embedding-multimodal-bridgetower: support air-gapped environment

comps/embeddings/deployment/docker_compose/compose.yaml

@@ -101,6 +101,20 @@ services:
       multimodal-bridgetower-embedding-gaudi-serving:
         condition: service_healthy
 
+  multimodal-bridgetower-embedding-server-offline:
+    <<: *multimodal-bridgetower-embedding-config
+    container_name: embedding-multimodal-bridgetower-server-offline
+    depends_on:
+      multimodal-bridgetower-embedding-serving-offline:
+        condition: service_healthy
+
+  multimodal-bridgetower-embedding-gaudi-server-offline:
+    <<: *multimodal-bridgetower-embedding-config
+    container_name: embedding-multimodal-bridgetower-gaudi-server-offline
+    depends_on:
+      multimodal-bridgetower-embedding-gaudi-serving-offline:
+        condition: service_healthy
+
 networks:
   default:
     driver: bridge

comps/embeddings/src/README_bridgetower.md

@@ -105,3 +105,34 @@ curl -X POST http://0.0.0.0:6600/v1/embeddings \
 ```
 
 This request will return an embedding representing the semantic meaning of the input text.
+
+## Running in the air gapped environment
+
+The following steps are common for running the dataprep microservice in an air gapped environment (a.k.a. environment with no internet access).
+
+1. Download the necessary data
+
+```shell
+# Download model
+export DATA_PATH="<model data directory>"
+huggingface-cli download --cache-dir $DATA_PATH BridgeTower/bridgetower-large-itm-mlm-itc
+# Download image for warmup
+cd $DATA_PATH
+wget https://llava-vl.github.io/static/images/view.jpg
+```
+
+2. launch the `embedding-multimodal-bridgetower` microservice with the following settings:
+
+- For Gaudi HPU:
+
+```bash
+export DATA_PATH="<model data directory>"
+docker compose up multimodal-bridgetower-embedding-gaudi-serving-offline multimodal-bridgetower-embedding-gaudi-server-offline -d
+```
+
+- For Xeon CPU:
+
+```bash
+export DATA_PATH="<model data directory>"
+docker compose up multimodal-bridgetower-embedding-serving-offline multimodal-bridgetower-embedding-server-offline -d
+```

comps/third_parties/bridgetower/deployment/docker_compose/compose.yaml

@@ -45,6 +45,59 @@ services:
       retries: 18
       start_period: 30s
 
+  multimodal-bridgetower-embedding-serving-offline:
+    image: ${REGISTRY:-opea}/embedding-multimodal-bridgetower:${TAG:-latest}
+    container_name: multimodal-bridgetower-embedding-serving-offline
+    ports:
+      - ${EMBEDDER_PORT:-12400}:${EMBEDDER_PORT:-12400}
+    ipc: host
+    environment:
+      HF_HUB_OFFLINE: 1
+      HF_HUB_CACHE: /data
+      # Use non-existing proxy to mimic air gapped environment
+      no_proxy: localhost,127.0.0.1,${offline_no_proxy}
+      http_proxy: http://localhost:7777
+      https_proxy: http://localhost:7777
+      PORT: ${EMBEDDER_PORT}
+    restart: unless-stopped
+    volumes:
+      - "${DATA_PATH:-./data}:/data"
+    healthcheck:
+      test: ["CMD-SHELL", "http_proxy='' curl -f http://localhost:${EMBEDDER_PORT}/v1/health_check"]
+      interval: 10s
+      timeout: 6s
+      retries: 18
+      start_period: 30s
+
+
+  multimodal-bridgetower-embedding-gaudi-serving-offline:
+    image: ${REGISTRY:-opea}/embedding-multimodal-bridgetower-gaudi:${TAG:-latest}
+    container_name: multimodal-bridgetower-embedding-gaudi-serving-offline
+    ports:
+      - ${EMBEDDER_PORT:-12400}:${EMBEDDER_PORT:-12400}
+    ipc: host
+    environment:
+      HF_HUB_OFFLINE: 1
+      HF_HUB_CACHE: /data
+      # Use non-existing proxy to mimic air gapped environment
+      no_proxy: localhost,127.0.0.1,${offline_no_proxy}
+      http_proxy: http://localhost:7777
+      https_proxy: http://localhost:7777
+      PORT: ${EMBEDDER_PORT}
+      HABANA_VISIBLE_DEVICES: all
+    volumes:
+      - "${DATA_PATH:-./data}:/data"
+    runtime: habana
+    cap_add:
+      - SYS_NICE
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD-SHELL", "http_proxy='' curl -f http://localhost:${EMBEDDER_PORT}/v1/health_check"]
+      interval: 10s
+      timeout: 6s
+      retries: 18
+      start_period: 30s
+
 networks:
   default:
     driver: bridge

comps/third_parties/bridgetower/src/README.md

@@ -44,6 +44,39 @@ cd comps/third_parties/bridgetower/deployment/docker_compose/
 docker compose -f compose.yaml up -d multimodal-bridgetower-embedding-serving
 ```
 
+## Running in the air gapped environment
+
+The following steps are common for running the dataprep microservice in an air gapped environment (a.k.a. environment with no internet access).
+
+1. Download the necessary data
+
+```shell
+# Download model
+export DATA_PATH="<model data directory>"
+huggingface-cli download --cache-dir $DATA_PATH BridgeTower/bridgetower-large-itm-mlm-itc
+# Download image for warmup
+cd $DATA_PATH
+wget https://llava-vl.github.io/static/images/view.jpg
+```
+
+2. launch the `embedding-multimodal-bridgetower` microservice with the following settings:
+
+- For Gaudi HPU:
+
+```bash
+export DATA_PATH="<model data directory>"
+cd comps/third_parties/bridgetower/deployment/docker_compose/
+docker compose -f compose.yaml up -d multimodal-bridgetower-embedding-gaudi-serving-offline
+```
+
+- For Xeon CPU:
+
+```bash
+export DATA_PATH="<model data directory>"
+cd comps/third_parties/bridgetower/deployment/docker_compose/
+docker compose -f compose.yaml up -d multimodal-bridgetower-embedding-serving-offline
+```
+
 ## 🚀3. Access the service
 
 Then you need to test your MMEI service using the following commands:

comps/third_parties/bridgetower/src/bridgetower_server.py

@@ -131,11 +131,18 @@ async def get_woker_status():
 
     # warmup
     print("Warmup...")
-    image_paths = ["https://llava-vl.github.io/static/images/view.jpg"]
+    is_offline = os.getenv("HF_HUB_OFFLINE") == "1"
+    if is_offline:
+        image_paths = ["/data/view.jpg"]
+    else:
+        image_paths = ["https://llava-vl.github.io/static/images/view.jpg"]
     example_prompts = ["This is test image!"]
     images = []
     for image_path in image_paths:
-        images.append(PIL.Image.open(requests.get(image_path, stream=True, timeout=3000).raw))
+        if is_offline:
+            images.append(PIL.Image.open(image_path))
+        else:
+            images.append(PIL.Image.open(requests.get(image_path, stream=True, timeout=3000).raw))
     for i in range(args.warmup):
         embedder.embed_image_text_pairs(
             example_prompts,