Neither company was endeared to Apple, as the squabble had many industry people speculating that Apple may look into AMD processors, even though AMD had very few competitive offerings in the laptop space. Predictably, Intel then filed suit against Nvidia, throwing Apple's plans into disarray. Nvidia pulling a fast one on Intel put Apple in the center of its own controversial strategy. Intel was much more central to Apple as a business partner, and Intel enjoyed Apple in its company roster. At the time, Intel's integrated GPUs were pretty bad and could not support OpenCL, thus limiting the amount of offloading to the GPU that Apple could reliably bank with the OS. It'd allowed Apple to stop using the underwhelming Intel integrated GPUs and unify them to mirror the desktops. The advantage was that Apple was going to be able to simplify its GPU strategy. Apple was the first PC maker to adopt Nvidia's new chipset. Then in 2008, Nvidia produced Nehalem-based chipsets that bypassed the Intel Northbridge (Memory controller) and South Bridge (I/0 controller) chipset. In 2004, Intel and NVIDIA joined forces for a patent licensing agreement for Intel CPUs with integrated memory controllers, the MCP79 and the MCP89. To understand this, we have to jump back to 2004. Apple pulled into a legal battle that was primarily between Nvidia and Intel. The year 2008 is when the relationship with NVidia changed during a flurry of events. As important as Apple was during this time frame, it wasn't the goliath it is today. Still, Apple continued to offer plenty of Nvidia options. In 2004, 30-Inch Apple's Cinema Display release was delayed by Nvidia's GeForce 8600 Ultra yields, not producing the cards in a timely enough fashion for Apple's liking. The first Mac to ship with an Nvidia chipset was the Nvidia GeForce 2 MX, with the G4 Digital Audio in 2001, and Apple would also at the same time ship the PowerMacs with an option GeForce 3 GPU. I don't have any particular insider info, but what I do have is the power of hind-sight. Over the years wrote a few popular guides on using Nvidia GPUs on the Mac and wrote a lot about Mac GPUs as part of my monstrous The Definitive Classic Mac Pro (2006-2012) Upgrade Guide. It was a crazy leap of faith as I read some guy who claimed to have done it on (once a powerhouse of a website for power users) and then reported back the steps I used to flash the card to the community. It's a particular topic that interests me as it dates back to when I bought my first Nvidia GPU in 2001, a VisionTek GeForce 3, and used DOS with nvflash.exe to load the Mac Firmware onto the GPU. I've tried to piece together the narrative as told by many news reports over the years, much of it I read as it was happening. It's Apple's management doesn't want Nvidia support in macOS, and that's a bad sign for the Mac Pro is a great first stop, but it's a bit dated and self-referential. Still, I have to give them credit as they've followed the Apple/Nvidia saga better than any other publication. It that should also fail, your best bet is to install without CUDA support.The video version differs slightly as it includes more personal ancedotes and asides.Īppleinsider isn't my favorite source for Apple news as it's too evangelical, generally portraying Apple as the protagonist in its reporting. If that should be the case I recommend to use Nvidia Docker (hopefully it has mac support) with a pytorch container from MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ NO_CUDA=1 python setup.py installĬheck that NO_CUDA, this issue as also been mentioned HERE in the forums and it seems to be that it could be an issue caused by the OS and driver versions. There are issues on the git reporting that behavior here which suggest to add something like: # if you are updating an existing checkoutĪnd finally set up the conda variable and install: export CMAKE_PREFIX_PATH=$ Then this (which I assume you've already done): git clone -recursive conda install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing (that installs some requirements) Which returned /Library/Developer/CommandLineTools/usr/include/c++/v1/string.hĭoesn't this mean I already have string.h?Īre you installing from a conda env? According to the github this should work: So I tried: $ find /Library/Developer/CommandLineTools/usr -type f -name string.h I searched on stackoverflow and came up with this. In terminal, I got fatal error: 'string.h' file not found MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py install Therefore, I followed instructions on Pytorch and installed Anaconda and Cuda. I tried verfication on and constructing a randomly initialized tensor works just fine. I have installed Cuda 10.0 version of pytorch. GPU driver and CUDA is not enabled and accessible by PyTorch.
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