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TensorFlow Template

Modified 2021-11-03 by tanij

This section describes the basic procedure for making a submission with a model trained in using TensorFlow. It can be used as a starting point for any of the LF, LFV, and LFI challenges.

That you have setup your accounts.

That you meet the software requirement.

9 GB free space.

You make a submission to all of the LF* challenges and can view their status and output.

The video is at

TensorFlow Template


Modified 2020-11-07 by Andrea Censi

Clone the template repo:

$ git clone

Change into the directory:

$ cd challenge-aido_LF-template-tensorflow

Either make a submission with:

$ dts challenges submit --challenge CHALLENGE_NAME

where you can find a list of the open challenges here.

Or, run local evaluation with:

$ dts challenges evaluate --challenge CHALLENGE_NAME

Verify your submission(s)

Modified 2019-04-15 by Liam Paull

This will make a number of submissions (as described below). You can track the status of these submissions in the command line with:

$ dts challenges follow --submission SUBMISSION_NUMBER

or through your browser by navigating the webpage:

where SUBMISSION_NUMBER should be replaced with the number of the submission which is reported in the terminal output.

Anatomy of the submission

Modified 2020-11-06 by Liam Paull

The submission consists of all of the basic files that required for a basic submission. Below we will highlight the specifics with respect to this template.

Modified 2020-11-07 by Liam Paull

The only difference in is that we are initializing our model:

from model import TfInference
    # define observation and output shapes
    self.model = TfInference(observation_shape=(1,) + expect_shape,
                                 # this is the shape of the image we get.
                                 action_shape=(1, 2),  # we need to output v, omega.
                                 graph_location='tf_models/')  # this is the folder where our models are stored.
    self.current_image = np.zeros(expect_shape)

and then we call our model to compute an action with the following code:

def compute_action(self, observation):
        action = self.model.predict(observation)
        return action.astype(float)

Note that we also can require the presence of a GPU with the environment variable AIDO_REQUIRE_GPU and then the solution will fail if a GPU is not found.

Model files

Modified 2020-11-06 by Liam Paull

The other additional files are the following:


The directory tf_models/ contains the Tensorflow learned models (the ones that you have trained).

The code is the code that runs the Tensorflow model.