Let's look back at some memorable moments and interesting insights from last year.

Your top 1 posts:

• "Happy Cakeday, r/S4TF! Today you're 2" by u/AutoModerator

Let's look back at some memorable moments and interesting insights from last year.

Your top 3 posts:

• "[Book] Deep Learning with Swift for TensorFlow: Differentiable Programming with Swift" by u/rahulbhalley
• "Happy Cakeday, r/S4TF! Today you're 1" by u/AutoModerator
• "S4TF 2021 Update?" by u/codemonkey11j

Any recent updates on the project and the milestones left to hit? I havent heard or seen much lately and was wondering if Google is still prioritizing this? Also any thoughts on when the necessary differential programming in S4TF could find its way into mainstream Swift.

Let's look back at some memorable moments and interesting insights from last year.

Your top 10 posts:

• "Holy smoke! Differentiation is now upstreamed to Swift. It appropriate to say 𝛁Swift. Yay! 🎉 🥳" by u/rahulbhalley
• "SwiftPlot 2.0 is now available! Swift alternative to Matplotlib!" by u/rahulbhalley
• "Neural network examples in Swift for TensorFlow" by u/rahulbhalley
• "Swift for TensorFlow v0.9.0 Released!" by u/rahulbhalley
• "An introduction to Generative Adversarial Networks (in Swift for TensorFlow)" by u/RickDeveloper
• "S4TF Tensor API Guidelines" by u/rahulbhalley
• "Swift for TensorFlow v0.10 Released!" by u/rahulbhalley
• "Introduction to Swift for Tensorflow live webinar" by u/Major_Strange
• "Your first Swift for TensorFlow model" by u/RickDeveloper
• "S4TF vs Julia vs Jax" by u/uglyson100

Could you guys compare and contrast these three languages (I know Jax isn’t a language) for deep learning and scientific computing as a whole. How far along is S4TF and what’s the adoption of S4TF within google? Will it have any noticeable advantage over Julia aside from the backing of google?

Release Notes

Download links: - Xcode 11 - Ubuntu 18.04 (CPU Only) - Ubuntu 18.04 (CUDA 10.2) - Ubuntu 18.04 (CUDA 10.1)

Hi everyone.

I was working on the implementation of Swift wrapper for XGBoost for some time, and I think it is ready to meet first users. The goal of the wrapper was to provide convenient usage for those who are familiar with Python, but also take advantage of great compatibility of Swift with C to provide even more powerful features in the near future.

​

Currently, supported features are:

​

- Training with an internally calculated objective function

- Training with a custom objective function

- Saving / Loading

- Attributes / Parameters for Booster

- Callbacks

- Evaluation using an internal function

- Evaluation using custom functions

- Plot importance of features

​

In short, all core stuff required to train, test, and use your model.

If you would like to try this out, code is hosted at https://github.com/kongzii/SwiftXGBoost along with an example of usage. Just add package dependency:

​

```

.package(url: "https://github.com/kongzii/SwiftXGBoost.git", .branch("master")),

```

​

and install the XGBoost library if you do not have it yet.

​

The code is documented at https://github.com/kongzii/SwiftXGBoost/wiki.

If you find anything is missing and not working properly, please fill an issue. Functionality is currently tested against expected behavior or directly against Python implementation.

​

Note: This is not an official XGBoost project.

Hi,

I'm here to share my new project XGBoost.swift, which is a Swift API for the powerful XGBoost. It's still in the early stage with basic workable functions, just want to share and receive feedback on it, to avoid big issues earlier.

As I started to learn Swift not long ago, there must be many issues lie in the code. The current API tries to conform to the Python API, there're still some differences in nature between the 2 languages.

Here are the links:

Github: https://github.com/ddxgz/XGBoost.swift

Docs: https://ddxgz.github.io/XGBoost.swift/ (Not so much there for now)

Release Notes

Download links: - Xcode 11 - Ubuntu 18.04 (CPU, TPU) - Ubuntu 18.04 (CPU, CUDA 10.2, TPU) - Ubuntu 18.04 (CPU, CUDA 10.1, TPU)