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!
- 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
- Documentation
The porting guide highlights the key differences between the current cuDNN and MIOpen APIs.
Install the ROCm MIOpen implementation (assuming you already have the ‘rocm’ and ‘rocm-opencl-dev” package installed):
sudo apt-get install miopengemm miopen-opencl
sudo apt-get install miopengemm miopen-hip
Or you can build from source code
Framework | Status | MIOpen Enabled | Upstreamed | Current Repository |
---|---|---|---|---|
Caffe | Public | Yes | https://github.com/ROCmSoftwarePlatform/hipCaffe | |
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 |
HIPnn | Upstreaming | Development | https://github.com/ROCmSoftwarePlatform/cunn_hip | |
PyTorch | Development | Development | ||
MxNet | Development | Development | https://github.com/ROCmSoftwarePlatform/mxnet | |
CNTK | Development | Development |