Optimizing Training Workloads for GPU Clusters

This article outlines best practices focused on optimizing training workloads on GPU clusters. It is intended for machine learning engineers, infrastructure specialists, and MLOps teams seeking to maximize throughput, reliability, and cost-efficiency.

1. Cluster Planning

Cluster Sizing

Avoid over-provisioning at the outset. Start with a smaller configuration to benchmark training throughput and memory usage, then scale as needed. Cluster sizing depends on:

• GPU Type: Select based on performance characteristics. For example:
  • NVIDIA A100 or H100 for transformer-based models.
  • L4 or A10G for inference-heavy or low-latency workloads.
• Training modern machine learning models—especially large language models and multimodal systems—requires careful orchestration of compute, storage, and data pipelines. GPU clusters offer the performance necessary for these workloads, but without deliberate planning and system-level optimizations, teams often face underutilized resources and unpredictable bottlenecks.
• Model Architecture: LLMs and vision transformers require more memory and bandwidth. Video, robotics, and biology applications often have mixed CPU/GPU demands.
• Batch Size and Sequence Length: These parameters significantly influence memory requirements and should be tested during scaling trials.
• Dataset Size: Very large datasets may require preprocessing pipelines that balance throughput against memory and network bandwidth.

Data Placement

• Position datasets close to the GPU nodes to minimize latency. Use node-local NVMe or high-throughput parallel file systems like Lustre or BeeGFS.
• Account for data transfer time when ingesting from external object stores. Tools like rclone, gsutil, or aws s3 sync should be tested for throughput.
• Validate that your storage layer supports the IOPS and bandwidth required for high-throughput model training.

Orchestration: Kubernetes vs. Slurm

• Kubernetes is container-native, supports autoscaling, and integrates well with modern ML stacks. GPU support requires proper deployment of device plugins and runtime class configuration.
• Slurm provides mature support for tightly coupled HPC workloads, especially those requiring MPI or RDMA-based communication.

Teams should choose based on workload characteristics and operational experience.

Software Stack Compatibility

• Ensure GPU drivers, CUDA, cuDNN, and container runtimes are aligned across the cluster.
• Mismatches are a common cause of runtime errors and degraded performance. Version pinning in Docker images and CI testing pipelines is recommended.
• Validate NCCL versions and settings when configuring multi-node communication.
  
  

2. Pre-Training Validations

Access Verification

• Confirm basic access to the cluster using CLI tools (kubectl, gcloud, aws, etc.).
• Verify kubeconfig files and authentication to the Kubernetes API.
• Ensure separation between control plane (CPU nodes, services) and data plane (GPU worker nodes).

Hardware Health Checks

Run baseline commands to verify node readiness and hardware integrity:

• nvidia-smi: Reports GPU status, memory usage, temperature, and ECC errors.
• kubectl get nodes -o wide: Ensures GPU nodes are schedulable and reporting correctly.
• Use nvidia-smi topo -m or NCCL tests to verify GPU-to-GPU communication topology (especially important with NVLink or InfiniBand).
• ECC errors should be addressed before training begins, as they can indicate hardware instability.

System Configuration

• Validate Docker image layers, CUDA libraries, and model framework dependencies.
• For high-performance networking, ensure RDMA interfaces (e.g., via RoCE or Infiniband) are properly configured and visible to the container runtime.
• Confirm resource quotas, limits, and scheduling policies are not constraining GPU workloads.
  
  

3. Optimization Techniques

Workload Profiling

Understanding how your model utilizes compute and memory is the basis for optimization. Use profiling tools such as:

• nvidia-smi dmon or DCGM for real-time GPU metrics.
• Framework-level profilers (e.g., PyTorch Profiler, TensorFlow Profiler) for operation-level insights.

Identify time spent in data loading, forward/backward pass, communication, and loss computation.

Data Pipeline Optimization

• Avoid CPU bottlenecks in preprocessing by using multi-threaded or GPU-accelerated data loaders.
• For image or video tasks, preprocess data into efficient binary formats such as TFRecord or WebDataset.
• If using a distributed filesystem, pre-stage datasets to node-local storage to reduce runtime contention.

Storage Strategies

• Use local NVMe when performance is critical and the dataset can be partitioned across nodes.
• Parallel file systems are better suited for very large datasets but require tuning to minimize metadata contention and improve aggregate throughput.
• Monitor disk IO using tools like iostat, nmon, or custom Prometheus exporters.

Minimizing Network Overhead

• Use NCCL’s ring or tree communication algorithms based on topology and message size.
• Enable topology-aware scheduling to co-locate workers with low-latency interconnects.
• Minimize cross-node communication in early training stages (e.g., model sharding, gradient accumulation).

Monitoring and Observability

• Set up dashboards with GPU metrics (utilization, power draw, memory bandwidth) and node metrics (CPU load, memory usage, network throughput).
• Use nvidia-smi, DCGM, and Kubernetes metrics-server as primary data sources.
• Implement log-based alerts for node failures, container restarts, and GPU errors (e.g., via Prometheus + Grafana or Fluentd + Loki).

Failure Recovery

• Use periodic checkpointing to resume training from intermediate states in case of preemption or hardware failure.
• Monitor SSD health metrics if using local caching. Failures can lead to silent data loss.
• Use autoscaling policies that can detect and replace unresponsive or failed GPU nodes.
  

4. Conclusion

Effective training on GPU clusters requires more than access to powerful hardware. It demands coordination across orchestration systems, storage configurations, data pipelines, and runtime environments. The upfront investment in planning and validation pays dividends in reduced downtime, faster experimentation, and lower operational costs.

Together AI’s infrastructure platform supports instant cluster provisioning and comes pre-configured with the necessary software stack to streamline these steps. Users can try an instant cluster, review documentation, or join the support community to further optimize their training pipelines.

• Instant Clusters: together.ai/instant
• Documentation: docs.together.ai
• Support Community: discord.com/invite/9Rk6sSeWEG