Homebrew, iTerm2, and Visual Studio Code are probably your best bets out of the 31 options considered. 'Quick access to a large repository of open source software' is the primary reason people pick Homebrew over the competition. This page is powered by a knowledgeable community that helps you make an informed decision. Easily build widgets and make them available across iOS, iPadOS, and macOS using the WidgetKit framework and the new widget API for SwiftUI. Widgets now come in multiple sizes, and users can visit the new widget gallery to search, preview sizes, and place them anywhere on the Home screen to access important details at a glance. Welcome to my second tutorial of the macOS app development! If you have not read my first tutorial on macOS app development, it may be helpful for you to check it out first as I will not go into some of the stuff, which were already mentioned there. In this tutorial, we will be looking into custom views, audio playback and one of the most commonly used feature: File Upload!
I have never written anything using XCode. I have written in other languages such as Basic, Pascal, Assembler, Java, PHP and Python.
Free apps for mac computer. I just never got around to learning C or C++, which is what XCode looks like to me, but it also resembles Python in my opinion. I have written a program in Python and I would like to convert it to a native macOS application and possibly even offer it through the App Store for macOS one day.
I know XCode and Swift just went through a major revision with the release of macOS Catalina and the updates and information is still propogating for the learning sites and ebooks.
I have attempted to follow the Apple Developer Tutorial for the Landmarks application because it shows that it will teach the learner how to deploy the application to all Apple platforms, but that tutorial has some inconsistencies that a newbie to XCode like myself has difficulty overcoming once I started section 2 (I've submitted Feedback to Apple for this issue).
I have no real interest in developing for the iOS platform, or any other platform for that matter, at this time. So, I am looking for a macOS specific developer course or book I can use for learning XCode 11.*/SwiftUI 5.* environments.
I checked out the free 24 page download for Hacking macOS by Paul Hudson from the Hacking With Swift site, but the in the 24 page sample it starts off having you create a macOS application and choosing 'Cocoa Application', which is not an option in XCode 11.1. Is it the same and Apple just changed the name to just 'App'? I'm skeptical to buy something when right off the bat there is an inconsistency with what the reader is encountering between the book and real world. Anyone have experience learning from that author? Back in my day the O'Reilly books were some of my goto sources for learning different languages. I looked there and it seems they're focused on iOS app development and nothing appears to be targeted for recent macOS development.
Learn Macos Development
I've searched through the forums for this question and didn't find anything that was as specific as this question.
Thank you!
Tom
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Every company is sucking up data scientists and machine learning engineers. You usually hear that serious machine learning needs a beefy computer and a high-end Nvidia graphics card. While that might have been true a few years ago, Apple has been stepping up its machine learning game quite a bit. Let’s take a look at where machine learning is on macOS now and what we can expect soon.
2019 Started StrongMore Cores, More Memory
The new MacBook Pro’s 6 cores and 32 GB of memory make on-device machine learning faster than ever.
Depending on the problem you are trying to solve, you might not be using the GPU at all. Scikit-learn and some others only support the CPU, with no plans to add GPU support.
eGPU Support
If you are in the domain of neural networks or other tools that would benefit from GPU, macOS Mojave brought good news: It added support for external graphics cards (eGPUs).
(Well, for some. macOS only supports AMD eGPUs. This won’t let you use Nvidia’s parallel computing platform CUDA. Nvidia have stepped into the gap to try to provide eGPU macOS drivers, but they are slow to release updates for new versions of macOS, and those drivers lack Apple’s support.)
Neural Engine
2018’s iPhones and new iPad Pro run on the A12 and A12X Bionic chips, which include an 8-core Neural Engine. Apple has opened the Neural Engine to third-party developers. The Neural Engine runs Metal and Core ML code faster than ever, so on-device predictions and computer vision work better than ever. This makes on-device machine learning usable where it wouldn’t have been before.
Experience Report![]()
I have been doing neural network training on my 2017 MacBook Pro using an external AMD Vega Frontier Edition graphics card. I have been amazed at macOS’s ability to get the most out of this card.
PlaidML
To put this to work, I relied on Intel’s PlaidML. PlaidML supports Nvidia, AMD, and Intel GPUs. In May 2018, it even added support for Metal. I have taken Keras code written to be executed on top of TensorFlow, changed Keras’s backend to be PlaidML, and, without any other changes, I was now training my network on my Vega chipset on top of Metal, instead of OpenCL.
What about Core ML?
Why didn’t I just use Core ML, an Apple framework that also uses Metal? Because Core ML cannot train models. Once you have a trained model, though, Core ML is the right tool to run them efficiently on device and with great Xcode integration.
Metal
GPU programming is not easy. CUDA makes managing stuff like migrating data from CPU memory to GPU memory and back again a bit easier. Metal plays much the same role: Based on the code you ask it to execute, Metal selects the processor best-suited for the job, whether the CPU, GPU, or, if you’re on an iOS device, the Neural Engine. Metal takes care of sending memory and work to the best processor.
Many have mixed feelings about Metal. But my experience using it for machine learning left me entirely in love with the framework. I discovered Metal inserts a bit of Apple magic into the mix.
When training a neural network, you have to pick the batch size, and your system’s VRAM limits this. The number also changes based on the data you’re processing. With CUDA and OpenCL, your training run will crash with an “out of memory” error if it turns out to be too big for your VRAM.
When I got to 99.8% of my GPU’s available 16GB of RAM, my model wasn’t crashing under Metal the way it did under OpenCL. Instead, my Python memory usage jumped from 8GB to around 11GB.
When I went over the VRAM size, Metal didn’t crash. Instead, it started using RAM.
This VRAM management is pretty amazing. While using RAM is slower than staying in VRAM, it beats crashing, or having to spend thousands of dollars on a beefier machine. Delete app on macos. Training on My MBP
Select between apps mac. The new MacBook Pro’s Vega GPU has only 4GB of VRAM. Metal’s ability to transparently switch to RAM makes this workable.
I have yet to have issues loading models, augmenting data, or training complex models. I have done all of these using my 2017 MacBook Pro with an eGPU.
I ran a few benchmarks in training the “Hello World” of computer vision, the MNIST dataset. The test was to do 3 epochs of training:
![]()
You’ll find a bit more detail in the table below.
Mac Os Development Tools3 Epochs training run of the MNIST dataset on a simple Neural Network
Looking Forward
Thanks to Apple’s hard work, macOS Machine Learning is only going to get better. Learning speed will increase, and tools will improve.
TensorFlow on Metal
Apple announced at their WWDC 2018 State of the Union that they are working with Google to bring TensorFlow to Metal. I was initially just excited to know TensorFlow would soon be able to do GPU programming on the Mac. However, knowing what Metal is capable of, I can’t wait for the release to come out some time in Q1 of 2019. Factor in Swift for TensorFlow, and Apple are making quite the contribution to Machine Learning.
Create ML
Not all jobs require low-level tools like TensorFlow and scikit-learn. Apple released Create ML this year. It is currently limited to only a few kinds of problems, but it has made making some models for iOS so easy that, with a dataset in hand, you can have a model on your phone in no time.
Turi Create
Create ML is not Apple’s only project. Turi Create provides a bit more control than Create ML, but it still doesn’t require the in-depth knowledge of Neural Networks that TensorFlow would need. Turi Create is well-suited to many kinds of machine learning problems. It does a lot with transfer learning, which works well for smaller startups that need accurate models but lack the data needed to fine-tune a model. Version 5 added GPU support for a few of its models. They say more will support GPUs soon.
Mac Os Basics
Unfortunately, my experience with Turi Create was marred by lots of bugs and poor documentation. I eventually abandonded it to build Neural Networks directly with Keras. But Turi Create continues to improve, and I’m very excited to see where it is in a few years.
Conclusion
It’s an exciting time to get started with Machine Learning on macOS. Tools are getting better all the time. You can use tools like Keras on top of PlaidML now, and TensorFlow is expected to come to Metal later this quarter (2019Q1). There are great eGPU cases on the market, and high-end AMD GPUs have flooded the used market thanks to the crypto crash.
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