PyTorch 1.6.0a + ROCm 3.3 for AMD RadeonGPU @ Apr 20th, 2020 Update and upgrade to latest packages. sudo apt update
sudo apt -y dist-upgrade
Install "Non Uniform Memory Access" dev package. sudo apt install -y libnuma-dev
Add the ROCm apt repository wget -q -O - http://repo.radeon.com/rocm/apt/debian/rocm.gpg.key | sudo apt-key add -
echo 'deb [arch=amd64] http://repo.radeon.com/rocm/apt/debian/ xenial main' | sudo tee /etc/apt/sources.list.d/rocm.list
sudo apt update
sudo apt install rocm-dkms
sudo reboot
AMDGPU
NVIDIA
Description
rocm-smi
nvidia-smi
GPU information command.
clinfo
clinfo
OpenCL GPU information command.
Test for rocm installations # Make sure to recognized GPUs as file descriptor.
ls /dev/dri/
# >> card0 renderD128
# Make sure to recognized GPUs.
/opt/rocm/bin/rocm-smi
# ========================ROCm System Management Interface========================
# ================================================================================
# GPU Temp AvgPwr SCLK MCLK Fan Perf PwrCap VRAM% GPU%
# 0 35.0c 18.0W 808Mhz 350Mhz 21.96% auto 250.0W 0% 0%
# ================================================================================
# ==============================End of ROCm SMI Log ==============================
# Make sure to recognized GPUs using OpenCL.
/opt/rocm/opencl/bin/x86_64/clinfo
# Number of platforms: 1
# Platform Profile: FULL_PROFILE
# Platform Version: OpenCL 2.1 AMD-APP (3098.0)
# Platform Name: AMD Accelerated Parallel Processing
# Platform Vendor: Advanced Micro Devices, Inc.
# Platform Extensions: cl_khr_icd cl_amd_event_callback cl_amd_offline_devices
#
#
# Platform Name: AMD Accelerated Parallel Processing
# Number of devices: 1
# Device Type: CL_DEVICE_TYPE_GPU
# Vendor ID: 1002h
# Board name: Vega 20
# Device Topology: PCI[ B#3, D#0, F#0 ]
# Max compute units: 60
# .
# .
# .
echo 'export PATH=$PATH:/opt/rocm/bin:/opt/rocm/profiler/bin:/opt/rocm/opencl/bin/x86_64' |
sudo tee -a /etc/profile.d/rocm.sh
sudo reboot
Set permissions. To access the GPU you must be a user in the video group To add your user to the video group run sudo usermod -a -G video $LOGNAME
Preparing for installing pytorch Install ROCm, mkl PyTorch/TensorFlow dependencies
sudo apt install -y gcc cmake clang ccache llvm ocl-icd-opencl-dev python3-pip
cd /tmp
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
sudo apt-get update && sudo apt-get install intel-mkl-64bit-2018.2-046
sudo apt install -y rocrand rocblas miopen-hip miopengemm rocfft rocprim rocsparse rocm-cmake rocm-dev rocm-device-libs rocm-libs rccl hipcub rocthrust
export PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/opencl/bin:$PATH
export USE_LLVM=/opt/llvm
export LLVM_DIR=/opt/llvm/lib/cmake/llvm
You can choose either to install from wheels or build from source
Python
ROCm
PyTorch
GPU
S
3.5
3.3
1.6.0a GFX900
✓
3.5
3.3
1.6.0a GFX906
✓
3.7
3.3
1.6.0a
GFX900
-
3.7
3.3
1.6.0a
GFX906
-
3.5
2.9
1.3.0a GFX900
✓
3.5
2.9
1.3.0a GFX906
✓
3.7
2.9
1.3.0a
GFX900
-
3.7
2.9
1.3.0a
GFX906
-
GFX Code
Architecture
Products
GFX806
Polaris Series
RX550/RX560/RX570/RX580/RX590 ...
GFX900
Vega10 Series
Vega64/Vega56/MI25/WX9100/FrontierEdition ...
GFX906
Vega20 Series
RadeonVII/MI50/MI60 ...
# RadeonVII(GFX906) ROCm3.3 PyTorch1.6.0a
sudo pip3 install http://install.aieater.com/libs/pytorch/rocm3.3/gfx906/torch-1.6.0a0+d83509e-cp35-cp35m-linux_x86_64.whl torchvision
# Vega64(GFX900) ROCm3.3 PyTorch1.6.0a
sudo pip3 install http://install.aieater.com/libs/pytorch/rocm3.3/gfx900/torch-1.6.0a0+d83509e-cp35-cp35m-linux_x86_64.whl torchvision
continue to test the installation.
Source build for developers git clone https://github.com/pytorch/pytorch.git
cd pytorch
'Hipify' PyTorch source by executing python tools/amd_build/build_amd.py python3 tools/amd_build/build_amd.py
Alternative to get a fixed version. wget http://install.aieater.com/libs/pytorch/sources/pytorch1.6.0.tar.gz
This pytorch project has already hippified, and cloned sub modules.
Required environment variables
GFX Code
Architecture
Products
GFX806
Polaris Series
RX550/RX560/RX570/RX580/RX590 ...
GFX900
Vega10 Series
Vega64/Vega56/MI25/WX9100/FrontierEdition ...
GFX906
Vega20 Series
RadeonVII/MI50/MI60 ...
#export HCC_AMDGPU_TARGET=gfx806 #(RX550/RX560/RX570/RX580/RX590 ...)
export HCC_AMDGPU_TARGET=gfx900 #(Vega64/Vega56/MI25/WX9100/FrontierEdition ...)
#export HCC_AMDGPU_TARGET=gfx906 #(RadeonVII/MI50/MI60 ...)
export USE_NINJA=1
export MAX_JOBS=8
export HIP_PLATFORM=hcc
echo $HCC_AMDGPU_TARGET
Install cmake and requirements to Build and install pip3 install -r requirements.txt
python3 setup.py install --user
Distribution build for wheel GPU visibly masking and multiple GPUs
AMDGPU
NVIDIA
Description
export=HIP_VISIBLE_DEVICES=
export=CUDA_VISIBLE_DEVICES=
CPU
export=HIP_VISIBLE_DEVICES=0
export=CUDA_VISIBLE_DEVICES=0
Single GPU
export=HIP_VISIBLE_DEVICES=0,1
export=CUDA_VISIBLE_DEVICES=0,1
Multiple GPUs
Make sure to recognize GPU device via PyTorch. # Check GPU is available or not.
python3 -c 'import torch;print("GPU:",torch.cuda.is_available())'
# GPU: True
python3 -c 'import torch;print("DeviceID:",str(torch.cuda.current_device()))'
# DeviceID: 0
python3 -c 'import torch;print("DeviceName:",str(torch.cuda.get_device_name(torch.cuda.current_device())))'
# DeviceName: Vega 20
Make sure everything is working PYTORCH_TEST_WITH_ROCM=1 python3 test/run_test.py --verbose
Also see, AMDGPU - ROCm Caffe/PyTorch/Tensorflow 1.x installation, official, introduction on docker