README.md

Prize Qualification Baselines

This directory contains the baseline(s) that submissions must beat to qualify for prizes, see the Scoring Section of the competition rules.

Externally Tuned Ruleset

JAX

The prize qualification baseline submissions for JAX are:

• reference_algorithms/prize_qualification_baselines/external_tuning/jax_nadamw_target_setting.py
• feference_algorithms/prize_qualification_baselines/external_tuning/jax_nadamw_full_budget.py

Example command:

python3 submission_runner.py \
    --framework=jax \
    --data_dir=<data_dir> \
    --experiment_dir=<experiment_dir> \
    --experiment_name=<experiment_name> \
    --workload=<workload> \
    --submission_path=reference_algorithms/prize_qualification_baselines/external_tuning/jax_nadamw_target_setting.py \
    --tuning_search_space=reference_algorithms/prize_qualification_baselines/external_tuning/tuning_search_space.json

PyTorch

The prize qualification baseline submissionss for PyTorch are:

• reference_algorithms/prize_qualification_baselines/external_tuning/pytorch_nadamw_target_setting.py
• feference_algorithms/prize_qualification_baselines/external_tuning/pytorch_nadamw_full_budget.py

Example command:

torchrun --redirects 1:0,2:0,3:0,4:0,5:0,6:0,7:0 --standalone --nnodes=1 --nproc_per_node=8 submission_runner.py \
    --framework=pytorch \
    --data_dir=<data_dir> \
    --experiment_dir=<experiment_dir> \
    --experiment_name=t<experiment_name> \
    --workload=<workload>\
    --submission_path=reference_algorithms/prize_qualification_baselines/external_tuning/pytorch_nadamw_target_setting.py \
    --tuning_search_space=reference_algorithms/prize_qualification_baselines/external_tuning/tuning_search_space.json

Self-tuning Ruleset

JAX

The prize qualification baseline submissionss for jax are:

• reference_algorithms/prize_qualification_baselines/external_tuning/jax_nadamw_target_setting.py
• feference_algorithms/prize_qualification_baselines/external_tuning/jax_nadamw_full_budget.py

Example command:

python3 submission_runner.py \
    --framework=jax \
    --data_dir=<data_dir> \
    --experiment_dir=<experiment_dir> \
    --experiment_name=<experiment_name> \
    --workload=<workload> \
    --submission_path=reference_algorithms/prize_qualification_baselines/external_tuning/jax_nadamw_target_setting.py \
    --tuning_ruleset=self

PyTorch

The prize qualification baseline submissionss for PyTorch are:

• reference_algorithms/prize_qualification_baselines/external_tuning/pytorch_nadamw_target_setting.py
• feference_algorithms/prize_qualification_baselines/external_tuning/pytorch_nadamw_full_budget.py

Example command:

torchrun --redirects 1:0,2:0,3:0,4:0,5:0,6:0,7:0 --standalone --nnodes=1 --nproc_per_node=8 submission_runner.py \
    --framework=pytorch \
    --data_dir=<data_dir> \
    --experiment_dir=<experiment_dir> \
    --experiment_name=t<experiment_name> \
    --workload=<workload>\
    --submission_path=reference_algorithms/prize_qualification_baselines/external_tuning/pytorch_nadamw_target_setting.py \
    --tuning_ruleset=self