![]() Unlike brew way Python3 will be put in /Library/Frameworks/amework with a soft link is already created in /usr/local/bin/python3 so which python3 returns /usr/local/bin/python3. Open the installation package and follow the wizard. ![]() Choose the version from Python website where I chose Python 3.6.6 for macOS which is the newest version of Python 3.6 supporting tensorflow so far. In case you would not want Python 3.7.0 just like I did, a direct way to install Python3 is via the official distribution. Lsvirtualenv # list all virtualenv cdvirtualenv # cd into activated virtualenv directory showvirtualenv # show details of virtualenv env cpvirtualenv # copy virtualenv workon # no parametor to show all virtualenv cdproject # cd into projects directory of activated virtualenv Install Python as framework Open App Store and search xcode then the first result should be your choice.Īfter XCode installation, open the app and check License Agreement and then install XCode command line tool in the terminal. Install XCodeīefore installing Homebrew you need first install XCode which is an integrated development environment for macOS containing a suite of software development tools by Apple for creating apps for iPhone, iPad, Mac and other Apple products. The following steps shows the full path to install Python3 via Homebrew. Homebrew is called the missing package manager for macOS and it is a hot word if you search on the Internet for softwares installation on macOS. I like to install necessary packages for myself so I choose the Homebrew way. The easiest method is might to use Anaconda a integral distribution for python which is popular for scientific computing. The best way I found on the Internet is from the page of David Culley where he gives several alternatives to set up a Python3 working environment. I decided to install Python3 althought macOS comes out with a native python environment Python 2.7.10. Optimizer=tf.( 0.When I got a Macbook Pro, the first thing I thought was how to deploy a developing environment for programming and data analysis on Mac OS. Normalize_img, num_parallel_calls=tf.)ĭs_train = ds_train.shuffle(ds_examples)ĭs_train = ds_train.prefetch(tf.)ĭs_test = ds_test.prefetch(tf.) Running A Sample Code (MNIST) (ds_train, ds_test), ds_info = tfds.load(ĭef normalize_img( image, label): """Normalizes images: `uint8` -> `float32`.""" return tf.cast(image, tf.float32) / 255., label Print( "Num CPUs Available: ", len(tf._physical_devices( 'CPU'))) Print( "Num GPUs Available: ", len(tf._physical_devices( 'GPU'))) ![]() Print( "TensorFlow version:", tf._version_) Execute: jupyter-lab to open a Jupyter Notebook and run the following code:.Execute: pip install tensorflow-datasets pandas jupyterlab to install relevant dependencies to run sample code.Execute: pip install tensorflow-macos to install MacOS arm64 version of TensorFlow.Run: pip install tensorflow-metal to install Apple's Metal GPU APIs for TensorFlow.Run: conda install -c apple tensorflow-deps to install Apple's TensorFlow dependencies.Activate the environment: conda activate tf.Create an anaconda environment: conda create -n tf.Install miniforge from brew: brew install miniforge.Anaconda and Miniforge cannot co-exist together. Note: Uninstall Anaconda/Anaconda Navigator and other related previously installed version of conda-based installations.
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