CodeContests is a competitive programming dataset for machine-learning. This dataset was used when training AlphaCode. AlphaCode has been published in Science, with a preprint on arXiv.
It consists of programming problems, from a variety of sources:
| Site | URL | Source |
|---|---|---|
| Aizu | https://judge.u-aizu.ac.jp | CodeNet |
| AtCoder | https://atcoder.jp | CodeNet |
| CodeChef | https://www.codechef.com | description2code |
| Codeforces | https://codeforces.com | description2code and Codeforces |
| HackerEarth | https://www.hackerearth.com | description2code |
Problems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.
First install bazel and verify it builds correctly (we only support Linux with clang, but other platforms might work):
bazel build -c opt :print_names_and_sourcesInstall the Cloud SDK, which
provides the gsutil utility. You can then download the full data (~3GiB) with,
e.g:
gsutil -m cp -r gs://dm-code_contests /tmp
The data consists of ContestProblem protocol buffers in
Riegeli format. See contest_problem.proto
for the protocol buffer definition and documentation of its fields.
The dataset contains three splits:
| Split | Filename |
|---|---|
| Training | code_contests_train.riegeli-*-of-00128 |
| Validation | code_contests_valid.riegeli |
| Test | code_contests_test.riegeli |
There is example code for iterating over the dataset in C++ (in
print_names.cc) and Python (in print_names_and_sources.py). For example, you
can print the source and name of each problem in the validation data by
installing bazel and then
running:
bazel run -c opt \
:print_names_and_sources /tmp/dm-code_contests/code_contests_valid.riegeli
Or do the same for the training data with the following command (which will print around 13000 lines of output):
bazel run -c opt \
:print_names_and_sources /tmp/dm-code_contests/code_contests_train.riegeli*
The execution subdirectory contains code for executing a solution and
evaluating whether it solves a problem. solve_example demonstrates this
functionality, and can be run with e.g.
bazel run -c opt execution:solve_example -- \
--valid_path=/tmp/dm-code_contests/code_contests_valid.riegeli
Note, for the last command you should see one Compilation failed and two
Compilation succeeded, if you see three Compilation failed then there is
likely an issue with the Python version used, please install and try several
ones before reporting a bug.
The execution code defaults to using Python 3.9 and 2.7, located at
/usr/bin/python3.9 and /usr/bin/python2.7, with standard libraries at
/usr/lib/python3.9 and /usr/lib/python2.7. These can be changed with the
flags defined in py_locations.cc, for example:
bazel run -c opt execution:solve_example -- \
--valid_path=/tmp/dm-code_contests/code_contests_valid.riegeli \
--python3_path=/usr/bin/python3.10 --python3_library_paths=/usr/lib/python3.10
In Debian/Ubuntu you can install specific Python versions with
sudo apt install python3.9 python3.10 python3.11
and you can check if you have some version installed by which provides output:
which python3.11
Note that the Python used for building with bazel and for executing inside the sandbox can be different.
The incorrect and correct solutions attached to problems are not guaranteed to compile and execute in the exact same way as in their original contest website (for example different compiler versions or flags or different library versions). Some of the solutions will fail compilation, or will produce sandbox violations, especially if they are incorrect.
We recommend running the following before reporting bugs, which wipes out the bazel state and sometimes fixes transient errors.
bazel clean --expunge
rm -rf ~/.cache/bazel
This repository is supported on Linux, compiled with clang.
People on MacOS have reported this error: #5
Windows have reported this error: #9
If you use this dataset or code, please cite this paper:
@article{
doi:10.1126/science.abq1158,
author = {Yujia Li and David Choi and Junyoung Chung and Nate Kushman and Julian Schrittwieser and R{\'e}mi Leblond and Tom Eccles and James Keeling and Felix Gimeno and Agustin Dal Lago and Thomas Hubert and Peter Choy and Cyprien de Masson d’Autume and Igor Babuschkin and Xinyun Chen and Po-Sen Huang and Johannes Welbl and Sven Gowal and Alexey Cherepanov and James Molloy and Daniel J. Mankowitz and Esme Sutherland Robson and Pushmeet Kohli and Nando de Freitas and Koray Kavukcuoglu and Oriol Vinyals },
title = {Competition-level code generation with AlphaCode},
journal = {Science},
volume = {378},
number = {6624},
pages = {1092-1097},
year = {2022},
doi = {10.1126/science.abq1158},
URL = {https://www.science.org/doi/abs/10.1126/science.abq1158},
eprint = {https://www.science.org/doi/pdf/10.1126/science.abq1158},
abstract = {Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist programmers or even generate programs themselves could make programming more productive and accessible. Recent transformer-based neural network models show impressive code generation abilities yet still perform poorly on more complex tasks requiring problem-solving skills, such as competitive programming problems. Here, we introduce AlphaCode, a system for code generation that achieved an average ranking in the top 54.3\% in simulated evaluations on recent programming competitions on the Codeforces platform. AlphaCode solves problems by generating millions of diverse programs using specially trained transformer-based networks and then filtering and clustering those programs to a maximum of just 10 submissions. This result marks the first time an artificial intelligence system has performed competitively in programming competitions. Computer programming competitions are popular tests among programmers that require critical thinking informed by experience and creating solutions to unforeseen problems, both of which are key aspects of human intelligence but challenging to mimic by machine learning models. Using self-supervised learning and an encoder-decoder transformer architecture, Li et al. developed AlphaCode, a deep-learning model that can achieve approximately human-level performance on the Codeforces platform, which regularly hosts these competitions and attracts numerous participants worldwide (see the Perspective by Kolter). The development of such coding platforms could have a huge impact on programmers’ productivity. It may even change the culture of programming by shifting human work to formulating problems, with machine learning being the main one responsible for generating and executing codes. —YS Modern machine learning systems can achieve average human-level performance in popular competitive programming contests.}}
The code is licensed under the Apache 2.0 License.
All non-code materials provided are made available under the terms of the CC BY 4.0 license (Creative Commons Attribution 4.0 International license).
We gratefully acknowledge the contributions of the following:
- Codeforces materials are sourced from http://codeforces.com.
- Description2Code materials are sourced from: Description2Code Dataset, licensed under the MIT open source license, copyright not specified.
- CodeNet materials are sourced from: Project_CodeNet, licensed under Apache 2.0, copyright not specified.
Use of the third-party software, libraries code or data may be governed by separate terms and conditions or license provisions. Your use of the third-party software, libraries or code may be subject to any such terms. We make no representations here with respect to rights or abilities to use any such materials.
This is not an official Google product.
