# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
from pathlib import Path
from syne_tune.experiments.benchmark_definitions.common import RealBenchmarkDefinition
from benchmarking.training_scripts.resnet_cifar10.resnet_cifar10 import (
METRIC_NAME,
RESOURCE_ATTR,
MAX_RESOURCE_ATTR,
_config_space,
)
from syne_tune.remote.constants import (
DEFAULT_GPU_INSTANCE_1GPU,
DEFAULT_GPU_INSTANCE_4GPU,
)
[docs]
def resnet_cifar10_benchmark(sagemaker_backend: bool = False, **kwargs):
if sagemaker_backend:
instance_type = DEFAULT_GPU_INSTANCE_1GPU
else:
# For local backend, GPU cores serve different workers, so we
# need more memory
instance_type = DEFAULT_GPU_INSTANCE_4GPU
config_space = dict(
_config_space,
**{MAX_RESOURCE_ATTR: 27},
dataset_path="./",
num_gpus=1,
)
_kwargs = dict(
script=Path(__file__).parent.parent
/ "training_scripts"
/ "resnet_cifar10"
/ "resnet_cifar10.py",
config_space=config_space,
max_wallclock_time=3 * 3600,
n_workers=4,
instance_type=instance_type,
metric=METRIC_NAME,
mode="max",
max_resource_attr=MAX_RESOURCE_ATTR,
resource_attr=RESOURCE_ATTR,
framework="PyTorch",
)
_kwargs.update(kwargs)
return RealBenchmarkDefinition(**_kwargs)
# Support for cost models:
#
# from benchmarking.utils import get_cost_model_for_batch_size
# from benchmarking.training_scripts.resnet_cifar10.resnet_cifar10 import (
# BATCH_SIZE_LOWER,
# BATCH_SIZE_UPPER,
# BATCH_SIZE_KEY,
# )
# cost_model = get_cost_model_for_batch_size(
# cost_model_type="quadratic_spline",
# batch_size_key = BATCH_SIZE_KEY,
# batch_size_range = (BATCH_SIZE_LOWER, BATCH_SIZE_UPPER),
# )