Track notebooks, scripts & functions

This guide explains how to use track() & finish() to track notebook & scripts along with their inputs and outputs. For tracking pipelines, see: Pipelines – workflow managers.

# !pip install 'lamindb[jupyter]'
!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 tracking inputs & outputs of a run.

import lamindb as ln

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

# your code

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.

Assign a uid to a notebook or script

If you want to retain one version history when renaming notebooks and scripts, pass a uid to ln.track(), e.g.

ln.track("9priar0hoE5u0000")

To obtain a uid value, copy it from the logging statement (Transform('9priar0hoE5u0000')) when running ln.track() a first time without passing a uid.

Load a notebook or script

On the hub, search or filter the transform page and then load a script or notebook on the CLI. For example,

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

Query a notebook or script

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.latest_run.report  # report of latest run
transform.latest_run.environment  # environment of latest run
transform.runs  # all runs

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.

Track parameters

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

Track run parameters

Before tracking parameter values, you need to define valid parameters, e.g.:

import lamindb as ln

ln.Param(name="input_dir", dtype="str").save()
ln.Param(name="learning_rate", dtype="float").save()
ln.Param(name="preprocess_params", dtype="dict").save()

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→ connected lamindb: testuser1/test-track

Param(name='preprocess_params', dtype='dict', created_by_id=1, space_id=1, created_at=2025-02-20 07:26:53 UTC)

Upon running the below script without those parameters defined, you’ll get a ValidationError from which you can copy & paste the definitions.

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,  # nested parameter names & values in dictionaries are not validated
            "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('PeT0sAzkmK1a0000'), started new Run('XkKpSmsE...') at 2025-02-20 07:26:57 UTC
→ params: input_dir=./mydataset, learning_rate=0.01, preprocess_params={'downsample': True, 'normalization': 'the_good_one'}

→ finished Run('XkKpSmsE') after 2s at 2025-02-20 07:26:59 UTC

Query by run parameters

Query for all runs that match a certain parameters:

ln.Run.params.filter(
    learning_rate=0.01, input_dir="./mydataset", preprocess_params__downsample=True
).df()

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	uid	name	started_at	finished_at	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	created_at	created_by_id	_aux	_branch_code
id
1	XkKpSmsEBuV2GzNS0PH9	None	2025-02-20 07:26:57.685682+00:00	2025-02-20 07:26:59.915196+00:00	None	None	True	0	1	1	2	None	1	None	2025-02-20 07:26:57.686000+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

• nested keys like "downsample" in a dictionary do not appear in Param and hence, do not get validated

Access parameters of a run

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

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

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

Here is how it looks on the hub.

Explore all parameter values

If you want to query all parameter values across all runs, use ParamValue.

ln.core.ParamValue.df(include=["param__name", "created_by__handle"])

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	value	hash	space_id	param__name	created_by__handle
id
1	./mydataset	None	1	input_dir	testuser1
2	0.01	None	1	learning_rate	testuser1
3	{'downsample': True, 'normalization': 'the_goo...	None	1	preprocess_params	testuser1

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assert run.params.get_values() == {
    "input_dir": "./mydataset",
    "learning_rate": 0.01,
    "preprocess_params": {"downsample": True, "normalization": "the_good_one"},
}

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 ULabel, e.g., transform.ulabels.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

Track functions

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

In a notebook

import lamindb as ln

ln.track()  # track this notebook
ln.Param(name="subset_rows", dtype="int").save()  # define parameters
ln.Param(name="subset_cols", dtype="int").save()
ln.Param(name="input_artifact_key", dtype="str").save()
ln.Param(name="output_artifact_key", dtype="str").save()

→ created Transform('BeSHIWm1Apj50000'), started new Run('Pg9btxt8...') at 2025-02-20 07:27:02 UTC

→ notebook imports: lamindb==1.1.0

Param(name='output_artifact_key', dtype='str', created_by_id=1, run_id=2, space_id=1, created_at=2025-02-20 07:27:03 UTC)

Define a function and decorate it with tracked():

@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_df(new_data, key=output_artifact_key).save()

Prepare a test dataset:

df = ln.core.datasets.small_dataset1(otype="DataFrame")
input_artifact_key = "my_analysis/dataset.parquet"
artifact = ln.Artifact.from_df(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='wbFPOvWJUSCVqtDvf7E1', started_at=2025-02-20 07:27:03 UTC, finished_at=2025-02-20 07:27:03 UTC, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=2, created_at=2025-02-20 07:27:03 UTC)

This is the function that created it:

subsetted_artifact.run.transform

Transform(uid='4uLHhZwAEuOR0000', is_latest=True, key='track.ipynb/subset_dataframe.py', type='function', hash='F_wwrfFs6zmzMGVilG2Prg', space_id=1, created_by_id=1, created_at=2025-02-20 07:27:03 UTC)

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_df(new_data, key=output_artifact_key).save()\n'

These are all versions of this function:

subsetted_artifact.run.transform.versions.df()

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	created_at	created_by_id	_aux	_branch_code
id
3	4uLHhZwAEuOR0000	track.ipynb/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n inpu...	F_wwrfFs6zmzMGVilG2Prg	None	None	1	None	None	True	2025-02-20 07:27:03.931000+00:00	1	None	1

This is the initating run that triggered the function call:

subsetted_artifact.run.initiated_by_run

Run(uid='Pg9btxt8CMJDYeIZEtR5', started_at=2025-02-20 07:27:02 UTC, space_id=1, transform_id=2, created_by_id=1, created_at=2025-02-20 07:27:02 UTC)

This is the transform of the initiating run:

subsetted_artifact.run.initiated_by_run.transform

Transform(uid='BeSHIWm1Apj50000', is_latest=True, key='track.ipynb', description='Track notebooks, scripts & functions', type='notebook', space_id=1, created_by_id=1, created_at=2025-02-20 07:27:02 UTC)

These are the parameters of the run:

subsetted_artifact.run.params.get_values()

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

These input artifacts:

subsetted_artifact.run.input_artifacts.df()

	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	run_id	created_at	created_by_id	_aux	_branch_code
id
3	74oqAUwDrNO3zgax0000	my_analysis/dataset.parquet	None	.parquet	dataset	DataFrame	6586	6iyId6A4cyhKxhls4UxsQA	None	3	md5	True	False	1	1	None	None	True	2	2025-02-20 07:27:03.911000+00:00	1	None	1

These are output artifacts:

subsetted_artifact.run.output_artifacts.df()

	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	run_id	created_at	created_by_id	_aux	_branch_code
id
4	VocyZKAnGUWU3WZs0000	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3233	LiK5RaETbDxoDhxEn9X3Vw	None	2	md5	True	False	1	1	None	None	True	3	2025-02-20 07:27:03.977000+00:00	1	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='a5dvK4J3mKsBKjDyd2Ra', started_at=2025-02-20 07:27:04 UTC, finished_at=2025-02-20 07:27:04 UTC, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=2, created_at=2025-02-20 07:27:04 UTC)

With new parameters:

subsetted_artifact.run.params.get_values()

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

And a new version of the output artifact:

subsetted_artifact.run.output_artifacts.df()

	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	run_id	created_at	created_by_id	_aux	_branch_code
id
5	VocyZKAnGUWU3WZs0001	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3839	4IcM2NblBj81aKsrT_8c1Q	None	2	md5	True	False	1	1	None	None	True	4	2025-02-20 07:27:04.939000+00:00	1	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	run_id	created_at	created_by_id	_aux	_branch_code
id
5	VocyZKAnGUWU3WZs0001	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3839	4IcM2NblBj81aKsrT_8c1Q	None	2	md5	True	False	1	1	None	None	True	4	2025-02-20 07:27:04.939000+00:00	1	None	1
4	VocyZKAnGUWU3WZs0000	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3233	LiK5RaETbDxoDhxEn9X3Vw	None	2	md5	True	False	1	1	None	None	False	3	2025-02-20 07:27:03.977000+00:00	1	None	1
3	74oqAUwDrNO3zgax0000	my_analysis/dataset.parquet	None	.parquet	dataset	DataFrame	6586	6iyId6A4cyhKxhls4UxsQA	None	3	md5	True	False	1	1	None	None	True	2	2025-02-20 07:27:03.911000+00:00	1	None	1

Param

	name	dtype	is_type	_expect_many	space_id	type_id	run_id	created_at	created_by_id	_aux	_branch_code
id
7	output_artifact_key	str	None	False	1	None	2.0	2025-02-20 07:27:03.848000+00:00	1	None	1
6	input_artifact_key	str	None	False	1	None	2.0	2025-02-20 07:27:03.842000+00:00	1	None	1
5	subset_cols	int	None	False	1	None	2.0	2025-02-20 07:27:03.836000+00:00	1	None	1
4	subset_rows	int	None	False	1	None	2.0	2025-02-20 07:27:03.829000+00:00	1	None	1
3	preprocess_params	dict	None	False	1	None	NaN	2025-02-20 07:26:53.812000+00:00	1	None	1
2	learning_rate	float	None	False	1	None	NaN	2025-02-20 07:26:53.806000+00:00	1	None	1
1	input_dir	str	None	False	1	None	NaN	2025-02-20 07:26:53.795000+00:00	1	None	1

ParamValue

	value	hash	space_id	param_id	created_at	created_by_id	_aux	_branch_code
id
1	./mydataset	None	1	1	2025-02-20 07:26:57.703000+00:00	1	None	1
2	0.01	None	1	2	2025-02-20 07:26:57.703000+00:00	1	None	1
3	{'downsample': True, 'normalization': 'the_goo...	None	1	3	2025-02-20 07:26:57.703000+00:00	1	None	1
4	my_analysis/dataset.parquet	None	1	6	2025-02-20 07:27:03.952000+00:00	1	None	1
5	my_analysis/dataset_subsetted.parquet	None	1	7	2025-02-20 07:27:03.952000+00:00	1	None	1
6	2	None	1	4	2025-02-20 07:27:03.952000+00:00	1	None	1
7	2	None	1	5	2025-02-20 07:27:03.952000+00:00	1	None	1

Run

	uid	name	started_at	finished_at	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	created_at	created_by_id	_aux	_branch_code
id
1	XkKpSmsEBuV2GzNS0PH9	None	2025-02-20 07:26:57.685682+00:00	2025-02-20 07:26:59.915196+00:00	None	None	True	0	1	1	2.0	None	1.0	NaN	2025-02-20 07:26:57.686000+00:00	1	None	1
2	Pg9btxt8CMJDYeIZEtR5	None	2025-02-20 07:27:02.363724+00:00	NaT	None	None	None	0	1	2	NaN	None	NaN	NaN	2025-02-20 07:27:02.364000+00:00	1	None	1
3	wbFPOvWJUSCVqtDvf7E1	None	2025-02-20 07:27:03.934017+00:00	2025-02-20 07:27:03.979776+00:00	None	None	None	0	1	3	NaN	None	NaN	2.0	2025-02-20 07:27:03.934000+00:00	1	None	1
4	a5dvK4J3mKsBKjDyd2Ra	None	2025-02-20 07:27:04.893114+00:00	2025-02-20 07:27:04.941573+00:00	None	None	None	0	1	3	NaN	None	NaN	2.0	2025-02-20 07:27:04.893000+00:00	1	None	1

Storage

	uid	root	description	type	region	instance_uid	space_id	run_id	created_at	created_by_id	_aux	_branch_code
id
1	5XEdtIpS0TbV	/home/runner/work/lamindb/lamindb/docs/test-track	None	local	None	73KPGC58ahU9	1	None	2025-02-20 07:26:48.191000+00:00	1	None	1

Transform

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	created_at	created_by_id	_aux	_branch_code
id
3	4uLHhZwAEuOR0000	track.ipynb/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n inpu...	F_wwrfFs6zmzMGVilG2Prg	None	None	1	None	None	True	2025-02-20 07:27:03.931000+00:00	1	None	1
2	BeSHIWm1Apj50000	track.ipynb	Track notebooks, scripts & functions	notebook	None	None	None	None	1	None	None	True	2025-02-20 07:27:02.357000+00:00	1	None	1
1	PeT0sAzkmK1a0000	run-track-with-params.py	run-track-with-params.py	script	import argparse\nimport lamindb as ln\n\nif __...	nRUs3ZjuVTbKtBmSXpVQ5A	None	None	1	None	None	True	2025-02-20 07:26:57.682000+00:00	1	None	1

In a script

run-workflow.py

import argparse
import lamindb as ln

ln.Param(name="run_workflow_subset", dtype=bool).save()


@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_df(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 = {"run_workflow_subset": args.subset}

    ln.track(params=params)

    if args.subset:
        df = ln.core.datasets.small_dataset1(otype="DataFrame")
        artifact = ln.Artifact.from_df(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('8BQpD2CacJp20000'), started new Run('lN57iDFz...') at 2025-02-20 07:27:08 UTC
→ params: run_workflow_subset=True
→ found artifact with same hash: Artifact(uid='74oqAUwDrNO3zgax0000', is_latest=True, key='my_analysis/dataset.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=6586, hash='6iyId6A4cyhKxhls4UxsQA', n_observations=3, space_id=1, storage_id=1, run_id=2, created_by_id=1, created_at=2025-02-20 07:27:03 UTC); to track this artifact as an input, use: ln.Artifact.get()

→ found artifact with same hash: Artifact(uid='VocyZKAnGUWU3WZs0001', is_latest=True, key='my_analysis/dataset_subsetted.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=3839, hash='4IcM2NblBj81aKsrT_8c1Q', n_observations=2, space_id=1, storage_id=1, run_id=4, created_by_id=1, created_at=2025-02-20 07:27:04 UTC); to track this artifact as an input, use: ln.Artifact.get()
→ found artifact with same hash: Artifact(uid='M88CQvNCsi15LZPv0000', is_latest=True, description='log streams of run XkKpSmsEBuV2GzNS0PH9', suffix='.txt', size=0, hash='1B2M2Y8AsgTpgAmY7PhCfg', space_id=1, storage_id=1, created_by_id=1, created_at=2025-02-20 07:26:59 UTC); to track this artifact as an input, use: ln.Artifact.get()
! updated description from log streams of run XkKpSmsEBuV2GzNS0PH9 to log streams of run lN57iDFzv3K8UYNflo28
→ finished Run('lN57iDFz') after 1s at 2025-02-20 07:27:10 UTC

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	run_id	created_at	created_by_id	_aux	_branch_code
id
5	VocyZKAnGUWU3WZs0001	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3839	4IcM2NblBj81aKsrT_8c1Q	None	2	md5	True	False	1	1	None	None	True	6	2025-02-20 07:27:04.939000+00:00	1	None	1
4	VocyZKAnGUWU3WZs0000	my_analysis/dataset_subsetted.parquet	None	.parquet	dataset	DataFrame	3233	LiK5RaETbDxoDhxEn9X3Vw	None	2	md5	True	False	1	1	None	None	False	3	2025-02-20 07:27:03.977000+00:00	1	None	1
3	74oqAUwDrNO3zgax0000	my_analysis/dataset.parquet	None	.parquet	dataset	DataFrame	6586	6iyId6A4cyhKxhls4UxsQA	None	3	md5	True	False	1	1	None	None	True	5	2025-02-20 07:27:03.911000+00:00	1	None	1

Param

	name	dtype	is_type	_expect_many	space_id	type_id	run_id	created_at	created_by_id	_aux	_branch_code
id
8	run_workflow_subset	bool	None	False	1	None	NaN	2025-02-20 07:27:08.944000+00:00	1	None	1
7	output_artifact_key	str	None	False	1	None	2.0	2025-02-20 07:27:03.848000+00:00	1	None	1
6	input_artifact_key	str	None	False	1	None	2.0	2025-02-20 07:27:03.842000+00:00	1	None	1
5	subset_cols	int	None	False	1	None	2.0	2025-02-20 07:27:03.836000+00:00	1	None	1
4	subset_rows	int	None	False	1	None	2.0	2025-02-20 07:27:03.829000+00:00	1	None	1
3	preprocess_params	dict	None	False	1	None	NaN	2025-02-20 07:26:53.812000+00:00	1	None	1
2	learning_rate	float	None	False	1	None	NaN	2025-02-20 07:26:53.806000+00:00	1	None	1

ParamValue

	value	hash	space_id	param_id	created_at	created_by_id	_aux	_branch_code
id
1	./mydataset	None	1	1	2025-02-20 07:26:57.703000+00:00	1	None	1
2	0.01	None	1	2	2025-02-20 07:26:57.703000+00:00	1	None	1
3	{'downsample': True, 'normalization': 'the_goo...	None	1	3	2025-02-20 07:26:57.703000+00:00	1	None	1
4	my_analysis/dataset.parquet	None	1	6	2025-02-20 07:27:03.952000+00:00	1	None	1
5	my_analysis/dataset_subsetted.parquet	None	1	7	2025-02-20 07:27:03.952000+00:00	1	None	1
6	2	None	1	4	2025-02-20 07:27:03.952000+00:00	1	None	1
7	2	None	1	5	2025-02-20 07:27:03.952000+00:00	1	None	1

Run

	uid	name	started_at	finished_at	reference	reference_type	_is_consecutive	_status_code	space_id	transform_id	report_id	_logfile_id	environment_id	initiated_by_run_id	created_at	created_by_id	_aux	_branch_code
id
1	XkKpSmsEBuV2GzNS0PH9	None	2025-02-20 07:26:57.685682+00:00	2025-02-20 07:26:59.915196+00:00	None	None	True	0	1	1	2.0	None	1.0	NaN	2025-02-20 07:26:57.686000+00:00	1	None	1
2	Pg9btxt8CMJDYeIZEtR5	None	2025-02-20 07:27:02.363724+00:00	NaT	None	None	None	0	1	2	NaN	None	NaN	NaN	2025-02-20 07:27:02.364000+00:00	1	None	1
3	wbFPOvWJUSCVqtDvf7E1	None	2025-02-20 07:27:03.934017+00:00	2025-02-20 07:27:03.979776+00:00	None	None	None	0	1	3	NaN	None	NaN	2.0	2025-02-20 07:27:03.934000+00:00	1	None	1
4	a5dvK4J3mKsBKjDyd2Ra	None	2025-02-20 07:27:04.893114+00:00	2025-02-20 07:27:04.941573+00:00	None	None	None	0	1	3	NaN	None	NaN	2.0	2025-02-20 07:27:04.893000+00:00	1	None	1
5	lN57iDFzv3K8UYNflo28	None	2025-02-20 07:27:08.959137+00:00	2025-02-20 07:27:10.502766+00:00	None	None	True	0	1	4	2.0	None	1.0	NaN	2025-02-20 07:27:08.960000+00:00	1	None	1
6	67J9rfiac8zpLJScohmN	None	2025-02-20 07:27:10.456457+00:00	2025-02-20 07:27:10.498614+00:00	None	None	None	0	1	5	NaN	None	NaN	5.0	2025-02-20 07:27:10.456000+00:00	1	None	1

Storage

	uid	root	description	type	region	instance_uid	space_id	run_id	created_at	created_by_id	_aux	_branch_code
id
1	5XEdtIpS0TbV	/home/runner/work/lamindb/lamindb/docs/test-track	None	local	None	73KPGC58ahU9	1	None	2025-02-20 07:26:48.191000+00:00	1	None	1

Transform

	uid	key	description	type	source_code	hash	reference	reference_type	space_id	_template_id	version	is_latest	created_at	created_by_id	_aux	_branch_code
id
5	D22n4w3ICx0d0000	run-workflow.py/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n arti...	Dqbr_hMfHs17EhbPXP_PyQ	None	None	1	None	None	True	2025-02-20 07:27:10.454000+00:00	1	None	1
4	8BQpD2CacJp20000	run-workflow.py	run-workflow.py	script	import argparse\nimport lamindb as ln\n\nln.Pa...	yqr8j5hTUulVRzv4J-o9SQ	None	None	1	None	None	True	2025-02-20 07:27:08.956000+00:00	1	None	1
3	4uLHhZwAEuOR0000	track.ipynb/subset_dataframe.py	None	function	@ln.tracked()\ndef subset_dataframe(\n inpu...	F_wwrfFs6zmzMGVilG2Prg	None	None	1	None	None	True	2025-02-20 07:27:03.931000+00:00	1	None	1
2	BeSHIWm1Apj50000	track.ipynb	Track notebooks, scripts & functions	notebook	None	None	None	None	1	None	None	True	2025-02-20 07:27:02.357000+00:00	1	None	1
1	PeT0sAzkmK1a0000	run-track-with-params.py	run-track-with-params.py	script	import argparse\nimport lamindb as ln\n\nif __...	nRUs3ZjuVTbKtBmSXpVQ5A	None	None	1	None	None	True	2025-02-20 07:26:57.682000+00:00	1	None	1

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# clean up test instance
!rm -r ./test-track
!lamin delete --force test-track

• deleting instance testuser1/test-track