DeepDetect Docker images

This repository contains the Dockerfiles for building the CPU and GPU images for deepdetect.

Also see https://hub.docker.com/u/beniz/starred/ for pre-built images

The docker images contain:

• a running dede server ready to be used, no install required
• googlenet and resnet_50 pre-trained image classification models, in /opt/models/

This allows to run the container and set an image classification model based on deep (residual) nets in two short command line calls.

Getting and running official images

docker pull beniz/deepdetect_cpu

or

docker pull beniz/deepdetect_gpu

Running the CPU image

docker run -d -p 8080:8080 beniz/deepdetect_cpu

dede server is now listening on your port 8080:

curl http://localhost:8080/info

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

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\":false}},\"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"}]}}}

Running the GPU image

This requires nvidia-docker in order for the local GPUs to be made accessible by the container.

The following steps are required:

• install nvidia-docker: https://github.com/NVIDIA/nvidia-docker
• run with

nvidia-docker run -d -p 8080:8080 beniz/deepdetect_gpu

Notes:

• nvidia-docker requires docker >= 1.9

To test on image classification on GPU:

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"}}

and

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\"]}"

Try the POST call twice: first time loads the net so it takes slightly below a second, then second call should yield a time around 100ms as reported in the output JSON.

Access to server logs

To look at server logs, use

docker logs -f <container name>

where can be obtained via docker ps

Example:

• start container and server:

> docker run -d -p 8080:8080 beniz/deepdetect_cpu

• look for container:

> docker ps
CONTAINER ID        IMAGE                  COMMAND                  CREATED              STATUS              PORTS                    NAMES
d9944734d5d6        beniz/deepdetect_cpu   "/bin/sh -c './dede -"   17 seconds ago       Up 16 seconds       0.0.0.0:8080->8080/tcp   loving_shaw

• access server logs:

> docker logs -f loving_shaw 

DeepDetect [ commit 4e2c9f4cbd55eeba3a93fae71d9d62377e91ffa5 ]
Running DeepDetect HTTP server on 0.0.0.0:8080

• share a volume with the image:

docker run -d -p 8080:8080 -v /path/to/volume:/mnt beniz/deepdetect_cpu

where path/to/volume is the path to your local volume that you'd like to attach to /opt/deepdetect/. This is useful for sharing / saving models, etc...

Building an image

Example goes with the CPU image:

cd cpu
docker build -t beniz/deepdetect_cpu --no-cache .