Welcome to the annual Danish National Championship in AI hosted by Ambolt AI, Danish Data Science Academy and Pioneer Centre for Artificial Intelligence
In this repository, you will find all the information needed to participate in the event. Please read the entire information before proceeding to the use cases, and please make sure to read the full description of every use case. You will be granted points for every use case that you provide a submission for and a total score will be calculated based on the individual submissions.
Below you can find the three use cases for the DM i AI 2023 event.
Within each use case, you find a description together with a template that can be used to setup an API endpoint.
The API endpoint will be used for submission and is required. Emily can help with setting up the API, but you should feel free to set them up on your own. The requirements for the API endpoints are specified in the respective use cases.
- Lunar Lander
- AI Text Detector
- Tumor Segmentation
Clone this GitHub repository to download Emily templates for all three use cases.
git clone https://github.com/amboltio/DM-i-AI-2023.git
Inside the DM-i-AI-2023 folder, you will find the three use cases. To open a use case with Emily type emily open <use-case> e.g. emily open XXX to open the last use case.
Emily can assist you with developing the required API endpoints for the use cases. Together with every use case a predefined and documented template is provided to ensure correct API endpoints and DTOs for the specific use case. You can find the documentation of the entire framework here.
The use cases have been built on top of the FastAPI framework, and can be used to specify endpoints in every use case. Come hang out and talk to other competitors of the event on our Discord channel. Discuss the use cases with each other or get in touch with the organizers, to solve issues or questions that may arise during the competition. Join here!
You are not required to use Emily for competing in this event, however, we strongly recommend using Emily if you are not an expert in developing APIs and microservices. If you do not choose to use Emily, you should check the individual template and find the requirements for the different API endpoints. These have to be exactly the same for the evaluation service to work. Inside <use-case>/models/dtos.py you can find information on the request and response DTOs, describing the input and output requirements for your API.
You can only submit once per use case. We highly recommend that you validate your solution before submitting. You can do this on the submission form by using the QUEUE VALIDATION ATTEMPT button. You can validate as many times as you like, but you can only evaluate once per use case. When you queue validation, your score from the run will show up on the scoreboard, so you can see how you compare to the other teams.
When you validate your solution on the submission form, it will be evaluated on a validation set. When you submit your solution and get the final score for that use case, your solution will be evaluated on a test set which is different from the validation set. This means that the score you obtained when validating your solution may be different from the score you get when evaluating. Therefore, we encourage you not to overfit to the validation set!
The scoreboard will display a score for each use case and a "total score". The individual score reflects the placement your best model has achieved relative to the other participants' models.The total score is simply an average of your individual scores.
This format also means that you can lose points / be overtaken by other teams during the week if they submit a model that is better than yours.
The deadline for submission is Friday the 17th of November at 14:00.Upon completion of the contest, the top 5 highest-ranking teams will be asked to submit their training code and the trained models for validation no later than Saturday the 18th of November at 14:00 (24 hours after the deadline). The submissions will be validated by our Scientific Jury who will get back to everyone within top 5 to let them know their placement.
When you are doing the submission, we are expecting you to host the server at which the REST API can be deployed. You can sign up to Azure for Students, where you will get free credits that you can use to create a virtual machine. We expect you all to be able to do this, since the competition is only for students. Alternatively, you can also deploy your submission locally (This requires a public IP).The following contains the necessary links for creating a virtual machine:
- Creating a linux virtual machine
- Install and configure xrdp to use Remote Desktop
- Create an inbound security Rule (This ensures that the API endpoints can be accessed when submitting)
Please make sure to get a server up and running early in the competition, and make sure to get connection to the evaluation service as quickly as possible, so if you have any server related issues, we can catch them early and not close to deadline!
Q: Can I use a pretrained model I found on the internet?
A: Yes you are allowed to use pretrained models. If you can find a pretrained model fitting your purpose, you would save a lot of time, just like you would do if you were solving a problem for a company.
Q: Should we gather our own data?
A: This depends on the individual use case. If you believe you can create a better model with more data, you should go gather the data yourself. We are only supplying a limited amount of data, as we want you to get creative in your approach to each use case.
Q: What if I have already used my Azure student credits?
A: If you have already used your credicts, reach out to us on [email protected] and we will help you out. However, we cannot provide you with GPU servers, so remember to design your solutions such that they can run inference within the time constraints specified for the independent use cases.
Please note, that we do not provide servers for training! You are expected to train your models and solutions using your own hardware, Google Colab, etc.
Q: How do I use Emily to deploy my service?
A: Emily can help you with deployment of your service, in most cases you can get around deployment by typing emily deploy <your-project>, you will be asked several questions guiding your towards deployment on your server. In this guide you can read more about how to get started using Emily.