| <!--- 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. --> |
| |
| Frequently Asked Questions |
| ========================== |
| This document contains frequently asked questions. |
| |
| How to Install |
| -------------- |
| See [Installation](http://docs.tvm.ai/install/) |
| |
| TVM's relation to Other IR/DSL Projects |
| --------------------------------------- |
| There are usually two levels of abstractions of IR in the deep learning systems. |
| NNVM, TensorFlow's XLA and Intel's ngraph uses computation graph representation. |
| This representation is high level, and can be helpful to perform generic optimizations |
| such as memory reuse, layout transformation and automatic differentiation. |
| |
| TVM adopts a low level representation, that explicitly express the choice of memory |
| layout, parallelization pattern, locality and hardware primitives etc. |
| This level of IR is closer to directly target hardwares. |
| The low level IR adopt ideas from existing image processing languages like Halide, darkroom |
| and loop transformation tools like loopy and polyhedra based analysis. |
| We specifically focus of expressing deep learning workloads(e.g. recurrence), |
| optimization for different hardware backends and embedding with frameworks to provide |
| end-to-end compilation stack. |
| |
| |
| TVM's relation to libDNN cuDNN |
| ------------------------------ |
| TVM can incorporate these library as external calls. One goal of TVM is to be able to |
| generate high performing kernels. We will evolve TVM an incremental manner as |
| we learn from the technics of manual kernel crafting and add these as primitives in DSL. |
| See also [TVM Operator Inventory](https://github.com/apache/incubator-tvm/tree/master/topi) for |
| recipes of operators in TVM. |
