<jupyter_start><jupyter_text>Experiment<jupyter_code>experiment_name = 'FishyBusiness'

experiment = Experiment(workspace=ws, name=experiment_name)
experiment.start_logging()<jupyter_output><empty_output><jupyter_text>Estimator<jupyter_code>'''
script_params = {
    '--data-folder': ds.path('mnist').as_mount(),
    '--regularization': 0.05
}
'''

estimator = Estimator(source_directory=source_directory,
                      entry_script='extract_frames.py',
                      #script_params=script_params,
                      compute_target=compute_target)
run = experiment.submit(config=estimator)
run
RunDetails(run).show()
run.wait_for_completion(show_output=True)<jupyter_output><empty_output><jupyter_text>Pipeline<jupyter_code>videos = DataReference(datastore=default_file_store,
                       data_reference_name='raw_data',
                       path_on_datastore=target_path)
frames = PipelineData('raw_frames', datastore=default_file_store)
conda_dependencies = CondaDependencies()
#conda_dependencies.add_conda_package('opencv')

run_configuration = RunConfiguration()
run_configuration.environment.docker.enabled = True
run_configuration.environment.docker.base_image = DEFAULT_CPU_IMAGE
run_configuration.environment.python.user_managed_dependencies = False
run_configuration.environment.python.conda_dependencies = conda_dependencies
run_configuration.target = compute_target
extract_frames_step = PythonScriptStep(name='extract_frames',
                                       source_directory=source_directory,
                                       script_name='extract_frames.py',
                                       #arguments=["--raw_data", videos, "--raw_frames", frames],
                                       #inputs=[videos],
                                       #outputs=[frames],
                                       runconfig=run_configuration,
                                       allow_reuse=False)
pipeline = Pipeline(workspace=ws, steps=[extract_frames_step])
pipeline_run = Experiment(ws, 'extract_frames').submit(pipeline)
RunDetails(pipeline_run).show()
pipeline_run.wait_for_completion(show_output=True)
children = pipeline_run.get_children()
for child in children:
    status = child.get_status()
    print('Script:', child.name, 'status:', status)
    
    if status == "Failed":
        joblog = child.get_job_log()
        print('job log:', joblog)<jupyter_output>Script: extract_frames status: Finished<jupyter_text>ScriptRunConfig<jupyter_code>run_configuration.environment.docker.enabled = False
run_configuration.environment.python.user_managed_dependencies = True
run_configuration.target = compute_target
script_run_config = ScriptRunConfig(source_directory=source_directory,
                                    script='extract_frames.py',
                                    run_config=run_configuration,)
run = experiment.submit(script_run_config)
run
RunDetails(run).show()
run.wait_for_completion(show_output=True)<jupyter_output>RunId: FishyBusiness_1558269074_d346b7f4

Streaming azureml-logs/80_driver_log.txt
========================================

Warning: Couldn't instantiate AppInsights telemetry client. Telemetry disabled.
Warning: Unable to import azureml.history. Output collection disabled.
In train.py
As a data scientist, this is where I use my training code.


The experiment completed successfully. Finalizing run...
Logging experiment finalizing status in history service
Warning: Couldn't instantiate AppInsights telemetry client. Telemetry disabled.

Execution Summary
=================
RunId: FishyBusiness_1558269074_d346b7f4

Warnings:
This compute target type doesn't support non-Docker runs; overriding run configuration enable Docker.
{'fraction_non_alphanumeric': 0.10026737967914438, 'fraction_numerical': 0.009358288770053475, 'mean_word_length': 3.8146718146718146, 'pattern_counts': {'":': 1, '<': 15, '<?xml version=': 0, '>': 15, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 9, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '401002', 'license': 'MIT', 'n_tokens_mistral': 1075, 'n_tokens_neox': 1011, 'n_words': 171, 'path': 'v1/notebooks/Estimator v Pipeline v ScriptRunConfig.ipynb', 'repo_name': 'Muflhi01/HealthyCountryAI', 'size': '3740'}