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Five Python Libraries For Machine Learning

Five Python Libraries For Machine Learning - This article is about Five Python Libraries For Machine Learning that describes how they are useful in Machine Learning projects.

Machine learning is fascinating, but the task is difficult and complex. It usually involves lots of manual labor–assembling workflows pipelines and establishing data sources, and then shunting back and forth between on-prem and cloud-based resources.

If you have more instruments, have them under your tool belt to make it easier to do this task and make it easier, the more efficient. It’s a good thing; Python is a powerful toolbox of an open-source language employed in big data and machine learning. 

In This Article You Will Know About Five Python Libraries For Machine Learning. 

Five Python Libraries For Machine Learning – Before moving ahead, let’s know a bit about Python Tools For Data Science.

Table of Contents

1. PyWren

Straightforward and with a solid premise, PyWren allows you to use Python-based scientific computing applications in several instances using AWS Lambda functions. An overview for the program on The New Stack describes PyWren employing the AWS Lambda as a considerable processor that can handle projects that can be cut and diced into smaller tasks that require no massive amount of storage or memory to be run.

The downside of lambda is that they aren’t able to run for longer than 300 seconds maximum. Suppose you’re looking for a task to take only a few minutes, and you need to execute it hundreds of times over an entire dataset. In that case, PyWren may be a suitable option to run that job in the cloud to the extent that it’s not available on personal hardware.

2. Tfdeploy

Google’s TensorFlow framework is taking off fast with the advent of the full 1.0 version. A common query about it is how can I benefit from the models I create in TensorFlow without using TensorFlow itself?

To deploy a partial solution to this question. It converts the trained TensorFlow model into “a simple NumPy-based callable,” which means that the model can be utilized in Python using Tfdeploy along with NumPy math-and-stats library. NumPy math-and states library as the sole dependencies. A majority of the actions performed within TensorFlow can be done using Tfdeploy. You can extend the library’s capabilities with the help of conventional Python analogies (such as overloading classes).

This is the bad news: Tfdeploy does not provide GPU acceleration, at least because NumPy does not do this. The creator of Tfdeploy recommends using the GNUMPy project as an alternative.

3. Luigi

Writing batch jobs is typically only one of the steps to process massive amounts of data. You must also string all of the tasks together into something that resembles an automated workflow or pipeline. Luigi was developed in collaboration with Spotify and named after the plumber famousized by Nintendo. It was designed to “address each of the issues that are typically encountered in long-running batch processing.”

With Luigi, a programmer can select various process data tasks–“a Hive query, a Hadoop job written in Java and a Spark task in Scala or dumping a table from a database”–and build an automated workflow that executes from beginning to end. The complete description of the job, as well as its dependencies, are written in Python modules, not XML configuration files or some other data format. This means that it can be integrated with other Python-related projects.

4. Kubelib

If you’re considering using Kubernetes to orchestrate your tool to run machine learning tasks, The last thing you’ll want to do is allow the use of Kubernetes to cause more problems than it can solve. Kubelik is an array of Pythonic interfaces for Kubernetes designed initially to help Jenkins’s scripting. However, it can be used in conjunction with Jenkins also as it can do everything accessible via the Kubectl CLI or Kubernetes API.

5. PyTorch

It’s important to remember this recently announced and highly-publicized enhancement to the Python world: implementing the Torch machine learning framework. PyTorch does not just adapt Torch to Python but also provides other features, like GPU acceleration and an application that allows multiprocessing to be performed using shared memory (for splitting jobs across several cores). The best part is that it provides GPU-powered alternatives to certain not accelerated functions in NumPy.


PyTorch in Python

One of the main reasons people select PyTorch is that the code they review is quite simple to comprehend. The framework was constructed and crafted to be compatible with Python instead of constantly working against it.

Because of the fast execution mode PyTorch operates under; it is more than the static execution graph used by traditional TensorFlow. It’s accessible to custom PyTorch classes or the standard Python methods, all from printing () statements to creating flame graphs using stack trace examples. All of this is highly welcome to other frameworks for data science such as Pandas and Scikit-learn.

Pytorch Great Community

It is a beautiful thing to be part of the PyTorch. The main website,, has excellent documentation that is kept up to date with PyTorch releases and an outstanding tutorial collection that includes everything from an hour-long review of the core features of PyTorch to more in-depth explanations of how to enhance the library using customized C++ operators. While the tutorials could use a little more standardization around things like training/validation/test splits and training loops, they are an invaluable resource, especially when a new feature is introduced.

In addition to the official documentation, Beyond the official documentation, the Discourse-based community at is a fantastic source where you can connect with and get assistance from the core PyTorch developers. With over 15 hundred posts every week, it’s a friendly, active and lively community.

Faster Deep Learning Training Than TensorFlow

TensorFlow and PyTorch are extremely close regarding the speed of deep learning training. Models that contain many parameters require more processes. There is a lot of computational work needed for each update of the gradient, which is why, as the number of parameters, the training times will increase exceptionally quickly. A study that evaluated the deep-learning frameworks revealed, “When you need advanced models that could be improved with speed being of most importance, you should consider taking some time to develop the TensorFlow and Pytorch process.”

Easier To Learn and Simpler to Code

PyTorch is more straightforward to master than other deep learning libraries because it isn’t far from traditional programming methods. The documentation for PyTorch is also incredibly impressive and helpful for novices.

If you find anything incorrect in the above-discussed topic and have further questions, please comment below.

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