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
Hide code cell output 
→ 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()
Hide code cell output 
→ connected lamindb: testuser1/test-track

Param(name='preprocess_params', dtype='dict', created_by_id=1, space_id=1, created_at=2025-02-18 13:41:10 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
Hide code cell output 
→ connected lamindb: testuser1/test-track

→ created Transform('j7Ecw9WoDmgs0000'), started new Run('Qvbucg6h...') at 2025-02-18 13:41:14 UTC
→ params: input_dir=./mydataset, learning_rate=0.01, preprocess_params={'downsample': True, 'normalization': 'the_good_one'}

→ finished Run('Qvbucg6h') after 2s at 2025-02-18 13:41:16 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()
Hide code cell output
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 Qvbucg6hc7fSfyQp0aK2 None 2025-02-18 13:41:14.027840+00:00 2025-02-18 13:41:16.065501+00:00 None None True 0 1 1 2 None 1 None 2025-02-18 13:41:14.028000+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()
Hide code cell output 
{'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 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"])
Hide code cell output
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
Hide code cell content 
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('Hgy4VKDv3anM0000'), started new Run('lBVxeHIL...') at 2025-02-18 13:41:18 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=2025-02-18 13:41:19 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()
_images/fbd42790068ab4f0065a8e6588c18ed5e32ffc6c5ddba8bd43382c404c29c873.svg

This is the run that created the subsetted_artifact:

subsetted_artifact.run

Run(uid='K0mfvaqZti09j1AaJq44', started_at=2025-02-18 13:41:19 UTC, finished_at=2025-02-18 13:41:19 UTC, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=2, created_at=2025-02-18 13:41:19 UTC)

This is the function that created it:

subsetted_artifact.run.transform

Transform(uid='r0W2E5c3booT0000', is_latest=True, key='track.ipynb/subset_dataframe.py', type='function', hash='F_wwrfFs6zmzMGVilG2Prg', space_id=1, created_by_id=1, created_at=2025-02-18 13:41:19 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 r0W2E5c3booT0000 track.ipynb/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n inpu... F_wwrfFs6zmzMGVilG2Prg None None 1 None None True 2025-02-18 13:41:19.848000+00:00 1 None 1

This is the initating run that triggered the function call:

subsetted_artifact.run.initiated_by_run

Run(uid='lBVxeHILVLR7iB0vwCDL', started_at=2025-02-18 13:41:18 UTC, space_id=1, transform_id=2, created_by_id=1, created_at=2025-02-18 13:41:18 UTC)

This is the transform of the initiating run:

subsetted_artifact.run.initiated_by_run.transform

Transform(uid='Hgy4VKDv3anM0000', is_latest=True, key='track.ipynb', description='Track notebooks, scripts & functions', type='notebook', space_id=1, created_by_id=1, created_at=2025-02-18 13:41:18 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 PhKAkwD9McPMysfR0000 my_analysis/dataset.parquet None .parquet dataset DataFrame 6586 yMhflyJSY9zEHe9ew5-JqA None None md5 True False 1 1 None None True 2 2025-02-18 13:41:19.827000+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 GGCIFFTYUfmJn5JS0000 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3233 7jKFlRPwu3gPRxVnkb6qaw None None md5 True False 1 1 None None True 3 2025-02-18 13:41:19.901000+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()
Hide code cell output 
→ creating new artifact version for key='my_analysis/dataset_subsetted.parquet' (storage: '/home/runner/work/lamindb/lamindb/docs/test-track')
_images/4993bb540cf9ba0b815141ae56e6fe8c925d689cfec0c2c2f305d84a3a134ca7.svg

We created a new run:

subsetted_artifact.run

Run(uid='ayroJ8z2cuoa8xEZsbgM', started_at=2025-02-18 13:41:20 UTC, finished_at=2025-02-18 13:41:20 UTC, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=2, created_at=2025-02-18 13:41:20 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 GGCIFFTYUfmJn5JS0001 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3839 s5a7sMZMWEXie9Oy-UDK5g None None md5 True False 1 1 None None True 4 2025-02-18 13:41:20.424000+00:00 1 None 1

See the state of the database:

ln.view()
Hide code cell output 
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 GGCIFFTYUfmJn5JS0001 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3839 s5a7sMZMWEXie9Oy-UDK5g None None md5 True False 1 1 None None True 4 2025-02-18 13:41:20.424000+00:00 1 None 1
4 GGCIFFTYUfmJn5JS0000 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3233 7jKFlRPwu3gPRxVnkb6qaw None None md5 True False 1 1 None None False 3 2025-02-18 13:41:19.901000+00:00 1 None 1
3 PhKAkwD9McPMysfR0000 my_analysis/dataset.parquet None .parquet dataset DataFrame 6586 yMhflyJSY9zEHe9ew5-JqA None None md5 True False 1 1 None None True 2 2025-02-18 13:41:19.827000+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 2025-02-18 13:41:19.763000+00:00 1 None 1
6 input_artifact_key str None False 1 None 2.0 2025-02-18 13:41:19.756000+00:00 1 None 1
5 subset_cols int None False 1 None 2.0 2025-02-18 13:41:19.749000+00:00 1 None 1
4 subset_rows int None False 1 None 2.0 2025-02-18 13:41:19.742000+00:00 1 None 1
3 preprocess_params dict None False 1 None NaN 2025-02-18 13:41:10.129000+00:00 1 None 1
2 learning_rate float None False 1 None NaN 2025-02-18 13:41:10.123000+00:00 1 None 1
1 input_dir str None False 1 None NaN 2025-02-18 13:41:10.111000+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 2025-02-18 13:41:14.048000+00:00 1 None 1
2 0.01 None 1 2 2025-02-18 13:41:14.048000+00:00 1 None 1
3 {'downsample': True, 'normalization': 'the_goo... None 1 3 2025-02-18 13:41:14.048000+00:00 1 None 1
4 my_analysis/dataset.parquet None 1 6 2025-02-18 13:41:19.872000+00:00 1 None 1
5 my_analysis/dataset_subsetted.parquet None 1 7 2025-02-18 13:41:19.872000+00:00 1 None 1
6 2 None 1 4 2025-02-18 13:41:19.872000+00:00 1 None 1
7 2 None 1 5 2025-02-18 13:41:19.872000+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 Qvbucg6hc7fSfyQp0aK2 None 2025-02-18 13:41:14.027840+00:00 2025-02-18 13:41:16.065501+00:00 None None True 0 1 1 2.0 None 1.0 NaN 2025-02-18 13:41:14.028000+00:00 1 None 1
2 lBVxeHILVLR7iB0vwCDL None 2025-02-18 13:41:18.252149+00:00 NaT None None None 0 1 2 NaN None NaN NaN 2025-02-18 13:41:18.253000+00:00 1 None 1
3 K0mfvaqZti09j1AaJq44 None 2025-02-18 13:41:19.852146+00:00 2025-02-18 13:41:19.904506+00:00 None None None 0 1 3 NaN None NaN 2.0 2025-02-18 13:41:19.852000+00:00 1 None 1
4 ayroJ8z2cuoa8xEZsbgM None 2025-02-18 13:41:20.364973+00:00 2025-02-18 13:41:20.429714+00:00 None None None 0 1 3 NaN None NaN 2.0 2025-02-18 13:41:20.365000+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 CPhhQrlufozK /home/runner/work/lamindb/lamindb/docs/test-track None local None 73KPGC58ahU9 1 None 2025-02-18 13:41:04.256000+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 r0W2E5c3booT0000 track.ipynb/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n inpu... F_wwrfFs6zmzMGVilG2Prg None None 1 None None True 2025-02-18 13:41:19.848000+00:00 1 None 1
2 Hgy4VKDv3anM0000 track.ipynb Track notebooks, scripts & functions notebook None None None None 1 None None True 2025-02-18 13:41:18.245000+00:00 1 None 1
1 j7Ecw9WoDmgs0000 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 2025-02-18 13:41:14.024000+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

→ created Transform('US4rlnbl6eKX0000'), started new Run('uiMoQAnZ...') at 2025-02-18 13:41:24 UTC
→ params: run_workflow_subset=True

→ found artifact with same hash: Artifact(uid='PhKAkwD9McPMysfR0000', is_latest=True, key='my_analysis/dataset.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=6586, hash='yMhflyJSY9zEHe9ew5-JqA', space_id=1, storage_id=1, run_id=2, created_by_id=1, created_at=2025-02-18 13:41:19 UTC); to track this artifact as an input, use: ln.Artifact.get()

→ found artifact with same hash: Artifact(uid='GGCIFFTYUfmJn5JS0001', is_latest=True, key='my_analysis/dataset_subsetted.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=3839, hash='s5a7sMZMWEXie9Oy-UDK5g', space_id=1, storage_id=1, run_id=4, created_by_id=1, created_at=2025-02-18 13:41:20 UTC); to track this artifact as an input, use: ln.Artifact.get()
→ found artifact with same hash: Artifact(uid='Xu8DxC35abee9ERp0000', is_latest=True, description='log streams of run Qvbucg6hc7fSfyQp0aK2', suffix='.txt', size=0, hash='1B2M2Y8AsgTpgAmY7PhCfg', space_id=1, storage_id=1, created_by_id=1, created_at=2025-02-18 13:41:16 UTC); to track this artifact as an input, use: ln.Artifact.get()
! updated description from log streams of run Qvbucg6hc7fSfyQp0aK2 to log streams of run uiMoQAnZPuk3OzmjA8la

→ finished Run('uiMoQAnZ') after 1s at 2025-02-18 13:41:26 UTC

ln.view()
Hide code cell output 
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 GGCIFFTYUfmJn5JS0001 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3839 s5a7sMZMWEXie9Oy-UDK5g None None md5 True False 1 1 None None True 6 2025-02-18 13:41:20.424000+00:00 1 None 1
4 GGCIFFTYUfmJn5JS0000 my_analysis/dataset_subsetted.parquet None .parquet dataset DataFrame 3233 7jKFlRPwu3gPRxVnkb6qaw None None md5 True False 1 1 None None False 3 2025-02-18 13:41:19.901000+00:00 1 None 1
3 PhKAkwD9McPMysfR0000 my_analysis/dataset.parquet None .parquet dataset DataFrame 6586 yMhflyJSY9zEHe9ew5-JqA None None md5 True False 1 1 None None True 5 2025-02-18 13:41:19.827000+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 2025-02-18 13:41:24.650000+00:00 1 None 1
7 output_artifact_key str None False 1 None 2.0 2025-02-18 13:41:19.763000+00:00 1 None 1
6 input_artifact_key str None False 1 None 2.0 2025-02-18 13:41:19.756000+00:00 1 None 1
5 subset_cols int None False 1 None 2.0 2025-02-18 13:41:19.749000+00:00 1 None 1
4 subset_rows int None False 1 None 2.0 2025-02-18 13:41:19.742000+00:00 1 None 1
3 preprocess_params dict None False 1 None NaN 2025-02-18 13:41:10.129000+00:00 1 None 1
2 learning_rate float None False 1 None NaN 2025-02-18 13:41:10.123000+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 2025-02-18 13:41:14.048000+00:00 1 None 1
2 0.01 None 1 2 2025-02-18 13:41:14.048000+00:00 1 None 1
3 {'downsample': True, 'normalization': 'the_goo... None 1 3 2025-02-18 13:41:14.048000+00:00 1 None 1
4 my_analysis/dataset.parquet None 1 6 2025-02-18 13:41:19.872000+00:00 1 None 1
5 my_analysis/dataset_subsetted.parquet None 1 7 2025-02-18 13:41:19.872000+00:00 1 None 1
6 2 None 1 4 2025-02-18 13:41:19.872000+00:00 1 None 1
7 2 None 1 5 2025-02-18 13:41:19.872000+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 Qvbucg6hc7fSfyQp0aK2 None 2025-02-18 13:41:14.027840+00:00 2025-02-18 13:41:16.065501+00:00 None None True 0 1 1 2.0 None 1.0 NaN 2025-02-18 13:41:14.028000+00:00 1 None 1
2 lBVxeHILVLR7iB0vwCDL None 2025-02-18 13:41:18.252149+00:00 NaT None None None 0 1 2 NaN None NaN NaN 2025-02-18 13:41:18.253000+00:00 1 None 1
3 K0mfvaqZti09j1AaJq44 None 2025-02-18 13:41:19.852146+00:00 2025-02-18 13:41:19.904506+00:00 None None None 0 1 3 NaN None NaN 2.0 2025-02-18 13:41:19.852000+00:00 1 None 1
4 ayroJ8z2cuoa8xEZsbgM None 2025-02-18 13:41:20.364973+00:00 2025-02-18 13:41:20.429714+00:00 None None None 0 1 3 NaN None NaN 2.0 2025-02-18 13:41:20.365000+00:00 1 None 1
5 uiMoQAnZPuk3OzmjA8la None 2025-02-18 13:41:24.667211+00:00 2025-02-18 13:41:26.278308+00:00 None None True 0 1 4 2.0 None 1.0 NaN 2025-02-18 13:41:24.669000+00:00 1 None 1
6 JMANsjf9YAnWI2np8J0s None 2025-02-18 13:41:26.221976+00:00 2025-02-18 13:41:26.272929+00:00 None None None 0 1 5 NaN None NaN 5.0 2025-02-18 13:41:26.222000+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 CPhhQrlufozK /home/runner/work/lamindb/lamindb/docs/test-track None local None 73KPGC58ahU9 1 None 2025-02-18 13:41:04.256000+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 iSQXxR4c0BOE0000 run-workflow.py/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n arti... Dqbr_hMfHs17EhbPXP_PyQ None None 1 None None True 2025-02-18 13:41:26.218000+00:00 1 None 1
4 US4rlnbl6eKX0000 run-workflow.py run-workflow.py script import argparse\nimport lamindb as ln\n\nln.Pa... yqr8j5hTUulVRzv4J-o9SQ None None 1 None None True 2025-02-18 13:41:24.663000+00:00 1 None 1
3 r0W2E5c3booT0000 track.ipynb/subset_dataframe.py None function @ln.tracked()\ndef subset_dataframe(\n inpu... F_wwrfFs6zmzMGVilG2Prg None None 1 None None True 2025-02-18 13:41:19.848000+00:00 1 None 1
2 Hgy4VKDv3anM0000 track.ipynb Track notebooks, scripts & functions notebook None None None None 1 None None True 2025-02-18 13:41:18.245000+00:00 1 None 1
1 j7Ecw9WoDmgs0000 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 2025-02-18 13:41:14.024000+00:00 1 None 1
Hide code cell content 
# clean up test instance
!rm -r ./test-track
!lamin delete --force test-track

• deleting instance testuser1/test-track