2023-02-26
Deep Learning with Nim
NIM is a statically typed, efficient language that, despite being less popular than Python or Java, has a unique set of characteristics making it ideal for specific use-cases. One of these involves implementing deep learning algorithms. This blog post will guide you through the process of getting started with deep learning in NIM and will explore some existing libraries such as SciNim, Arraymancer, and Flambleau.
What is NIM?
Nim is an expressive language that combines the readability of Python, speed of C, and expressiveness of metaprogramming. It has a strong static type system that prevents numerous bugs at compile-time, creating reliable and maintainable programs. What makes Nim interesting for deep learning is its efficiency and ability to interface with other programming languages, such as C, C++, and Python.
Deep Learning in NIM
NIM is a general-purpose programming language that is statically typed and compiled, which makes it fast and efficient. NIM has a syntax that is similar to Python, which makes it easy for developers who are familiar with Python to learn and use NIM. NIM has built-in support for multi-dimensional arrays, which is essential for implementing deep learning algorithms. NIM also has a powerful macro system, which enables developers to write code that is concise and easy to read.
Deep Learning Libraries in NIM
To implement deep learning models in NIM, it’s crucial to be aware of some libraries that facilitate the task. This article will focus on three significant NIM libraries: SciNim, Arraymancer, and Flambleau.
SciNim
SciNim is not a single library but more of a collective that provides scientific computation libraries for NIM. The aim is to provide high-speed, robust, and easy-to-use libraries for numeric computation. Let's look at two significant libraries under SciNim:
- getting-started: Serves as a blueprint to demonstrate how to use the other libraries.
- nimblas: Basic linear algebra bindings used by other modules.
- Flambleau: Nim bindings to libtorch
Although SciNim is still relatively new, it can already perform a lot of scientific computing tasks effectively.
Arraymancer
Arraymancer is a tensor (N-dimensional array) project in Nim. The term "Arraymancer" is a combination of Array (data structure) and Necromancer (a magician who can revive the dead), symbolizing the revival of dead data through arrays.
As a powerful numerical library, Arraymancer provides elegance and speed for machine learning and deep learning applications. It relies heavily on metaprogramming to provide an intuitive syntax for tensor operations. Features include:
- Array operations - It supports basic arithmetic, matrix multiplication, and numerical computations, which are crucial elements for any deep learning task.
- Deep learning - With support for manual and automatic differentiation, it makes defining and training deep learning models straightforward.
- Interoperability - Arraymancer can use and provide data to C, Python, and OpenCL, allowing for straightforward integration with existing codebases or libraries.
Flambleau
Flambleau, one of the newest additions to the Nim ecosystem, is a wrapper around PyTorch through the C++ API.
While being still in development, Flambleau already offers:
- Tensors, Variables, and Functions - Gives you first-class citizens to work with deep learning.
- Auto-differentiation - Simplifies backpropagation in gradient descent algorithms.
- Neural Networks - Implement and train complex neural architectures.
Even though Flambleau's capabilities are currently limited, it presents a significant future contribution to the field of deep learning, particularly for those programming in Nim.