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The simple start

In order to help you get started as quickly as possible we’ve put together this example where we use an example Docker Image on our platform. All you need to do is follow the steps, and it will teach you how to use our platform.

About The WgetWorker

The wgetworker is really just a packaged version of ‘wget’, the unix command line utility that downloads something from the internet. Wget takes an address of a website as input like so: wget For details of this container look at the source on and

If you have docker installed on your machine you can also run it as:

$ docker run nerdalize/wgetworker

Run it on Nerdalize

  1. Make sure you have downloaded and installed the command line interface.

  2. Start your workload.

    $ nerd workload start nerdalize/wgetworker
    Workload created with ID: caa9ffb86d65b70f8903

    Great! Now you’ve started the ‘nerdalize/wgetworker’ on the Nerdalize cloud. It is waiting for a task, or input.

  3. Create a task.

    Our wgetworker container expects as input argument the URL of a website. So we will pass such URL as the task.

    $ nerd task create <workload_id> --
    Started task with ID: 1496926723804988010

    Note that we used -- to separate the arguments that we give to the Nerdalize CLI and the arguments that we give to the container.

    If we had run the docker image locally it would have looked like this:

    $ docker run nerdalize/wgetworker
  4. Download the data this job has produced.

    $ nerd dataset list 
    DATASET ID             CREATED
    c0e6718e82b6099d4535   5 minutes ago

    This lists all the datasets that belong to the current project, both those that were uploaded by you, as those that were automatically created to store the output data.

    $ nerd dataset download <dataset_id> <target_dir>
  5. Stop your workload.

    $ nerd workload stop <workload_id>

    If you don’t stop your workload it will keep running, waiting for you to start new tasks.


You have successfully run your first workload on the Nerdalize cloud. That deserves a tweet! Now start running your own workloads, or continue to learn about using datasets.