add caffe2 training example

cloud_ml_samples/caffe2/linear_regression/setup.py

@@ -0,0 +1,16 @@
+# Copyright 2017 Xiaomi, Inc.
+#
+# Licensed 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.
+
+import setuptools
+setuptools.setup(name='caffe2-trainer', version='1.0', packages=['trainer'])

cloud_ml_samples/caffe2/linear_regression/trainer/task.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+
+# Copyright 2017 Xiaomi, Inc.
+#
+# Licensed 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 caffe2.python import core, cnn, workspace
+
+
+def main():
+  init_net = core.Net("init")
+  # The ground truth parameters.
+  W_gt = init_net.GivenTensorFill(
+      [], "W_gt", shape=[1, 2], values=[2.0, 1.5])
+  B_gt = init_net.GivenTensorFill([], "B_gt", shape=[1], values=[0.5])
+  # Constant value ONE is used in weighted sum when updating parameters.
+  ONE = init_net.ConstantFill([], "ONE", shape=[1], value=1.)
+  # ITER is the iterator count.
+  ITER = init_net.ConstantFill([], "ITER", shape=[1], value=0, dtype=core.DataType.INT32)
+
+  # For the parameters to be learned: we randomly initialize weight
+  # from [-1, 1] and init bias with 0.0.
+  W = init_net.UniformFill([], "W", shape=[1, 2], min=-1., max=1.)
+  B = init_net.ConstantFill([], "B", shape=[1], value=0.0)
+  print('Created init net.')
+
+
+  train_net = core.Net("train")
+  # First, we generate random samples of X and create the ground truth.
+  X = train_net.GaussianFill([], "X", shape=[64, 2], mean=0.0, std=1.0, run_once=0)
+  Y_gt = X.FC([W_gt, B_gt], "Y_gt")
+  # We add Gaussian noise to the ground truth
+  noise = train_net.GaussianFill([], "noise", shape=[64, 1], mean=0.0, std=1.0, run_once=0)
+  Y_noise = Y_gt.Add(noise, "Y_noise")
+  # Note that we do not need to propagate the gradients back through Y_noise,
+  # so we mark StopGradient to notify the auto differentiating algorithm
+  # to ignore this path.
+  Y_noise = Y_noise.StopGradient([], "Y_noise")
+
+  # Now, for the normal linear regression prediction, this is all we need.
+  Y_pred = X.FC([W, B], "Y_pred")
+
+  # The loss function is computed by a squared L2 distance, and then averaged
+  # over all items in the minibatch.
+  dist = train_net.SquaredL2Distance([Y_noise, Y_pred], "dist")
+  loss = dist.AveragedLoss([], ["loss"])
+
+  # Get gradients for all the computations above.
+  gradient_map = train_net.AddGradientOperators([loss])
+
+  # Increment the iteration by one.
+  train_net.Iter(ITER, ITER)
+  # Compute the learning rate that corresponds to the iteration.
+  LR = train_net.LearningRate(ITER, "LR", base_lr=-0.1,
+                              policy="step", stepsize=20, gamma=0.9)
+
+  # Weighted sum
+  train_net.WeightedSum([W, ONE, gradient_map[W], LR], W)
+  train_net.WeightedSum([B, ONE, gradient_map[B], LR], B)
+
+  workspace.RunNetOnce(init_net)
+  workspace.CreateNet(train_net)
+
+  print("Before training, W is: {}".format(workspace.FetchBlob("W")))
+  print("Before training, B is: {}".format(workspace.FetchBlob("B")))
+
+  for i in range(100):
+      workspace.RunNet(train_net.Proto().name)
+
+  print("After training, W is: {}".format(workspace.FetchBlob("W")))
+  print("After training, B is: {}".format(workspace.FetchBlob("B")))
+  print("Ground truth W is: {}".format(workspace.FetchBlob("W_gt")))
+  print("Ground truth B is: {}".format(workspace.FetchBlob("B_gt")))
+
+
+if __name__ == "__main__":
+  main()

cloud_ml_sdk/cloud_ml_sdk/client.py

@@ -342,20 +342,30 @@ def update_model_service(self, model_name, model_version, update_json, org_id=No
     else:
       return response.content
 
-  def get_model_service_logs(self, model_name, model_version, org_id=None):
+  def get_model_service_logs(self, model_name, model_version, org_id=None, replica_index=None):
     """Get logs of the model service.
 
     Args:
       model_name: The name of the model service.
       model_version: The version of the model service.
+      org_id: the client's orgid
+      replica_index: the replica's index, 
 
     Returns:
       The logs of the model service.
     """
     if org_id:
-      url = self._model_url + "/" + model_name + "/" + model_version + "/logs" + "?org_id=" + org_id
+      if replica_index:
+        url = self._model_url + "/" + model_name + "/" + model_version + "/logs" + "?org_id=" + org_id + \
+              "&replica=" + replica_index
+      else:
+        url = self._model_url + "/" + model_name + "/" + model_version + "/logs" + "?org_id=" + org_id
     else:
-      url = self._model_url + "/" + model_name + "/" + model_version + "/logs"
+      if replica_index:
+        url = self._model_url + "/" + model_name + "/" + model_version + "/logs" + "?replica=" + replica_index
+      else:
+        url = self._model_url + "/" + model_name + "/" + model_version + "/logs"
+
     response = requests.get(url, auth=self._auth)
     if response.ok:
       return json.loads(response.content.decode("utf-8"))

cloud_ml_sdk/cloud_ml_sdk/command/command.py

@@ -298,6 +298,12 @@ def main():
   models_logs_parser.add_argument("model_name", help="The name of the model")
   models_logs_parser.add_argument(
       "model_version", help="The version of the model")
+  models_logs_parser.add_argument(
+      "-ri",
+      "--replica",
+      dest="replica_index",
+      help="The replica index"
+  )
   models_logs_parser.set_defaults(func=util.get_model_logs)
 
   # subcommand of models: metrics

cloud_ml_sdk/cloud_ml_sdk/command/util.py

@@ -776,13 +776,20 @@ def get_model_logs(args):
 
   client = CloudMlClient()
   if "org_id" in args:
-    response = client.get_model_service_logs(args.model_name, args.model_version, args.org_id)
+    if args.replica_index:
+      response = client.get_model_service_logs(args.model_name, args.model_version, args.org_id, args.replica_index)
+    else:
+      response = client.get_model_service_logs(args.model_name, args.model_version, args.org_id)
   else:
-    response = client.get_model_service_logs(args.model_name, args.model_version)
+    if args.replica_index:
+      response = client.get_model_service_logs(args.model_name, args.model_version, replica_index=args.replica_index)
+    else:
+      response = client.get_model_service_logs(args.model_name, args.model_version)
   if not isinstance(response, str):
-    print(response["logs"])
-  else:
-    print("response: {}".format(response))
+    if "error" in response:
+      print(response['message'])
+    else:
+      print(response["logs"])
 
 
 def get_model_metrics(args):