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()