def download_model_def(args: Namespace) -> None:
resp = bindings.get_GetModelDef(setup_session(args),
experimentId=args.experiment_id)
with args.output_dir.joinpath(str(args.experiment_id)).open("wb") as f:
f.write(base64.b64decode(resp.b64Tgz))
def archive(args: Namespace) -> None:
bindings.post_ArchiveExperiment(setup_session(args), id=args.experiment_id)
print("Archived experiment {}".format(args.experiment_id))
def describe(args: Namespace) -> None:
session = setup_session(args)
exps = []
for experiment_id in args.experiment_ids.split(","):
r = bindings.get_GetExperiment(session, experimentId=experiment_id)
if args.json:
exps.append(r.to_json())
else:
exps.append(r.experiment)
if args.json:
print(json.dumps(exps, indent=4))
return
# Display overall experiment information.
headers = [
"Experiment ID",
"State",
"Progress",
"Start Time",
"End Time",
"Name",
"Description",
"Archived",
"Resource Pool",
"Labels",
]
values = [[
exp.id,
exp.state.value.replace("STATE_", ""),
render.format_percent(exp.progress),
render.format_time(exp.startTime),
render.format_time(exp.endTime),
exp.name,
exp.description,
exp.archived,
exp.resourcePool,
", ".join(sorted(exp.labels or [])),
] for exp in exps]
if not args.outdir:
outfile = None
print("Experiment:")
else:
outfile = args.outdir.joinpath("experiments.csv")
render.tabulate_or_csv(headers, values, args.csv, outfile)
# Display trial-related information.
trials_for_experiment: Dict[str, Sequence[bindings.trialv1Trial]] = {}
for exp in exps:
trials_for_experiment[exp.id] = bindings.get_GetExperimentTrials(
session, experimentId=exp.id).trials
headers = [
"Trial ID", "Experiment ID", "State", "Start Time", "End Time",
"H-Params"
]
values = [[
trial.id,
exp.id,
trial.state.value.replace("STATE_", ""),
render.format_time(trial.startTime),
render.format_time(trial.endTime),
json.dumps(trial.hparams, indent=4),
] for exp in exps for trial in trials_for_experiment[exp.id]]
if not args.outdir:
outfile = None
print("\nTrials:")
else:
outfile = args.outdir.joinpath("trials.csv")
render.tabulate_or_csv(headers, values, args.csv, outfile)
# Display step-related information.
t_metrics_headers: List[str] = []
t_metrics_names: List[str] = []
v_metrics_headers: List[str] = []
v_metrics_names: List[str] = []
if args.metrics:
# Accumulate the scalar training and validation metric names from all provided experiments.
for exp in exps:
sample_trial = trials_for_experiment[exp.id][0]
sample_workloads = bindings.get_GetTrial(
session, trialId=sample_trial.id).workloads
t_metrics_names += scalar_training_metrics_names(sample_workloads)
v_metrics_names += scalar_validation_metrics_names(
sample_workloads)
t_metrics_names = sorted(set(t_metrics_names))
t_metrics_headers = [
"Training Metric: {}".format(name) for name in t_metrics_names
]
v_metrics_names = sorted(set(v_metrics_names))
v_metrics_headers = [
"Validation Metric: {}".format(name) for name in v_metrics_names
]
headers = (["Trial ID", "# of Batches", "State", "Report Time"] +
t_metrics_headers + [
"Checkpoint State",
"Checkpoint Report Time",
"Validation State",
"Validation Report Time",
] + v_metrics_headers)
wl_output: Dict[int, List[Any]] = {}
for exp in exps:
for trial in trials_for_experiment[exp.id]:
workloads = bindings.get_GetTrial(session,
trialId=trial.id).workloads
for workload in workloads:
t_metrics_fields = []
wl_detail: Optional[
Union[bindings.v1MetricsWorkload,
bindings.v1CheckpointWorkload]] = None
if workload.training:
wl_detail = workload.training
for name in t_metrics_names:
if wl_detail.metrics and (name in wl_detail.metrics):
t_metrics_fields.append(wl_detail.metrics[name])
else:
t_metrics_fields.append(None)
else:
t_metrics_fields = [None for name in t_metrics_names]
if workload.checkpoint:
wl_detail = workload.checkpoint
if workload.checkpoint and wl_detail:
checkpoint_state = wl_detail.state.value
checkpoint_end_time = wl_detail.endTime
else:
checkpoint_state = ""
checkpoint_end_time = None
v_metrics_fields = []
if workload.validation:
wl_detail = workload.validation
validation_state = wl_detail.state.value
validation_end_time = wl_detail.endTime
for name in v_metrics_names:
if wl_detail.metrics and (name in wl_detail.metrics):
v_metrics_fields.append(wl_detail.metrics[name])
else:
v_metrics_fields.append(None)
else:
validation_state = ""
validation_end_time = None
v_metrics_fields = [None for name in v_metrics_names]
if wl_detail:
if wl_detail.totalBatches in wl_output:
# condense training, checkpoints, validation workloads into one step-like
# row for compatibility with previous versions of describe
merge_row = wl_output[wl_detail.totalBatches]
merge_row[3] = max(
merge_row[3],
render.format_time(wl_detail.endTime))
for idx, tfield in enumerate(t_metrics_fields):
if tfield and merge_row[4 + idx] is None:
merge_row[4 + idx] = tfield
start_checkpoint = 4 + len(t_metrics_fields)
if checkpoint_state:
merge_row[
start_checkpoint] = checkpoint_state.replace(
"STATE_", "")
merge_row[start_checkpoint +
1] = render.format_time(
checkpoint_end_time)
if validation_end_time:
merge_row[start_checkpoint +
3] = render.format_time(
validation_end_time)
if validation_state:
merge_row[start_checkpoint +
2] = validation_state.replace(
"STATE_", "")
for idx, vfield in enumerate(v_metrics_fields):
if vfield and merge_row[start_checkpoint + idx +
4] is None:
merge_row[start_checkpoint + idx + 4] = vfield
else:
row = ([
trial.id,
wl_detail.totalBatches,
wl_detail.state.value.replace("STATE_", ""),
render.format_time(wl_detail.endTime),
] + t_metrics_fields + [
checkpoint_state.replace("STATE_", ""),
render.format_time(checkpoint_end_time),
validation_state.replace("STATE_", ""),
render.format_time(validation_end_time),
] + v_metrics_fields)
wl_output[wl_detail.totalBatches] = row
if not args.outdir:
outfile = None
print("\nWorkloads:")
else:
outfile = args.outdir.joinpath("workloads.csv")
values = sorted(wl_output.values(), key=lambda a: int(a[1]))
render.tabulate_or_csv(headers, values, args.csv, outfile)
def config(args: Namespace) -> None:
result = bindings.get_GetExperiment(setup_session(args),
experimentId=args.experiment_id).config
yaml.safe_dump(result, stream=sys.stdout, default_flow_style=False)
def pause(args: Namespace) -> None:
bindings.post_PauseExperiment(setup_session(args), id=args.experiment_id)
print("Paused experiment {}".format(args.experiment_id))
def cancel(args: Namespace) -> None:
bindings.post_CancelExperiment(setup_session(args), id=args.experiment_id)
print("Canceled experiment {}".format(args.experiment_id))
def kill_experiment(args: Namespace) -> None:
bindings.post_KillExperiment(setup_session(args), id=args.experiment_id)
print("Killed experiment {}".format(args.experiment_id))
def process_updates(args: Namespace) -> None:
session = setup_session(args)
for arg in args.operation:
inputs = validate_operation_args(arg)
_single_update(session=session, **inputs)
def unarchive_workspace(args: Namespace) -> None:
sess = setup_session(args)
current = workspace_by_name(sess, args.workspace_name)
bindings.post_UnarchiveWorkspace(sess, id=current.id)
print(f"Successfully un-archived workspace {args.workspace_name}.")
