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()),
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--mode", dest="mode", default="cluster")
args = parser.parse_args()
config = {
"hyperparameters": {
"hidden_size": 2,
"learning_rate": 0.1,
"global_batch_size": 4,
"optimizer": "sgd",
"shuffle": False,
}
}
context = estimator.init(
config=config, mode=experimental.Mode(args.mode), context_dir=str(pathlib.Path.cwd())
)
batch_size = context.get_per_slot_batch_size()
shuffle = context.get_hparam("shuffle")
context.serving_input_receiver_fns = build_serving_input_receiver_fns()
context.train_and_evaluate(
build_estimator(context),
tf.estimator.TrainSpec(
xor_input_fn(context=context, batch_size=batch_size, shuffle=shuffle), max_steps=1
),
tf.estimator.EvalSpec(xor_input_fn(context=context, batch_size=batch_size, shuffle=False)),
)