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PyWren over IBM Cloud Functions and IBM Cloud Object Storage

What is PyWren

PyWren is an open source project whose goals are massively scaling the execution of Python code and its dependencies on serverless computing platforms and monitoring the results. PyWren delivers the user’s code into the serverless platform without requiring knowledge of how functions are invoked and run.

PyWren provides great value for the variety of uses cases, like processing data in object storage, running embarrassingly parallel compute jobs (e.g. Monte-Carlo simulations), enriching data with additional attributes and many more

PyWren and IBM Cloud

This repository is based on PyWren main branch and adapted for IBM Cloud Functions and IBM Cloud Object Storage. PyWren for IBM Cloud is based on Docker images and we also extended PyWren to execute a reduce function, which now enables PyWren to run complete map reduce flows. In extending PyWren to work with IBM Cloud Object Storage, we also added a partition discovery component that allows PyWren to process large amounts of data stored in the IBM Cloud Object Storage. See changelog for more details.

This is still a beta version and is rapidly changed so please keep yourself updated.

This document describes the steps to use PyWren-IBM-Cloud over IBM Cloud Functions and IBM Cloud Object Storage (COS)

Initial Requirements

  • IBM Cloud Function account, as described here. Make sure you can run end-to-end example with Python.
  • IBM Cloud Object Storage account
  • Python 3.6 (preferable) or Python 3.5

PyWren Setup

Install PyWren

Clone the repository and run the setup script:

git clone https://github.com/pywren/pywren-ibm-cloud
or
git clone git@github.com:pywren/pywren-ibm-cloud.git

Navigate into pywren-ibm-cloud folder

cd pywren-ibm-cloud/pywren

If you plan to develop code, stay in the master branch. Otherwise obtain the most recent stable release version from the release tab. For example, if release is v1.0.0 then execute

git checkout v1.0.0

Build and install

python3 setup.py install --force

or

pip install -U .

Deploy PyWren main runtime

You need to deploy the PyWren runtime to your IBM Cloud Functions namespace and create the main PyWren action. PyWren main action is responsible to execute Python functions inside PyWren runtime within IBM Cloud Functions. The strong requirement here is to match Python versions between the client and the runtime. The runtime may also contain additional packages which your code depends on.

PyWren-IBM-Cloud shipped with default runtime:

Runtime name Python version Packages included
pywren_3.6 3.6 list of packages

To deploy the default runtime, navigate into runtime folder and execute:

./deploy_runtime

This script will automatically create a Python 3.6 action named pywren_3.6 which is based on python:3.6 IBM docker image (Debian Jessie). This action is the main runtime used to run functions within IBM Cloud Functions with PyWren.

If your client uses different Python version or there is need to add additional packages to the runtime, then it is necessary to build a custom runtime. Detail instructions can be found here.

Configuration

Configure PyWren client with access details to your Cloud Object Storage account and with your IBM Cloud Functions account.

Access details to IBM Cloud Functions can be obtained here. Details on your COS account can be obtained from the "service credentials" page on the UI of your COS account. More details on "service credentials" can be obtained here

Summary of configuration keys

Group Key Default Mandatory Additional info
pywren storage_bucket yes Any bucket that exists in your COS account. This will be used by PyWren for intermediate data
pywren storage_prefix pywren.jobs no Storage prefix is a virtual sub-directory in the bucket, to provide better control over location where PyWren writes temporary data. The COS location will be storage_bucket/storage_prefix
pywren data_cleaner False no If set to True, then cleaner will automatically delete temporary data that was written into storage_bucket/storage_prefix
ibm_cf endpoint yes IBM Cloud Functions hostname. Endpoint is the value of 'host' from api-key. Make sure to use https:// prefix
ibm_cf namespace yes IBM Cloud Functions namespace. Value of CURRENT NAMESPACE from api-key
ibm_cf api_key yes IBM Cloud Functions api key. Value of api key from api-key
ibm_cf action_timeout 600000 no Default timeout
ibm_cf action_memory 512 no Default memory
ibm_cos endpoint yes Endpoint to your COS account. Make sure to use full path. for example https://s3-api.us-geo.objectstorage.softlayer.net
ibm_cos api_key yes API Key to your COS account

There are two options to configure PyWren:

Using configuration file

Copy the pywren/ibmcf/default_config.yaml.template into ~/.pywren_config

Edit ~/.pywren_config and configure the following entries:

pywren: 
    storage_bucket: <BUCKET_NAME>
    storage_prefix: <pywren.jobs>
    data_cleaner : <True / False>

ibm_cf:
    # Obtain all values from https://console.bluemix.net/openwhisk/learn/api-key

    # endpoint is the value of 'host'
    # make sure to use https:// as prefix
    endpoint    : <CF_API_ENDPOINT>
    # namespace = value of CURRENT NAMESPACE
    namespace   : <CF_NAMESPACE>
    api_key     : <CF_API_KEY>
   
ibm_cos:
    # make sure to use full path.
    # for example https://s3-api.us-geo.objectstorage.softlayer.net
    endpoint   : <COS_API_ENDPOINT>
    # this is preferable authentication method for IBM COS
    api_key    : <COS_API_KEY>
    # alternatively you may use HMAC authentication method
    # access_key : <ACCESS_KEY>
    # secret_key : <SECRET_KEY>

You can choose different name for the config file or keep into different folder. If this is the case make sure you configure system variable

PYWREN_CONFIG_FILE=<LOCATION OF THE CONFIG FILE>

Using configuration file you can obtain PyWren executor with:

import pywren_ibm_cloud as pywren
pw = pywren.ibm_cf_executor()

Configuration in the runtime

This option allows you pass all the configuration details as part of the PyWren invocation in runtime. All you need is to configure a Python dictionary with keys and values, for example:

config = {'pywren' : {'storage_bucket' : 'BUCKET_NAME'},

          'ibm_cf':  {'endpoint': 'CF_API_ENDPOINT', 
                      'namespace': 'CF_NAMESPACE', 
                      'api_key': 'CF_API_KEY'}, 

          'ibm_cos': {'endpoint': 'COS_API_ENDPOINT', 
                      'api_key': 'COS_API_KEY'}
         }

Having configuration allows you to provide it to the PyWren as follows:

import pywren_ibm_cloud as pywren
pw = pywren.ibm_cf_executor(config=config)

Verify

To test that all is working, run the testpywren.py located in the test folder with the arguments listed below.

For initializing test files using IBM Cloud Object Storage service, execute once from the project root folder:

python3 test/testpywren.py init

and then execute:

Command Explanation
python3 test/testpywren.py test all PyWren's functionality
python3 test/testpywren.py pywren test PyWren without Cloud Object Storage service
python3 test/testpywren.py pywren_cos test PyWren using Cloud Object Storage service only
python3 test/testpywren.py <FUNC_NAME> run a specific test function by its name as implemented in the test file

To clean test files stored in Cloud Object Storage service, execute:

python3 test/testpywren.py clean

NOTE: The test script assumes that a local PyWren's config file was set correctly.

To edit tests' data, open the data file located in the test folder and simply add or remove text URL files.

How to use PyWren for IBM Cloud Functions

  1. Single function execution example.

    import pywren_ibm_cloud as pywren

def my_function(x):
    return x + 7

pw = pywren.ibm_cf_executor()
pw.call_async(my_function, 3)
result = pw.get_result()

  2. Multiple function execution (Map).

    To run multiple functions in parallel, the executor contains a method called map() which applies a function to a list of data in the cloud. The map() method will launch one function for each entry of the list. To get the results of a map() call get_result() method. The results are returned within an ordered list, where each element of the list is the result of one invocation. For example, in the next code PyWren will launch one function for each value within iterdata:

    import pywren_ibm_cloud as pywren

iterdata = [1, 2, 3, 4] 

def my_map_function(x):
    return x + 7

pw = pywren.ibm_cf_executor()
pw.map(my_map_function, iterdata)
result = pw.get_result()

    and result will be: [8, 9, 10, 11]

  3. Multiple function execution with reduce (map-reduce).

    PyWren allows to run a reduce function over the results of the map. The map_reduce() method waits until it gets the results from all the map functions, and then launches the reduce function. By default the reduce method waits locally to get all the results. This approach does not consumes CPU time in Cloud Functions, but it has the tradeoff of greater data transfers because it has to download all the results and then upload them again for processing with the reduce function. After call the map_reduce(), it is possible to get the result from it by calling the get_result() method.

    import pywren_ibm_cloud as pywren

iterdata = [1, 2, 3, 4] 

def my_map_function(x):
    return x + 7

def my_reduce_function(results):
    total = 0
    for map_result in results:
        total = total + map_result
    return total

pw = pywren.ibm_cf_executor()
pw.map_reduce(my_map_function, iterdata, my_reduce_function)
result = pw.get_result()

    In this example the result will be 38

    By default the reducer waits locally for the results, and then launches the reduce() function in the cloud. You can change this behaviour and make the reducer waits remotely for the results by setting the reducer_wait_local parameter of the map_reduce() method to False.

    pw.map_reduce(my_map_function, iterdata, my_reduce_function, reducer_wait_local=False)

Using PyWren to process data from IBM Cloud Object Storage

PyWren for IBM Cloud functions has a built-in method for processing data objects from the IBM Cloud Object Storage.

We designed a partitioner within the map_reduce() method which is configurable by specifying the size of the chunk. The input to the partitioner may be either a list of data objects, a list of URLs or the entire bucket itself. The partitioner is activated inside PyWren and it responsible to split the objects into smaller chunks. It executes one my_map_function for each object chunk and when all executions are completed, the partitioner executes the my_reduce_function. The reduce function will wait for all the partial results before processing them.

In the parameters of the my_map_function function you must specify a parameter called data_stream. This variable allows access to the data stream of the object.

map_reduce method has different signatures as shown in the following examples

map_reduce where partitioner get the list of objects

import pywren_ibm_cloud as pywren

iterdata = ['bucket1/object1', 'bucket1/object2', 'bucket1/object3'] 

def my_map_function(key, data_stream):
    for line in data_stream:
        # Do some process
    return partial_intersting_data

def my_reduce_function(results):
    for partial_intersting_data in results:
        # Do some process
    return final_result

chunk_size = 4*1024**2  # 4MB

pw = pywren.ibm_cf_executor()
pw.map_reduce(my_map_function, iterdata, my_reduce_function, chunk_size)
result = pw.get_result()
method method signature
pw.map_reduce(my_map_function, iterdata, my_reduce_function, chunk_size) iterdata contains list of objects in the format of bucket_name/object_name
my_map_function(key, data_stream) key is an entry from iterdata that is assigned to the invocation

map_reduce where partitioner gets entire bucket

Commonly, a dataset may contains hundreds or thousands of files, so the previous approach where you have to specify each object one by one is not well suited in this case. With this new map_reduce() method you can specify, instead, the bucket name which contains all the object of the dataset.

import pywren_ibm_cloud as pywren

bucket_name = 'my_data_bucket'

def my_map_function(bucket, key, data_stream, ibm_cos):
    for line in data_stream:
        # Do some process
    return partial_intersting_data

def my_reduce_function(results):
    for partial_intersting_data in results:
        # Do some process
    return final_result

chunk_size = 4*1024**2  # 4MB

pw = pywren.ibm_cf_executor()
pw.map_reduce(my_map_function, bucket_name, my_reduce_function, chunk_size)
result = pw.get_result()
  • If chunk_size=None then partitioner's granularity is a single object.
method method signature
pw.map_reduce(my_map_function, bucket_name, my_reduce_function, chunk_size) bucket_name contains the name of the bucket
my_map_function(bucket, key, data_stream, ibm_cos) key is a data object from bucket that is assigned to the invocation. ibm_cos is an optional parameter which provides a boto3_client (see here)

map_reduce where partitioner gets the list of urls

import pywren_ibm_cloud as pywren

iterdata = ['http://myurl/myobject1', 'http://myurl/myobject1'] 

def my_map_function(url, data_stream):
    for line in data_stream:
        # Do some process
    return partial_intersting_data

def my_reduce_function(results):
    for partial_intersting_data in results:
        # Do some process
    return final_result

chunk_size = 4*1024**2  # 4MB

pw = pywren.ibm_cf_executor()
pw.map_reduce(my_map_function, iterdata, my_reduce_function, chunk_size)
result = pw.get_result()
method method signature
pw.map_reduce(my_map_function, iterdata, my_reduce_function, chunk_size) iterdata contains list of objects in the format of http://myurl/myobject.data
my_map_function(url, data_stream) url is an entry from iterdata that is assigned to the invocation

Reducer granularity

By default there will be one reducer for all the objects. If you need one reducer for each object, you must set the parameter reducer_one_per_object=True into the map_reduce() method.

pw.map_reduce(my_map_function, bucket_name, my_reduce_function, 
              chunk_size, reducer_one_per_object=True)

Geting boto3 client from any map function

Any map function can get ibm_cos parameter which is boto3_client. This allows you to access your IBM COS account from any map function, for example:

import pywren_ibm_cloud as pywren

iterdata = [1, 2, 3, 4]

def my_map_function(x, ibm_cos):
    data_object = ibm_cos.get_object(Bucket='mybucket', Key='mydata.data')
    # Do some process over the object
    return x + 7

pw = pywren.ibm_cf_executor()
pw.map(my_map_function, iterdata)
result = pw.get_result()

PyWren and IBM Watson Studio

You can use PyWren inside an IBM Watson Studio notebook in order to execute parallel data analytics by using IBM Cloud functions.

How to install PyWren within IBM Watson Studio

It is possible to use PyWren inside an IBM Watson Studio notebook in order to execute parallel data analytics by using IBM Cloud functions. As the current IBM Watson Studio runtimes does not contains the PyWren package, it is needed to install it. Add these lines at the beginning of the notebook:

try:
    import pywren_ibm_cloud as pywren
except:
    !curl -fsSL "https://git.io/fhe9X" | sh
    import pywren_ibm_cloud as pywren

Deploy PyWren runtime to your IBM Cloud Functions

You can create PyWren runtime from the notebook itself:

from pywren_ibm_cloud.deployutil import clone_runtime
clone_runtime('<dockerhub_space>/<name>:<version>', config, 'pywren-ibm-cloud')

Additional resources