Merge pull request #1 from RadeonOpenCompute/master

Pull from upstream

Deep_learning/MXNet.rst

@@ -0,0 +1,152 @@
+.. _mxnet:
+
+=========
+MXNet 
+=========
+
+.. image:: MXNet_image1.png
+
+MXNet is a deep learning framework that has been ported to the HIP port of MXNet. It works both on HIP/ROCm and HIP/CUDA platforms.
+Mxnet makes use of rocBLAS,rocRAND,hcFFT and MIOpen APIs.
+ It allows you to mix `symbolic and imperative programming <https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts>`_ to **maximize** efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.
+
+MXNet is more than a deep learning project. It is a collection of `blue prints and guidelines <https://mxnet.incubator.apache.org/architecture/index.html#deep-learning-system-design-concepts>`_ for building deep learning systems, and interesting insights of DL systems for hackers.
+
+Installation Guide for MXNet library
+#####################################
+
+Prerequisites
+**************
+
+`GCC 4.8 <https://gcc.gnu.org/gcc-4.8/>`_ or later to compile C++ 11.
+`GNU Make <https://www.gnu.org/software/make/>`_
+
+**Install Dependencies to build mxnet for HIP/ROCm**
+
+* Install ROCm following AMD `ROCm's Installation Guide <http://rocm-documentation.readthedocs.io/en/latest/Installation_Guide/Installation-Guide.html#installing-from-amd-rocm-repositories>`_ to setup MXNet with GPU support.
+
+* Install ROCm Libraries
+
+ ::
+  
+  sudo apt install -y rocm-device-libs rocm-libs rocblas hipblas rocrand rocfft
+
+* Install ROCm opencl
+
+ ::
+
+  sudo apt install -y rocm-opencl rocm-opencl-dev
+
+* Install MIOpen for acceleration
+
+ ::
+
+  sudo apt install -y miopengemm miopen-hip
+
+* Install rocthrust,rocprim, hipcub Libraries
+
+ ::
+
+  sudo apt install -y rocthrust rocprim hipcub
+ 
+ 
+**Install Dependencies to build mxnet for HIP/CUDA**
+
+Install CUDA following the NVIDIA’s `installation guide <http://docs.nvidia.com/cuda/cuda-installation-guide-linux/>`_ to setup MXNet with GPU support
+
+.. note:: 
+   * Make sure to add CUDA install path to LD_LIBRARY_PATH 
+   * Example - export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
+
+Install the dependencies hipblas, rocrand, hcfft from source.
+
+Build the MXNet library
+########################
+
+**Step 1: Install build tools.**
+::
+ $ sudo apt-get update
+ $ sudo apt-get install -y build-essential 
+ 
+**Step 2: Install OpenBLAS.** 
+MXNet uses BLAS and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK libraries - OpenBLAS, ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+::
+ $ sudo apt-get install -y libopenblas-dev liblapack-dev libomp-dev libatlas-dev libatlas-base-dev
+
+**Step 3: Install OpenCV.**
+Install OpenCV <https://opencv.org/>`_ here.
+MXNet uses OpenCV for efficient image loading and augmentation operations.
+::
+ $ sudo apt-get install -y libopencv-dev
+ 
+
+ 
+**Step 4: Download MXNet sources and build MXNet core shared library.**
+::
+ $ git clone --recursive https://github.com/ROCmSoftwarePlatform/mxnet.git
+ $ cd mxnet
+ $ export PATH=/opt/rocm/bin:$PATH
+
+**Step 5:**
+
+**To compile on HCC PLATFORM(HIP/ROCm):**
+::
+ $ export HIP_PLATFORM=hcc
+
+**To compile on NVCC PLATFORM(HIP/CUDA):**
+::
+ $ export HIP_PLATFORM=nvcc
+ 
+
+ 
+**Step 6: To enable MIOpen for higher acceleration :**
+::
+ USE_CUDNN=1  
+ 
+
+**Step 7:**
+**If building on CPU:**
+::
+ make -jn(n=number of cores) USE_GPU=0 (For Ubuntu 16.04)
+ make -jn(n=number of cores)  CXX=g++-6 USE_GPU=0 (For Ubuntu 18.04)
+ 
+**If building on GPU:**
+::
+ make -jn(n=number of cores) USE_GPU=1 (For Ubuntu 16.04)
+ make -jn(n=number of cores)  CXX=g++-6 USE_GPU=1 (For Ubuntu 18.04) 
+ 
+
+On succesfull compilation a library called libmxnet.so is created in mxnet/lib path.
+
+**Note:**
+ 1. USE_CUDA(to build on GPU), USE_CUDNN(for acceleration) flags can be changed in make/config.mk.
+ 2. To compile on HIP/CUDA make sure to set USE_CUDA_PATH to right CUDA installation path in make/config.mk. In most cases it is - /usr/local/cuda.
+
+
+Install the MXNet Python binding
+##################################
+
+**Step 1: Install prerequisites - python, setup-tools, python-pip and numpy.**
+::
+ $ sudo apt-get install -y python-dev python-setuptools python-numpy python-pip python-scipy
+ $ sudo apt-get install python-tk
+ $ sudo apt install -y fftw3 fftw3-dev pkg-config
+
+
+
+**Step 2: Install the MXNet Python binding.**
+::
+ $ cd python
+ $ sudo python setup.py install 
+
+**Step 3: Execute sample example**
+::
+ $ cd example/
+ $ cd bayesian-methods/
+
+ To run on gpu change mx.cpu() to mx.gpu() in python script (Example- bdk_demo.py)
+
+::
+ $ python bdk_demo.py
+
+

Deep_learning/caffe.rst

@@ -136,6 +136,26 @@ Steps::
    ./scripts/download_model_binary.py models/bvlc_reference_caffenet
    ./build/examples/cpp_classification/classification.bin \ models/bvlc_reference_caffenet/deploy.prototxt \models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \data/ilsvrc12/imagenet_mean.binaryproto \data/ilsvrc12/synset_words.txt \examples/images/cat.jpg
 
+Soumith's Convnet benchmarks
++++++++++++++++++++++++++++++++
+
+Steps:
+
+::
+  
+  git clone https://github.com/soumith/convnet-benchmarks.git
+  cd convnet-benchmarks/caffe
+
+
+OPTIONAL: reduce the batch sizes to avoid running out of memory for GoogleNet and VGG. For example, these configs work on Fiji: sed -i 's|input_dim: 128|input_dim: 8|1' imagenet_winners/googlenet.prototxt
+
+::
+
+  export CAFFE_ROOT=/path/to/your/caffe/installation
+  sed -i 's#./caffe/build/tools/caffe#$CAFFE_ROOT/build/tools/caffe#' ./run_imagenet.sh
+  ./run_imagenet.sh
+
+
 Known Issues
 -------------