IntelliJ IDEA vs. Pytorch

This is a superb tool if your project involves a lot of backend development, especially in Java/Spring Boot and Kotlin. The support for the front end is great as well, but some developers may prefer to use the GitHub copilot add-on. I especially love using the GitHub copilot add-on. It may be less appropriate if your project requires heavy use of HotSwaps for backend debugging, as sometimes the support for that can be limited.

Incentivized

They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

Incentivized

• Unit testing: Fully integrated into IntelliJ IDEA. Your unit tests will run smoothly and efficiently, with excellent debugging tools for when things get tricky.
• Spring integration: Our Spring project using Maven works flawlessly in IntelliJ IDEA. I know firsthand that Apache is also easily and readily supported too. The integration is seamless and very easy to set up using IntelliJ IDEA's set up wizard when importing new projects.
• Customization: IntelliJ IDEA comes out of the box with a bunch of handy shortcuts, as well as text prediction, syntax error detection, and other tools to help keep your code clean. But even better is that it allows for total customization of shortcuts you can easily create to suit your needs.

Incentivized

• flexibility
• Clean code, close to the algorithm.
• Fast
• Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
• Versatile, can work efficiently on text/audio/image/tabular datasets.

Incentivized

• Finding if a feature exists or not in IntelliJ IDEA can be challenging.
• For example, if we know how to format a particular file, the command is Ctrl+Alt+Shift+L, but if we don't, then finding it is difficult.
• Setting up a project interpreter and directory structure might not be intuitive at first.
• Git integration can be improved. For example, it isn't easy to rebase using UI in IntelliJ IDEA.

Incentivized

• Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow

Incentivized

VS Code is maturing and has a Scala plugin now. The overall experience with VS Code - for web development at least - is very snappy/fast. IntelliJ feels a bit sluggish in comparison. If that Scala plugin for VS Code is deemed mature enough - we may not bother renewing and resort to the Community Edition if we need it.

Incentivized

There is always room for improvement, but I haven't met any IDE that I liked more so far. Even if it did not fit a use case right out of the box, there is always a way to configure how it works to do just that.

Incentivized

The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.

Incentivized

Customer support is really good in the case of IntelliJ. If you are paying for this product then, the company makes sure that you will get all the services adequately. Regular update patches are provided to improve the IDE. An online bug report makes it easier for the developers to find the solution as fast as possible. The large online community also helps to find the various solutions to the issues.

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This installs just like any other application - its pretty straight forward. Perhaps licensing could be more challenging - but if you use the cloud licensing they offer its as simple as having engineers login to the application and it just works.

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Eclipse is just so old, like a dinosaur, compared to IntelliJ. There are still formats that Eclipse supports better, especially old and/or propriety ones. Still, most of the modern software development needs can be done on IntelliJ, & in a much better way, some of them are not even supported on Eclipse.

Incentivized

Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.

Incentivized

• Easier to find bugs and debugs, thus reducing man hours and generating immediate dollar impact.
• Coding time is lessened, which in turn again reduces man hours and generates immediate dollar impact.
• Refactoring code is more innovative and easy here, resulting in more maintainable code.

Incentivized

• The ability to make models as never before
• Being able to control the bias of models was not done before the arrival of Pytorch in our company

Incentivized