βοΈπΊπΈ Mega Chess SF Hackathon: Train AI Chess Bots on GPUs
βSpend 2 days training AI Chess bots on GPUs in SF or Sydney.
βπ TLDR:
βJoin our Mega Chess Hackathon to get hands-on experience with rapid iteration cluster training using Strong Computeβs Instant SuperComputer (ISC). This event will focus on training deep neural networks to play chess.
βUnlike traditional training environments, the ISC simplifies and accelerates job execution on shared GPU clusters, allowing training to start within seconds. Shared resources are efficiently managed, and large datasets are readily accessible. Experiment with different models, datasets, and hyperparameters to develop the best chess AI over two days.
βThe winner will receive a $10k-$100k compute grant for open-source AI research purposes*. ππ°
β
βπ Details:
βWeβre providing 2-day access to 48 GPU clusters. Twenty teams will compete, blending human intuition, tonnes of data, and innovative techniques to create the ultimate chess AI.
βThe full set of rules and gameplay will be shared with participants ahead of time on Discord.
β
β𧩠Task:
βTrain a deep neural network to play chess. Inputs to the neural network during inference (game play) will be (1) the current game history in Portable Game Notation (PGN) using Standard Algebraic Notation (SAN) and (2) a possible move in SAN. Inference output from the model will be a numeric score representing your modelβs strength of preference for that move.
β
βπ Dataset:
βDatasets that will be made available include (1) board states evaluated by StockFish and (2) PGNs of historic games. Code for generating training data, as well as example data loading pipelines will be provided for reference. Participants can also use their own resources.
β
βπ§ Models:
βParticipants are free to implement any model architecture that satisfies the input and output constraints described above. Example model architectures will be supplied, which demonstrate application of computer vision (e.g. Convolutional Neural Network & Transformer) and autoregressive language model (e.g. Transformer) architectures. Models must meet memory and inference time constraints to be determined. Models must be trained from scratch during the competition.
β
βπ» Compute:
βAccess to multiple 48x 24GB Ampere clusters and dedicated GPU workstations on the Strong Compute Instant Super Computer platform. Compute credits will be provided during the competition.
β
βπ Judging:
βEach teamβs model will play off in a tournament to determine the competition winner.
β
βπ Prize:
βThe winning team will receive one of Strong Computeβs $10-100K grants for open-source AI research or further development of open chess models.
β
βπ Participant Requirements:
βExperience with PyTorch and cluster training is preferred but not mandatory.
βSkills in large-scale clusters (64+ GPUs) are highly valued.
βTeams of 1 to 3 members are allowed. Include your team membersβ names when registering. Approved participants will receive a link to our Discord competition channel.
βπ Locations:
βSan Francisco, USA: Venue details will be shared upon successful application. πΊπΈ
βSydney, Australia: Register here π¦πΊ
βποΈ Event Schedule:
βSan Francisco:
βFriday:
β6:00 pm: Drinks, snacks + networking
β6:30 pm: Set-up, onboarding, test training run + inference
β7:30 pm: Competition begins π
β8:30 pm: Dinner
βLate: hacking
βSaturday:
β9:00 am: Day two start, breakfast
β1:00 pm: Lunch
β7:30 pm: Dinner
β8:00 pm: Final submissions π
β9:00 pm: Competition begins βοΈ
β11:00 pm: Winners announced π
βStrong Compute
βStrong Compute provides infrastructure management capability for rapid artificial intelligence development.
βP.S. We're hiring and this is a great way for us to get to know each other https://strongcompute.com/jobs
βSupported by community partner:
Build Club: the home for top AI engineers in APAC
β*Refer to our research grants for requirements.