Building Python runtime locally

Pre-requisites

Docker
curl, wget, or Postman

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

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

Build docker

Build using Python 3.7 (recommended). This tutorial assumes you're building with python 3.7. Run local_build.sh with the correct parameters to build docker

cd /tutorials
chmod 755 local_build.sh
cd ..
./tutorials/local_build.sh -r python3Action -t action-python-v3.7:1.0-SNAPSHOT

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

docker images

You should see an image that looks something like:

action-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>/action-python-v3.7:1.0-SNAPSHOT

Run docker on localhost with either the following commands:

docker run -p 127.0.0.1:80:8080/tcp --name=bloom_whisker --rm -it action-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 action-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

docker ps -a

You should see a container named bloom_whisker being run.

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 agains 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 prefered 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

You can perform the same steps as above using Postman application. Make sure you have the correct request type set and the respective body. Also set the correct headers key value pairs, which for us is "Content-Type: application/json"

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

Creating functions with arguments

If your container still running from the previuous example you must stop it and re-run it before proceding. 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 disticntion between what needs to be send 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

Advanced function

This function will calculate the nth Fibonacci number. 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

Notes

At this point you can edit python-fib-run.json and try other fib_n values. All you have to do is save python-fib-run.json and trigger the function again. Notice that here we're just modifying the parameters of our function; therefore, there's no need to re-run/re-initialize our container that contains our Python runtime.
You can also automate most of this process through docker actions by using invoke.py

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Building Python runtime locally

Pre-requisites

Creating functions with arguments

Advanced function

Notes

FilesExpand file tree

local_build.md

Latest commit

History

local_build.md

File metadata and controls

Building Python runtime locally

Pre-requisites

Creating functions with arguments

Advanced function

Notes