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from pathlib import Path
import yaml
from huggingface_hub import HfApi, HfFileSystem, hf_hub_download
from mlip_arena.models import MLIP
from mlip_arena.models import REGISTRY as MODEL_REGISTRY
# from .run import md as MD
# __all__ = ["MD"]
with open(Path(__file__).parent / "registry.yaml") as f:
REGISTRY = yaml.safe_load(f)
class Task:
def __init__(self):
self.name: str = self.__class__.__name__ # display name on the leaderboard
def run_local(self, model: MLIP):
"""Run the task using the given model and return the results."""
raise NotImplementedError
def run_hf(self, model: MLIP):
"""Run the task using the given model and return the results."""
raise NotImplementedError
# Calcualte evaluation metrics and postprocessed data
api = HfApi()
api.upload_file(
path_or_fileobj="results.json",
path_in_repo=f"{self.__class__.__name__}/{model.__class__.__name__}/results.json", # Upload to a specific folder
repo_id="atomind/mlip-arena",
repo_type="dataset",
)
def run_nersc(self, model: MLIP):
"""Run the task using the given model and return the results."""
raise NotImplementedError
def get_results(self):
"""Get the results from the task."""
# fs = HfFileSystem()
# files = fs.glob(f"datasets/atomind/mlip-arena/{self.__class__.__name__}/*/*.json")
for model, metadata in MODEL_REGISTRY.items():
results = hf_hub_download(
repo_id="atomind/mlip-arena",
filename="results.json",
subfolder=f"{self.__class__.__name__}/{model}",
repo_type="dataset",
revision=None,
)
return results
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