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.

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:

  1. Builds an Apptainer image with a snapshot of your cloned repository.

  2. Runs the ParCa.

  3. Runs one simulation.

  4. Runs the mass fraction analysis.

All output files will be saved to a test_sherlock directory in your cloned repository. You can modify the workflow output directory by changing the out_dir option under emitter_arg in the config JSON. See Configuration for a description of the Sherlock-specific configuration options and Running Workflows for details about running a workflow on Sherlock.

To run scripts on Sherlock outside a workflow, see Interactive Container. To run scripts on Sherlock through a SLURM batch script, see Non-Interactive Container.

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:

  1. Nextflow: NXF_EDGE=1 nextflow self-update

  2. 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).

Warning

~ and environment variables like $SCRATCH are not expanded in the configuration JSON. See the warning box at Workflows.

Running Workflows

With these options in the configuration JSON, a workflow can be started by running python3 runscripts/workflow.py --config {}, substituting in the path to your config JSON.

Warning

Remember to use python3 to start workflows instead of python.

This command should be run on a login node (no need to request a compute node). If build_image is true in your config JSON, the terminal will report that a SLURM job was submitted to build the container image. When the image build job starts, the terminal will report the build progress.

Note

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

Warning

Do not make any changes to your cloned repository or close your SSH connection until the build has finished.

Once the build has finished, the terminal will report that a SLURM job was submitted for the Nextflow workflow orchestrator before exiting back to the shell. At this point, you are free to close your connection, start additional workflows, etc. Unlike workflows run locally, Sherlock’s containerized workflows mean any changes made to the repository after the container image has been built will not affect the running workflow.

Once started, the Nextflow job will stay alive for the duration of the workflow (up to 7 days) and submit new SLURM jobs as needed.

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).

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 reusing a previously built image as follows: set build_image to false and container_image to the path of said 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, either run these manually following Interactive Container or create a SLURM batch script to run these analyses following Non-Interactive Container.

Interactive Container

Warning

The following steps should be run on a compute node. See the Sherlock documentation for details.

The maximum resource request for an interactive compute node is 2 hours, 4 CPU cores, and 8GB RAM/core. Scripts that require more resources should be submitted as SLURM batch scripts to the mcovert or owners partition (see Non-Interactive Container).

To run scripts on Sherlock, you must have either:

  • Previously run a workflow on Sherlock and have access to the built container image

  • Built a container image manually using runscripts/container/build-image.sh with the -a flag

Start an interactive container with your full image path (see the warning box at Workflows) by navigating to your cloned repository and running:

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.

The above command launches a container containing a snapshot of your cloned repository as it was when the image was built. This snapshot is located at /vEcoli inside the container and is mostly intended to guarantee reproducibility for troubleshooting failed workflow jobs. More specifically, users who wish to debug a failed workflow job should:

  1. Start an interactive container with the image used to run the workflow.

  2. Use nano to add breakpoints (import ipdb; ipdb.set_trace()) to the relevant scripts in /vEcoli.

  3. Navigate to the working directory (see Troubleshooting) for the job that you want to debug.

  4. Invoke bash .command.sh to run the failing task and pause upon reaching your breakpoints, allowing you to inspect variables and step through the code.

Warning

~ and environment variables like $SCRATCH do not work inside the container. Follow the instructions in the warning box at Workflows outside the container to get the full path to use inside the container.

Danger

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

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:

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

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 may be a long startup delay due to package updates. To avoid this, build a new image with runscripts/container/build-image.sh -i container_image -a, replacing container_image with a path for the image to build.

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, replacing container_image with the pat to your container image and {} with the path to your configuration JSON:

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

This feature is intended for use in SLURM batch scripts to manually run analysis scripts with custom resource requests. Make sure to include one of the following directives at the top of your script:

  • #SBATCH --partition=owners: The big advantage of this partition is that you can request very large amounts of resources (for example, dozens of cores). The major downsides are that queue times may be long and other users may preempt your job at any moment, though this is anecdotally rare for jobs under an hour long.

  • #SBATCH --partition=mcovert: Best for high priority scripts (short queue time) that you cannot risk being preempted. The number of available cores is 32 minus whatever is currently being used by other users in the mcovert partition. Importantly, if all 32 cores are in use by mcovert users, not only will your script have to wait for resources to free up, so will any workflows. As such, treat this partition as a limited resource reserved for high priority jobs.

Just as with interactive containers, to run scripts directly from your cloned repository and not the snapshot, add the -d flag drop the /vEcoli/ prefix from script names. Note that changing files in your cloned repository may affect SLURM batch jobs submitted with this flag.

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. Additionally, 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:

  1. Completely follow the local setup instructions in the README (install uv, etc).

  2. Delete the following lines from runscripts/nextflow/config.template:

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

  1. 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. Additionally, you will need to replace the SLURM submission directives in runscripts.workflow.main() with equivalent directives for your scheduler.

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.