PyTorch: From Research to Production
Dynamic graphs, intuitive debugging, thriving research ecosystem. We move fast during experimentation and scale reliably for production inference.
What we deliver with PyTorch
Experiment structure that survives change
Tracked runs, pinned environments, and seed control, so results are comparable and the best run can be reproduced on demand.
Distributed training
DistributedDataParallel across nodes, gradient checkpointing when memory is tight, and checkpointing that survives a preemption.
Export and inference paths
TorchScript or ONNX export validated against the eager model, with batch and streaming serving both measured before launch.
Fine-tuning and adaptation
LoRA and parameter-efficient fine-tuning where full training is unnecessary, with evaluation harnesses that catch regressions.
GPU cost control
Right-sized instance types, spot capacity for training, and inference batching that keeps utilisation above the point where cost per prediction makes sense.
How we work with PyTorch
PyTorch rewards speed of iteration, which is exactly why research code tends to outrun its engineering. We keep the fast loop but put a boundary around it: experiments run freely, and only code with a fixed environment, a saved config, and an evaluation set is allowed to become a service.
Before any model goes live we build the boring parts — a load test at expected peak, a rollback to the previous weights, and a monitor on input drift. Those three things decide whether an incident is a five-minute fix or a week of explanations.
PyTorch in production: where it breaks
The same handful of problems show up on almost every engagement. Naming them early is cheaper than discovering them at 2am.
Unpinned CUDA and library versions
A rebuild breaks the environment and results shift. Pinned images make training environments disposable and repeatable.
Checkpoint sprawl
Hundreds of unlabelled weights files, none clearly the one in production. We keep a registry with lineage and a documented rollback target.
Synchronous inference under load
Single-request serving collapses under concurrency. Dynamic batching and warm workers bring latency back within budget.
When to use PyTorch
Research teams that need their models operated, not just published.
Fine-tuning and adaptation work on open-weight language models.
Recommender and vision prototypes heading toward real traffic.