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Deeplearning on MacOS M-series Processors |
2022-08-06T18:25:22.000Z |
2023-08-12T14:06:28.323Z |
it is funny that macOS still supports AMD GPUs, means any intel Mac (not M-series!) can now utilize internal/external AMD GPUs as long as frameworks like jax and pytorch support MPS/Metal.
calling python code from swift using pythonkit:
func downloadVideo(link: String){
let sys = Python.import("sys")
sys.path.append(dirPath)
let example = Python.import("sample")
let response = example.downloadVideo(link, dirPath)
videoPath = String(response)
}run macos in docker with kvm
it is used for coreml inference, not training
first, download macos montery using the mac. then, install it on hackintosh, with associated nvidia drivers. next test gpu avalibility via system info panel. then install xcode commandline tools and check coreml avalibility
darling is at its very premature stage, just like the wine. now it is testing something called "darlingserver" which is a full userspace implementation and is prone to tons of problems. swift repl is not working and installing xcode commandline tools 14 will hang this thing. i suggest you to do light model training on macbook air and convert it to onnx if want to use it everywhere.
before reinstallation of darling, make sure you have removed all darling related files by checking updatedb; locate darling | grep -v <compile directory>
visit here to install darling from source (maybe that's the only way)
if want to install darling on kali, you must outsource all deeplearning models to other disks, and collect all other big files to somewhere else or trash them. use systemwide user broadcast method to warn me if any of the disk is missing. use automatic symlink change method to adapt the external disk mountpoint changes.
darling can install xcode commandline tools with macos sdk, so maybe it can run coreml models with swift using cpu. gpu support is currently not known. maybe that requires metal support.
consider using external gpus (eGPUs) with thunderbolt 3 and AMD GPUs to avoid overheating. currently that can only be done with intel Macs.
battery life is currently bad for intel/amd notebooks of x86/64 architecture.
heavy lifting jobs are likely to be run on Mac Studio with M1 Ultra and 128GB RAM. Macbook Air M1 with 8GB RAM is simply not feasible.
aside of Apple platforms, these APIs are virtually useless.
to run these on other non-apple machines, you need to tweak and install macOS on x86-64 platforms with macOS supported GPUs(may have low performance), which will definitely not taking any advantage of huge shared RAM with CPU, and may run poorly on CoreML/CreateML, may not support deepspeed stage 2/3 or BMI(big model inference)
Non-Supported NVIDIA Cards, use AMD GPU instead
High Sierra no longer supports NVIDIA Mac. Mojave – Catalina – BigSur only works with AMD graphics and Intel onboard graphics and only a very small number of old NVIDIA products. Suppose you have GTX 1070, 1080, and the like, you can not use High Sierra onwards because Nvidia does not provide any updates for Mac and can not be used in any other way. In general, the graphics of the Turing, Pascal, and Maxwell series will never be supported again. The latest Mac version that can use this series of graphics is High Sierra.using tensorflow metal plugin, which sets up miniforge and install tensorflow-metal within.
install without miniforge(works!)
pip3 install tensorflow-macos tensorflow-metalvalidation:
python3 -c "import tensorflow as tf; physical_devices = tf.config.list_physical_devices('GPU'); print('Num GPUs:', len(physical_devices)); print(physical_devices)"install from nightly release channel, with minimum system version requirements 12.3 (which this machine had been qualified after system update, now 12.5)
# MPS acceleration is available on MacOS 12.3+
pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpuvalidation
python3 -c "import torch; print('MPS avaliable:',torch.backends.mps.is_available()); print('Built with MPS:',torch.backends.mps.is_built())"use pythonkit
import CreateMLCreateML is similar to any other AutoML tools, like AutoKeras, AutoTrain by Huggingface (works by training against a selected set of user-provided models)
curated, largest coreml models collection
CoreML models can be created by CreateML and some customization can be done via protocol MLCustomLayer.
onnxruntime can run onnx models on CoreML, via c#, since that library is maintained by microsoft.
to install c# on macos:
brew install dotnet-sdkto install and launch dotnet repl:
dotnet tool install -g dotnet-repl
dotnet replconvert into onnx first, then run on onnxruntime.
paddlepaddle itself currently only supports running on M1 CPU only via rosetta 2.
Swift Core ML 3 implementations of GPT-2, DistilGPT-2, BERT, and DistilBERT for Question answering.
train image classifier and text classifier in which CreateMLUI is deprecated (gone)
train source code classifier with flightschool which is a free swift tutorial books provider
classifying sounds with coreml return sound type along with timestamp
detect human pose using coreml
apple speech recognization api request
text classification using createml
FCRN-DepthPrediction Depth Estimation
Predict the depth from a single image. View Models
MNIST Drawing Classification
Classify a single handwritten digit (supports digits 0-9). View Model
UpdatableDrawingClassifier Drawing Classification
Drawing classifier that learns to recognize new drawings based on a K-Nearest Neighbors model (KNN). View Model and Code Sample
MobileNetV2 Image Classification
The MobileNetv2 architecture trained to classify the dominant object in a camera frame or image. View Models and Code Sample
Resnet50 Image Classification
A Residual Neural Network that will classify the dominant object in a camera frame or image. View Models and Code Sample
SqueezeNet Image Classification
A small Deep Neural Network architecture that classifies the dominant object in a camera frame or image. View Models and Code Sample
DeeplabV3 Image Segmentation
Segment the pixels of a camera frame or image into a predefined set of classes. View Models
YOLOv3 Object Detection
Locate and classify 80 different types of objects present in a camera frame or image. View Models and Code Sample
YOLOv3-Tiny Object Detection
Locate and classify 80 different types of objects present in a camera frame or image. View Models and Code Sample
PoseNet Pose Estimation
Estimates up to 17 joint positions for each person in an image. View Models and Code Sample Text
BERT-SQuAD Question Answering
Find answers to questions about paragraphs of text. View Model and Code Sample
Apple Machine Learning Related APIs (may need user permission within or without xcode by means of Info.plist or something)
Vision Build features that can process and analyze images and video using computer vision.
View Vision framework
Image Classification Automatically identify the content in images.
View API
Image Saliency Quantify and visualize the key part of an image or where in the image people are likely to look.
View API
Image Alignment Analyze and manage the alignment of images.
View API
Image Similarity Generate a feature print to compute distance between images.
View API
Object Detection Find and label objects in images.
View API
Object Tracking Track moving objects in video.
View API
Trajectory Detection Detect the trajectory of objects in motion in video.
View API
Contour Detection Trace the edges of objects and features in images and video.
View API
Text Detection Detect regions of visible text in images.
View API
Text Recognition Find, recognize, and extract text from images.
View API
Face Detection Detect human faces in images.
View API
Face Tracking Track faces from a camera feed in real time.
View API
Face Landmarks Find facial features in images by detecting landmarks on faces.
View API
Face Capture Quality Compare face capture quality in a set of images.
View API
Human Body Detection Find regions that contain human bodies in images.
View API
Body Pose Detect landmarks on people in images and video.
View API
Hand Pose Detect landmarks on human hands in images and video.
View API
Animal Recognition Find cats and dogs in images.
View API
Barcode Detection Detect and analyze barcodes in images.
View API
Rectangle Detection Find rectangular regions in images.
View API
Horizon Detection Determine the horizon angle in images.
View API
Optical Flow Analyze the pattern of motion of objects between consecutive video frames.
View API
Person Segmentation New Produce a matte image for a person in an image.
View API
Document Detection New Detect rectangular regions in images that contain text.
View API
Natural Language Analyze natural language text and deduce its language-specific metadata.
View Natural Language framework
Tokenization Enumerate the words in text strings.
View API
Language Identification Recognize the language of bodies of text.
View API
Named Entity Recognition Use a linguistic tagger to name entities in a string.
View API
Part of Speech Tagging Classify nouns, verbs, adjectives, and other parts of speech in a string.
View API
Word Embedding Get a vector representation for any word and find similarity between two words or nearest neighbors for a word.
View API
Sentence Embedding Get a vector representation for any string and find similarity between two strings.
View API
Sentiment Analysis Score text as positive, negative, or neutral based on the sentiment.
View API
Speech Take advantage of speech recognition and saliency features for a variety of languages.
View Speech framework
Speech Recognition Recognize and analyze speech in audio and get back data like transcripts.
View API
Sound Analysis Analyze audio and recognize it as a particular type, such as laughter or applause.
View Sound Analysis framework
Sound Classification Analyze sounds in audio using the built-in sound classifier or a custom Core ML sound classification model.
View API