 2 additions & 2 deletions
tests/scripts/run-notebook-test.sh

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -10,9 +10,9 @@ aws s3 --region us-west-2 cp ./dist/sagemaker-*.tar.gz s3://sagemaker-python-sdk
aws s3 cp s3://sagemaker-mead-cli/mead-nb-test.tar.gz mead-nb-test.tar.gz
tar -xzf mead-nb-test.tar.gz
git clone --depth 1 https://github.com/awslabs/amazon-sagemaker-examples.git
JAVA_HOME=$(get-java-home)
export JAVA_HOME=$(get-java-home)
echo "set JAVA_HOME=$JAVA_HOME"
SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
export SAGEMAKER_ROLE_ARN=$(get-sagemaker-role-arn)
echo "set SAGEMAKER_ROLE_ARN=$SAGEMAKER_ROLE_ARN"
./runtime/bin/mead-run-nb-test \
--instance-type ml.c4.8xlarge \
Expand Down

11 changes: 11 additions & 0 deletions
tests/unit/test_estimator.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -315,6 +315,17 @@ def test_augmented_manifest(sagemaker_session):
 assert s3_data_source['AttributeNames'] == ['foo', 'bar']


def test_s3_input_mode(sagemaker_session):
 expected_input_mode = 'Pipe'
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
 enable_cloudwatch_metrics=True)
 fw.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_shuffle_config(sagemaker_session):
 fw = DummyFramework(entry_point=SCRIPT_PATH, role='DummyRole', sagemaker_session=sagemaker_session,
 train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
Expand Down

26 changes: 26 additions & 0 deletions
tests/unit/test_tuner.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -28,6 +28,7 @@
from sagemaker.tuner import (_TuningJob, create_identical_dataset_and_algorithm_tuner,
 create_transfer_learning_tuner, HyperparameterTuner, WarmStartConfig,
 WarmStartTypes)
from sagemaker.session import s3_input

DATA_DIR = os.path.join(os.path.dirname(__file__), '..', 'data')
MODEL_DATA = "s3://bucket/model.tar.gz"
Expand DownExpand Up@@ -286,6 +287,31 @@ def test_fit_mxnet_with_vpc_config(sagemaker_session, tuner):
 assert tune_kwargs['vpc_config'] == {'Subnets': subnets, 'SecurityGroupIds': security_group_ids}


def test_s3_input_mode(sagemaker_session, tuner):
 expected_input_mode = 'Pipe'

 script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'failure_script.py')
 mxnet = MXNet(entry_point=script_path,
 role=ROLE,
 framework_version=FRAMEWORK_VERSION,
 train_instance_count=TRAIN_INSTANCE_COUNT,
 train_instance_type=TRAIN_INSTANCE_TYPE,
 sagemaker_session=sagemaker_session)
 tuner.estimator = mxnet

 tags = [{'Name': 'some-tag-without-a-value'}]
 tuner.tags = tags

 hyperparameter_ranges = {'num_components': IntegerParameter(2, 4),
 'algorithm_mode': CategoricalParameter(['regular', 'randomized'])}
 tuner._hyperparameter_ranges = hyperparameter_ranges

 tuner.fit(inputs=s3_input('s3://mybucket/train_manifest', input_mode=expected_input_mode))

 actual_input_mode = sagemaker_session.method_calls[1][2]['input_mode']
 assert actual_input_mode == expected_input_mode


def test_fit_pca_with_inter_container_traffic_encryption_flag(sagemaker_session, tuner):
 pca = PCA(ROLE, TRAIN_INSTANCE_COUNT, TRAIN_INSTANCE_TYPE, NUM_COMPONENTS,
 base_job_name='pca', sagemaker_session=sagemaker_session,
Expand Down