Machine learning

How to translate using your custom AutoML model in Python

If you’ve successfully trained your first custom AutoML neuronal translation model, the next step is to integrate it into your application.

Here’s a python3 utility class that easily allows you to translate using your custom model:

class GNTMAutoMLTranslationDriver(object):
    Custom AutoML model translator.

    Usage example (be sure to use your own model here!):

    >>> translator = GNTMAutoMLTranslationDriver('myproject-101472', 'TRL455090968000816104449')
    >>> translator.translate("This is a translation test")
    def __init__(self, project_id, model_id):
        self.client = automl_v1beta1.PredictionServiceClient()
        self._name = 'projects/{}/locations/us-central1/models/{}'.format(project_id, model_id)
    def translate(self, engl):
        payload = {'text_snippet': {'content': engl}}
        params = {}
        request = self.client.predict(self._name, payload, params)
        return request.payload[0].translation.translated_content.content

See the class documentation for a usage example. Most of the code is also present in the official AutoML example, but I had to figure out some parts for myself, e.g. how to extract the string from the protobuf (request.payload[0].translation.translated_content.content).

Also note that AutoML is currently in Beta and therefore the API might change without prior notice.

Posted by Uli Köhler in Machine learning, Python

Which version on CuDNN should you install for TensorFlow GPU on Ubuntu?


You’ve followed my previous blogpost

Fixing TensorFlow cannot open shared object file on Ubuntu

on how to install CuBLAS etc. in order to get TensorFlow working.

Now you are getting an error message similar to this:

ImportError: cannot open shared object file: No such file or directory

You are wondering how you can install CuDNN as it’s not available from your


In order to install CuDNN, first go to the NVIDIA CuDNN page. At the time of writing this, downloading CuDNN is only possible if you have an NVIDIA account, so you need to register (click on Join) if you dont have one or Login if you already have one.

On the CuDNN download page you have several versions of CuDNN to choose from. Don’t just download the newest one as TensorFlow requires a specific one.

Look at your error message: It tells you that TensorFlow is missing – can you see the 6 in that string? That means that you need CuDNN 6.x(TensorFlow 1.5.0, at the time of writing this, always requires CuDNN 6.x). Although you can install CuDNN 7.x, 8.x, 9.x in parallel to 6.x,

Once you have selected the correct version, you need to select a package type.

The first important choice is whether you want a developer package or just the runtime package. You don’t need the developer package to run TensorFlow, even if you are developing applications using TensorFlow! Just select the runtime package.

Regarding the type of package, of course if you are on Linux, you absolutely need to select a linux package. If you use Ubuntu 16.04+, the easiest option is to select cuDNN v6.0 Runtime Library for Ubuntu16.04 (Deb) – even though the name suggest it supports only 16.04, this package worked flawlessly for me on Ubuntu 17.04 and 17.10 as well.

I recommend to download the Ubuntu 16.04 DEB package option unless you have a specific reason not to use it.

Posted by Uli Köhler in GPU, Machine learning