Nerd Maze Mac OS
Mac.ICY; The Mac and here (USA) and blog and Flickr; MAC Crew (France) Mac1 (UK) Macia Crew; M.A.D. Mad C; Mad Stylers; Made514 and here; Madjecks; Mafeel and here; Mahon; Mak; Make; Victor Malagon and here; Male one; Malitz; Mamio; Mambo; Mambo Kings; Mania and here; ManOne and on GuerillaOne; Mare139 and sketches and his blog; Maria and here. The latest version of the Mac OS X operating system, numbered 10.5 and called “Leopard,” was released last night. I went out and bought it and upgraded the iMac that I’m writing this post on. It’s a nice upgrade, with all sorts of little features. Find the best information and most relevant links on all topics related toThis domain may be for sale! ComicNerd fits seamlessly with all of your other favorite Mac apps and helps you easily read and manage all of your comics (.cbr,.cbz,.rar,.zip) in one place. A clean and simple reader. ComicNerd's reader is designed to be fast and easy to use.
12 Features You Should Try After Upgrading to Mac OS Catalina
Comment Chloe
Discover everything you need to know about upgrading to the Mac OS Catalina!
There are plenty of new features that arrived on your Mac after making the upgrade to OS Catalina. This latest upgrade has some amazing features that you won’t want to miss. Read on to learn more about these incredible features.
The New and Improved “iTunes”
iTunes hasn’t been able to meet the current needs of users these days, which is why Apple has decided to break it up into 3 different programs: Apple TV, Apple Podcasts, and Apple Music. Apple Music is where you would play and store your music. Apple Podcasts is where you manage your podcasts or subscribe to new ones. Apple TV is where you handle your television and movies.
Access iPad Apps on Your Mac
Thanks to Catalina, developers can now build apps for MacOS by using a new tool called Catalyst. This allows developers to create apps for both MacOS and iPads, allowing Mac users a broader range of apps.
Screen Time Limits
With this new update, you can put screen time limits on your children and even yourself if you need to.
Second Screen Display
Sidecar is a great new feature that allows you to use your iPad as a second display for your Mac. You can use this feature through connecting both devices or through Wi-Fi.
Block Contacts in Your Email App
There is now a feature in your email app that allows you to block any unwanted emails. When you block a sender, any email from that sender will automatically go into the trash.
Better Accessibility
The accessibility controls are greatly improved thanks to the Catalina through using voice controls. You can also add custom words so that it can easily recognize the words that you often use.
Better Photo Organization
A new feature in Catalina is that there is a tab that allows you to organize your pictures based on the Months, Days, and Years they were taken. It also determines the best picture within that collection and showcases them.
Apple Arcade
This is a new game subscription service offers Mac users over 100 new and exclusive games. Apple works with game creators to create these new games, which aren’t available anywhere else.
Find My App
The new Find My app can be used across all of your Apple devices, helping you find any of these devices. It can also use other user’s devices to help find your device if it is offline.
Better Notes
Notes gets a significant upgrade with Catalina. The checklist feature is easier to use, the search function is more thorough, and the new gallery view.
Easier to Set Reminders
Reminders also gets a significant upgrade with this new OS. When you make plans in your Messages app, you will get a prompt that recognizes this and asks if you want to set a reminder about them.
Security and Privacy Upgrades
Activation Lock is a new feature that prevents any device that was lost or stolen from being opened without the person who has the iCloud account attached to the device.
< Back to Our BlogEvery 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 Strong
More 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.
Nerd Maze Mac Os 11
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.
Training on My MBP
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:
- TensorFlow running on the CPU took about 130 seconds an epoch: 1 hour total.
- The Radeon Pro 560 built into the computer could do one epoch in about 47 seconds: 25 minutes total.
- My AMD Vega Frontier Edition eGPU with Metal was clocking in at about 25 seconds: 10 minutes total.
You’ll find a bit more detail in the table below.
3 Epochs training run of the MNIST dataset on a simple Neural Network
| Average per Epoch | Total | Configuration |
|---|---|---|
| 130.3s | 391s | TensorFlow on Intel CPU |
| 47.6s | 143s | Metal on Radeon Pro 560 (Mac’s Built in GPU) |
| 42.0s | 126s | OpenCL on Vega Frontier Edition |
| 25.6s | 77s | Metal on Vega Frontier Edition |
| N/A | N/A | Metal on Intel Graphics HD (crashed – feature was experimental) |
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
Nerd Maze Mac Os 11
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.
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
Mac Os Mojave
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.
Nerd Maze Mac Os Catalina
We'd love to hear from you
From training to building products, companies of all sizes trust us with transforming their project vision into reality.