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[IBCDPE-749] Clarify points in documentation (#36)
* Update README.md and .env.example
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.env.example

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# IMPORTANT: The ':' and '@' characters before and after
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# the access token are required. Do not omit them.
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#
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# Examples:
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# Structure:
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# export SEVENBRIDGES_CONNECTION_URI=sbg://:<access-token>@<api-base-endpoint>[/?project=<project-id>]
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# Examples:
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# export SEVENBRIDGES_CONNECTION_URI=sbg://:f560[...]bf9d@cavatica-api.sbgenomics.com/v2
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# export SEVENBRIDGES_CONNECTION_URI=sbg://:f560[...]bf9d@cavatica-api.sbgenomics.com/v2/?project=bgrande/sandbox
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# IMPORTANT: The ':' and '@' characters before and after
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# the access token are required. Do not omit them.
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#
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# Examples:
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# Structure:
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# export NEXTFLOWTOWER_CONNECTION_URI=tower://:<access-token>@<api-base-endpoint>[/?workspace=<organization-name>/<workspace-name>]
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# Examples:
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# export NEXTFLOWTOWER_CONNECTION_URI=tower://:eyJ0[...]MA==@api.tower.nf
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# export NEXTFLOWTOWER_CONNECTION_URI=tower://:eyJ0[...]MA==@tower.sagebionetworks.org/api/?workspace=sage-bionetworks/example-project
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# export NEXTFLOWTOWER_CONNECTION_URI=tower://:eyJ0[...]MA==@tower-dev.sagebionetworks.org/api/?workspace=sage-bionetworks/example-dev-project

README.md

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This repository includes a demonstration script called [`demo.py`](demo.py), which showcases how you can use `py-orca` to launch and monitor your workflows on Nextflow Tower. Specifically, it illustrates how to process an RNA-seq dataset using a series of workflow runs, namely `nf-synstage`, `nf-core/rnaseq`, and `nf-synindex`. `py-orca` can be used with any Python-compatible workflow management system to orchestrate each step (_e.g._ Airflow, Prefect, Dagster). The demonstration script uses [Metaflow](https://metaflow.org/) because it's easy to run locally and has an intuitive syntax.
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The script assumes that the following environment variables are set.
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Refer to [`.env.example`](.env.example) for the format of their values as well as examples. You can set these environment variables using whatever method you prefer (_e.g._ using an `.env` file, sourcing a shell script).
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The script assumes that the following environment variables are set. Before setting them up, ensure that you have an AWS profile configured for a role that has access to the dev/ops tower workspace you plan to launch your workflows from. You can set these environment variables using whatever method you prefer (_e.g._ using an `.env` file, sourcing a shell script, etc).
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Refer to [`.env.example`](.env.example) for the format of their values as well as examples.
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- `NEXTFLOWTOWER_CONNECTION_URI`
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- `SYNAPSE_CONNECTION_URI`
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- `AWS_PROFILE` (or another source of AWS credentials)
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Once your environment is set, you can create a virtual environment, install the Python dependencies, and run the demonstration script (after downloading it) as follows. Note that you will need to update the `s3_prefix` parameter so that it points to an S3 bucket that is accessible to your Tower workspace.
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### Manually creating a virtual environment
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### Creating and setting up your py-`orca` virtual environment and executing `demo.py`
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Below are the instructions for creating and setting up your virtual environment and executing the `demo.py`. If you would like to set up a developer environment with the relevant dependencies, you can execute the shell script [dev_setup](https://github.com/Sage-Bionetworks-Workflows/py-orca/blob/main/dev_setup.sh) in a clone of this repository stored on your machine.
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```bash
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# Create and activate a Python virtual environment (tested with Python 3.10)
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python3 -m venv venv/

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