Make your own submission

In order to participate in the Shift Challenge 2022 you have to submit your algorithm wrapped in a docker file.

Create your own docker

In order to create your algorithm docker you have to write your own code in process.py. More specifically you have to inherit from ShippingBaseAlgorithm and implement the load_model and predict template methods. You can also do anything you want in the init of your class. Do not change the source code of ShippingBaseAlgorithm as you may face some issues in the submission phase. The template docker works with pandas DataFrame that is convenient regarding tabular data tasks.

• load_model: In this method you have to define the way that your model is loaded. This generally depends on the library you use to train your model and the way you serialise the model in the training phase. You are free to use any python library for your model (include the libraries in the ./requirements.txt). All the dependencies, in terms of source code and files, should be stored in the ./model folder. In our example docker tutorial we load all 10 member of our ensemble together with the required file about the data scaling.

• predict: Write your code in order to make the inference of the model. In general, the output should be a pandas DataFrame containing 3 columns ("time_id", "power", "uncertainty"). You can use the self.form_outputs to ensure that your data is on the correct format.

Tips:

The docker takes into account specific folders and files as described in the Dockerfile. So you can add and modify the following folder or files.

• process.py (write your inference process)
• /utils (you can add as many pythons files as you want)
• /model (you can add models, data statistics and python scripts)

In case that the template does not meet the requirements of your method you can create an algorithm docker from scratch as described in the documentation of grand-challenge. We are strongly advised you to change the current template to create your docker.

There are specific requirements about the input and the output path and the output format. You should read your input from /input/merchant-vessel-features.json and write your output in /output/vessel-power-estimates.json. You check the output format in the /test/expected_output.json .

Development

For the development phase of the algorithm docker you can modify the following paths on the /utils/eval_utils.py to match your local path.

DEFAULT_OUTPUT_PATH = Path("/output")
DEFAULT_INPUT_PATH = Path("/input")
DEFAULT_MODEL_PATH = Path("/opt/algorithm/model")

Test you algorithm docker

You can test your algorithm by running the ./test.sh file. The sample of test input data is stored in the ./test folder.

If you face problem with you submission while your ./test.sh work fine is probably due to memory error. To extensively validate your submissions, create a fake test dataset of 2.600.000 points and store it in the /test folder.

Who do I talk to?

If you have any question don't hesitate to concat us.

e.tsompopoulou@deepsea.ai
a.nikitakis@deepsea.ai