update hooks

06_gpu_and_ml/embeddings/wikipedia/download.py

@@ -8,7 +8,7 @@
 
 # We define our Modal Resources that we'll need
 volume = Volume.persisted("embedding-wikipedia")
-image = Image.debian_slim().pip_install("datasets")
+image = Image.debian_slim().pip_install("datasets", "apache_beam")
 stub = Stub(image=image)
 
 

06_gpu_and_ml/embeddings/wikipedia/main.py

@@ -2,7 +2,7 @@
 import json
 import subprocess
 
-from modal import Image, Secret, Stub, Volume, gpu, method
+from modal import Image, Secret, Stub, Volume, gpu, method, build, enter, exit
 
 # We first set out configuration variables for our script.
 ## Embedding Containers Configuration
@@ -66,17 +66,12 @@ def spawn_server() -> subprocess.Popen:
                 )
 
 
-def download_model():
-    spawn_server()
-
-
 tei_image = (
     Image.from_registry(
         "ghcr.io/huggingface/text-embeddings-inference:86-0.4.0",
         add_python="3.10",
     )
     .dockerfile_commands("ENTRYPOINT []")
-    .run_function(download_model, gpu=GPU_CONFIG)
     .pip_install("httpx")
 )
 
@@ -129,14 +124,20 @@ def generate_batches(xs, batch_size):
     retries=3,
 )
 class TextEmbeddingsInference:
-    def __enter__(self):
+    @build()
+    def download_model(self):
+        spawn_server()
+
+    @enter()
+    def open_connection(self):
         # If the process is running for a long time, the client does not seem to close the connections, results in a pool timeout
         from httpx import AsyncClient
 
         self.process = spawn_server()
         self.client = AsyncClient(base_url="http://127.0.0.1:8000", timeout=30)
 
-    def __exit__(self, _exc_type, _exc_value, _traceback):
+    @exit()
+    def terminate_connection(self, _exc_type, _exc_value, _traceback):
         self.process.terminate()
 
     async def _embed(self, chunk_batch):
@@ -259,7 +260,7 @@ def upload_result_to_hf(batch_size: int) -> None:
         CHECKPOINT_DIR: EMBEDDING_CHECKPOINT_VOLUME,
     },
     timeout=86400,
-    secret=Secret.from_name("huggingface-credentials"),
+    secret=Secret.from_name("huggingface-secret"),
 )
 def embed_dataset(down_scale: float = 1, batch_size: int = 512 * 50):
     """