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We offer cost-effective and optimized NVIDIA GPU rental servers for PyTorch with CUDA.
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Here are 6 Key Benefits of PyTorch Lightning, a popular high-level framework built on top of PyTorch.
PyTorch Lightning separates engineering from research logic. You write less boilerplate code (e.g., for training loops, validation steps, logging), and focus more on the model itself.
Easily train on multiple GPUs, TPUs, or multiple nodes with just a few lines of code. No need to manually write distributed training logic (like DDP or Horovod setup).
Lightning works on everything from your local machine to cloud GPU servers, Kubernetes, and high-performance clusters — with the same code.
Integrated support for: TensorBoard, WandB, MLflow, Model checkpointing, and Early stopping and learning rate schedulers.
Lightning handles training, validation, testing, and prediction loops internally, ensuring consistent and reproducible results.
Easily extend or customize behavior using plugins and callbacks: Mixed precision training (AMP), Gradient accumulation, Custom learning rate schedulers and Profilers and debuggers.
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