Get Started

This guide provides step-by-step instructions to quickly deploy and test the Multimodal Embedding Serving microservice.

Prerequisites

Before you begin, confirm the following:

• System Requirements: Your system meets the minimum requirements.

• Docker Installed: Install Docker if needed. See Get Docker.

This guide assumes basic familiarity with Docker commands and terminal usage.

Environment Variables Reference

Model Configuration

• EMBEDDING_MODEL_NAME - The model to use (e.g., “CLIP/clip-vit-b-16”). Refer to the Supported Models list for additional choices.

• EMBEDDING_DEVICE - Device for inference (CPU/GPU, default: CPU)

• EMBEDDING_USE_OV - Enable OpenVINO optimization (true/false, default: false)

• EMBEDDING_OV_MODELS_DIR - Directory for OpenVINO models (default: ./ov-models)

Model Handler Performance

• INFER_BATCH_SIZE - Batch size for inference (default: 64). Compiles model to accept fixed batch input. Padding or split is done to accommodate dynamic input sizes.

• PREPROCESS_WORKERS - Number of parallel preprocessing workers (default: min(16, cpu_count * 2)). Higher is better but yields diminishing returns if > number of CPU cores.

Video Frame Extraction

These variables control the video frame extraction pipeline performance and memory usage.

Extraction Performance

• VIDEO_FRAME_BATCH_SIZE - Batch size for video frame extraction (default: 64)

• VIDEO_FRAME_DECODER_WORKERS - Number of workers for video frame decoding (default: 8)

• VIDEO_FRAME_QUEUE_SIZE - Queue size for frame extraction pipeline (default: 32)

Shared Memory Configuration

• VIDEO_FRAME_SHM_POOL_BLOCK_SIZE - Shared memory block size in bytes (default: 192010803 = 6,220,800 bytes for 1080p RGB)

• VIDEO_FRAME_SHM_POOL_BLOCKS_MULTIPLIER - Multiplier for total shared memory blocks (default: 2)

  • Total blocks = VIDEO_FRAME_BATCH_SIZE × VIDEO_FRAME_SHM_POOL_BLOCKS_MULTIPLIER

Logging

• VIDEO_FRAME_LOG_LEVEL - Logging level for video frame extraction (DEBUG/INFO/WARNING/ERROR/CRITICAL, default: INFO)

Set Environment Values

Basic Setup

Set the required environment variables before launching the service.

export EMBEDDING_MODEL_NAME=CLIP/clip-vit-b-32

Refer to the Supported Models list for additional choices.

NOTE: You can change the model, OpenVINO conversion, device, or tokenization parameters by editing setup.sh.

Configure the Registry

export REGISTRY_URL=intel
export TAG=latest

Configuration Examples

Basic CPU setup (default):

export EMBEDDING_MODEL_NAME=CLIP/clip-vit-b-32

GPU acceleration with OpenVINO:

export EMBEDDING_MODEL_NAME=CLIP/clip-vit-b-32
export EMBEDDING_DEVICE=GPU
export EMBEDDING_USE_OV=true

High Performance Video Processing:

export EMBEDDING_MODEL_NAME=CLIP/clip-vit-b-32
export VIDEO_FRAME_BATCH_SIZE=256
export VIDEO_FRAME_DECODER_WORKERS=8
export VIDEO_FRAME_SHM_POOL_BLOCK_SIZE=$((1920 * 1080 * 3))  # 6MB for 1080p
export VIDEO_FRAME_SHM_POOL_BLOCKS_MULTIPLIER=2
export INFER_BATCH_SIZE=64
export PREPROCESS_WORKERS=16

Memory-Constrained Environment:

export EMBEDDING_MODEL_NAME=CLIP/clip-vit-b-32
export VIDEO_FRAME_BATCH_SIZE=64
export VIDEO_FRAME_DECODER_WORKERS=4
export VIDEO_FRAME_SHM_POOL_BLOCK_SIZE=$((1280 * 720 * 3))  # 2.8MB for 720p
export VIDEO_FRAME_SHM_POOL_BLOCKS_MULTIPLIER=2

With OpenVINO Optimization on GPU:

export EMBEDDING_MODEL_NAME=CLIP/clip-vit-b-16
export EMBEDDING_USE_OV=true
export EMBEDDING_DEVICE=GPU
export INFER_BATCH_SIZE=64
export PREPROCESS_WORKERS=16

Debug Mode with Detailed Logging:

export VIDEO_FRAME_LOG_LEVEL=INFO
export EMBEDDING_DEVICE=CPU

Performance Tuning Guide

For Video Processing Bottlenecks

1. Increase VIDEO_FRAME_BATCH_SIZE (trades memory for throughput)

2. Increase VIDEO_FRAME_DECODER_WORKERS (limited by CPU cores)

3. Increase VIDEO_FRAME_QUEUE_SIZE if frames are being dropped

For Memory Constraints

1. Decrease VIDEO_FRAME_BATCH_SIZE

2. Decrease VIDEO_FRAME_SHM_POOL_BLOCKS_MULTIPLIER

3. Reduce VIDEO_FRAME_SHM_POOL_BLOCK_SIZE if processing lower resolutions

For Inference Performance

1. Increase INFER_BATCH_SIZE and PREPROCESS_WORKERS

2. Enable OpenVINO: EMBEDDING_USE_OV=true

3. Use GPU if available: EMBEDDING_DEVICE=GPU

Set the environment variables

Set the environment with default values by running the below command. Note that this needs to be run anytime the environment variables are changed. For example: if running on GPU, additional environment variables will need to be set.

source setup.sh

Quick Start with Docker

You can build the Docker image or pull a prebuilt image from the configured registry and tag. For prebuilt image, the setup script will configure the necessary variables to pull the right version of the image.

Running the Server with CPU

docker compose -f docker/compose.yaml up -d

Verify the deployment by running the below command. The user should see a healthy status printed on the console.

curl --location --request GET 'http://localhost:9777/health'

Running the Server with GPU

1. Configure GPU Device

# Automatic GPU selection
export EMBEDDING_DEVICE=GPU

# Specific GPU index (if applicable)
export EMBEDDING_DEVICE=GPU.0

2. Run Setup Script

source setup.sh

Note: When EMBEDDING_DEVICE=GPU is set, setup.sh applies GPU-friendly defaults, including setting EMBEDDING_USE_OV=true.

3. Start the Service

docker compose -f docker/compose.yaml up -d

4. Verify GPU Configuration

# Check service health
curl --location --request GET 'http://localhost:9777/health'

# Inspect active model capabilities
curl --location --request GET 'http://localhost:9777/model/capabilities'

Stop the Multimodal Embedding microservice

docker compose -f docker/compose.yaml down

Sample CURL Commands

The following samples mirror the accompanying Postman collection. All requests target http://localhost:9777.

Text Embedding

curl --location 'http://localhost:9777/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "input": {
    "type": "text",
    "text": "Sample input text1"
  },
  "model": "CLIP/clip-vit-b-32",
  "encoding_format": "float"
}'

Document Embedding (multiple texts)

curl --location 'http://localhost:9777/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "input": {
    "type": "text",
    "text": ["Sample input text1", "Sample input text2"]
  },
  "model": "CLIP/clip-vit-b-32",
  "encoding_format": "float"
}'

Image URL Embedding

curl --location 'http://localhost:9777/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "input": {
    "type": "image_url",
    "image_url": "https://i.ytimg.com/vi/H_8J2YfMpY0/sddefault.jpg"
  },
  "model": "CLIP/clip-vit-b-32",
  "encoding_format": "float"
}'

Image Base64 Embedding

curl --location 'http://localhost:9777/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "model": "CLIP/clip-vit-b-32",
  "encoding_format": "float",
  "input": {
    "type": "image_base64",
    "image_base64": "<image base64 value here>"
  }
}'

Video Frames Embedding

curl --location 'http://localhost:9777/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "model": "CLIP/clip-vit-b-32",
  "encoding_format": "float",
  "input": {
    "type": "video_frames",
    "video_frames": [
      {
        "type": "image_url",
        "image_url": "https://i.ytimg.com/vi/H_8J2YfMpY0/sddefault.jpg"
      },
      {
        "type": "image_base64",
        "image_base64": "<image base64 value here>"
      }
    ]
  }
}'

Video URL Embedding (with segment config)

curl --location 'http://localhost:9777/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "model": "CLIP/clip-vit-b-32",
  "encoding_format": "float",
  "input": {
    "type": "video_url",
    "video_url": "https://sample-videos.com/video321/mp4/720/big_buck_bunny_720p_10mb.mp4",
    "segment_config": {
      "startOffsetSec": 0,
      "clip_duration": -1,
      "num_frames": 64,
      "frame_indexes": [1, 10, 20]
    }
  }
}'

Video Base64 Embedding

set num_frames: 0 to process all the frames.

curl --location 'http://localhost:9777/embeddings' \
--header 'Content-Type: application/json' \
--data '{
  "model": "CLIP/clip-vit-b-32",
  "encoding_format": "float",
  "input": {
    "type": "video_base64",
    "segment_config": {
      "startOffsetSec": 0,
      "clip_duration": -1,
      "num_frames": 64
    },
    "video_base64": "<video base64 value here>"
  }
}'

Models, Current Model, and Capabilities

# List all available models
curl --location --request GET 'http://localhost:9777/models'

# Inspect the currently loaded model
curl --location --request GET 'http://localhost:9777/model/current'

# View modality support for the active model
curl --location --request GET 'http://localhost:9777/model/capabilities'

Troubleshooting

1. Docker container fails to start

• Run docker logs multimodal-embedding-serving to inspect failures.

• Ensure required ports (default 9777) are available.

2. Cannot access the microservice

• Confirm the containers are running:

  docker ps
  

• Verify EMBEDDING_MODEL_NAME points to a supported entry and rerun source setup.sh if you make changes.

3. GPU runtime errors

• Check Intel GPU device nodes:

  ls -la /dev/dri
  

• Confirm EMBEDDING_USE_OV=true for best performance with OpenVINO on GPU.

Supporting Resources

• Overview

• System Requirements

• How to Build from Source

• Supported Models

• API Reference

• SDK Usage

• Wheel Installation