DLRMBenchmark

Repository for showcasing DLRM v2 functionality on a single AMD node (8x MI3xx). This codebase is not meant to highlight the peak achievable performance. The model has been derived from the ML Perf DLRM v2 repo.

Installation

Requirements

• Single-node w/ 8x MI300X/325X/350X
• Docker / Torch wheels for ROCm

Setup

1. Clone repository:

   git clone https://github.com/AMD-AGI/DLRMBenchmark.git
   

2. Update permissions to 777 for all shell scripts
3. Container with ROCm, PyTorch, FBGEMM and torchrec installed are available at https://hub.docker.com/r/rocm/pytorch-training/. Pull the container: docker pull rocm/pytorch-training:v25.10_gfx942
4. Launch container. Ensure all required paths including codebase are mounted (similar to /home_dir/).

   docker run -d \
   --ipc=host \
   -v /dev/shm:/dev/shm \
   -v /home_dir/:/home_dir/ \
   -e USER=$user -e UID=$uid -e GID=$gid \
   --device=/dev/kfd \
   --device=/dev/dri \
   --device=/dev/infiniband \
   --ulimit memlock=-1:-1 \
   --shm-size 32G \
   --cap-add=SYS_PTRACE \
   --security-opt seccomp=unconfined \
   --group-add video \
   --network=host \
   --name dlrm_demo \
   -it rocm/pytorch-training:v25.10_gfx942 \
   tail -f /dev/null
   

Model Training

1. Start interactive shell session within container: docker exec -it dlrm_demo bash
2. Modify training configuration (if required) in training_config.sh
3. Launch training using ./launch_training_single_node.sh. Check for training progress in the ./training_logs folder. Upon completion, the final row in the results.csv file shows the mean rec/s.