Using Bodo Cloud Platform

Creating Clusters

In the left bar click on Clusters (or click on the second step in the Onboarding list):


This will take you to the Clusters page. At the top right corner, click on Create Cluster which opens the cluster creation form. First, choose a name for your cluster and check the EFA checkbox if you want to use EFA-enabled nodes (only available on AWS). Then, select the type of nodes in the cluster to be created from the Instance type dropdown list.

Note: If the Instance type dropdown list does not populate, either the credentials are not entered properly or they are not valid. Please go back to Setting AWS Credentials or Setting Azure Credentials and make sure you complete it with valid credentials.

Next, enter the number of nodes for your cluster in Number of Instances. and choose the Bodo Version to be installed on your cluster. Typically the three latest Bodo Releases are available.

Note: If the Bodo Version dropdown list does not populate, either the credentials are not entered properly or the permissions to Bodo’s Images have not been granted to your account. Please go back Setting AWS Credentials or Setting Azure Credentials and make sure you complete it with valid credentials and that images have been successfully shared with your AWS or Azure account.

Then, select a value for Cluster auto shutdown. This is the amount of time of inactivity after which the platform will remove the cluster automatically. Activity is determined through attached notebooks (see Attaching a Notebook to a Cluster) and jobs (see Running a Job). Therefore, if you don’t plan to attach a notebook or a job to this cluster (and use it via ssh instead), it’s recommended to set this to Never, since otherwise the cluster will be removed after the set time.


Finally click on CREATE. You will see that a new task for creating the cluster has been created.


The status is updated to INPROGRESS when the task starts executing and cluster creation is in progress.


You can click on the Details drop down to monitor the progress for the cluster creation.


Once the cluster is successfully created and ready to use, the status is updated to FINISHED.


Attaching a Notebook to a Cluster

Go to the notebooks page by clicking on Notebooks in the left bar (or on the third green step in the Onboarding list at the top).


This will take you to the Notebooks page. At the top right corner, click on the Create Notebook button which opens the notebook creation form. Choose a name for your notebook and select the type of node that will host the notebook from the Instance type drop down list. Note that this node is for running the Jupyter notebook itself, and will not run cluster workloads. Lastly, select a cluster for attaching the notebook from the Cluster drop down menu and and click on CREATE.


After clicking CREATE, a new task for creating the notebook and its corresponding node is created.


The status updates to INPROGRESS when the task starts executing.


After creating the notebook, the platform runs readiness probe checks:


The notebook is ready to use after all checks are complete. OPEN NOTEBOOK will open the notebook in the current browser page, while the dropdown allows opening the notebook in a new tab.


Connecting to a Cluster

We recommend interacting with clusters primarily through Jupyter notebooks and Jobs. However, it may be necessary to connect directly to a cluster in some cases. You can either connect through a notebook terminal (recommended), or ssh directly from your machine. The latter requires providing your ssh public key during cluster creation.

Connecting with a Notebook Terminal

Follow the steps in Creating Clusters and Attaching a Notebook to a Cluster to attach a Notebook to a cluster.

Then, go the cluster tab and find your cluster. Click on DETAILS and copy the cluster UUID.


Next, go to the notebooks tab and select OPEN NOTEBOOK. In the Launcher, click on Terminal.


Through this terminal, you can interact with the /shared folder, which is shared by all the instances in the cluster and the Notebook instance. Follow the steps in Verify your Connection, to interact directly with your cluster.

SSH From Your Machine

First, navigate to the clusters tabs and select Create a Cluster. Click on Show Advanced and add your public key in SSH Public Key. Then, click on Add your IP in the Access from IP address section to enable accessing your cluster from your machine.


Fill the rest of the form by following the steps in Creating Clusters.

In the clusters tab, select your cluster and click on DETAILS to find the list of IP addresses for your cluster nodes. Use any of the IP addresses as the ssh destination. In addition, also copy the cluster UUID which will be needed to execute commands across the cluster.


In any ssh agent, you can connect to one of your nodes with:

ssh -i <path_to_private_key> bodo@<IP_ADDRESS>

To add additional ssh options please refer to the documentation for your ssh agent.

Verify your Connection

Once you have connected to a node in your cluster, you should verify that you can run operations across all the instances in the cluster.

  1. Verify the path to the hostfile for your cluster. You can find it by running:

    ls -la /shared/.hostfile-<CLUSTER UUID>
  2. Check that you can run a command across you cluster. To do this, run:

    mpiexec -n <TOTAL_CORE_COUNT> -f /shared/.hostfile-<CLUSTER UUID> hostname

    This will print one line per each core in the cluster, with one unique hostname per cluster node.

    Your cluster’s TOTAL_CORE_COUNT is usually half the number of vCPUs on each instance times the number of instances in your cluster. For example, if you have a 4 instance cluster of c5.4xlarge, then your TOTAL_CORE_COUNT is 32.

  3. Verify that you can run a python command across your cluster. For example, run:

    mpiexec -n <TOTAL_CORE_COUNT> -f /shared/.hostfile-<CLUSTER_UUID> python --version

If all commands succeed, you should be able to execute workloads across your cluster. You can place scripts and small data that are shared across cluster nodes in /shared. However, external storage, such as S3, should be used for reading and writing large data.

Running a Job

Bodo Cloud Platform has support for running scheduled (and immediate) Python jobs without the need for Jupyter Notebooks. To create a Job, navigate to the Jobs page by selecting Jobs in the left bar.


This pages displays any INPROGRESS jobs you have previously scheduled and allows you to schedule new Jobs. At the top right corner, click on CREATE JOB. This opens a job creation form.

First, select a name for your job and specify the cluster on which you want to deploy your job. If you have an existing cluster that is not currently bound to a notebook or another job, you can select this cluster from the dropdown menu. Alternatively, you can create a cluster specifically for this job by selecting the NEW button next to the cluster dropdown menu. When creating a cluster specifically for a job, note that the cluster is only used for that job and is removed once the job completes. After selecting your cluster, indicate when you want your job to be executed in the Schedule section. Then, enter the Command that you want to execute inside this cluster.

Note: This command is automatically prepended with mpiexec -n <CORE_COUNT> python. For example, to run a file with the argument 1, you would enter the command 1.

To specify your source code location, fill in the Path line with a valid Git URL or S3 URI (only available on AWS) that leads to a repository containing your code.

Note: When selecting a GitHub URL, you should select the URL available at the top of your web browser and NOT the path when cloning the repository, i.e. your path SHOULD NOT end in .git. If selecting an S3 URI, your S3 bucket must be in the same region as your cluster.


If you are cloning a private repository, you need to provide the platform with valid Git credentials to download your repository. To do so, select Show advanced in the bottom right of the form. Then in Workspace username, enter your Git username and in Workspace password enter either your password or a valid Github Access Token. The advanced options also allow you to specify a particular commit or branch with Workspace reference and to load other custom environment variables in Other.

Note: If your Github Account uses 2FA please use a Github Access Token to avoid any possible authentication issues.

Once your form is complete, select CREATE to begin your job.


Once you’ve provided all the necessary details, select CREATE to begin your job. You will see a NEW task created in your jobs page.


If you created a cluster specifically for this job, a new cluster will also appear in your clusters page.


Your job will begin once it reaches its scheduled time and any necessary clusters have been created. Then your job will transition to being INPROGRESS.


At this point your job will execute your desired command. Once it finishes executing, your job will transition to FINISHED status. You can find any stdout information that you may need by pressing DETAILS followed by SHOW LOGS. If a cluster was specifically created for this job, it will be deleted after the job finishes.


Note: Bodo DOES NOT preserve artifacts written to local storage. If you have any information that you need to persist and later review, you should write to external storage, such as Amazon S3. You may also write to stdout/stderr, but output logs may be truncated, so it should not be considered reliable for large outputs that need to be read later.