def test_delete_model(self, mocker):
mock_method = mocker.patch.object(MlflowClient, "delete_model_version")
client = ModelsClient()
name = "simple-nn-model"
client.delete_model(name, '1')
mock_method.assert_called_once_with(name="simple-nn-model",
version="1")
def test_log_model(self, mocker):
mock_method = mocker.patch.object(ModelsClient, "log_model")
client = ModelsClient()
model = LinearNNModel()
name = "simple-nn-model"
client.log_model(name, model)
mock_method.assert_called_once_with("simple-nn-model", model)
def test_save_model(self, mocker):
mock_method = mocker.patch.object(ModelsClient, "save_model")
client = ModelsClient()
model = LinearNNModel()
name = "simple-nn-model"
client.save_model("pytorch", model, name)
mock_method.assert_called_once_with("pytorch", model,
"simple-nn-model")
def test_update_model(self, mocker):
mock_method = mocker.patch.object(MlflowClient,
"rename_registered_model")
client = ModelsClient()
name = "simple-nn-model"
new_name = "new-simple-nn-model"
client.update_model(name, new_name)
mock_method.assert_called_once_with(name="simple-nn-model",
new_name="new-simple-nn-model")
def test_load_model(self):
client = ModelsClient()
name = "simple-nn-model"
version = "1"
model = client.load_model(name, version)
x = np.float32([[1.0], [2.0]])
y = model.predict(x)
assert y.shape[0] == 2
assert y.shape[1] == 1
def test_load_model(self, mocker):
mock_method = mocker.patch.object(mlflow.pyfunc, "load_model")
mock_method.return_value = mlflow.pytorch._PyTorchWrapper(
LinearNNModel())
client = ModelsClient()
name = "simple-nn-model"
version = "1"
model = client.load_model(name, version)
mock_method.assert_called_once_with(
model_uri="models:/simple-nn-model/1")
x = np.float32([[1.0], [2.0]])
y = model.predict(x)
assert y.shape[0] == 2
assert y.shape[1] == 1
"""
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
"""
from submarine import ModelsClient
import random
import time
if __name__ == "__main__":
modelClient = ModelsClient()
with modelClient.start() as run:
modelClient.log_param("learning_rate", random.random())
for i in range(100):
time.sleep(1)
modelClient.log_metric("mse", random.random() * 100, i)
modelClient.log_metric("acc", random.random(), i)
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
"""
from submarine import ModelsClient
import numpy as np
import torch
from submarine import ModelsClient
class LinearNNModel(torch.nn.Module):
def __init__(self):
super(LinearNNModel, self).__init__()
self.linear = torch.nn.Linear(2, 1) # One in and one out
def forward(self, x):
y_pred = self.linear(x)
return y_pred
if __name__ == "__main__":
client = ModelsClient()
net = LinearNNModel()
client.log_model("simple-nn-model", net)
def models_client_fixture():
client = ModelsClient("http://localhost:5001", "http://localhost:9000")
return client
BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync
with strategy.scope():
ds_train = make_datasets_unbatched().batch(BATCH_SIZE).repeat()
options = tf.data.Options()
options.experimental_distribute.auto_shard_policy = \
tf.data.experimental.AutoShardPolicy.DATA
ds_train = ds_train.with_options(options)
# Model building/compiling need to be within `strategy.scope()`.
multi_worker_model = build_and_compile_cnn_model()
class MyCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
# monitor the loss and accuracy
print(logs)
modelClient.log_metrics(
{
"loss": logs["loss"],
"accuracy": logs["accuracy"]
}, epoch)
with modelClient.start() as run:
multi_worker_model.fit(ds_train,
epochs=10,
steps_per_epoch=70,
callbacks=[MyCallback()])
if __name__ == '__main__':
modelClient = ModelsClient()
main()
def test_delete_model(self):
client = ModelsClient()
name = "simple-nn-model"
client.delete_model(name, '1')
def test_update_model(self):
client = ModelsClient()
name = "simple-nn-model"
new_name = "new-simple-nn-model"
client.update_model(name, new_name)
def test_log_model(self):
client = ModelsClient()
model = LinearNNModel()
name = "simple-nn-model"
client.log_model(name, model)
"""
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
"""
from submarine import ModelsClient
import random
import time
if __name__ == "__main__":
periscope = ModelsClient()
with periscope.start() as run:
periscope.log_param("learning_rate", random.random())
for i in range(100):
time.sleep(1)
periscope.log_metric("mse", random.random() * 100, i)
periscope.log_metric("acc", random.random(), i)
