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"""Handle Manager."""
import json
import logging
import math
import numbers
import time
from collections import defaultdict
from queue import Queue
from threading import Event
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Sequence,
Tuple,
cast,
)
from wandb.errors.links import url_registry
from wandb.proto.wandb_internal_pb2 import (
HistoryRecord,
InternalMessages,
MetricRecord,
Record,
Result,
RunRecord,
SampledHistoryItem,
SummaryItem,
SummaryRecord,
SummaryRecordRequest,
SystemMetricSample,
SystemMetricsBuffer,
)
from ..interface.interface_queue import InterfaceQueue
from ..lib import handler_util, proto_util
from ..wandb_metadata import Metadata
from . import context, sample, tb_watcher
from .settings_static import SettingsStatic
from .system.system_monitor import SystemMonitor
if TYPE_CHECKING:
from wandb.proto.wandb_internal_pb2 import MetricSummary
SummaryDict = Dict[str, Any]
logger = logging.getLogger(__name__)
# Update (March 5, 2024): Since ~2020/2021, when constructing the summary
# object, we had replaced the artifact path for media types with the latest
# artifact path. The primary purpose of this was to support live updating of
# media objects in the UI (since the default artifact path was fully qualified
# and would not update). However, in March of 2024, a bug was discovered with
# this approach which causes this path to be incorrect in cases where the media
# object is logged to another artifact before being logged to the run. Setting
# this to `False` disables this copy behavior. The impact is that users will
# need to refresh to see updates. Ironically, this updating behavior is not
# currently supported in the UI, so the impact of this change is minimal.
REPLACE_SUMMARY_ART_PATH_WITH_LATEST = False
def _dict_nested_set(target: Dict[str, Any], key_list: Sequence[str], v: Any) -> None:
# recurse down the dictionary structure:
for k in key_list[:-1]:
target.setdefault(k, {})
new_target = target.get(k)
if TYPE_CHECKING:
new_target = cast(Dict[str, Any], new_target)
target = new_target
# use the last element of the key to write the leaf:
target[key_list[-1]] = v
class HandleManager:
_consolidated_summary: SummaryDict
_sampled_history: Dict[str, sample.UniformSampleAccumulator]
_partial_history: Dict[str, Any]
_run_proto: Optional[RunRecord]
_settings: SettingsStatic
_record_q: "Queue[Record]"
_result_q: "Queue[Result]"
_stopped: Event
_writer_q: "Queue[Record]"
_interface: InterfaceQueue
_system_monitor: Optional[SystemMonitor]
_tb_watcher: Optional[tb_watcher.TBWatcher]
_metric_defines: Dict[str, MetricRecord]
_metric_globs: Dict[str, MetricRecord]
_metric_track: Dict[Tuple[str, ...], float]
_metric_copy: Dict[Tuple[str, ...], Any]
_track_time: Optional[float]
_accumulate_time: float
_run_start_time: Optional[float]
_context_keeper: context.ContextKeeper
def __init__(
self,
settings: SettingsStatic,
record_q: "Queue[Record]",
result_q: "Queue[Result]",
stopped: Event,
writer_q: "Queue[Record]",
interface: InterfaceQueue,
context_keeper: context.ContextKeeper,
) -> None:
self._settings = settings
self._record_q = record_q
self._result_q = result_q
self._stopped = stopped
self._writer_q = writer_q
self._interface = interface
self._context_keeper = context_keeper
self._tb_watcher = None
self._system_monitor = None
self._metadata: Optional[Metadata] = None
self._step = 0
self._track_time = None
self._accumulate_time = 0
self._run_start_time = None
# keep track of summary from key/val updates
self._consolidated_summary = dict()
self._sampled_history = defaultdict(sample.UniformSampleAccumulator)
self._run_proto = None
self._partial_history = dict()
self._metric_defines = defaultdict(MetricRecord)
self._metric_globs = defaultdict(MetricRecord)
self._metric_track = dict()
self._metric_copy = dict()
self._internal_messages = InternalMessages()
self._dropped_history = False
def __len__(self) -> int:
return self._record_q.qsize()
def handle(self, record: Record) -> None:
self._context_keeper.add_from_record(record)
record_type = record.WhichOneof("record_type")
assert record_type
handler_str = "handle_" + record_type
handler: Callable[[Record], None] = getattr(self, handler_str, None) # type: ignore
assert handler, f"unknown handle: {handler_str}" # type: ignore
handler(record)
def handle_request(self, record: Record) -> None:
request_type = record.request.WhichOneof("request_type")
assert request_type
handler_str = "handle_request_" + request_type
handler: Callable[[Record], None] = getattr(self, handler_str, None) # type: ignore
if request_type != "network_status":
logger.debug(f"handle_request: {request_type}")
assert handler, f"unknown handle: {handler_str}" # type: ignore
handler(record)
def _dispatch_record(self, record: Record, always_send: bool = False) -> None:
if always_send:
record.control.always_send = True
self._writer_q.put(record)
def _respond_result(self, result: Result) -> None:
context_id = context.context_id_from_result(result)
self._context_keeper.release(context_id)
self._result_q.put(result)
def debounce(self) -> None:
pass
def handle_request_cancel(self, record: Record) -> None:
self._dispatch_record(record)
def handle_request_metadata(self, record: Record) -> None:
logger.warning("Metadata updates are ignored when using the legacy service.")
def handle_request_defer(self, record: Record) -> None:
defer = record.request.defer
state = defer.state
logger.info(f"handle defer: {state}")
# only handle flush tb (sender handles the rest)
if state == defer.FLUSH_STATS:
# TODO(jhr): this could block so we dont really want to call shutdown
# from handler thread
if self._system_monitor is not None:
self._system_monitor.finish()
elif state == defer.FLUSH_TB:
if self._tb_watcher:
# shutdown tensorboard workers so we get all metrics flushed
self._tb_watcher.finish()
self._tb_watcher = None
elif state == defer.FLUSH_PARTIAL_HISTORY:
self._flush_partial_history()
elif state == defer.FLUSH_SUM:
self._save_summary(self._consolidated_summary, flush=True)
# defer is used to drive the sender finish state machine
self._dispatch_record(record, always_send=True)
def handle_request_python_packages(self, record: Record) -> None:
self._dispatch_record(record)
def handle_run(self, record: Record) -> None:
if self._settings._offline:
self._run_proto = record.run
result = proto_util._result_from_record(record)
result.run_result.run.CopyFrom(record.run)
self._respond_result(result)
self._dispatch_record(record)
def handle_stats(self, record: Record) -> None:
self._dispatch_record(record)
def handle_config(self, record: Record) -> None:
self._dispatch_record(record)
def handle_output(self, record: Record) -> None:
self._dispatch_record(record)
def handle_output_raw(self, record: Record) -> None:
self._dispatch_record(record)
def handle_files(self, record: Record) -> None:
self._dispatch_record(record)
def handle_request_link_artifact(self, record: Record) -> None:
self._dispatch_record(record)
def handle_use_artifact(self, record: Record) -> None:
self._dispatch_record(record)
def handle_artifact(self, record: Record) -> None:
self._dispatch_record(record)
def handle_alert(self, record: Record) -> None:
self._dispatch_record(record)
def _save_summary(self, summary_dict: SummaryDict, flush: bool = False) -> None:
summary = SummaryRecord()
for k, v in summary_dict.items():
update = summary.update.add()
update.key = k
update.value_json = json.dumps(v)
if flush:
record = Record(summary=summary)
self._dispatch_record(record)
elif not self._settings._offline:
# Send this summary update as a request since we aren't persisting every update
summary_record = SummaryRecordRequest(summary=summary)
request_record = self._interface._make_request(
summary_record=summary_record
)
self._dispatch_record(request_record)
def _save_history(
self,
history: HistoryRecord,
) -> None:
for item in history.item:
# TODO(jhr) save nested keys?
k = item.key
v = json.loads(item.value_json)
if isinstance(v, numbers.Real):
self._sampled_history[k].add(v)
def _update_summary_metrics(
self,
s: "MetricSummary",
kl: List[str],
v: "numbers.Real",
float_v: float,
goal_max: Optional[bool],
) -> bool:
updated = False
best_key: Optional[Tuple[str, ...]] = None
if s.none:
return False
if s.copy:
# non-key list copy already done in _update_summary
if len(kl) > 1:
_dict_nested_set(self._consolidated_summary, kl, v)
return True
if s.last:
last_key = tuple(kl + ["last"])
old_last = self._metric_track.get(last_key)
if old_last is None or float_v != old_last:
self._metric_track[last_key] = float_v
_dict_nested_set(self._consolidated_summary, last_key, v)
updated = True
if s.best:
best_key = tuple(kl + ["best"])
if s.max or best_key and goal_max:
max_key = tuple(kl + ["max"])
old_max = self._metric_track.get(max_key)
if old_max is None or float_v > old_max:
self._metric_track[max_key] = float_v
if s.max:
_dict_nested_set(self._consolidated_summary, max_key, v)
updated = True
if best_key:
_dict_nested_set(self._consolidated_summary, best_key, v)
updated = True
# defaulting to minimize if goal is not specified
if s.min or best_key and not goal_max:
min_key = tuple(kl + ["min"])
old_min = self._metric_track.get(min_key)
if old_min is None or float_v < old_min:
self._metric_track[min_key] = float_v
if s.min:
_dict_nested_set(self._consolidated_summary, min_key, v)
updated = True
if best_key:
_dict_nested_set(self._consolidated_summary, best_key, v)
updated = True
if s.mean:
tot_key = tuple(kl + ["tot"])
num_key = tuple(kl + ["num"])
avg_key = tuple(kl + ["mean"])
tot = self._metric_track.get(tot_key, 0.0)
num = self._metric_track.get(num_key, 0)
tot += float_v
num += 1
self._metric_track[tot_key] = tot
self._metric_track[num_key] = num
_dict_nested_set(self._consolidated_summary, avg_key, tot / num)
updated = True
return updated
def _update_summary_leaf(
self,
kl: List[str],
v: Any,
d: Optional[MetricRecord] = None,
) -> bool:
has_summary = d and d.HasField("summary")
if len(kl) == 1:
copy_key = tuple(kl)
old_copy = self._metric_copy.get(copy_key)
if old_copy is None or v != old_copy:
self._metric_copy[copy_key] = v
# Store copy metric if not specified, or copy behavior
if not has_summary or (d and d.summary.copy):
self._consolidated_summary[kl[0]] = v
return True
if not d:
return False
if not has_summary:
return False
if not isinstance(v, numbers.Real):
return False
if math.isnan(v):
return False
float_v = float(v)
goal_max = None
if d.goal:
goal_max = d.goal == d.GOAL_MAXIMIZE
if self._update_summary_metrics(
d.summary, kl=kl, v=v, float_v=float_v, goal_max=goal_max
):
return True
return False
def _update_summary_list(
self,
kl: List[str],
v: Any,
d: Optional[MetricRecord] = None,
) -> bool:
metric_key = ".".join([k.replace(".", "\\.") for k in kl])
d = self._metric_defines.get(metric_key, d)
# if the dict has _type key, it's a wandb table object
if isinstance(v, dict) and not handler_util.metric_is_wandb_dict(v):
updated = False
for nk, nv in v.items():
if self._update_summary_list(kl=kl[:] + [nk], v=nv, d=d):
updated = True
return updated
# If the dict is a media object, update the pointer to the latest alias
elif (
REPLACE_SUMMARY_ART_PATH_WITH_LATEST
and isinstance(v, dict)
and handler_util.metric_is_wandb_dict(v)
):
if "_latest_artifact_path" in v and "artifact_path" in v:
# TODO: Make non-destructive?
v["artifact_path"] = v["_latest_artifact_path"]
updated = self._update_summary_leaf(kl=kl, v=v, d=d)
return updated
def _update_summary_media_objects(self, v: Dict[str, Any]) -> Dict[str, Any]:
# For now, non-recursive - just top level
for nk, nv in v.items():
if REPLACE_SUMMARY_ART_PATH_WITH_LATEST and (
isinstance(nv, dict)
and handler_util.metric_is_wandb_dict(nv)
and "_latest_artifact_path" in nv
and "artifact_path" in nv
):
# TODO: Make non-destructive?
nv["artifact_path"] = nv["_latest_artifact_path"]
v[nk] = nv
return v
def _update_summary(self, history_dict: Dict[str, Any]) -> List[str]:
# keep old behavior fast path if no define metrics have been used
if not self._metric_defines:
history_dict = self._update_summary_media_objects(history_dict)
self._consolidated_summary.update(history_dict)
return list(history_dict.keys())
updated_keys = []
for k, v in history_dict.items():
if self._update_summary_list(kl=[k], v=v):
updated_keys.append(k)
return updated_keys
def _history_assign_step(
self,
history: HistoryRecord,
history_dict: Dict[str, Any],
) -> None:
has_step = history.HasField("step")
item = history.item.add()
item.key = "_step"
if has_step:
step = history.step.num
history_dict["_step"] = step
item.value_json = json.dumps(step)
self._step = step + 1
else:
history_dict["_step"] = self._step
item.value_json = json.dumps(self._step)
self._step += 1
def _history_define_metric(self, hkey: str) -> Optional[MetricRecord]:
"""Check for hkey match in glob metrics and return the defined metric."""
# Dont define metric for internal metrics
if hkey.startswith("_"):
return None
for k, mglob in self._metric_globs.items():
if k.endswith("*"):
if hkey.startswith(k[:-1]):
m = MetricRecord()
m.CopyFrom(mglob)
m.ClearField("glob_name")
m.options.defined = False
m.name = hkey
return m
return None
def _history_update_leaf(
self,
kl: List[str],
v: Any,
history_dict: Dict[str, Any],
update_history: Dict[str, Any],
) -> None:
hkey = ".".join([k.replace(".", "\\.") for k in kl])
m = self._metric_defines.get(hkey)
if not m:
m = self._history_define_metric(hkey)
if not m:
return
mr = Record()
mr.metric.CopyFrom(m)
mr.control.local = True # Dont store this, just send it
self._handle_defined_metric(mr)
if m.options.step_sync and m.step_metric:
if m.step_metric not in history_dict:
copy_key = tuple([m.step_metric])
step = self._metric_copy.get(copy_key)
if step is not None:
update_history[m.step_metric] = step
def _history_update_list(
self,
kl: List[str],
v: Any,
history_dict: Dict[str, Any],
update_history: Dict[str, Any],
) -> None:
if isinstance(v, dict):
for nk, nv in v.items():
self._history_update_list(
kl=kl[:] + [nk],
v=nv,
history_dict=history_dict,
update_history=update_history,
)
return
self._history_update_leaf(
kl=kl, v=v, history_dict=history_dict, update_history=update_history
)
def _history_update(
self,
history: HistoryRecord,
history_dict: Dict[str, Any],
) -> None:
# if syncing an old run, we can skip this logic
if history_dict.get("_step") is None:
self._history_assign_step(history, history_dict)
update_history: Dict[str, Any] = {}
# Look for metric matches
if self._metric_defines or self._metric_globs:
for hkey, hval in history_dict.items():
self._history_update_list([hkey], hval, history_dict, update_history)
if update_history:
history_dict.update(update_history)
for k, v in update_history.items():
item = history.item.add()
item.key = k
item.value_json = json.dumps(v)
def handle_history(self, record: Record) -> None:
history_dict = proto_util.dict_from_proto_list(record.history.item)
# Inject _runtime if it is not present
if history_dict is not None:
if "_runtime" not in history_dict:
self._history_assign_runtime(record.history, history_dict)
self._history_update(record.history, history_dict)
self._dispatch_record(record)
self._save_history(record.history)
# update summary from history
updated_keys = self._update_summary(history_dict)
if updated_keys:
updated_items = {k: self._consolidated_summary[k] for k in updated_keys}
self._save_summary(updated_items)
def _flush_partial_history(
self,
step: Optional[int] = None,
) -> None:
if not self._partial_history:
return
history = HistoryRecord()
for k, v in self._partial_history.items():
item = history.item.add()
item.key = k
item.value_json = json.dumps(v)
if step is not None:
history.step.num = step
self.handle_history(Record(history=history))
self._partial_history = {}
def handle_request_sender_mark_report(self, record: Record) -> None:
self._dispatch_record(record, always_send=True)
def handle_request_status_report(self, record: Record) -> None:
self._dispatch_record(record, always_send=True)
def handle_request_partial_history(self, record: Record) -> None:
partial_history = record.request.partial_history
flush = None
if partial_history.HasField("action"):
flush = partial_history.action.flush
step = None
if partial_history.HasField("step"):
step = partial_history.step.num
history_dict = proto_util.dict_from_proto_list(partial_history.item)
if step is not None:
if step < self._step:
if not self._dropped_history:
message = (
"Step only supports monotonically increasing values, use define_metric to set a custom x "
f"axis. For details see: {url_registry.url('define-metric')}"
)
self._internal_messages.warning.append(message)
self._dropped_history = True
message = (
f"(User provided step: {step} is less than current step: {self._step}. "
f"Dropping entry: {history_dict})."
)
self._internal_messages.warning.append(message)
return
elif step > self._step:
self._flush_partial_history()
self._step = step
elif flush is None:
flush = True
self._partial_history.update(history_dict)
if flush:
self._flush_partial_history(self._step)
def handle_summary(self, record: Record) -> None:
summary = record.summary
for item in summary.update:
if len(item.nested_key) > 0:
# we use either key or nested_key -- not both
assert item.key == ""
key = tuple(item.nested_key)
else:
# no counter-assertion here, because technically
# summary[""] is valid
key = (item.key,)
target = self._consolidated_summary
# recurse down the dictionary structure:
for prop in key[:-1]:
target = target[prop]
# use the last element of the key to write the leaf:
target[key[-1]] = json.loads(item.value_json)
for item in summary.remove:
if len(item.nested_key) > 0:
# we use either key or nested_key -- not both
assert item.key == ""
key = tuple(item.nested_key)
else:
# no counter-assertion here, because technically
# summary[""] is valid
key = (item.key,)
target = self._consolidated_summary
# recurse down the dictionary structure:
for prop in key[:-1]:
target = target[prop]
# use the last element of the key to erase the leaf:
del target[key[-1]]
self._save_summary(self._consolidated_summary)
def handle_exit(self, record: Record) -> None:
if self._track_time is not None:
self._accumulate_time += time.time() - self._track_time
record.exit.runtime = int(self._accumulate_time)
self._dispatch_record(record, always_send=True)
def handle_final(self, record: Record) -> None:
self._dispatch_record(record, always_send=True)
def handle_preempting(self, record: Record) -> None:
self._dispatch_record(record)
def handle_header(self, record: Record) -> None:
self._dispatch_record(record)
def handle_footer(self, record: Record) -> None:
self._dispatch_record(record)
def handle_request_attach(self, record: Record) -> None:
result = proto_util._result_from_record(record)
attach_id = record.request.attach.attach_id
assert attach_id
assert self._run_proto
result.response.attach_response.run.CopyFrom(self._run_proto)
self._respond_result(result)
def handle_request_log_artifact(self, record: Record) -> None:
self._dispatch_record(record)
def handle_telemetry(self, record: Record) -> None:
self._dispatch_record(record)
def handle_request_run_start(self, record: Record) -> None:
run_start = record.request.run_start
assert run_start
assert run_start.run
self._run_proto = run_start.run
self._run_start_time = run_start.run.start_time.ToMicroseconds() / 1e6
self._track_time = time.time()
if run_start.run.resumed and run_start.run.runtime:
self._accumulate_time = run_start.run.runtime
else:
self._accumulate_time = 0
# system monitor
self._system_monitor = SystemMonitor(
self._settings,
self._interface,
)
if not (
self._settings.x_disable_stats or self._settings.x_disable_machine_info
):
self._system_monitor.start()
if (
not (self._settings.x_disable_meta or self._settings.x_disable_machine_info)
and not run_start.run.resumed
):
try:
self._metadata = Metadata(**self._system_monitor.probe(publish=True))
except Exception:
logger.exception("Error probing system metadata.")
self._tb_watcher = tb_watcher.TBWatcher(
self._settings, interface=self._interface, run_proto=run_start.run
)
if run_start.run.resumed or run_start.run.forked:
self._step = run_start.run.starting_step
result = proto_util._result_from_record(record)
self._respond_result(result)
def handle_request_resume(self, record: Record) -> None:
if self._system_monitor is not None:
logger.info("starting system metrics thread")
self._system_monitor.start()
if self._track_time is not None:
self._accumulate_time += time.time() - self._track_time
self._track_time = time.time()
def handle_request_pause(self, record: Record) -> None:
if self._system_monitor is not None:
logger.info("stopping system metrics thread")
self._system_monitor.finish()
if self._track_time is not None:
self._accumulate_time += time.time() - self._track_time
self._track_time = None
def handle_request_poll_exit(self, record: Record) -> None:
self._dispatch_record(record, always_send=True)
def handle_request_stop_status(self, record: Record) -> None:
self._dispatch_record(record)
def handle_request_network_status(self, record: Record) -> None:
self._dispatch_record(record)
def handle_request_internal_messages(self, record: Record) -> None:
result = proto_util._result_from_record(record)
result.response.internal_messages_response.messages.CopyFrom(
self._internal_messages
)
self._internal_messages.Clear()
self._respond_result(result)
def handle_request_status(self, record: Record) -> None:
result = proto_util._result_from_record(record)
self._respond_result(result)
def handle_request_get_summary(self, record: Record) -> None:
result = proto_util._result_from_record(record)
for key, value in self._consolidated_summary.items():
item = SummaryItem()
item.key = key
item.value_json = json.dumps(value)
result.response.get_summary_response.item.append(item)
self._respond_result(result)
def handle_request_get_system_metrics(self, record: Record) -> None:
result = proto_util._result_from_record(record)
if self._system_monitor is None:
return
buffer = self._system_monitor.buffer
for key, samples in buffer.items():
buff = []
for s in samples:
sms = SystemMetricSample()
sms.timestamp.FromMicroseconds(int(s[0] * 1e6))
sms.value = s[1]
buff.append(sms)
result.response.get_system_metrics_response.system_metrics[key].CopyFrom(
SystemMetricsBuffer(record=buff)
)
self._respond_result(result)
def handle_request_get_system_metadata(self, record: Record) -> None:
result = proto_util._result_from_record(record)
if self._system_monitor is None or self._metadata is None:
return
result.response.get_system_metadata_response.metadata.CopyFrom(
self._metadata.to_proto()
)
self._respond_result(result)
def handle_tbrecord(self, record: Record) -> None:
logger.info("handling tbrecord: %s", record)
if self._tb_watcher:
tbrecord = record.tbrecord
self._tb_watcher.add(tbrecord.log_dir, tbrecord.save, tbrecord.root_dir)
self._dispatch_record(record)
def _handle_defined_metric(self, record: Record) -> None:
metric = record.metric
if metric._control.overwrite:
self._metric_defines[metric.name].CopyFrom(metric)
else:
self._metric_defines[metric.name].MergeFrom(metric)
# before dispatching, make sure step_metric is defined, if not define it and
# dispatch it locally first
metric = self._metric_defines[metric.name]
if metric.step_metric and metric.step_metric not in self._metric_defines:
m = MetricRecord(name=metric.step_metric)
self._metric_defines[metric.step_metric] = m
mr = Record()
mr.metric.CopyFrom(m)
mr.control.local = True # Don't store this, just send it
self._dispatch_record(mr)
self._dispatch_record(record)
def _handle_glob_metric(self, record: Record) -> None:
metric = record.metric
if metric._control.overwrite:
self._metric_globs[metric.glob_name].CopyFrom(metric)
else:
self._metric_globs[metric.glob_name].MergeFrom(metric)
self._dispatch_record(record)
def handle_metric(self, record: Record) -> None:
"""Handle MetricRecord.
Walkthrough of the life of a MetricRecord:
Metric defined:
- run.define_metric() parses arguments create wandb_metric.Metric
- build MetricRecord publish to interface
- handler (this function) keeps list of metrics published:
- self._metric_defines: Fully defined metrics
- self._metric_globs: metrics that have a wildcard
- dispatch writer and sender thread
- writer: records are saved to persistent store
- sender: fully defined metrics get mapped into metadata for UI
History logged:
- handle_history
- check if metric matches _metric_defines
- if not, check if metric matches _metric_globs
- if _metric globs match, generate defined metric and call _handle_metric
Args:
record (Record): Metric record to process
"""
if record.metric.name:
self._handle_defined_metric(record)
elif record.metric.glob_name:
self._handle_glob_metric(record)
def handle_request_sampled_history(self, record: Record) -> None:
result = proto_util._result_from_record(record)
for key, sampled in self._sampled_history.items():
item = SampledHistoryItem()
item.key = key
values: Iterable[Any] = sampled.get()
if all(isinstance(i, numbers.Integral) for i in values):
try:
item.values_int.extend(values)
except ValueError:
# it is safe to ignore these as this is for display information
pass
elif all(isinstance(i, numbers.Real) for i in values):
item.values_float.extend(values)
result.response.sampled_history_response.item.append(item)
self._respond_result(result)
def handle_request_keepalive(self, record: Record) -> None:
"""Handle a keepalive request.
Keepalive is a noop, we just want to verify transport is alive.
"""
def handle_request_run_status(self, record: Record) -> None:
self._dispatch_record(record, always_send=True)
def handle_request_shutdown(self, record: Record) -> None:
# TODO(jhr): should we drain things and stop new requests from coming in?
result = proto_util._result_from_record(record)
self._respond_result(result)
self._stopped.set()
def handle_request_operations(self, record: Record) -> None:
"""No-op. Not implemented for the legacy-service."""
self._respond_result(proto_util._result_from_record(record))
def finish(self) -> None:
logger.info("shutting down handler")
if self._system_monitor is not None:
self._system_monitor.finish()
if self._tb_watcher:
self._tb_watcher.finish()
# self._context_keeper._debug_print_orphans()
def __next__(self) -> Record:
return self._record_q.get(block=True)
next = __next__
def _history_assign_runtime(
self,
history: HistoryRecord,
history_dict: Dict[str, Any],
) -> None:
# _runtime calculation is meaningless if there is no _timestamp
if "_timestamp" not in history_dict:
return
# if it is offline sync, self._run_start_time is None
# in that case set it to the first tfevent timestamp
if self._run_start_time is None:
self._run_start_time = history_dict["_timestamp"]
history_dict["_runtime"] = history_dict["_timestamp"] - self._run_start_time
item = history.item.add()
item.key = "_runtime"
item.value_json = json.dumps(history_dict[item.key])
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