def _log_batch():
_validate_batch_log_api_req(_get_request_json())
request_message = _get_request_message(LogBatch())
metrics = [Metric.from_proto(proto_metric) for proto_metric in request_message.metrics]
params = [Param.from_proto(proto_param) for proto_param in request_message.params]
tags = [RunTag.from_proto(proto_tag) for proto_tag in request_message.tags]
_get_store().log_batch(run_id=request_message.run_id, metrics=metrics, params=params, tags=tags)
response_message = LogBatch.Response()
response = Response(mimetype='application/json')
response.set_data(message_to_json(response_message))
return response
def get_metric_history(self, run_uuid, metric_key):
"""
Returns all logged value for a given metric.
:param run_uuid: Unique identifier for run
:param metric_key: Metric name within the run
:return: A list of float values logged for the give metric if logged, else empty list
"""
req_body = _message_to_json(GetMetricHistory(run_uuid=run_uuid, metric_key=metric_key))
response_proto = self._call_endpoint(GetMetricHistory, req_body)
return [Metric.from_proto(metric).value for metric in response_proto.metrics]
def get_metric(self, run_uuid, metric_key):
"""
Returns the last logged value for a given metric.
:param run_uuid: Unique identifier for run
:param metric_key: Metric name within the run
:return: A single float value for the give metric if logged, else None
"""
req_body = _message_to_json(GetMetric(run_uuid=run_uuid, metric_key=metric_key))
response_proto = self._call_endpoint(GetMetric, req_body)
return Metric.from_proto(response_proto.metric)
def get_metric_history(self, run_uuid, metric_key):
"""
Return all logged values for a given metric.
:param run_uuid: Unique identifier for run
:param metric_key: Metric name within the run
:return: A list of :py:class:`mlflow.entities.Metric` entities if logged, else empty list
"""
req_body = message_to_json(GetMetricHistory(run_uuid=run_uuid, metric_key=metric_key))
response_proto = self._call_endpoint(GetMetricHistory, req_body)
return [Metric.from_proto(metric) for metric in response_proto.metrics]
def test_creation_and_hydration(self):
key = random_str()
value = 10000
ts = int(time.time())
metric = Metric(key, value, ts)
self._check(metric, key, value, ts)
as_dict = {"key": key, "value": value, "timestamp": ts}
self.assertEqual(dict(metric), as_dict)
proto = metric.to_proto()
metric2 = metric.from_proto(proto)
self._check(metric2, key, value, ts)
metric3 = Metric.from_dictionary(as_dict)
self._check(metric3, key, value, ts)
def test_creation_and_hydration():
key = random_str()
value = 10000
ts = int(1000 * time.time())
step = random_int()
metric = Metric(key, value, ts, step)
_check(metric, key, value, ts, step)
as_dict = {"key": key, "value": value, "timestamp": ts, "step": step}
assert dict(metric) == as_dict
proto = metric.to_proto()
metric2 = metric.from_proto(proto)
_check(metric2, key, value, ts, step)
metric3 = Metric.from_dictionary(as_dict)
_check(metric3, key, value, ts, step)
def test_parse_dict_int_as_string_backcompat():
in_json = {"timestamp": "123"}
message = ProtoMetric()
parse_dict(in_json, message)
experiment = Metric.from_proto(message)
assert experiment.timestamp == 123
