It’s Time for a Distributed Learning for Machine Learning – Next Generation ML Tools

Machine Learning today is one of the hottest aspects of computer Science.

-Steve Ballmer

There are two ways for accelerating machine learning workloads- Vertical scaling or horizontal scaling.

But in terms of the degree of distribution within a machine learning ecosystem, they are classified into three categories which are- centralized, decentralized, and fully distributed. 

The distributed system is more appropriate and efficient than a centralized or any other system.

So, here we’ve enlisted some of the popular tools that enable distributed Machine learning.

Best Tools for Distributed Machine Learning for 2021

1. DistBelief

DistBelief is one of the most important tools for Distributed Machine Learning.

It was developed by Google and able to support data and model parallel training with huge capability like tens or thousands of CPU cores. 

DistBelief can also handle the training of a giant model with 1.7 billion parameters.

2. Apache Spark

Many Machine Learning algorithms have transformations in Linear Algebra and that are highly iterative in nature. 

For such tasks, the paradigm of the map and the reduced operations are not suitable. To resolve this problem, the introduction of Apache Spark took place.

The core difference between MapReduce and Spark is that the MapReduce tasks require you to write all the data to the disks to execute it. 

The Spark, on the other hand, can keep all data in memory and saves expensive reads from the disk.

3. MapReduce and Hadoop

Developed by Google, MapReduce is an efficient framework for processing large amounts of data.

Processing of data in a distributed setting follows a two-phase process. 

In the first phase, which is the map phase, the data is split into tuples.

It then follows the second phase, the reduce phase, where these tuples are grouped to generate a single output value per key. 

In every phase of execution, Hadoop and MapReduce both depend on distributed file systems.

4. Caffe2

Caffe2 is a deep learning framework that distributes machine learning by using AllReduce algorithms.

It does this by using NCCL and custom code. 

NCCL between GPUs on a single host and custom code between hosts based on Facebook’s Gloo library.

5. TensorFlow

TensorFlow emerged from DistBelief and both were developed by Google.

It borrows the concepts of computation graph and parameter server from DistBelief. 

TensorFlow doesn’t require any custom code composed of fundamental math operations for defining a new type of neural network layer.

6. Microsoft Cognitive Toolkit

There are various ways one can achieve data-parallel distribution by using Microsoft Cognitive Toolkit.

Most of them use the Ring AllReduce tactic. Over fault-tolerance, it makes the trade-off of linear scalability. 

7. Petuum

To keep track of the model being trained, Petuum uses a Parameter server paradigm.

Petuum provides an abstraction layer that enables it to run on systems using HDFS and Hadoop job scheduler. 

This simplifies the compatibility with the pre-existing clusters.

To achieve good scalability on large datasets, ML’s error tolerance, dependencies, and non-uniform convergence are the aims of this approach. 

8. DIANNE( Distributed Artificial Neural Networks)

DIANNE is a Java-based distributed deep learning framework.

For executing the necessary computations, it uses the torch native backend. 

It enables model-parallelism as each building block of the neural network is deployed on a specific node.

9. MXNet

MXNet can easily achieve linear speedup on a small cluster of 10 machines equipped with a GPU as compared to a single machine when training GoogleNet.

Like TensorFlow, the representation of models is just as dataflow graphs.  

10. Baidu AllReduce

To train stochastic gradient descent models on separate mini-batches of the training data, common high-performance computing technology is used by AllReduce. 

Baidu claims to have linear speedup when applying this technique to train deep learning networks. 

Summary

So these are some of the important tools for Distributed Machine Learning.

These tools have plenty of advantages and make the task of developers easier. 

All it requires is to use the right tool in the right situation to get the most benefit out of these tools.