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AI Model Training Pipeline

Build a high-performance, automated pipeline for training large scale AI models.

Architecture Overview​

This architecture leverages a mix of standard Virtual Instances for data preprocessing and powerful Bare Metal GPU nodes for the intensive training phase.

Workflow Steps​

  1. Ingestion: Raw training data is uploaded to Object Storage (S3-compatible).
  2. Preprocessing: A fleet of CPU-optimized Virtual Instances spins up to clean, tokenize, and format the data.
  3. Storage: Processed data is written to a high-throughput shared file system (e.g., connected via NVMe over Fabric) accessible by the GPU nodes.
  4. Training: The Job Scheduler detects new data and launches a distributed training job across multiple Bare Metal H100 servers.
  5. Checkpointing: Model weights are saved periodically to the Model Registry / Object Storage.

Infrastructure Requirements​

  • Compute: Bare metal instances with 8x NVIDIA H100 GPUs for training; general purpose CPU instances for preprocessing.
  • Networking: High-bandwidth (100Gbps+) private networking between GPU nodes for gradient synchronization (NCCL).
  • Storage: Low-latency block storage for the shared training dataset bucket.