CraftOS vs TFLite LiteRT TensorFlow Test

CraftOS-PC is a fantasy terminal that allows you to write and run programs inside an ‘80s-style text console. CraftOS-PC emulates the popular Minecraft mod "ComputerCraft", which adds programmable computers to Minecraft using the Lua programming language. CraftOS-PC takes this experience outside Minecraft to allow you to run the same programs anywhere you go. CraftOS-PC provides a set of functions (called APIs) that make it super easy to do simple tasks such as write text to the screen, read files, and more. The simplicity of these functions makes CraftOS-PC great for new programmers, but their power makes it possible to write all sorts of complex programs with less code. If you aren’t ready to write programs yet, there’s already a large number of programs for ComputerCraft that will work in CraftOS-PC, ranging from simple games to whole graphical operating systems. These can be downloaded through the built-in Pastebin and GitHub Gist clients. • Full Lua 5.2+ scripting environment and command-line REPL • 16-color text-based terminal display • Expansive virtual filesystem for program and data storage • Built-in shell with syntax similar to most desktop shells • APIs to easily access terminal, filesystem, Internet, event queue, and more • Built-in programs make it easy to navigate & edit files without a single line of code • Plenty of help documents to assist programmers • Compatibility with thousands of existing ComputerCraft programs • Over 3x faster than Minecraft mod and comparable emulators • Emulation of all peripherals available in ComputerCraft • Easily access configuration from inside CraftOS • Exclusive graphics mode providing up to 256-color, pixel-based screen manipulation • Edit Lua scripts from CraftOS or other code editing apps • Open-source app makes it easy to suggest and contribute changes Documentation on all of the APIs that ComputerCraft provides are available at https://tweaked.cc, and CraftOS-PC’s unique APIs are described at https://www.craftos-pc.cc/docs/. Join the CraftOS-PC community at https://www.craftos-pc.cc/discord!

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.