Track notebooks, scripts & functions

For tracking pipelines, see: Pipelines – workflow managers.

# pip install lamindb
!lamin init --storage ./test-track

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→ initialized lamindb: testuser1/test-track

Track a notebook or script

Call track() to register your notebook or script as a transform and start capturing inputs & outputs of a run.

import lamindb as ln

ln.track()  # initiate a tracked notebook/script run

# your code automatically tracks inputs & outputs

ln.finish()  # mark run as finished, save execution report, source code & environment

Here is how a notebook with run report looks on the hub.

Explore it here.

You find your notebooks and scripts in the Transform registry (along with pipelines & functions). Run stores executions. You can use all usual ways of querying to obtain one or multiple transform records, e.g.:

transform = ln.Transform.get(key="my_analyses/my_notebook.ipynb")
transform.source_code  # source code
transform.runs  # all runs
transform.latest_run.report  # report of latest run
transform.latest_run.environment  # environment of latest run

To load a notebook or script from the hub, search or filter the transform page and use the CLI.

lamin load https://lamin.ai/laminlabs/lamindata/transform/13VINnFk89PE

Use nested paths

If no working directory is set for LaminDB, script & notebooks keys equal their filenames. If you configure a working directory, script & notebooks keys equal the relative path in the working directory.

To set the working directory to your current shell working directory, run:

lamin settings set work-dir .

You can see the current status by running:

lamin info

The default is that no working directory is set.

Use projects

You can link the entities created during a run to a project.

import lamindb as ln

my_project = ln.Project(name="My project").save()  # create a project

ln.track(project="My project")  # auto-link entities to "My project"

ln.Artifact(
    ln.examples.datasets.file_fcs(), key="my_file.fcs"
).save()  # save an artifact

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→ connected lamindb: testuser1/test-track

→ created Transform('MUdUquLQhLyh0000', key='track.ipynb'), started new Run('I5Ool2E4Y9csOlos') at 2025-10-16 13:19:20 UTC

→ notebook imports: lamindb==1.13.1

• recommendation: to identify the notebook across renames, pass the uid: ln.track("MUdUquLQhLyh", project="My project")

Artifact(uid='tn8M7n0QZnQn7qLT0000', is_latest=True, key='my_file.fcs', suffix='.fcs', size=19330507, hash='rCPvmZB19xs4zHZ7p_-Wrg', branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-10-16 13:19:25 UTC, is_locked=False)

Filter entities by project, e.g., artifacts:

ln.Artifact.filter(projects=my_project).to_dataframe()

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	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	is_locked	run_id	created_at	created_by_id	_aux	_real_key	branch_id
id
1	tn8M7n0QZnQn7qLT0000	my_file.fcs	None	.fcs	None	None	19330507	rCPvmZB19xs4zHZ7p_-Wrg	None	None	md5	True	False	1	1	None	None	True	False	1	2025-10-16 13:19:25.953000+00:00	1	{'af': {'0': True}}	None	1

Access entities linked to a project.

display(my_project.artifacts.to_dataframe())
display(my_project.transforms.to_dataframe())
display(my_project.runs.to_dataframe())

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	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	is_locked	run_id	created_at	created_by_id	_aux	_real_key	branch_id
id
1	tn8M7n0QZnQn7qLT0000	my_file.fcs	None	.fcs	None	None	19330507	rCPvmZB19xs4zHZ7p_-Wrg	None	None	md5	True	False	1	1	None	None	True	False	1	2025-10-16 13:19:25.953000+00:00	1	{'af': {'0': True}}	None	1

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	is_locked	created_at	created_by_id	_aux	branch_id
id
1	MUdUquLQhLyh0000	track.ipynb	Track notebooks, scripts & functions	notebook	None	None	None	None	1	None	None	True	False	2025-10-16 13:19:20.537000+00:00	1	None	1

	uid	name	started_at	finished_at	params	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	is_locked	created_at	created_by_id	_aux	branch_id
id
1	I5Ool2E4Y9csOlos	None	2025-10-16 13:19:20.547205+00:00	None	None	None	None	None	-1	1	1	None	None	None	None	False	2025-10-16 13:19:20.548000+00:00	1	None	1

Use spaces

You can write the entities created during a run into a space that you configure on LaminHub. This is particularly useful if you want to restrict access to a space. Note that this doesn’t affect bionty entities who should typically be commonly accessible.

ln.track(space="Our team space")

Track parameters

In addition to tracking source code, run reports & environments, you can track run parameters.

Track run parameters

Let’s look at the following script, which has a few parameters.

run_track_with_params.py

import argparse
import lamindb as ln

if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument("--input-dir", type=str)
    p.add_argument("--downsample", action="store_true")
    p.add_argument("--learning-rate", type=float)
    args = p.parse_args()
    params = {
        "input_dir": args.input_dir,
        "learning_rate": args.learning_rate,
        "preprocess_params": {
            "downsample": args.downsample,
            "normalization": "the_good_one",
        },
    }
    ln.track(params=params)

    # your code

    ln.finish()

Run the script.

!python scripts/run_track_with_params.py  --input-dir ./mydataset --learning-rate 0.01 --downsample

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→ connected lamindb: testuser1/test-track

→ created Transform('s6TQmexGMTBc0000', key='run_track_with_params.py'), started new Run('HI5EsMupjY3Fhx86') at 2025-10-16 13:19:29 UTC
→ params: input_dir=./mydataset, learning_rate=0.01, preprocess_params={'downsample': True, 'normalization': 'the_good_one'}
• recommendation: to identify the script across renames, pass the uid: ln.track("s6TQmexGMTBc", params={...})

Query by run parameters

Query for all runs that match a certain parameters:

import lamindb as ln

ln.Run.filter(
    params__learning_rate=0.01,
    params__input_dir="./mydataset",
    params__preprocess_params__downsample=True,
).to_dataframe()

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	uid	name	started_at	finished_at	params	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	is_locked	created_at	created_by_id	_aux	branch_id
id
2	HI5EsMupjY3Fhx86	None	2025-10-16 13:19:29.134250+00:00	2025-10-16 13:19:30.414785+00:00	{'input_dir': './mydataset', 'learning_rate': ...	None	None	True	0	1	2	3	None	2	None	False	2025-10-16 13:19:29.135000+00:00	1	None	1

Note that preprocess_params__downsample=True traverses the dictionary preprocess_params to find the key "downsample" and match it to True.

Access parameters of a run

Below is how you get the parameter values that were used for a given run.

run = ln.Run.filter(params__learning_rate=0.01).order_by("-started_at").first()
run.params

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{'input_dir': './mydataset',
 'learning_rate': 0.01,
 'preprocess_params': {'downsample': True, 'normalization': 'the_good_one'}}

Track functions

If you want more-fined-grained data lineage tracking, use the tracked() decorator.

@ln.tracked()
def subset_dataframe(
    input_artifact_key: str,
    output_artifact_key: str,
    subset_rows: int = 2,
    subset_cols: int = 2,
) -> None:
    artifact = ln.Artifact.get(key=input_artifact_key)
    dataset = artifact.load()
    new_data = dataset.iloc[:subset_rows, :subset_cols]
    ln.Artifact.from_dataframe(new_data, key=output_artifact_key).save()

Prepare a test dataset:

df = ln.examples.datasets.mini_immuno.get_dataset1(otype="DataFrame")
input_artifact_key = "my_analysis/dataset.parquet"
artifact = ln.Artifact.from_dataframe(df, key=input_artifact_key).save()

Run the function with default params:

ouput_artifact_key = input_artifact_key.replace(".parquet", "_subsetted.parquet")
subset_dataframe(input_artifact_key, ouput_artifact_key)

Query for the output:

subsetted_artifact = ln.Artifact.get(key=ouput_artifact_key)
subsetted_artifact.view_lineage()

This is the run that created the subsetted_artifact:

subsetted_artifact.run

Run(uid='BbfyvFkP2kndXvyO', started_at=2025-10-16 13:19:31 UTC, finished_at=2025-10-16 13:19:31 UTC, params={'input_artifact_key': 'my_analysis/dataset.parquet', 'output_artifact_key': 'my_analysis/dataset_subsetted.parquet', 'subset_rows': 2, 'subset_cols': 2}, branch_id=1, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=1, created_at=2025-10-16 13:19:31 UTC, is_locked=False)

This is the function that created it:

subsetted_artifact.run.transform

Transform(uid='cZ3CfclU5GWZ0000', is_latest=True, key='track.ipynb/subset_dataframe.py', type='function', hash='CUqkJpolJY1Q1tqyCoWIWg', branch_id=1, space_id=1, created_by_id=1, created_at=2025-10-16 13:19:31 UTC, is_locked=False)

This is the source code of this function:

subsetted_artifact.run.transform.source_code

'@ln.tracked()\ndef subset_dataframe(\n    input_artifact_key: str,\n    output_artifact_key: str,\n    subset_rows: int = 2,\n    subset_cols: int = 2,\n) -> None:\n    artifact = ln.Artifact.get(key=input_artifact_key)\n    dataset = artifact.load()\n    new_data = dataset.iloc[:subset_rows, :subset_cols]\n    ln.Artifact.from_dataframe(new_data, key=output_artifact_key).save()\n'

These are all versions of this function:

subsetted_artifact.run.transform.versions.to_dataframe()

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	is_locked	created_at	created_by_id	_aux	branch_id
id
3	cZ3CfclU5GWZ0000	track.ipynb/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n inpu...	CUqkJpolJY1Q1tqyCoWIWg	None	None	1	None	None	True	False	2025-10-16 13:19:31.040000+00:00	1	None	1

This is the initating run that triggered the function call:

subsetted_artifact.run.initiated_by_run

Run(uid='I5Ool2E4Y9csOlos', started_at=2025-10-16 13:19:20 UTC, branch_id=1, space_id=1, transform_id=1, created_by_id=1, created_at=2025-10-16 13:19:20 UTC, is_locked=False)

This is the transform of the initiating run:

subsetted_artifact.run.initiated_by_run.transform

Transform(uid='MUdUquLQhLyh0000', is_latest=True, key='track.ipynb', description='Track notebooks, scripts & functions', type='notebook', branch_id=1, space_id=1, created_by_id=1, created_at=2025-10-16 13:19:20 UTC, is_locked=False)

These are the parameters of the run:

subsetted_artifact.run.params

{'input_artifact_key': 'my_analysis/dataset.parquet',
 'output_artifact_key': 'my_analysis/dataset_subsetted.parquet',
 'subset_rows': 2,
 'subset_cols': 2}

These are the input artifacts:

subsetted_artifact.run.input_artifacts.to_dataframe()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	is_locked	run_id	created_at	created_by_id	_aux	_real_key	branch_id
id
4	g1I5yA7nbdpSDTAz0000	my_analysis/dataset.parquet	None	.parquet	dataset	DataFrame	9868	wvfEBPwHL3XHiAb-o8fU6Q	None	3	md5	True	False	1	1	None	None	True	False	1	2025-10-16 13:19:31.013000+00:00	1	{'af': {'0': True}}	None	1

These are output artifacts:

subsetted_artifact.run.output_artifacts.to_dataframe()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	is_locked	run_id	created_at	created_by_id	_aux	_real_key	branch_id
id
5	Vft4wxzeOKEoX9GX0000	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3238	UM8d9C-x_2fbc_46BScp8A	None	2	md5	True	False	1	1	None	None	True	False	3	2025-10-16 13:19:31.066000+00:00	1	{'af': {'0': True}}	None	1

Re-run the function with a different parameter:

subsetted_artifact = subset_dataframe(
    input_artifact_key, ouput_artifact_key, subset_cols=3
)
subsetted_artifact = ln.Artifact.get(key=ouput_artifact_key)
subsetted_artifact.view_lineage()

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→ creating new artifact version for key='my_analysis/dataset_subsetted.parquet' (storage: '/home/runner/work/lamindb/lamindb/docs/test-track')

We created a new run:

subsetted_artifact.run

Run(uid='M3MRl3o07mu4JaV8', started_at=2025-10-16 13:19:32 UTC, finished_at=2025-10-16 13:19:32 UTC, params={'input_artifact_key': 'my_analysis/dataset.parquet', 'output_artifact_key': 'my_analysis/dataset_subsetted.parquet', 'subset_rows': 2, 'subset_cols': 3}, branch_id=1, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=1, created_at=2025-10-16 13:19:32 UTC, is_locked=False)

With new parameters:

subsetted_artifact.run.params

{'input_artifact_key': 'my_analysis/dataset.parquet',
 'output_artifact_key': 'my_analysis/dataset_subsetted.parquet',
 'subset_rows': 2,
 'subset_cols': 3}

And a new version of the output artifact:

subsetted_artifact.run.output_artifacts.to_dataframe()

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	is_locked	run_id	created_at	created_by_id	_aux	_real_key	branch_id
id
6	Vft4wxzeOKEoX9GX0001	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3852	7WGuLVamVyBMhPb2qRE_tA	None	2	md5	True	False	1	1	None	None	True	False	4	2025-10-16 13:19:32.488000+00:00	1	{'af': {'0': True}}	None	1

See the state of the database:

ln.view()

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Artifact

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	is_locked	run_id	created_at	created_by_id	_aux	_real_key	branch_id
id
1	tn8M7n0QZnQn7qLT0000	my_file.fcs	None	.fcs	None	None	19330507	rCPvmZB19xs4zHZ7p_-Wrg	None	NaN	md5	True	False	1	1	None	None	True	False	1	2025-10-16 13:19:25.953000+00:00	1	{'af': {'0': True}}	None	1
4	g1I5yA7nbdpSDTAz0000	my_analysis/dataset.parquet	None	.parquet	dataset	DataFrame	9868	wvfEBPwHL3XHiAb-o8fU6Q	None	3.0	md5	True	False	1	1	None	None	True	False	1	2025-10-16 13:19:31.013000+00:00	1	{'af': {'0': True}}	None	1
5	Vft4wxzeOKEoX9GX0000	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3238	UM8d9C-x_2fbc_46BScp8A	None	2.0	md5	True	False	1	1	None	None	False	False	3	2025-10-16 13:19:31.066000+00:00	1	{'af': {'0': True}}	None	1
6	Vft4wxzeOKEoX9GX0001	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3852	7WGuLVamVyBMhPb2qRE_tA	None	2.0	md5	True	False	1	1	None	None	True	False	4	2025-10-16 13:19:32.488000+00:00	1	{'af': {'0': True}}	None	1

Project

	uid	name	description	is_type	abbr	url	start_date	end_date	_status_code	space_id	type_id	is_locked	run_id	created_at	created_by_id	_aux	branch_id
id
1	b20yzsJzsS7G	My project	None	False	None	None	None	None	0	1	None	False	None	2025-10-16 13:19:19.262000+00:00	1	None	1

Run

	uid	name	started_at	finished_at	params	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	is_locked	created_at	created_by_id	_aux	branch_id
id
1	I5Ool2E4Y9csOlos	None	2025-10-16 13:19:20.547205+00:00	NaT	None	None	None	None	-1	1	1	NaN	None	NaN	NaN	False	2025-10-16 13:19:20.548000+00:00	1	None	1
2	HI5EsMupjY3Fhx86	None	2025-10-16 13:19:29.134250+00:00	2025-10-16 13:19:30.414785+00:00	{'input_dir': './mydataset', 'learning_rate': ...	None	None	True	0	1	2	3.0	None	2.0	NaN	False	2025-10-16 13:19:29.135000+00:00	1	None	1
3	BbfyvFkP2kndXvyO	None	2025-10-16 13:19:31.044637+00:00	2025-10-16 13:19:31.075403+00:00	{'input_artifact_key': 'my_analysis/dataset.pa...	None	None	None	0	1	3	NaN	None	NaN	1.0	False	2025-10-16 13:19:31.045000+00:00	1	None	1
4	M3MRl3o07mu4JaV8	None	2025-10-16 13:19:32.464498+00:00	2025-10-16 13:19:32.497155+00:00	{'input_artifact_key': 'my_analysis/dataset.pa...	None	None	None	0	1	3	NaN	None	NaN	1.0	False	2025-10-16 13:19:32.465000+00:00	1	None	1

Storage

	uid	root	description	type	region	instance_uid	space_id	is_locked	run_id	created_at	created_by_id	_aux	branch_id
id
1	2vQ99VsjaU1N	/home/runner/work/lamindb/lamindb/docs/test-track	None	local	None	73KPGC58ahU9	1	False	None	2025-10-16 13:19:15.693000+00:00	1	None	1

Transform

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	is_locked	created_at	created_by_id	_aux	branch_id
id
1	MUdUquLQhLyh0000	track.ipynb	Track notebooks, scripts & functions	notebook	None	None	None	None	1	None	None	True	False	2025-10-16 13:19:20.537000+00:00	1	None	1
2	s6TQmexGMTBc0000	run_track_with_params.py	None	script	import argparse\nimport lamindb as ln\n\nif __...	5RBz7zJICeKE1OSmg7gEdQ	None	None	1	None	None	True	False	2025-10-16 13:19:29.131000+00:00	1	None	1
3	cZ3CfclU5GWZ0000	track.ipynb/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n inpu...	CUqkJpolJY1Q1tqyCoWIWg	None	None	1	None	None	True	False	2025-10-16 13:19:31.040000+00:00	1	None	1

In a script

run_workflow.py

import argparse
import lamindb as ln


@ln.tracked()
def subset_dataframe(
    artifact: ln.Artifact,
    subset_rows: int = 2,
    subset_cols: int = 2,
    run: ln.Run | None = None,
) -> ln.Artifact:
    dataset = artifact.load(is_run_input=run)
    new_data = dataset.iloc[:subset_rows, :subset_cols]
    new_key = artifact.key.replace(".parquet", "_subsetted.parquet")
    return ln.Artifact.from_dataframe(new_data, key=new_key, run=run).save()


if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument("--subset", action="store_true")
    args = p.parse_args()

    params = {"is_subset": args.subset}

    ln.track(params=params)

    if args.subset:
        df = ln.examples.datasets.mini_immuno.get_dataset1(otype="DataFrame")
        artifact = ln.Artifact.from_dataframe(
            df, key="my_analysis/dataset.parquet"
        ).save()
        subsetted_artifact = subset_dataframe(artifact)

    ln.finish()

!python scripts/run_workflow.py --subset

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→ connected lamindb: testuser1/test-track

→ created Transform('sFydgjof7Lie0000', key='run_workflow.py'), started new Run('MA2eubYwRDmdMQ7z') at 2025-10-16 13:19:35 UTC
→ params: is_subset=True
• recommendation: to identify the script across renames, pass the uid: ln.track("sFydgjof7Lie", params={...})

→ returning artifact with same hash: Artifact(uid='g1I5yA7nbdpSDTAz0000', is_latest=True, key='my_analysis/dataset.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=9868, hash='wvfEBPwHL3XHiAb-o8fU6Q', n_observations=3, branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-10-16 13:19:31 UTC, is_locked=False); to track this artifact as an input, use: ln.Artifact.get()
! cannot infer feature type of: None, returning '?
! skipping param run because dtype not JSON serializable
→ returning artifact with same hash: Artifact(uid='Vft4wxzeOKEoX9GX0001', is_latest=True, key='my_analysis/dataset_subsetted.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=3852, hash='7WGuLVamVyBMhPb2qRE_tA', n_observations=2, branch_id=1, space_id=1, storage_id=1, run_id=4, created_by_id=1, created_at=2025-10-16 13:19:32 UTC, is_locked=False); to track this artifact as an input, use: ln.Artifact.get()

→ returning artifact with same hash: Artifact(uid='L0ST0mguEK5zWSbc0000', is_latest=True, description='log streams of run HI5EsMupjY3Fhx86', suffix='.txt', kind='__lamindb_run__', size=0, hash='1B2M2Y8AsgTpgAmY7PhCfg', branch_id=1, space_id=1, storage_id=1, created_by_id=1, created_at=2025-10-16 13:19:30 UTC, is_locked=False); to track this artifact as an input, use: ln.Artifact.get()
! updated description from log streams of run HI5EsMupjY3Fhx86 to log streams of run MA2eubYwRDmdMQ7z

ln.view()

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Artifact

	uid	key	description	suffix	kind	otype	size	hash	n_files	n_observations	_hash_type	_key_is_virtual	_overwrite_versions	space_id	storage_id	schema_id	version	is_latest	is_locked	run_id	created_at	created_by_id	_aux	_real_key	branch_id
id
1	tn8M7n0QZnQn7qLT0000	my_file.fcs	None	.fcs	None	None	19330507	rCPvmZB19xs4zHZ7p_-Wrg	None	NaN	md5	True	False	1	1	None	None	True	False	1	2025-10-16 13:19:25.953000+00:00	1	{'af': {'0': True}}	None	1
4	g1I5yA7nbdpSDTAz0000	my_analysis/dataset.parquet	None	.parquet	dataset	DataFrame	9868	wvfEBPwHL3XHiAb-o8fU6Q	None	3.0	md5	True	False	1	1	None	None	True	False	1	2025-10-16 13:19:31.013000+00:00	1	{'af': {'0': True}}	None	1
5	Vft4wxzeOKEoX9GX0000	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3238	UM8d9C-x_2fbc_46BScp8A	None	2.0	md5	True	False	1	1	None	None	False	False	3	2025-10-16 13:19:31.066000+00:00	1	{'af': {'0': True}}	None	1
6	Vft4wxzeOKEoX9GX0001	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3852	7WGuLVamVyBMhPb2qRE_tA	None	2.0	md5	True	False	1	1	None	None	True	False	4	2025-10-16 13:19:32.488000+00:00	1	{'af': {'0': True}}	None	1

Project

	uid	name	description	is_type	abbr	url	start_date	end_date	_status_code	space_id	type_id	is_locked	run_id	created_at	created_by_id	_aux	branch_id
id
1	b20yzsJzsS7G	My project	None	False	None	None	None	None	0	1	None	False	None	2025-10-16 13:19:19.262000+00:00	1	None	1

Run

	uid	name	started_at	finished_at	params	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	is_locked	created_at	created_by_id	_aux	branch_id
id
1	I5Ool2E4Y9csOlos	None	2025-10-16 13:19:20.547205+00:00	NaT	None	None	None	None	-1	1	1	NaN	None	NaN	NaN	False	2025-10-16 13:19:20.548000+00:00	1	None	1
2	HI5EsMupjY3Fhx86	None	2025-10-16 13:19:29.134250+00:00	2025-10-16 13:19:30.414785+00:00	{'input_dir': './mydataset', 'learning_rate': ...	None	None	True	0	1	2	3.0	None	2.0	NaN	False	2025-10-16 13:19:29.135000+00:00	1	None	1
3	BbfyvFkP2kndXvyO	None	2025-10-16 13:19:31.044637+00:00	2025-10-16 13:19:31.075403+00:00	{'input_artifact_key': 'my_analysis/dataset.pa...	None	None	None	0	1	3	NaN	None	NaN	1.0	False	2025-10-16 13:19:31.045000+00:00	1	None	1
4	M3MRl3o07mu4JaV8	None	2025-10-16 13:19:32.464498+00:00	2025-10-16 13:19:32.497155+00:00	{'input_artifact_key': 'my_analysis/dataset.pa...	None	None	None	0	1	3	NaN	None	NaN	1.0	False	2025-10-16 13:19:32.465000+00:00	1	None	1
5	MA2eubYwRDmdMQ7z	None	2025-10-16 13:19:35.737513+00:00	2025-10-16 13:19:37.132374+00:00	{'is_subset': True}	None	None	True	0	1	4	3.0	None	2.0	NaN	False	2025-10-16 13:19:35.738000+00:00	1	None	1
6	HNhmBbHqWUe4TxMB	None	2025-10-16 13:19:37.106117+00:00	2025-10-16 13:19:37.129787+00:00	{'artifact': 'Artifact[g1I5yA7nbdpSDTAz0000]',...	None	None	None	0	1	5	NaN	None	NaN	5.0	False	2025-10-16 13:19:37.106000+00:00	1	None	1

Storage

	uid	root	description	type	region	instance_uid	space_id	is_locked	run_id	created_at	created_by_id	_aux	branch_id
id
1	2vQ99VsjaU1N	/home/runner/work/lamindb/lamindb/docs/test-track	None	local	None	73KPGC58ahU9	1	False	None	2025-10-16 13:19:15.693000+00:00	1	None	1

Transform

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	is_locked	created_at	created_by_id	_aux	branch_id
id
1	MUdUquLQhLyh0000	track.ipynb	Track notebooks, scripts & functions	notebook	None	None	None	None	1	None	None	True	False	2025-10-16 13:19:20.537000+00:00	1	None	1
2	s6TQmexGMTBc0000	run_track_with_params.py	None	script	import argparse\nimport lamindb as ln\n\nif __...	5RBz7zJICeKE1OSmg7gEdQ	None	None	1	None	None	True	False	2025-10-16 13:19:29.131000+00:00	1	None	1
3	cZ3CfclU5GWZ0000	track.ipynb/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n inpu...	CUqkJpolJY1Q1tqyCoWIWg	None	None	1	None	None	True	False	2025-10-16 13:19:31.040000+00:00	1	None	1
4	sFydgjof7Lie0000	run_workflow.py	None	script	import argparse\nimport lamindb as ln\n\n\n@ln...	fwij4oyLV27mmm9f2GVY_A	None	None	1	None	None	True	False	2025-10-16 13:19:35.735000+00:00	1	None	1
5	88muOREZ3pVI0000	run_workflow.py/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n arti...	9NYMDP5l5Iuu9F8VrO3vWQ	None	None	1	None	None	True	False	2025-10-16 13:19:37.104000+00:00	1	None	1

Sync scripts with git

To sync with your git commit, add the following line to your script:

ln.settings.sync_git_repo = <YOUR-GIT-REPO-URL>

synced_with_git.py

import lamindb as ln

ln.settings.sync_git_repo = "https://github.com/..."
ln.track()
# your code
ln.finish()

You’ll now see the GitHub emoji clickable on the hub.

Manage notebook templates

A notebook acts like a template upon using lamin load to load it. Consider you run:

lamin load https://lamin.ai/account/instance/transform/Akd7gx7Y9oVO0000

Upon running the returned notebook, you’ll automatically create a new version and be able to browse it via the version dropdown on the UI.

Additionally, you can:

• label using Record, e.g., transform.records.add(template_label)

• tag with an indicative version string, e.g., transform.version = "T1"; transform.save()

Saving a notebook as an artifact

Sometimes you might want to save a notebook as an artifact. This is how you can do it:

lamin save template1.ipynb --key templates/template1.ipynb --description "Template for analysis type 1" --registry artifact

A few checks at the end of this notebook:

assert run.params == {
    "input_dir": "./mydataset",
    "learning_rate": 0.01,
    "preprocess_params": {"downsample": True, "normalization": "the_good_one"},
}
assert my_project.artifacts.exists()
assert my_project.transforms.exists()
assert my_project.runs.exists()