Tue, Dec 21, 2021
Welcome to a new edition (7th!) of our yearly Top Python Libraries list!
Starting in December 2015 — and uninterruptedly since then — we have been compiling the best Python libraries that are launched or popularized every year (or late the previous year). It all started as a "Top 10" series, but although we still have 10 main picks, we are nowadays listing so many more libraries. The work the Python community has been doing is just too good, and we want to give YOU a chance to find these great libraries in case they haven't yet crossed your path.
In case you are not a fan of most top-10-style posts, bear with us and give this a chance. This is not your typical list of obvious choices everybody knows (no
TensorFlow here). I promise most of the libraries we picked will be new to you. We do our homework!
As a final disclaimer, we are an AI Solutions company, which will become evident in the picks. If you think we have missed some great libraries, especially for folks doing full-stack development, please mention them in the comments!
Let's get started!
For several years, Django REST Framework has been sort of the go-to option to build RESTful APIs in Django applications. But in 2021, a new contender was born.
Meet Django Ninja, a fast web framework for building APIs with Django (which recently released 4.0 by the way) and, as we've come to expect from several of these libraries, type hints!
It makes it truly simple to build a REST API in which you get type casting and validation for your parameters (thanks again to Pydantic) and documentation for free. Adding to that, autocomplete in your favorite editor just works, and the library supports async views!
You'll have an ORM that looks very intuitive, has great editor support (code completion, inline errors), and also data validation and documentation. As usual, the library's documentation is top-notch. What will the surprise for next year be, tiangolo? 😂
You might be already familiar with numpy and its arrays. They are the central data structure: basically a grid of values (matrices for 2D or tensors for higher dimensions). They enable vectorized operations over chunks of data (like broadcasting), which leverage parallelism and optimizations in low level libraries, and therefore can run much faster than regular Python
But values in NumPy arrays have to be all of the same type. They cannot express variable-length structures. Although you can set
object, this is not enough.
Awkward Arrays come to the rescue. They look like regular arrays to the user, but underneath, they are a general tree-like data structure (like JSON). They will store data efficiently, contiguous in memory and operate with it using compiled, vectorized code, just like NumPy does.
Consider the example listed in the project's GitHub README:
And the following snippets:
Both snippets generate the same output:
But not only is 2. much more concise, it is also orders of magnitude faster and uses less memory. When paired with Numba, it can be even faster. Sweet!
Jupyter Notebooks are a great tool, but writing code in a browser and losing all the features of your favorite IDE is not that great. Even worse, notebooks often mean a lot of trouble for version control and collaboration as they ultimately are stored as JSON files.
Jupytext is a Jupyter plugin that comes to solve these issues and lets you save notebooks as markdown or scripts in several languages. With the resulting, plain-text files, it's easy to share them in version control, merge changes done by other people, and even use IDEs and their nice autocomplete or type checking features.
A must-have in the arsenal of the data scientist in 2021.
(Also related, check Jupyter Ascending from our extra picks).
If you are in the data science world, you have probably heard about Streamlit (which made it to our top 10 in 2019). Streamlit makes it easy to turn data scripts into shareable web apps, so you can demo your results as an actual app and not a Jupyter Notebook.
Gradio is a new tool that takes this a step further: if you want to build a demo of a ML model, it makes things even easier than Streamlit.
It's the simplest way to create a web UI specific to your model, and let a user play around by changing parameters using sliders, or uploading images, writing text and even recording voice.
Definitely a step in the right direction to make models more accessible and data scientists focus on what matters most to them, rather than UI work.
It looks like Gradio has just been acquired by HuggingFace!
Originally used for training more robust models in computer vision, data augmentation has quickly proved crucial for all machine learning disciplines. As we know, labeled data is scarce, so getting the most out of it is very important. Moreover, data augmentation is at the heart of several disciplines that have greatly advanced SoTA in 2021, like self-supervised learning.
AugLy, by Facebook Research (now Meta Research) is a data augmentation library that supports over 100 types of augmentations in audio, images, text, and video. The augmentations can be configured with metadata, and composed to achieve the effects you want.
Apart from the usual image scale, flipping, resizing or color jittering you can usually find in other libraries, there are many non-classical types of augmentation: turning images into memes or screenshots, overlaying text to images, changing some words to their emoji equivalents, or Instagram-like filters. Definitely, one to keep on your radar!
Until large, multi-task pre-trained multi-lingual models become great at zero-shot learning, we are going to continue needing labeled data to solve specific business problems using NLP.
But labeling data is expensive. The problem gets worse if your use case is on a language that is not English, where labeled data is more abundant.
skweak helps you solve the problem of label scarcity using a technique called weak supervision. The idea is simple: you define a set of labeling functions with some heuristics that can automatically label your document (say using gazetteers, regular expressions, or even other weak ML models), and
skweak can aggregate the outputs of these into a single most likely label, using a statistical model.
It's really cool to see it in action and will save you a lot of effort in manual labeling. Also, it integrates with spaCy and therefore can be used in your existing NLP pipeline.
After teams of data scientists and ML engineers have done their work for the past few months, a ML model gets into production. It starts receiving data and sending its predictions to populate very important dashboards. The ML people move to tackle some other, important problem. Business stakeholders are happy. Or should they?
In reality, there are a lot of things that can go wrong, often in unexpected ways. Maybe the kind of data that the model is receiving in production is different than what it was used for training, causing it to underperform. Maybe the data is right, but the predictions start slowly misbehaving, leading you to make all sorts of bad business decisions in the process (losing lots of money!).
Our advice: if you are going to rely on ML continuously in time, you need to have a ML monitoring system in place that will alert you when stuff goes wrong.
Welcome to Evidently, a tool that helps evaluate ML models during validation, and monitor them in production. This tool will not directly detect anomalies in the data but will help you detect what is called data drift and target drift, as well as model performance in production if you happen to obtain ground truth labels (which is often the case if there are humans in the loop).
Evidently can build interactive visual reports that can be checked manually by data scientists to make sure everything is ok, and can also generate and JSON profiles from pandas
csv files that can be integrated into automated prediction pipelines or used with other tools.
Here is a video demonstrating the use of Evidently in a Jupyter Notebook. It makes it easy, right?
There is a quiet revolution happening behind the scenes. You surely have been using search engines like Google for a while, but might not have noticed how they are substantially better than what they were a few years ago.
What has been happening: keyword-based search is being slowly phased out.
The newcomer? Neural search. It's all about using the representations that deep neural networks can learn, to power the components of a search system. Instead of splitting your text in discrete tokens and using that for matching, neural search feeds the entire text to neural networks, which turn it into vectors. Then, the vectors in space that are closer to those, can be your matching documents. So if you search for synonyms of "please help me", you might get "I need some assistance" as the first match, even if those don't share a single word.
But it doesn't only work on text. It can work on virtually any kind of data. You can search images that match some text. You can query for images that look like other images. Audio, video... you name it.
Jina is a neural search framework that empowers anyone to build scalable deep learning search applications in minutes. It basically provides you with abstractions that will make your life much easier while implementing neural search systems, both from the code and deploy perspective. It has a distributed architecture, is scalable, and cloud-native.
Accompanying Jina, Finetuner allows you to finetune the neural network representations to get the best results for neural search tasks. It provides a web interface for the human-in-the-loop approach. First, you get results with the pre-trained NN for a batch and start selecting which results you like the most and which ones you don't. Based on this, Finetuner will adjust the weights of the NN and present to you a new batch, with results that will become increasingly better.
We are very excited to see what 2022 will bring to these libraries!
The unfortunate reality of data science is that the majority of time is not spent in tuning models or thinking about clever approaches to novel problems. Nope. Data scientists spend a lot of their time fetching data, putting it in the right format, and writing boilerplate code.
For dealing with important quantities of data (several GBs), there is also the need to build infrastructure code that can support each workflow.
But as the tools mature, maybe this becomes a problem of the past?
Meet Hub, a dataset format with a simple API that can help you work with any kind of dataset without worrying about where it is stored and how large it is. It makes sure the data is stored in a compressed format (chunked arrays) that are basically binary blobs that can be stored anywhere. Yes, this means you can use any storage option like AWS S3, a GCP bucket, or — if you dare — local storage, transparently and without changes in your code.
Did we mention Hub works lazily, meaning the data is only fetched when needed? You don't need to have a multi-TB hard drive to work with multi-TB datasets. There is also an API for connecting Hub datasets to your most used tools like PyTorch or TensorFlow, building pipelines, and also doing data version control. And you can do distributed transformations to your datasets. And visualize them. And, who knows what else is coming in 2022? Exciting!
CUDA error: out of memoryin just 1 line of code.
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