| --- |
| id: device |
| title: Device |
| --- |
| |
| <!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you 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. --> |
| |
| Device代表一个有内存和计算单元的硬件设备。所有的[Tensor操作](./tensor)都由常驻设备安排执行的，Tensor内存也由设备的内存管理器管理。因此，需要在Device类中实现内存和执行的优化。 |
| |
| |
| ## 特定设备 |
| |
| 目前，SINGA支持三种设备： |
| |
| 1. CudaGPU：用于运行Cuda代码的Nvidia GPU。 |
| 2. CppCPU：用于运行Cpp代码的CPU。 |
| 3. OpenclGPU：用于运行OpenCL代码的GPU卡。 |
| |
| ## 用法示例 |
| |
| 下面的代码提供了创建设备的例子： |
| |
| ```python |
| from singa import device |
| cuda = device.create_cuda_gpu_on(0) # use GPU card of ID 0 |
| host = device.get_default_device() # get the default host device (a CppCPU) |
| ary1 = device.create_cuda_gpus(2) # create 2 devices, starting from ID 0 |
| ary2 = device.create_cuda_gpus([0,2]) # create 2 devices on ID 0 and 2 |
| ``` |
