Analysis runner to help make analysis results reproducible
Project description
Analysis runner
This tool helps to improve analysis provenance and theoretical reproducibility by automating the following aspects:
- Allow quick iteration using an environment that resembles production.
- Only allow access to production datasets through code that has been reviewed.
- Link the output data with the exact program invocation of how the data has been generated.
One of our main workflow pipeline systems at the CPG is Hail Batch. By default, its pipelines are defined by running a Python program locally and submitting the resulting DAG to the Hail Batch server. By specifying a repo, commit, and file, this tool will run your script inside a "driver" image on Hail Batch, with the correct permissions.
All invocations are logged to metamist, in the analysis-runner page.
When using the analysis-runner, the jobs are run as a specific Hail Batch service account to give appropriate permissions based on the dataset, and access level ("test", "standard", or "full", as documented in the team docs storage policies). This helps with bucket permission management and billing budgets.
By default, we run your script in a driver image, that contains a number of common tools - but you can in fact run any container inside the cpg-common artifact registry (and any container if running using the test access level).
The analysis-runner is also integrated with our Cromwell server to run WDL based workflows.
CLI
The analysis-runner CLI is used to start pipelines based on a GitHub repository, commit, and command to run.
First, make sure that your environment provides Python 3.10 or newer. We recommend using pyenv to manage your python versions
pyenv install 3.10.12
pyenv global 3.10.12
> python3 --version
Python 3.10.12
Then install the analysis-runner Python package using pip:
python3 -m pip install analysis-runner
Run analysis-runner --help to see usage information.
Make sure that you're logged into GCP with application-default credentials:
gcloud auth application-default login
If you're in the directory of the project you want to run, you can omit the --commit and --repository parameters, which will use your current git remote and commit HEAD.
For example:
analysis-runner \
--dataset <dataset> \
--description <description> \
--access-level <level> \
--output-dir <directory-within-bucket> \
script_to_run.py with arguments
<level> corresponds to an access level as defined in the storage policies.
<directory-within-bucket> does not contain a prefix like gs://cpg-fewgenomes-main/. For example, if you want your results to be stored in gs://cpg-fewgenomes-main/1kg_pca/v2, specify --output-dir 1kg_pca/v2.
If you provide a --repository, you MUST supply a --commit <SHA>, e.g.:
analysis-runner \
--repository my-approved-repo \
--commit <commit-sha> \
--dataset <dataset> \
--description <description> \
--access-level <level>
--output-dir <directory-within-bucket> \
script_to_run.py with arguments
For more examples (including for running an R script and dataproc), see the examples directory.
GitHub Authentication
If you are submitting an analysis-runner job that needs to clone a private repository owned by populationgenomics on GitHub (eg submitting a script to analysis-runner from a private repository), the analysis-runner should insert the following items into your config.toml:
[infrastructure]
git_credentials_secret_name = '<ask_software_team_for_secret_name>'
git_credentials_secret_project = '<ask_software_team_for_secret_project>'
If you are specifying multiple configuration files, please don't accidentally override these values.
Custom Docker images
The default driver image that's used to run scripts comes with Hail and some statistics libraries preinstalled (see the corresponding Hail Dockerfile). It's possible to use any custom Docker image instead, using the --image parameter. Note that any such image needs to contain the critical dependencies as specified in the base image.
For R scripts, we add the R-tidyverse set of packages to the base image, see the corresponding R Dockerfile and the R example for more details.
Helper for Hail Batch
The analysis-runner package has a number of functions that make it easier to run reproducible analysis through Hail Batch.
This is installed in the analysis runner driver image, ie: you can access the analysis_runner module when running scripts through the analysis-runner.
Checking out a git repository at the current commit
from cpg_utils.hail_batch import get_batch
from analysis_runner.git import (
prepare_git_job,
get_repo_name_from_current_directory,
get_git_commit_ref_of_current_repository,
)
b = get_batch(name='do-some-analysis')
j = b.new_job('checkout_repo')
prepare_git_job(
job=j,
# you could specify a name here, like 'analysis-runner'
repo_name=get_repo_name_from_current_directory(),
# you could specify the specific commit here, eg: '1be7bb44de6182d834d9bbac6036b841f459a11a'
commit=get_git_commit_ref_of_current_repository(),
)
# Now, the working directory of j is the checkout out repository
j.command('examples/bash/hello.sh')
Running a dataproc script
from cpg_utils.hail_batch import get_batch
from analysis_runner.dataproc import setup_dataproc
b = get_batch(name='do-some-analysis')
# starts up a cluster, and submits a script to the cluster,
# see the definition for more information about how you can configure the cluster
# https://github.com/populationgenomics/analysis-runner/blob/main/analysis_runner/dataproc.py#L80
cluster = dataproc.setup_dataproc(
b,
max_age='1h',
packages=['click', 'selenium'],
cluster_name='My Cluster with max-age=1h',
)
cluster.add_job('examples/dataproc/query.py', job_name='example')
Development
You can ignore this section if you just want to run the tool.
To set up a development environment for the analysis runner using pip, run the following:
pip install -r requirements-dev.txt
pip install --editable .
Deployment
The server can be deployed by manually running the deploy_server.yaml GitHub action. This will also deploy the driver image.
The CLI tool is shipped as a pip package, this happens automatically on pushes to main.py. To build a new version, you should add a bump2version commit to your branch.
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