Description

This curriculum spans the technical and operational complexity of a multi-workshop program for building and operating HPC-powered machine learning systems, comparable to the internal capability development seen in enterprises deploying large-scale ML infrastructure across cloud and on-prem environments.

Module 1: Architecting Scalable Compute Infrastructure for ML Workloads

Selecting between on-premises GPU clusters and cloud-based HPC instances based on data residency requirements and burst scalability needs.
Designing multi-tenant Kubernetes clusters with GPU partitioning to isolate workloads across business units.
Implementing NVMe-over-Fabrics storage tiers to reduce I/O bottlenecks during large-scale model training.
Configuring RDMA-enabled networking (RoCE or InfiniBand) for low-latency communication in distributed training jobs.
Establishing cost governance policies for preemptible/spot instances in cloud environments to balance budget and training continuity.
Integrating hardware health monitoring tools to detect GPU memory degradation and compute node failures proactively.

Module 2: Distributed Training Frameworks and Optimization

Choosing between data parallelism, model parallelism, and pipeline parallelism based on model size and batch constraints.
Implementing gradient accumulation strategies to simulate large batch sizes on limited GPU memory.
Configuring mixed-precision training with Tensor Cores while managing numerical stability in loss scaling.
Deploying Horovod or PyTorch Distributed with collective communication optimizations for all-reduce operations.
Managing checkpointing frequency and format (e.g., Safetensors vs. Pickle) to balance fault tolerance and storage overhead.
Optimizing learning rate schedules in multi-node environments to prevent convergence instability due to delayed gradients.

Module 3: Data Pipeline Engineering at Scale

Designing sharded dataset formats (e.g., TFRecord, WebDataset) to enable efficient parallel data loading across nodes.
Implementing asynchronous data prefetching and decoding pipelines using DALI or TensorFlow tf.data.
Applying data augmentation on GPU to offload preprocessing from CPU and reduce pipeline latency.
Managing data versioning and lineage tracking in distributed environments using Delta Lake or DVC.
Enforcing data access controls and encryption in transit for sensitive training data across distributed nodes.
Monitoring data drift in streaming pipelines using statistical tests and triggering retraining workflows automatically.

Module 4: Model Compilation and Hardware Acceleration

Applying model quantization (INT8, FP16) using TensorRT or ONNX Runtime while measuring accuracy degradation thresholds.
Using Just-in-Time (JIT) compilers like TorchScript or JAX XLA to optimize computational graphs for specific hardware.
Partitioning models across multiple accelerators (e.g., TPU pods, multi-GPU) using automatic sharding strategies.
Integrating FPGA-based inference accelerators for low-latency, high-throughput scoring in real-time applications.
Profiling kernel execution times using Nsight Systems to identify bottlenecks in fused operations.
Managing firmware and driver compatibility across heterogeneous compute environments (GPU, TPU, ASIC).

Module 5: Performance Monitoring and Observability

Deploying Prometheus and Grafana to monitor GPU utilization, memory pressure, and inter-node communication latency.
Instrumenting training jobs with custom metrics (e.g., steps per second, loss per epoch) for performance benchmarking.
Correlating system-level telemetry (CPU, memory, disk I/O) with model convergence behavior to detect resource starvation.
Setting up distributed tracing for end-to-end latency analysis in multi-stage ML pipelines.
Establishing alerting thresholds for abnormal job termination or prolonged idle compute states.
Archiving performance profiles for audit and reproducibility in regulated business environments.

Module 6: Governance, Security, and Compliance in HPC-ML Systems

Implementing role-based access control (RBAC) for HPC clusters to restrict model training and data access by team.
Encrypting model checkpoints and intermediate artifacts at rest using KMS-integrated storage solutions.
Conducting periodic vulnerability scans on container images used in distributed training environments.
Enforcing model provenance tracking to meet regulatory requirements for audit and explainability.
Applying data masking or differential privacy techniques in training when handling PII at scale.
Documenting infrastructure configurations and change logs for compliance with SOC 2 or ISO 27001 standards.

Module 7: Cost Management and Resource Orchestration

Implementing dynamic scaling policies in Kubernetes based on queue depth and job priority in ML training pipelines.
Using spot instance fallback logic to maintain training continuity during cloud provider capacity interruptions.
Allocating GPU quotas per team to prevent resource monopolization in shared HPC environments.
Applying job scheduling algorithms (e.g., fair share, gang scheduling) to optimize cluster utilization.
Tracking cost attribution per model training run using cloud billing tags and custom metadata.
Automating cluster shutdown for non-production environments during off-peak hours to reduce idle spend.

Module 8: Integration with Business-Critical Applications

Designing low-latency inference APIs using Triton Inference Server with dynamic batching for high concurrency.
Implementing A/B testing frameworks to compare HPC-trained models in production traffic safely.
Integrating model outputs with enterprise data warehouses for downstream reporting and analytics.
Ensuring SLA compliance for inference endpoints by configuring autoscaling and circuit breakers.
Deploying shadow mode inference to validate new models against live traffic without impacting decisions.
Establishing rollback procedures for model versions that degrade business KPIs post-deployment.