Posts by Phani Vaddadi
DGL in the Real World: Running GNNs on Real Use Cases
- 20 August 2025
In our previous blog post, we introduced the Deep Graph Library (DGL) and highlighted how its support on the AMD ROCm platform unlocks scalable, performant graph neural networks (GNNs) on AMD GPUs. That post focused on the why — the growing relevance of graph workloads and what it means to bring that capability to AMD’s accelerated computing ecosystem.
Graph Neural Networks at Scale: DGL with ROCm on AMD Hardware
- 31 July 2025
This blog introduces the Deep Graph Library (DGL) and explores its significance on AMD hardware for enabling scalable, performant graph neural networks.
Accelerating Parallel Programming in Python with Taichi Lang on AMD GPUs
- 31 July 2025
Taichi Lang is an open-source, imperative, parallel programming language for high-performance numerical computation. It is embedded in Python and uses just-in-time (JIT) compiler frameworks (e.g. LLVM) to offload the compute-intensive Python code to the native GPU or CPU instructions. The language has broad applications spanning real-time physical simulation, numerical computation, augmented reality, artificial intelligence, vision and robotics, visual effects in films and games, general-purpose computing, and much more [1].
Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration
- 24 April 2025
In this blog post, we provide an overview of Volcano Engine Reinforcement Learning for LLMs (verl) and discuss its benefits in large-scale reinforcement learning from human feedback (RLHF). We also detail the modifications made to the codebase to optimize verl’s performance on AMD Instinct GPUs. Next, we walk through the process of building the Docker image using a Dockerfile on the user side, along with training scripts tailored for both single-node and multi-node setups. Lastly, we present verl’s performance results, focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and accelerate your RLHF training with ROCm-optimized performance.
Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs
- 23 March 2025
Training massive deep-learning models requires a balance of efficiency and scalability. In the context of the Transformers architecture, Mixture of Experts (MoE) models are massive machine learning architectures characterized for dividing tasks among multiple specialized sub-networks or “experts”. A gating network determines the expert to which a given input should be routed, enabling the model to handle complex tasks more efficiently by using the specialized capabilities of each expert. This dynamic routing mechanism allows MoE models to scale efficiently, activating only a subset of the network for each input, therefore reducing computational load while maintaining high model capacity.