Docker

Quickstart instructions are below, for more information on DeepDetect docker images, see https://github.com/jolibrain/deepdetect/tree/master/docker.

Installation steps:

  • Pull the docker image
  
  • Run the container with:
  

Usage

Get info from the DeepDetect server:


curl http://localhost:8080/info

Yields something like:


{
  "status":{
    "code":200,
    "msg":"OK"
  },
  "head":{
    "method":"/info",
    "version":"0.1",
    "branch":"master",
    "commit":"c8556f0b3e7d970bcd9861b910f9eae87cfd4b0c",
    "services":[]
  }
}

The image embeds googlenet and resnet_50 image classification models, see how to use them without effort below.

To use a pre-trained model from outside the docker image, see how to share a volume in the Docker Tips section.

Here is how to do a simple image classification service and prediction test:

Service creation


curl -X PUT "http://localhost:8080/services/imageserv" -d '{
  "mllib":"caffe",
  "description":"image classification service",
  "type":"supervised",
  "parameters":{
    "input":{
      "connector":"image"
    },
    "mllib":{
      "nclasses":1000
    }
  },
  "model":{
    "repository":"/opt/models/ggnet/"
  }
}'

{
  "status":{
    "code":201,
    "msg":"Created"
  }
}

Image classification


curl -X POST "http://localhost:8080/predict" -d '{
  "service":"imageserv",
  "parameters":{
    "input":{
      "width":224,
      "height":224
    },
    "output":{
      "best":3
    },
    "mllib":{
      "gpu":true
    }
  },
  "data":[
    "http://i.ytimg.com/vi/0vxOhd4qlnA/maxresdefault.jpg"
  ]
}'

{
  "status":{
    "code":200,
    "msg":"OK"
  },
  "head":{
    "method":"/predict",
    "time":852.0,
    "service":"imageserv"
  },
  "body":{
    "predictions":{
      "uri":"http://i.ytimg.com/vi/0vxOhd4qlnA/maxresdefault.jpg",
      "classes":[
        {
          "prob":0.2255125343799591,
          "cat":"n03868863 oxygen mask"
        },
        {
          "prob":0.20917612314224244,
          "cat":"n03127747 crash helmet"
        },
        {
          "last":true,
          "prob":0.07399296760559082,
          "cat":"n03379051 football helmet"
        }
      ]
    }
  }
}

Docker tips

Use models from outside the container:


docker run -d -p 8080:8080 -v /path/to/models:/opt/models/ beniz/deepdetect_gpu

where path/to/models is the path to your local models you’d like to use with the DeepDetect container.

You are now ready to use any available model.

Looking at DeepDetect server logs:


docker logs -f 

where container name can be obtained via docker ps