Google Cloud TPU

Start by spinning up a TPU VM, attaching it to your project, and pointing it at your training data in Cloud Storage. Pick a topology that fits your goal: v5e pod slices for cost‑efficient fine‑tuning and high‑throughput inference, or v5p for large, multi‑host training runs. Use the provided TensorFlow, JAX, or PyTorch/XLA images, or bring your own Docker container. Build an input pipeline with tf.data or JAX prefetch that reads TFRecords or WebDataset shards from GCS, caches hot samples, and overlaps host I/O with device compute. Enable bfloat16 mixed precision, set a reproducible seed, and verify a single‑chip run before scaling out.

When you’re ready to scale, shard your model and data across chips using SPMD via pjit/pmap (JAX) or tf.distribute strategies (TensorFlow). Define a global batch size, tune per‑device microbatches if memory is tight, and use gradient accumulation to preserve optimizer behavior. Profile with the TPU profiler and TensorBoard to spot input stalls, all‑reduce hotspots, or matmul shapes that miss peak throughput. Checkpoint to GCS with frequent, atomic writes so preemptions are painless, and resume training by restoring optimizer state. Launch parallel hyperparameter sweeps with Vertex AI or your own orchestrator, pinning each trial to a specific pod slice to keep utilization high.

For generative workloads, fine‑tune LLMs with LoRA/QLoRA or train instruction heads for use cases like document drafting, code suggestions, and chatbot assistants. Offload tokenization to CPU threads, pack sequences to reduce padding, and monitor tokens/sec per chip. To serve, deploy a TPU‑backed inference service with dynamic batching and request prioritization, or host on Vertex AI for managed autoscaling. Set sensible defaults (temperature, top_p, max_tokens), enable streaming responses, and log prompts/completions for safety and offline evaluation. For image workflows, run diffusion models on v5e for batch content generation (creative variants, product shots) and export to GCS with metadata for asset management.

Operationalize your stack with CI/CD and reproducible environments. Use Vertex AI Pipelines or GKE Jobs to schedule training, attach preemptible TPUs for non‑urgent runs, and checkpoint every N steps to tolerate interruptions. Track spend with budgets and alerts, tag jobs by team or project, and capture metrics (throughput, step time, first token latency) in Cloud Monitoring. Lock down access with IAM and VPC‑SC, keep artifacts in Artifact Registry, and audit every deployment. A/B test new models behind a canary TPU pool, roll back instantly if KPIs regress, and document the exact model, data snapshot, and flags used in production.