hipCaffe: the HIP Port of Caffe


This repository hosts the HIP port of Caffe (or hipCaffe, for short). For details on HIP, please refer here. This HIP-ported framework is able to target both AMD ROCm and Nvidia CUDA devices from the same source code. Hardware-specific optimized library calls are also supported within this codebase.


Hardware Requirements

  • For ROCm hardware requirements, see here .

Software and Driver Requirements

  • For ROCm software requirements, see here


AMD ROCm Installation

For further background information on ROCm, refer here.

Installing ROCm Debian packages:


wget -qO - $PKG_REPO/rocm.gpg.key | sudo apt-key add -

sudo sh -c "echo deb [arch=amd64] $PKG_REPO xenial main > /etc/apt/sources.list.d/rocm.list"

sudo apt-get update

sudo apt-get install rocm rocm-utils rocm-opencl rocm-opencl-dev rocm-profiler cxlactivitylogger

echo 'export PATH=/opt/rocm/bin:$PATH' >> $HOME/.bashrc

echo 'export LD_LIBRARY_PATH=/opt/rocm/lib:$LD_LIBRARY_PATH' >> $HOME/.bashrc

source $HOME/.bashrc

sudo reboot

Then, verify the installation. Double-check your kernel (at a minimum, you should see “kfd” in the name):

uname -r

In addition, check that you can run the simple HSA vector_copy sample application:

cd /opt/rocm/hsa/sample

Pre-requisites Installation

Install Caffe dependencies:

sudo apt-get install \
       pkg-config \
       protobuf-compiler \
       libprotobuf-dev \
       libleveldb-dev \
       libsnappy-dev \
       libhdf5-serial-dev \
       libatlas-base-dev \
       libboost-all-dev \
       libgflags-dev \
       libgoogle-glog-dev \
       liblmdb-dev \
       python-numpy python-scipy python3-dev python-yaml python-pip \
       libopencv-dev \
       libfftw3-dev \

Install the necessary ROCm compute libraries:

sudo apt-get install rocm-libs miopen-hip miopengemm

hipCaffe Build Steps

Clone hipCaffe:

git clone https://github.com/ROCmSoftwarePlatform/hipCaffe.git

cd hipCaffe

You may need to modify the Makefile.config file for your own installation. Then, build it:

cp ./Makefile.config.example ./Makefile.config

To improve build time, consider invoking parallel make with the “-j$(nproc)” flag.

Unit Testing

Run the following commands to perform unit testing of different components of Caffe.

make test

Example Workloads

MNIST training



CIFAR-10 training


./build/tools/caffe train --solver=examples/cifar10/cifar10_quick_solver.prototxt

CaffeNet inference


./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


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

Known Issues

Temp workaround for multi-GPU data transfer error

Sometimes when training with multiple GPUs, we hit this type of error signature:

*** SIGSEGV (@0x0) received by PID 57122 (TID 0x7fd841500b80) from PID 0; stack trace: ***
    @     0x7fd8409a1390 (unknown)
    @     0x7fd8400a71f7 (unknown)
    @     0x7fd840515263 (unknown)
    @     0x7fd81f5ef907 UnpinnedCopyEngine::CopyHostToDevice()
    @     0x7fd81f5d3bb9 HSACopy::syncCopyExt()
    @     0x7fd81f5d28bc Kalmar::HSAQueue::copy_ext()
    @     0x7fd8410dba5b ihipStream_t::locked_copySync()
    @     0x7fd8411030bf hipMemcpy
    @           0x6cfd43 caffe::caffe_gpu_rng_uniform()
    @           0x5a32ba caffe::DropoutLayer<>::Forward_gpu()
    @           0x430bbf caffe::Layer<>::Forward()
    @           0x6fefe7 caffe::Net<>::ForwardFromTo()
    @           0x6feeff caffe::Net<>::Forward()
    @           0x801e8c caffe::Solver<>::Step()
    @           0x8015c3 caffe::Solver<>::Solve()
    @           0x71a277 caffe::P2PSync<>::Run()
    @           0x42dcbc train()

See this comment.

In short, here’s the temporary workaround: