Royce  Reinger

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6 Best Rust Machine Learning Library

In today's post we will learn about 6 Best Rust Machine Learning Library. 

What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (PDF, 481 KB) (link resides outside IBM) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat seems trivial, but it’s considered a major milestone in the field of artificial intelligence.

Over the last couple of decades, the technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix’s recommendation engine and self-driving cars.

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.

Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.

Table of contents:

  • Autumnai/leaf - Open Machine Intelligence framework. Abandoned project. The most updated fork is spearow/juice.
  • Huggingface/tokenizers - Hugging Face's tokenizers for modern NLP pipelines written in Rust (original implementation) with bindings for Python.
  • LaurentMazare/tch-rs - Rust language bindings for PyTorch. 
  • Maciejkula/rustlearn - Machine learning crate for Rust. 
  • Rust-ml/linfa - Machine learning framework.
  • Tensorflow/rust - Rust language bindings for TensorFlow.

1 - Autumnai/leaf:

Open Machine Intelligence framework. Abandoned project. The most updated fork is spearow/juice.

Leaf is a open Machine Learning Framework for hackers to build classical, deep or hybrid machine learning applications. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning.

Leaf has one of the simplest APIs, is lean and tries to introduce minimal technical debt to your stack.

See the [Leaf - Machine Learning for Hackers][leaf-book] book for more.

Leaf is a few months old, but thanks to its architecture and Rust, it is already one of the fastest Machine Intelligence Frameworks available.

See more Deep Neural Networks benchmarks on [Deep Learning Benchmarks][deep-learning-benchmarks-website].

Leaf is portable. Run it on CPUs, GPUs, and FPGAs, on machines with an OS, or on machines without one. Run it with OpenCL or CUDA. Credit goes to Collenchyma and Rust.

Leaf is part of the [Autumn][autumn] Machine Intelligence Platform, which is working on making AI algorithms 100x more computational efficient.

We see Leaf as the core of constructing high-performance machine intelligence applications. Leaf's design makes it easy to publish independent modules to make e.g. deep reinforcement learning, visualization and monitoring, network distribution, automated preprocessing or scaleable production deployment easily accessible for everyone.

Disclaimer: Leaf is currently in an early stage of development. If you are experiencing any bugs with features that have been implemented, feel free to create a issue.

Getting Started

Documentation

To learn how to build classical, deep or hybrid machine learning applications with Leaf, check out the [Leaf - Machine Learning for Hackers][leaf-book] book.

For additional information see the Rust API Documentation or the [Autumn Website][autumn].

Or start by running the Leaf examples.

We are providing a Leaf examples repository, where we and others publish executable machine learning models build with Leaf. It features a CLI for easy usage and has a detailed guide in the project README.md.

Leaf comes with an examples directory as well, which features popular neural networks (e.g. Alexnet, Overfeat, VGG). To run them on your machine, just follow the install guide, clone this repoistory and then run

# The examples currently require CUDA support.
cargo run --release --no-default-features --features cuda --example benchmarks alexnet

Installation

Leaf is build in Rust. If you are new to Rust you can install Rust as detailed here. We also recommend taking a look at the official Rust - Getting Started Guide.

To start building a machine learning application (Rust only for now. Wrappers are welcome) and you are using Cargo, just add Leaf to your Cargo.toml:

[dependencies]
leaf = "0.2.1"

If you are on a machine that doesn't have support for CUDA or OpenCL you can selectively enable them like this in your Cargo.toml:

[dependencies]
leaf = { version = "0.2.1", default-features = false }

[features]
default = ["native"] # include only the ones you want to use, in this case "native"
native  = ["leaf/native"]
cuda    = ["leaf/cuda"]
opencl  = ["leaf/opencl"]

More information on the use of feature flags in Leaf can be found in FEATURE-FLAGS.md

Contributing

If you want to start hacking on Leaf (e.g. adding a new Layer) you should start with forking and cloning the repository.

We have more instructions to help you get started in the CONTRIBUTING.md.

We also has a near real-time collaboration culture, which happens here on Github and on the Leaf Gitter Channel.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as below, without any additional terms or conditions.

View on Github

2 - Huggingface/tokenizers:

Hugging Face's tokenizers for modern NLP pipelines written in Rust (original implementation) with bindings for Python.

Main features:

  • Train new vocabularies and tokenize, using today's most used tokenizers.
  • Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU.
  • Easy to use, but also extremely versatile.
  • Designed for research and production.
  • Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token.
  • Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.

Quick example using Python:

Choose your model between Byte-Pair Encoding, WordPiece or Unigram and instantiate a tokenizer:

from tokenizers import Tokenizer
from tokenizers.models import BPE

tokenizer = Tokenizer(BPE())

You can customize how pre-tokenization (e.g., splitting into words) is done:

from tokenizers.pre_tokenizers import Whitespace

tokenizer.pre_tokenizer = Whitespace()

Then training your tokenizer on a set of files just takes two lines of codes:

from tokenizers.trainers import BpeTrainer

trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.train(files=["wiki.train.raw", "wiki.valid.raw", "wiki.test.raw"], trainer=trainer)

Once your tokenizer is trained, encode any text with just one line:

output = tokenizer.encode("Hello, y'all! How are you 😁 ?")
print(output.tokens)
# ["Hello", ",", "y", "'", "all", "!", "How", "are", "you", "[UNK]", "?"]

Check the python documentation or the python quicktour to learn more!

View on Github

3 - LaurentMazare/tch-rs:

Rust language bindings for PyTorch. 

Rust bindings for the C++ api of PyTorch. The goal of the tch crate is to provide some thin wrappers around the C++ PyTorch api (a.k.a. libtorch). It aims at staying as close as possible to the original C++ api. More idiomatic rust bindings could then be developed on top of this. 

Getting Started

This crate requires the C++ PyTorch library (libtorch) in version v1.12.0 to be available on your system. You can either:

  • Use the system-wide libtorch installation (default).
  • Install libtorch manually and let the build script know about it via the LIBTORCH environment variable.
  • When a system-wide libtorch can't be found and LIBTORCH is not set, the build script will download a pre-built binary version of libtorch. By default a CPU version is used. The TORCH_CUDA_VERSION environment variable can be set to cu113 in order to get a pre-built binary using CUDA 11.3.

System-wide Libtorch

The build script will look for a system-wide libtorch library in the following locations:

  • In Linux: /usr/lib/libtorch.so

Libtorch Manual Install

  • Get libtorch from the PyTorch website download section and extract the content of the zip file.
  • For Linux users, add the following to your .bashrc or equivalent, where /path/to/libtorch is the path to the directory that was created when unzipping the file.
export LIBTORCH=/path/to/libtorch
export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH

For Windows users, assuming that X:\path\to\libtorch is the unzipped libtorch directory.

  • Navigate to Control Panel -> View advanced system settings -> Environment variables.
  • Create the LIBTORCH variable and set it to X:\path\to\libtorch.
  • Append X:\path\to\libtorch\lib to the Path variable.
$Env:LIBTORCH = "X:\path\to\libtorch"
$Env:Path += ";X:\path\to\libtorch\lib"
  • You should now be able to run some examples, e.g. cargo run --example basics.

Examples

Basic Tensor Operations

This crate provides a tensor type which wraps PyTorch tensors. Here is a minimal example of how to perform some tensor operations.

use tch::Tensor;

fn main() {
    let t = Tensor::of_slice(&[3, 1, 4, 1, 5]);
    let t = t * 2;
    t.print();
}

View on Github

4 - Maciejkula/rustlearn:

Machine learning crate for Rust.

Introduction

This crate contains reasonably effective implementations of a number of common machine learning algorithms.

At the moment, rustlearn uses its own basic dense and sparse array types, but I will be happy to use something more robust once a clear winner in that space emerges.

Parallelization

A number of models support both parallel model fitting and prediction.

Model serialization

Model serialization is supported via serde.

Using rustlearn

Usage should be straightforward.

  • import the prelude for all the linear algebra primitives and common traits:
use rustlearn::prelude::*;
  • import individual models and utilities from submodules:
use rustlearn::prelude::*;

use rustlearn::linear_models::sgdclassifier::Hyperparameters;
// more imports

Examples

Logistic regression

use rustlearn::prelude::*;
use rustlearn::datasets::iris;
use rustlearn::cross_validation::CrossValidation;
use rustlearn::linear_models::sgdclassifier::Hyperparameters;
use rustlearn::metrics::accuracy_score;


let (X, y) = iris::load_data();

let num_splits = 10;
let num_epochs = 5;

let mut accuracy = 0.0;

for (train_idx, test_idx) in CrossValidation::new(X.rows(), num_splits) {

    let X_train = X.get_rows(&train_idx);
    let y_train = y.get_rows(&train_idx);
    let X_test = X.get_rows(&test_idx);
    let y_test = y.get_rows(&test_idx);

    let mut model = Hyperparameters::new(X.cols())
                                    .learning_rate(0.5)
                                    .l2_penalty(0.0)
                                    .l1_penalty(0.0)
                                    .one_vs_rest();

    for _ in 0..num_epochs {
        model.fit(&X_train, &y_train).unwrap();
    }

    let prediction = model.predict(&X_test).unwrap();
    accuracy += accuracy_score(&y_test, &prediction);
}

accuracy /= num_splits as f32;

View on Github

5 - Rust-ml/linfa:

Machine learning framework.

linfa (Italian) / sap (English):

The vital circulating fluid of a plant.

linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust.

Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks.

Current state

Where does linfa stand right now? Are we learning yet?

linfa currently provides sub-packages with the following algorithms:

NamePurposeStatusCategoryNotes
clusteringData clusteringTested / BenchmarkedUnsupervised learningClustering of unlabeled data; contains K-Means, Gaussian-Mixture-Model, DBSCAN and OPTICS
kernelKernel methods for data transformationTestedPre-processingMaps feature vector into higher-dimensional space
linearLinear regressionTestedPartial fitContains Ordinary Least Squares (OLS), Generalized Linear Models (GLM)
elasticnetElastic NetTestedSupervised learningLinear regression with elastic net constraints
logisticLogistic regressionTestedPartial fitBuilds two-class logistic regression models
reductionDimensionality reductionTestedPre-processingDiffusion mapping and Principal Component Analysis (PCA)
treesDecision treesTested / BenchmarkedSupervised learningLinear decision trees
svmSupport Vector MachinesTestedSupervised learningClassification or regression analysis of labeled datasets
hierarchicalAgglomerative hierarchical clusteringTestedUnsupervised learningCluster and build hierarchy of clusters
bayesNaive BayesTestedSupervised learningContains Gaussian Naive Bayes
icaIndependent component analysisTestedUnsupervised learningContains FastICA implementation
plsPartial Least SquaresTestedSupervised learningContains PLS estimators for dimensionality reduction and regression
tsneDimensionality reductionTestedUnsupervised learningContains exact solution and Barnes-Hut approximation t-SNE
preprocessingNormalization & VectorizationTested / BenchmarkedPre-processingContains data normalization/whitening and count vectorization/tf-idf
nnNearest Neighbours & DistancesTested / BenchmarkedPre-processingSpatial index structures and distance functions
ftrlFollow The Reguralized Leader - proximalTested / BenchmarkedPartial fitContains L1 and L2 regularization. Possible incremental update

We believe that only a significant community effort can nurture, build, and sustain a machine learning ecosystem in Rust - there is no other way forward.

If this strikes a chord with you, please take a look at the roadmap and get involved!

BLAS/Lapack backend

Some algorithm crates need to use an external library for linear algebra routines. By default, we use a pure-Rust implementation. However, you can also choose an external BLAS/LAPACK backend library instead, by enabling the blas feature and a feature corresponding to your BLAS backend. Currently you can choose between the following BLAS/LAPACK backends: openblas, netblas or intel-mkl.

BackendLinuxWindowsmacOS
OpenBLAS✔️--
Netlib✔️--
Intel MKL✔️✔️✔️

Each BLAS backend has two features available. The feature allows you to choose between linking the BLAS library in your system or statically building the library. For example, the features for the intel-mkl backend are intel-mkl-static and intel-mkl-system.

An example set of Cargo flags for enabling the Intel MKL backend on an algorithm crate is --features blas,linfa/intel-mkl-system. Note that the BLAS backend features are defined on the linfa crate, and should only be specified for the final executable.

View on Github

6 - Tensorflow/rust:

Rust language bindings for TensorFlow.

Getting Started

Since this crate depends on the TensorFlow C API, it needs to be downloaded or compiled first. This crate will automatically download or compile the TensorFlow shared libraries for you, but it is also possible to manually install TensorFlow and the crate will pick it up accordingly.

Prerequisites

If the TensorFlow shared libraries can already be found on your system, they will be used. If your system is x86-64 Linux or Mac, a prebuilt binary will be downloaded, and no special prerequisites are needed.

Otherwise, the following dependencies are needed to compile and build this crate, which involves compiling TensorFlow itself:

  • git
  • bazel
  • Python Dependencies numpy, dev, pip and wheel
  • Optionally, CUDA packages to support GPU-based processing

The TensorFlow website provides detailed instructions on how to obtain and install said dependencies, so if you are unsure please check out the docs for further details.

Some of the examples use TensorFlow code written in Python and require a full TensorFlow installation.

The minimum supported Rust version is 1.58.

Usage

Add this to your Cargo.toml:

[dependencies]
tensorflow = "0.19.1"

and this to your crate root:

extern crate tensorflow;

Then run cargo build -j 1. The tensorflow-sys crate's build.rs now either downloads a pre-built, basic CPU only binary (the default) or compiles TensorFlow if forced to by an environment variable. If TensorFlow is compiled during this process, since the full compilation is very memory intensive, we recommend using the -j 1 flag which tells cargo to use only one task, which in turn tells TensorFlow to build with only one task. Though, if you have a lot of RAM, you can obviously use a higher value.

To include the especially unstable API (which is currently the expr module), use --features tensorflow_unstable.

For now, please see the Examples for more details on how to use this binding.

Tensor Max Display

When printing or debugging a tensor, it will print every element by default, this can be modified by changing an environment variable:

TF_RUST_DISPLAY_MAX=5

Which will truncate the values if they exceed the limit:

let values: Vec<u64> = (0..100000).collect();
let t = Tensor::new(&[2, 50000]).with_values(&values).unwrap();
dbg!(t);
t = Tensor<u64> {
    values: [
        [0, 1, 2, 3, 4, ...],
        ...
    ],
    dtype: uint64,
    shape: [2, 50000]
}

GPU Support

To enable GPU support, use the tensorflow_gpu feature in your Cargo.toml:

[dependencies]
tensorflow = { version = "0.19.1", features = ["tensorflow_gpu"] }

View on Github

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