• Building locally
• Using OpenWhisk Actions.

• Docker
• curl, wget, or Postman

0. Choose/create a folder of your liking
1. Clone this repo:

git clone https://github.com/apache/openwhisk-runtime-python
cd openwhisk-runtime-python

2. Build docker

Build using Python 3.7 (recommended)

docker build -t actionloop-python-v3.7:1.0-SNAPSHOT $(pwd)/core/python3ActionLoop

This tutorial assumes you're building with python 3.7. But if you want to use python 2.7 you can use:

docker build -t actionloop-python-v2.7:1.0-SNAPSHOT $(pwd)/core/python2ActionLoop

2.1. Check docker IMAGE ID (3rd column) for repository actionloop-python-v3.7

docker images

You should see an image that looks something like:

actionloop-python-v3.7         1.0-SNAPSHOT ...

2.2. Tag image (Optional step). Required if you’re pushing your docker image to a registry e.g. dockerHub

docker tag <docker_image_ID> <dockerHub_username>/actionloop-python-v3.7:1.0-SNAPSHOT

3. Run docker on localhost with either the following commands:

docker run -p 127.0.0.1:80:8080/tcp --name=bloom_whisker --rm -it actionloop-python-v3.7:1.0-SNAPSHOT

Or run the container in the background (Add -d (detached) to the command above)

docker run -d -p 127.0.0.1:80:8080/tcp --name=bloom_whisker --rm -it actionloop-python-v3.7:1.0-SNAPSHOT

Note: If you run your docker container in the background you'll want to stop it with:

docker stop <container_id>

Where <container_id> is obtained from docker ps command bellow

Lists all running containers

docker ps

or

docker ps -a

You shoulkd see a container named bloom_whisker being run

4. Create your function (note that each container can only hold one function) In this first example we'll be creating a very simple function Create a json file called python-data-init-run.json which will contain the function that looks something like the following: NOTE: value of code is the actual payload and must match the syntax of the target runtime language, in this case python

{
   "value": {
      "name" : "python-helloworld",
      "main" : "main",
      "binary" : false,
      "code" : "def main(args): return {'payload': 'Hello World!'}"
   }
}

To issue the action against the running runtime, we must first make a request against the init API We need to issue POST requests to init our function Using curl (the option -d signifies we're issuing a POST request)

curl -d "@python-data-init-run.json" -H "Content-Type: application/json" http://localhost/init

Using wget (the option --post-file signifies we're issuing a POST request)

wget --post-file=python-data-init-run.json --header="Content-Type: application/json" http://localhost/init

The above can also be achieved with Postman by setting the headers and body accordingly

Client expected response:

{"ok":true}

Server will remain silent in this case

Now we can invoke/run our function against the run API with: Using curl POST request

curl -d "@python-data-init-run.json" -H "Content-Type: application/json" http://localhost/run

Or using GET request

curl --data-binary "@python-data-init-run.json" -H "Content-Type: application/json" http://localhost/run

Or Using wget POST request. The -O- is to redirect wget response to stdout.

wget -O- --post-file=python-data-init-run.json --header="Content-Type: application/json" http://localhost/run

Or using GET request

wget -O- --body-file=python-data-init-run.json --method=GET --header="Content-Type: application/json" http://localhost/run

The above can also be achieved with Postman by setting the headers and body accordingly.

You noticed that we’re passing the same file python-data-init-run.json from function initialization request to trigger the function. That’s not necessary and not recommended since to trigger a function all we need is to pass the parameters of the function. So in the above example, it's preferred if we create a file called python-data-params.json that looks like the following:

{
   "value": {}
}

And trigger the function with the following (it also works with wget and postman equivalents):

curl --data-binary "@python-data-params.json" -H "Content-Type: application/json" http://localhost/run

After you trigger the function with one of the above commands you should expect the following client response:

{"payload": "Hello World!"}

And Server expected response:

XXX_THE_END_OF_A_WHISK_ACTIVATION_XXX
XXX_THE_END_OF_A_WHISK_ACTIVATION_XXX

If your container still running from the previous example you must stop it and re-run it before proceeding. Remember that each python runtime can only hold one function (which cannot be overrided due to security reasons) Create a json file called python-data-init-params.json which will contain the function to be initialized that looks like the following:

{
   "value": {
      "name": "python-helloworld-with-params",
      "main" : "main",
      "binary" : false,
      "code" : "def main(args): return {'payload': 'Hello ' + args.get('name') + ' from ' + args.get('place') + '!!!'}"
   }
}

Also create a json file python-data-run-params.json which will contain the parameters to the function used to trigger it. Notice here we're creating 2 separate file from the beginning since this is good practice to make the distinction between what needs to be sent via the init API and what needs to be sent via the run API:

{
   "value": {
      "name": "UFO",
      "place": "Mars"
   }
}

Now, all we have to do is initialize and trigger our function. First, to initialize our function make sure your python runtime container is running if not, spin the container by following step 3. Issue a POST request against the init API with the following command: Using curl:

curl -d "@python-data-init-params.json" -H "Content-Type: application/json" http://localhost/init

Using wget:

wget --post-file=python-data-init-params.json --header="Content-Type: application/json" http://localhost/init

Client expected response:

{"ok":true}

Server will remain silent in this case

Second, to run/trigger the function issue requests against the run API with the following command: Using curl with POST:

curl -d "@python-data-run-params.json" -H "Content-Type: application/json" http://localhost/run

Or using curl with GET:

curl --data-binary "@python-data-run-params.json" -H "Content-Type: application/json" http://localhost/run

Or Using wget with POST:

wget -O- --post-file=python-data-run-params.json --header="Content-Type: application/json" http://localhost/run

Or using wget with GET:

wget -O- --body-file=python-data-run-params.json --method=GET --header="Content-Type: application/json" http://localhost/run

After you trigger the function with one of the above commands you should expect the following client response:

{"payload": "Hello UFO from Mars!!!"}

And Server expected response:

XXX_THE_END_OF_A_WHISK_ACTIVATION_XXX
XXX_THE_END_OF_A_WHISK_ACTIVATION_XXX

This is the function we’re trying to create. It calculates the nth number of the Fibonacci sequence recursively in O(n) time

def fibonacci(n, mem):
   if (n == 0 or n == 1):
      return 1
   if (mem[n] == -1):
      mem[n] = fibonacci(n-1, mem) + fibonacci(n-2, mem)
   return mem[n]

def main(args):
   n = int(args.get('fib_n'))
   mem = [-1 for i in range(n+1)]
   result = fibonacci(n, mem)
   key = 'Fibonacci of n == ' + str(n)
   return {key: result}

Create a json file called python-fib-init.json to initialize our fibonacci function and insert the following. (It’s the same code as above but since we can’t have a string span multiple lines in JSON we need to put all this code in one line and this is how we do it. It’s ugly but not much we can do here)

{
   "value": {
      "name": "python-recursive-fibonacci",
      "main" : "main",
      "binary" : false,
      "code" : "def fibonacci(n, mem):\n\tif (n == 0 or n == 1):\n\t\treturn 1\n\tif (mem[n] == -1):\n\t\tmem[n] = fibonacci(n-1, mem) + fibonacci(n-2, mem)\n\treturn mem[n]\n\ndef main(args):\n\tn = int(args.get('fib_n'))\n\tmem = [-1 for i in range(n+1)]\n\tresult = fibonacci(n, mem)\n\tkey = 'Fibonacci of n == ' + str(n)\n\treturn {key: result}"
   }
}

Create a json file called python-fib-run.json which will be used to run/trigger our function with the appropriate argument:

{
   "value": {
      "fib_n": "40"
   }
}

Now we’re all set. Make sure your python runtime container is running if not, spin the container by following step 3. Initialize our fibonacci function by issuing a POST request against the init API with the following command: Using curl:

curl -d "@python-fib-init.json" -H "Content-Type: application/json" http://localhost/init

Using wget:

wget --post-file=python-fib-init.json --header="Content-Type: application/json" http://localhost/init

Client expected response:

{"ok":true}

You've noticed by now that init API always returns {"ok":true} for a successful initialized function. And the server, again, will remain silent

Trigger the function by running/triggering the function with a request against the run API with the following command: Using curl with POST:

curl -d "@python-fib-run.json" -H "Content-Type: application/json" http://localhost/run

Using curl with GET:

curl --data-binary "@python-fib-run.json" -H "Content-Type: application/json" http://localhost/run

Using wget with POST:

wget -O- --post-file=python-fib-run.json --header="Content-Type: application/json" http://localhost/run

Using wget with GET:

wget -O- --body-file=python-fib-run.json --method=GET --header="Content-Type: application/json" http://localhost/run

After you trigger the function with one of the above commands you should expect the following client response:

{"Fibonacci of n == 40": 165580141}

And Server expected response:

XXX_THE_END_OF_A_WHISK_ACTIVATION_XXX
XXX_THE_END_OF_A_WHISK_ACTIVATION_XXX

• Gradle
• Docker
• OpenWhisk CLI wsk

The runtimes are built using Gradle. The file settings.gradle lists the images that are build by default. To build all those images, run the following command.

./gradlew distDocker

You can optionally build a specific image by modifying the Gradle command. For example:

./gradlew core:python3ActionLoop:distDocker

The build will produce Docker images such as actionloop-python-v3.7 and will also tag the same image with the whisk/ prefix. The latter is a convenience, which if you're testing with a local OpenWhisk stack, allows you to skip pushing the image to Docker Hub.

The image will need to be pushed to Docker Hub if you want to test it with a hosted OpenWhisk installation.

The Gradle build parameters dockerImagePrefix and dockerRegistry can be configured for your Docker Registry. Make sure you are logged in first with the docker CLI.

• Use the docker CLI to login. The following assume you will substitute $DOCKER_USER with an appropriate value.

  docker login --username $DOCKER_USER
  

• Now build, tag and push the image accordingly.

  ./gradlew distDocker -PdockerImagePrefix=$DOCKER_USER -PdockerRegistry=docker.io
  

You can now use this image as an OpenWhisk action. For example, to use the image actionloop-python-v3.7 as an action runtime, you would run the following command.

wsk action update myAction myAction.py --docker $DOCKER_USER/actionloop-python-v3.7

There are suites of tests that are generic for all runtimes, and some that are specific to a runtime version. To run all tests, there are two steps.

First, you need to create an OpenWhisk snapshot release. Do this from your OpenWhisk home directory.

./gradlew install

Now you can build and run the tests in this repository.

./gradlew tests:test

Gradle allows you to selectively run tests. For example, the following command runs tests which match the given pattern and excludes all others.

./gradlew :tests:test --tests *ActionLoopContainerTests*

This action runtime enables developers to create AI Services with OpenWhisk. It comes with preinstalled libraries useful for running machine learning and deep learning inferences. Read more about this runtime here.

Follow these steps to import the project into your IntelliJ IDE.

• Import project as gradle project.
• Make sure the working directory is root of the project/repo.