In a previous post we used gRPC to call Python code from Go. gRPC is a great framework, but there is a performance cost to it. Every function call needs to marshal the arguments using protobuf, make a network call over HTTP/2, and then un-marshal the result using
In this blog post, we’ll get rid of the networking layer and to some extent, the marshalling. We’ll do this by using cgo to interact with Python as a shared library.
I’m not going to cover all of the code in detail in order to keep this blog size down. You can find all the code on github and I did my best to provide proper documentation. Feel free to reach out and ask me questions if you don’t understand something.
And finally, if you want to follow along, you’ll need to install the following (apart from Go):
- Python 3.8
- A C compiler (such as gcc)…
With many of us around the globe under shelter in place due to COVID-19 video calls have become a lot more common. In particular, ZOOM has controversially become very popular. Arguably Zoom’s most interesting feature is the “Virtual Background” support which allows users to replace the background behind them in their webcam video feed with any image (or video)…
An interactive deep learning book with code, math, and discussions, based on the NumPy interface.
- Multi framework: Cortex supports TensorFlow, PyTorch, scikit-learn, XGBoost, and more.
- Autoscaling: Cortex automatically scales APIs to handle production workloads.
- CPU / GPU support: Cortex can run inference on CPU or GPU infrastructure.
- Spot instances: Cortex supports EC2 spot instances.
- Rolling updates: Cortex updates deployed APIs without any downtime.
- Log streaming: Cortex streams logs from deployed models to your CLI.
- Prediction monitoring: Cortex monitors network metrics and tracks predictions.
- Minimal configuration: Cortex deployments are defined in a single
Khan Academy is embarking on a huge effort to rebuild our server software on a more modern stack in Go.
At Khan Academy, we don’t shy away from a challenge. After all, we’re a non-profit with a mission to provide a “free world-class education to anyone, anywhere”. Challenges don’t get much bigger than that.
Our mission requires us to create and maintain software to provide tools which help teachers and coaches who work with students, and a personalized learning experience both in and out of school. Millions of people rely on our servers each month to provide a wide variety of features we’ve built up over the past ten years.
Ten years is a long time in technology! We chose Python as our backend server language and it has been a productive choice for us. Of course, ten years ago we chose Python 2 because Python 3 was still very new and not well supported.
In this article, we will discuss how any organisation can use deep learning to automate ID card information extraction, data entry and reviewing procedures to achieve greater efficiency and cut costs. We will review different deep learning approaches that have been used in the past for this problem, compare the results and look into the latest in the field. We will discuss graph neural networks and how they are being used for digitization.
While we will be looking at the specific use-case of ID cards, anyone dealing with any form of documents, invoices and receipts, etc and is interested in building a technical understanding of how deep learning and OCR can solve the problem will find the information useful.
Handling character encodings in Python or any other language can at times seem painful. Places such as Stack Overflow have thousands of questions stemming from confusion over exceptions like
UnicodeEncodeError. This tutorial is designed to clear the
Exception fog and illustrate that working with text and binary data in Python 3 can be a smooth experience. Python’s Unicode support is strong and robust, but it takes some time to master.
This tutorial is different because it’s not language-agnostic but instead deliberately Python-centric. You’ll still get a language-agnostic primer, but you’ll then dive into illustrations in Python, with text-heavy paragraphs kept to a minimum. You’ll see how to use concepts of character encodings in live Python code.
As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below.
PySnooper is a poor man’s debugger.
You’re trying to figure out why your Python code isn’t doing what you think it should be doing. You’d love to use a full-fledged debugger with breakpoints and watches, but you can’t be bothered to set one up right now.
You want to know which lines are running and which aren’t, and what the values of the local variables are.
Most people would use
print lines, in strategic locations, some of them showing the values of variables.
PySnooper lets you do the same, except instead of carefully crafting the right
print lines, you just add one decorator line to the function you’re interested in. You’ll get a play-by-play log of your function, including which lines ran and when, and exactly when local variables were changed.
What makes PySnooper stand out from all other code intelligence tools? You can use it in your shitty, sprawling enterprise codebase without having to do any setup. Just slap the decorator on, as shown below, and redirect the output to a dedicated log file by specifying its path as the first argument.
This is the seventh article in my series of articles on Python for NLP. In my previous article, I explained how to perform topic modeling using Latent Dirichlet Allocation and Non-Negative Matrix factorization. We used the Scikit-Learn library to perform topic modeling.
In this article, we will explore TextBlob, which is another extremely powerful NLP library for Python. TextBlob is built upon NLTK and provides an easy to use interface to the NLTK library. We will see how TextBlob can be used to perform a variety of NLP tasks ranging from parts-of-speech tagging to sentiment analysis, and language translation to text classification.