def setUp(self):
self.p = promote.Promote('fakeuser', 'fakeapike',
'https://www.fakeurl.com/')
self.p.deployment_file = os.path.join(os.path.dirname(__file__),
'sample-model',
'deploy_clfModel.py')
self.p.deployment_dir = os.path.dirname(self.p.deployment_file)
def promote(self):
sys.stdout.write("Promoting Container... ")
sys.stdout.flush()
prom = promote.Promote(self.image)
prom.run()
sys.stdout.write("Done")
sys.stdout.flush()
def testInvalidDeploymentFile(self):
originalArgv = copy.copy(sys.argv)
sys.argv[0] = 'foo'
try:
promote.Promote('fakeuser', 'fakeapike',
'https://www.fakeurl.com/')
raise Exception("shouldn't get here")
except Exception as ex:
self.assertIsNotNone(ex)
sys.argv = originalArgv
def testInvalidDeploymentDir(self):
# TODO: this is just a subset of testInvalidDeploymentFile. it's a stupid test.
originalArgv = copy.copy(sys.argv)
sys.argv[0] = 'foo'
try:
promote.Promote('fakeuser', 'fakeapike',
'https://www.fakeurl.com/')
raise Exception("shouldn't get here")
except Exception as ex:
self.assertIsNotNone(ex)
sys.argv = originalArgv
# h2o will automatically hold on to the prediction data, which is a feature we don't need in promote
# you must use h2o.remove() to remove this prediction from memory, otherwise you will eventually run
# out of memory for your model.
# http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/h2o.html?highlight=remove#h2o.remove
h2o.remove(h2o_data)
h2o.remove(prediction)
# Return the result as our response.
return {'response': model_response}
USERNAME = '******'
API_KEY = 'your_api_key'
PROMOTE_URL = 'http://www.promote_url.com'
p = promote.Promote(USERNAME, API_KEY, PROMOTE_URL)
# add metadata
p.metadata.logloss = float("{0:.5f}".format(model.logloss()))
# test data
TESTDATA = {"C1": 4.9, "C2": 3, "C3": 1.4, "C4": 0.2}
# test model locally
print(helloh2o(TESTDATA))
# test that TESTDATA is valid json
p.deploy("helloh2o",
helloh2o,
TESTDATA,
confirm=True,
import helpers
from sklearn.externals import joblib
clf = joblib.load('./pickles/model_weights.pkl')
with tf as tensorflow:
tf.session()
tf.load('./pickles/model_weights.pkl')
t = [1,2,3,4,5]
import promote
p = promote.Promote("colin", "789asdf879h789a79f79sf79s", "https://sandbox.c.yhat.com/")
# schema is optional
@schema(Schema([{'name': And(str, len)}]))
def promoteModel(data):
tf.evaluate(testdata)
if prediction not in t:
return 'error'
else:
return prediction
# testdata is optional
testdata = {'name': 'greg'}
promoteModel(testdata)
p.deploy("HelloModel", promoteModel, testdata=testdata, confirm=True, dry_run=False, verbose=0)
