Add batch size parameter to CLI for Sentence Transformers

packages/backend/embedding_atlas/cli.py

@@ -130,6 +130,12 @@ def find_available_port(start_port: int, max_attempts: int = 10, host="localhost
     default=False,
     help="Allow execution of remote code when loading models from Hugging Face Hub.",
 )
+@click.option(
+    "--batch-size",
+    type=int,
+    default=None,
+    help="Batch size for processing embeddings (default: 32 for text, 16 for images). Larger values use more memory but may be faster.",
+)
 @click.option(
     "--x",
     "x_column",
@@ -207,6 +213,7 @@ def main(
     enable_projection: bool,
     model: str | None,
     trust_remote_code: bool,
+    batch_size: int | None,
     x_column: str | None,
     y_column: str | None,
     neighbors_column: str | None,
@@ -280,6 +287,7 @@ def main(
                     neighbors=new_neighbors_column,
                     model=model,
                     trust_remote_code=trust_remote_code,
+                    batch_size=batch_size,
                     umap_args=umap_args,
                 )
             elif image is not None:
@@ -291,6 +299,7 @@ def main(
                     neighbors=new_neighbors_column,
                     model=model,
                     trust_remote_code=trust_remote_code,
+                    batch_size=batch_size,
                     umap_args=umap_args,
                 )
             else:

packages/backend/embedding_atlas/projection.py

@@ -92,6 +92,7 @@ def _projection_for_texts(
     texts: list[str],
     model: str | None = None,
     trust_remote_code: bool = False,
+    batch_size: int | None = None,
     umap_args: dict = {},
 ) -> Projection:
     if model is None:
@@ -102,6 +103,7 @@ def _projection_for_texts(
             "version": 1,
             "texts": texts,
             "model": model,
+            "batch_size": batch_size,
             "umap_args": umap_args,
         }
     )
@@ -115,11 +117,16 @@ def _projection_for_texts(
     # Import on demand.
     from sentence_transformers import SentenceTransformer
 
+    # Set default batch size if not provided
+    if batch_size is None:
+        batch_size = 32
+        logger.info("Using default batch size of %d for text. Adjust with --batch-size if you encounter memory issues or want to speed up processing.", batch_size)
+
     logger.info("Loading model %s...", model)
     transformer = SentenceTransformer(model, trust_remote_code=trust_remote_code)
 
-    logger.info("Running embedding for %d texts...", len(texts))
-    hidden_vectors = transformer.encode(texts)
+    logger.info("Running embedding for %d texts with batch size %d...", len(texts), batch_size)
+    hidden_vectors = transformer.encode(texts, batch_size=batch_size)
 
     result = _run_umap(hidden_vectors, umap_args)
     Projection.save(cpath, result)
@@ -130,6 +137,7 @@ def _projection_for_images(
     images: list,
     model: str | None = None,
     trust_remote_code: bool = False,
+    batch_size: int | None = None,
     umap_args: dict = {},
 ) -> Projection:
     if model is None:
@@ -140,6 +148,7 @@ def _projection_for_images(
             "version": 1,
             "images": images,
             "model": model,
+            "batch_size": batch_size,
             "umap_args": umap_args,
         }
     )
@@ -170,9 +179,13 @@ def load_image(value):
 
     pipe = pipeline("image-feature-extraction", model=model, device_map="auto")
 
-    logger.info("Running embedding for %d images...", len(images))
+    # Set default batch size if not provided
+    if batch_size is None:
+        batch_size = 16
+        logger.info("Using default batch size of %d for images. Adjust with --batch-size if you encounter memory issues or want to speed up processing.", batch_size)
+
+    logger.info("Running embedding for %d images with batch size %d...", len(images), batch_size)
     tensors = []
-    batch_size = 16
 
     current_batch = []
 
@@ -207,6 +220,7 @@ def compute_text_projection(
     neighbors: str | None = "neighbors",
     model: str | None = None,
     trust_remote_code: bool = False,
+    batch_size: int | None = None,
     umap_args: dict = {},
 ):
     """
@@ -225,6 +239,8 @@ def compute_text_projection(
         model: str, name or path of the SentenceTransformer model to use for embedding.
         trust_remote_code: bool, whether to trust and execute remote code when loading
             the model from HuggingFace Hub. Default is False.
+        batch_size: int, batch size for processing embeddings. Larger values use more 
+            memory but may be faster. Default is 32.
         umap_args: dict, additional keyword arguments to pass to the UMAP algorithm
             (e.g., n_neighbors, min_dist, metric).
 
@@ -237,6 +253,7 @@ def compute_text_projection(
         list(text_series),
         model=model,
         trust_remote_code=trust_remote_code,
+        batch_size=batch_size,
         umap_args=umap_args,
     )
     data_frame[x] = proj.projection[:, 0]
@@ -314,6 +331,7 @@ def compute_image_projection(
     neighbors: str | None = "neighbors",
     model: str | None = None,
     trust_remote_code: bool = False,
+    batch_size: int | None = None,
     umap_args: dict = {},
 ):
     """
@@ -332,6 +350,8 @@ def compute_image_projection(
         model: str, name or path of the model to use for embedding.
         trust_remote_code: bool, whether to trust and execute remote code when loading
             the model from HuggingFace Hub. Default is False.
+        batch_size: int, batch size for processing images. Larger values use more 
+            memory but may be faster. Default is 16.
         umap_args: dict, additional keyword arguments to pass to the UMAP algorithm
             (e.g., n_neighbors, min_dist, metric).
 
@@ -344,6 +364,7 @@ def compute_image_projection(
         list(image_series),
         model=model,
         trust_remote_code=trust_remote_code,
+        batch_size=batch_size,
         umap_args=umap_args,
     )
     data_frame[x] = proj.projection[:, 0]