Deploying Apps and ML models on mesosphere DC/OS

Have you ever thought of your data centers and cloud infrastructure ( private and public ) as one big computer? where you can deploy your applications with a click of a button, without worrying too much about the underlying infrastructure? well … DCOS allows you to manage your infrastructure from a single point, offering you the possibility to run distributed applications, containers, services, jobs while maintaining a certain abstraction from the infrastructure layer, as long as it provides computing, storage, and networking capabilities.

After deploying my ML model on a kubernates Cluster, a lambda function, I will deploy it on a DCOS cluster.

what is DCOS:

DCOS is a datacenter operating system, DC/OS is itself a distributed system, a cluster manager, a container platform, and an operating system.

DC/OS Architecture Layers

DCOS manages the 3 layers of software, platform, and infrastructure.

the dashboard :

Screen Shot 2018-09-03 at 7.43.36 PM

the catalog:

DCOS UI offers a catalog of certified and community packages that the users can install in seconds , like kafka, spark, hadoop, MySQL ..

 

 

Deploying Apps and ML models on DCOS :

the application I’m deploying is a web server running the model I created in my previous posts to make predictions.

DCOS relies on an application definition file that looks like this :

app.json :

{
    "volumes": null,
    "id": "mlpregv3",
    "cmd": "python server.py",
    "instances": 1,
    "cpus": 1,
    "mem": 128,
    "disk": 0,
    "gpus": 0,

    "container": {
        "type": "DOCKER",
        "docker": {
            "image": "mbenachour/dcos-mlpreg:1",
            "forcePullImage": false,
            "privileged": false,
            "network": "HOST",
            "portMappings": [
                { "containerPort": 8088, "hostPort": 8088 }
            ]
        }
    }
}

 

the rest of the code can be found in my GitHub repo

after you configure your DCOS CLI and log in, you can run this command :

Screen Shot 2018-09-03 at 8.01.37 PM

if we take a look at the UI we can see that app/web server has been deployed :

Screen Shot 2018-09-03 at 8.03.35 PM

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