CODEWITHSUNDEEP
tensorflowgoogle-cloud-mlcudnn
7hacker
7hacker

Reputation: 1988

Cloud ML Tensorflow and Cudnn versions compatibility

I would like to use Tensorflow 1.3 (and maybe 1.4) on Cloud ML. Im running jobs on multi-GPU machines on Cloud ML

I do that by specifying the tensorflow version in the setup.py as shown below:

from setuptools import setup

REQUIRED_PACKAGES = ['tensorflow==1.3.0']

setup(
    name='my-image-classification',
    install_requires=REQUIRED_PACKAGES,
    version='1.0',
    packages=['my_image_classification',
              'my_image_classification/foo',
              'my_image_classification/bar',
              'my_image_classification/utils'],
    )

What is the cudnn library that is installed on Cloud ML? Is it compatible with tensorflow 1.3 and tensorflow 1.3+ ?

I was able to start the jobs, but the performance is 10X lower than the expected value, and I'm curious if there is a problem with the underlying linking of Libraries

Edit:

I'm pretty confident now that the Cudnn versions on Cloud ML dont match what is required for Tensorflow 1.3. I noticed that Tensorflow 1.3 jobs are missing the "Creating Tensorflow device (/gpu:0...) " Logs which appear when I run a job with the default available Tensorflow on cloud ml

Upvotes: 0

Views: 191

Answers (1)

rhaertel80
rhaertel80

Reputation: 8399

DISCLAIMER: using anything but 1.0, 1.2 is not officially supported as of 2017/11/01.

You need to specify the GPU-enabled version of TensorFlow:

REQUIRED_PACKAGES = ['tensorflow-gpu==1.3.0']

But the version of pip is out-of-date so you need to force that to update first.

Upvotes: 1

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