| .. 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. |
| |
| |
| Tensor |
| ======== |
| |
| Each Tensor instance is a multi-dimensional array allocated on a specific |
| Device instance. Tensor instances store variables and provide |
| linear algebra operations over different types of hardware devices without user |
| awareness. Note that users need to make sure the tensor operands are |
| allocated on the same device except copy functions. |
| |
| |
| Tensor implementation |
| --------------------- |
| |
| SINGA has three different sets of implmentations of Tensor functions, one for each |
| type of Device. |
| |
| * 'tensor_math_cpp.h' implements operations using Cpp (with CBLAS) for CppGPU devices. |
| * 'tensor_math_cuda.h' implements operations using Cuda (with cuBLAS) for CudaGPU devices. |
| * 'tensor_math_opencl.h' implements operations using OpenCL for OpenclGPU devices. |
| |
| Python API |
| ---------- |
| |
| |
| .. automodule:: singa.tensor |
| :members: |