def report_evaluation_metrics(self, model_outputs, labels):
"""
report evaluation metrics to ps.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for name, output in model_outputs.items():
output = np.concatenate(output)
serialize_ndarray(output, req.model_outputs[name])
labels = np.concatenate(labels)
serialize_ndarray(labels, req.labels)
req.worker_id = self._worker_id
self._stub.report_evaluation_metrics(req)
def report_evaluation_metrics(self, model_outputs, labels):
"""
report evaluation metrics to ps.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for name, output in model_outputs.items():
output = np.concatenate(output)
emplace_tensor_pb_from_ndarray(req.model_outputs,
output,
name=name)
labels = np.concatenate(labels)
tensor = Tensor(values=labels)
serialize_tensor(tensor, req.labels)
self._stub.report_evaluation_metrics(req)
def report_evaluation_metrics(self, model_outputs, labels):
"""
report evaluation metrics to ps, return (accepted, model_version)
from rpc call.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
if not isinstance(model_outputs, dict):
model_outputs = {MetricsDictKey.MODEL_OUTPUT: model_outputs}
for name, output in model_outputs.items():
req.model_outputs[name].CopyFrom(ndarray_to_tensor(output.numpy()))
req.labels.CopyFrom(ndarray_to_tensor(labels.numpy()))
req.model_version = self._model_version
res = self._stub.ReportEvaluationMetrics(req)
return res.accepted, res.model_version
def report_evaluation_metrics(self, evaluation_metrics):
"""
report evaluation metrics to ps, return (accepted, model_version)
from rpc call.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for k, v in evaluation_metrics.items():
v_np = v.numpy()
# If scalar, convert to numpy 1D array with size 1
if not v_np.shape:
v_np = v_np.reshape(1)
req.evaluation_metrics[k].CopyFrom(ndarray_to_tensor(v_np))
req.model_version = self._model_version
res = self._stub.ReportEvaluationMetrics(req)
return res.accepted, res.model_version
def report_evaluation_metrics(self, model_outputs, labels):
"""
report evaluation metrics to ps, return (accepted, model_version)
from rpc call.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for name, output in model_outputs.items():
output = np.concatenate(output)
emplace_tensor_pb_from_ndarray(req.model_outputs,
output,
name=name)
labels = np.concatenate(labels)
tensor = Tensor(values=labels)
serialize_tensor(tensor, req.labels)
req.model_version = self._model_version if self._use_multi_ps else -1
res = self._stub.ReportEvaluationMetrics(req)
return res.accepted, res.model_version
def report_evaluation_metrics(self, model_outputs, labels):
"""Report evaluation metrics to master.
Args:
model_outputs: dict
the evaluation result on training.
labels: numpy array
the labels on training dataset.
"""
req = elasticdl_pb2.ReportEvaluationMetricsRequest()
for name, output in model_outputs.items():
output = np.concatenate(output)
serialize_ndarray(output, req.model_outputs[name])
labels = np.concatenate(labels)
serialize_ndarray(labels, req.labels)
req.worker_id = self._worker_id
self._stub.report_evaluation_metrics(req)
