def _get_ws():
global work_space
if work_space is not None:
return work_space
sk = _get_sk()
work_space = skil.WorkSpace(sk)
return work_space
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_work_space_by_id():
global work_space
global work_space_id
sk = _get_sk()
work_space = skil.WorkSpace(sk, name='test_ws')
ws_id = work_space.id
work_space_id = ws_id
work_space2 = skil.get_workspace_by_id(sk, ws_id)
assert work_space.name == work_space2.name
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_work_space_creation():
global work_space
work_space = skil.WorkSpace(sk)
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))
import skil
from keras.models import model_from_config
import json
# Load Keras model you want to train
with open('keras_config.json', 'r') as f:
model = model_from_config(json.load(f))
model.compile(loss='categorical_crossentropy', optimizer='sgd')
# Create a SKIL model from it
skil_server = skil.Skil()
ws = skil.WorkSpace(skil_server)
experiment = skil.Experiment(ws)
model = skil.Model(model, model_id='keras_mnist_mlp_42',
name='keras', experiment=experiment)
# Register compute and storage resources.
s3 = skil.resources.storage.S3(
skil_server, 's3_resource', 'bucket_name', 'region')
emr = skil.resources.compute.EMR(
skil_server, 'emr_cluster', 'region', 'credential_uri', 'cluster_id')
# Define your general training setup
training_config = skil.jobs.TrainingJobConfiguration(
skil_model=model, num_epochs=10, eval_type='ROC_MULTI_CLASS',
storage_resource=s3, compute_resource=emr,
data_set_provider_class='MnistProvider',
eval_data_set_provider_class='MnistProvider',
output_path='.')
# Optionally specify a distributed training config.
def test_work_space_deletion():
sk = _get_sk()
work_space = skil.WorkSpace(sk)
work_space.delete()
def test_work_space_creation():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
work_space.delete()
def test_experiment_creation():
sk = skil.Skil()
work_space = skil.WorkSpace(sk)
exp = skil.Experiment(work_space)
work_space.delete()
exp.delete()
