# Copyright (c) 2022, NVIDIA CORPORATION. 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
import os
from abc import ABC, abstractmethod
from nvflare.apis.resource_manager_spec import ResourceConsumerSpec
class _Consumer(ABC):
@abstractmethod
def consume(self, resources: list):
pass
class _GPUConsumer(_Consumer):
def __init__(self):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
def consume(self, resources: list):
"""Consumes resources.
Note that this class did not check physically if those GPUs exist.
"""
gpu_numbers = [str(x) for x in resources]
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(gpu_numbers)
[docs]class ListResourceConsumer(ResourceConsumerSpec):
def __init__(self):
"""This class can be used with ListResourceManager.
Users can add custom _Consumer in the resource_consumer_map to handle new resource type.
"""
super().__init__()
self.resource_consumer_map = {"gpu": _GPUConsumer()}
[docs] def consume(self, resources: dict):
for key, consumer in self.resource_consumer_map.items():
if key in resources:
consumer.consume(resources[key])