SimpleGPSApp Lesson 01 vs TFLite LiteRT TensorFlow Test

GREAT FUN - GPS 3D MapKit & Speedometer iOS Project - showing RAW GPS Speed of MTS (Meters per Second) & hundredths MPH & KMPH! On the way to Full Level 5 Self Driving for a new EV we are developing (by Summer 2022), this XCODE project is yours, with lots of useful, very simplified code/methods you can copy and use in your own project, Xcode 13, Swift 5, available to copy to your desktop using iTunes/Music File Share. Beginner to Advanced iOS Developer Lessons! We have always learned best and quickest by looking at working App's. Included in this lesson is the full XCODE PROJECT itself for you to use in your own App projects, saving you months of headache to find the answers!:*) We will make you understand! This is the first in a series to help ALL other Apple iOS, iPhone/iPad App Developers, Beginners to Advanced. We've kept the code very, very simple so a 9 year old could get it! Feel free to use any part of this project in your own projects. These are things that have taken months to learn all in this very optimized code for iPhone/iPad App. The Xcode Project is included within the app and can be copied from the iTunes/Music File Share app to your desktop. Highlights in this lesson includes; LocationManager, CLLocationManager, GPS, Speed MPH and KMPH conversion, 3D MapKit, Tracking Mode, iTunes File Sharing, Web/URL link, Buttons with Automated Titles, printing app events to Console/Debug Area, converting large decimal numbers to 2 place, labels, buttons, switches, Case/Logic Switch in Swift, rounding to integer of decimal numbers, variables, integers, text, hidden lessons include a vertical slider and much more! We have plans for many more in the near future! AND if you're interested in getting involved in our LEVEL 5, FULL SELF DRIVING EV PROJECT (for Summer 2022), contact us via our WebSite! CODING IS FUN! JOHNAVATAR.com Pacific Palisades, CA 90402

How fast does your .tflite model actually run on this iPhone? Pick CPU, GPU or Neural Engine. Get real latency and QPS. No Mac, no Xcode project, no Bazel build, no cloud, no account. Open a .tflite file from the Files app, pick an accelerator, run it. A real measurement on the exact device you care about, in about a minute — no Bazel workspace, no benchmark tool built from source. PICK THE ACCELERATOR - Neural Engine — attaches the Core ML delegate, so supported operators run on the Apple Neural Engine - GPU — attaches the GPU delegate, backed by Metal - CPU — the plain interpreter, with a thread count you choose: 1, 2, 4, 6 or 8 One configuration per run: run it, change the delegate or thread count, load it again, compare the numbers yourself. WHAT YOU GET - Latency: mean, min and max, in milliseconds - Queries per second - Total queries completed and total run duration - A query count you set per run, from 50 to 5,000 invocations Mean, min and max time the model inference call only — no pre-processing, no post-processing. Duration covers the whole loop. The screen stays awake during a run. WHAT YOU SEE ABOUT THE MODEL - Every input and output tensor: index, name, shape and data type (uInt8, int32, float16, float32 and the rest) - Model file size and framework - Live app memory usage against the memory available to the app, refreshed while the model runs - A device tab with model identifier, system version, disk space, and the exact TensorFlow Lite runtime version this build links against A MobileNet model ships in the app and loads on launch, so you see a real measurement before importing your own. READ THIS BEFORE YOU TRUST A NUMBER Inference runs on a zeroed dummy input. The app fills every input tensor with zeros and invokes the model, so what you measure is the compute cost of the graph on the accelerator you picked — latency and throughput, nothing else. It is not an accuracy test. It will not tell you whether your model gives the correct answer, and it does not show output tensor values. Ask it "how fast", not "how correct". What it does not do, stated up front: no side-by-side accelerator comparison in one run, no per-layer or per-operator profiling, no result export, no image or real-data input. It opens .tflite files only. PRIVACY Your model never leaves the device. Loading, inspection, inference and timing all happen locally on your iPhone or iPad. No account, no sign-in — your models and your results are never uploaded. TensorFlow Lite is now called LiteRT; the format and the .tflite extension are unchanged. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. Not affiliated with or endorsed by Google.