🤖 AI / Machine Learning28 min
Designing a Deep Learning Framework: Autograd, Tensors & GPU Acceleration
A
Muhammad Asim Chattha
Software Developer & Cybersecurity Researcher
#Deep Learning#CUDA#Autograd#GPU Computing#Python
Building your own deep learning framework is the ultimate way to understand how PyTorch and TensorFlow work internally.
Framework Components
- **Tensor Library**: N-dimensional array operations with broadcasting, indexing, and stride-based memory layout
- **Autograd Engine**: Reverse-mode automatic differentiation with a dynamic computation graph and gradient checkpointing
- **NN Modules**: Sequential, Linear, Conv2d, BatchNorm, Dropout — implementing forward and backward passes
- **Optimizers**: SGD with momentum, Adam, AdamW, and learning rate schedulers (cosine, one-cycle)
- **CUDA Backend**: Writing custom CUDA kernels with cuBLAS and cuDNN integration
- **Data Pipeline**: Dataset and DataLoader abstractions with prefetching and multiprocessing