default="cluster")
args = parser.parse_args()
config = {
"data": {
"url":
"https://s3-us-west-2.amazonaws.com/determined-ai-test-data/pytorch_mnist.tar.gz"
},
"hyperparameters": {
"learning_rate": det.Log(minval=-3.0, maxval=-1.0, base=10),
"dropout": det.Double(minval=0.2, maxval=0.8),
"global_batch_size": det.Constant(value=64),
"n_filters1": det.Constant(value=32),
"n_filters2": det.Constant(value=32),
},
"searcher": {
"name": "single",
"metric": "validation_error",
"max_steps": 20,
"smaller_is_better": True,
},
}
config.update(json.loads(args.config))
experimental.create(
trial_def=model_def.MNistTrial,
config=config,
mode=experimental.Mode(args.mode),
context_dir=str(pathlib.Path.cwd()),
)
)
return model
def build_training_data_loader(self):
return np.zeros(1), np.zeros(1)
def build_validation_data_loader(self):
return np.zeros(1), np.zeros(1)
if __name__ == "__main__":
experimental.create(
trial_def=RuntimeErrorTrial,
config={
"description": "keras_runtime_error",
"hyperparameters": {
"global_batch_size": det.Constant(1)
},
"searcher": {
"metric": "accuracy"
},
"data_layer": {
"type": "lfs",
"container_storage_path": "/tmp"
},
},
local=True,
test=True,
context_dir=str(pathlib.Path.cwd()),
)
dataset_url = (
"https://determined-ai-public-datasets.s3-us-west-2.amazonaws.com/"
"PennFudanPed/PennFudanPed.zip")
config = {
"data": {
"url": dataset_url
},
"hyperparameters": {
"learning_rate": det.Constant(value=0.005),
"momentum": det.Constant(value=0.9),
"weight_decay": det.Constant(value=0.0005),
"global_batch_size": det.Constant(value=2),
},
"batches_per_step": 1,
"searcher": {
"name": "single",
"metric": "val_avg_iou",
"max_steps": 16,
"smaller_is_better": False,
},
}
config.update(json.loads(args.config))
experimental.create(
trial_def=model_def.ObjectDetectionTrial,
config=config,
local=args.local,
test=args.test,
context_dir=str(pathlib.Path.cwd()),
)
default="{}",
)
parser.add_argument("--local",
action="store_true",
help="Specifies local mode")
parser.add_argument("--test",
action="store_true",
help="Specifies test mode")
args = parser.parse_args()
config = {
"hyperparameters": {
"global_batch_size": det.Constant(value=32),
"dense1": det.Constant(value=128),
},
"searcher": {
"name": "single",
"metric": "val_accuracy",
"max_steps": 40
},
}
config.update(json.loads(args.config))
experimental.create(
trial_def=model_def.FashionMNISTTrial,
config=config,
local=args.local,
test=args.test,
context_dir=str(pathlib.Path.cwd()),
)
},
"smaller_is_better": True,
},
"data": {
"data_dir": "/tmp/data",
"task": "MRPC",
"model_name_or_path": "bert-base-uncased",
"output_mode": "classification",
"path_to_mrpc": "",
"download_data": True,
},
"hyperparameters": {
"global_batch_size": det.Constant(value=24),
"model_type": det.Constant(value="bert"),
"learning_rate": det.Constant(value=0.00002),
"lr_scheduler_epoch_freq": det.Constant(value=1),
"adam_epsilon": det.Constant(value=1e-8),
"weight_decay": det.Constant(value=0),
"num_warmup_steps": det.Constant(value=0),
"num_training_steps": det.Constant(value=459),
"max_seq_length": det.Constant(value=128),
},
}
experimental.create(
trial_def=model_def.BertPytorch,
mode=experimental.Mode(args.mode),
context_dir=str(pathlib.Path.cwd()),
config=config,
)
return {"val_loss": loss}
def build_training_data_loader(self) -> pytorch.DataLoader:
return pytorch.DataLoader(
OnesDataset(), batch_size=self.context.get_per_slot_batch_size())
def build_validation_data_loader(self) -> pytorch.DataLoader:
return pytorch.DataLoader(
OnesDataset(), batch_size=self.context.get_per_slot_batch_size())
if __name__ == "__main__":
conf = yaml.safe_load("""
description: test-native-api-local-test-mode
hyperparameters:
global_batch_size: 32
scheduling_unit: 1
searcher:
name: single
metric: val_loss
max_length:
batches: 1
smaller_is_better: true
max_restarts: 0
""")
experimental.create(OneVarTrial,
conf,
context_dir=".",
local=True,
test=True)
def build_training_data_loader(self):
return pytorch.DataLoader(
OnesDataset(), batch_size=self.context.get_per_slot_batch_size())
def build_validation_data_loader(self):
return pytorch.DataLoader(
OnesDataset(), batch_size=self.context.get_per_slot_batch_size())
if __name__ == "__main__":
conf = yaml.safe_load("""
description: noop-pytorch-native-api
data:
model_type: single_output
hyperparameters:
global_batch_size: 32
scheduling_unit: 1
searcher:
name: single
metric: validation_error
max_length:
batches: 3
smaller_is_better: true
max_restarts: 0
min_checkpoint_period:
batches: 1
min_validation_period:
batches: 1
""")
experimental.create(NoopPytorchTrial, conf, context_dir=".")
