/
test_uploader.py
1973 lines (1715 loc) · 76.5 KB
/
test_uploader.py
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# -*- coding: utf-8 -*-
# Copyright 2021 Google LLC
#
# 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.
#
"""Tests for uploader.py."""
import datetime
import functools
import logging
import os
import re
import tempfile
from unittest import mock
import grpc
import grpc_testing
from tensorboard.compat.proto import event_pb2
from tensorboard.compat.proto import graph_pb2
from tensorboard.compat.proto import meta_graph_pb2
from tensorboard.compat.proto import summary_pb2
from tensorboard.compat.proto import tensor_pb2
from tensorboard.compat.proto import types_pb2
from tensorboard.plugins.scalar import metadata as scalars_metadata
from tensorboard.plugins.graph import metadata as graphs_metadata
from tensorboard.summary import v1 as summary_v1
from tensorboard.uploader import logdir_loader
from tensorboard.uploader import upload_tracker
from tensorboard.uploader import util
from tensorboard.uploader.proto import server_info_pb2
import tensorflow as tf
from google.api_core import datetime_helpers
from google.cloud.aiplatform.tensorboard import uploader_utils
from google.cloud.aiplatform.tensorboard.plugins.tf_profiler import profile_uploader
import google.cloud.aiplatform.tensorboard.uploader as uploader_lib
from google.cloud import storage
from google.cloud.aiplatform_v1.services.tensorboard_service import (
client as tensorboard_service_client,
)
from google.cloud.aiplatform_v1.services.tensorboard_service.transports import (
grpc as transports_grpc,
)
from google.cloud.aiplatform_v1.types import tensorboard_data
from google.cloud.aiplatform_v1.types import tensorboard_service
from google.cloud.aiplatform_v1.types import (
tensorboard_experiment as tensorboard_experiment_type,
)
from google.cloud.aiplatform_v1.types import tensorboard_run as tensorboard_run_type
from google.cloud.aiplatform_v1.types import (
tensorboard_time_series as tensorboard_time_series_type,
)
from google.protobuf import timestamp_pb2
from google.protobuf import message
data_compat = uploader_lib.event_file_loader.data_compat
dataclass_compat = uploader_lib.event_file_loader.dataclass_compat
scalar_v2_pb = summary_v1._scalar_summary.scalar_pb
image_pb = summary_v1._image_summary.pb
_SCALARS_HISTOGRAMS_AND_GRAPHS = frozenset(
(scalars_metadata.PLUGIN_NAME, graphs_metadata.PLUGIN_NAME,)
)
# Sentinel for `_create_*` helpers, for arguments for which we want to
# supply a default other than the `None` used by the code under test.
_USE_DEFAULT = object()
_TEST_EXPERIMENT_NAME = "test-experiment"
_TEST_TENSORBOARD_RESOURCE_NAME = (
"projects/test_project/locations/us-central1/tensorboards/test_tensorboard"
)
_TEST_LOG_DIR_NAME = "/logs/foo"
_TEST_RUN_NAME = "test-run"
_TEST_ONE_PLATFORM_EXPERIMENT_NAME = "{}/experiments/{}".format(
_TEST_TENSORBOARD_RESOURCE_NAME, _TEST_EXPERIMENT_NAME
)
_TEST_ONE_PLATFORM_RUN_NAME = "{}/runs/{}".format(
_TEST_ONE_PLATFORM_EXPERIMENT_NAME, _TEST_RUN_NAME
)
_TEST_TIME_SERIES_NAME = "test-time-series"
_TEST_ONE_PLATFORM_TIME_SERIES_NAME = "{}/timeSeries/{}".format(
_TEST_ONE_PLATFORM_RUN_NAME, _TEST_TIME_SERIES_NAME
)
_TEST_BLOB_STORAGE_FOLDER = "test_folder"
def _create_example_graph_bytes(large_attr_size):
graph_def = graph_pb2.GraphDef()
graph_def.node.add(name="alice", op="Person")
graph_def.node.add(name="bob", op="Person")
graph_def.node[1].attr["small"].s = b"small_attr_value"
graph_def.node[1].attr["large"].s = b"l" * large_attr_size
graph_def.node.add(name="friendship", op="Friendship", input=["alice", "bob"])
return graph_def.SerializeToString()
class AbortUploadError(Exception):
"""Exception used in testing to abort the upload process."""
def _create_mock_client():
# Create a stub instance (using a test channel) in order to derive a mock
# from it with autospec enabled. Mocking TensorBoardWriterServiceStub itself
# doesn't work with autospec because grpc constructs stubs via metaclassing.
def create_experiment_response(
tensorboard_experiment_id=None,
tensorboard_experiment=None, # pylint: disable=unused-argument
parent=None,
): # pylint: disable=unused-argument
tensorboard_experiment_id = (
"{}/experiments/{}".format(parent, tensorboard_experiment_id)
if parent
else tensorboard_experiment_id
)
return tensorboard_experiment_type.TensorboardExperiment(
name=tensorboard_experiment_id
)
def create_run_response(
tensorboard_run=None, # pylint: disable=unused-argument
tensorboard_run_id=None,
parent=None,
): # pylint: disable=unused-argument
tensorboard_run_id = (
"{}/runs/{}".format(parent, tensorboard_run_id)
if parent
else tensorboard_run_id
)
return tensorboard_run_type.TensorboardRun(name=tensorboard_run_id)
def create_tensorboard_time_series(
tensorboard_time_series=None, parent=None
): # pylint: disable=unused-argument
name = (
"{}/timeSeries/{}".format(parent, tensorboard_time_series.display_name)
if parent
else tensorboard_time_series.display_name
)
return tensorboard_time_series_type.TensorboardTimeSeries(
name=name, display_name=tensorboard_time_series.display_name,
)
test_channel = grpc_testing.channel(
service_descriptors=[], time=grpc_testing.strict_real_time()
)
mock_client = mock.Mock(
spec=tensorboard_service_client.TensorboardServiceClient(
transport=transports_grpc.TensorboardServiceGrpcTransport(
channel=test_channel
)
)
)
mock_client.create_tensorboard_experiment.side_effect = create_experiment_response
mock_client.create_tensorboard_run.side_effect = create_run_response
mock_client.create_tensorboard_time_series.side_effect = (
create_tensorboard_time_series
)
return mock_client
def _create_mock_blob_storage():
mock_blob_storage = mock.Mock()
mock_blob_storage.mock_add_spec(storage.Bucket)
return mock_blob_storage
def _create_uploader(
writer_client=_USE_DEFAULT,
logdir=None,
max_scalar_request_size=_USE_DEFAULT,
max_tensor_request_size=_USE_DEFAULT,
max_tensor_point_size=_USE_DEFAULT,
max_blob_request_size=_USE_DEFAULT,
max_blob_size=_USE_DEFAULT,
logdir_poll_rate_limiter=_USE_DEFAULT,
rpc_rate_limiter=_USE_DEFAULT,
experiment_name=_TEST_EXPERIMENT_NAME,
tensorboard_resource_name=_TEST_TENSORBOARD_RESOURCE_NAME,
blob_storage_bucket=None,
blob_storage_folder=_TEST_BLOB_STORAGE_FOLDER,
description=None,
verbosity=0, # Use 0 to minimize littering the test output.
one_shot=None,
allowed_plugins=_SCALARS_HISTOGRAMS_AND_GRAPHS,
):
if writer_client is _USE_DEFAULT:
writer_client = _create_mock_client()
if max_scalar_request_size is _USE_DEFAULT:
max_scalar_request_size = 128000
if max_tensor_request_size is _USE_DEFAULT:
max_tensor_request_size = 512000
if max_blob_request_size is _USE_DEFAULT:
max_blob_request_size = 128000
if max_blob_size is _USE_DEFAULT:
max_blob_size = 12345
if max_tensor_point_size is _USE_DEFAULT:
max_tensor_point_size = 16000
if logdir_poll_rate_limiter is _USE_DEFAULT:
logdir_poll_rate_limiter = util.RateLimiter(0)
if rpc_rate_limiter is _USE_DEFAULT:
rpc_rate_limiter = util.RateLimiter(0)
upload_limits = server_info_pb2.UploadLimits(
max_scalar_request_size=max_scalar_request_size,
max_tensor_request_size=max_tensor_request_size,
max_tensor_point_size=max_tensor_point_size,
max_blob_request_size=max_blob_request_size,
max_blob_size=max_blob_size,
)
return uploader_lib.TensorBoardUploader(
experiment_name=experiment_name,
tensorboard_resource_name=tensorboard_resource_name,
writer_client=writer_client,
logdir=logdir,
allowed_plugins=allowed_plugins,
upload_limits=upload_limits,
blob_storage_bucket=blob_storage_bucket,
blob_storage_folder=blob_storage_folder,
logdir_poll_rate_limiter=logdir_poll_rate_limiter,
rpc_rate_limiter=rpc_rate_limiter,
description=description,
verbosity=verbosity,
one_shot=one_shot,
)
def _create_dispatcher(
experiment_resource_name, api=None, allowed_plugins=_USE_DEFAULT, logdir=None,
):
if api is _USE_DEFAULT:
api = _create_mock_client()
if allowed_plugins is _USE_DEFAULT:
allowed_plugins = _SCALARS_HISTOGRAMS_AND_GRAPHS
upload_limits = server_info_pb2.UploadLimits(
max_scalar_request_size=128000,
max_tensor_request_size=128000,
max_tensor_point_size=52000,
max_blob_request_size=128000,
)
rpc_rate_limiter = util.RateLimiter(0)
tensor_rpc_rate_limiter = util.RateLimiter(0)
blob_rpc_rate_limiter = util.RateLimiter(0)
one_platform_resource_manager = uploader_utils.OnePlatformResourceManager(
experiment_resource_name, api
)
request_sender = uploader_lib._BatchedRequestSender(
experiment_resource_name=experiment_resource_name,
api=api,
allowed_plugins=allowed_plugins,
upload_limits=upload_limits,
rpc_rate_limiter=rpc_rate_limiter,
tensor_rpc_rate_limiter=tensor_rpc_rate_limiter,
blob_rpc_rate_limiter=blob_rpc_rate_limiter,
blob_storage_bucket=None,
blob_storage_folder=None,
one_platform_resource_manager=one_platform_resource_manager,
tracker=upload_tracker.UploadTracker(verbosity=0),
)
additional_senders = {}
if "profile" in allowed_plugins:
additional_senders["profile"] = profile_uploader.ProfileRequestSender(
experiment_resource_name=experiment_resource_name,
api=api,
upload_limits=upload_limits,
blob_rpc_rate_limiter=util.RateLimiter(0),
blob_storage_bucket=_create_mock_blob_storage(),
source_bucket=_create_mock_blob_storage(),
blob_storage_folder=None,
tracker=upload_tracker.UploadTracker(verbosity=0),
logdir=logdir,
)
return uploader_lib._Dispatcher(
request_sender=request_sender, additional_senders=additional_senders,
)
def _create_scalar_request_sender(
experiment_resource_id, api=_USE_DEFAULT, max_request_size=_USE_DEFAULT
):
if api is _USE_DEFAULT:
api = _create_mock_client()
if max_request_size is _USE_DEFAULT:
max_request_size = 128000
return uploader_lib._ScalarBatchedRequestSender(
experiment_resource_id=experiment_resource_id,
one_platform_resource_manager=uploader_utils.OnePlatformResourceManager(
experiment_resource_id, api
),
api=api,
rpc_rate_limiter=util.RateLimiter(0),
max_request_size=max_request_size,
tracker=upload_tracker.UploadTracker(verbosity=0),
)
def _create_file_request_sender(
run_resource_id,
api=_USE_DEFAULT,
max_blob_request_size=_USE_DEFAULT,
max_blob_size=_USE_DEFAULT,
blob_storage_folder=None,
blob_storage_bucket=_USE_DEFAULT,
source_bucket=_USE_DEFAULT,
):
if api is _USE_DEFAULT:
api = _create_mock_client()
if max_blob_request_size is _USE_DEFAULT:
max_blob_request_size = 128000
if blob_storage_bucket is _USE_DEFAULT:
blob_storage_bucket = _create_mock_blob_storage()
if source_bucket is _USE_DEFAULT:
source_bucket = _create_mock_blob_storage()
if max_blob_size is _USE_DEFAULT:
max_blob_size = 128000
return profile_uploader._FileRequestSender(
run_resource_id=run_resource_id,
api=api,
rpc_rate_limiter=util.RateLimiter(0),
max_blob_request_size=max_blob_request_size,
max_blob_size=max_blob_size,
blob_storage_bucket=blob_storage_bucket,
blob_storage_folder=blob_storage_folder,
tracker=upload_tracker.UploadTracker(verbosity=0),
source_bucket=source_bucket,
)
def _scalar_event(tag, value):
return event_pb2.Event(summary=scalar_v2_pb(tag, value))
def _grpc_error(code, details):
# Monkey patch insertion for the methods a real grpc.RpcError would have.
error = grpc.RpcError("RPC error %r: %s" % (code, details))
error.code = lambda: code
error.details = lambda: details
return error
def _timestamp_pb(nanos):
result = timestamp_pb2.Timestamp()
result.FromNanoseconds(nanos)
return result
class FileWriter(tf.compat.v1.summary.FileWriter):
"""FileWriter for test.
TensorFlow FileWriter uses TensorFlow's Protobuf Python binding
which is largely discouraged in TensorBoard. We do not want a
TB.Writer but require one for testing in integrational style
(writing out event files and use the real event readers).
"""
def __init__(self, *args, **kwargs):
# Briefly enter graph mode context so this testing FileWriter can be
# created from an eager mode context without triggering a usage error.
with tf.compat.v1.Graph().as_default():
super(FileWriter, self).__init__(*args, **kwargs)
def add_test_summary(self, tag, simple_value=1.0, step=None):
"""Convenience for writing a simple summary for a given tag."""
value = summary_pb2.Summary.Value(tag=tag, simple_value=simple_value)
summary = summary_pb2.Summary(value=[value])
self.add_summary(summary, global_step=step)
def add_test_tensor_summary(self, tag, tensor, step=None, value_metadata=None):
"""Convenience for writing a simple summary for a given tag."""
value = summary_pb2.Summary.Value(
tag=tag, tensor=tensor, metadata=value_metadata
)
summary = summary_pb2.Summary(value=[value])
self.add_summary(summary, global_step=step)
def add_event(self, event):
if isinstance(event, event_pb2.Event):
tf_event = tf.compat.v1.Event.FromString(event.SerializeToString())
else:
tf_event = event
if not isinstance(event, bytes):
logging.error(
"Added TensorFlow event proto. "
"Please prefer TensorBoard copy of the proto"
)
super(FileWriter, self).add_event(tf_event)
def add_summary(self, summary, global_step=None):
if isinstance(summary, summary_pb2.Summary):
tf_summary = tf.compat.v1.Summary.FromString(summary.SerializeToString())
else:
tf_summary = summary
if not isinstance(summary, bytes):
logging.error(
"Added TensorFlow summary proto. "
"Please prefer TensorBoard copy of the proto"
)
super(FileWriter, self).add_summary(tf_summary, global_step)
def add_session_log(self, session_log, global_step=None):
if isinstance(session_log, event_pb2.SessionLog):
tf_session_log = tf.compat.v1.SessionLog.FromString(
session_log.SerializeToString()
)
else:
tf_session_log = session_log
if not isinstance(session_log, bytes):
logging.error(
"Added TensorFlow session_log proto. "
"Please prefer TensorBoard copy of the proto"
)
super(FileWriter, self).add_session_log(tf_session_log, global_step)
def add_graph(self, graph, global_step=None, graph_def=None):
if isinstance(graph_def, graph_pb2.GraphDef):
tf_graph_def = tf.compat.v1.GraphDef.FromString(
graph_def.SerializeToString()
)
else:
tf_graph_def = graph_def
super(FileWriter, self).add_graph(
graph, global_step=global_step, graph_def=tf_graph_def
)
def add_meta_graph(self, meta_graph_def, global_step=None):
if isinstance(meta_graph_def, meta_graph_pb2.MetaGraphDef):
tf_meta_graph_def = tf.compat.v1.MetaGraphDef.FromString(
meta_graph_def.SerializeToString()
)
else:
tf_meta_graph_def = meta_graph_def
super(FileWriter, self).add_meta_graph(
meta_graph_def=tf_meta_graph_def, global_step=global_step
)
class TensorboardUploaderTest(tf.test.TestCase):
def test_create_experiment(self):
logdir = _TEST_LOG_DIR_NAME
uploader = _create_uploader(_create_mock_client(), logdir)
uploader.create_experiment()
self.assertEqual(uploader._experiment.name, _TEST_ONE_PLATFORM_EXPERIMENT_NAME)
def test_create_experiment_with_name(self):
logdir = _TEST_LOG_DIR_NAME
mock_client = _create_mock_client()
new_name = "This is the new name"
uploader = _create_uploader(mock_client, logdir, experiment_name=new_name)
uploader.create_experiment()
mock_client.create_tensorboard_experiment.assert_called_once()
call_args = mock_client.create_tensorboard_experiment.call_args
self.assertEqual(
call_args[1]["tensorboard_experiment"],
tensorboard_experiment_type.TensorboardExperiment(),
)
self.assertEqual(call_args[1]["parent"], _TEST_TENSORBOARD_RESOURCE_NAME)
self.assertEqual(call_args[1]["tensorboard_experiment_id"], new_name)
def test_create_experiment_with_description(self):
logdir = _TEST_LOG_DIR_NAME
mock_client = _create_mock_client()
new_description = """
**description**"
may have "strange" unicode chars 🌴 \\/<>
"""
uploader = _create_uploader(mock_client, logdir, description=new_description)
uploader.create_experiment()
self.assertEqual(uploader._experiment_name, _TEST_EXPERIMENT_NAME)
mock_client.create_tensorboard_experiment.assert_called_once()
call_args = mock_client.create_tensorboard_experiment.call_args
tb_experiment = tensorboard_experiment_type.TensorboardExperiment(
description=new_description
)
expected_call_args = mock.call(
parent=_TEST_TENSORBOARD_RESOURCE_NAME,
tensorboard_experiment_id=_TEST_EXPERIMENT_NAME,
tensorboard_experiment=tb_experiment,
)
self.assertEqual(expected_call_args, call_args)
def test_create_experiment_with_all_metadata(self):
logdir = _TEST_LOG_DIR_NAME
mock_client = _create_mock_client()
new_description = """
**description**"
may have "strange" unicode chars 🌴 \\/<>
"""
new_name = "This is a cool name."
uploader = _create_uploader(
mock_client, logdir, experiment_name=new_name, description=new_description
)
uploader.create_experiment()
self.assertEqual(uploader._experiment_name, new_name)
mock_client.create_tensorboard_experiment.assert_called_once()
call_args = mock_client.create_tensorboard_experiment.call_args
tb_experiment = tensorboard_experiment_type.TensorboardExperiment(
description=new_description
)
expected_call_args = mock.call(
parent=_TEST_TENSORBOARD_RESOURCE_NAME,
tensorboard_experiment_id=new_name,
tensorboard_experiment=tb_experiment,
)
self.assertEqual(call_args, expected_call_args)
def test_start_uploading_without_create_experiment_fails(self):
mock_client = _create_mock_client()
uploader = _create_uploader(mock_client, _TEST_LOG_DIR_NAME)
with self.assertRaisesRegex(RuntimeError, "call create_experiment()"):
uploader.start_uploading()
def test_start_uploading_scalars(self):
mock_client = _create_mock_client()
mock_rate_limiter = mock.create_autospec(util.RateLimiter)
mock_tensor_rate_limiter = mock.create_autospec(util.RateLimiter)
mock_blob_rate_limiter = mock.create_autospec(util.RateLimiter)
mock_tracker = mock.MagicMock()
with mock.patch.object(
upload_tracker, "UploadTracker", return_value=mock_tracker
):
uploader = _create_uploader(
writer_client=mock_client,
logdir=_TEST_LOG_DIR_NAME,
# Send each Event below in a separate WriteScalarRequest
max_scalar_request_size=200,
rpc_rate_limiter=mock_rate_limiter,
verbosity=1, # In order to test the upload tracker.
)
uploader.create_experiment()
mock_logdir_loader = mock.create_autospec(logdir_loader.LogdirLoader)
mock_logdir_loader.get_run_events.side_effect = [
{
"run 1": _apply_compat(
[_scalar_event("1.1", 5.0), _scalar_event("1.2", 5.0)]
),
"run 2": _apply_compat(
[_scalar_event("2.1", 5.0), _scalar_event("2.2", 5.0)]
),
},
{
"run 3": _apply_compat(
[_scalar_event("3.1", 5.0), _scalar_event("3.2", 5.0)]
),
"run 4": _apply_compat(
[_scalar_event("4.1", 5.0), _scalar_event("4.2", 5.0)]
),
"run 5": _apply_compat(
[_scalar_event("5.1", 5.0), _scalar_event("5.2", 5.0)]
),
},
AbortUploadError,
]
with mock.patch.object(
uploader, "_logdir_loader", mock_logdir_loader
), self.assertRaises(AbortUploadError):
uploader.start_uploading()
self.assertEqual(5, mock_client.write_tensorboard_experiment_data.call_count)
self.assertEqual(5, mock_rate_limiter.tick.call_count)
self.assertEqual(0, mock_tensor_rate_limiter.tick.call_count)
self.assertEqual(0, mock_blob_rate_limiter.tick.call_count)
# Check upload tracker calls.
self.assertEqual(mock_tracker.send_tracker.call_count, 2)
self.assertEqual(mock_tracker.scalars_tracker.call_count, 5)
self.assertLen(mock_tracker.scalars_tracker.call_args[0], 1)
self.assertEqual(mock_tracker.tensors_tracker.call_count, 0)
self.assertEqual(mock_tracker.blob_tracker.call_count, 0)
def test_start_uploading_scalars_one_shot(self):
"""Check that one-shot uploading stops without AbortUploadError."""
def batch_create_runs(parent, requests):
# pylint: disable=unused-argument
tb_runs = []
for request in requests:
tb_run = tensorboard_run_type.TensorboardRun(request.tensorboard_run)
tb_run.name = "{}/runs/{}".format(
request.parent, request.tensorboard_run_id
)
tb_runs.append(tb_run)
return tensorboard_service.BatchCreateTensorboardRunsResponse(
tensorboard_runs=tb_runs
)
def batch_create_time_series(parent, requests):
# pylint: disable=unused-argument
tb_time_series = []
for request in requests:
ts = tensorboard_time_series_type.TensorboardTimeSeries(
request.tensorboard_time_series
)
ts.name = "{}/timeSeries/{}".format(
request.parent, request.tensorboard_time_series.display_name
)
tb_time_series.append(ts)
return tensorboard_service.BatchCreateTensorboardTimeSeriesResponse(
tensorboard_time_series=tb_time_series
)
mock_client = _create_mock_client()
mock_client.batch_create_tensorboard_runs.side_effect = batch_create_runs
mock_client.batch_create_tensorboard_time_series.side_effect = (
batch_create_time_series
)
mock_rate_limiter = mock.create_autospec(util.RateLimiter)
mock_tracker = mock.MagicMock()
with mock.patch.object(
upload_tracker, "UploadTracker", return_value=mock_tracker
):
uploader = _create_uploader(
writer_client=mock_client,
logdir=_TEST_LOG_DIR_NAME,
# Send each Event below in a separate WriteScalarRequest
max_scalar_request_size=200,
rpc_rate_limiter=mock_rate_limiter,
verbosity=1, # In order to test the upload tracker.
one_shot=True,
)
uploader.create_experiment()
mock_logdir_loader = mock.create_autospec(logdir_loader.LogdirLoader)
mock_logdir_loader.get_run_events.side_effect = [
{
"run 1": _apply_compat(
[_scalar_event("tag_1.1", 5.0), _scalar_event("tag_1.2", 5.0)]
),
"run 2": _apply_compat(
[_scalar_event("tag_2.1", 5.0), _scalar_event("tag_2.2", 5.0)]
),
},
# Note the lack of AbortUploadError here.
]
mock_logdir_loader_pre_create = mock.create_autospec(logdir_loader.LogdirLoader)
mock_logdir_loader_pre_create.get_run_events.side_effect = [
{
"run 1": _apply_compat(
[_scalar_event("tag_1.1", 5.0), _scalar_event("tag_1.2", 5.0)]
),
"run 2": _apply_compat(
[_scalar_event("tag_2.1", 5.0), _scalar_event("tag_2.2", 5.0)]
),
},
# Note the lack of AbortUploadError here.
]
with mock.patch.object(uploader, "_logdir_loader", mock_logdir_loader):
with mock.patch.object(
uploader, "_logdir_loader_pre_create", mock_logdir_loader_pre_create
):
uploader.start_uploading()
self.assertEqual(2, mock_client.write_tensorboard_experiment_data.call_count)
self.assertEqual(2, mock_rate_limiter.tick.call_count)
# Check upload tracker calls.
self.assertEqual(mock_tracker.send_tracker.call_count, 1)
self.assertEqual(mock_tracker.scalars_tracker.call_count, 2)
self.assertLen(mock_tracker.scalars_tracker.call_args[0], 1)
self.assertEqual(mock_tracker.tensors_tracker.call_count, 0)
self.assertEqual(mock_tracker.blob_tracker.call_count, 0)
def test_upload_empty_logdir(self):
logdir = self.get_temp_dir()
mock_client = _create_mock_client()
uploader = _create_uploader(mock_client, logdir)
uploader.create_experiment()
uploader._upload_once()
mock_client.write_tensorboard_experiment_data.assert_not_called()
def test_upload_polls_slowly_once_done(self):
class SuccessError(Exception):
pass
mock_rate_limiter = mock.create_autospec(util.RateLimiter)
upload_call_count_box = [0]
def mock_upload_once():
upload_call_count_box[0] += 1
tick_count = mock_rate_limiter.tick.call_count
self.assertEqual(tick_count, upload_call_count_box[0])
if tick_count >= 3:
raise SuccessError()
uploader = _create_uploader(
logdir=self.get_temp_dir(), logdir_poll_rate_limiter=mock_rate_limiter,
)
uploader._upload_once = mock_upload_once
uploader.create_experiment()
with self.assertRaises(SuccessError):
uploader.start_uploading()
def test_upload_swallows_rpc_failure(self):
logdir = self.get_temp_dir()
with FileWriter(logdir) as writer:
writer.add_test_summary("foo")
mock_client = _create_mock_client()
uploader = _create_uploader(mock_client, logdir)
uploader.create_experiment()
error = _grpc_error(grpc.StatusCode.INTERNAL, "Failure")
mock_client.write_tensorboard_experiment_data.side_effect = error
uploader._upload_once()
mock_client.write_tensorboard_experiment_data.assert_called_once()
def test_upload_full_logdir(self):
logdir = self.get_temp_dir()
mock_client = _create_mock_client()
uploader = _create_uploader(mock_client, logdir)
uploader.create_experiment()
# Convenience helpers for constructing expected requests.
data = tensorboard_data.TimeSeriesData
point = tensorboard_data.TimeSeriesDataPoint
scalar = tensorboard_data.Scalar
# First round
writer = FileWriter(logdir)
metadata = summary_pb2.SummaryMetadata(
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name="scalars", content=b"12345"
),
data_class=summary_pb2.DATA_CLASS_SCALAR,
)
writer.add_test_summary("foo", simple_value=5.0, step=1)
writer.add_test_summary("foo", simple_value=6.0, step=2)
writer.add_test_summary("foo", simple_value=7.0, step=3)
writer.add_test_tensor_summary(
"bar",
tensor=tensor_pb2.TensorProto(dtype=types_pb2.DT_FLOAT, float_val=[8.0]),
step=3,
value_metadata=metadata,
)
writer.flush()
writer_a = FileWriter(os.path.join(logdir, "a"))
writer_a.add_test_summary("qux", simple_value=9.0, step=2)
writer_a.flush()
uploader._upload_once()
self.assertEqual(3, mock_client.create_tensorboard_time_series.call_count)
call_args_list = mock_client.create_tensorboard_time_series.call_args_list
request = call_args_list[1][1]["tensorboard_time_series"]
self.assertEqual("scalars", request.plugin_name)
self.assertEqual(b"12345", request.plugin_data)
self.assertEqual(1, mock_client.write_tensorboard_experiment_data.call_count)
call_args_list = mock_client.write_tensorboard_experiment_data.call_args_list
request1, request2 = (
call_args_list[0][1]["write_run_data_requests"][0].time_series_data,
call_args_list[0][1]["write_run_data_requests"][1].time_series_data,
)
_clear_wall_times(request1)
_clear_wall_times(request2)
expected_request1 = [
data(
tensorboard_time_series_id="foo",
value_type=tensorboard_time_series_type.TensorboardTimeSeries.ValueType.SCALAR,
values=[
point(step=1, scalar=scalar(value=5.0)),
point(step=2, scalar=scalar(value=6.0)),
point(step=3, scalar=scalar(value=7.0)),
],
),
data(
tensorboard_time_series_id="bar",
value_type=tensorboard_time_series_type.TensorboardTimeSeries.ValueType.SCALAR,
values=[point(step=3, scalar=scalar(value=8.0))],
),
]
expected_request2 = [
data(
tensorboard_time_series_id="qux",
value_type=tensorboard_time_series_type.TensorboardTimeSeries.ValueType.SCALAR,
values=[point(step=2, scalar=scalar(value=9.0))],
)
]
self.assertProtoEquals(expected_request1[0], request1[0])
self.assertProtoEquals(expected_request1[1], request1[1])
self.assertProtoEquals(expected_request2[0], request2[0])
mock_client.write_tensorboard_experiment_data.reset_mock()
# Second round
writer.add_test_summary("foo", simple_value=10.0, step=5)
writer.add_test_summary("baz", simple_value=11.0, step=1)
writer.flush()
writer_b = FileWriter(os.path.join(logdir, "b"))
writer_b.add_test_summary("xyz", simple_value=12.0, step=1)
writer_b.flush()
uploader._upload_once()
self.assertEqual(1, mock_client.write_tensorboard_experiment_data.call_count)
call_args_list = mock_client.write_tensorboard_experiment_data.call_args_list
request3, request4 = (
call_args_list[0][1]["write_run_data_requests"][0].time_series_data,
call_args_list[0][1]["write_run_data_requests"][1].time_series_data,
)
_clear_wall_times(request3)
_clear_wall_times(request4)
expected_request3 = [
data(
tensorboard_time_series_id="foo",
value_type=tensorboard_time_series_type.TensorboardTimeSeries.ValueType.SCALAR,
values=[point(step=5, scalar=scalar(value=10.0))],
),
data(
tensorboard_time_series_id="baz",
value_type=tensorboard_time_series_type.TensorboardTimeSeries.ValueType.SCALAR,
values=[point(step=1, scalar=scalar(value=11.0))],
),
]
expected_request4 = [
data(
tensorboard_time_series_id="xyz",
value_type=tensorboard_time_series_type.TensorboardTimeSeries.ValueType.SCALAR,
values=[point(step=1, scalar=scalar(value=12.0))],
)
]
self.assertProtoEquals(expected_request3[0], request3[0])
self.assertProtoEquals(expected_request3[1], request3[1])
self.assertProtoEquals(expected_request4[0], request4[0])
mock_client.write_tensorboard_experiment_data.reset_mock()
# Empty third round
uploader._upload_once()
mock_client.write_tensorboard_experiment_data.assert_not_called()
def test_verbosity_zero_creates_upload_tracker_with_verbosity_zero(self):
mock_client = _create_mock_client()
mock_tracker = mock.MagicMock()
with mock.patch.object(
upload_tracker, "UploadTracker", return_value=mock_tracker
) as mock_constructor:
uploader = _create_uploader(
mock_client,
_TEST_LOG_DIR_NAME,
verbosity=0, # Explicitly set verbosity to 0.
)
uploader.create_experiment()
mock_logdir_loader = mock.create_autospec(logdir_loader.LogdirLoader)
mock_logdir_loader.get_run_events.side_effect = [
{
"run 1": _apply_compat(
[_scalar_event("1.1", 5.0), _scalar_event("1.2", 5.0)]
),
},
AbortUploadError,
]
with mock.patch.object(
uploader, "_logdir_loader", mock_logdir_loader
), self.assertRaises(AbortUploadError):
uploader.start_uploading()
self.assertEqual(mock_constructor.call_count, 1)
self.assertEqual(mock_constructor.call_args[1], {"verbosity": 0})
self.assertEqual(mock_tracker.scalars_tracker.call_count, 1)
def test_start_uploading_graphs(self):
mock_client = _create_mock_client()
mock_rate_limiter = mock.create_autospec(util.RateLimiter)
mock_bucket = mock.create_autospec(storage.Bucket)
mock_blob = mock.create_autospec(storage.Blob)
mock_bucket.blob.return_value = mock_blob
mock_tracker = mock.MagicMock()
def create_time_series(tensorboard_time_series, parent=None):
return tensorboard_time_series_type.TensorboardTimeSeries(
name=_TEST_ONE_PLATFORM_TIME_SERIES_NAME,
display_name=tensorboard_time_series.display_name,
)
mock_client.create_tensorboard_time_series.side_effect = create_time_series
with mock.patch.object(
upload_tracker, "UploadTracker", return_value=mock_tracker
):
uploader = _create_uploader(
writer_client=mock_client,
logdir=_TEST_LOG_DIR_NAME,
max_blob_request_size=1000,
rpc_rate_limiter=mock_rate_limiter,
blob_storage_bucket=mock_bucket,
verbosity=1, # In order to test tracker.
)
uploader.create_experiment()
# Of course a real Event stream will never produce the same Event twice,
# but is this test context it's fine to reuse this one.
graph_event = event_pb2.Event(graph_def=_create_example_graph_bytes(950))
expected_graph_def = graph_pb2.GraphDef.FromString(graph_event.graph_def)
mock_logdir_loader = mock.create_autospec(logdir_loader.LogdirLoader)
mock_logdir_loader.get_run_events.side_effect = [
{
"run 1": _apply_compat([graph_event, graph_event]),
"run 2": _apply_compat([graph_event, graph_event]),
},
{
"run 3": _apply_compat([graph_event, graph_event]),
"run 4": _apply_compat([graph_event, graph_event]),
"run 5": _apply_compat([graph_event, graph_event]),
},
AbortUploadError,
]
with mock.patch.object(
uploader, "_logdir_loader", mock_logdir_loader
), self.assertRaises(AbortUploadError):
uploader.start_uploading()
self.assertEqual(1, mock_client.create_tensorboard_experiment.call_count)
self.assertEqual(10, mock_bucket.blob.call_count)
blob_ids = set()
for call in mock_bucket.blob.call_args_list:
request = call[0][0]
m = re.match(
"test_folder/tensorboard-.*/test-experiment/.*/{}/(.*)".format(
_TEST_TIME_SERIES_NAME
),
request,
)
self.assertIsNotNone(m)
blob_ids.add(m[1])
for call in mock_blob.upload_from_string.call_args_list:
request = call[0][0]
actual_graph_def = graph_pb2.GraphDef.FromString(request)
self.assertProtoEquals(expected_graph_def, actual_graph_def)
for call in mock_client.write_tensorboard_experiment_data.call_args_list:
kargs = call[1]
time_series_data = kargs["write_run_data_requests"][0].time_series_data
self.assertEqual(len(time_series_data), 1)
self.assertEqual(
time_series_data[0].tensorboard_time_series_id, _TEST_TIME_SERIES_NAME
)
self.assertEqual(len(time_series_data[0].values), 2)
blobs = time_series_data[0].values[0].blobs.values
self.assertEqual(len(blobs), 1)
self.assertIn(blobs[0].id, blob_ids)
# Check upload tracker calls.
self.assertEqual(mock_tracker.send_tracker.call_count, 2)
self.assertEqual(mock_tracker.scalars_tracker.call_count, 0)
self.assertEqual(mock_tracker.tensors_tracker.call_count, 0)
self.assertEqual(mock_tracker.blob_tracker.call_count, 12)
def test_filter_graphs(self):
# Three graphs: one short, one long, one corrupt.
bytes_0 = _create_example_graph_bytes(123)
bytes_1 = _create_example_graph_bytes(9999)
# invalid (truncated) proto: length-delimited field 1 (0x0a) of
# length 0x7f specified, but only len("bogus") = 5 bytes given
# <https://developers.google.com/protocol-buffers/docs/encoding>
bytes_2 = b"\x0a\x7fbogus"
logdir = self.get_temp_dir()
for (i, b) in enumerate([bytes_0, bytes_1, bytes_2]):
run_dir = os.path.join(logdir, "run_%04d" % i)
event = event_pb2.Event(step=0, wall_time=123 * i, graph_def=b)
with FileWriter(run_dir) as writer:
writer.add_event(event)
limiter = mock.create_autospec(util.RateLimiter)
limiter.tick.side_effect = [None, AbortUploadError]
mock_bucket = mock.create_autospec(storage.Bucket)
mock_blob = mock.create_autospec(storage.Blob)
mock_bucket.blob.return_value = mock_blob
mock_client = _create_mock_client()
def create_time_series(tensorboard_time_series, parent=None):