Bird’s eye view

Here, you’ll backtrace file transformations through notebooks, pipelines & app uploads in a research project based on Schmidt22.

It’s a mix of a guide & a demo usecase.

Why should I care about data lineage?

Data lineage enables to trace the origin of biological insights, verify experimental outcomes, meet regulatory standards, and increase the reproducibility & reliability research.

While tracking data lineage is easier when it’s governed by deterministic pipelines, it becomes hard when its governed by interactive human-driven analyses.

This is where LaminDB fills a gap in the tools space.

Setup

We need an instance:

!lamin init --storage ./mydata

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💡 creating schemas: core==0.45.3 
🌱 saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-16 21:59:21)
🌱 saved: Storage(id='IWuJyZVy', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-08-16 21:59:21, created_by_id='DzTjkKse')
✅ loaded instance: testuser1/mydata
💡 did not register local instance on hub (if you want, call `lamin register`)

Import lamindb:

import lamindb as ln

✅ loaded instance: testuser1/mydata (lamindb 0.50.5)

We’ll need toy data:

assert ln.setup.settings.user.handle == "testuser1"
bfx_run_output = ln.dev.datasets.dir_scrnaseq_cellranger(
    "perturbseq", basedir=ln.settings.storage, output_only=False
)
ln.track(ln.Transform(name="Chromium 10x upload", type="pipeline"))
ln.File(bfx_run_output.parent / "fastq/perturbseq_R1_001.fastq.gz").save()
ln.File(bfx_run_output.parent / "fastq/perturbseq_R2_001.fastq.gz").save()
ln.setup.login("testuser2")

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🌱 saved: Transform(id='o8CUeNo7nkprz8', name='Chromium 10x upload', stem_id='o8CUeNo7nkpr', version='0', type='pipeline', updated_at=2023-08-16 21:59:22, created_by_id='DzTjkKse')

🌱 saved: Run(id='7jOHdO6bziRTcnlUK7qX', run_at=2023-08-16 21:59:22, transform_id='o8CUeNo7nkprz8', created_by_id='DzTjkKse')

💡 file in storage '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata' with key 'fastq/perturbseq_R1_001.fastq.gz'

💡 file in storage '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata' with key 'fastq/perturbseq_R2_001.fastq.gz'

✅ logged in with email testuser2@lamin.ai and id bKeW4T6E

🔶 record with similar name exist! did you mean to load it?

	id	__ratio__
name
Test User1	DzTjkKse	90.0

🌱 saved: User(id='bKeW4T6E', handle='testuser2', email='testuser2@lamin.ai', name='Test User2', updated_at=2023-08-16 21:59:23)

Track a bioinformatics pipeline

When working with a pipeline, we’ll register it before running it.

This only happens once and could be done by anyone on your team.

transform = ln.Transform(name="Cell Ranger", version="7.2.0", type="pipeline")

ln.User.filter().df()

	handle	email	name	updated_at
id
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-08-16 21:59:21
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-16 21:59:23

transform

Transform(id='YL2Qjuqj3p5PsM', name='Cell Ranger', stem_id='YL2Qjuqj3p5P', version='7.2.0', type='pipeline', created_by_id='bKeW4T6E')

ln.track(transform)

🌱 saved: Transform(id='YL2Qjuqj3p5PsM', name='Cell Ranger', stem_id='YL2Qjuqj3p5P', version='7.2.0', type='pipeline', updated_at=2023-08-16 21:59:23, created_by_id='bKeW4T6E')

🌱 saved: Run(id='QJqwvGBQVMXPvvbYsl2W', run_at=2023-08-16 21:59:23, transform_id='YL2Qjuqj3p5PsM', created_by_id='bKeW4T6E')

Now, let’s stage a few files from an instrument upload:

files = ln.File.filter(key__startswith="fastq/perturbseq").all()
filepaths = [file.stage() for file in files]

💡 adding file q3cJ3SSDwMFEnMjjlcB3 as input for run QJqwvGBQVMXPvvbYsl2W, adding parent transform o8CUeNo7nkprz8

💡 adding file CqvJ44vzSBWQVSdXIdcX as input for run QJqwvGBQVMXPvvbYsl2W, adding parent transform o8CUeNo7nkprz8

Assume we processed them and obtained 3 output files in a folder 'filtered_feature_bc_matrix':

output_files = ln.File.from_dir("./mydata/perturbseq/filtered_feature_bc_matrix/")
ln.save(output_files)

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✅ created 3 files from directory using storage /home/runner/work/lamin-usecases/lamin-usecases/docs/mydata and key = perturbseq/filtered_feature_bc_matrix/

🌱 storing file 'F4MS8ehlJiBGlFRljNPl' with key 'perturbseq/filtered_feature_bc_matrix/features.tsv.gz'

🌱 storing file 'qlhwSf74ngeLURGwQ9p6' with key 'perturbseq/filtered_feature_bc_matrix/matrix.mtx.gz'

🌱 storing file 'ZOL5CJlojxzMw2TEJGjA' with key 'perturbseq/filtered_feature_bc_matrix/barcodes.tsv.gz'

Let’s look at the data lineage at this stage:

output_files[0].view_lineage()

And let’s keep running the Cell Ranger pipeline in the background.

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transform = ln.Transform(
    name="Preprocess Cell Ranger outputs", version="2.0", type="pipeline"
)
ln.track(transform)
[f.stage() for f in output_files]
filepath = ln.dev.datasets.schmidt22_perturbseq(basedir=ln.settings.storage)
file = ln.File(filepath, description="perturbseq counts")
file.save()

🌱 saved: Transform(id='GlOTy7VbChAJ0b', name='Preprocess Cell Ranger outputs', stem_id='GlOTy7VbChAJ', version='2.0', type='pipeline', updated_at=2023-08-16 21:59:24, created_by_id='bKeW4T6E')

🌱 saved: Run(id='M0L3ec0jJjkt9O0u83Cx', run_at=2023-08-16 21:59:24, transform_id='GlOTy7VbChAJ0b', created_by_id='bKeW4T6E')

💡 adding file F4MS8ehlJiBGlFRljNPl as input for run M0L3ec0jJjkt9O0u83Cx, adding parent transform YL2Qjuqj3p5PsM

💡 adding file qlhwSf74ngeLURGwQ9p6 as input for run M0L3ec0jJjkt9O0u83Cx, adding parent transform YL2Qjuqj3p5PsM

💡 adding file ZOL5CJlojxzMw2TEJGjA as input for run M0L3ec0jJjkt9O0u83Cx, adding parent transform YL2Qjuqj3p5PsM

💡 file in storage '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata' with key 'schmidt22_perturbseq.h5ad'

💡 file is AnnDataLike, consider using File.from_anndata() to link var_names and obs.columns as features

Track app upload & analytics

The hidden cell below simulates additional analytic steps including:

• uploading phenotypic screen data

• scRNA-seq analysis

• analyses of the integrated datasets

Show code cell content Hide code cell content

# app upload
ln.setup.login("testuser1")
transform = ln.Transform(name="Upload GWS CRISPRa result", type="app")
ln.track(transform)

# upload and analyze the GWS data
filepath = ln.dev.datasets.schmidt22_crispra_gws_IFNG(ln.settings.storage)
file = ln.File(filepath, description="Raw data of schmidt22 crispra GWS")
file.save()
ln.setup.login("testuser2")
transform = ln.Transform(name="GWS CRIPSRa analysis", type="notebook")
ln.track(transform)

file_wgs = ln.File.filter(key="schmidt22-crispra-gws-IFNG.csv").one()
df = file_wgs.load().set_index("id")
hits_df = df[df["pos|fdr"] < 0.01].copy()
file_hits = ln.File(hits_df, description="hits from schmidt22 crispra GWS")
file_hits.save()

✅ logged in with email testuser1@lamin.ai and id DzTjkKse

🌱 saved: Transform(id='6LHjNUWqkCa8z8', name='Upload GWS CRISPRa result', stem_id='6LHjNUWqkCa8', version='0', type='app', updated_at=2023-08-16 21:59:26, created_by_id='DzTjkKse')

🌱 saved: Run(id='misq3GBByruI8WTGqOvJ', run_at=2023-08-16 21:59:26, transform_id='6LHjNUWqkCa8z8', created_by_id='DzTjkKse')

💡 file in storage '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata' with key 'schmidt22-crispra-gws-IFNG.csv'

✅ logged in with email testuser2@lamin.ai and id bKeW4T6E

🌱 saved: Transform(id='id9YimYt3pIRz8', name='GWS CRIPSRa analysis', stem_id='id9YimYt3pIR', version='0', type='notebook', updated_at=2023-08-16 21:59:28, created_by_id='bKeW4T6E')

🌱 saved: Run(id='dRLETvJddC64NtIPkIdq', run_at=2023-08-16 21:59:28, transform_id='id9YimYt3pIRz8', created_by_id='bKeW4T6E')

💡 adding file SOSzRwtqj9gnhMF7F0Fq as input for run dRLETvJddC64NtIPkIdq, adding parent transform 6LHjNUWqkCa8z8

💡 file will be copied to default storage upon `save()` with key 'VuUcmkNkygNaQAJcC59i.parquet'

💡 file is a dataframe, consider using File.from_df() to link column names as features

🌱 storing file 'VuUcmkNkygNaQAJcC59i' with key '.lamindb/VuUcmkNkygNaQAJcC59i.parquet'

Let’s see how the data lineage of this looks:

file = ln.File.filter(description="hits from schmidt22 crispra GWS").one()
file.view_lineage()

In the backgound, somebody integrated and analyzed the outputs of the app upload and the Cell Ranger pipeline:

Show code cell content Hide code cell content

# Let us add analytics on top of the cell ranger pipeline and the phenotypic screening
transform = ln.Transform(
    name="Perform single cell analysis, integrating with CRISPRa screen",
    type="notebook",
)
ln.track(transform)

file_ps = ln.File.filter(description__icontains="perturbseq").one()
adata = file_ps.load()
screen_hits = file_hits.load()
import scanpy as sc

sc.tl.score_genes(adata, adata.var_names.intersection(screen_hits.index).tolist())
filesuffix = "_fig1_score-wgs-hits.png"
sc.pl.umap(adata, color="score", show=False, save=filesuffix)
filepath = f"figures/umap{filesuffix}"
file = ln.File(filepath, key=filepath)
file.save()
filesuffix = "fig2_score-wgs-hits-per-cluster.png"
sc.pl.matrixplot(
    adata, groupby="cluster_name", var_names=["score"], show=False, save=filesuffix
)
filepath = f"figures/matrixplot_{filesuffix}"
file = ln.File(filepath, key=filepath)
file.save()

🌱 saved: Transform(id='WcjRp2t7TEuyz8', name='Perform single cell analysis, integrating with CRISPRa screen', stem_id='WcjRp2t7TEuy', version='0', type='notebook', updated_at=2023-08-16 21:59:28, created_by_id='bKeW4T6E')

🌱 saved: Run(id='04IPweATnqEZroiklIYq', run_at=2023-08-16 21:59:28, transform_id='WcjRp2t7TEuyz8', created_by_id='bKeW4T6E')

💡 adding file r0qE8pTcEoF7KQubMSdP as input for run 04IPweATnqEZroiklIYq, adding parent transform GlOTy7VbChAJ0b

💡 adding file VuUcmkNkygNaQAJcC59i as input for run 04IPweATnqEZroiklIYq, adding parent transform id9YimYt3pIRz8

WARNING: saving figure to file figures/umap_fig1_score-wgs-hits.png

💡 file will be copied to default storage upon `save()` with key 'figures/umap_fig1_score-wgs-hits.png'

🌱 storing file 'HHgQhiBtpBL5axw1tQj2' with key 'figures/umap_fig1_score-wgs-hits.png'

WARNING: saving figure to file figures/matrixplot_fig2_score-wgs-hits-per-cluster.png

💡 file will be copied to default storage upon `save()` with key 'figures/matrixplot_fig2_score-wgs-hits-per-cluster.png'

🌱 storing file '9JJTJD1CfBfb4JVCl1W9' with key 'figures/matrixplot_fig2_score-wgs-hits-per-cluster.png'

The outcome of it are a few figures stored as image files. Let’s query one of them and look at the data lineage:

Track notebooks

We’d now like to track the current Jupyter notebook to continue the work:

ln.track()

💡 notebook imports: lamindb==0.50.5 scanpy==1.9.3

🌱 saved: Transform(id='1LCd8kco9lZUz8', name='Bird's eye view', short_name='birds-eye', stem_id='1LCd8kco9lZU', version='0', type=notebook, updated_at=2023-08-16 21:59:30, created_by_id='bKeW4T6E')

🌱 saved: Run(id='iIlHVKcd9Ii3UrKomCV7', run_at=2023-08-16 21:59:30, transform_id='1LCd8kco9lZUz8', created_by_id='bKeW4T6E')

Visualize data lineage

Let’s load one of the plots:

file = ln.File.filter(key__contains="figures/matrixplot").one()
file.stage()

💡 adding file 9JJTJD1CfBfb4JVCl1W9 as input for run iIlHVKcd9Ii3UrKomCV7, adding parent transform WcjRp2t7TEuyz8

PosixPath('/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/figures/matrixplot_fig2_score-wgs-hits-per-cluster.png')

We see that the image file is tracked as an input of the current notebook. The input is highlighted, the notebook follows at the bottom:

file.view_lineage()

Alternatively, we can also purely look at the sequence of transforms and ignore the files:

transform = ln.Transform.search("Bird's eye view", return_queryset=True).first()

transform.parents.df()

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
WcjRp2t7TEuyz8	Perform single cell analysis, integrating with...	None	WcjRp2t7TEuy	0	notebook	None	2023-08-16 21:59:30	bKeW4T6E

transform.view_parents()

Understand runs

We tracked pipeline and notebook runs through run_context, which stores a Transform and a Run record as a global context.

File objects are the inputs and outputs of runs.

What if I don’t want a global context?

Sometimes, we don’t want to create a global run context but manually pass a run when creating a file:

run = ln.Run(transform=transform)
ln.File(filepath, run=run)

When does a file appear as a run input?

When accessing a file via stage(), load() or backed(), two things happen:

1. The current run gets added to file.input_of

2. The transform of that file gets added as a parent of the current transform

You can then switch off auto-tracking of run inputs if you set ln.settings.track_run_inputs = False: Can I disable tracking run inputs?

You can also track run inputs on a case by case basis via is_run_input=True, e.g., here:

file.load(is_run_input=True)

Query by provenance

We can query or search for the notebook that created the file:

transform = ln.Transform.search("GWS CRIPSRa analysis", return_queryset=True).first()

And then find all the files created by that notebook:

ln.File.filter(transform=transform).df()

	storage_id	key	suffix	accessor	description	version	initial_version_id	size	hash	hash_type	transform_id	run_id	updated_at	created_by_id
id
VuUcmkNkygNaQAJcC59i	IWuJyZVy	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	yw5f-kMLJhaNhdEF-lhxOQ	md5	id9YimYt3pIRz8	dRLETvJddC64NtIPkIdq	2023-08-16 21:59:28	bKeW4T6E

Which transform ingested a given file?

file = ln.File.filter().first()
file.transform

Transform(id='o8CUeNo7nkprz8', name='Chromium 10x upload', stem_id='o8CUeNo7nkpr', version='0', type='pipeline', updated_at=2023-08-16 21:59:22, created_by_id='DzTjkKse')

And which user?

file.created_by

User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-16 21:59:26)

Which transforms were created by a given user?

users = ln.User.lookup()

ln.Transform.filter(created_by=users.testuser2).df()

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
YL2Qjuqj3p5PsM	Cell Ranger	None	YL2Qjuqj3p5P	7.2.0	pipeline	None	2023-08-16 21:59:23	bKeW4T6E
GlOTy7VbChAJ0b	Preprocess Cell Ranger outputs	None	GlOTy7VbChAJ	2.0	pipeline	None	2023-08-16 21:59:25	bKeW4T6E
id9YimYt3pIRz8	GWS CRIPSRa analysis	None	id9YimYt3pIR	0	notebook	None	2023-08-16 21:59:28	bKeW4T6E
WcjRp2t7TEuyz8	Perform single cell analysis, integrating with...	None	WcjRp2t7TEuy	0	notebook	None	2023-08-16 21:59:30	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	1LCd8kco9lZU	0	notebook	None	2023-08-16 21:59:30	bKeW4T6E

Which notebooks were created by a given user?

ln.Transform.filter(created_by=users.testuser2, type="notebook").df()

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
id9YimYt3pIRz8	GWS CRIPSRa analysis	None	id9YimYt3pIR	0	notebook	None	2023-08-16 21:59:28	bKeW4T6E
WcjRp2t7TEuyz8	Perform single cell analysis, integrating with...	None	WcjRp2t7TEuy	0	notebook	None	2023-08-16 21:59:30	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	1LCd8kco9lZU	0	notebook	None	2023-08-16 21:59:30	bKeW4T6E

We can also view all recent additions to the entire database:

ln.view()

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File

	storage_id	key	suffix	accessor	description	version	initial_version_id	size	hash	hash_type	transform_id	run_id	updated_at	created_by_id
id
9JJTJD1CfBfb4JVCl1W9	IWuJyZVy	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	None	28814	JYIPcat0YWYVCX3RVd3mww	md5	WcjRp2t7TEuyz8	04IPweATnqEZroiklIYq	2023-08-16 21:59:30	bKeW4T6E
HHgQhiBtpBL5axw1tQj2	IWuJyZVy	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	None	118999	laQjVk4gh70YFzaUyzbUNg	md5	WcjRp2t7TEuyz8	04IPweATnqEZroiklIYq	2023-08-16 21:59:29	bKeW4T6E
VuUcmkNkygNaQAJcC59i	IWuJyZVy	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	yw5f-kMLJhaNhdEF-lhxOQ	md5	id9YimYt3pIRz8	dRLETvJddC64NtIPkIdq	2023-08-16 21:59:28	bKeW4T6E
SOSzRwtqj9gnhMF7F0Fq	IWuJyZVy	schmidt22-crispra-gws-IFNG.csv	.csv	None	Raw data of schmidt22 crispra GWS	None	None	1729685	cUSH0oQ2w-WccO8_ViKRAQ	md5	6LHjNUWqkCa8z8	misq3GBByruI8WTGqOvJ	2023-08-16 21:59:26	DzTjkKse
r0qE8pTcEoF7KQubMSdP	IWuJyZVy	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	GlOTy7VbChAJ0b	M0L3ec0jJjkt9O0u83Cx	2023-08-16 21:59:25	bKeW4T6E
ZOL5CJlojxzMw2TEJGjA	IWuJyZVy	perturbseq/filtered_feature_bc_matrix/barcodes...	.tsv.gz	None	None	None	None	6	4c9GlDhpkQHxUfapmIfHZA	md5	YL2Qjuqj3p5PsM	QJqwvGBQVMXPvvbYsl2W	2023-08-16 21:59:23	bKeW4T6E
qlhwSf74ngeLURGwQ9p6	IWuJyZVy	perturbseq/filtered_feature_bc_matrix/matrix.m...	.mtx.gz	None	None	None	None	6	h5C1MAwIZs8TohL2IiTQLw	md5	YL2Qjuqj3p5PsM	QJqwvGBQVMXPvvbYsl2W	2023-08-16 21:59:23	bKeW4T6E
F4MS8ehlJiBGlFRljNPl	IWuJyZVy	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	None	6	fLIi4pGVmfs4RNdVj4ZQfw	md5	YL2Qjuqj3p5PsM	QJqwvGBQVMXPvvbYsl2W	2023-08-16 21:59:23	bKeW4T6E
CqvJ44vzSBWQVSdXIdcX	IWuJyZVy	fastq/perturbseq_R2_001.fastq.gz	.fastq.gz	None	None	None	None	6	8uh99YnFmsM1K32v2DZlwA	md5	o8CUeNo7nkprz8	7jOHdO6bziRTcnlUK7qX	2023-08-16 21:59:22	DzTjkKse
q3cJ3SSDwMFEnMjjlcB3	IWuJyZVy	fastq/perturbseq_R1_001.fastq.gz	.fastq.gz	None	None	None	None	6	Hob7PrSo6P4cTrzDAZlcpQ	md5	o8CUeNo7nkprz8	7jOHdO6bziRTcnlUK7qX	2023-08-16 21:59:22	DzTjkKse

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
7jOHdO6bziRTcnlUK7qX	o8CUeNo7nkprz8	2023-08-16 21:59:22	DzTjkKse	None	None
QJqwvGBQVMXPvvbYsl2W	YL2Qjuqj3p5PsM	2023-08-16 21:59:23	bKeW4T6E	None	None
M0L3ec0jJjkt9O0u83Cx	GlOTy7VbChAJ0b	2023-08-16 21:59:24	bKeW4T6E	None	None
misq3GBByruI8WTGqOvJ	6LHjNUWqkCa8z8	2023-08-16 21:59:26	DzTjkKse	None	None
dRLETvJddC64NtIPkIdq	id9YimYt3pIRz8	2023-08-16 21:59:28	bKeW4T6E	None	None
04IPweATnqEZroiklIYq	WcjRp2t7TEuyz8	2023-08-16 21:59:28	bKeW4T6E	None	None
iIlHVKcd9Ii3UrKomCV7	1LCd8kco9lZUz8	2023-08-16 21:59:30	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
IWuJyZVy	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-08-16 21:59:21	DzTjkKse

Transform

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
1LCd8kco9lZUz8	Bird's eye view	birds-eye	1LCd8kco9lZU	0	notebook	None	2023-08-16 21:59:30	bKeW4T6E
WcjRp2t7TEuyz8	Perform single cell analysis, integrating with...	None	WcjRp2t7TEuy	0	notebook	None	2023-08-16 21:59:30	bKeW4T6E
id9YimYt3pIRz8	GWS CRIPSRa analysis	None	id9YimYt3pIR	0	notebook	None	2023-08-16 21:59:28	bKeW4T6E
6LHjNUWqkCa8z8	Upload GWS CRISPRa result	None	6LHjNUWqkCa8	0	app	None	2023-08-16 21:59:26	DzTjkKse
GlOTy7VbChAJ0b	Preprocess Cell Ranger outputs	None	GlOTy7VbChAJ	2.0	pipeline	None	2023-08-16 21:59:25	bKeW4T6E
YL2Qjuqj3p5PsM	Cell Ranger	None	YL2Qjuqj3p5P	7.2.0	pipeline	None	2023-08-16 21:59:23	bKeW4T6E
o8CUeNo7nkprz8	Chromium 10x upload	None	o8CUeNo7nkpr	0	pipeline	None	2023-08-16 21:59:22	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-16 21:59:28
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-08-16 21:59:26

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!lamin login testuser1
!lamin delete --force mydata
!rm -r ./mydata

✅ logged in with email testuser1@lamin.ai and id DzTjkKse

💡 deleting instance testuser1/mydata
✅     deleted instance settings file: /home/runner/.lamin/instance--testuser1--mydata.env

✅     instance cache deleted
✅     deleted '.lndb' sqlite file
🔶     consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/mydata