LaminDB: Manage R&D data & analyses.
Project description
LaminDB: Data lake for biology
LaminDB is an API layer for your existing infrastructure to manage your existing data & analyses.
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 that is fully compatible with SQLAlchemy/SQLModel is 0.41.2.
Features
Free:
- Track data lineage across notebooks, pipelines & apps.
- Manage biological registries, ontologies & features.
- Persist, load & stream data objects with a single line of code.
- Query for anything, define & manage custom schemas.
- Manage data on your laptop, server or cloud infra.
- Use a mesh of distributed LaminDB instances for different teams and purposes.
- Share instances through a Hub akin to GitHub.
Enterprise:
- Explore & share data, submit samples & track lineage with LaminApp (deployable in your infra).
- Receive support, code templates & services for a BioTech data & analytics platform.
How does it work?
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. For details, see Architecture.
Installation
pip install lamindb # basic data lake
pip install 'lamindb[bionty]' # biological entities
pip install 'lamindb[nbproject]' # Jupyter notebook tracking
pip install 'lamindb[aws]' # AWS dependencies (s3fs, etc.)
pip install 'lamindb[gcp]' # GCP dependencies (gcfs, etc.)
Quick setup
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 instances (S3 bucket names, etc.).
- Sign up:
lamin signup <email> - Log in:
lamin login <handle> - Init a data lake:
lamin init --storage <default-storage-location>
Usage overview
import lamindb as ln
Store & load data artifacts
Store a DataFrame or an AnnData in default local or cloud storage:
df = pd.DataFrame({"feat1": [1, 2], "feat2": [3, 4]})
ln.File(df, name="My dataframe").save() # create a File object and save it
Get it back:
file = ln.File.select(name="My dataframe").one() # query for it
df = file.load() # load it into memory
Track & query data lineage
ln.File.select(created_by__handle="lizlemon").df() # all files ingested by lizlemon
ln.File.select().order_by("-updated_at").first() # latest updated file
Notebooks
Track a Jupyter Notebook:
ln.track() # auto-detect notebook metadata, save as a Transform, create a Run
ln.File("my_artifact.parquet").save() # this file is an output of the notebook run
When you query this file later on, you'll always know where it came from:
file = ln.File.select(name="my_artifact.parquet").one()
file.transform # gives you the notebook with title, filename, version, id, etc.
file.run # gives you the run of the notebook that created the file
Of course, you can also query for notebooks:
transforms = ln.Transform.select( # all notebooks with 'T cell' in the title created in 2022
name__contains="T cell", type="notebook", created_at__year=2022
).all()
ln.File.select(transform__in=transforms).all() # data artifacts created by these notebooks
Pipelines
To save a pipeline (complementary to workflow tools) to the Transform registry, call
ln.Transform(name="Awesom-O", version="0.41.2").save() # save a pipeline
To track a run of a registered pipeline:
transform = ln.Transform.select(name="Awesom-O", version="0.41.2").one() # select a pipeline from the registry
ln.track(transform) # create a new global run context
ln.File("s3://my_samples01/my_artifact.fastq.gz").save() # link file against run & transform
Now, you can query, e.g., for
ln.Run.select(transform__name="Awesom-O").order_by("-created_at").df() # get the latest pipeline runs
Lookup categoricals with auto-complete
When you're unsure about spellings, use a lookup object:
lookup = ln.Transform.lookup()
ln.Run.select(transform=lookup.awesome_o)
Manage biological registries
lamin init --storage ./myobjects --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.
Notebooks
- Find all guide notebooks here.
- You can run these notebooks in hosted versions of JupyterLab, e.g., Saturn Cloud, Google Vertex AI, and others or on Google Colab.
- Jupyter Lab & Notebook offer a fully interactive experience, VS Code & others require using the CLI (
lamin track my-notebook.ipynb)
Architecture
LaminDB consists of the lamindb Python package, which builds on a number of open-source packages:
- 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.
LaminHub & LaminApp are not open-sourced, neither are templates that model lab operations.
Lamin's packages build on the infrastructure listed above. Previously, they were based on SQLAlchemy/SQLModel instead of Django, and cloudpathlib instead of fsspec.
Documentation
Read the docs.
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