Top 5 C++ Libraries for Artificial Intelligence
Looking for the best C++ libraries to boost your AI development skills? Look no further! In this article, we will list and discuss the top 5 must-have C++ libraries for AI development in 2023. These libraries provide a wide range of features and functionality that can help you build powerful and efficient AI applications. Whether you're a beginner or an experienced developer, these libraries are sure to be a valuable asset.
what is C++?
C++ is a general-purpose programming language created by Danish computer scientist Bjarne Stroustrup at Bell Labs in 1979. It is a compiled language, which means that the code is translated into machine code before it is executed. C++ is a superset of the C programming language, which means that it includes all of the features of C, as well as additional features such as object-oriented programming, generic programming, and exception handling.
C++ is one of the most popular programming languages in the world, and is used to develop a wide variety of software, including operating systems, compilers, games, and embedded systems. It is known for its speed, efficiency, and flexibility.
Top 5 C++ Libraries for Artificial Intelligence
- frugally-deep
Use Keras models in C++ with ease
Introduction
Would you like to build/train a model using Keras/Python? And would you like to run the prediction (forward pass) on your model in C++ without linking your application against TensorFlow? Then frugally-deep is exactly for you.
frugally-deep
- is a small header-only library written in modern and pure C++.
- is very easy to integrate and use.
- depends only on FunctionalPlus, Eigen and json - also header-only libraries.
- supports inference (
model.predict) not only for sequential models but also for computational graphs with a more complex topology, created with the functional API. - re-implements a (small) subset of TensorFlow, i.e., the operations needed to support prediction.
- results in a much smaller binary size than linking against TensorFlow.
- works out-of-the-box also when compiled into a 32-bit executable. (Of course, 64 bit is fine too.)
- avoids temporarily allocating (potentially large chunks of) additional RAM during convolutions (by not materializing the im2col input matrix).
- utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. ;-)
- but is quite fast on one CPU core, and you can run multiple predictions in parallel, thus utilizing as many CPUs as you like to improve the overall prediction throughput of your application/pipeline.
Supported layer types
Add,Concatenate,Subtract,Multiply,Average,Maximum,Minimum,DotAveragePooling1D/2D/3D,GlobalAveragePooling1D/2D/3DBidirectional,TimeDistributed,GRU,LSTM,CuDNNGRU,CuDNNLSTMConv1D/2D,SeparableConv2D,DepthwiseConv2DCropping1D/2D/3D,ZeroPadding1D/2D/3D,CenterCropBatchNormalization,Dense,Flatten,NormalizationDropout,AlphaDropout,GaussianDropout,GaussianNoiseSpatialDropout1D,SpatialDropout2D,SpatialDropout3DActivityRegularizationRandomContrast,RandomFlip,RandomHeightRandomRotation,RandomTranslation,RandomWidth,RandomZoomMaxPooling1D/2D/3D,GlobalMaxPooling1D/2D/3DELU,LeakyReLU,ReLU,SeLU,PReLUSigmoid,Softmax,Softplus,TanhExponential,GELU,Softsign,RescalingUpSampling1D/2D,ResizingReshape,Permute,RepeatVectorEmbedding,CategoryEncodingAttention,AdditiveAttention
Source: https://github.com/Dobiasd/frugally-deep
2. Genann
Genann is a minimal, well-tested library for training and using feedforward artificial neural networks (ANN) in C. Its primary focus is on being simple, fast, reliable, and hackable. It achieves this by providing only the necessary functions and little extra.
Features
- C99 with no dependencies.
- Contained in a single source code and header file.
- Simple.
- Fast and thread-safe.
- Easily extendible.
- Implements backpropagation training.
- Compatible with alternative training methods (classic optimization, genetic algorithms, etc)
- Includes examples and test suite.
- Released under the zlib license - free for nearly any use.
Building
Genann is self-contained in two files: genann.c and genann.h. To use Genann, simply add those two files to your project.
Source: https://github.com/codeplea/genann
3. MXNet
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scalable to many GPUs and machines.
Apache MXNet is more than a deep learning project. It is a community on a mission of democratizing AI. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.
Licensed under an Apache-2.0 license.
Features
- NumPy-like programming interface, and is integrated with the new, easy-to-use Gluon 2.0 interface. NumPy users can easily adopt MXNet and start in deep learning.
- Automatic hybridization provides imperative programming with the performance of traditional symbolic programming.
- Lightweight, memory-efficient, and portable to smart devices through native cross-compilation support on ARM, and through ecosystem projects such as TVM, TensorRT, OpenVINO.
- Scales up to multi GPUs and distributed setting with auto parallelism through ps-lite, Horovod, and BytePS.
- Extensible backend that supports full customization, allowing integration with custom accelerator libraries and in-house hardware without the need to maintain a fork.
- Support for Python, Java, C++, R, Scala, Clojure, Go, Javascript, Perl, and Julia.
- Cloud-friendly and directly compatible with AWS and Azure.
Source: https://github.com/apache/mxnet
4. pytorch
PyTorch is a Python package that provides two high-level features:
- Tensor computation (like NumPy) with strong GPU acceleration
- Deep neural networks built on a tape-based autograd system
You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.
More About PyTorch
At a granular level, PyTorch is a library that consists of the following components:
| Component | Description |
|---|---|
| torch | A Tensor library like NumPy, with strong GPU support |
| torch.autograd | A tape-based automatic differentiation library that supports all differentiable Tensor operations in torch |
| torch.jit | A compilation stack (TorchScript) to create serializable and optimizable models from PyTorch code |
| torch.nn | A neural networks library deeply integrated with autograd designed for maximum flexibility |
| torch.multiprocessing | Python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and Hogwild training |
| torch.utils | DataLoader and other utility functions for convenience |
Usually, PyTorch is used either as:
- A replacement for NumPy to use the power of GPUs.
- A deep learning research platform that provides maximum flexibility and speed.
Elaborating Further:
A GPU-Ready Tensor Library
If you use NumPy, then you have used Tensors (a.k.a. ndarray).
PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the computation by a huge amount.
We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, mathematical operations, linear algebra, reductions. And they are fast!
Source: https://github.com/pytorch/pytorch
5. Flashlight
Flashlight is a fast, flexible machine learning library written entirely in C++ from the Facebook AI Research and the creators of Torch, TensorFlow, Eigen and Deep Speech. Its core features include:
- Total internal modifiability including internal APIs for tensor computation.
- A small footprint, with the core clocking in at under 10 MB and 20k lines of C++.
- High-performance defaults featuring just-in-time kernel compilation with modern C++ via the ArrayFire tensor library.
- An emphasis on efficiency and scale.
Native support in C++ and simple extensibility makes Flashlight a powerful research framework that enables fast iteration on new experimental setups and algorithms with little unopinionation and without sacrificing performance. In a single repository, Flashlight provides apps for research across multiple domains:
- Automatic speech recognition (formerly wav2letter project) — Documentation | Tutorial
- Image classification
- Object detection
- Language modeling
Project Layout
Flashlight is broken down into a few parts:
flashlight/libcontains kernels and standalone utilities for audio processing and more.flashlight/flis the core tensor interface and neural network library using the ArrayFire tensor library by default.flashlight/pkgare domain packages for speech, vision, and text built on the core.flashlight/appare applications of the core library to machine learning across domains.
