alienware15r3_ubuntu14 | install Ubuntu 14.04 on the Alienware 15 R3

by awesomebytes Python Version: 0.0.1 License: No License

X-Ray Key Features Code Snippets(3)Community Discussions Vulnerabilities Install Support

kandi X-RAY | alienware15r3_ubuntu14 Summary

alienware15r3_ubuntu14 is a Python library typically used in Tensorflow, Ubuntu applications. alienware15r3_ubuntu14 has no bugs, it has no vulnerabilities and it has low support. However alienware15r3_ubuntu14 build file is not available. You can download it from GitHub.

Instructions on how to install Ubuntu 14.04 on the Alienware 15 R3 (and installing cuda, tensorflow and disabling intel graphics card)

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Quality

Security

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Support

alienware15r3_ubuntu14 has a low active ecosystem.

It has 48 star(s) with 26 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 3 have been closed. On average issues are closed in 48 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of alienware15r3_ubuntu14 is 0.0.1

Quality

alienware15r3_ubuntu14 has 0 bugs and 0 code smells.

Security

alienware15r3_ubuntu14 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

alienware15r3_ubuntu14 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

alienware15r3_ubuntu14 does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

alienware15r3_ubuntu14 releases are available to install and integrate.

alienware15r3_ubuntu14 has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

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alienware15r3_ubuntu14 Key Features

No Key Features are available at this moment for alienware15r3_ubuntu14.

alienware15r3_ubuntu14 Examples and Code Snippets

How to install Ubuntu 14.04.05 on Alienware 15 R3,Testing installation

Python

Lines of Code : 99

License : No License

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/usr/local/cuda-8.0/bin/cuda-install-samples-8.0.sh ~

cd ~/NVIDIA_CUDA-8.0_Samples/1_Utilities/deviceQuery
make
optirun ./deviceQuery

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capab

How to install Ubuntu 14.04.05 on Alienware 15 R3,Test Tflearn installation

Python

Lines of Code : 62

License : No License

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sudo apt-get install python-h5py python-scipy

optirun python test_tflearn.py

 19:12:06
~/Downloads$ python test_tflearn.py 
Downloading Titanic dataset...
Succesfully downloaded titanic_dataset.csv 82865 bytes.
2017-03-21 19:12:26.354733: W tensorf

How to install Ubuntu 14.04.05 on Alienware 15 R3,Install Tensorflow and Nvidia CuDNN

Python

Lines of Code : 59

License : No License

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$ ls
libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb
libcudnn5_5.1.10-1+cuda8.0_amd64.deb
libcudnn5-doc_5.1.10-1+cuda8.0_amd64.deb

$ sudo dpkg -i libcudnn5*.deb

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cu

Community Discussions

No Community Discussions are available at this moment for alienware15r3_ubuntu14.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install alienware15r3_ubuntu14

Guide to install Ubuntu 14.04.05 on a brand new Alienware 15 R3 to dual boot with Windows 10 and be able to run CUDA code on the Nvidia GeForce 1070 GTX. The major part of this guide may be useful for Ubuntu 16.04.
In order for the installation wizard to be able to deal with your NVME disk (the SSD) you need the newest Gparted.
Just double click the Install Ubuntu 14.04.05 LTS desktop icon. Configure as you like BUT DON'T ENABLE DOWNLOAD UPDATES WHILE INSTALLING NOR INSTALL THIRD PARTY SOFTWARE. It will freeze your installation. If you don't believe me, just try and enjoy your reboot. Click on Something else. Now you should see some partitions like /dev/nvme0n1. If you don't, you missed some step. Now choose the free space partition that corresponds to the shrinked space we made before. For me it's 115343 MB. I'll just make a partition for / and another swap one. In order to be able to suspend in Ubuntu you'll need at least your amount of RAM as swap. I doubt very much it will actually work, but hey, you need to try. I have 16GB of RAM so I'll do 115343 - 17 * 1024 = 97884 MB partition. (Yeah that's a 17, I'm a bit lazy to check for how much exactly it should be). Click on that free space to be selected and click on the + symbol. Put your amount of MB for it (97884) in Size. Choose Logical as Type. Leave Location as Beginning of this space. Use as Ext4. Mount point as /. Then on the left free space, repeat the process but make it of type swap. IMPORTANT now you need to change the Device for boot loader installation to /dev/nvme0n1. Now you can click Install Now.
Once rebooted and in your freshly installed Ubuntu 14.04, you'll need to copy some files from the bootable pendrive. For the Wifi card QCA6174 you need a newer binary of the firmware (based on this askubuntu post).
Put a copy of the CUDA demos in your home:.
I'm following abhay.harpale.net blog post. Install Bazel, for Ubuntu 14.04 we have nice instructions.
Download the latest CuDNN v5.1. I choose the debians, note that you need to register to acess:
Install it.
Add to your LD_LIBRARY_PATH cuda, you probably want to add this to your .bashrc:
Install Tensorflow dependencies:
Install Tensorflow:
Now run the test script from the tflearn docs test_tflearn.py:.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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