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https://github.com/ROCm/hipCaffe

(Deprecated) hipCaffe: the HIP port of Caffe
https://github.com/ROCm/hipCaffe

Last synced: 10 days ago
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(Deprecated) hipCaffe: the HIP port of Caffe

Host: GitHub
URL: https://github.com/ROCm/hipCaffe
Owner: ROCm
License: other
Archived: true
Created: 2017-04-01T23:06:48.000Z (about 7 years ago)
Default Branch: hip
Last Pushed: 2024-05-01T21:56:34.000Z (about 2 months ago)
Last Synced: 2024-05-16T22:17:01.923Z (about 1 month ago)
Language: C++
Homepage:
Size: 29.4 MB
Stars: 124
Watchers: 19
Forks: 27
Open Issues: 17
Metadata Files:
- Readme: README.ROCm.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

Lists

Awesome-Caffe - hipCaffe-AMD’s Radeon Instinct GPUs
awesome-python-data-science - hipCaffe - The HIP port of Caffe. <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU"> (Deep Learning / Others)

README

# hipCaffe: the HIP Port of Caffe #

## Introduction ##

This repository hosts the HIP port of [Caffe](https://github.com/BVLC/caffe) (or hipCaffe, for short). For details on HIP, please refer [here](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP). 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.

## Prerequisites ##

### Hardware Requirements ###

* For ROCm hardware requirements, see [here](https://github.com/RadeonOpenCompute/ROCm/blob/master/README.md#supported-cpus)

### Software and Driver Requirements ###

* For ROCm software requirements, see [here](https://github.com/RadeonOpenCompute/ROCm/blob/master/README.md#the-latest-rocm-platform---rocm-15)

## Installation ##

### AMD ROCm Installation ###

For further background information on ROCm, refer [here](https://github.com/RadeonOpenCompute/ROCm/blob/master/README.md)

Install ROCm Debian packages:

PKG_REPO="http://repo.radeon.com/rocm/apt/debian/"

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-dkms rocm-utils rocm-opencl rocm-opencl-dev rocm-profiler cxlactivitylogger

Next, update your paths and reboot:

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 and the installed kernel modules:

uname -r

lsmod | grep kfd

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

pushd /opt/rocm/hsa/sample

make

./vector_copy

popd

### 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 \
libelf-dev

Install some misc development dependencies:

sudo apt-get install git wget

Install the necessary ROCm compute libraries:

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

### hipCaffe Build Steps ###

Clone hipCaffe (1.7.1 update: choosing the hip implementation):

git clone -b hip 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

make

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

./build/test/test_all.testbin

## Example Workloads ##

### MNIST training ###

Steps:

./data/mnist/get_mnist.sh

./examples/mnist/create_mnist.sh

./examples/mnist/train_lenet.sh

### CIFAR-10 training ###

Steps:

./data/cifar10/get_cifar10.sh

./examples/cifar10/create_cifar10.sh

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

### CaffeNet inference ###

Steps:

./data/ilsvrc12/get_ilsvrc_aux.sh

./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

### 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](https://github.com/ROCmSoftwarePlatform/hipCaffe/issues/11#issuecomment-318518802).

In short, here's the temporary workaround:
```
export HCC_UNPINNED_COPY_MODE=2
```

Please note that we have a long-term solution -- using a new RNG lib -- that we'll be pushing out soon.

## Tutorials

* [hipCaffe Quickstart Guide](http://rocm-documentation.readthedocs.io/en/latest/Tutorial/hipCaffe%20.html#hipcaffe)