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Update 6.4.1 release notes (#399)

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* remove extra file

* Update wording in RELEASE.md

* Update RELEASE.md

Co-authored-by: Jeffrey Novotny <jnovotny@amd.com>

* update amdsmi changelog

* install -> installed

t

---------

Co-authored-by: Jeffrey Novotny <jnovotny@amd.com>
This commit is contained in:
Peter Park
2025-05-14 15:41:12 -04:00
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parent d1debc7e45
commit 2a3c2fe5aa
2 changed files with 12 additions and 85 deletions
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RELEASE.md
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@@ -56,7 +56,7 @@ The ROCm Runfile Installer 6.4.1 adds the following improvements:
- Performance improvements for detecting a previous ROCm install.
- Removal of the extra `opt` directory created for the target during the ROCm installation. For example, installing to `target=/home/amd` now installs ROCm to `/home/amd/rocm-6.4.1` and not `/home/amd/opt/rocm-6.4.1`. For installs using `target=/`, the installation will continue to use `/opt/`.
- The Runfile Installer can be used to uninstall any Runfile-based installation of the driver.
- In the CLI interface, The `postrocm` argument can now be run separately from the `rocm` argument. In cases where `postrocm` was missed from the initial ROCm install, `postrocm` can now be run on the same target folder. For example, if you install ROCm 6.4.1 using: `install.run target=/myrocm rocm` you can run the post-installation separately using the command `install.run target=/myrocm/rocm-6.4.1 postrocm`.
- In the CLI interface, the `postrocm` argument can now be run separately from the `rocm` argument. In cases where `postrocm` was missed from the initial ROCm install, `postrocm` can now be run on the same target folder. For example, if you installed ROCm 6.4.1 using `install.run target=/myrocm rocm`, you can run the post-installation separately using the command `install.run target=/myrocm/rocm-6.4.1 postrocm`.
For more information, see [ROCm Runfile Installer](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-runfile-installer.html).
@@ -64,8 +64,7 @@ For more information, see [ROCm Runfile Installer](https://rocm.docs.amd.com/pro
ROCm documentation continues to be updated to provide clearer and more comprehensive guidance for a wider variety of user needs and use cases.
* [Tutorials for AI developers](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/) have been expanded with five
new tutorials. These tutorials are Jupyter notebook-based, easy-to-follow documents. They are ideal for AI developers who want to learn about specific topics, including inference, fine-tuning, and training.
* [Tutorials for AI developers](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/) have been expanded with five new tutorials. These tutorials are Jupyter notebook-based, easy-to-follow documents. They are ideal for AI developers who want to learn about specific topics, including inference, fine-tuning, and training. For more information about the changes, see [Changelog for the AI Developer Hub](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/changelog.html).
* The [Training a model with LLM Foundry](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry.html) performance testing guide has been added. This guide describes how to use the preconfigured [ROCm/pytorch-training](https://hub.docker.com/layers/rocm/pytorch-training/v25.5/images/sha256-d47850a9b25b4a7151f796a8d24d55ea17bba545573f0d50d54d3852f96ecde5) training environment and [https://github.com/ROCm/MAD](https://github.com/ROCm/MAD) to test the training performance of the LLM Foundry framework on AMD Instinct MI325X and MI300X accelerators using the [MPT-30B](https://huggingface.co/mosaicml/mpt-30b) model.
* The [Training a model with PyTorch](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html) performance testing guide has been updated to feature the latest [ROCm/pytorch-training](https://hub.docker.com/layers/rocm/pytorch-training/v25.5/images/sha256-d47850a9b25b4a7151f796a8d24d55ea17bba545573f0d50d54d3852f96ecde5) Docker image (a preconfigured training environment with ROCm and PyTorch). Support for [Llama 3.3 70B](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) has been added.
* The [Training a model with JAX MaxText](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.html) performance testing guide has been updated to feature the latest [ROCm/jax-training](https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.5/images/sha256-4e0516358a227cae8f552fb866ec07e2edcf244756f02e7b40212abfbab5217b) Docker image (a preconfigured training environment with ROCm, JAX, and [MaxText](https://github.com/AI-Hypercomputer/maxtext)). Support for [Llama 3.3 70B](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) has been added.
@@ -426,8 +425,17 @@ For a historical overview of ROCm component updates, see the {doc}`ROCm consolid
* Dumping CPER entries from RAS tool `amdsmi_get_gpu_cper_entries()` to Python and C APIs.
- Dumping CPER entries consist of `amdsmi_cper_hdr_t`.
- Dumping CPER entries is also enabled in the CLI interface through `sudo amd-smi ras --cper`.
* `amdsmi_get_gpu_busy_percent` to the C API.
#### Resolved
#### Changed
* Modified VRAM display for amd-smi monitor -v.
#### Optimized
* Improved load times for CLI commands when the GPU has multiple parititons.
#### Resolved issues
* Fixed partition enumeration in `amd-smi list -e`, `amdsmi_get_gpu_enumeration_info()`, `amdsmi_enumeration_info_t`, `drm_card`, and `drm_render` fields.