HPC Clusters

vEcoli uses Nextflow and Apptainer containers to run on high-performance computing (HPC) clusters. For users with access to the Covert Lab’s partition on Sherlock, follow the instructions in the Sherlock section. For users looking to run the model on other HPC clusters, follow the instructions in the Other Clusters section.

To speed up HPC workflows, vEcoli supports the HyperQueue executor. See HyperQueue for more information.

Sherlock

On Sherlock, once a workflow is started with runscripts.workflow, runscripts/container/build-image.sh builds an Apptainer image with a minimal snapshot of your cloned repository. Nextflow starts containers using this image to run the steps of the workflow. To run or interact with the model outside of runscripts.workflow, start an interactive container by following the steps in Interactive Container.

Note

Files that match the patterns in .dockerignore are excluded from the Apptainer image.

Setup

Note

The following setup applies to members of the Covert Lab only.

After cloning the model repository to your home directory, add the following lines to your ~/.bash_profile, then close and reopen your SSH connection:

# Load newer Git, Java (for Nextflow), and PyArrow
module load system git java/21.0.4 py-pyarrow
# Include shared Nextflow and HyperQueue installations on PATH
export PATH=$PATH:$GROUP_HOME/vEcoli_env

Warning

If you have any lines in your ~/.bash_profile, ~/.bashrc, or ~/.profile that modify the PATH variable, make sure that which python3 returns the Sherlock-managed Python 3 from the py-pyarrow module (/share/software/user/open/python/3.xx.x/bin/python3). If not, comment out lines that modify the PATH variable (for example, pyenv-related), until the right Python is found.

Then, run the following to test your setup:

python3 runscripts/workflow.py --config ecoli/composites/ecoli_configs/test_sherlock.json

This will run a small workflow that:

Builds an Apptainer image with a snapshot of your cloned repository and saves it to $SCRATCH/test_image.
Runs the ParCa.
Runs one simulation.
Runs the mass fraction analysis.

All output files will be saved to $SCRATCH/test_sherlock. See Configuration for a description of the configuration options and Running Workflow for a details about running a workflow on Sherlock.

Note

The above setup is sufficient to run workflows on Sherlock. However, if you have a compelling reason to update the shared Nextflow or HyperQueue binaries, navigate to $GROUP_HOME/vEcoli_env and run:

Nextflow: NXF_EDGE=1 nextflow self-update
HyperQueue: See HyperQueue.

Configuration

To tell vEcoli that you are running on Sherlock, you MUST include the following keys in your configuration JSON (note the top-level sherlock key):

{
  "sherlock": {
    # Boolean, whether to build a fresh Apptainer image. If files that are
    # not excluded by .dockerignore did not change since your last build,
    # you can set this to false to skip building the image.
    "build_image": true,
    # Path (relative or absolute, including file name) of Apptainer image to
    # build (or use directly, if build_image is false)
    "container_image": "",
    # Boolean, whether to use HyperQueue executor for simulation jobs
    # (see HyperQueue section below)
    "hyperqueue": true,
    # Boolean, denotes that a workflow is being run as part of Jenkins
    # continuous integration testing. Randomizes the initial seed and
    # ensures that all STDOUT and STDERR is piped to the launching process
    # so they can be reported by GitHub
    "jenkins": false
  }
}

In addition to these options, you MUST set the emitter output directory (see description of emitter_arg in JSON Config Files) to a path with enough space to store your workflow outputs. We recommend setting this to a location in your $SCRATCH directory (e.g. /scratch/users/{username}/out).

Running Workflow

With these options in the configuration JSON, a workflow can be started by running python3 runscripts/workflow.py --config {}.json on a login node. This submits a job that will run the Nextflow workflow orchestrator with a 7-day time limit on the lab’s dedicated partition. The workflow orchestrator will automatically submit jobs for each step in the workflow: one for the ParCa, one to create variants, one for each cell, and one for each analysis.

Warning

Remember to use python3 to start the workflow instead of python.

If you are trying to run a workflow that takes longer than 7 days, you can use the resume functionality (see Fault Tolerance). Alternatively, consider running your workflow on Google Cloud, which has no maximum workflow runtime (see Google Cloud).

Note

Unlike workflows run locally, Sherlock workflows are run using containers with a snapshot of the repository.

When you run python3 runscripts/workflow.py, you will get a message that a SLURM job was submitted to build the image. When that job starts, you will get terminal output showing the build progress. Avoid making any changes to your cloned repository until the build has finished. The built image will contain a snapshot of your cloned repository. Any changes made to the repository after the container image has been built will not affect the running workflow.

You can start additional, concurrent workflows that each build a new image with different modifications to the cloned repository. However, if possible, we recommend designing your code to accept options through the config JSON which modify the behavior of your workflow without modifying core code. This allows you to save time by setting build_image to false and container_image to the path of a previously built image.

There is a 4 hour time limit on each job in the workflow, including analyses. This is a generous limit designed to accomodate very slow-dividing cells. Generally, we recommend that users exclude analysis scripts which take more than a few minutes from their workflow configuration. Instead, create a SLURM batch script to run these analyses following Non-Interactive Container.

Interactive Container

To debug a failed job in a workflow, you must locate the container image that was used for that workflow. You can refer to the container_image key in the config JSON saved to the workflow output directory (see Output). Start an interactive container with that image name as follows:

runscripts/container/interactive.sh -i container_image -a

Note

Inside the interactive container, you can safely use python directly in addition to the usual uv commands.

Now, inside the container, navigate to /vEcoli and add breakpoints to scripts as you see fit. Finally, navigate to the working directory (see Troubleshooting) for the task that you want to debug. By invoking bash .command.sh, the job will run and pause upon reaching your breakpoints, allowing you to inspect variables and step through the code.

Warning

Any changes that you make to /vEcoli inside the container are discarded when the container terminates.

The files located in /vEcoli are a copy of the repository (excluding files ignored by .dockerignore) at the time the workflow was launched. To start an interactive container that reflects the current state of your cloned repository, navigate to your cloned repository and run the above command with the -d flag to start a “development” container.

In this mode, instead of editing source files in /vEcoli, you can directly edit the source files in your cloned repository and have those changes immediately reflected when running those scripts inside the container. Because you are just modifying your cloned repository, any code changes you make will persist after the container terminates and can be tracked using Git version control.

Note

If the image you use to start a development container was built with an outdated version of uv.lock or pyproject.toml, there will be a delay on startup while uv updates the packages. To avoid this, build a new image with runscripts/container/build-image.sh.

Non-Interactive Container

To run any script inside a container without starting an interactive session, use the same command as Interactive Container but specify a command using the -c flag. For example, to run the ParCa process, navigate to your cloned repository and run the following command:

runscripts/container/interactive.sh -i container_image -c "python /vEcoli/runscripts/parca.py --config {}"

Note

We strongly recommend sticking to running files from the snapshot of the repository included in the container image at /vEcoli. If you want to run a script from your cloned repository with all changes reflected, add the -d flag and drop the /vEcoli/ prefix from the script name.

This is particularly useful for writing SLURM batch scripts to manually run analysis scripts with custom resource requests (e.g. more than default 4 hours, 1 CPU, 4 GB RAM in workflow).

Other Clusters

Nextflow has support for a wide array of HPC schedulers. If your HPC cluster uses a supported scheduler, you can likely run vEcoli on it with fairly minimal modifications.

Prerequisites

The following are required:

Nextflow (requires Java)
PyArrow
Git clone vEcoli to a location that is accessible from all nodes in your cluster

If your cluster has Apptainer (formerly known as Singularity) installed, check to see if it is configured to automatically mount all filesystems (see Apptainer docs). If not, you may run into errors when running workflows because Apptainer containers are read-only. You may be able to resolve this by adding --writeable-tmpfs to containerOptions for the sherlock and sherlock-hq profiles in runscripts/nextflow/config.template.

If this does not work, Nextflow allows users to define beforeScript and afterScript directives for each process that we can potentially use to create and clean up Apptainer overlay files. Then, the containerOptions directive can be modified to start containers with these overlays. However, the simplest solution is likely to set up vEcoli as if Apptainer was not available (see below). Note that if Apptainer is not configured to automount filesystems, you will need to manually specify paths to mount when debugging with interactive containers (see Interactive Container). This can be done using the -p argument for runscripts/container/interactive.sh.

If your cluster does not have Apptainer, you can try the following steps:

Completely follow the local setup instructions in the README (install uv, etc).
Delete the following lines from runscripts/nextflow/config.template:

process.container = 'IMAGE_NAME'
...
apptainer.enabled = true

Make sure to always set build_runtime_image to false in your config JSONs (see Configuration)

Cluster Options

If your HPC cluster uses the SLURM scheduler, you can use vEcoli on that cluster by changing the queue option in runscripts/nextflow/config.template and all instances of --partition=QUEUE(S) in runscripts.workflow to the right queue(s) for your cluster.

If your HPC cluster uses a different scheduler, refer to the Nextflow executor documentation for more information on configuring the right executor. Beyond changing queue names as described above, this could be as simple as modifying the executor directives for the sherlock and sherlock_hq profiles in runscripts/nextflow/config.template.

HyperQueue

HyperQueue is a job scheduler that is designed to run on top of a traditional HPC scheduler like SLURM. It consists of a head server that can automatically allocate worker jobs using the underlying HPC scheduler. These worker jobs can be configured to persist for long enough to complete multiple tasks, greatly reducing the overhead of job submission and queuing, especially for shorter jobs.

HyperQueue is distributed as a pre-built binary on GitHub. Unfortunately, this binary is built with a newer version of GLIBC than is available on Sherlock, necessitating a rebuild from source. A binary built in this way is available in $GROUP_HOME/vEcoli_env to users with access to the Covert Lab’s partition on Sherlock. This is added to PATH in the Sherlock setup instructions, and unless you have a compelling reason to update it, no further action is required.

Users who want or need to build from source should follow these instructions.