modelEvalReg = PythonScriptStep(
name="Evaluate and Register Model",
script_name="evaluate_model.py",
arguments=["--release_id", 0, '--model_name', model_name],
compute_target=compute_target_cpu,
source_directory=source_directory,
runconfig=run_config,
allow_reuse=False)
modelEvalReg.run_after(trainingStep)
print("Model Evaluation and Registration Step is Created")
from azureml.pipeline.core import Pipeline
from azureml.core import Experiment
pipeline = Pipeline(workspace=ws,
steps=[extractDataStep, trainingStep, modelEvalReg])
pipeline_run = Experiment(ws, pipeline_experiment_name).submit(pipeline)
pipeline_run.wait_for_completion(show_output=True, raise_on_error=True)
published_pipeline = pipeline_run.publish_pipeline(
name=pipeline_name,
description=
"Steps are: data preparation, training, model validation and model registration",
version="0.1",
continue_on_step_failure=False)
## Free up compute resource(s) after computation is completed!
print(f'Deleting compute resource: [{cluster_name_cpu}]')
compute_target_cpu.delete()
# RunDetails(pipeline_run1).show()
step_runs = pipeline_run1.get_children()
for step_run in step_runs:
status = step_run.get_status()
print('Script:', step_run.name, 'status:', status)
# Change this if you want to see details even if the Step has succeeded.
if status == "Failed":
joblog = step_run.get_job_log()
print('job log:', joblog)
pipeline_param = PipelineParameter(name="pipeline_arg", default_value=10)
published_pipeline1 = pipeline_run1.publish_pipeline(
name="Published_Titanic_Pipeline_Notebook",
description="Titanic_Pipeline_Notebook Published Pipeline Description",
version="1.0")
from azureml.pipeline.core import PublishedPipeline
import requests
response = requests.post(published_pipeline1.endpoint,
json={
"ExperimentName": "Titanic_Pipeline_Notebook",
"ParameterAssignments": {
"pipeline_arg": 20
}
})
arguments=["--modelName", modelName],
runconfig=run_config,
allow_reuse=False)
print("Scoring and output Step created")
##------------- Create Pipeline
trainingStep.run_after(preprocessingStep)
scoringStep.run_after(trainingStep)
qualityMLPipeline = Pipeline(workspace=ws, steps=[scoringStep])
print("Quality Prediction pipeline is built")
qualityMLPipeline.validate()
print("Quality Prediction pipeline simple validation complete")
##------------- Submit an Experiement using the Pipeline
pipelineRun = Experiment(ws, 'quality_prediction_gb').submit(qualityMLPipeline)
print("Quality Prediction pipeline submitted for execution")
##------------- Publish Pipeline
#publishedPipeline = qualityMLPipeline.publish(name="NewQualityPrediction-Pipeline", description="Quality Prediction pipeline",version="0.1")
publishedPipeline = pipelineRun.publish_pipeline(
name="NewQualityPrediction-Pipeline",
description="Quality Prediction pipeline",
version="0.1")
print("Newly published pipeline id => ", publishedPipeline.id)
print("Newly published pipeline endpoint => ", publishedPipeline.endpoint)
allow_reuse=False)
from azureml.core import Experiment
from azureml.pipeline.core import Pipeline
pipeline = Pipeline(workspace=ws, steps=[batch_score_step])
pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline)
pipeline_run.wait_for_completion(show_output=True)
import pandas as pd
batch_run = next(pipeline_run.get_children())
batch_output = batch_run.get_output_data("scores")
batch_output.download(local_path="inception_results")
for root, dirs, files in os.walk("inception_results"):
for file in files:
if file.endswith("parallel_run_step.txt"):
result_file = os.path.join(root, file)
df = pd.read_csv(result_file, delimiter=":", header=None)
df.columns = ["Filename", "Prediction"]
print("Prediction has ", df.shape[0], " rows")
df.head(10)
published_pipeline = pipeline_run.publish_pipeline(
name="Inception_v3_scoring",
description="Batch scoring using Inception v3 model",
version="1.0")
published_pipeline
for root, dirs, files in os.walk('results'):
for file in files:
if file.endswith('parallel_run_step.txt'):
result_file = os.path.join(root,file)
# Load and display the results
df = pd.read_csv(result_file, delimiter=":", header=None)
df.columns = ["File", "Prediction"]
print(df)
#step 5 - publish
#REST service
published_pipeline = pipeline_run.publish_pipeline(name='Batch_Prediction_Pipeline',
description='Batch pipeline',
version='1.0')
rest_endpoint = published_pipeline.endpoint
#use endpoint to start job
import requests
response = requests.post(rest_endpoint,
headers=auth_header,
json={"ExperimentName": "Batch_Prediction"})
run_id = response.json()["Id"]
#have it run auto
from azureml.pipeline.core import ScheduleRecurrence, Schedule
