Python Programming
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EuroPython 2020: Our keynotes
Conference tickets are available on our registration page. We hope to see lots of you at the conference from July 23-26. Rest assured that we’ll make this a great event again — even within the limitations of running the conference online.
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Full Stack Python: How to Report Errors in Flask Web Apps with Sentry
Flask web applications are highly customizable by developers thanks to the framework's extension-based architecture, but that flexibility can sometimes lead to more errors when you run the application due to rough edges between the libraries.
Reporting errors is crucial to running a well-functioning Flask web application, so this tutorial will guide you through adding a free, basic Sentry configuration to a fresh Flask project.
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PyCharm EAP#3 is out!
PyCharm EAP #3 is out and it’s almost releasing time!! If you are like us you are also looking forward to the end of the month! We have been talking about new features for the last month and today we will take a deeper look into two very exciting ones. For the full list, check our release notes.
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The Home Stretch - Building SaaS #63
In this episode, we return to the homeschool application that I’m building. I’m in the final stretch of changes that need to happen to make the product minimally viable. We worked on a template, wrote some model methods, and did a bunch of automated testing.
We started by adding students to the context of the students index page. With the students in the context, we updated the index page to display the list of students.
After the students were available, we had to check their enrolled status in a school year. That logic doesn’t belong in the template so we worked out the changes needed for the view.
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py.CheckIO: Find out more about Python by searching the solutions
As you might have noticed, for two weeks we haven’t made our usual newsletter mailouts. But we definitely weren’t wasting any time. CheckiO team was actually preparing some important updates, which we want to share with you.
That’s a common knowledge that CheckiO originated from the idea of practical learning through shared solutions. This means that in our portals you can learn not only by solving the coding tasks, but also by checking out and analyzing the solutions made by other users. In view of this, our next step became a logical continuation of this ideology.
Since the creation of CheckiO, we’ve gathered nearly half a million of different solutions. Now, using the Solution Search feature, which becomes available from the 2nd Level, you can easily find any solution you need. Like you can look for the usage examples of an itertools.groupby function. You just need to enter it into the search field and you’ll see multiple solutions. Or you can type ‘itertools’ and you’ll be presented with all of the solutions where this module had been used. It’s fast, efficient and quite handy. The feature is still in the beta testing mode though.
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Data science workflows on Kubernetes with Kubeflow pipelines: Part 2
Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is a part of the Kubeflow project that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. For more on Kubeflow, read our Kubernetes for data science: meet Kubeflow post.
In this blog series, we demystify Kubeflow pipelines and showcase this method to produce reusable and reproducible data science.
In Part 1, we covered WHY Kubeflow brings the right standardization to data science workflows. Now, let’s see HOW you can accomplish that with Kubeflow Pipelines.
In Part 2 of this blog series, we’ll work on building your first Kubeflow Pipeline as you gain an understanding of how it’s used to deploy reusable and reproducible ML pipelines.
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