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[tune][minor] formatting examples, fix travis (#5869)

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* formatting

* formatting
This commit is contained in:
Richard Liaw
2019-10-08 17:58:43 -07:00
committed by Eric Liang
parent a851d7eb87
commit 1181924077
6 changed files with 70 additions and 80 deletions
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python/ray/tune/examples/async_hyperband_example.py
+9 -14
View File
@@ -70,18 +70,13 @@ if __name__ == "__main__":
run(MyTrainableClass,
name="asynchyperband_test",
scheduler=ahb,
**{
"stop": {
"training_iteration": 1 if args.smoke_test else 99999
},
"num_samples": 20,
"resources_per_trial": {
"cpu": 1,
"gpu": 0
},
"config": {
"width": sample_from(
lambda spec: 10 + int(90 * random.random())),
"height": sample_from(lambda spec: int(100 * random.random())),
},
stop={"training_iteration": 1 if args.smoke_test else 99999},
num_samples=20,
resources_per_trial={
"cpu": 1,
"gpu": 0
},
config={
"width": sample_from(lambda spec: 10 + int(90 * random.random())),
"height": sample_from(lambda spec: int(100 * random.random())),
})
python/ray/tune/examples/mnist_pytorch_trainable.py
+15 -17
View File
@@ -69,23 +69,21 @@ if __name__ == "__main__":
analysis = tune.run(
TrainMNIST,
scheduler=sched,
**{
"stop": {
"mean_accuracy": 0.95,
"training_iteration": 3 if args.smoke_test else 20,
},
"resources_per_trial": {
"cpu": 3,
"gpu": int(args.use_gpu)
},
"num_samples": 1 if args.smoke_test else 20,
"checkpoint_at_end": True,
"checkpoint_freq": 3,
"config": {
"args": args,
"lr": tune.uniform(0.001, 0.1),
"momentum": tune.uniform(0.1, 0.9),
}
stop={
"mean_accuracy": 0.95,
"training_iteration": 3 if args.smoke_test else 20,
},
resources_per_trial={
"cpu": 3,
"gpu": int(args.use_gpu)
},
num_samples=1 if args.smoke_test else 20,
checkpoint_at_end=True,
checkpoint_freq=3,
config={
"args": args,
"lr": tune.uniform(0.001, 0.1),
"momentum": tune.uniform(0.1, 0.9),
})
print("Best config is:", analysis.get_best_config(metric="mean_accuracy"))
python/ray/tune/examples/pbt_example.py
+9 -11
View File
@@ -112,15 +112,13 @@ if __name__ == "__main__":
scheduler=pbt,
reuse_actors=True,
verbose=False,
**{
"stop": {
"training_iteration": 2000,
},
"num_samples": 4,
"config": {
"lr": 0.0001,
# note: this parameter is perturbed but has no effect on
# the model training in this example
"some_other_factor": 1,
},
stop={
"training_iteration": 2000,
},
num_samples=4,
config={
"lr": 0.0001,
# note: this parameter is perturbed but has no effect on
# the model training in this example
"some_other_factor": 1,
})
python/ray/tune/examples/pbt_ppo_example.py
+19 -21
View File
@@ -53,26 +53,24 @@ if __name__ == "__main__":
"PPO",
name="pbt_humanoid_test",
scheduler=pbt,
**{
"num_samples": 8,
"config": {
"env": "Humanoid-v1",
"kl_coeff": 1.0,
"num_workers": 8,
"num_gpus": 1,
"model": {
"free_log_std": True
},
# These params are tuned from a fixed starting value.
"lambda": 0.95,
"clip_param": 0.2,
"lr": 1e-4,
# These params start off randomly drawn from a set.
"num_sgd_iter": sample_from(
lambda spec: random.choice([10, 20, 30])),
"sgd_minibatch_size": sample_from(
lambda spec: random.choice([128, 512, 2048])),
"train_batch_size": sample_from(
lambda spec: random.choice([10000, 20000, 40000]))
num_samples=8,
config={
"env": "Humanoid-v1",
"kl_coeff": 1.0,
"num_workers": 8,
"num_gpus": 1,
"model": {
"free_log_std": True
},
# These params are tuned from a fixed starting value.
"lambda": 0.95,
"clip_param": 0.2,
"lr": 1e-4,
# These params start off randomly drawn from a set.
"num_sgd_iter": sample_from(
lambda spec: random.choice([10, 20, 30])),
"sgd_minibatch_size": sample_from(
lambda spec: random.choice([128, 512, 2048])),
"train_batch_size": sample_from(
lambda spec: random.choice([10000, 20000, 40000]))
})
python/ray/tune/examples/tune_cifar10_gluon.py
+14 -16
View File
@@ -206,20 +206,18 @@ if __name__ == "__main__":
name=args.expname,
verbose=2,
scheduler=sched,
**{
"stop": {
"mean_accuracy": 0.98,
"training_iteration": 1 if args.smoke_test else args.epochs
},
"resources_per_trial": {
"cpu": int(args.num_workers),
"gpu": int(args.num_gpus)
},
"num_samples": 1 if args.smoke_test else args.num_samples,
"config": {
"lr": tune.sample_from(
lambda spec: np.power(10.0, np.random.uniform(-4, -1))),
"momentum": tune.sample_from(
lambda spec: np.random.uniform(0.85, 0.95)),
}
stop={
"mean_accuracy": 0.98,
"training_iteration": 1 if args.smoke_test else args.epochs
},
resources_per_trial={
"cpu": int(args.num_workers),
"gpu": int(args.num_gpus)
},
num_samples=1 if args.smoke_test else args.num_samples,
config={
"lr": tune.sample_from(
lambda spec: np.power(10.0, np.random.uniform(-4, -1))),
"momentum": tune.sample_from(
lambda spec: np.random.uniform(0.85, 0.95)),
})