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Jan 27, 2018 TensorFlow-GPU 1.5 is here! Support for CUDA Toolkit 9.0 and CUDNN 7.0 is added. In this video we'll go step by step on how to install the new CUDA libraries and install tensorflow-GPU 1.5 CUDA. Jan 07, 2018 Install Anaconda Python 3.6 version and Tensorflow on Window 10 64bit. 1.Go to to download Anaconda Python 3.6 version for Window. Oct 10, 2018 conda create -name tfgpu tensorflow-gpu. Well is that it? This command will create an environment first named with ‘tfgpu’ and will install all the packages required by tensorflow-gpu including the cuda and cuDNN compatible verisons. Package ‘tensorflow’. The tensorflow package provides access to the complete TensorFlow API from within R. For additional documentation on the tensorflow package seetf 9 tf Main TensorFlow module Description Interface to main TensorFlow module. Provides access to top level classes and functions as well as.
Virtual machine
- Download the latest Xubuntu image.
- Download and install Oracle VM VirtualBox.
- Create a new virtual machine with the following specifications:
- Version: Ubuntu (64-bit)
- RAM: Half of available
- Hard disk: Fixed, 50GB
- Open the virtual machine's settings and add the following settings:
- System > Processor: Maximum allowed in green zone
- Display > Video Memory: 80MB
- Display > Enable 3D Acceleration: True
- Storage > Controller: IDE > Empty: Choose Xubuntu image
- Run the virtual machine and proceed with installation using the following settings:
- Choose 'Do something else'
- Select 1024MB for swap
- Select the rest as ext4 mounted at root (/)
- After installation, enable guest additions:
- Devices > Insert Guest Additions
- Install guest additions by entering the following command in terminal:
- Restart the virtual machine.
- Install pip and virtualenv by entering the following command in terminal:
- Create a virtualenv environment by entering the following command in terminal:
- Activate the virtualenv environment by entering the following command in terminal:
- Install TensorFlow in the active virtualenv environment by entering one the following commands in terminal:
Optional
Install git
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Install Keras
Install PIL
Install h5py
Create shared folder
- Go to Devices > Shared Folders > Shared Folders Settings
- Add new shared folder with auto-mount
- Give user read and write permissions to folder by entering the following command in terminal:
- Restart virtual machine
These instructions were adapted from here.
The following was tested on the hive machines (e.g.
hive10.cs.berkeley.edu
).Install cuDNN and setup CUDA environment variables
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Download and install cuDNN.
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Uncompress and copy the cuDNN files into the
~/local
directory.Define CUDA and cuDNN environment variables and add them to the path so that TensorFlow can find them. You may add these at the end of your
~/.bashrc
so that they are automatically defined next time you log in (assuming you are using bash as your shell).Install Bazel (needed to compile TensorFlow)
Download and install Bazel.
If the installation was successful, the compiled Basel binaries should be in
~/bin
. Add this directory to your PATH
.Install TensorFlow
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Clone the TensorFlow repo and initialize all submodules using their default settings.
Configure the TensorFlow options to build it with GPU support using CUDA compute capability 3.0 (this is what the instructional machines support) via the unofficial settings.
You will be asked for settings and you should specify the following answers (or press ENTER to specify the default one).
Build TensorFlow using Bazel. Jump starter model j5c09me user manual. This should take a few minutes.
Install the built binaries using
pip
so that your python program can find the TensorFlow libaries.Tensorflow-gpu Package Manual Download Free
If the installation was succesful, you should be able to import and use TensorFlow in python. Make sure you start python outside the
tensorflow
directory.