document ARM (#83)

.travis.yml

@@ -38,13 +38,3 @@ matrix:
   - os: linux
     arch: arm64
     env: DOCKERFILE=Dockerfile.arm32v7 IMAGE=arm ARCH=arm32v7
-  - if: type = push AND (branch = master OR tag IS present)
-    stage: "Docker Manifest"
-    addons:
-      apt:
-        packages:
-          - docker-ce
-    install:
-    before_script:
-    script:
-    after_success:

README.md

@@ -48,10 +48,16 @@ Y. Song, S. Guadarrama, K. Murphy_, ["Speed/accuracy trade-offs for modern convo
 
 To run the docker image, which automatically starts the model serving API, run:
 
-```
+Intel CPUs:
+```bash
 $ docker run -it -p 5000:5000 codait/max-object-detector
 ```
 
+ARM CPUs (eg Raspberry Pi):
+```bash
+$ docker run -it -p 5000:5000 codait/max-object-detector:arm-arm32v7-latest
+```
+
 This will pull a pre-built image from Docker Hub (or use an existing image if already cached locally) and run it.
 If you'd rather checkout and build the model locally you can follow the [run locally](#run-locally) steps below.
 
@@ -65,7 +71,7 @@ You can also deploy the model on Kubernetes using the latest docker image on Doc
 
 On your Kubernetes cluster, run the following commands:
 
-```
+```bash
 $ kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Object-Detector/master/max-object-detector.yaml
 ```
 
@@ -87,30 +93,36 @@ A more elaborate tutorial on how to deploy this MAX model to production on [IBM
 
 Clone this repository locally. In a terminal, run the following command:
 
-```
+```bash
 $ git clone https://github.com/IBM/MAX-Object-Detector.git
 ```
 
 Change directory into the repository base folder:
 
-```
+```bash
 $ cd MAX-Object-Detector
 ```
 
-To build the docker image locally, run: 
+To build the docker image locally for Intel CPUs, run: 
 
-```
+```bash
 $ docker build -t max-object-detector .
 ```
 
+For ARM CPUs (eg Raspberry Pi), run:
+
+```bash
+$ docker build -f Dockerfile.arm32v7 -t max-object-detector .
+```
+
 All required model assets will be downloaded during the build process. _Note_ that currently this docker image is CPU only (we will add support for GPU images later).
 
 
 ### 2. Deploy the Model
 
 To run the docker image, which automatically starts the model serving API, run:
 
-```
+```bash
 $ docker run -it -p 5000:5000 max-object-detector
 ```
 
@@ -124,7 +136,7 @@ Use the `model/predict` endpoint to load a test image (you can use one of the te
 
 You can also test it on the command line, for example:
 
-```
+```bash
 $ curl -F "image=@samples/dog-human.jpg" -XPOST http://127.0.0.1:5000/model/predict
 ```
 
@@ -162,7 +174,7 @@ You should see a JSON response like that below:
 
 You can also control the probability threshold for what objects are returned using the `threshold` argument like below:
 
-```
+```bash
 $ curl -F "image=@samples/dog-human.jpg" -XPOST http://127.0.0.1:5000/model/predict?threshold=0.5
 ```
 
@@ -175,7 +187,7 @@ The default value for `threshold` is `0.7`.
 
 Run the following command from the model repo base folder, in a new terminal window:
 
-```
+```bash
 $ jupyter notebook
 ```
 
@@ -205,7 +217,7 @@ and provides interactive visualization of the bounding boxes and their related l
 
 If you wish to disable the web app, start the model serving API by running:
 
-```
+```bash
 $ docker run -it -p 5000:5000 -e DISABLE_WEB_APP=true codait/max-object-detector
 ```