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=".")