Participate

To get started and submit your first model, you will need to pass through the following steps.

Register

Creating an account on the CrunchDAO platform will allow you to be identified and get access to the competition dataset. Follow the link below to join the competition.

Home - CrunchDAO Account

Submit

Two distinct formats of submission are accepted for the competitions:

• Jupyter Notebook (.ipynb), which is a self-contained version of the code

• Python Script (.py), which allows more flexibility and to split the code into multiple files

All the work you submit remains your exclusive property. The Crunch Foundation guarantees the privacy of both client data and competitors' code.

Jupyter Notebook

Notebook users can use the Quickstarters provided by CrunchDAO to quickly experiment with a working solution that users can tinker with.

Setting the Environment

Before trying to execute any cell, users must set up their environment by copying the command available on the competition page:

Run the commands to set up your environment and download the data to be ready to go:

# Upgrade the Crunch-CLI to the latest version
%pip install crunch-cli --upgrade

# Authenticates yourself, it will downloads your last submission and the data
!crunch setup <competition name> <model name> --notebook --token <token>

Learn more about how setup tokens work and if it is safe to leak them.

Users can now load the data locally:

# Load the notebook, run me once
import crunch
crunch = crunch.load_notebook()

# Load the data, re-run me if you corrupt the dataframes
X_train, y_train, X_test = crunch.load_data()

When users are satisfied with their work, they can easily test their implementation:

# Run a local test
crunch.test()

Submitting your Notebook

After testing the code, users need to have access to the .ipynb file.

• If you are on Google Colab: File > Download > Download .ipynb

• If you are on Kaggle: File > Download Notebook

• If you are on Jupyter Lab: File > Download

Then submit on the Submit a Notebook page:

Some model files can also be uploaded along with the notebook, which will be stored in the resources/ directory.

The notebook is automatically converted to a Python script, keeping only the functions, imports, and classes. Everything else will be commented out.

Specifying package versions

Since submitting a notebook does not include a requirements.txt, users can instead specify the version of a package using import-level requirement specifiers in a comment on the same line.

# Valid statements
import pandas # == 1.3
import sklearn # >= 1.2, < 2.0
import tqdm # [foo, bar]
import scikit # ~= 1.4.2
from requests import Session # == 1.5

Specifying multiple times will cause the submission to be rejected if they are different.

# Inconsistant versions will be rejected
import pandas # == 1.3
import pandas # == 1.5

Specifying versions on standard libraries does nothing (but they will still be rejected if there is an inconsistent version).

# Will be ignored
import os # == 1.3
import sys # == 1.5

If an optional dependency is required for the code to work properly, an import statement must be added, even if the code does not use it directly.

import castle.algorithms

# Keep me, I am needed by castle
import torch

Python Script

Script users can use the Quickstarters provided by CrunchDAO to know what the structure should be.

A mandatory main.py is required to have both functions (train and infer) in order for your code to run properly.

Setting the Environment

Before starting to work, users must setup their environment which will be similar to a git repository.

Run the commands to set up your environment and download the data to be ready to go:

# Upgrade the Crunch-CLI to the latest version
$ pip install crunch-cli --upgrade

# Authenticates yourself, it will downloads your last submission and the data
$ crunch setup <competition name> <model name> --token <token> [directory]

# Change the directory to the configured environment
$ cd <directory>

Learn more about how setup tokens work and if it is safe to leak them.

Directory Layouts

# Example of a folder structure.
# The data files may change depending on the competition.
$ tree
.
├── data
│   ├── X_test.parquet
│   ├── X_train.parquet
│   └── y_train.parquet
├── main.py
├── requirements.txt
└── resources

3 directories, 5 files

File / Directory	Reason
data/	Directory containing the data of the competition, should never be modified by the user. Always kept up to date by the CLI.
main.py	Code entry point. Must contain the train() and infer() function. Can import other files if necessary. Learn more...
requirements.txt	List of packages used by your code. They are installed before your code is invoked.
resources/	Directory where your model should be stored. The content is persisted across runs during the transition between the Submission and Out-of-Sample phases.

Local Testing

When users are satisfied with their work, they can easily test their implementation:

# Run a local test using a shell command
$ crunch test

Pushing your Code

After the code has been tested, the submission needs to be uploaded to the server.

The message is optional and is just a label for users to know what they did.

$ crunch push --message "hello world"

Remember to include all your dependencies in a requirements.txt file.

Hybrid

For some complex setups, users may need to use the CLI to submit a Jupyter Notebook. This can happen if they want to submit with a large pre-trained model, or they want to include non-PyPI packages.

It will be very similar to the Python Script setup:

• Setting the Environment, like for a Python Script.

• Remove the main.py.

• Move your notebook to the project directory and name it main.ipynb.

The main name can be changed by using the --main-file <new_file_name>.py option.

If done correctly, before each crunch push, the CLI will first convert the notebook to a script file before sending it.

Note that package version specifiers will not work and the requirements.txt file must be updated manually.

Setup Tokens

The site generates new tokens every minute, and each token can only be used once within a 3-minute timeframe.

This prevents any problems if your token is accidentally shared, as it will likely have already been used or expired. Even the team shares their expired tokens in Quickstarters.

This token allows the CLI to download the data and submit your submission on your behalf.

Run

Checking your submission

A successful submission.

The system parses your work to retrieve the code of the interface functions (train() and infer()) and their dependencies. By clicking on the right arrow, you can access the contents of your submission.

The view of a submission once properly uploaded

Running in the Cloud

Once you've submitted, it's time to make sure your model can run in the cloud environment. Click on a submission and then click the Run in the Cloud button.

Click Run in the Cloud to start your run

Your code is fed a standard epoch of data and the system simulates an inference.

If your submission ran properly, you'll see the status as successful.

A successful run means that the system will be able to call your code on new data to produce the inferences for that customer.

Debugging with the logs

If your run crashes or you want to better understand how your code behaved, you can review the logs.

How to check your execution logs

Logs of a run

Due to abuse, only the first 1,500 lines of a user's code logs will be displayed.