Prior to using the tensorflow R package you need to install a version of Python and TensorFlow on your system. Below we describe how to install to do this as well the various options available for customizing your installation.
Note that this article principally covers the use of the R install_tensorflow() function, which provides an easy to use wrapper for the various steps required to install TensorFlow. You can also choose to install TensorFlow manually (as described at ). In that case the Custom Installation section covers how to arrange for the tensorflow R package to use the version you installed.
Download Tensorflow Mac
This will provide you with a default installation of TensorFlow suitable for use with the tensorflow R package. Read on if you want to learn about additional installation options, including installing a version of TensorFlow that takes advantage of Nvidia GPUs if you have the correct CUDA libraries installed.
Note that these instructions will install the latest master branch of tensorflow. If you want to install a specific branch (such as a release branch), pass -b to the git clone command and --recurse-submodules for r0.8 and earlier to fetch the protobuf library that TensorFlow depends on.
Finally, you will also want to install the CUDA Deep Neural Network (cuDNN v5) library which currently requires an Accelerated Computing Developer Program account. Once you have it downloaded locally, you can unzip and move the header and libraries to your local CUDA Toolkit folder:
Note that this setup still requires you to rebuild the //tensorflow/tools/pip_package:build_pip_package target every time you change a C++ file; add, delete, or move any python file; or if you change bazel build rules.
TensorFlow pip package depends on protobuf pip package version 3.0.0b2. Protobuf's pip package downloaded from PyPI (when running pip install protobuf) is a Python only library, that has Python implementations of proto serialization/deserialization which can be 10x-50x slower than the C++ implementation. Protobuf also supports a binary extension for the Python package that contains fast C++ based proto parsing. This extension is not available in the standard Python only PIP package. We have created a custom binary pip package for protobuf that contains the binary extension. Follow these instructions to install the custom binary protobuf pip package :
Install the above package after you have installed TensorFlow via pip, as the standard pip install tensorflow would install the python only pip package. The above pip package will over-write the existing protobuf package. Note that the binary pip package already has support for protobuf larger than 64MB, that should fix errors such as these :
Note that you must activate the virtualenv environment each time youuse TensorFlow in a new shell. If the virtualenv environment is notcurrently active (that is, the prompt is not (tensorflow), invokeone of the following commands:
We are relying on Stack Overflow to document TensorFlow installation problemsand their remedies. The following table contains links to Stack Overflowanswers for some common installation problems.If you encounter an error message or otherinstallation problem not listed in the following table, search for iton Stack Overflow. If Stack Overflow doesn't show the error message,ask a new question about it on Stack Overflow and specifythe tensorflow tag.
Windows & OSX build support for node-gyp requires Python 2.7. Be sure to have this version before installing @tensorflow/tfjs-node or @tensorflow/tfjs-node-gpu. Machines with Python 3.x will not install the bindings properly.
If you want to publish an addon library with your own libtensorflow binary, you can host the custom libtensorflow binary and optional pre-compiled node addon module on the cloud service you choose, and add a custom-binary.json file in scripts folder with the following information:
There are options to install the driver when you install the CUDA Toolkit 8.0, but I preferred to install the driver first, to make sure I have the latest version. Go to this URL and download the latest version. At this time, it is 8.0.83:
For its specifications, the result is not bad, but I actually expected more from the new chip. Training time at 1000 epochs was 11814 seconds. Apparently there is still some need for optimization as far as large trainig sets are concerned. The same experience was also made in this blog: -vidhya/m1-mac-mini-scores-higher-than-my-nvidia-rtx-2080ti-in-tensorflow-speed-test-9f3db2b02d74
The pip installer will automatically gather all the other required dependencies. You will see each individual download and installation until the software is fully installed. A successful installation will appear as shown in the following screenshot:
First, we need to install Python 3.5.x or 3.6.x 64-bit from the following links: -352/ -362/ Make sure that you download the 64-bit version of Python where the name of the installation has amd64, such as python-3.6.2-amd64.exe. The Python 3.6.2 installation looks like this:
Miniforge installed when installing tensorflow-macos defaults to python for ARM64 architecture. PyTorch for Rosetta 2 emulation requires Python for x86_64. Although the system python supports both architectures, to avoid using it I chose to install another miniforge, this time built for x86_64.
In contrast to TensorFlow 1.x, where different Python packages needed to be installed for one to run TensorFlow on either their CPU or GPU (namely tensorflow and tensorflow-gpu), TensorFlow 2.x only requires that the tensorflow package is installed and automatically checks to see if a GPU can be successfully registered.
To download the models you can either use Git to clone the TensorFlow Models repository inside the TensorFlow folder, or you can simply download it as a ZIP and extract its contents inside the TensorFlow folder. To keep things consistent, in the latter case you will have to rename the extracted folder models-master to models.
To install the library we will create an environment in Anaconda with python 3.5 we name it tensorflow. However, you may choose your own desired name for it. Open command prompt (or terminal) and type:
To support all algorithms, Install MPI for Windows (you need to download and install msmpisetup.exe) and follow the instructions on how to install Stable-Baselines with MPI support in following section.
I have Intelpython 2021.03 for mac os x (10.13.6 ), the current version of Intelpython (2022.1) can't allow to install tensorflow (2.0 for mac) and py-xgboost (xgboost doesn't install) because of python 3.9. 2ff7e9595c