def test_model_creation_2():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
exp = skil.Experiment(work_space)
model = skil.Model('keras_mnist.h5', experiment=exp)
work_space.delete()
exp.delete()
model.delete()
def test_transform_creation_2():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
exp = skil.Experiment(work_space)
transform = skil.Transform('iris_tp.json', experiment=exp)
transform.add_evaluation(0.42)
work_space.delete()
exp.delete()
transform.delete()
def test_service_creation():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
exp = skil.Experiment(work_space)
model = skil.Model('keras_mnist.h5', experiment=exp)
model.add_evaluation(0.95)
dep = skil.Deployment(sk)
model.deploy(dep)
work_space.delete()
exp.delete()
model.delete()
dep.delete()
def test_base_inference_job():
skil_server = skil.Skil()
model = skil.Model('keras_mnist.h5')
res = skil.resources.compute.EMR(skil_server, 'name244', 'region', 'creds',
'id')
conf = InferenceJobConfiguration(model, 32, res, res, './', "DSP")
# job = InferenceJob(skil_server, conf)
# job.inference_config()
# with pytest.raises(Exception):
# job.run()
res.delete()
def test_base_training_job():
skil_server = skil.Skil()
model = skil.Model('keras_mnist.h5')
res = skil.resources.compute.EMR(skil_server, 'name234', 'region', 'creds',
'id')
conf = TrainingJobConfiguration(model, 10, "acc", "EvalDSP", res, res,
'./', "DSP")
distributed_config = ParameterAveraging(8, 32)
# TODO "jobArgs" does not get recognize"
# job = TrainingJob(skil_server, conf, distributed_config)
# job._training_job_args()
# with pytest.raises(Exception):
# job.run()
res.delete()
def __init__(self,
model_file_name,
id=None,
name=None,
version=None,
experiment=None,
labels='',
verbose=False):
if not experiment:
self.skil = skil.Skil()
self.work_space = skil.workspaces.WorkSpace(self.skil)
self.experiment = skil.experiments.Experiment(self.work_space)
else:
self.experiment = experiment
self.work_space = experiment.work_space
self.skil = self.work_space.skil
self.skil.upload_model(os.path.join(os.getcwd(), model_file_name))
self.model_name = model_file_name
self.model_path = self.skil.get_model_path(model_file_name)
self.id = id if id else uuid.uuid1()
self.name = name if name else model_file_name
self.version = version if version else 1
self.evaluations = {}
add_model_instance_response = self.skil.api.add_model_instance(
self.skil.server_id,
skil_client.ModelInstanceEntity(uri=self.model_path,
model_id=id,
model_labels=labels,
model_name=name,
model_version=self.version,
created=int(
round(time.time() * 1000)),
experiment_id=self.experiment.id))
if verbose:
self.skil.printer.pprint(add_model_instance_response)
def _get_sk():
global _sk
if _sk is None:
_sk = skil.Skil()
return _sk
def test_skil_creation():
global sk
sk = skil.Skil()
import skil
import numpy as np
skil_server = skil.Skil()
work_space = skil.WorkSpace(skil_server)
experiment = skil.Experiment(work_space)
transform = skil.Transform(transform='iris_tp.json', experiment=experiment)
model = skil.Model(model='iris_model.h5', experiment=experiment)
deployment = skil.Deployment(skil_server)
pipeline = skil.Pipeline(deployment, model, transform)
with open('iris.data', 'r') as f:
data = np.array(f.readlines())
print(pipeline.predict(data))
def test_work_space_creation():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
work_space.delete()
def test_skil_creation():
sk = skil.Skil()
def test_deployment_creation():
sk = skil.Skil()
dep = skil.Deployment(sk)
dep.delete()
def test_experiment_creation():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
exp = skil.Experiment(work_space)
work_space.delete()
exp.delete()
