In addition to Tensorflow v1. Both whl packages and docker containers are available below. For details, see here. Note : firstly, configure docker environment for ROCm information here. Pull the docker images for Tensorflow releases with ROCm backend support. The size of these docker images is about 7 Gb. More information about tensorflow docker images can be found here.
The official github repository is here. Source code. Or you can build from source code. Currently both the backends cannot be installed on the same system simultaneously. If a different backend other than what currently exists on the system is desired, please uninstall the existing backend completely and then install the new backend.
Using Docker gives you portability and access to a pre-built docker container that has been rigorously tested within AMD. This might also save on the compilation time and should perform exactly as it did when tested, without facing potential installation issues.
This option provides a docker image which has PyTorch pre-installed. This will automatically download the image if it does not exist on the host. You can also pass -v argument to mount any data directories from the host onto the container. PyTorch supports the ROCm platform by providing tested wheel packages. In this example, ROCm 4. A pre-built base Docker image is used to build PyTorch in this option. The base docker has all dependencies installed, including ROCm, torch-vision, Conda packages, and the compiler tool-chain.
By default, PyTorch builds for gfx , gfx, and gfx architectures simultaneously. To determine your AMD uarch, run. Instead of using a pre-built base Docker image, a custom base Docker image can be built using scripts from the PyTorch repository.
This will utilize a standard Docker image from operating system maintainers and install all the dependencies required to build PyTorch, including ROCm, torch-vision, Conda packages, and the compiler tool-chain. To determine your AMD uarch, run rocminfo grep gfx. PyTorch unit tests can be used to validate a PyTorch installation. Alternatively, you can manually run the unit tests to validate the PyTorch installation fully.
Test if PyTorch is installed and accessible by importing the torch package in Python. Note, do not run in the PyTorch git folder. Run the unit tests to validate the PyTorch installation fully. Run the following command from the PyTorch home directory. This will first install some dependencies, such as a supported TorchVision version for PyTorch.
TorchVision is used in some PyTorch tests for loading models. NOTE: Some tests may be skipped, as appropriate, based on your system configuration. All features of PyTorch are not supported on ROCm, and the tests that evaluate these features are skipped.
In addition, depending on the host memory, or the number of available GPUs, other tests may be skipped. No test should fail if the compilation and installation are correct. The PyTorch examples repository provides basic examples that exercise the functionality of the framework. MNIST database is a collection of handwritten digits, that may be used to train a Convolutional Neural Network for handwriting recognition.
Alternatively, ImageNet is a database of images used to train a network for visual object recognition. In this case, pip3 install -r requirements. This requires your host system to have rocm It is recommended to add the user to the docker group to run docker as a non-root user, please refer here. This option provides a docker image which has Caffe2 installed.
You can also pass -v argument to mount any data directories on to the container. After cloning the pytorch repository, you can build your own Caffe2 ROCm docker image. Navigate to pytorch repo and run. More information about the performance database can be found here. In the cache directory there exists a directory for each version of MIOpen. If the compiler changes, or the user modifies the kernels then the cache must be deleted for the MIOpen version in use; e.
More information about the cache can be found here. MIOpen's kernel cache directory is versioned so that users' cached kernels will not collide when upgrading from earlier version. The configuration can be changed after running cmake by using ccmake :. OR cmake-gui : cmake-gui..
The ccmake program can be downloaded as the Linux package cmake-curses-gui , but is not available on windows. The library can be built, from the build directory using the 'Release' configuration:. MIOpen provides an application-driver which can be used to execute any one particular layer in isolation and measure performance and verification of the library. The driver can be built using the MIOpenDriver target:. Documentation on how to run the driver is here.
This will build a local searchable web site inside the. Documentation is built using generated using Doxygen and should be installed separately. Depending on your setup sudo may be required for the pip install. All the code is formatted using clang-format.
To format a file, use:. Also, githooks can be installed to format the code per-commit:. If Ubuntu v16 is used then the Boost packages can also be installed by:. Note: MIOpen by default will attempt to build with Boost statically linked libraries. If it is needed, the user can build with dynamically linked Boost libraries by using this flag during the configruation stage:. The half header needs to be installed from here. The easiest way is to use docker.
You can build the top-level docker file:. Then to enter the development environment use docker run , for example:. Prebuilt docker images can be found on ROCm's public docker hub here. Skip to content. Star MIT License. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
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MIOpen provides an application-driver which can be used to execute any one particular layer in isolation and measure performance and verification of the library. Advanced Micro Devices, Inc's open source deep learning library. Sources and binaries can be found at MIOpen's GitHub site. Contents: MIOpen Release notes. Deep neural networks can be decomposed into a series of different operators. MIOpen, AMD's open-source deep learning primitives library for GPUs.