Code for Model Training – Reproducible, Scalable Training Pipelines
We build clean, configurable training codebases using modern ML tooling so teams can iterate quickly and ship reliably.
From single-GPU experiments to distributed training, we standardize configs, logging, evaluation, and CI to keep your research and product in sync.
What we deliver
- Frameworks: PyTorch, Transformers, Lightning/Accelerate
- Config-driven training (YAML/JSON) with experiment tracking
- Data loaders with streaming and sharding
- Evaluation harness (metrics, regression tests, checkpoints)
- Mixed precision, gradient accumulation, LoRA/QLoRA, DDP
- Hyperparameter search & sweeps
- Observability: logs, traces, dashboards
- Packaging, CI, and artifacts for deployment
Why it matters
- Reproducible experiments and reliable releases
- Faster iteration loops for research and product
- Cost-efficient scaling from prototype to production
- Clear ownership and maintainable code quality
Ready to Get Started with Code for Model Training – Reproducible, Scalable Training Pipelines?
Let's discuss how Bhavitech can help you implement code for model training – reproducible, scalable training pipelines for your business.
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