Deeplearning On Macos M-Series Processors

This article covers deep learning on MacOS, exploring the use of M-series processors and AMD GPUs for training. It discusses training with a MacBook Air, using TensorFlow Metal and PyTorch with MPS. The article also delves into CoreML image analysis techniques and AutoML for Apple’s M1 chips. Additionally, it covers image/video analysis methods, Optical Flow, Person Segmentation, NLP APIs, sentiment analysis, speech recognition, and sound classification using DNNs.

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:

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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

neural engine

it is used for coreml inference, not training

run coreml on hackintosh

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

run coreml with swift on linux

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.

thermal and battery life concerns, and more

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.

tensorflow with m1 support

using tensorflow metal plugin, which sets up miniforge and install tensorflow-metal within.

install without miniforge(works!)

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pip3 install tensorflow-macos tensorflow-metal

validation:

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python3 -c "import tensorflow as tf; physical_devices = tf.config.list_physical_devices('GPU'); print('Num GPUs:', len(physical_devices)); print(physical_devices)"

pytorch with m1 support, using MPS (Metal performance shader)

install from nightly release channel, with minimum system version requirements 12.3 (which this machine had been qualified after system update, now 12.5)

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# MPS acceleration is available on MacOS 12.3+
pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu

validation

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python3 -c "import torch; print('MPS avaliable:',torch.backends.mps.is_available()); print('Built with MPS:',torch.backends.mps.is_built())"

run python inside swift

use pythonkit

automatic machine learning using CreateML

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import CreateML

CreateML is similar to any other AutoML tools, like AutoKeras, AutoTrain by Huggingface (works by training against a selected set of user-provided models)

using CoreML

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:

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brew install dotnet-sdk

to install and launch dotnet repl:

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dotnet tool install -g dotnet-repl
dotnet repl

paddlepaddle support

convert 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

pytorch mps backend

text classification using createml

onnx model zoo

Getting CoreML Models

CoreML Model Zoo

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

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