def create_experiment(Experiment_name, Experiment_description=None):
try:
experiment = Experiment.load(experiment_name=Experiment_name)
except Exception as ex:
if "ResourceNotFound" in str(ex):
experiment = Experiment.create(experiment_name=Experiment_name,
description=Experiment_description)
def set_experiment_config(experiment_basename=None):
'''
Optionally takes an base name for the experiment. Has a hard dependency on boto3 installation.
Creates a new experiment using the basename, otherwise simply uses autogluon as basename.
May run into issues on Experiments' requirements for basename config downstream.
'''
now = int(time.time())
if experiment_basename:
experiment_name = '{}-autogluon-{}'.format(experiment_basename, now)
else:
experiment_name = 'autogluon-{}'.format(now)
try:
client = boto3.Session().client('sagemaker')
except:
print(
'You need to install boto3 to create an experiment. Try pip install --upgrade boto3'
)
return ''
try:
Experiment.create(
experiment_name=experiment_name,
description="Running AutoGluon Tabular with SageMaker Experiments",
sagemaker_boto_client=client)
print(
'Created an experiment named {}, you should be able to see this in SageMaker Studio right now.'
.format(experiment_name))
except:
print(
'Could not create the experiment. Is your basename properly configured? Also try installing the sagemaker experiments SDK with pip install sagemaker-experiments.'
)
return ''
return experiment_name
def _test_training_function(ecr_image, sagemaker_session, instance_type,
framework_version, py_version):
if py_version is None or '2' in py_version:
pytest.skip('Skipping python2 {}'.format(py_version))
return
from smexperiments.experiment import Experiment
from smexperiments.trial import Trial
from smexperiments.trial_component import TrialComponent
sm_client = sagemaker_session.sagemaker_client
random.seed(f"{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}")
unique_id = random.randint(1, 6000)
experiment_name = f"tf-container-integ-test-{unique_id}-{int(time.time())}"
experiment = Experiment.create(
experiment_name=experiment_name,
description="Integration test experiment from sagemaker-tf-container",
sagemaker_boto_client=sm_client,
)
trial_name = f"tf-container-integ-test-{unique_id}-{int(time.time())}"
trial = Trial.create(experiment_name=experiment_name,
trial_name=trial_name,
sagemaker_boto_client=sm_client)
training_job_name = utils.unique_name_from_base(
"test-tf-experiments-mnist")
# create a training job and wait for it to complete
with timeout(minutes=15):
resource_path = os.path.join(os.path.dirname(__file__), "..", "..",
"resources")
script = os.path.join(resource_path, "mnist", "mnist.py")
estimator = TensorFlow(
entry_point=script,
role="SageMakerRole",
instance_type=instance_type,
instance_count=1,
sagemaker_session=sagemaker_session,
image_uri=ecr_image,
framework_version=framework_version,
script_mode=True,
)
inputs = estimator.sagemaker_session.upload_data(
path=os.path.join(resource_path, "mnist", "data"),
key_prefix="scriptmode/mnist")
estimator.fit(inputs, job_name=training_job_name)
training_job = sm_client.describe_training_job(
TrainingJobName=training_job_name)
training_job_arn = training_job["TrainingJobArn"]
# verify trial component auto created from the training job
trial_components = list(
TrialComponent.list(source_arn=training_job_arn,
sagemaker_boto_client=sm_client))
trial_component_summary = trial_components[0]
trial_component = TrialComponent.load(
trial_component_name=trial_component_summary.trial_component_name,
sagemaker_boto_client=sm_client,
)
# associate the trial component with the trial
trial.add_trial_component(trial_component)
# cleanup
trial.remove_trial_component(trial_component_summary.trial_component_name)
trial_component.delete()
trial.delete()
experiment.delete()
def test_training(sagemaker_session, ecr_image, instance_type, instance_count):
from smexperiments.experiment import Experiment
from smexperiments.trial import Trial
from smexperiments.trial_component import TrialComponent
sm_client = sagemaker_session.sagemaker_client
experiment_name = "mxnet-container-integ-test-{}".format(int(time.time()))
experiment = Experiment.create(
experiment_name=experiment_name,
description=
"Integration test experiment from sagemaker-mxnet-container",
sagemaker_boto_client=sm_client,
)
trial_name = "mxnet-container-integ-test-{}".format(int(time.time()))
trial = Trial.create(experiment_name=experiment_name,
trial_name=trial_name,
sagemaker_boto_client=sm_client)
hyperparameters = {
"random_seed": True,
"num_steps": 50,
"smdebug_path": "/opt/ml/output/tensors",
"epochs": 1,
}
mx = MXNet(
entry_point=SCRIPT_PATH,
role="SageMakerRole",
train_instance_count=instance_count,
train_instance_type=instance_type,
sagemaker_session=sagemaker_session,
image_name=ecr_image,
hyperparameters=hyperparameters,
)
training_job_name = utils.unique_name_from_base("test-mxnet-image")
# create a training job and wait for it to complete
with timeout(minutes=15):
prefix = "mxnet_mnist_gluon_basic_hook_demo/{}".format(
utils.sagemaker_timestamp())
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(DATA_PATH, "train"),
key_prefix=prefix + "/train")
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(DATA_PATH, "test"), key_prefix=prefix + "/test")
mx.fit({
"train": train_input,
"test": test_input
},
job_name=training_job_name,
wait=False)
training_job = sm_client.describe_training_job(
TrainingJobName=training_job_name)
training_job_arn = training_job["TrainingJobArn"]
# verify trial component auto created from the training job
trial_component_summary = None
attempts = 0
while True:
trial_components = list(
TrialComponent.list(source_arn=training_job_arn,
sagemaker_boto_client=sm_client))
if len(trial_components) > 0:
trial_component_summary = trial_components[0]
break
if attempts < 10:
attempts += 1
sleep(500)
assert trial_component_summary is not None
trial_component = TrialComponent.load(
trial_component_name=trial_component_summary.trial_component_name,
sagemaker_boto_client=sm_client,
)
# associate the trial component with the trial
trial.add_trial_component(trial_component)
# cleanup
trial.remove_trial_component(trial_component_summary.trial_component_name)
trial_component.delete()
trial.delete()
experiment.delete()
def test_training(sagemaker_session, ecr_image, instance_type,
framework_version):
sm_client = sagemaker_session.sagemaker_client
experiment_name = "tf-container-integ-test-{}".format(int(time.time()))
experiment = Experiment.create(
experiment_name=experiment_name,
description="Integration test experiment from sagemaker-tf-container",
sagemaker_boto_client=sm_client,
)
trial_name = "tf-container-integ-test-{}".format(int(time.time()))
trial = Trial.create(experiment_name=experiment_name,
trial_name=trial_name,
sagemaker_boto_client=sm_client)
training_job_name = utils.unique_name_from_base(
"test-tf-experiments-mnist")
# create a training job and wait for it to complete
with timeout(minutes=DEFAULT_TIMEOUT):
resource_path = os.path.join(os.path.dirname(__file__), "..", "..",
"resources")
script = os.path.join(resource_path, "mnist", "mnist.py")
estimator = TensorFlow(
entry_point=script,
role="SageMakerRole",
train_instance_type=instance_type,
train_instance_count=1,
sagemaker_session=sagemaker_session,
image_name=ecr_image,
framework_version=framework_version,
script_mode=True,
)
inputs = estimator.sagemaker_session.upload_data(
path=os.path.join(resource_path, "mnist", "data"),
key_prefix="scriptmode/mnist")
estimator.fit(inputs, job_name=training_job_name)
training_job = sm_client.describe_training_job(
TrainingJobName=training_job_name)
training_job_arn = training_job["TrainingJobArn"]
# verify trial component auto created from the training job
trial_components = list(
TrialComponent.list(source_arn=training_job_arn,
sagemaker_boto_client=sm_client))
trial_component_summary = trial_components[0]
trial_component = TrialComponent.load(
trial_component_name=trial_component_summary.trial_component_name,
sagemaker_boto_client=sm_client,
)
# associate the trial component with the trial
trial.add_trial_component(trial_component)
# cleanup
trial.remove_trial_component(trial_component_summary.trial_component_name)
trial_component.delete()
trial.delete()
experiment.delete()
def main(): # pragma: no cover
"""The main harness that creates or updates and runs the pipeline.
Creates or updates the pipeline and runs it.
"""
parser = argparse.ArgumentParser(
"Creates or updates and runs the pipeline for the pipeline script.")
parser.add_argument(
"-n",
"--module-name",
dest="module_name",
type=str,
help="The module name of the pipeline to import.",
)
parser.add_argument(
"-kwargs",
"--kwargs",
dest="kwargs",
default=None,
help=
"Dict string of keyword arguments for the pipeline generation (if supported)",
)
parser.add_argument(
"-role-arn",
"--role-arn",
dest="role_arn",
type=str,
help="The role arn for the pipeline service execution role.",
)
parser.add_argument(
"-description",
"--description",
dest="description",
type=str,
default=None,
help="The description of the pipeline.",
)
parser.add_argument(
"-tags",
"--tags",
dest="tags",
default=None,
help=
"""List of dict strings of '[{"Key": "string", "Value": "string"}, ..]'""",
)
args = parser.parse_args()
if args.module_name is None or args.role_arn is None:
parser.print_help()
sys.exit(2)
tags = convert_struct(args.tags)
try:
pipeline = get_pipeline_driver(args.module_name, args.kwargs)
print(
"###### Creating/updating a SageMaker Pipeline with the following definition:"
)
parsed = json.loads(pipeline.definition())
print(json.dumps(parsed, indent=2, sort_keys=True))
upsert_response = pipeline.upsert(role_arn=args.role_arn,
description=args.description,
tags=tags)
print(
"\n###### Created/Updated SageMaker Pipeline: Response received:")
print(upsert_response)
execution = pipeline.start()
print(
f"\n###### Execution started with PipelineExecutionArn: {execution.arn}"
)
# Now we describe execution instance and list the steps in the execution to find out more about the execution.
execution_run = execution.describe()
print(execution_run)
# Create or Load the 'Experiment'
try:
experiment = Experiment.create(
experiment_name=pipeline.name,
description='Amazon Customer Reviews BERT Pipeline Experiment')
except:
experiment = Experiment.load(experiment_name=pipeline.name)
print('Experiment name: {}'.format(experiment.experiment_name))
# Add Execution Run as Trial to Experiments
execution_run_name = execution_run['PipelineExecutionDisplayName']
print(execution_run_name)
# Create the `Trial`
timestamp = int(time.time())
trial = Trial.create(trial_name=execution_run_name,
experiment_name=experiment.experiment_name,
sagemaker_boto_client=sm)
trial_name = trial.trial_name
print('Trial name: {}'.format(trial_name))
######################################################
## Parse Pipeline Definition For Processing Job Args
######################################################
processing_param_dict = {}
for step in parsed['Steps']:
print('step: {}'.format(step))
if step['Name'] == 'Processing':
print('Step Name is Processing...')
arg_list = step['Arguments']['AppSpecification'][
'ContainerArguments']
print(arg_list)
num_args = len(arg_list)
print(num_args)
# arguments are (key, value) pairs in this list, so we extract them in pairs
# using [i] and [i+1] indexes and stepping by 2 through the list
for i in range(0, num_args, 2):
key = arg_list[i].replace('--', '')
value = arg_list[i + 1]
print('arg key: {}'.format(key))
print('arg value: {}'.format(value))
processing_param_dict[key] = value
##############################
## Wait For Execution To Finish
##############################
print("Waiting for the execution to finish...")
execution.wait()
print("\n#####Execution completed. Execution step details:")
# List Execution Steps
print(execution.list_steps())
# List All Artifacts Generated By The Pipeline
processing_job_name = None
training_job_name = None
from sagemaker.lineage.visualizer import LineageTableVisualizer
viz = LineageTableVisualizer(sagemaker.session.Session())
for execution_step in reversed(execution.list_steps()):
print(execution_step)
# We are doing this because there appears to be a bug of this LineageTableVisualizer handling the Processing Step
if execution_step['StepName'] == 'Processing':
processing_job_name = execution_step['Metadata'][
'ProcessingJob']['Arn'].split('/')[-1]
print(processing_job_name)
#display(viz.show(processing_job_name=processing_job_name))
elif execution_step['StepName'] == 'Train':
training_job_name = execution_step['Metadata']['TrainingJob'][
'Arn'].split('/')[-1]
print(training_job_name)
#display(viz.show(training_job_name=training_job_name))
else:
#display(viz.show(pipeline_execution_step=execution_step))
time.sleep(5)
# Add Trial Compontents To Experiment Trial
processing_job_tc = '{}-aws-processing-job'.format(processing_job_name)
print(processing_job_tc)
# -aws-processing-job is the default name assigned by ProcessingJob
response = sm.associate_trial_component(
TrialComponentName=processing_job_tc, TrialName=trial_name)
# -aws-training-job is the default name assigned by TrainingJob
training_job_tc = '{}-aws-training-job'.format(training_job_name)
print(training_job_tc)
response = sm.associate_trial_component(
TrialComponentName=training_job_tc, TrialName=trial_name)
##############
# Log Additional Parameters within Trial
##############
print('Logging Processing Job Parameters within Experiment Trial...')
processing_job_tracker = tracker.Tracker.load(
trial_component_name=processing_job_tc)
for key, value in processing_param_dict.items():
print('key: {}, value: {}'.format(key, value))
processing_job_tracker.log_parameters({key: str(value)})
# must save after logging
processing_job_tracker.trial_component.save()
except Exception as e: # pylint: disable=W0703
print(f"Exception: {e}")
sys.exit(1)
import time, os, sys
import sagemaker, boto3
import numpy as np
import pandas as pd
import itertools
from pprint import pprint
sess = boto3.Session()
sm = sess.client('sagemaker')
role = sagemaker.get_execution_role()
sagemaker_session = sagemaker.Session(boto_session=sess)
bucket_name = sagemaker_session.default_bucket()
from smexperiments.experiment import Experiment
from smexperiments.trial import Trial
from smexperiments.trial_component import TrialComponent
from smexperiments.tracker import Tracker
training_experiment = Experiment.create(
experiment_name=f"test-experiment-{int(time.time())}",
description="This is a Test ",
sagemaker_boto_client=sm)
def setup_workflow(project, purpose, workflow_execution_role, script_dir,
ecr_repository):
""" to setup all needed for a step function with sagemaker.
arg:
project: project name under sagemaker
purpose: subproject
workflow_execution_role: arn to execute step functions
script_dir: processing file name, like a .py file
ecr_repository: ecr repository name
return:
workflow: a stepfunctions.workflow.Workflow instance
example:
PROJECT = '[dpt-proj-2022]'
PURPOSE = '[processing]'
WORKFLOW_EXECUTION_ROLE = "arn:aws-cn:iam::[*********]:role/[**************]"
SCRIPT_DIR = "[processing].py"
ECR_REPOSITORY = '[ecr-2022]'
"""
# SageMaker Session setup
# ========================================================================================
# SageMaker Session
# ====================================
account_id = boto3.client('sts').get_caller_identity().get('Account')
role = sagemaker.get_execution_role()
# Storage
# ====================================
session = sagemaker.Session()
region = session.boto_region_name
s3_output = session.default_bucket()
# Code storage
# ==================
s3_prefix = '{}/{}'.format(project, purpose)
s3_prefix_code = '{}/code'.format(s3_prefix)
s3CodePath = 's3://{}/{}/code'.format(s3_output, s3_prefix)
## preprocess & prediction
script_list = [script_dir]
for script in script_list:
session.upload_data(script,
bucket=session.default_bucket(),
key_prefix=s3_prefix_code)
# ECR environment
# ====================================
uri_suffix = 'amazonaws.com.cn'
tag = ':latest'
ecr_repository_uri = '{}.dkr.ecr.{}.{}/{}'.format(account_id, region,
uri_suffix,
ecr_repository + tag)
# SageMaker Experiments setup
# ========================================================================================
experiment = Experiment.create(
experiment_name="{}-{}".format(project, int(time.time())),
description="machine learning project",
sagemaker_boto_client=boto3.client('sagemaker'))
print(experiment)
execution_input = ExecutionInput(schema={
"ProcessingJobName": str,
"ResultPath": str,
})
# setup script processor
script_processor = ScriptProcessor(command=['python3'],
image_uri=ecr_repository_uri,
role=role,
instance_count=1,
instance_type='ml.m5.4xlarge')
# Step
# ========================================================================================
optimizing_step = steps.ProcessingStep(
"Processing Step",
processor=script_processor,
job_name=execution_input["ProcessingJobName"],
inputs=[
ProcessingInput(source=s3CodePath,
destination='/opt/ml/processing/input/code',
input_name='code')
],
outputs=[
ProcessingOutput(output_name=purpose,
destination=execution_input["ResultPath"],
source='/opt/ml/processing/{}'.format(purpose))
],
container_entrypoint=[
"python3", "/opt/ml/processing/input/code/" + script_dir
],
)
# Fail Sate
# ========================================================================================
failed_state = steps.states.Fail("Processing Workflow failed",
cause="SageMakerProcessingJobFailed")
catch_state_processing = steps.states.Catch(
error_equals=["States.TaskFailed"], next_step=failed_state)
# Create Workflow
# ========================================================================================
optimizing_step.add_catch(catch_state_processing)
workflow_name = workflow_name = "workflow-{}-{}".format(project,
purpose).upper()
workflow_graph = steps.Chain([optimizing_step])
workflow = Workflow(name=workflow_name,
definition=workflow_graph,
role=workflow_execution_role)
workflow.create()
return workflow
def _test_training_function(ecr_image, sagemaker_session, instance_type,
framework_version):
sm_client = sagemaker_session.sagemaker_client
random.seed(f"{datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')}")
unique_id = random.randint(1, 6000)
experiment_name = f"tf-container-integ-test-{unique_id}-{int(time.time())}"
experiment = Experiment.create(
experiment_name=experiment_name,
description="Integration test experiment from sagemaker-tf-container",
sagemaker_boto_client=sm_client,
)
trial_name = f"tf-container-integ-test-{unique_id}-{int(time.time())}"
trial = Trial.create(experiment_name=experiment_name,
trial_name=trial_name,
sagemaker_boto_client=sm_client)
training_job_name = utils.unique_name_from_base(
"test-tf-experiments-mnist")
# create a training job and wait for it to complete
with timeout(minutes=DEFAULT_TIMEOUT):
resource_path = os.path.join(os.path.dirname(__file__), "..", "..",
"resources")
script = os.path.join(resource_path, "mnist", "mnist.py")
estimator = TensorFlow(
model_dir=False,
entry_point=script,
role="SageMakerRole",
instance_type=instance_type,
instance_count=1,
sagemaker_session=sagemaker_session,
image_uri=ecr_image,
framework_version=framework_version,
)
inputs = estimator.sagemaker_session.upload_data(
path=os.path.join(resource_path, "mnist", "data"),
key_prefix="scriptmode/mnist")
estimator.fit(inputs, job_name=training_job_name)
training_job = sm_client.describe_training_job(
TrainingJobName=training_job_name)
training_job_arn = training_job["TrainingJobArn"]
# verify trial component auto created from the training job
trial_components = list(
TrialComponent.list(source_arn=training_job_arn,
sagemaker_boto_client=sm_client))
trial_component_summary = trial_components[0]
trial_component = TrialComponent.load(
trial_component_name=trial_component_summary.trial_component_name,
sagemaker_boto_client=sm_client,
)
# associate the trial component with the trial
trial.add_trial_component(trial_component)
# cleanup
trial.remove_trial_component(trial_component_summary.trial_component_name)
trial_component.delete()
trial.delete()
# Prevent throttling to avoid deleting experiment before it's updated with trial deletion
time.sleep(1.2)
experiment.delete()
def test_training(sagemaker_session, ecr_image, instance_type):
from smexperiments.experiment import Experiment
from smexperiments.trial import Trial
from smexperiments.trial_component import TrialComponent
sm_client = sagemaker_session.sagemaker_client
experiment_name = "pytorch-container-integ-test-{}".format(int(
time.time()))
experiment = Experiment.create(
experiment_name=experiment_name,
description=
"Integration test full customer e2e from sagemaker-pytorch-container",
sagemaker_boto_client=sm_client,
)
trial_name = "pytorch-container-integ-test-{}".format(int(time.time()))
trial = Trial.create(experiment_name=experiment_name,
trial_name=trial_name,
sagemaker_boto_client=sm_client)
hyperparameters = {
"random_seed": True,
"num_steps": 50,
"smdebug_path": "/opt/ml/output/tensors",
"epochs": 1,
"data_dir": training_dir,
}
training_job_name = utils.unique_name_from_base(
"test-pytorch-experiments-image")
# create a training job and wait for it to complete
with timeout(minutes=DEFAULT_TIMEOUT):
pytorch = PyTorch(
entry_point=smdebug_mnist_script,
role="SageMakerRole",
train_instance_count=1,
train_instance_type=instance_type,
sagemaker_session=sagemaker_session,
image_name=ecr_image,
hyperparameters=hyperparameters,
)
training_input = pytorch.sagemaker_session.upload_data(
path=training_dir, key_prefix="pytorch/mnist")
pytorch.fit({"training": training_input}, job_name=training_job_name)
training_job = sm_client.describe_training_job(
TrainingJobName=training_job_name)
training_job_arn = training_job["TrainingJobArn"]
# verify trial component auto created from the training job
trial_components = list(
TrialComponent.list(source_arn=training_job_arn,
sagemaker_boto_client=sm_client))
trial_component_summary = trial_components[0]
trial_component = TrialComponent.load(
trial_component_name=trial_component_summary.trial_component_name,
sagemaker_boto_client=sm_client,
)
# associate the trial component with the trial
trial.add_trial_component(trial_component)
# cleanup
trial.remove_trial_component(trial_component_summary.trial_component_name)
trial_component.delete()
trial.delete()
experiment.delete()