Deep Learning on ROCm
ROCm Tensorflow v1.10 Release
We are excited to announce the release of ROCm enabled TensorFlow v1.10 for AMD GPUs.
First, you’ll need to install the open-source ROCm 1.9.0 stack. Details can be found here: https://rocm.github.io/ROCmInstall.html Then, install these other relevant ROCm packages:
sudo apt update sudo apt install rocm-libs miopen-hip cxlactivitylogger
And finally, install TensorFlow itself (via the Python Package Index):
sudo apt install wget python3-pip # Pip3 install the whl package from PyPI pip3 install --user tensorflow-rocm
Now that Tensorflow v1.10 is installed!
Tensorflow More Resources
Tensorflow docker images are also publicly available, more details can be found here: https://hub.docker.com/r/rocm/tensorflow/
Please connect with us for any questions, our official github repository is here: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream
ROCm MIOpen v1.5 Release
Announcing our new Foundation for Deep Learning acceleration MIOpen 1.5 which introduces support for Convolution Neural Network (CNN) acceleration — built to run on top of the ROCm software stack!
This release includes the following:
- Layer fusion API for inference with convolution, bias, batch norm, and activation operators
- Deep Convolution Solvers optimized for both forward and backward propagation
- Optimized Convolutions including Winograd and FFT transformations
- Optimized GEMM’s for Deep Learning
- Pooling, Softmax, Activations, Gradient Algorithms Batch Normalization, and LR Normalization
- MIOpen describes data as 4-D tensors ‒ Tensors 4D NCHW format
- Support for OpenCL and HIP enabled frameworks API’s
- MIOpen Driver enables the testing of forward/backward calls of any particular layer in MIOpen.
- Binary Package support for Ubuntu 16.04 and Fedora 24
- Source code at https://github.com/ROCmSoftwarePlatform/MIOpen
Porting from cuDNN to MIOpen
The porting guide highlights the key differences between the current cuDNN and MIOpen APIs.
The ROCm 1.9 has prebuilt packages for MIOpen
Install the ROCm MIOpen implementation (assuming you already have the ‘rocm’ and ‘rocm-opencl-dev” package installed):
For just OpenCL development
sudo apt-get install miopengemm miopen-opencl
For HIP development
sudo apt-get install miopengemm miopen-hip
Or you can build from source code
Deep Learning Framework support for ROCm
|Framework||Status||MIOpen Enabled||Upstreamed||Current Repository|
|Tensorflow||Development||Yes||CLA in Progress||Notes: Working on NCCL and XLA enablement, Running|
|Caffe2||Upstreaming||Yes||CLA in Progress||https://github.com/ROCmSoftwarePlatform/caffe2|
|Torch HIP||Upstreaming||Development||In process||https://github.com/ROCmSoftwarePlatform/cutorch_hip|