Nebelung: Python wrapper for the Firecloud API

This package provides a wrapper around the Firecloud package and performs a similar, though cat-themed, function as dalmation.

Installation

Nebelung requires Python 3.11 or later.

uv add nebelung # or `poetry add nebelung`, `pip install nebelung`, etc.

Usage

The package has two classes, TerraWorkspace and TerraWorkflow, and a variety of utility functions that wrap a subset of Firecloud API functionality.

Workspaces

from nebelung.terra_workspace import TerraWorkspace

terra_workspace = TerraWorkspace(
    workspace_namespace="terra_workspace_namespace",
    workspace_name="terra_workspace_name",
    owners=["user1@example.com", "group@firecloud.org"],
)

Entities

# get a workspace data table as a Pandas data frame
df = terra_workspace.get_entities("sample")

# get a workspace data table as a Pandas data frame typed with Pandera
# (`YourPanderaSchema` should subclass `nebelung.types.CoercedDataFrame`)
df = terra_workspace.get_entities("sample", YourPanderaSchema)   

# upsert a data frame to a workspace data table
terra_workspace.upload_entities(df)  # first column of `df` should be, e.g., `entity:sample_id` 

# create a sample set named, e.g., `sample_2024-08-21T17-24-19_call_cnvs"
sample_set_id = terra_workspace.create_entity_set(
    entity_type="sample",
    entity_ids=["sample_id1", "sample_id2"], 
    suffix="call_cnvs",
)

Workflow outputs

# collect workflow outputs from successful jobs as a list of `nebelung.types.TaskResult` objects 
outputs = terra_workspace.collect_workflow_outputs() 

# collect workflow outputs from successful jobs submitted in the last week
import datetime
a_week_ago = datetime.datetime.now() - datetime.timedelta(days=7)
outputs = terra_workspace.collect_workflow_outputs(since=a_week_ago)

Workflow

Here, a "workflow" (standard data pipeline terminology) comprises a "method" and "method config" (Terra terminology).

The standard method for making a WDL-based workflow available in a Terra workspace is to configure the git repo to push to Dockstore. Although this would be the recommended technique to make a workflow available publicly, there are several drawbacks:

• The git repo must be public (for GCP-backed Terra workspaces at least).
• Every change to the method (WDL) or method config (JSON) requires creating and pushing a git commit.
• The workflow isn't updated on Dockstore immediately, since it depends on continuous deployment (CD).
• The Dockstore UI doesn't provide great visibility into CD build failures and their causes.

An alternative to Dockstore is to push the WDL directly to Firecloud. However, that API endpoint doesn't support uploading a WDL script that imports other local WDL scripts, nor a zip file of cross-referenced WDL scripts (like Cromwell does). The endpoint will accept WDL that imports other scripts via URLs, but currently only from the githubusercontent.com domain.

Method persistence with GitHub gists

Thus, Nebelung (ab)uses GitHub gists to persist all the WDL scripts for a workflow as multiple files belonging to a single gist, then uploads the top-level WDL script's code to Firecloud. Any import "./path/to/included/script.wdl" as other_script statement is rewritten so that the imported script is persisted in the gist and thus imported from a https://gist.githubusercontent.com URL. This happens recursively, so local imports can have their own local imports.

Method config

To aid in automation and make it easier to submit jobs manually without filling out many fields in the job submission UI, a JSON-formatted method config is also required, e.g.:

{
  "deleted": false,
  "inputs": {
    "call_cnvs.sample_id": "this.sample_id"
  },
  "methodConfigVersion": 1,
  "methodRepoMethod": {
    "methodNamespace": "omics_pipelines",
    "methodName": "call_cnvs",
    "methodVersion": 1
  },
  "namespace": "omics_pipelines",
  "name": "call_cnvs",
  "outputs": {
    "call_cnvs.segs": "this.segments"
  },
  "rootEntityType": "sample"
}

• Both methods and method configs have their own namespaces. To simplify things, the above example uses the same sets of values for both. This approach might not be ideal if your methods and their configs are not one-to-one.
• The TerraWorkspace.update_workflow method will replace the methodVersion with an auto-incrementing version number based on the latest method's "snapshot ID" each time the method is updated. The methodConfigVersion should be incremented manually if desired.

Inputs

To allow for method/workflow inputs to be used as a "test parameter config" JSON file on Dockstore, a optional separate JSON file for this part of the method config can be specified, e.g.:

{
  "call_cnvs.some_static_file": "gs://bucket/file.txt"
}

The object from the inputs JSON file is merged into the inputs of the method config, with the method config's values taking precedence.

Using the inputs JSON file for static values and the method config's inputs object for this.column_name mappings is recommended to keep the former unopinionated and appropriate for distribution on Dockstore.

Validation

To avoid persisting potentially invalid WDL, update_workflow also validates all the WDL scripts with WOMtool first.

Example

See also the example module module in this repo.

import os
from pathlib import Path
from nebelung.terra_workflow import TerraWorkflow

# download the latest WOMtool from https://github.com/broadinstitute/cromwell/releases
os.environ["WOMTOOL_JAR"] = "/path/to/womtool.jar"

# generate a Github personal access token (fine-grained) at 
# https://github.com/settings/tokens?type=beta
# with the "Read and Write access to gists" permission 
os.environ["GITHUB_PAT"] = "github_pat_..."

terra_workflow = TerraWorkflow(
    method_namespace="omics_pipelines", # should match `methodRepoMethod.methodNamespace` from method config
    method_name="call_cnvs", # should match `methodRepoMethod.name` from method config
    method_config_namespace="omics_pipelines", # should match `namespace` from method config
    method_config_name="call_cnvs", # should match `name` from method config
    method_synopsis="This method calls CNVs.",
    workflow_wdl_path=Path("/path/to/call_cnvs.wdl").resolve(),
    method_config_json_path=Path("/path/to/call_cnvs.json").resolve(),
    workflow_inputs_json_path=Path("/path/to/call_cnvs_inputs.json").resolve(),
    github_pat="github_pat_...", # (if not using the GITHUB_PAT ENV variable) 
    womtool_jar="/path/to/womtool.jar", # (if not using the WOMTOOL_JAR ENV variable) 
)

# create or update a workflow (i.e. method and method config) directly in Firecloud
terra_workspace.update_workflow(terra_workflow, n_snapshots_to_keep=20)

# submit a job
terra_workspace.submit_workflow_run(
    terra_workflow,
    # any arguments below are passed to `firecloud_api.create_submission`
    entity="sample_2024-08-21T17-24-19_call_cnvs", # from `create_entity_set`
    etype="sample_set", # data type of the `entity` arg
    expression="this.samples", # the root entity (i.e. the WDL expects a single sample)
    use_callcache=True,
    use_reference_disks=False,
    memory_retry_multiplier=1.2,
)

Call Firecloud API directly

All calls to the Firecloud API made internally by Nebelung are retried automatically (with a backoff function) in the case of a networking-related error. This function also detects other errors returned by the API and parses the JSON response if the call was successful.

To use this functionality in the cases where Nebelung doesn't provide an endpoint wrapper, import the Firecloud API and the call_firecloud_api function:

from firecloud import api as firecloud_api
from nebelung.utils import call_firecloud_api

# get a job submission
result = call_firecloud_api(
    firecloud_api.get_submission,
    namespace="terra_workspace_namespace",
    workspace="terra_workspace_name",
    max_retries=1,
    # kwargs for `get_submission`
    submission_id="<uuid>",
)

Development

To update the package on pipy.org, update the version in pyproject.toml and run uv build && uv publish.