def stream_experiment_data(request, **kwargs):
self.assertEqual(kwargs["metadata"], grpc_util.version_metadata())
for run in ("train_1", "train_2"):
for tag in ("dense_1/kernel", "dense_1/bias", "text/test"):
response = export_service_pb2.StreamExperimentDataResponse(
)
response.run_name = run
response.tag_name = tag
display_name = "%s:%s" % (request.experiment_id, tag)
response.tag_metadata.CopyFrom(
test_util.scalar_metadata(display_name))
for step in range(2):
response.tensors.steps.append(step)
response.tensors.wall_times.add(
seconds=1571084520 + step,
nanos=862939144 if run == "train_1" else 962939144,
)
if tag != "text/test":
response.tensors.values.append(
tensor_util.make_tensor_proto(
np.ones([3, 2]) * step))
else:
response.tensors.values.append(
tensor_util.make_tensor_proto(
np.full([3], "a" * (step + 1))))
yield response
def stream_experiment_data(request, **kwargs):
self.assertEqual(request.experiment_id, "789")
self.assertEqual(kwargs["metadata"], grpc_util.version_metadata())
for run in ("train", "test"):
for tag in ("accuracy", "loss"):
response = export_service_pb2.StreamExperimentDataResponse(
)
response.run_name = run
response.tag_name = tag
display_name = "%s:%s" % (request.experiment_id, tag)
response.tag_metadata.CopyFrom(
test_util.scalar_metadata(display_name))
for step in range(10):
response.points.steps.append(step)
if tag == "loss":
if run == "train":
value = 1.0 / (step + 1)
seconds = step
else:
value = -1.0 / (step + 1)
seconds = 600 + step
else: # "accuracy"
if run == "train":
value = 1.0 / (10 - step)
seconds = step * 2
else:
value = -1.0 / (10 - step)
seconds = 600 + step * 2
response.points.values.append(value)
response.points.wall_times.add(seconds=seconds,
nanos=0)
yield response
def stream_experiment_data(request, **kwargs):
self.assertEqual(request.experiment_id, "789")
self.assertEqual(kwargs["metadata"], grpc_util.version_metadata())
response = export_service_pb2.StreamExperimentDataResponse()
response.run_name = "train"
response.tag_name = "batch_loss"
response.points.steps.append(0)
response.points.values.append(0.5)
response.points.wall_times.add(seconds=0, nanos=0)
response.points.steps.append(1)
response.points.values.append(0.25)
response.points.wall_times.add(seconds=1, nanos=0)
yield response
response = export_service_pb2.StreamExperimentDataResponse()
response.run_name = "train"
response.tag_name = "epoch_loss"
response.points.steps.append(0)
response.points.values.append(0.375)
response.points.wall_times.add(seconds=2, nanos=0)
yield response
def stream_experiment_data(request, **kwargs):
self.assertEqual(kwargs["metadata"], grpc_util.version_metadata())
for run in ("train", "test"):
for tag in ("accuracy", "loss"):
response = export_service_pb2.StreamExperimentDataResponse()
response.run_name = run
response.tag_name = tag
display_name = "%s:%s" % (request.experiment_id, tag)
response.tag_metadata.CopyFrom(
test_util.scalar_metadata(display_name))
for step in range(10):
response.points.steps.append(step)
response.points.values.append(2.0 * step)
response.points.wall_times.add(
seconds=1571084520 + step, nanos=862939144)
yield response
def test_handles_outdir_with_no_slash(self):
oldcwd = os.getcwd()
try:
os.chdir(self.get_temp_dir())
mock_api_client = self._create_mock_api_client()
mock_api_client.StreamExperiments.return_value = iter(
[_make_experiments_response(["123"])])
mock_api_client.StreamExperimentData.return_value = iter(
[export_service_pb2.StreamExperimentDataResponse()])
exporter = exporter_lib.TensorBoardExporter(
mock_api_client, "outdir")
generator = exporter.export()
self.assertEqual(list(generator), ["123"])
self.assertTrue(os.path.isdir("outdir"))
finally:
os.chdir(oldcwd)
def stream_experiment_data(request, **kwargs):
self.assertEqual(kwargs["metadata"], grpc_util.version_metadata())
tag = "__default_graph__"
for run in ("train", "test"):
response = export_service_pb2.StreamExperimentDataResponse()
response.run_name = run
response.tag_name = tag
display_name = "%s:%s" % (request.experiment_id, tag)
response.tag_metadata.CopyFrom(
summary_pb2.SummaryMetadata(
data_class=summary_pb2.DATA_CLASS_BLOB_SEQUENCE
)
)
for step in range(1):
response.blob_sequences.steps.append(step)
response.blob_sequences.wall_times.add(
seconds=1571084520 + step, nanos=862939144
)
blob_sequence = blob_pb2.BlobSequence()
if run == "train":
# A finished blob sequence.
blob = blob_pb2.Blob(
blob_id="%s_blob" % run,
state=blob_pb2.BlobState.BLOB_STATE_CURRENT,
)
blob_sequence.entries.append(
blob_pb2.BlobSequenceEntry(blob=blob)
)
# An unfinished blob sequence.
blob = blob_pb2.Blob(
state=blob_pb2.BlobState.BLOB_STATE_UNFINALIZED,
)
blob_sequence.entries.append(
blob_pb2.BlobSequenceEntry(blob=blob)
)
elif run == "test":
blob_sequence.entries.append(
# `blob` unspecified: a hole in the blob sequence.
blob_pb2.BlobSequenceEntry()
)
response.blob_sequences.values.append(blob_sequence)
yield response
