Learn PYTORCH with Real Code Examples

Install Python 3.8+

Install PyTorch with pip or conda: https://pytorch.org/get-started/

Verify installation by importing torch and running torch.rand(3,3)

Check GPU availability: torch.cuda.is_available()

Run a small model example to confirm functionality

Install Python 3.8+

Create virtual environment

Install PyTorch via pip/conda

Verify GPU availability

Run a small model example

main.py

models/

data_preprocessing.py

utils/

notebooks/

pip install torch - install

python main.py - run script

torchserve - launch serving API

python -m unittest - run tests

torch.jit.save - export TorchScript model

UTF-8 support in Python

Multilingual datasets via TorchText

Locale-independent code

Custom preprocessing for different languages

Integration with NLP libraries like HuggingFace

Python and cross-platform support

GPU acceleration when available

Eager execution simplifies experimentation

Integrates with common Python libraries

Cloud-based GPU/TPU support possible

Matplotlib/Seaborn for plotting

TensorBoard integration via torch.utils.tensorboard

Jupyter notebook for experimentation

Optional dashboards for metrics

Custom visualization of tensors and activations

Save/load model state_dict

Checkpoint training states

Random seeds for reproducibility

Callbacks for custom runtime behavior

Version experiments and models

Split datasets for training, validation, testing

Data augmentation via torchvision transforms

Efficient batching with DataLoader

Save preprocessed datasets for reuse

Store model checkpoints and configurations