Gaurav Kaila gauravkaila

API	Free Subscription	Paid Subscription 1	Paid Subscription 2	Paid Subscription 3	Link
Clarifai	First 5000 ops	Pre-Built Models: $1.20 / 1000 ops	Custom Models: $3.20 / 1000 operations	NA	https://clarifai.com/pricing
Sightengine	First 2000 ops	Starter: $2.9/1000 ops	Growth: $2.475/1000 ops	Pro: $1.995/1000 ops	https://sightengine.com/pricing
DeepAI	NA	First 1M ops: $1.00/1000	Next 9M ops: $0.80/1000	Next 90M ops: $0.60/1000	https://deepai.org/pricing
Nudedetect	First 5000 ops	Pro: $4.95/1000 ops	Ultra: $4.58/1000 ops	Mega: $2.995/1000 ops	https://rapidapi.com/Netspark/api/Nude%20Detect/pricing
Vrate	First 7500 ops	Pro: $0.99/1000 ops	Mega: $0.89/1000 ops	Ultra: $0.49/1000 ops	https://vrate.net/#!/pricing
Picpurify	First 2000 ops	Pro: $1.63/1000 ops	Ultra: $1.41/1000 ops	Mega: $1.25/1000 ops	https://www.picpurify.com/pricing.html
Nanonets	First 1000 ops	Small: $0.90/1000 ops	Medium: $0.99/1000 ops	Large: custom	https://nanonets.com/pricing/

	for i in Image.list(workspace = ws,image_name=config['docker']['docker_image_name']):
	if i.version == int(config['deploy']['docker_image_version']):
	image = i

	from azureml.core.webservice import AciWebservice

	aciconfig = AciWebservice.deploy_configuration(cpu_cores = int(config['deploy']['cpu_cores']),
	memory_gb = int(config['deploy']['memory']),
	tags = {'area': "diabetes", 'type': "regression"},
	description = "test")

	# Create docker image
	from azureml.core.image import Image, ContainerImage

	image_config = ContainerImage.image_configuration(runtime= "python",
	execution_script=config['docker']['path_scoring_script'],
	conda_file=config['docker']['conda_env_file'],
	tags = {'area': "diabetes", 'type': "regression"},
	description = "test")

	image = Image.create(name = config['docker']['docker_image_name'],

	# Create conda environment for docker image
	from azureml.core.conda_dependencies import CondaDependencies

	# DEFINE CONDA DEPENDENCIES
	myenv = CondaDependencies.create(pip_packages=ast.literal_eval(config['docker']['pip_packages']),conda_packages=ast.literal_eval(config['train']['conda_packages']))
	myenv.add_pip_package("pynacl==1.2.1")

	# CREATE CONDA ENVIRONMENT FILE
	with open(config['docker']['conda_env_file'],"w") as f:
	f.write(myenv.serialize_to_string())

	# Retrive registered model by version and name
	from azureml.core.model import Model

	regression_models = Model.list(workspace=ws,name=config['train']['model_name'])
	for m in regression_models:
	if m.version == int(config['docker']['model_version']):
	model = m

	# Register the model
	print('Registering model...')
	model = run.register_model(model_name=config['train']['model_name'], model_path='./outputs/ridge_1.pkl')
	print('Done registering model.')

	src = ScriptRunConfig(source_directory='./',
	script= config['train']['script'],
	run_config=conda_run_config,
	# pass the datastore reference as a parameter to the training script
	arguments=['--data-folder', str(ds.as_download())]
	)
	run = exp.submit(config=src)
	run.wait_for_completion(show_output=True)

	# get the default datastore and upload data from local folder to VM
	ds = ws.get_default_datastore()
	print(ds.name, ds.datastore_type, ds.account_name, ds.container_name)

	# Upload data to default data storage
	data_folder = config['train']['data_folder']
	ds.upload(config['train']['local_directory'],target_path=data_folder,overwrite=True)
	print ('Finished Uploading Data.')

	# Create an experiment
	from azureml.core import Experiment
	# NOTE: New experiment is created if one with the following name is not found
	experiment_name = config['train']['experiment_name']
	exp = Experiment(workspace=ws, name=experiment_name)

	# Create a target compute - VM
	from azureml.core.compute import DsvmCompute
	from azureml.core.compute_target import ComputeTargetException