Hello Friends! It is very obvious that we encounter a situation where we have to create a pipeline for our tasks. Specially In machine learning there, you have so many tasks to perform like data preparation, model training, etc. Even in data preparation, there could be n numbers of tasks like cleaning , scaling, and validation. In order to handle these tasks, you may use distributive computing. Well! At a high level, you at least need some Task queue Management Frameworks. This will reduce your over head tasks. This post will assist you on the Top 5 Task queue Management Frameworks in Python.

Top 5 Task queue Management Frameworks in Python

Before you start, I would like to acknowledge that the ranking in the task queue managers are not meant that one which is coming after another is lesser efficient.It is just an order to display the information. Each framework has its own advantages and disadvantages. Let’s start –

1. Celery: Distributed Task Queue –

It supports distribute message passing. The core for Celery is in python but its different version support multiple languages. You will awesome documentation on celery like – How to use celery, related bugs and their fixes etc. There is big community support for celery. Obviously, celery is my first choice for task queue management. Once it comes to the implementation, It has a simple interface. Celery support multiple message broker like Rabbit MQ, Redis, BeanStalk etc.

2. Redis Queue –

Awesome implementation in python. You may put jobs in a queue and handle them with n numbers of workers. This is a very popular framework. Actually, you will so many applications which is built on the top of Redis Queue.

3. huey –

The back end for Huey is Redis but It does not mean they both are perfectly similar. But yes you may say Huey is a simpler version for Redis. Apart from scheduling stuff, it can also help you in retrying failure events. Corporations have high adaptability for Huey. In Huey you may create a separate pipeline for distributed tasks. You may use a timer to automate and schedule the jobs.

4. Taskmaster

It is a masterpiece in handling a large number of tasks. See! it works like the above mention but with the difference of handling a large number of queue tasks. If you have such a requirement, it comes the first place for you.

taskmaster-queue-manager — taskmaster- queue- manager

5. Others –

As I never put a single name in the last bucket. How can I do with Task queue Management Frameworks? I will provide you with a couple of names here. You may go through them –

5.1 – Kuyruk

5.2 -Dramatiq

Conclusion –

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Data Science Learner Team