feat: Add epoch-based deterministic shuffling to generate_batch endpoint

tools/server/server.py

@@ -73,9 +73,25 @@ def create_app(config: ServerConfig) -> FastAPI:
             raise HTTPException(status_code=404, detail=f"Experiment '{name}' not found")
         return {"status": "deleted"}
 
+    def permute_index(idx: int, epoch_seed: int, dataset_size: int) -> int:
+        """Generate a deterministic permuted index without materializing full permutation.
+        
+        Args:
+            idx: Original index to permute
+            epoch_seed: Seed for this epoch's permutation
+            dataset_size: Size of the dataset
+            
+        Returns:
+            Permuted index in range [0, dataset_size)
+        """
+        # Combine index with epoch seed for unique mapping
+        rng = Random(epoch_seed + idx)
+        # Generate a random number and map it to dataset size range
+        return rng.randrange(0, dataset_size)
+
     @app.get("/experiments/{name}/batch", response_model=BatchResponse)
     async def generate_batch(name: str, base_index: int, batch_size: int, epoch: int = 0):
-        """Generate a batch of raw entries"""
+        """Generate a batch of raw entries with epoch-based shuffling"""
         # Validate parameters
         if base_index < 0:
             raise HTTPException(status_code=400, detail="base_index must be non-negative")
@@ -87,9 +103,15 @@ def create_app(config: ServerConfig) -> FastAPI:
             raise HTTPException(status_code=404, detail=f"Experiment '{name}' not found")
 
         try:
+            dataset_size = len(experiment.dataset)
+            base_seed = experiment.config.seed if experiment.config.seed is not None else 0
+            epoch_seed = base_seed + (epoch * dataset_size)
+            
             entries = []
             for i in range(base_index, base_index + batch_size):
-                entry = experiment.dataset[i]
+                # Get permuted index for this position
+                shuffled_idx = permute_index(i, epoch_seed, dataset_size)
+                entry = experiment.dataset[shuffled_idx]
 
                 # Create BatchEntry with minimal required data
                 batch_entry = BatchEntry(