![]() ![]() bin/activate # If using bash, sh, ksh, or zsh$ source. ![]() The target directory needs to be activated as virtual python environment with either of the following commands: However, it can be changed to any other directory based on the selection of the developer. The target directory will be the top of the virtual Python environment tree. $ virtualenv –system-site-packages targetDirectory # for Python 2.7$ virtualenv –system-site-packages -p python3 targetDirectory # for Python 3.n Once the virtual Python environment has been upgraded, the following commands can be leveraged to create the virtual Python environment. $ sudo easy_install pip $ pip install –upgrade virtualenv The following commands set up the installation of pip and virtual Python environment for TensorFlow. The option three virtual Python environment is preferred. Once the macOS terminal is started, the following instructions can be beneficial to complete the installation. $ pip install tensorflow # Python 2.7 CPU support $ pip3 install tensorflow # Python 3.n CPU supportĪ short TensorFlow program can be run in the system after clearing the syntax to ensure that the installation has been successful and system has been set up with Tensorflow development environment. Once, the installation or upgrade is successfully complete, TensorFlow can be installed with the following command based on the system environment: $ sudo easy_install –upgrade pip$ sudo easy_install –upgrade six It’s always a good idea to update the pip with the latest version: $ pip -V # for Python 2.7$ pip3 -V # for Python 3.n You can identify the system version of Pip through the following commands. Pip is for Python 2.7 and Pip3 is for Python 3.X version. ![]() If macOS does not have Python environment, it has to be installed before attempting to install TensorFlow. The installation through native Pip expects a prior environment of either Python 3.X+ or Python 2.7. $ docker run -it -p 8888:8888 tensorflow/tensorflow The following command initiates setting the Jupyter environment for Docker container to run TensorFlow programs from Jupyter notebooks. $ docker run -it tensorflow/tensorflow bash The following command can be run to launch the TensorFlow programs from shell. The TensorFlow binary images are also available on DockerHub. Running embedding visualizations from TensorBoard insider the container will require setting the localhost to 6006. In case of running the TensorFlow program from the Jupyter Notebook, the hostPort and containerPort can be set to 8888. If the TensorFlow programs are run from the Shell, -p hostPort:containerPortwill be optional. $ docker run -it -p hostPort:containerPortTensorFlowImage The Docker container can be launched with the following command: The Docker installation now contains entire TensorFlow environment in a TensorFlow binary image. Once the download of the docker is initiated on macOS, a sample docker run hello-world can be run to verify the validity of the Docker installation. The Docker platform also delivers developer tools for running command line, compose, and Docker notary command line with automatic updates pushed through the Docker development environment. Docker Community Edition for macOS.ĭocker Community Edition for macOS dockerizes the applications on mac with Hypervisor framework, filesystem, and networking with a complete development environment. This may require creation of docker ID and password to be part of the Docker Store.įigure 1. Download the Docker Community Edition for macOS from the Docker Store. The system should be equipped with at least 4 GB RAM. It’s recommended to have macOS High Sierra version 10.13.4 for minimizing the problems with docker installation. However, macOS High Sierra resolved majority of the issues encountered from the earlier version of macOS. Any operating systems such as mac OS El Capitan or above are supported by Docker. The hardware support can be verified with the command sysctl kern.hv_support in the terminal. The new Docker for mac Hyperkit VM creates the virtual environment with the minimum hardware support of Mac hardware created no earlier than 2010 with Intel’s hardware support for virtualization of memory management in unrestricted mode. The installation of Docker on a mac machine does not affect any machines created with the Docker machine. ![]() The Docker CE edition installation is compatible with AWS and Azure cloud platforms on CentOS, Debian, Fedora, Ubuntu, and Linux platforms. The Docker CE supports both on-premise and cloud platforms. There are couple of update channels available through Docker community edition such as Stable and Edge. The docker community edition is the preferred platform for building container based applications on macOS.
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