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76 lines
3.8 KiB
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76 lines
3.8 KiB
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:orphan:
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.. meta::
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:description: Taichi compatibility
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:keywords: GPU, Taichi compatibility
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.. version-set:: rocm_version latest
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*******************************************************************************
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Taichi compatibility
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*******************************************************************************
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`Taichi <https://www.taichi-lang.org/>`_ is an open-source, imperative, and parallel
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programming language designed for high-performance numerical computation.
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Embedded in Python, it leverages just-in-time (JIT) compilation frameworks such as LLVM to accelerate
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compute-intensive Python code by compiling it to native GPU or CPU instructions.
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Taichi is widely used across various domains, including real-time physical simulation,
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numerical computing, augmented reality, artificial intelligence, computer vision, robotics,
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visual effects in film and gaming, and general-purpose computing.
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* ROCm support for Taichi is hosted in the official `https://github.com/ROCm/taichi <https://github.com/ROCm/taichi>`_ repository.
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* Due to independent compatibility considerations, this location differs from the `https://github.com/taichi-dev <https://github.com/taichi-dev>`_ upstream repository.
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* Use the prebuilt :ref:`Docker image <taichi-docker-compat>` with ROCm, PyTorch, and Taichi preinstalled.
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* See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` to install and get started.
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.. note::
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Taichi is supported on ROCm 6.3.2.
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Supported devices and features
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===============================================================================
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There is support through the ROCm software stack for all Taichi GPU features on AMD Instinct MI250X and MI210X series GPUs with the exception of Taichi’s GPU rendering system, CGUI.
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AMD Instinct MI300X series GPUs will be supported by November.
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.. _taichi-recommendations:
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Use cases and recommendations
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================================================================================
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To fully leverage Taichi's performance capabilities in compute-intensive tasks, it is best to adhere to specific coding patterns and utilize Taichi decorators.
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A collection of example use cases is available in the `https://github.com/ROCm/taichi_examples <https://github.com/ROCm/taichi_examples>`_ repository,
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providing practical insights and foundational knowledge for working with the Taichi programming language.
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You can also refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_ to search for Taichi examples and best practices to optimize your workflows on AMD GPUs.
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.. _taichi-docker-compat:
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Docker image compatibility
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================================================================================
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.. |docker-icon| raw:: html
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<i class="fab fa-docker"></i>
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AMD validates and publishes ready-made `ROCm Taichi Docker images <https://hub.docker.com/r/rocm/taichi/tags>`_
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with ROCm backends on Docker Hub. The following Docker image tags and associated inventories
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represent the latest Taichi version from the official Docker Hub.
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The Docker images have been validated for `ROCm 6.3.2 <https://rocm.docs.amd.com/en/docs-6.3.2/about/release-notes.html>`_.
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Click |docker-icon| to view the image on Docker Hub.
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.. list-table::
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:header-rows: 1
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:class: docker-image-compatibility
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* - Docker image
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- ROCm
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- Taichi
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- Ubuntu
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- Python
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* - .. raw:: html
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<a href="https://hub.docker.com/layers/rocm/taichi/taichi-1.8.0b1_rocm6.3.2_ubuntu22.04_py3.10.12/images/sha256-e016964a751e6a92199032d23e70fa3a564fff8555afe85cd718f8aa63f11fc6"><i class="fab fa-docker fa-lg"></i> rocm/taichi</a>
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- `6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_
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- `1.8.0b1 <https://github.com/taichi-dev/taichi>`_
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- 22.04
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- `3.10.12 <https://www.python.org/downloads/release/python-31012/>`_ |