If your Mac is from 2015 or earlier it’s not a feature you will be able to enjoy (unless you have a 27in iMac from that era). However, if you want to operate multiple Macs and iPads with one keyboard, mouse and trackpad you’ll need a newer Mac. The new feature also makes it extremely easy to copy content between devices using drag-and-drop. All you have to do to switch devices is move the pointer until it crosses the edge of the screen. Once it arrives Universal Control will let you use the same keyboard and mouse for all the Macs and iPads you are working on. Promises that it will launch later in 2021. However the company is now working on developing the feature, and Universal Control wasn’t available at the time that macOS Monterey launched. It will be possible to share content at a lower resolution to supported Mac models using older iPhone, iPad, and Mac models if you set ‘Allow AirPlay for’ to ‘Everyone’ or ‘Anyone on the same network’ though. If you want to use AirPlay to send content to your Mac from an iPhone, iPad, or another Mac, or to use your Mac as an AirPlay speaker, you’ll need a MacBook Pro (2018 and later), MacBook Air (2018 and later), iMac (2019 and later), iMac Pro (2017), Mac mini (2020 and later), Mac Pro (2019), iPhone 7 and later, iPad Pro (2nd generation and later), iPad Air (3rd generation and later), iPad (6th generation and later), and iPad mini (5th generation and later). This file has been truncated.If your Mac predates 2018 you won’t be able to experience the feature. You can take a look at the DLC requirements here: DeepLabCut/DeepLabCut/blob/1c9f59818ba2e960827167f52715e127eebd63bd/requirements.txt ipython The idea there is to recreate what DLC needs in a native arm64 python 3.8 (or 3.9 – I’ve been using 3.9 exclusively for tensorflow) env with Apple tensorflow and the metal plugin. 08:16:23.926585: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. I have only run testscript.py and not a full project, and it only runs in “light mode” (no GUI) but, the GPU was engaged during the training sets (python GPU usage went to 95%) and the following confirmation statements. I think I got DLC to install on an M1 macbook pro, running macOS Monterey 12.0.1. Installing DLC on mac with M1 and engaging GPU Development Python -m pip install tensorpack tf_slim tables conda-environments/DEEPLABCUT_M1.yamlĬonda install filterpy ruamel.yaml imgaug numba scikit-image scikit-learn statsmodels tqdm moviepy We are currently working with a local copy of dlc (but identical to the main GITversion) and the following code successfully installed everything (including the GUI): conda env create -f. My jobs are running ok on colab, but I really really prefer to run in an environment I can control. When the training ends the memory is not released and the only way I recover it is by ctrl-Z the pythonw. The same task now is using 50GB and running at 20-30% when I’m lucky. Unfortunately I did not record enough details on Big Sur, but my recollection is I was using most of the 16GB I have on this M1 mini, and the GPU was running at above 90%. A training that took ~10 minutes before is taking 6 hours now. Once we switched to Monterey it is a disaster. I have succeeded in running dlc on Big Sur, very efficiently.
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