def test_eq(self): x1 = provider.ScalarTimeSeries(77, 1234.5, b"\x12", "one", "two") x2 = provider.ScalarTimeSeries(77, 1234.5, b"\x12", "one", "two") x3 = provider.ScalarTimeSeries(66, 4321.0, b"\x7F", "hmm", "hum") self.assertEqual(x1, x2) self.assertNotEqual(x1, x3) self.assertNotEqual(x1, object())
def test_list_scalars(self):
res = data_provider_pb2.ListScalarsResponse()
run1 = res.runs.add(run_name="val")
tag11 = run1.tags.add(tag_name="accuracy")
tag11.metadata.max_step = 7
tag11.metadata.max_wall_time = 7.77
tag11.metadata.summary_metadata.plugin_data.content = b"magic"
tag11.metadata.summary_metadata.display_name = "Accuracy"
tag11.metadata.summary_metadata.summary_description = "hey"
tag12 = run1.tags.add(tag_name="xent")
tag12.metadata.max_step = 8
tag12.metadata.max_wall_time = 8.88
run2 = res.runs.add(run_name="test")
tag21 = run2.tags.add(tag_name="accuracy")
tag21.metadata.max_step = 9
tag21.metadata.max_wall_time = 9.99
self.stub.ListScalars.return_value = res
actual = self.provider.list_scalars(
self.ctx,
experiment_id="123",
plugin_name="scalars",
run_tag_filter=provider.RunTagFilter(tags=["xent", "accuracy"]),
)
expected = {
"val": {
"accuracy": provider.ScalarTimeSeries(
max_step=7,
max_wall_time=7.77,
plugin_content=b"magic",
description="hey",
display_name="Accuracy",
),
"xent": provider.ScalarTimeSeries(
max_step=8,
max_wall_time=8.88,
plugin_content=b"",
description="",
display_name="",
),
},
"test": {
"accuracy": provider.ScalarTimeSeries(
max_step=9,
max_wall_time=9.99,
plugin_content=b"",
description="",
display_name="",
),
},
}
self.assertEqual(actual, expected)
req = data_provider_pb2.ListScalarsRequest()
req.experiment_id = "123"
req.plugin_filter.plugin_name = "scalars"
req.run_tag_filter.tags.names.extend(["accuracy", "xent"]) # sorted
self.stub.ListScalars.assert_called_once_with(req)
def test_hash(self): x1 = provider.ScalarTimeSeries(77, 1234.5, b"\x12", "one", "two") x2 = provider.ScalarTimeSeries(77, 1234.5, b"\x12", "one", "two") x3 = provider.ScalarTimeSeries(66, 4321.0, b"\x7F", "hmm", "hum") self.assertEqual(hash(x1), hash(x2)) # The next check is technically not required by the `__hash__` # contract, but _should_ pass; failure on this assertion would at # least warrant some scrutiny. self.assertNotEqual(hash(x1), hash(x3))
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 list_scalars(self, experiment_id, plugin_name, run_tag_filter=None):
del experiment_id # ignored for now
run_tag_content = self._multiplexer.PluginRunToTagToContent(
plugin_name)
result = {}
if run_tag_filter is None:
run_tag_filter = provider.RunTagFilter(runs=None, tags=None)
for (run, tag_to_content) in six.iteritems(run_tag_content):
result_for_run = {}
for tag in tag_to_content:
if not self._test_run_tag(run_tag_filter, run, tag):
continue
result[run] = result_for_run
max_step = None
max_wall_time = None
for event in self._multiplexer.Tensors(run, tag):
if max_step is None or max_step < event.step:
max_step = event.step
if max_wall_time is None or max_wall_time < event.wall_time:
max_wall_time = event.wall_time
summary_metadata = self._multiplexer.SummaryMetadata(run, tag)
result_for_run[tag] = provider.ScalarTimeSeries(
max_step=max_step,
max_wall_time=max_wall_time,
plugin_content=summary_metadata.plugin_data.content,
description=summary_metadata.summary_description,
display_name=summary_metadata.display_name,
)
return result
def _scalar_time_series(self, max_step, max_wall_time, plugin_content,
description, display_name):
# Helper to use explicit kwargs.
return provider.ScalarTimeSeries(
max_step=max_step,
max_wall_time=max_wall_time,
plugin_content=plugin_content,
description=description,
display_name=display_name,
)
def test_repr(self):
x = provider.ScalarTimeSeries(
max_step=77,
max_wall_time=1234.5,
plugin_content=b"AB\xCD\xEF!\x00",
description="test test",
display_name="one two",
)
repr_ = repr(x)
self.assertIn(repr(x.max_step), repr_)
self.assertIn(repr(x.max_wall_time), repr_)
self.assertIn(repr(x.plugin_content), repr_)
self.assertIn(repr(x.description), repr_)
self.assertIn(repr(x.display_name), repr_)
def list_scalars(
self, ctx, *, experiment_id, plugin_name, run_tag_filter=None
):
self._validate_eid(experiment_id)
run_name = "%s/train" % experiment_id
tag_name = "loss.%s" % plugin_name
return {
run_name: {
tag_name: provider.ScalarTimeSeries(
max_step=2,
max_wall_time=0.5,
plugin_content=b"",
description="Hello from %s" % self._name,
display_name="loss",
)
}
}
