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[tune] Update pbt_transformers example #40125

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[tune] Update pbt_transformers example #40125

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13 changes: 5 additions & 8 deletions python/ray/tune/examples/pbt_transformers/pbt_transformers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,17 +102,17 @@ def get_model():
)

tune_config = {
"per_device_train_batch_size": 32,
"per_device_train_batch_size": tune.choice([16, 32, 64]),
"per_device_eval_batch_size": 32,
"num_train_epochs": tune.choice([2, 3, 4, 5]),
"max_steps": 1 if smoke_test else -1, # Used for smoke test.
"num_train_epochs": 2 if smoke_test else tune.choice([2, 3, 4, 5]),
}

scheduler = PopulationBasedTraining(
time_attr="training_iteration",
metric="eval_acc",
mode="max",
perturbation_interval=1,
synch=True,
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We could also pass this as a **kwargs parameter so that the regular example has sync=False - happy either way

hyperparam_mutations={
"weight_decay": tune.uniform(0.0, 0.3),
"learning_rate": tune.uniform(1e-5, 5e-5),
Expand All @@ -137,14 +137,11 @@ def get_model():
resources_per_trial={"cpu": 1, "gpu": gpus_per_trial},
scheduler=scheduler,
checkpoint_config=CheckpointConfig(
num_to_keep=1,
num_to_keep=3,
checkpoint_score_attribute="training_iteration",
),
stop={"training_iteration": 1} if smoke_test else None,
progress_reporter=reporter,
local_dir="~/ray_results/",
name="tune_transformer_pbt",
log_to_file=True,
)


Expand All @@ -158,7 +155,7 @@ def get_model():
args, _ = parser.parse_known_args()

if args.smoke_test:
tune_transformer(num_samples=1, gpus_per_trial=0, smoke_test=True)
tune_transformer(num_samples=2, gpus_per_trial=0, smoke_test=True)
else:
# You can change the number of GPUs here:
tune_transformer(num_samples=8, gpus_per_trial=1)