LaminDB: Manage R&D data & analyses.
Project description
LaminDB
Open-source data lake & feature store for biology.
Public beta: Currently only recommended for collaborators as we still make breaking changes.
Update 2023-06-14:
- We completed a major migration from SQLAlchemy/SQLModel to Django, available in 0.42.0.
- The last version before the migration is 0.41.2.
Introduction
LaminDB is an open-source Python library to:
- Manage files & datasets while tracking provenance across pipelines, notebooks & app uploads.
- Manage biological registries, ontologies, features & schemas.
- Enhance integrity through built-in data validation and idempotent, ACID operations.
- Query, search, look up, save, load and stream with one API.
- Collaborate across a mesh of LaminDB instances.
LaminApp is a data management app built on LaminDB. If LaminDB ~ git, LaminApp ~ GitHub.
LaminApp, support, code templates & auto-dispatched integration tests for a BioTech data & analytics platform are currently only available on an enterprise plan. LaminApp is available for your cloud infrastructure or hosted by us.
Quickstart
Installation and sign-up take no time: Run pip install lamindb and lamin signup <email> on the command line.
Then, init a LaminDB instance with local or cloud default storage like you'd init a git repository:
$ lamin init --storage ./mydata # or s3://my-bucket, gs://my-bucket
Import lamindb:
import lamindb as ln
Manage data objects
Store a DataFrame object:
df = pd.DataFrame({"feat1": [1, 2], "feat2": [3, 4]}) # AnnData works, too
ln.File(df, description="Data batch 1").save() # create a File object and save/upload it
If you don't have specific metadata in mind, run a search:
ln.File.search("batch 1")
You have the full power of SQL to query for metadata, but the simplest query for a file is:
file = ln.File.select(description="Data batch 1").one() # get exactly one result
Once you queried or searched it, load a file back into memory:
df = file.load()
Or get a backed accessor to stream its content from the cloud:
backed = file.backed() # currently works for AnnData, zarr, HDF5, not yet for DataFrame
Manage files
The same API works for any file:
file = ln.File("s3://my-bucket/images/image001.jpg") # or a local path
file.save() # register the file
Query by key (the relative path within your storage):
file.select(key__startswith="images/").df() # all files in folder "images/" in default storage
Auto-complete categoricals
When you're unsure about spellings, use a lookup object:
users = ln.User.lookup()
ln.File.select(created_by=users.lizlemon)
Track & query data lineage
In addition to basic provenance information (created_by, created_at,
created_by), you can track which notebooks & pipelines
transformed files.
Notebooks
Track a Jupyter Notebook:
ln.track() # auto-detect & save notebook metadata
ln.File("my_artifact.parquet").save() # this file is now aware that it was saved in this notebook
When you query the file, later on, you'll know from which notebook it came:
file = ln.File.select(description="my_artifact.parquet").one() # query for a file
file.transform # the notebook with id, title, filename, version, etc.
file.run # the specific run of the notebook that created the file
Alternatively, you can query for notebooks and find the files written by them:
transforms = ln.Transform.select(type="notebook", created_at__year=2022).search("T cell").all()
ln.File.select(transform__in=transforms).df() # the files created by these notebooks
Pipelines
This works like for notebooks just that you need to provide pipeline metadata yourself.
To save a pipeline to the Transform registry, call
ln.Transform(name="Awesom-O", version="0.41.2").save() # save a pipeline, optionally with metadata
Track a pipeline run:
transform = ln.Transform.select(name="Awesom-O", version="0.41.2").one() # select pipeline from the registry
ln.track(transform) # create a new global run context
ln.File("s3://my_samples01/my_artifact.fastq.gz").save() # file gets auto-linked against run & transform
Now, you can query for the latest pipeline runs:
ln.Run.select(transform=transform).order_by("-created_at").df() # get the latest pipeline runs
Load your instance from anywhere
If provided with access, others can load your instance via:
$ lamin load myaccount/mydata
Manage biological registries
lamin init --storage ./bioartifacts --schema bionty
...
Track biological features
...
Track biological samples
...
Manage custom schemas
- Create a GitHub repository with Django ORMs similar to github.com/laminlabs/lnschema-lamin1
- Create & deploy migrations via
lamin migrate createandlamin migrate deploy
It's fastest if we do this for you based on our templates within an enterprise plan, but you can fully manage the process yourself.
Setup
Installation
pip install lamindb # basic data management
You can configure the installation using extras, e.g.,
pip install 'lamindb[jupyter,bionty,fcs,aws]'
Supported extras are:
jupyter # Track Jupyter notebooks
bionty # Manage basic biological entities
fcs # Manage .fcs files (flow cytometry)
zarr # Store & stream arrays with zarr
aws # AWS (s3fs, etc.)
gcp # Google Cloud (gcfs, etc.)
postgres # Postgres server
Docker
Here is a way of running LaminDB in a docker: github.com/laminlabs/lamindb-docker.
Sign up
Why do I have to sign up?
- Data lineage requires a user identity (who modified which data when?).
- Collaboration requires a user identity (who shares this with me?).
Signing up takes 1 min.
We do not store any of your data, but only basic metadata about you (email address, etc.) & your LaminDB instances (S3 bucket names, etc.).
- Sign up:
lamin signup <email> - Log in:
lamin login <handle>
How does it work?
Dependencies
LaminDB builds semantics of R&D and biology onto well-established tools:
- SQLite & Postgres for SQL databases using Django ORM (previously: SQLModel)
- S3, GCP & local storage for object storage using fsspec
- Configurable storage formats: pyarrow, anndata, zarr, etc.
- Biological knowledge sources & ontologies: see Bionty
LaminDB is open source.
Architecture
LaminDB consists of the lamindb Python package (repository here) with its components:
- bionty: Basic biological entities (usable standalone).
- lamindb-setup: Setup & configure LaminDB, client for Lamin Hub.
- lnschema-core: Core schema, ORMs to model data objects & data lineage.
- lnschema-bionty: Bionty schema, ORMs that are coupled to Bionty's entities.
- lnschema-lamin1: Exemplary configured schema to track samples, treatments, etc.
- nbproject: Parse metadata from Jupyter notebooks.
- lamin-utils: Utilities for LaminDB and Bionty.
- readfcs: FCS file reader.
LaminHub & LaminApp are not open-sourced, and neither are templates that model lab operations.
Notebooks
- Find all guide notebooks here.
- You can run these notebooks in hosted versions of JupyterLab, e.g., Saturn Cloud, Google Vertex AI, Google Colab, and others.
- Jupyter Lab & Notebook offer a fully interactive experience, VS Code & others require using the CLI to track notebooks:
lamin track my-notebook.ipynb
Documentation
Read the docs.
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