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DeploymentsContainer

GPU Speech to text container

Learn about the Speechmatics Transcription GPU container system

Prerequisites​

System requirements​

The system must have:

  • Nvidia GPU(s) with at least 16GB of GPU memory
  • Nvidia drivers (see below for supported versions)
  • CUDA compute capability of 7.5-9.0 inclusive, which corresponds to the Turing, Ampere, Lovelace, Hopper architectures. Cards with the Volta architecture or below are not able to run the models
  • 24 GB RAM
  • The nvidia-container-toolkit installed
  • Docker version > 19.03

The raw image size of the GPU Inference Container is around 15GB.

See Performance and cost for more information on the performance and cost of the container.

Nvidia drivers​

The GPU Inference Container is based on CUDA 12.3.2, which requires NVIDIA Driver release 545 or later. If you are running on a data center GPU (e.g, a T4) you can use drivers: 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later R535), or 545.23 (or later R545).

Driver installation can be validated by running nvidia-smi. This command should return the Nvidia driver version and show additional information about the GPU(s).

Cloud instances​

The GPU node can be provisioned in the cloud.

Running the image​

Currently, each GPU Inference Container can only run on a single GPU. If a system has more than one GPU, the device must be specified using CUDA_VISIBLE_DEVICES or selecting the device using the --gpus argument. See Nvidia/CUDA documentation for details.

docker run --rm -it \
  -v $PWD/license.json:/license.json \
  --gpus '"device=0"' \
  -e CUDA_VISIBLE_DEVICES \
  -p 8001:8001 \
  speechmaticspublic.azurecr.io/sm-gpu-inference-server-en:13.0.0

When the Container starts you should see output similar to this, indicating that the server has started and is ready to serve requests.

I1215 11:43:57.300390 1 server.cc:633]
+----------------------+---------+--------+
| Model                | Version | Status |
+----------------------+---------+--------+
| am_en_enhanced       | 1       | READY  |
| am_en_standard       | 1       | READY  |
| body_enhanced        | 1       | READY  |
| body_standard        | 1       | READY  |
| diar_enhanced        | 1       | READY  |
| diar_standard        | 1       | READY  |
| ensemble_en_enhanced | 1       | READY  |
| ensemble_en_standard | 1       | READY  |
| lm_en_enhanced       | 1       | READY  |
+----------------------+---------+--------+
...
I1215 11:43:57.375233 1 grpc_server.cc:4819] Started GRPCInferenceService at 0.0.0.0:8001
I1215 11:43:57.375473 1 http_server.cc:3477] Started HTTPService at 0.0.0.0:8000
I1215 11:43:57.417749 1 http_server.cc:184] Started Metrics Service at 0.0.0.0:8002

Batch and Real-time inference​

The Inference server can run in two modes: batch, for processing whole files and returning the transcript at the end, and real-time for processing audio streams. The default mode is batch. To configure the GPU server for real-time, set the environment variable SM_BATCH_MODE=false by passing it into the docker run command.

The modes correspond to the two types of client speech Container, which are distinguished by their name:

  • rt-asr-transcriber-en:<version>
  • batch-asr-transcriber-en:<version>

The server can only support one of these modes at once.

Linking to a GPU inference container​

Once the GPU Server is running, follow the Instructions for Linking a CPU Container.

Running only one operating point​

Operating Points represent different levels of model complexity. To save GPU memory for throughput, you can run the server with only one Operating Point loaded. To do this, pass the SM_OPERATING_POINT environment variable to the container and set it to either standard or enhanced.

When running the all language standard Operating Point GPU inference server you must set the SM_OPERATING_POINT environment variable to standard

Monitoring the server​

The inference server is based on Nvidia's Triton architecture and as such can be monitored using Triton's inbuilt Prometheus metrics, or the GRPC/HTTP APIs. To expose these, configure an external mapping for port 8002(Prometheus) or 8000(HTTP).

Operating points in GPU inference​

When inference is outsourced to a GPU server, alternative GPU-specific models are used, so you should not expect to see identical results compared to CPU-based inference. For convenience, the GPU models are also designated as 'standard' and 'enhanced'.

Docker Compose example​

This Docker Compose file will create a Speechmatics GPU Inference Server:

(assumes your license.json file is in the current working directory)

docker-compose.yml
version: "3.8"

networks:
  transcriber:
    driver: bridge

services:
  triton:
    image: speechmaticspublic.azurecr.io/sm-gpu-inference-server-en:13.0.0
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              ### Limit to N GPUs
              # count: 1
              ### Pick specific GPUs by device ID
              # device_ids:
              #   - 0
              #   - 3
              capabilities:
                - gpu
    container_name: triton
    networks:
      - transcriber
    expose:
      - 8000/tcp
      - 8001/tcp
      - 8002/tcp
    environment:
      - NVIDIA_DRIVER_CAPABILITIES=all
      - NVIDIA_VISIBLE_DEVICES=all
      - CUDA_VISIBLE_DEVICES=0
    volumes:
      - $PWD/license.json:/license.json:ro