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toto-2.0: load model once across tasks; tirex-2: unpin datasets #176

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Merged
shchur merged 1 commit into from
Aug 19, 2026
Merged

toto-2.0: load model once across tasks; tirex-2: unpin datasets #176

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1 change: 0 additions & 1 deletion models/tirex-2/requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,4 +1,3 @@
tirex-2==0.1.0
fev
datasets==3.6.0
pyarrow>=21.0.0
8 changes: 6 additions & 2 deletions models/toto-2.0/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@ def __init__(
self.decode_block_size = decode_block_size
self.as_univariate = as_univariate
self.device = device
self._model = None

def _fit_predict(self, task: fev.Task) -> list[datasets.DatasetDict]:
import torch
Expand All @@ -40,15 +41,18 @@ def _fit_predict(self, task: fev.Task) -> list[datasets.DatasetDict]:

target_columns = ["target"] if self.as_univariate else task.target_columns

model = PretrainedToto2.from_pretrained(fev.utils.maybe_cache_from_s3(self.model_path))
if self._model is None: # load the large model once and reuse it across tasks
self._model = PretrainedToto2.from_pretrained(fev.utils.maybe_cache_from_s3(self.model_path))
self._model = self._model.to(self.device).eval()

config = Toto2GluonTSModelConfig(
prediction_length=task.horizon,
context_length=self.context_length,
target_dim=len(target_columns),
decode_block_size=self.decode_block_size,
quantiles=task.quantile_levels,
)
gts_model = Toto2GluonTSModel(model.to(self.device).eval(), config)
gts_model = Toto2GluonTSModel(self._model, config)
predictor = gts_model.create_predictor(batch_size=self.batch_size, device=self.device)

logging.getLogger("gluonts").setLevel(100)
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