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nmdc_automation

An automation framework for running sequential metagenome analysis jobs and making the outputs available as metadata in the NMDC database, and data objects on the NMDC data portal.

Table of Contents

Installation

Requirements

  • mongodb-community needs to be installed and running on the local machine
  • Python 3.11 or later
  • Poetry

Poetry Installation instructions can be found Here

MongoDB Installation

Install MongoDB using Homebrew on MacOS:

brew tap mongodb/brew
brew install mongodb-community
brew services start mongodb-community

Full Mongodb installation instructions for Mac can be found here

Ensure that the mongodb service is running:

brew services start mongodb-community

Installation

  1. Clone the repository

    git clone https://github.com/microbiomedata/nmdc_automation.git

  2. Install the required packages

    cd nmdc_automation  
poetry install

  3. Activate the poetry environment

    poetry env activate

    OR

    poetry shell

Depending on Poetry version

  1. Run the tests 
    make test

Overview

System Components

Scheduler

The Scheduler polls the NMDC database based upon an Allowlist of DataGeneration IDs. Based on an allowed data-generation ID, the scheduler examines WorkflowExecutions and DataObjects that was_informed_by by the data generation, and builds a graph of Workflow Process Nodes.

A Workflow Process Node is a representation of:

  • workflow - the workflow configuration, from workflows.yaml. The "recipe" for the given type of analysis
    • workflow.children - the child workflow recipes that can be run after this workflow
  • process - the planned process, from the NMDC database. The "instance" of a workflow execution or data generation from the NMDC database
  • parent - the parent workflow process node, if any
  • children - the child workflow process nodes, if any
Workflow Process Node Mermaid Diagram: 
erDiagram
    WorkflowProcessNode ||--|| PlannedProcess: "process"
    PlannedProcess ||-- |{ DataObject: "has_input / has_output"
    WorkflowProcessNode }|--|| WorkflowConfig: "workflow"
    WorkflowConfig ||--o{ WorkflowConfig: "children"
    WorkflowProcessNode |o--o| WorkflowProcessNode: "parent"
    WorkflowProcessNode |o--o{ WorkflowProcessNode: "children"
Loading

When the Scheduler finds a node where:

  1. The node has a workflow configuration in node.workflow.children
  2. The node DOES NOT have a child node in node.children
  3. The required inputs for the child workflow are available in node's process outputs
Scheduler Process Mermaid Diagram: 
erDiagram
    WPNode_Sequencing ||--|| WPNode_ReadsQC: "children nodes"
    WPNode_Sequencing ||--|| WConfig_Sequencing: "workflow"
    WConfig_Sequencing ||--o{ WConfig_ReadsQC: "children workflows"
    WPNode_Sequencing ||--|| Process_Sequencing: "process"
    Process_Sequencing ||-- |{ SequencingData: "has_output"
    WPNode_ReadsQC ||--|| Process_ReadsQC: "process"
    Process_ReadsQC ||--|{ SequencingData: "has_input"
    Process_ReadsQC ||-- |{ ReadsQCData: "has_output"
    WPNode_ReadsQC ||--|| WConfig_ReadsQC: "workflow"
    WConfig_ReadsQC ||--o{ WConfig_Assembly: "children workflows"
Loading

In this case the Scheduler will "schedule" a new job by creating a Job configuration from:

  • the workflow configuration from node.workflow.children
  • input data from node.data_objects

and writing this to the jobs collection in the NMDC database

Watcher

The Watcher "watches" the jobs table in the NMDC database looking for unclaimed jobs. If found, the Watcher will create a WorkflowJob to manage the analysis job. The watcher will then periodically poll each workflow job for its status and process successful or failed jobs when they are complete

WorkflowJob

A WorkflowJob consists of a WorkflowStateManager and a JobRunner and is responsible for preparing the required inputs for an analysis job, submitting it to the job running service.

The default job running service is JAWS. The legacy job running service is a self-managed SLURM/Condor/Cromwell stack running on Permutter.

Details can be found in README_Slurm.md

The JobRunner is also responsible for processing the resulting data and metadata when the job completes.
The watcher maintains a record of it's current activity in a State File

System Configuration

Site Config

Site-specific configuration is provided by a .toml file and defines some parameters that are used across the workflow process including

  1. URL and credentials for NMDC API
  2. Staging and Data filesystem locations for the site
  3. Job Runner service URLs
  4. Path to the state file

Workflow Definitions : Workflow definitions in a .yaml file describing each analysis step, specifying:

  1. Name, type, version, WDL and git repository for each workflow
  2. Inputs, Outputs and Workflow Execution steps
  3. Data Object Types, description and name templates for processing workflow output data

Instructions (for NERSC / Perlmutter environment)

Running the Scheduler on NERSC Rancher2

The Scheduler is a Dockerized application running on Rancher. To initialize the Scheduler for new DataGeneration IDs, the following steps:

  1. On Rancher, go to Deployments, select Production from the clusters list, and find the Scheduler in either nmdc or nmdc-dev
  2. Click on the Scheduler and select run shell
  3. In the shell, cd /conf
  4. Update the file allow.lst with the Data Generation IDs that you want to schedule
    1. Copy the list of data-generation IDs to you clipboard
    2. In the shell, delete the existing allow list rm allow.lst
    3. Replace the file with your copied list:
      1. cat >allow.lst
      2. Paste your IDs command-v
      3. Ensure a blank line at the end with a return
      4. Terminate the cat command using control-d
  5. The default log level is INFO if you want to change it to DEBUG for more verbose logging, run the following command:
    1. export NMDC_LOG_LEVEL=DEBUG
  6. Restart the scheduler. In the shell, in /conf: ./run_scheduler.sh
    1. If running tests on dev, make sure to check ./run_scheduler.sh -h for options.
  7. Ensure the scheduler is running by checking sched.log

Running the Watcher on NERSC Perlmutter

The watcher is a python application which runs on a login node on Perlmutter. The following instructions all assume the user is logged in as user [email protected]

  1. Get an ssh key - in your home directory: ./sshproxy.sh -u <your_nersc_username> -c nmdcda
  2. Log in using the key ssh -i .ssh/nmdcda [email protected]

Watcher code and config files can be found

  • /global/homes/n/nmdcda/nmdc_automation/prod
  • /global/homes/n/nmdcda/nmdc_automation/dev

Check the Watcher Status

  1. Check the last node the watcher was running on

    (base) nmdcda@perlmutter:login07:~> cd nmdc_automation/dev
(base) nmdcda@perlmutter:login07:~/nmdc_automation/dev> cat host-dev.last
login24

  2. ssh to that node

    (base) nmdcda@perlmutter:login07:~/nmdc_automation/dev> ssh login24

  3. Check for the watcher process

    (base) nmdcda@perlmutter:login24:~> ps aux | grep watcher
nmdcda    115825  0.0  0.0   8236   848 pts/94   S+   09:33   0:00 grep watcher
nmdcda   2044781  0.4  0.0 146420 113668 ?       S    Mar06   5:42 python -m nmdc_automation.run_process.run_workflows watcher --config /global/homes/n/nmdcda/nmdc_automation/prod/site_configuration_nersc_prod.toml --jaws daemon
nmdcda   2044782  0.0  0.0   5504   744 ?        S    Mar06   0:00 tee -a watcher-prod.log

  4. IF we are going to shut down the Watcher (without restarting), we need to kill the existing process

    (base) nmdcda@perlmutter:login24:~> kill -9 2044781

Note

This will also terminate the tee process that is writing to the log file. To restart the Watcher with older versions of the ./run.sh script, manual termination of the existing process was necessary with kill -9 2044781. However, the new run_watcher.sh script now handles killing and restarting the Watcher.

Set-Up and Configuration

  1. Ensure you have the latest nmdc_automation code.
    1. cd nmdc_automation
    2. git status / git switch main if not on main branch
    3. git fetch origin
    4. git pull
  2. Setup NMDC automation environment with conda and poetry.
    1. load conda: eval "$__conda_setup"
    2. in the nmdc_automation directory, install the nmdc_automation project with poetry install
    3. poetry shell to use the environment
Example Setup: 
(nersc-python) nmdcda@perlmutter:login38:~> pwd
/global/homes/n/nmdcda
(nersc-python) nmdcda@perlmutter:login38:~> cd nmdc_automation/dev/
(nersc-python) nmdcda@perlmutter:login38:~/nmdc_automation/dev> eval "$__conda_setup"
(base) nmdcda@perlmutter:login38:~/nmdc_automation/dev> cd nmdc_automation/
(base) nmdcda@perlmutter:login38:~/nmdc_automation/dev/nmdc_automation> poetry install
Installing dependencies from lock file

No dependencies to install or update

Installing the current project: nmdc-automation (0.1.0)
(base) nmdcda@perlmutter:login38:~/nmdc_automation/dev/nmdc_automation> poetry shell
Spawning shell within /global/cfs/cdirs/m3408/nmdc_automation/dev/nmdc_automation/.venv
. /global/cfs/cdirs/m3408/nmdc_automation/dev/nmdc_automation/.venv/bin/activate
(base) nmdcda@perlmutter:login38:~/nmdc_automation/dev/nmdc_automation> . /global/cfs/cdirs/m3408/nmdc_automation/dev/nmdc_automation/.venv/bin/activate
(nmdc-automation-py3.11) (base) nmdcda@perlmutter:login38:~/nmdc_automation/dev/nmdc_automation>

The poetry shell command will activate the environment for the current shell session. Environment (nmdc-automation-py3.11) will be displayed in the prompt.

Running the Watcher

We run the Watcher using nohup (No Hangup) - this prevents the Watcher process from being terminated when the user's terminal session ends. This will cause stdout and stderr to be written to a file names nohup.out in addition to being written to the watcher-[dev/prod].log file.

  1. change to the working prod or dev directory
  • /global/homes/n/nmdcda/nmdc_automation/prod
  • /global/homes/n/nmdcda/nmdc_automation/dev
  1. rm nohup.out (Long term logging is captured in the watcher-[dev/prod].log file, which is retained)
  2. nohup ./run_watcher_dev.sh & (for dev) OR nohup ./run_watcher_prod.sh & (for prod)

Note

These scripts use the JAWS service to run jobs. If you want to use SLURM/Condor, use run_dev_slurm.sh or run_prod_slurm.sh

Monitoring the Watcher

Same process as as Checking the Watcher Status

JAWS

JAWS is the default job running service. It is a Cromwell-based service that runs jobs on NERSC and other compute resources. Documentation can be found here.

With the jaws_jobid from the agent.state files, you can check the status of the job in the JAWS service

JAWS Status call: 
> jaws status 109288
{
  "compute_site_id": "nmdc",
  "cpu_hours": null,
  "cromwell_run_id": "0fddc559-833e-4e14-9fa5-1e3d485b232d",
  "id": 109288,
  "input_site_id": "nmdc",
  "json_file": "/tmp/tmpeoq9a5p_.json",
  "output_dir": null,
  "result": null,
  "status": "running",
  "status_detail": "The run is being executed; you can check `tasks` for more detail",
  "submitted": "2025-05-01 11:22:45",
  "tag": "nmdc:dgns-11-sm8wyy89/nmdc:wfrqc-11-7fgdsy18.1",
  "team_id": "nmdc",
  "updated": "2025-05-01 11:40:44",
  "user_id": "nmdcda",
  "wdl_file": "/tmp/tmpq0l3fk0n.wdl",
  "workflow_name": "nmdc_rqcfilter",
  "workflow_root": "/pscratch/sd/n/nmjaws/nmdc-prod/cromwell-executions/nmdc_rqcfilter/0fddc559-833e-4e14-9fa5-1e3d485b232d"
}
NMDC Database

  1. Query the jobs table in the NMDC database based on was_informed_by a specific DataGeneration ID

    db.getCollection("jobs").find({
    "config.was_informed_by": "nmdc:omprc-11-sdyccb57"
})

    Similarly, you can query workflow_executions to find results based on was_informed_by a specific DataGeneration ID

    db.getCollection("workflow_execution_set").find({
    "was_informed_by": "nmdc:omprc-11-sdyccb57"
})

  2. Job document example

    Example database entry 
    {
    "workflow" : {
        "id" : "Metagenome Assembly: v1.0.9"
    },
    "id" : "nmdc:9380c834-fab7-11ef-b4bd-0a13321f5970",
    "created_at" : "2025-03-06T18:19:43.000+0000",
    "config" : {
        "git_repo" : "https://github.com/microbiomedata/metaAssembly",
        "release" : "v1.0.9",
        "wdl" : "jgi_assembly.wdl",
        "activity_id" : "nmdc:wfmgas-12-k8dxr170.1",
        "activity_set" : "workflow_execution_set",
        "was_informed_by" : "nmdc:omprc-11-sdyccb57",
        "trigger_activity" : "nmdc:wfrqc-12-dvn15085.1",
        "iteration" : 1,
        "input_prefix" : "jgi_metaAssembly",
        "inputs" : {
            "input_files" : "https://data.microbiomedata.org/data/nmdc:omprc-11-sdyccb57/nmdc:wfrqc-12-dvn15085.1/nmdc_wfrqc-12-dvn15085.1_filtered.fastq.gz",
            "proj" : "nmdc:wfmgas-12-k8dxr170.1",
            "shortRead" : false
        },
        "input_data_objects" : [],
        "activity" : {},
        "outputs" : []
    },
    "claims" : [ ]
}

Things to note:

  • config.was_informed_by is the DataGeneration ID that is the root of this job
  • config.trigger_activity is the WorkflowExecution ID that triggered this job
  • config.inputs are the inputs to the job
  • claims a list of workers that have claimed the job. If this list is empty, the job is available to be claimed. If the list is not empty, the job is being processed by a worker - example: 
    {
    "op_id" : "nmdc:sys0z232qf64",
    "site_id" : "NERSC"
}

This refers to the operation and site that is processing the job.

Watcher State File

The Watcher maintains a state file with job configuration, metadata and status information. The location of the state file is defined in the site configuration file. For dev this location is: /global/cfs/cdirs/m3408/var/dev/agent.state

Example State File Entry 
{
      "workflow": {
        "id": "Metagenome Assembly: v1.0.9"
      },
      "created_at": "2025-03-06T18:19:43",
      "config": {
        "git_repo": "https://github.com/microbiomedata/metaAssembly",
        "release": "v1.0.9",
        "wdl": "jgi_assembly.wdl",
        "activity_id": "nmdc:wfmgas-12-k8dxr170.1",
        "activity_set": "workflow_execution_set",
        "was_informed_by": "nmdc:omprc-11-sdyccb57",
        "trigger_activity": "nmdc:wfrqc-12-dvn15085.1",
        "iteration": 1,
        "input_prefix": "jgi_metaAssembly",
        "inputs": {
          "input_files": "https://data.microbiomedata.org/data/nmdc:omprc-11-sdyccb57/nmdc:wfrqc-12-dvn15085.1/nmdc_wfrqc-12-dvn15085.1_filtered.fastq.gz",
          "proj": "nmdc:wfmgas-12-k8dxr170.1",
          "shortRead": false
        },
        "input_data_objects": [],
        "activity": {},
        "outputs": []
      },
      "claims": [],
      "opid": "nmdc:sys0z232qf64",
      "done": true,
      "start": "2025-03-06T19:24:52.176365+00:00",
      "cromwell_jobid": "0b138671-824d-496a-b681-24fb6cb207b3",
      "last_status": "Failed",
      "nmdc_jobid": "nmdc:9380c834-fab7-11ef-b4bd-0a13321f5970",
      "failed_count": 3
    }

Similar to a jobs record, with these additional things to note:

  • done is a boolean indicating if the job is complete
  • cromwell_jobid is the job ID from the Cromwell service
  • last_status is the last known status of the job - this is updated by the watcher
  • failed_count is the number of times the job has failed

Handling Failed Jobs

By default, the Watcher will retry a failed job 1 additional time via jaws resubmit. If the job fails again, the Watcher will mark the job as done and update the status to Failed.

Some things to note:

For jobs that have failed for with a transient incomplete data download, these may be resolved by invoking the jaws download $jaws_jobid command

For jobs that may have failed due to Cromwell or other system errors and need to be resubmitted, use the API release endpoint to mark a claimed job as failed and have JAWS resubmit the job if the JAWS job itself cannot be resubmitted. This will increase the claims array in the jobs record by 1.

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