def format_experiment(e: Any) -> List[Any]:
result = [
e["id"],
e["owner"]["username"],
e["config"]["description"],
e["state"],
render.format_percent(e["progress"]),
render.format_time(e["start_time"]),
render.format_time(e["end_time"]),
]
if args.all:
result.append(e["archived"])
return result
def format_experiment(e: Any) -> List[Any]:
result = [
e.id,
e.owner.username,
e.config["description"],
e.state,
render.format_percent(e.progress),
render.format_time(e.start_time),
render.format_time(e.end_time),
]
if args.all:
result.append(e.archived)
return result
def describe(args: Namespace) -> None:
docs = []
for experiment_id in args.experiment_ids.split(","):
if args.metrics:
r = api.get(args.master,
"experiments/{}/metrics/summary".format(experiment_id))
else:
r = api.get(args.master, "experiments/{}".format(experiment_id))
docs.append(r.json())
if args.json:
print(json.dumps(docs, indent=4))
return
# Display overall experiment information.
headers = [
"Experiment ID",
"State",
"Progress",
"Start Time",
"End Time",
"Description",
"Archived",
"Labels",
]
values = [[
doc["id"],
doc["state"],
render.format_percent(doc["progress"]),
render.format_time(doc.get("start_time")),
render.format_time(doc.get("end_time")),
doc["config"].get("description"),
doc["archived"],
", ".join(sorted(doc["config"].get("labels", []))),
] for doc in docs]
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.
headers = [
"Trial ID", "Experiment ID", "State", "Start Time", "End Time",
"H-Params"
]
values = [[
trial["id"],
doc["id"],
trial["state"],
render.format_time(trial.get("start_time")),
render.format_time(trial.get("end_time")),
json.dumps(trial["hparams"], indent=4),
] for doc in docs for trial in doc["trials"]]
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.
if args.metrics:
# Accumulate the scalar training and validation metric names from all provided experiments.
t_metrics_names = sorted(
{n
for doc in docs for n in scalar_training_metrics_names(doc)})
t_metrics_headers = [
"Training Metric: {}".format(name) for name in t_metrics_names
]
v_metrics_names = sorted(
{n
for doc in docs for n in scalar_validation_metrics_names(doc)})
v_metrics_headers = [
"Validation Metric: {}".format(name) for name in v_metrics_names
]
else:
t_metrics_headers = []
v_metrics_headers = []
headers = (["Trial ID", "Step ID", "State", "Start Time", "End Time"] +
t_metrics_headers + [
"Checkpoint State",
"Checkpoint Start Time",
"Checkpoint End Time",
"Validation State",
"Validation Start Time",
"Validation End Time",
] + v_metrics_headers)
values = []
for doc in docs:
for trial in doc["trials"]:
for step in trial["steps"]:
t_metrics_fields = []
if step.get("metrics"):
avg_metrics = step["metrics"]["avg_metrics"]
for name in t_metrics_names:
if name in avg_metrics:
t_metrics_fields.append(avg_metrics[name])
else:
t_metrics_fields.append(None)
checkpoint = step.get("checkpoint")
if checkpoint:
checkpoint_state = checkpoint["state"]
checkpoint_start_time = checkpoint.get("start_time")
checkpoint_end_time = checkpoint.get("end_time")
else:
checkpoint_state = None
checkpoint_start_time = None
checkpoint_end_time = None
validation = step.get("validation")
if validation:
validation_state = validation["state"]
validation_start_time = validation.get("start_time")
validation_end_time = validation.get("end_time")
else:
validation_state = None
validation_start_time = None
validation_end_time = None
if args.metrics:
v_metrics_fields = [
api.metric.get_validation_metric(name, validation)
for name in v_metrics_names
]
else:
v_metrics_fields = []
row = ([
step["trial_id"],
step["id"],
step["state"],
render.format_time(step.get("start_time")),
render.format_time(step.get("end_time")),
] + t_metrics_fields + [
checkpoint_state,
render.format_time(checkpoint_start_time),
render.format_time(checkpoint_end_time),
validation_state,
render.format_time(validation_start_time),
render.format_time(validation_end_time),
] + v_metrics_fields)
values.append(row)
if not args.outdir:
outfile = None
print("\nSteps:")
else:
outfile = args.outdir.joinpath("steps.csv")
render.tabulate_or_csv(headers, values, args.csv, outfile)
def describe(args: Namespace) -> None:
ids = [int(x) for x in args.experiment_ids.split(",")]
q = api.GraphQLQuery(args.master)
exps = q.op.experiments(where=gql.experiments_bool_exp(
id=gql.Int_comparison_exp(_in=ids)))
exps.archived()
exps.config()
exps.end_time()
exps.id()
exps.progress()
exps.start_time()
exps.state()
trials = exps.trials(order_by=[gql.trials_order_by(id=gql.order_by.asc)])
trials.end_time()
trials.hparams()
trials.id()
trials.start_time()
trials.state()
steps = trials.steps(order_by=[gql.steps_order_by(id=gql.order_by.asc)])
steps.end_time()
steps.id()
steps.start_time()
steps.state()
steps.trial_id()
steps.checkpoint.end_time()
steps.checkpoint.start_time()
steps.checkpoint.state()
steps.validation.end_time()
steps.validation.start_time()
steps.validation.state()
if args.metrics:
steps.metrics(path="avg_metrics")
steps.validation.metrics()
resp = q.send()
# Re-sort the experiment objects to match the original order.
exps_by_id = {e.id: e for e in resp.experiments}
experiments = [exps_by_id[id] for id in ids]
if args.json:
print(json.dumps(resp.__to_json_value__()["experiments"], indent=4))
return
# Display overall experiment information.
headers = [
"Experiment ID",
"State",
"Progress",
"Start Time",
"End Time",
"Description",
"Archived",
"Labels",
]
values = [[
e.id,
e.state,
render.format_percent(e.progress),
render.format_time(e.start_time),
render.format_time(e.end_time),
e.config.get("description"),
e.archived,
", ".join(sorted(e.config.get("labels", []))),
] for e in experiments]
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.
headers = [
"Trial ID", "Experiment ID", "State", "Start Time", "End Time",
"H-Params"
]
values = [[
t.id,
e.id,
t.state,
render.format_time(t.start_time),
render.format_time(t.end_time),
json.dumps(t.hparams, indent=4),
] for e in experiments for t in e.trials]
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.
if args.metrics:
# Accumulate the scalar training and validation metric names from all provided experiments.
t_metrics_names = sorted(
{n
for e in experiments for n in scalar_training_metrics_names(e)})
t_metrics_headers = [
"Training Metric: {}".format(name) for name in t_metrics_names
]
v_metrics_names = sorted({
n
for e in experiments for n in scalar_validation_metrics_names(e)
})
v_metrics_headers = [
"Validation Metric: {}".format(name) for name in v_metrics_names
]
else:
t_metrics_headers = []
v_metrics_headers = []
headers = (["Trial ID", "Step ID", "State", "Start Time", "End Time"] +
t_metrics_headers + [
"Checkpoint State",
"Checkpoint Start Time",
"Checkpoint End Time",
"Validation State",
"Validation Start Time",
"Validation End Time",
] + v_metrics_headers)
values = []
for e in experiments:
for t in e.trials:
for step in t.steps:
t_metrics_fields = []
if hasattr(step, "metrics"):
avg_metrics = step.metrics
for name in t_metrics_names:
if name in avg_metrics:
t_metrics_fields.append(avg_metrics[name])
else:
t_metrics_fields.append(None)
checkpoint = step.checkpoint
if checkpoint:
checkpoint_state = checkpoint.state
checkpoint_start_time = checkpoint.start_time
checkpoint_end_time = checkpoint.end_time
else:
checkpoint_state = None
checkpoint_start_time = None
checkpoint_end_time = None
validation = step.validation
if validation:
validation_state = validation.state
validation_start_time = validation.start_time
validation_end_time = validation.end_time
else:
validation_state = None
validation_start_time = None
validation_end_time = None
if args.metrics:
v_metrics_fields = [
api.metric.get_validation_metric(name, validation)
for name in v_metrics_names
]
else:
v_metrics_fields = []
row = ([
step.trial_id,
step.id,
step.state,
render.format_time(step.start_time),
render.format_time(step.end_time),
] + t_metrics_fields + [
checkpoint_state,
render.format_time(checkpoint_start_time),
render.format_time(checkpoint_end_time),
validation_state,
render.format_time(validation_start_time),
render.format_time(validation_end_time),
] + v_metrics_fields)
values.append(row)
if not args.outdir:
outfile = None
print("\nSteps:")
else:
outfile = args.outdir.joinpath("steps.csv")
render.tabulate_or_csv(headers, values, args.csv, outfile)
