Track notebooks, scripts & functions

For tracking pipelines, see: Pipelines – workflow managers.

# pip install 'lamindb[jupyter]'
!lamin init --storage ./test-track
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! not connected, call: ln.connect('account/name')
 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 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.core.datasets.file_fcs(), key="my_file.fcs").save()  # save an artifact
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 connected lamindb: testuser1/test-track
 created Transform('P7xeu7ByPEAg0000'), started new Run('Qr1837TA...') at 2025-07-01 19:13:01 UTC
 notebook imports: lamindb==1.7a1
 recommendation: to identify the notebook across renames, pass the uid: ln.track("P7xeu7ByPEAg", project="My project")
Artifact(uid='Y6jGDdFNYR1d7GU20000', 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=2025-07-01 19:13:03 UTC)

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

ln.Artifact.filter(projects=my_project).df()
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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 run_id created_at created_by_id _aux branch_id
id
1 Y6jGDdFNYR1d7GU20000 my_file.fcs None .fcs None None 19330507 rCPvmZB19xs4zHZ7p_-Wrg None None md5 True False 1 1 None None True 1 2025-07-01 19:13:03.918000+00:00 1 {'af': {'0': True}} 1

Access entities linked to a project.

display(my_project.artifacts.df())
display(my_project.transforms.df())
display(my_project.runs.df())
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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 run_id created_at created_by_id _aux branch_id
id
1 Y6jGDdFNYR1d7GU20000 my_file.fcs None .fcs None None 19330507 rCPvmZB19xs4zHZ7p_-Wrg None None md5 True False 1 1 None None True 1 2025-07-01 19:13:03.918000+00:00 1 {'af': {'0': True}} 1
uid key description type source_code hash reference reference_type space_id _template_id version is_latest created_at created_by_id _aux branch_id
id
1 P7xeu7ByPEAg0000 track.ipynb Track notebooks, scripts & functions notebook None None None None 1 None None True 2025-07-01 19:13:01.038000+00:00 1 None 1
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_id
id
1 Qr1837TAFCHHGRUh None 2025-07-01 19:13:01.049685+00:00 None None None None 0 1 1 None None None None 2025-07-01 19:13:01.050000+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

First, define valid parameters, e.g.:

ln.Feature(name="input_dir", dtype=str).save()
ln.Feature(name="learning_rate", dtype=float).save()
ln.Feature(name="preprocess_params", dtype="dict").save()
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Feature(uid='EeG9NXiCBIGz', name='preprocess_params', dtype='dict', array_rank=0, array_size=0, branch_id=1, space_id=1, created_by_id=1, run_id=1, created_at=2025-07-01 19:13:04 UTC)

If you hadn’t defined these parameters, you’d get a ValidationError in the following script.

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
! Starting bulk_create for 3 RunFeatureValue records in batches of 10000
 created Transform('9995OOwxVdZC0000'), started new Run('Z5BkUJ58...') at 2025-07-01 19:13:06 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("9995OOwxVdZC", params={...})
 finished Run('Z5BkUJ58') after 1s at 2025-07-01 19:13:08 UTC

Query by run parameters

Query for all runs that match a certain parameters:

ln.Run.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_id
id
2 Z5BkUJ58ReJzTcl9 None 2025-07-01 19:13:06.856943+00:00 2025-07-01 19:13:08.015739+00:00 None None True 0 1 2 3 None 2 None 2025-07-01 19:13:06.857000+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 Feature 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.filter(learning_rate=0.01).order_by("-started_at").first()
run.features.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.
image

Explore parameter values

If you want to query all parameter values together with other feature values, use FeatureValue.

ln.models.FeatureValue.df(include=["feature__name", "created_by__handle"])
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value hash feature__name created_by__handle
id
1 ./mydataset 71I4KdtOlqWZYoR9KaVTvw input_dir testuser1
2 0.01 BIF-_RHBU2Sm7COXgAOIYg learning_rate testuser1
3 {'downsample': True, 'normalization': 'the_goo... 4ehQH8UO25aNM181K_gloQ preprocess_params testuser1

Track functions

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

In a notebook

ln.Feature(name="subset_rows", dtype="int").save()  # define parameters
ln.Feature(name="subset_cols", dtype="int").save()
ln.Feature(name="input_artifact_key", dtype="str").save()
ln.Feature(name="output_artifact_key", dtype="str").save()
Feature(uid='5M9OxqetONO9', name='output_artifact_key', dtype='str', array_rank=0, array_size=0, branch_id=1, space_id=1, created_by_id=1, run_id=1, created_at=2025-07-01 19:13:08 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)
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! Starting bulk_create for 4 RunFeatureValue records in batches of 10000

Query for the output:

subsetted_artifact = ln.Artifact.get(key=ouput_artifact_key)
subsetted_artifact.view_lineage()
_images/ecd9fde1cd7ccd50982e9d873b32f616d64e758c9e9cbf99c78a5e7cb96951bc.svg

This is the run that created the subsetted_artifact:

subsetted_artifact.run
Run(uid='0p4fwQp7Rc9u8otK', started_at=2025-07-01 19:13:08 UTC, finished_at=2025-07-01 19:13:08 UTC, branch_id=1, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=1, created_at=2025-07-01 19:13:08 UTC)

This is the function that created it:

subsetted_artifact.run.transform
Transform(uid='02JdyxnvNRXB0000', is_latest=True, key='track.ipynb/subset_dataframe.py', type='function', hash='F_wwrfFs6zmzMGVilG2Prg', branch_id=1, space_id=1, created_by_id=1, created_at=2025-07-01 19:13:08 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_id
id
3 02JdyxnvNRXB0000 track.ipynb/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n inpu... F_wwrfFs6zmzMGVilG2Prg None None 1 None None True 2025-07-01 19:13:08.650000+00:00 1 None 1

This is the initating run that triggered the function call:

subsetted_artifact.run.initiated_by_run
Run(uid='Qr1837TAFCHHGRUh', started_at=2025-07-01 19:13:01 UTC, branch_id=1, space_id=1, transform_id=1, created_by_id=1, created_at=2025-07-01 19:13:01 UTC)

This is the transform of the initiating run:

subsetted_artifact.run.initiated_by_run.transform
Transform(uid='P7xeu7ByPEAg0000', 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=2025-07-01 19:13:01 UTC)

These are the parameters of the run:

subsetted_artifact.run.features.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_id
id
4 zUOTyknKog6o0qDw0000 my_analysis/dataset.parquet None .parquet dataset DataFrame 9868 8-_BZRWEGUQzd8T8U2DCsA None 3 md5 True False 1 1 None None True 1 2025-07-01 19:13:08.625000+00:00 1 {'af': {'0': True}} 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_id
id
5 Ntw2bDFkEycuDK3R0000 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3238 qyOwv-ZalTsQ3ba7phCLkg None 2 md5 True False 1 1 None None True 3 2025-07-01 19:13:08.709000+00:00 1 {'af': {'0': True}} 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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! Starting bulk_create for 4 RunFeatureValue records in batches of 10000
 creating new artifact version for key='my_analysis/dataset_subsetted.parquet' (storage: '/home/runner/work/lamindb/lamindb/docs/test-track')
_images/be448b98f82ee0116471ba2931e5c00e849ca68a390be286756cc9fc506c371f.svg

We created a new run:

subsetted_artifact.run
Run(uid='7DQUK1Axg0yAyOLh', started_at=2025-07-01 19:13:09 UTC, finished_at=2025-07-01 19:13:09 UTC, branch_id=1, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=1, created_at=2025-07-01 19:13:09 UTC)

With new parameters:

subsetted_artifact.run.features.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_id
id
6 Ntw2bDFkEycuDK3R0001 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3852 t7OPyiQSIk2_01X7zZxvrA None 2 md5 True False 1 1 None None True 4 2025-07-01 19:13:09.483000+00:00 1 {'af': {'0': True}} 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_id
id
6 Ntw2bDFkEycuDK3R0001 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3852 t7OPyiQSIk2_01X7zZxvrA None 2.0 md5 True False 1 1 None None True 4 2025-07-01 19:13:09.483000+00:00 1 {'af': {'0': True}} 1
5 Ntw2bDFkEycuDK3R0000 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3238 qyOwv-ZalTsQ3ba7phCLkg None 2.0 md5 True False 1 1 None None False 3 2025-07-01 19:13:08.709000+00:00 1 {'af': {'0': True}} 1
4 zUOTyknKog6o0qDw0000 my_analysis/dataset.parquet None .parquet dataset DataFrame 9868 8-_BZRWEGUQzd8T8U2DCsA None 3.0 md5 True False 1 1 None None True 1 2025-07-01 19:13:08.625000+00:00 1 {'af': {'0': True}} 1
1 Y6jGDdFNYR1d7GU20000 my_file.fcs None .fcs None None 19330507 rCPvmZB19xs4zHZ7p_-Wrg None NaN md5 True False 1 1 None None True 1 2025-07-01 19:13:03.918000+00:00 1 {'af': {'0': True}} 1
Feature
uid name dtype is_type unit description array_rank array_size array_shape proxy_dtype synonyms _expect_many _curation space_id type_id run_id created_at created_by_id _aux branch_id
id
7 5M9OxqetONO9 output_artifact_key str None None None 0 0 None None None None None 1 None 1 2025-07-01 19:13:08.565000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
6 szjVlKbwehWJ input_artifact_key str None None None 0 0 None None None None None 1 None 1 2025-07-01 19:13:08.556000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
5 fL233mKFvyoa subset_cols int None None None 0 0 None None None None None 1 None 1 2025-07-01 19:13:08.546000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
4 OXsmd7QbQn6b subset_rows int None None None 0 0 None None None None None 1 None 1 2025-07-01 19:13:08.536000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
3 EeG9NXiCBIGz preprocess_params dict None None None 0 0 None None None None None 1 None 1 2025-07-01 19:13:04.112000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
2 FtZVcoqOTETY learning_rate float None None None 0 0 None None None None None 1 None 1 2025-07-01 19:13:04.099000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
1 mC3R1uBceDV8 input_dir str None None None 0 0 None None None None None 1 None 1 2025-07-01 19:13:04.088000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
FeatureValue
value hash space_id feature_id run_id created_at created_by_id _aux branch_id
id
1 ./mydataset 71I4KdtOlqWZYoR9KaVTvw 1 1 NaN 2025-07-01 19:13:06.879000+00:00 1 None 1
2 0.01 BIF-_RHBU2Sm7COXgAOIYg 1 2 NaN 2025-07-01 19:13:06.881000+00:00 1 None 1
3 {'downsample': True, 'normalization': 'the_goo... 4ehQH8UO25aNM181K_gloQ 1 3 NaN 2025-07-01 19:13:06.883000+00:00 1 None 1
4 2 yB5yjZ1ML2NvBn-JzBSGLA 1 4 1.0 2025-07-01 19:13:08.673000+00:00 1 None 1
5 2 yB5yjZ1ML2NvBn-JzBSGLA 1 5 1.0 2025-07-01 19:13:08.676000+00:00 1 None 1
6 my_analysis/dataset.parquet 1ImgyYl4KlCl3XCd-aQE9Q 1 6 1.0 2025-07-01 19:13:08.678000+00:00 1 None 1
7 my_analysis/dataset_subsetted.parquet G9luXJ51Hi4-Csrifos0Lw 1 7 1.0 2025-07-01 19:13:08.681000+00:00 1 None 1
Project
uid name is_type abbr url start_date end_date _status_code space_id type_id run_id created_at created_by_id _aux branch_id
id
1 b0HjvPVxFdg2 My project False None None None None 0 1 None None 2025-07-01 19:12:59.909000+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_id
id
1 Qr1837TAFCHHGRUh None 2025-07-01 19:13:01.049685+00:00 NaT None None None 0 1 1 NaN None NaN NaN 2025-07-01 19:13:01.050000+00:00 1 None 1
2 Z5BkUJ58ReJzTcl9 None 2025-07-01 19:13:06.856943+00:00 2025-07-01 19:13:08.015739+00:00 None None True 0 1 2 3.0 None 2.0 NaN 2025-07-01 19:13:06.857000+00:00 1 None 1
3 0p4fwQp7Rc9u8otK None 2025-07-01 19:13:08.655385+00:00 2025-07-01 19:13:08.719868+00:00 None None None 0 1 3 NaN None NaN 1.0 2025-07-01 19:13:08.655000+00:00 1 None 1
4 7DQUK1Axg0yAyOLh None 2025-07-01 19:13:09.431437+00:00 2025-07-01 19:13:09.492711+00:00 None None None 0 1 3 NaN None NaN 1.0 2025-07-01 19:13:09.431000+00:00 1 None 1
Storage
uid root description type region instance_uid space_id run_id created_at created_by_id _aux branch_id
id
1 eU5TXvFdF23w /home/runner/work/lamindb/lamindb/docs/test-track None local None 73KPGC58ahU9 1 None 2025-07-01 19:12:56.811000+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_id
id
3 02JdyxnvNRXB0000 track.ipynb/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n inpu... F_wwrfFs6zmzMGVilG2Prg None None 1 None None True 2025-07-01 19:13:08.650000+00:00 1 None 1
2 9995OOwxVdZC0000 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 2025-07-01 19:13:06.853000+00:00 1 None 1
1 P7xeu7ByPEAg0000 track.ipynb Track notebooks, scripts & functions notebook None None None None 1 None None True 2025-07-01 19:13:01.038000+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
Hide code cell output
 connected lamindb: testuser1/test-track
! Starting bulk_create for 1 RunFeatureValue records in batches of 10000
 created Transform('BmREU4bprq5o0000'), started new Run('bYcr1H6p...') at 2025-07-01 19:13:12 UTC
→ params: run_workflow_subset=True
 recommendation: to identify the script across renames, pass the uid: ln.track("BmREU4bprq5o", params={...})
 returning existing artifact with same hash: Artifact(uid='zUOTyknKog6o0qDw0000', is_latest=True, key='my_analysis/dataset.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=9868, hash='8-_BZRWEGUQzd8T8U2DCsA', n_observations=3, branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-07-01 19:13:08 UTC); to track this artifact as an input, use: ln.Artifact.get()
! Starting bulk_create for 2 RunFeatureValue records in batches of 10000
 returning existing artifact with same hash: Artifact(uid='Ntw2bDFkEycuDK3R0001', is_latest=True, key='my_analysis/dataset_subsetted.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=3852, hash='t7OPyiQSIk2_01X7zZxvrA', n_observations=2, branch_id=1, space_id=1, storage_id=1, run_id=4, created_by_id=1, created_at=2025-07-01 19:13:09 UTC); to track this artifact as an input, use: ln.Artifact.get()
 returning existing artifact with same hash: Artifact(uid='lBLlyBdDHg4y2nzf0000', is_latest=True, description='log streams of run Z5BkUJ58ReJzTcl9', suffix='.txt', kind='__lamindb_run__', size=0, hash='1B2M2Y8AsgTpgAmY7PhCfg', branch_id=1, space_id=1, storage_id=1, created_by_id=1, created_at=2025-07-01 19:13:08 UTC); to track this artifact as an input, use: ln.Artifact.get()
! updated description from log streams of run Z5BkUJ58ReJzTcl9 to log streams of run bYcr1H6pInMIJNMd
 finished Run('bYcr1H6p') after 0s at 2025-07-01 19:13:13 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_id
id
6 Ntw2bDFkEycuDK3R0001 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3852 t7OPyiQSIk2_01X7zZxvrA None 2.0 md5 True False 1 1 None None True 4 2025-07-01 19:13:09.483000+00:00 1 {'af': {'0': True}} 1
5 Ntw2bDFkEycuDK3R0000 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3238 qyOwv-ZalTsQ3ba7phCLkg None 2.0 md5 True False 1 1 None None False 3 2025-07-01 19:13:08.709000+00:00 1 {'af': {'0': True}} 1
4 zUOTyknKog6o0qDw0000 my_analysis/dataset.parquet None .parquet dataset DataFrame 9868 8-_BZRWEGUQzd8T8U2DCsA None 3.0 md5 True False 1 1 None None True 1 2025-07-01 19:13:08.625000+00:00 1 {'af': {'0': True}} 1
1 Y6jGDdFNYR1d7GU20000 my_file.fcs None .fcs None None 19330507 rCPvmZB19xs4zHZ7p_-Wrg None NaN md5 True False 1 1 None None True 1 2025-07-01 19:13:03.918000+00:00 1 {'af': {'0': True}} 1
Feature
uid name dtype is_type unit description array_rank array_size array_shape proxy_dtype synonyms _expect_many _curation space_id type_id run_id created_at created_by_id _aux branch_id
id
8 9uv6EMzUGL7X run_workflow_subset bool None None None 0 0 None None None None None 1 None NaN 2025-07-01 19:13:12.577000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
7 5M9OxqetONO9 output_artifact_key str None None None 0 0 None None None None None 1 None 1.0 2025-07-01 19:13:08.565000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
6 szjVlKbwehWJ input_artifact_key str None None None 0 0 None None None None None 1 None 1.0 2025-07-01 19:13:08.556000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
5 fL233mKFvyoa subset_cols int None None None 0 0 None None None None None 1 None 1.0 2025-07-01 19:13:08.546000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
4 OXsmd7QbQn6b subset_rows int None None None 0 0 None None None None None 1 None 1.0 2025-07-01 19:13:08.536000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
3 EeG9NXiCBIGz preprocess_params dict None None None 0 0 None None None None None 1 None 1.0 2025-07-01 19:13:04.112000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
2 FtZVcoqOTETY learning_rate float None None None 0 0 None None None None None 1 None 1.0 2025-07-01 19:13:04.099000+00:00 1 {'af': {'0': None, '1': True, '2': False}} 1
FeatureValue
value hash space_id feature_id run_id created_at created_by_id _aux branch_id
id
1 ./mydataset 71I4KdtOlqWZYoR9KaVTvw 1 1 NaN 2025-07-01 19:13:06.879000+00:00 1 None 1
2 0.01 BIF-_RHBU2Sm7COXgAOIYg 1 2 NaN 2025-07-01 19:13:06.881000+00:00 1 None 1
3 {'downsample': True, 'normalization': 'the_goo... 4ehQH8UO25aNM181K_gloQ 1 3 NaN 2025-07-01 19:13:06.883000+00:00 1 None 1
4 2 yB5yjZ1ML2NvBn-JzBSGLA 1 4 1.0 2025-07-01 19:13:08.673000+00:00 1 None 1
5 2 yB5yjZ1ML2NvBn-JzBSGLA 1 5 1.0 2025-07-01 19:13:08.676000+00:00 1 None 1
6 my_analysis/dataset.parquet 1ImgyYl4KlCl3XCd-aQE9Q 1 6 1.0 2025-07-01 19:13:08.678000+00:00 1 None 1
7 my_analysis/dataset_subsetted.parquet G9luXJ51Hi4-Csrifos0Lw 1 7 1.0 2025-07-01 19:13:08.681000+00:00 1 None 1
Project
uid name is_type abbr url start_date end_date _status_code space_id type_id run_id created_at created_by_id _aux branch_id
id
1 b0HjvPVxFdg2 My project False None None None None 0 1 None None 2025-07-01 19:12:59.909000+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_id
id
1 Qr1837TAFCHHGRUh None 2025-07-01 19:13:01.049685+00:00 NaT None None None 0 1 1 NaN None NaN NaN 2025-07-01 19:13:01.050000+00:00 1 None 1
2 Z5BkUJ58ReJzTcl9 None 2025-07-01 19:13:06.856943+00:00 2025-07-01 19:13:08.015739+00:00 None None True 0 1 2 3.0 None 2.0 NaN 2025-07-01 19:13:06.857000+00:00 1 None 1
3 0p4fwQp7Rc9u8otK None 2025-07-01 19:13:08.655385+00:00 2025-07-01 19:13:08.719868+00:00 None None None 0 1 3 NaN None NaN 1.0 2025-07-01 19:13:08.655000+00:00 1 None 1
4 7DQUK1Axg0yAyOLh None 2025-07-01 19:13:09.431437+00:00 2025-07-01 19:13:09.492711+00:00 None None None 0 1 3 NaN None NaN 1.0 2025-07-01 19:13:09.431000+00:00 1 None 1
5 bYcr1H6pInMIJNMd None 2025-07-01 19:13:12.595672+00:00 2025-07-01 19:13:13.590074+00:00 None None True 0 1 4 3.0 None 2.0 NaN 2025-07-01 19:13:12.596000+00:00 1 None 1
6 z8d1ghksJY0pvSs5 None 2025-07-01 19:13:13.536934+00:00 2025-07-01 19:13:13.584538+00:00 None None None 0 1 5 NaN None NaN 5.0 2025-07-01 19:13:13.537000+00:00 1 None 1
Storage
uid root description type region instance_uid space_id run_id created_at created_by_id _aux branch_id
id
1 eU5TXvFdF23w /home/runner/work/lamindb/lamindb/docs/test-track None local None 73KPGC58ahU9 1 None 2025-07-01 19:12:56.811000+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_id
id
5 7R6JfcmMHZSk0000 run_workflow.py/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n arti... Dqbr_hMfHs17EhbPXP_PyQ None None 1 None None True 2025-07-01 19:13:13.534000+00:00 1 None 1
4 BmREU4bprq5o0000 run_workflow.py run_workflow.py script import argparse\nimport lamindb as ln\n\nln.Pa... yqr8j5hTUulVRzv4J-o9SQ None None 1 None None True 2025-07-01 19:13:12.593000+00:00 1 None 1
3 02JdyxnvNRXB0000 track.ipynb/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n inpu... F_wwrfFs6zmzMGVilG2Prg None None 1 None None True 2025-07-01 19:13:08.650000+00:00 1 None 1
2 9995OOwxVdZC0000 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 2025-07-01 19:13:06.853000+00:00 1 None 1
1 P7xeu7ByPEAg0000 track.ipynb Track notebooks, scripts & functions notebook None None None None 1 None None True 2025-07-01 19:13:01.038000+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 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
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assert run.features.get_values() == {
    "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()

# clean up test instance
!rm -r ./test-track
!lamin delete --force test-track
 deleting instance testuser1/test-track