Reference Documentation

Parallelism and compute structure

JuliaHub supports fully heterogeneous parallel compute. It uses the Distributed standard library to parallelize across workers on potentially many machines. Each worker can support multiple threads with the builtin Threads module. Each machine can optionally have a computational GPU attached to it for further acceleration with the CUDA package.

All of these modes of operation are fully supported and JuliaHub ensures that all machines in the cluster have the appropriate files and dependencies in place. This means, for example, that you do not need to addprocs or set the number threads yourself; your code should use scale-invariant constructs like pmap, @distributed and @threads or other higher-level abstractions like those for DifferentialEquations.jl.

JuliaHub also scales seamlessly from simple single-file scripts to whole applications with many thousands of lines of code, dependencies (both private and public), binary artifacts, and data. Your IDE will automatically detect all such dependencies, bundle them together, and ensure their availability on every node.

Project, Manifest and Artifacts

When running from VS Code or Juno, these are pre-populated based upon your active project.

When running ad-hoc code, you may choose them manually.

Outputs

Set ENV["RESULTS"] to contain a simple JSON string to display certain values in the Output table. This is helpful for a handful of summary values to compare jobs at a glance on JuliaHub through the Details button. The contents of ENV["RESULTS"] are restricted to 1000 characters. If a longer value is set, it will be truncated.

You can further set the ENV["RESULTS_FILE"] to a local file on the filesystem that will be available for download after job completion. While there is only one results file per job, note that you can zip or tar multiple files together. The overall size of the results (the single file or the tar bundle) is limited to 5GiB. Upload of result exceeding that size is would fail, and they will not be available for download later.

Cluster design

Choose the number of cores and machines as appropriate:

Cluster design

It is possible to either start a job with single Julia process or with multiple Julia processes.

  • To start a job with single Julia process, select single-process in the first drop-down menu.

  • To start a job with multiple Julia processes, select distributed in the first drop-down menu.

The vCPU and memory selections will determine the node type/size on which the job will be run.

The actual number of Julia processes that will be started depends on whether one Julia process for each vCPU is selected or one Julia process for each node is selected.

If one Julia process for each vCPU is selected, then the total number of Julia processes = number of nodes * number of vCPUs in each node

If one Julia process for each node is selected, then the total number of Julia Processes = number of nodes

Note that one Julia process will always be designated to be the master and the remaining would be worker processes.

Logging

While running your job, live logs are available. Note that it can take a few minutes to launch the machines (longer for GPU machines). The logs are searchable through dynamic filters, with additional information available if you use the builtin [logging] module and related macros (like @info, @warn, and friends).

Secrets

Jobs can have access to secrets. Secrets can store sensitive information encrypted and make them available to your job at run time. This is available for use only in batch jobs as of now.

To configure a secret, log in to the JuliaHub browser site and click on the account settings icon on upper-right corner of the screen.

Open Secret Management

When the secrets management screen opens, it lists the existing secrets if some are configured already. Secrets are associated with namespaces. The default namespace of a regular user is the email id with which they logged in. One may see other namespaces corresponding to groups that they belong to. A secret created with a group namespace is accessible to all users who belong to that group. Note that group members will have both read and write access to secrets in the group.

Add or Delete Secret

In the Job environment, secrets from the user's own namespace can be accessed in jobs using the intuitive get_secret API: secretvalue = JuliaRunJob.get_secret(secretname).

There is also a secrets manager get API that can be used to access secrets from other namespaces a user may have access to when the user belongs to any group:

secretvalue = JuliaRunJob.get(
        JuliaRunJob.SECRETS_MANAGER[],
        joinpath(base64encode(secrets_namespace), secretname)
    )

Where:

  • SECRETS_MANAGER[] provides a handle to the secrets manager context for the job

  • secretname is the name of the secret to access

  • secrets_namespace is the namespace in which the secret exists

Billing

Set your credit card information in the Payments settings:

Setting CC info

Billing is done at per-second interval, but with a minimum 10 minutes per job. You may set the maximum time or cost for any given job, but note that jobs that exceed that limit will be killed without storing any outputs (although logs will still be available).

Connecting VSCode IDE to JuliaHub

Make sure you've got the main Julia plugin installed from the VSCode marketplace as discussed in the vscode Julia extension documentation.

Next, install the JuliaHub plugin from the VSCode marketplace. Once installed, use the "show JuliaHub" command to open the JuliaHub pane.

The JuliaHub plugin uses the package server setting from the Julia plugin. You'll need to set this to https://juliahub.com:

juliahub package setting