def main():
eta, sd = com.init_ray()
eta = 3 if eta == 1 else eta
MyTrainable = TorchTrainer.as_trainable(
data_creator=workload.data_creator,
model_creator=workload.model_creator,
loss_creator=workload.loss_creator,
optimizer_creator=workload.optimizer_creator,
training_operator_cls=workload.WLMOperator,
config={
"seed": sd,
"extra_fluid_trial_resources": {}
},
)
params = {
**com.run_options(__file__),
"stop": workload.create_stopper(),
**setup_tune_scheduler(),
}
analysis = tune.run(MyTrainable, **params)
dfs = analysis.trial_dataframes
for logdir, df in dfs.items():
ld = Path(logdir)
df.to_csv(ld / "trail_dataframe.csv")
def main():
num_worker, sd = com.init_ray()
MyTrainable_SyncBOHB = TorchTrainer.as_trainable(
data_creator=workload.data_creator,
model_creator=workload.model_creator,
loss_creator=workload.loss_creator,
optimizer_creator=workload.optimizer_creator,
config={
"seed": sd,
BATCH_SIZE: 64,
"extra_fluid_trial_resources": {}
},
)
params = {
**com.run_options(__file__),
"stop": workload.create_stopper(),
**setup_tune_scheduler(num_worker),
}
analysis = tune.run(MyTrainable_SyncBOHB, **params)
dfs = analysis.trial_dataframes
for logdir, df in dfs.items():
ld = Path(logdir)
df.to_csv(ld / "trail_dataframe.csv")
def main():
_, sd = com.init_ray()
workload.init_dcgan()
MyTrainable = TorchTrainer.as_trainable(
data_creator=workload.data_creator,
model_creator=workload.model_creator,
loss_creator=workload.loss_creator,
optimizer_creator=workload.optimizer_creator,
training_operator_cls=workload.GANOperator,
config={
"seed": sd,
**workload.static_config(),
},
)
params = {
**com.run_options(__file__),
"stop": workload.create_stopper(),
**setup_tune_scheduler(),
}
analysis = tune.run(MyTrainable, **params)
dfs = analysis.trial_dataframes
for logdir, df in dfs.items():
ld = Path(logdir)
df.to_csv(ld / "trail_dataframe.csv")