Run xcode-select -install to reinstall the command line tools, because sometimes the Xcode application fails to install certain header files. Open Xcode, and under preferences, select the 10.1 command line tools. Rename any other version of Xcode you have installed, and then copy it to /Applications. If you have a newer version or none at all, download it from the Apple Developer site. Install XcodeĬheck that you have installed Xcode version 10.1. Downgrading OS X requires creating a bootable USB memory stick installer and erasing your laptop's hard disk. If you have a newer version you will need to downgrade Apple banished CUDA with Mojave and later versions of the OS.
Downgrade to High SierraĬheck that you are running Mac OS X High Sierra (10.13.6). When you install a product or package using conda, you install it into an environment. For example, you may have one environment for testing and another environment for production. A conda environment is a directory that contains a specific collection of conda products and packages that you have installed. For example, I wish I could write like conda install scikit-learn for Python 3 environment and something like conda2 install scikit-learn for. Ideal way is to have totally separate Python environments. Now I'm wondering how to install properly second Anaconda (for work) with Python 2? I need both versions to work with iPython and Spyder IDE. The Anaconda parcel provides a static installation of Anaconda, based on Python 2.7, that can be used with Python and PySpark jobs on the cluster. If you have a CDH cluster, you can install the Anaconda parcel using Cloudera Manager. These instructions have been tested for: OS : MacOS High Sierra 10.13.6 (17G14042) Thanks to Jack Dyson for this write up based on an earlier version that I published earlier. There are numerous preliminary steps and 'gotchas'. The standard Mac distribution of Pytorch does not support CUDA, but it is supported if you compile Pytorch from source. If you have a newer MacBook Pro you are out of luck, because it either has a Radeon GPU or none at all. Although puny by modern standards, it provides about a 4X speedup over the cpu for Pytorch, and is fine for learning Pytorch and prototyping. This GPU has 384 cores and 1 GB of VRAM, and is CUDA capability 3. It has a CUDA-capable GPU, the NVIDIA GeForce GT 650M. The best laptop ever produced was the 2012-2014 Macbook Pro Retina with 15 inch display.
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