To work with Lithops you must configure both compute backend and a storage. Failing to configure them properly will prevent Lithops to submit workloads.

Lithops can work with almost any compute backend and storage any can be used with almost any cloud provider. You have multiple options to choose compute backend and storage backend based on your needs.

After you choose your compute and storage engine, you need to configure Lithops so it can use choosen compute and storage. Lithops configuration can be provided either in configuration file or provided in runtime via Python dictionary.

To configure Lithops through a configuration template file you have multiple options:

1. Create e new file called config in the ~/.lithops folder.

2. Create a new file called .lithops_config in the root directory of your project from where you will execute your Lithops scripts.

3. Create the config file in any other location and configure the LITHOPS_CONFIG_FILE system environment variable:

    LITHOPS_CONFIG_FILE=<CONFIG_FILE_LOCATION>
   

An alternative mode of configuration is to use a python dictionary. This option allows to pass all the configuration details as part of the Lithops invocation in runtime. An entire list of sections and keys is here

Choose your compute and storage engine from the table below

Standalone Compute Backends	Serverless Compute Backends	Storage Backends
Remote Virtual Machine IBM Virtual Private Cloud	IBM Cloud Functions IBM Code Engine Knative OpenWhisk AWS Lambda Google Cloud Functions Google Cloud Run Azure Functions Aliyun functions	IBM Cloud Object Storage AWS S3 Google Cloud Storage Azure Blob Storage Aliyun Object Storage Service Infinispan Ceph Redis OpenStack Swift

Students or academic stuff are welcome to follow IBM Academic Initiative, a special program that allows free trial of IBM Cloud for Academic institutions. This program is provided for students and faculty staff members, and allow up to 12 months of free usage. You can register your university email and get a free of charge account.

Test if Lithops is working properly:

import lithops

def hello_world(name):
    return 'Hello {}!'.format(name)

if __name__ == '__main__':
    fexec = lithops.FunctionExecutor()
    fexec.call_async(hello, 'World')
    print(fexec.get_result())

Example of providing configuration keys for IBM Cloud Functions and IBM Cloud Object Storage

import lithops

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

          'ibm_cf':  {'endpoint': 'ENDPOINT',
                      'namespace': 'NAMESPACE',
                      'api_key': 'API_KEY'},

          'ibm_cos': {'region': 'REGION',
                      'api_key': 'API_KEY'}}

def hello_world(name):
    return 'Hello {}!'.format(name)

if __name__ == '__main__':
    fexec = lithops.FunctionExecutor(config=config)
    fexec.call_async(hello, 'World')
    print(fexec.get_result())

By default, Lithops uses the storage backend to monitor function activations: Each function activation stores a file named {id}/status.json to the Object Storage when it finishes its execution. This file contains some statistics about the execution, including if the function activation ran successfully or not. Having these files, the default monitoring approach is based on polling the Object Store each X seconds to know which function activations have finished and which not.

As this default approach can slow-down the total application execution time, due to the number of requests it has to make against the object store, in Lithops we integrated a RabbitMQ service to monitor function activations in real-time. With RabbitMQ, the content of the {id}/status.json file is sent trough a queue. This speeds-up total application execution time, since Lithops only needs one connection to the messaging service to monitor all function activations. We currently support the AMQP protocol. To enable Lithops to use this service, add the AMQP_URL key into the rabbitmq section in the configuration, for example:

rabbitmq:
    amqp_url: <AMQP_URL>  # amqp://

In addition, activate the monitoring service by setting rabbitmq_monitor : True in the configuration (Lithops section):

lithops:
   rabbitmq_monitor: True

or in the executor by:

fexec = lithops.FunctionExecutor(rabbitmq_monitor=True)