Google Cloud TPU

When deadlines are tight and models are large, you need hardware that moves at your pace. Spin up a Cloud TPU VM from the Console or with gcloud, select a region with available quota, and choose a slice size that matches your phase: a single device for bring‑up, a modest Pod slice when scaling starts, and larger slices only after profiling. Attach a service account with least‑privilege access, mount Cloud Storage buckets, and preload data shards near the TPU region. Install JAX, TensorFlow, or PyTorch/XLA on the TPU VM, pin package versions, and record the TPU runtime you’re using for reproducibility. Convert hot paths to XLA‑friendly ops, enable mixed precision (bfloat16 or FP8 where supported), prefetch aggressively, and verify correctness on a small validation set before turning up the batch size.\n\nTo grow training without rewriting your codebase, add distributed primitives that match your framework. In TensorFlow, wrap the training loop with tf.distribute.TPUStrategy and move input pipelines to tf.data for parallel reads; in JAX, use pjit/pmap with SPMD partitioning and gradient rematerialization for long sequences; in PyTorch, launch workers with torch_xla.distributed.xla_multiprocessing and shard datasets per host. Start with compilation warmups, cache compiled graphs, and keep step times stable by locking batch shapes. Use TPU profiler, Cloud Monitoring, and TensorBoard traces to spot input stalls, underutilized cores, or long compile times. Save checkpoints to Cloud Storage every N steps using fast, resumable writes; test preemptible runs to cut costs and confirm you can recover cleanly. For hyperparameter sweeps, wire your trainer to Vertex AI Vizier or your preferred tuner and log metrics to Vertex Experiments for side‑by‑side comparisons.\n\nFor production inference, choose cost‑efficient slices and right‑size for latency targets. Export XLA‑compiled graphs (StableHLO/SavedModel where applicable), fuse operations on the critical path, and set micro‑batching by time windows to balance throughput and tail latency. Quantize where accuracy budgets allow and verify with A/B tests on held‑out traffic. Deploy as a container on GKE with TPU nodes or run services directly on TPU VMs; set horizontal autoscaling on queue depth or p95 latency, and place instances behind Cloud Load Balancing for regional failover. Keep a small CPU or GPU canary path to validate new releases, run shadow inference against upcoming checkpoints, and gate promotion on automated SLO checks.\n\nOperating at scale is about discipline, not luck. Use IAM to restrict who can create Pod slices, set budgets and alerts, and label jobs for cost attribution. Store datasets in Cloud Storage with lifecycle policies; co‑locate shards with TPUs to reduce egress and input wait. Build repeatable pipelines with Vertex AI Pipelines or Cloud Build so a single commit can preprocess data, train, evaluate, and publish artifacts. Typical patterns include fine‑tuning a vision transformer on millions of images, pretraining a multilingual language model with sharded tokenization, running nightly recommendation refresh jobs, and bulk classifying radiology studies under strict SLAs. The playbook is consistent: prototype on a single device, profile throughput, scale out with Pod slices, automate evaluation, and keep iterating until your metrics and costs line up with your goals.