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# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A data provider that talks to a gRPC server."""
import collections
import contextlib
import grpc
from tensorboard.util import tensor_util
from tensorboard.util import timing
from tensorboard import errors
from tensorboard.data import provider
from tensorboard.data.proto import data_provider_pb2
from tensorboard.data.proto import data_provider_pb2_grpc
def make_stub(channel):
"""Wraps a gRPC channel with a service stub."""
return data_provider_pb2_grpc.TensorBoardDataProviderStub(channel)
class GrpcDataProvider(provider.DataProvider):
"""Data provider that talks over gRPC."""
def __init__(self, addr, stub):
"""Initializes a GrpcDataProvider.
Args:
addr: String address of the remote peer. Used cosmetically for
data location.
stub: `data_provider_pb2_grpc.TensorBoardDataProviderStub`
value. See `make_stub` to construct one from a channel.
"""
self._addr = addr
self._stub = stub
def __str__(self):
return "GrpcDataProvider(addr=%r)" % self._addr
def experiment_metadata(self, ctx, *, experiment_id):
req = data_provider_pb2.GetExperimentRequest()
req.experiment_id = experiment_id
with _translate_grpc_error():
res = self._stub.GetExperiment(req)
res = provider.ExperimentMetadata(
data_location=res.data_location,
experiment_name=res.name,
experiment_description=res.description,
creation_time=_timestamp_proto_to_float(res.creation_time),
)
return res
def list_plugins(self, ctx, *, experiment_id):
req = data_provider_pb2.ListPluginsRequest()
req.experiment_id = experiment_id
with _translate_grpc_error():
res = self._stub.ListPlugins(req)
return [p.name for p in res.plugins]
def list_runs(self, ctx, *, experiment_id):
req = data_provider_pb2.ListRunsRequest()
req.experiment_id = experiment_id
with _translate_grpc_error():
res = self._stub.ListRuns(req)
return [
provider.Run(
run_id=run.name,
run_name=run.name,
start_time=run.start_time,
)
for run in res.runs
]
@timing.log_latency
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
@timing.log_latency
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
@timing.log_latency
def read_last_scalars(
self,
ctx,
*,
experiment_id,
plugin_name,
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)
# `ReadScalars` always includes the most recent datum, therefore
# downsampling to one means fetching the latest value.
req.downsample.num_points = 1
with timing.log_latency("_stub.ReadScalars"):
with _translate_grpc_error():
res = self._stub.ReadScalars(req)
with timing.log_latency("build result"):
result = collections.defaultdict(dict)
for run_entry in res.runs:
run_name = run_entry.run_name
for tag_entry in run_entry.tags:
d = tag_entry.data
# There should be no more than one datum in
# `tag_entry.data` since downsample was set to 1.
for step, wt, value in zip(d.step, d.wall_time, d.value):
result[run_name][tag_entry.tag_name] = (
provider.ScalarDatum(
step=step,
wall_time=wt,
value=value,
)
)
return result
@timing.log_latency
def list_tensors(
self, ctx, *, experiment_id, plugin_name, run_tag_filter=None
):
with timing.log_latency("build request"):
req = data_provider_pb2.ListTensorsRequest()
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.ListTensors"):
with _translate_grpc_error():
res = self._stub.ListTensors(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.TensorTimeSeries(
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
@timing.log_latency
def read_tensors(
self,
ctx,
*,
experiment_id,
plugin_name,
downsample=None,
run_tag_filter=None,
):
with timing.log_latency("build request"):
req = data_provider_pb2.ReadTensorsRequest()
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.ReadTensors"):
with _translate_grpc_error():
res = self._stub.ReadTensors(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.TensorDatum(
step=step,
wall_time=wt,
numpy=tensor_util.make_ndarray(value),
)
series.append(point)
return result
@timing.log_latency
def list_blob_sequences(
self, ctx, experiment_id, plugin_name, run_tag_filter=None
):
with timing.log_latency("build request"):
req = data_provider_pb2.ListBlobSequencesRequest()
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.ListBlobSequences"):
with _translate_grpc_error():
res = self._stub.ListBlobSequences(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.BlobSequenceTimeSeries(
max_step=time_series.max_step,
max_wall_time=time_series.max_wall_time,
max_length=time_series.max_length,
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
@timing.log_latency
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
@timing.log_latency
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)
@contextlib.contextmanager
def _translate_grpc_error():
try:
yield
except grpc.RpcError as e:
if e.code() == grpc.StatusCode.INVALID_ARGUMENT:
raise errors.InvalidArgumentError(e.details())
if e.code() == grpc.StatusCode.NOT_FOUND:
raise errors.NotFoundError(e.details())
if e.code() == grpc.StatusCode.PERMISSION_DENIED:
raise errors.PermissionDeniedError(e.details())
raise
def _populate_rtf(run_tag_filter, rtf_proto):
"""Copies `run_tag_filter` into `rtf_proto`."""
if run_tag_filter is None:
return
if run_tag_filter.runs is not None:
rtf_proto.runs.names[:] = sorted(run_tag_filter.runs)
if run_tag_filter.tags is not None:
rtf_proto.tags.names[:] = sorted(run_tag_filter.tags)
def _timestamp_proto_to_float(ts):
"""Converts `timestamp_pb2.Timestamp` to float seconds since epoch."""
return ts.ToNanoseconds() / 1e9
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