def read_scalars(
self,
ctx,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
with timing.log_latency("build request"):
req = data_provider_pb2.ReadScalarsRequest()
req.experiment_id = experiment_id
req.plugin_filter.plugin_name = plugin_name
_populate_rtf(run_tag_filter, req.run_tag_filter)
req.downsample.num_points = downsample
with timing.log_latency("_stub.ReadScalars"):
with _translate_grpc_error():
res = self._stub.ReadScalars(req)
with timing.log_latency("build result"):
result = {}
for run_entry in res.runs:
tags = {}
result[run_entry.run_name] = tags
for tag_entry in run_entry.tags:
series = []
tags[tag_entry.tag_name] = series
d = tag_entry.data
for (step, wt, value) in zip(d.step, d.wall_time, d.value):
point = provider.ScalarDatum(
step=step,
wall_time=wt,
value=value,
)
series.append(point)
return result
def list_scalars(
self, ctx, *, experiment_id, plugin_name, run_tag_filter=None
):
with timing.log_latency("build request"):
req = data_provider_pb2.ListScalarsRequest()
req.experiment_id = experiment_id
req.plugin_filter.plugin_name = plugin_name
_populate_rtf(run_tag_filter, req.run_tag_filter)
with timing.log_latency("_stub.ListScalars"):
with _translate_grpc_error():
res = self._stub.ListScalars(req)
with timing.log_latency("build result"):
result = {}
for run_entry in res.runs:
tags = {}
result[run_entry.run_name] = tags
for tag_entry in run_entry.tags:
time_series = tag_entry.metadata
tags[tag_entry.tag_name] = provider.ScalarTimeSeries(
max_step=time_series.max_step,
max_wall_time=time_series.max_wall_time,
plugin_content=time_series.summary_metadata.plugin_data.content,
description=time_series.summary_metadata.summary_description,
display_name=time_series.summary_metadata.display_name,
)
return result
def read_blob(self, ctx, blob_key):
with timing.log_latency("build request"):
req = data_provider_pb2.ReadBlobRequest()
req.blob_key = blob_key
with timing.log_latency("list(_stub.ReadBlob)"):
with _translate_grpc_error():
responses = list(self._stub.ReadBlob(req))
with timing.log_latency("build result"):
return b"".join(res.data for res in responses)
def test_decorator_implicit_name(self):
decorated = timing.log_latency(self.my_slow_function)
with self.assert_logs_matching(
"ENTER LogLatencyTest.my_slow_function"):
decorated()
# Again: make sure it's reusable.
with self.assert_logs_matching(
"ENTER LogLatencyTest.my_slow_function"):
decorated()
def read_blob_sequences(
self,
ctx,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
with timing.log_latency("build request"):
req = data_provider_pb2.ReadBlobSequencesRequest()
req.experiment_id = experiment_id
req.plugin_filter.plugin_name = plugin_name
_populate_rtf(run_tag_filter, req.run_tag_filter)
req.downsample.num_points = downsample
with timing.log_latency("_stub.ReadBlobSequences"):
with _translate_grpc_error():
res = self._stub.ReadBlobSequences(req)
with timing.log_latency("build result"):
result = {}
for run_entry in res.runs:
tags = {}
result[run_entry.run_name] = tags
for tag_entry in run_entry.tags:
series = []
tags[tag_entry.tag_name] = series
d = tag_entry.data
for (step, wt, blob_sequence) in zip(
d.step, d.wall_time, d.values
):
values = []
for ref in blob_sequence.blob_refs:
values.append(
provider.BlobReference(
blob_key=ref.blob_key, url=ref.url or None
)
)
point = provider.BlobSequenceDatum(
step=step, wall_time=wt, values=tuple(values)
)
series.append(point)
return result
def test_context_manager_explicit_name(self):
with self.assert_logs_matching("ENTER my_slow_region"):
with timing.log_latency("my_slow_region"):
pass
def test_decorator_explicit_name(self):
decorated = timing.log_latency("my_costly_block")(
self.my_slow_function)
with self.assert_logs_matching("ENTER my_costly_block"):
decorated()
