What is PyTorch?. 1.History | by Aysh | May, 2024

1.History

PyTorch is an open-source deep learning platform developed by Facebook. Its foundations are based on Torch, a deep learning library created in 2002 by Ronan Collobert, Koray Kavukcuoglu, and Léon Bottou. However, Torch had limitations in integration with Python and lacked a large community.

PyTorch, as an evolution of Torch, was built on top of a deep learning library called KarpathyNet. PyTorch was officially announced for the first time at NIPS 2016. Its dynamic computation graph approach and ease of use quickly gained popularity among researchers and industry professionals.

In 2018, PyTorch 1.0 was released. This version introduced the ability to switch between static and dynamic computation graphs, similar to TensorFlow. It also included many developments to facilitate distributed training and model deployment.

PyTorch continues to evolve, with new features being added regularly. In addition to contributions from Facebook, a global community also contributes to PyTorch’s development. PyTorch, which has become a standard in the field of deep learning, has been widely adopted by researchers and industry professionals. Today, PyTorch is one of the most commonly used tools for developing and deploying deep learning projects.

2.Key Features

• Dynamic Computation Graph: PyTorch utilizes a dynamic computation graph. This allows the computation graph to be built at runtime, providing flexibility in developing and modifying the graph during model development. This feature is particularly advantageous for models with variable input sizes or adaptable architectures.Easy Integration with
• Python: PyTorch is based on the Python programming language and fully leverages the power of Python. This enables easy usage and integration with the extensive Python ecosystem. Researchers and practitioners can harness the flexibility and ease of use of Python in PyTorch projects.
• High-Level API: PyTorch provides a high-level API, making it easier to build and train deep learning models. It follows a module-based structure with ready-to-use components for model creation and training. This allows researchers and practitioners to rapidly prototype their ideas.
• GPU Acceleration: PyTorch supports GPU acceleration and integrates with the CUDA library. This enables training deep learning models quickly on large datasets. Additionally, running your model on a GPU can significantly reduce training and inference times.
• Flexible Distributed Training: PyTorch offers a suite of tools to facilitate distributed training and model deployment. This is crucial for training on large-scale datasets and distributing models across different devices. PyTorch provides specialized tools and infrastructure to coordinate these operations.
• Community Support: PyTorch benefits from a large and active community, enabling users to solve problems and exchange knowledge. Additionally, this community contributes to the continuous improvement and enhancement of PyTorch. This makes PyTorch a user-friendly deep learning platform.

3.Uses of PyTorch

3.1.Tensors and Dynamic Computation Graph

PyTorch uses tensors as its fundamental data structure. Tensors represent multi-dimensional arrays and matrices and can be processed with GPU acceleration. PyTorch also employs a dynamic computation graph, meaning the computation graph is created at runtime. This allows for a more flexible and dynamic workflow. For example:

3.2.Building Deep Learning Models:

PyTorch features a module-based structure that allows users to easily build deep learning models. These modules include components such as layers, activation functions, and optimization algorithms. Models are derived from Python classes and have a forward method that defines forward and backward passes. For example:

3.3.Training and Validation:

PyTorch is used to train deep learning models using the training loop. In this loop, data is loaded, forward and backward passes are made through the model, and optimization steps are applied. PyTorch’s automatic differentiation feature makes it easier to compute gradients. For example:

4.Pytorch vs TensorFlow-Keras

4.1.Flexibility and Ease of Use:

• PyTorch offers a flexible approach using dynamic computation graphs. This allows for modifying and adapting the model during runtime, making it ideal for rapid prototyping. It directly uses Python, providing less abstraction and ease of use.
• On the other hand, TensorFlow-Keras provides a more structured approach. Keras, with its high-level API, simplifies model creation and training. The integration with TensorFlow 2.x combines the power of TensorFlow with the simplicity of Keras.

4.2.Community and Ecosystem:

• Both frameworks have large and active communities. However, TensorFlow’s community is larger and more diverse. TensorFlow benefits from widespread usage in large-scale industrial applications, resulting in extensive documentation, sample code, and auxiliary tools.
• PyTorch’s community is rapidly growing and actively contributing, especially in academic research and innovative projects.

4.3.Distributed Training and Model Deployment:

• Both frameworks support distributed training and model deployment. However, TensorFlow provides a more mature solution with integration into Google Cloud’s powerful infrastructure.
• While PyTorch offers solutions for distributed training, such as PyTorch BigGraph, TensorFlow’s dominance in this area is more pronounced.

In conclusion, PyTorch and TensorFlow-Keras are two powerful tools in the field of deep learning with similar capabilities. The choice depends on the project requirements, personal preferences, and existing infrastructure. PyTorch is suitable for flexibility and rapid prototyping, while TensorFlow-Keras is preferred for large-scale industrial projects. Each framework has its unique advantages, and learning both can be valuable for success in deep learning applications.