diff --git a/executors/accelerate/src/hypha/accelerate_executor/dataset.py b/executors/accelerate/src/hypha/accelerate_executor/dataset.py
index b7bce441..4731f5b4 100644
--- a/executors/accelerate/src/hypha/accelerate_executor/dataset.py
+++ b/executors/accelerate/src/hypha/accelerate_executor/dataset.py
@@ -1,3 +1,4 @@
+import os
 from collections.abc import Iterator
 from typing import Any
 
@@ -6,35 +7,50 @@
 from snappy import uncompress
 from torch.utils.data import IterableDataset
 
+from .api import fetch
+from .utils import get_preprocessor
+
 
 class IterableStreamDataSet(IterableDataset):  # type: ignore[type-arg]
     def __init__(
-        self,
-        data_file_iter: Iterator[str],
-        batch_size: int,
-        model_inputs: list[str],
-        processor_inputs: list[str],
-        preprocessor: Any | None,
+        self, socket_path: str, work_dir: str, fetch_path: str, batch_size: int, config: dict[str, Any]
     ) -> None:
         super(IterableStreamDataSet).__init__()  # type: ignore[misc]
-        self.data_iter = data_file_iter
+        self.socket_path = socket_path
+        self.work_dir = work_dir
+        self.fetch_path = fetch_path
+        self.config = config
         self.batch_size = batch_size
-        self.model_inputs = model_inputs
-        self.processor_inputs = processor_inputs
-        self.processor = preprocessor
+        self.model_inputs = config["model"]["input-names"]
+        self.processor_config = config.get("preprocessor", {})
+        self.processor_inputs = self.processor_config.get("input-names", [])
 
     def __iter__(self):  # type: ignore[no-untyped-def]
         # Don't need sharding each call to data_iter returns a unique instance
+        socket_path = self.socket_path
+        data_config = self.config["data"]
+        work_dir = self.work_dir
+
+        def wrap() -> Iterator[str]:
+            while True:
+                tensor_data = fetch(socket_path, data_config)
+                yield os.path.join(work_dir, tensor_data[0]["path"])
+
+        data_iter = iter(wrap())
+
+        processor = None
+        if self.processor_config:
+            processor = get_preprocessor(self.processor_config, self.fetch_path)
 
         # Holds the "remainder" from the previous file
         buffer = None
-        for path in self.data_iter:
+        for path in data_iter:
             with open(path, "rb") as file:
                 raw_bytes = uncompress(file.read())
             data = load(raw_bytes)
-            if self.processor:
+            if processor:
                 processed = {
-                    **{k: v[0] for k, v in self.processor(**{k: data.pop(k) for k in self.processor_inputs}).items()},
+                    **{k: v[0] for k, v in processor(**{k: data.pop(k) for k in self.processor_inputs}).items()},
                     **data,
                 }
             else:
diff --git a/executors/accelerate/src/hypha/accelerate_executor/training.py b/executors/accelerate/src/hypha/accelerate_executor/training.py
index f52bb644..394900a4 100644
--- a/executors/accelerate/src/hypha/accelerate_executor/training.py
+++ b/executors/accelerate/src/hypha/accelerate_executor/training.py
@@ -28,9 +28,7 @@
 from .model import get_model
 from .utils import (
     extract_gradients,
-    fetch_data,
     get_adam,
-    get_preprocessor,
     get_scheduler,
     merge_models,
     prepare_files,
@@ -105,17 +103,13 @@ def main(socket_path: str, work_dir: str, job_json: str) -> None:  # noqa: PLR09
         model = get_model(local_fetch_path, config["model"]["task"])
         optimizer = get_adam(config["optimizer"], model.parameters())
         scheduler = get_scheduler(config.get("scheduler"), optimizer)
-        preprocessor_config = config.get("preprocessor")
         data_loader = torch.utils.data.DataLoader(
-            IterableStreamDataSet(
-                fetch_data(socket_path, config["data"], work_dir),
-                config["batch_size"],
-                config["model"]["input-names"],
-                preprocessor_config["input-names"] if preprocessor_config else [],
-                get_preprocessor(preprocessor_config, local_fetch_path),
-            ),
+            IterableStreamDataSet(socket_path, work_dir, local_fetch_path, config["batch_size"], config),
             batch_size=None,
             pin_memory=True,
+            num_workers=4,
+            persistent_workers=True,
+            timeout=600,
         )
 
         model, optimizer, training_dataloader, scheduler = accelerator.prepare(model, optimizer, data_loader, scheduler)
diff --git a/executors/accelerate/src/hypha/accelerate_executor/utils.py b/executors/accelerate/src/hypha/accelerate_executor/utils.py
index b1164553..014bd403 100644
--- a/executors/accelerate/src/hypha/accelerate_executor/utils.py
+++ b/executors/accelerate/src/hypha/accelerate_executor/utils.py
@@ -1,5 +1,4 @@
-import os
-from collections.abc import Iterable, Iterator
+from collections.abc import Iterable
 from typing import Any
 
 import torch
@@ -23,7 +22,7 @@
     get_wsd_schedule,
 )
 
-from .api import Session, fetch
+from .api import Session
 
 
 def prepare_files(config: dict[str, Any], session: Session) -> None:
@@ -61,15 +60,6 @@ def get_adam(optimizer: dict[str, Any], parameters: Iterable[torch.Tensor]) -> O
     return torch.optim.AdamW(parameters, lr=lr)
 
 
-def fetch_data(socket_path: str, data: str, work_dir: str) -> Iterator[str]:
-    def wrap() -> Iterator[str]:
-        while True:
-            tensor_data = fetch(socket_path, data)
-            yield os.path.join(work_dir, tensor_data[0]["path"])
-
-    return iter(wrap())
-
-
 def get_loss_fn(loss_fn: str) -> Module:
     if loss_fn == "l1":
         return torch.nn.L1Loss()