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641 Release notes update post RC2 batch1 (#387)

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* Release highlight updated

* TOC updated for internal

* RC3 manifest added

* clarify docker image highlight

* update doc highlights

* RC3 changes added

* RC3 manifest added

* ROCm SMI version update

---------

Co-authored-by: Peter Park <peter.park@amd.com>
This commit is contained in:
Pratik Basyal
2025-05-06 15:07:54 -04:00
committed by GitHub
parent e28eac2fe1
commit 169f3bbe5e
6 changed files with 65 additions and 54 deletions
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RELEASE.md
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@@ -42,10 +42,12 @@ The following are notable new features and improvements in ROCm 6.4.1. For chang
AMD Instinct MI300X now supports DPX partition mode under NPS2 memory mode. For more partitioning information, see the [Deep dive into the MI300 compute and memory partition modes](https://rocm.blogs.amd.com/software-tools-optimization/compute-memory-modes/README.html) blog and [AMD Instinct MI300X system optimization](https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#change-gpu-partition-modes).
### Introducing the ROCm Data Science toolkit
The ROCm Data Science toolkit (or ROCm-DS) is an open-source software collection for high-performance data science applications built on the core ROCm platform. You can leverage ROCm-DS to accelerate both new and existing data science workloads, allowing you to execute intensive applications with larger datasets at lightning speed. ROCm-DS is in an early access state. Running production workloads is not recommended. For more information, see [AMD ROCm-DS Documentation](https://rocm.docs.amd.com/projects/rocm-ds/en/latest/index.html).
### ROCm Offline Installer Creator updates
The ROCm Offline Installer Creator 6.4.1 adds support for Debian 12 and enables the use of the SPACEBAR or ENTER keys for menu item selection in the GUI.
It also fixes an issue for “full” mode RHEL offline installer creation where GDM packages were uninstalled during offline installation. See [ROCm Offline Installer Creator](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-offline-installer.html) for more information.
The ROCm Offline Installer Creator 6.4.1 now allows you to use the SPACEBAR or ENTER keys for menu item selection in the GUI. It also adds support for Debian 12 and fixes an issue for “full” mode RHEL offline installer creation, where GDM packages were uninstalled during offline installation. See [ROCm Offline Installer Creator](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-offline-installer.html) for more information.
### ROCm Runfile Installer updates
@@ -64,10 +66,11 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
* [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.
* The [vLLM inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/vllm-benchmark.html?model=pyt_vllm_qwq-32b) guide has been updated to feature the latest [ROCm/vLLM](https://hub.docker.com/layers/rocm/vllm/instinct_main/images/sha256-ad9062dea3483d59dedb17c67f7c49f30eebd6eb37c3fac0a171fb19696cc845) Docker image. Support for the [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) model has been added.
* The [PyTorch inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/pytorch-inference-benchmark.html?model=pyt_clip_inference) guide has been added, featuring the [ROCm/PyTorch](https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-ab1d350b818b90123cfda31363019d11c0d41a8f12a19e3cb2cb40cf0261137d) Docker image with initial support for the [CLIP](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K) and [Chai-1](https://huggingface.co/chaidiscovery/chai-1) models.
* 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. 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. Support for [Llama 3.3 70B](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) has been added.
* 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.
* The [vLLM inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/vllm-benchmark.html?model=pyt_vllm_qwq-32b) guide has been updated to feature the latest [ROCm/vLLM](https://hub.docker.com/layers/rocm/vllm/instinct_main/images/sha256-ad9062dea3483d59dedb17c67f7c49f30eebd6eb37c3fac0a171fb19696cc845) Docker image (a preconfigured environment for inference with ROCm and [vLLM](https://docs.vllm.ai/en/latest/)). Support for the [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) model has been added.
* The [PyTorch inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/pytorch-inference-benchmark.html?model=pyt_clip_inference) guide has been added, featuring the [ROCm/PyTorch](https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-ab1d350b818b90123cfda31363019d11c0d41a8f12a19e3cb2cb40cf0261137d) Docker image (a preconfigured inference environment with ROCm and PyTorch) with initial support for the [CLIP](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K) and [Chai-1](https://huggingface.co/chaidiscovery/chai-1) models.
* The [Data types and precision support](https://rocm.docs.amd.com/en/latest/reference/precision-support.html) topic has been updated with new information in the library's precision support list.
* The deep learning frameworks compatibility pages have been updated with new information and are reorganized, making them easier to review. For more information, see [PyTorch compatibility](https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html), [TensorFlow compatibility](https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html), and [JAX compatibility](https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html).
@@ -154,7 +157,7 @@ Click {fab}`github` to go to the component's source code on GitHub.
<th rowspan="2"></th>
<th rowspan="2">Communication</th>
<td><a href="https://rocm.docs.amd.com/projects/rccl/en/docs-6.4.0/index.html">RCCL</a></td>
<td>2.22.3&nbsp;&Rightarrow;&nbsp;<a href="#rccl-2-23-4">2.23.4</a></td>
<td>2.22.3&nbsp;&Rightarrow;&nbsp;<a href="#rccl-2-22-3">2.22.3</td>
<td><a href="https://github.com/ROCm/rccl"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
@@ -276,7 +279,7 @@ Click {fab}`github` to go to the component's source code on GitHub.
<th rowspan="7">Tools</th>
<th rowspan="7">System management</th>
<td><a href="https://rocm.docs.amd.com/projects/amdsmi/en/docs-6.4.0/index.html">AMD SMI</a></td>
<td>25.3.0&nbsp;&Rightarrow;&nbsp;<a href="#amd-smi-25-4-0">25.4.0</a></td>
<td>25.3.0&nbsp;&Rightarrow;&nbsp;<a href="#amd-smi-25-4-2">25.4.2</a></td>
<td><a href="https://github.com/ROCm/amdsmi"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
@@ -291,7 +294,7 @@ Click {fab}`github` to go to the component's source code on GitHub.
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocm_smi_lib/en/docs-6.4.0/index.html">ROCm SMI</a></td>
<td>7.5.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-smi-7-6-0">7.6.0</a></td>
<td>7.5.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-smi-7-5-0">7.5.0</a></td>
<td><a href="https://github.com/ROCm/rocm_smi_lib"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
@@ -318,7 +321,7 @@ Click {fab}`github` to go to the component's source code on GitHub.
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocprofiler-systems/en/docs-6.4.0/index.html">ROCm Systems Profiler</a></td>
<td>1.0.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-systems-profiler-1-0-0">1.0.0</td>
<td>1.0.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-systems-profiler-1-0-1">1.0.1</td>
<td><a href="https://github.com/ROCm/rocprofiler-systems"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
@@ -416,7 +419,7 @@ The following sections describe key changes to ROCm components.
For a historical overview of ROCm component updates, see the {doc}`ROCm consolidated changelog </release/changelog>`.
```
### **AMD SMI** (25.4.0)
### **AMD SMI** (25.4.2)
#### Added
@@ -457,7 +460,7 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/roc
* Fixed an accuracy issue for some solutions using an `FP32` or `TF32` data type with a TT transpose.
### **RCCL** (2.23.4)
### **RCCL** (2.22.3)
#### Changed
@@ -466,7 +469,7 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/roc
#### Resolved issues
* Fixed an issue where early termination, in rare circumstances, could cause the application to stop responding by adding synchronization before destroying a proxy thread.
* Fixed the accuracy issue for the MSCCLPP `allreduc7` kernel in graph mode.
* Fixed the accuracy issue for the MSCCLPP `allreduce7` kernel in graph mode.
### **rocALUTION** (3.2.3)
@@ -493,7 +496,7 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/roc
- Fixed [ROCProfiler](https://rocm.docs.amd.com/projects/rocprofiler/en/docs-6.4.0/index.html) eval metrics and memory leaks.
### **ROCm SMI** (7.6.0)
### **ROCm SMI** (7.5.0)
#### Resolved issues
@@ -503,12 +506,16 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/roc
See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
```
### **ROCm Systems Profiler** (1.0.0)
### **ROCm Systems Profiler** (1.0.1)
#### Added
* How-to document for [network performance profiling](https://rocm.docs.amd.com/projects/rocprofiler-systems/en/amd-staging/how-to/nic-profiling.html) for standard Network Interface Cards (NICs).
#### Resolved issues
* Fixed a build issue with Dyninst on GCC 13.
### **ROCr Runtime** (1.15.0)
#### Resolved issues