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Why not use the Tensorflow Docker images? Or if you think they are too old, you can rebuild them manually, it will still be easier than installing all the depe
by flx42_ 11y ago
Why not use the Tensorflow Docker images?
Or if you think they are too old, you can rebuild them manually, it will still be easier than installing all the dependencies manually.
There is also an easier way of downloading cuDNN v2 (there is no such thing as cuDNN v6.5 by the way):
https://github.com/NVIDIA/nvidia-docker/blob/master/ubuntu-14.04/cuda/7.0/devel/cudnn2/Dockerfile#L13 https://github.com/NVIDIA/nvidia-docker/blob/master/ubuntu-1...
- dwiel 11y agoIs running the docker image on a fresh standard AMI [1] all it takes to get a working tensorflow backed by the GPU? There is nothing you need to install on the host OS? [1] for example: Ubuntu 14.04 (HVM) public ami, ami-06116566
- exxo_ 11y agoYou need the NVIDIA drivers and the nvidia-docker plugin. $ docker-machine create --driver amazonec2 --amazonec2-instance-type g2.2xlarge ... $ docker-machine ssh <host> # install the NVIDIA driver and nvidia-docker-plugin $ eval `docker-machine env <host>` $ ssh-add ~/.docker/machine/machines/<host>/id_rsa $ NV_HOST="ssh://ubuntu@<ip>:" nvidia-docker run mybuild/tensorflow Step 2 can be skipped if you use a custom AMI.