Track notebooks, scripts & functions¶
For tracking pipelines, see: Pipelines – workflow managers.
# pip install 'lamindb[jupyter]'
!lamin init --storage ./test-track
Show 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 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
Show code cell output
→ connected lamindb: testuser1/test-track
→ created Transform('TKQXkTqKXPCp0000'), started new Run('uvUULVsx...') at 2025-05-29 10:19:45 UTC
→ notebook imports: lamindb==1.6a1
• recommendation: to identify the notebook across renames, pass the uid: ln.track("TKQXkTqKXPCp", project="My project")
Artifact(uid='gKt5dYYnOTPHda4F0000', 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=2025-05-29 10:19:47 UTC)
Filter entities by project, e.g., artifacts:
ln.Artifact.filter(projects=my_project).df()
Show code cell output
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 | gKt5dYYnOTPHda4F0000 | my_file.fcs | None | .fcs | None | None | 19330507 | rCPvmZB19xs4zHZ7p_-Wrg | None | None | md5 | True | False | 1 | 1 | None | None | True | 1 | 2025-05-29 10:19:47.927000+00:00 | 1 | None | 1 |
Access entities linked to a project.
display(my_project.artifacts.df())
display(my_project.transforms.df())
display(my_project.runs.df())
Show code cell output
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 | gKt5dYYnOTPHda4F0000 | my_file.fcs | None | .fcs | None | None | 19330507 | rCPvmZB19xs4zHZ7p_-Wrg | None | None | md5 | True | False | 1 | 1 | None | None | True | 1 | 2025-05-29 10:19:47.927000+00:00 | 1 | None | 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 | TKQXkTqKXPCp0000 | track.ipynb | Track notebooks, scripts & functions | notebook | None | None | None | None | 1 | None | None | True | 2025-05-29 10:19:45.913000+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 | uvUULVsxq1nLvvaS | None | 2025-05-29 10:19:45.924069+00:00 | None | None | None | None | 0 | 1 | 1 | None | None | None | None | 2025-05-29 10:19:45.924000+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()
Show code cell output
Feature(uid='aOqd7QnDraQe', 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=2025-05-29 10:19:48 UTC)
If you hadn’t defined these parameters, you’d get a ValidationError
in the following script.
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
Show code cell output
→ connected lamindb: testuser1/test-track
→ created Transform('i5F0Ukhy6xrg0000'), started new Run('ywkultuc...') at 2025-05-29 10:19:50 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("i5F0Ukhy6xrg", params={...})
→ finished Run('ywkultuc') after 1s at 2025-05-29 10:19:51 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()
Show 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_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id | ||||||||||||||||||
2 | ywkultucvUzn6UdO | None | 2025-05-29 10:19:50.653376+00:00 | 2025-05-29 10:19:51.745997+00:00 | None | None | True | 0 | 1 | 2 | 3 | None | 2 | None | 2025-05-29 10:19:50.654000+00:00 | 1 | None | 1 |
Note that:
preprocess_params__downsample=True
traverses the dictionarypreprocess_params
to find the key"downsample"
and match it toTrue
nested keys like
"downsample"
in a dictionary do not appear inFeature
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()
Show 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.
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"])
Show code cell output
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='eIxOu3OUEEOP', 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=2025-05-29 10:19:52 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='tZUhOLq62659MKuT', started_at=2025-05-29 10:19:52 UTC, finished_at=2025-05-29 10:19:52 UTC, branch_id=1, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=1, created_at=2025-05-29 10:19:52 UTC)
This is the function that created it:
subsetted_artifact.run.transform
Transform(uid='YrSTlpY0RCk00000', 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=2025-05-29 10:19:52 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 | YrSTlpY0RCk00000 | track.ipynb/subset_dataframe.py | None | function | @ln.tracked()\ndef subset_dataframe(\n inpu... | F_wwrfFs6zmzMGVilG2Prg | None | None | 1 | None | None | True | 2025-05-29 10:19:52.330000+00:00 | 1 | None | 1 |
This is the initating run that triggered the function call:
subsetted_artifact.run.initiated_by_run
Run(uid='uvUULVsxq1nLvvaS', started_at=2025-05-29 10:19:45 UTC, branch_id=1, space_id=1, transform_id=1, created_by_id=1, created_at=2025-05-29 10:19:45 UTC)
This is the transform
of the initiating run:
subsetted_artifact.run.initiated_by_run.transform
Transform(uid='TKQXkTqKXPCp0000', 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=2025-05-29 10:19:45 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 | gHp3TtsX3RNT9U1d0000 | my_analysis/dataset.parquet | None | .parquet | dataset | DataFrame | 9108 | D2ZSlO6x7-OIfdf0MkTzRQ | None | 3 | md5 | True | False | 1 | 1 | None | None | True | 1 | 2025-05-29 10:19:52.309000+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_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id | |||||||||||||||||||||||
5 | UscBcrAtzZpUzKV80000 | my_analysis/dataset_subsetted.parquet | None | .parquet | dataset | DataFrame | 3238 | dNHL-WWN3PCVS9pyW8pKHA | None | 2 | md5 | True | False | 1 | 1 | None | None | True | 3 | 2025-05-29 10:19:52.382000+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()
Show code cell output
→ 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='24AT5Jq51dbCiiw5', started_at=2025-05-29 10:19:52 UTC, finished_at=2025-05-29 10:19:53 UTC, branch_id=1, space_id=1, transform_id=3, created_by_id=1, initiated_by_run_id=1, created_at=2025-05-29 10:19:52 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 | UscBcrAtzZpUzKV80001 | my_analysis/dataset_subsetted.parquet | None | .parquet | dataset | DataFrame | 3852 | gBAN-0lok9-D61VnFuHrAA | None | 2 | md5 | True | False | 1 | 1 | None | None | True | 4 | 2025-05-29 10:19:53.042000+00:00 | 1 | None | 1 |
See the state of the database:
ln.view()
Show 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_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id | |||||||||||||||||||||||
6 | UscBcrAtzZpUzKV80001 | my_analysis/dataset_subsetted.parquet | None | .parquet | dataset | DataFrame | 3852 | gBAN-0lok9-D61VnFuHrAA | None | 2.0 | md5 | True | False | 1 | 1 | None | None | True | 4.0 | 2025-05-29 10:19:53.042000+00:00 | 1 | None | 1 |
5 | UscBcrAtzZpUzKV80000 | my_analysis/dataset_subsetted.parquet | None | .parquet | dataset | DataFrame | 3238 | dNHL-WWN3PCVS9pyW8pKHA | None | 2.0 | md5 | True | False | 1 | 1 | None | None | False | 3.0 | 2025-05-29 10:19:52.382000+00:00 | 1 | None | 1 |
4 | gHp3TtsX3RNT9U1d0000 | my_analysis/dataset.parquet | None | .parquet | dataset | DataFrame | 9108 | D2ZSlO6x7-OIfdf0MkTzRQ | None | 3.0 | md5 | True | False | 1 | 1 | None | None | True | 1.0 | 2025-05-29 10:19:52.309000+00:00 | 1 | None | 1 |
3 | r3dQJYCiYFwVTcEY0000 | None | log streams of run ywkultucvUzn6UdO | .txt | __lamindb_run__ | None | 0 | 1B2M2Y8AsgTpgAmY7PhCfg | None | NaN | md5 | True | False | 1 | 1 | None | None | True | NaN | 2025-05-29 10:19:51.750000+00:00 | 1 | None | 1 |
2 | I0nWY20NcECnGNG80000 | None | requirements.txt | .txt | __lamindb_run__ | None | 3992 | 0xW_aLpu4vmJOrumPkmK3Q | None | NaN | md5 | True | False | 1 | 1 | None | None | True | NaN | 2025-05-29 10:19:51.743000+00:00 | 1 | None | 1 |
1 | gKt5dYYnOTPHda4F0000 | my_file.fcs | None | .fcs | None | None | 19330507 | rCPvmZB19xs4zHZ7p_-Wrg | None | NaN | md5 | True | False | 1 | 1 | None | None | True | 1.0 | 2025-05-29 10:19:47.927000+00:00 | 1 | None | 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 | eIxOu3OUEEOP | output_artifact_key | str | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:19:52.254000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
6 | 9W2Dv2m2n3JP | input_artifact_key | str | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:19:52.245000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
5 | N3jku7V4oY3l | subset_cols | int | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:19:52.233000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
4 | 9oV4wAkzf6rM | subset_rows | int | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:19:52.223000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
3 | aOqd7QnDraQe | preprocess_params | dict | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:19:48.087000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
2 | 4GpwrS78pQD9 | learning_rate | float | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:19:48.078000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
1 | lSa8RKg4djld | input_dir | str | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:19:48.069000+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 | 2025-05-29 10:19:50.670000+00:00 | 1 | None | 1 |
2 | 0.01 | BIF-_RHBU2Sm7COXgAOIYg | 1 | 2 | NaN | 2025-05-29 10:19:50.672000+00:00 | 1 | None | 1 |
3 | {'downsample': True, 'normalization': 'the_goo... | 4ehQH8UO25aNM181K_gloQ | 1 | 3 | NaN | 2025-05-29 10:19:50.674000+00:00 | 1 | None | 1 |
4 | 2 | yB5yjZ1ML2NvBn-JzBSGLA | 1 | 4 | 1.0 | 2025-05-29 10:19:52.352000+00:00 | 1 | None | 1 |
5 | 2 | yB5yjZ1ML2NvBn-JzBSGLA | 1 | 5 | 1.0 | 2025-05-29 10:19:52.354000+00:00 | 1 | None | 1 |
6 | my_analysis/dataset.parquet | 1ImgyYl4KlCl3XCd-aQE9Q | 1 | 6 | 1.0 | 2025-05-29 10:19:52.357000+00:00 | 1 | None | 1 |
7 | my_analysis/dataset_subsetted.parquet | G9luXJ51Hi4-Csrifos0Lw | 1 | 7 | 1.0 | 2025-05-29 10:19:52.359000+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 | iLouzfX8BtU2 | My project | False | None | None | None | None | 0 | 1 | None | None | 2025-05-29 10:19:45.045000+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 | uvUULVsxq1nLvvaS | None | 2025-05-29 10:19:45.924069+00:00 | NaT | None | None | None | 0 | 1 | 1 | NaN | None | NaN | NaN | 2025-05-29 10:19:45.924000+00:00 | 1 | None | 1 |
2 | ywkultucvUzn6UdO | None | 2025-05-29 10:19:50.653376+00:00 | 2025-05-29 10:19:51.745997+00:00 | None | None | True | 0 | 1 | 2 | 3.0 | None | 2.0 | NaN | 2025-05-29 10:19:50.654000+00:00 | 1 | None | 1 |
3 | tZUhOLq62659MKuT | None | 2025-05-29 10:19:52.335063+00:00 | 2025-05-29 10:19:52.387657+00:00 | None | None | None | 0 | 1 | 3 | NaN | None | NaN | 1.0 | 2025-05-29 10:19:52.335000+00:00 | 1 | None | 1 |
4 | 24AT5Jq51dbCiiw5 | None | 2025-05-29 10:19:52.997163+00:00 | 2025-05-29 10:19:53.048450+00:00 | None | None | None | 0 | 1 | 3 | NaN | None | NaN | 1.0 | 2025-05-29 10:19:52.997000+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 | EWfiiSeyG5xo | /home/runner/work/lamindb/lamindb/docs/test-track | None | local | None | 73KPGC58ahU9 | 1 | None | 2025-05-29 10:19:41.976000+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 | YrSTlpY0RCk00000 | track.ipynb/subset_dataframe.py | None | function | @ln.tracked()\ndef subset_dataframe(\n inpu... | F_wwrfFs6zmzMGVilG2Prg | None | None | 1 | None | None | True | 2025-05-29 10:19:52.330000+00:00 | 1 | None | 1 |
2 | i5F0Ukhy6xrg0000 | 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 | 2025-05-29 10:19:50.650000+00:00 | 1 | None | 1 |
1 | TKQXkTqKXPCp0000 | track.ipynb | Track notebooks, scripts & functions | notebook | None | None | None | None | 1 | None | None | True | 2025-05-29 10:19:45.913000+00:00 | 1 | None | 1 |
In a script¶
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
Show code cell output
→ connected lamindb: testuser1/test-track
→ created Transform('mJyK6DMmFSjh0000'), started new Run('X6Ok6k5K...') at 2025-05-29 10:19:56 UTC
→ params: run_workflow_subset=True
• recommendation: to identify the script across renames, pass the uid: ln.track("mJyK6DMmFSjh", params={...})
→ returning existing artifact with same hash: Artifact(uid='gHp3TtsX3RNT9U1d0000', is_latest=True, key='my_analysis/dataset.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=9108, hash='D2ZSlO6x7-OIfdf0MkTzRQ', n_observations=3, branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-05-29 10:19:52 UTC); to track this artifact as an input, use: ln.Artifact.get()
→ returning existing artifact with same hash: Artifact(uid='UscBcrAtzZpUzKV80001', is_latest=True, key='my_analysis/dataset_subsetted.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=3852, hash='gBAN-0lok9-D61VnFuHrAA', n_observations=2, branch_id=1, space_id=1, storage_id=1, run_id=4, created_by_id=1, created_at=2025-05-29 10:19:53 UTC); to track this artifact as an input, use: ln.Artifact.get()
→ returning existing artifact with same hash: Artifact(uid='r3dQJYCiYFwVTcEY0000', is_latest=True, description='log streams of run ywkultucvUzn6UdO', suffix='.txt', kind='__lamindb_run__', size=0, hash='1B2M2Y8AsgTpgAmY7PhCfg', branch_id=1, space_id=1, storage_id=1, created_by_id=1, created_at=2025-05-29 10:19:51 UTC); to track this artifact as an input, use: ln.Artifact.get()
! updated description from log streams of run ywkultucvUzn6UdO to log streams of run X6Ok6k5KLWCH3YVi
→ finished Run('X6Ok6k5K') after 0s at 2025-05-29 10:19:57 UTC
ln.view()
Show 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_id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id | |||||||||||||||||||||||
6 | UscBcrAtzZpUzKV80001 | my_analysis/dataset_subsetted.parquet | None | .parquet | dataset | DataFrame | 3852 | gBAN-0lok9-D61VnFuHrAA | None | 2.0 | md5 | True | False | 1 | 1 | None | None | True | 4.0 | 2025-05-29 10:19:53.042000+00:00 | 1 | None | 1 |
5 | UscBcrAtzZpUzKV80000 | my_analysis/dataset_subsetted.parquet | None | .parquet | dataset | DataFrame | 3238 | dNHL-WWN3PCVS9pyW8pKHA | None | 2.0 | md5 | True | False | 1 | 1 | None | None | False | 3.0 | 2025-05-29 10:19:52.382000+00:00 | 1 | None | 1 |
4 | gHp3TtsX3RNT9U1d0000 | my_analysis/dataset.parquet | None | .parquet | dataset | DataFrame | 9108 | D2ZSlO6x7-OIfdf0MkTzRQ | None | 3.0 | md5 | True | False | 1 | 1 | None | None | True | 1.0 | 2025-05-29 10:19:52.309000+00:00 | 1 | None | 1 |
3 | r3dQJYCiYFwVTcEY0000 | None | log streams of run X6Ok6k5KLWCH3YVi | .txt | __lamindb_run__ | None | 0 | 1B2M2Y8AsgTpgAmY7PhCfg | None | NaN | md5 | True | False | 1 | 1 | None | None | True | NaN | 2025-05-29 10:19:51.750000+00:00 | 1 | None | 1 |
2 | I0nWY20NcECnGNG80000 | None | requirements.txt | .txt | __lamindb_run__ | None | 3992 | 0xW_aLpu4vmJOrumPkmK3Q | None | NaN | md5 | True | False | 1 | 1 | None | None | True | NaN | 2025-05-29 10:19:51.743000+00:00 | 1 | None | 1 |
1 | gKt5dYYnOTPHda4F0000 | my_file.fcs | None | .fcs | None | None | 19330507 | rCPvmZB19xs4zHZ7p_-Wrg | None | NaN | md5 | True | False | 1 | 1 | None | None | True | 1.0 | 2025-05-29 10:19:47.927000+00:00 | 1 | None | 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 | nfNwjo69qpTA | run_workflow_subset | bool | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | NaN | 2025-05-29 10:19:55.800000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
7 | eIxOu3OUEEOP | output_artifact_key | str | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1.0 | 2025-05-29 10:19:52.254000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
6 | 9W2Dv2m2n3JP | input_artifact_key | str | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1.0 | 2025-05-29 10:19:52.245000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
5 | N3jku7V4oY3l | subset_cols | int | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1.0 | 2025-05-29 10:19:52.233000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
4 | 9oV4wAkzf6rM | subset_rows | int | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1.0 | 2025-05-29 10:19:52.223000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
3 | aOqd7QnDraQe | preprocess_params | dict | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1.0 | 2025-05-29 10:19:48.087000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
2 | 4GpwrS78pQD9 | learning_rate | float | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1.0 | 2025-05-29 10:19:48.078000+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 | 2025-05-29 10:19:50.670000+00:00 | 1 | None | 1 |
2 | 0.01 | BIF-_RHBU2Sm7COXgAOIYg | 1 | 2 | NaN | 2025-05-29 10:19:50.672000+00:00 | 1 | None | 1 |
3 | {'downsample': True, 'normalization': 'the_goo... | 4ehQH8UO25aNM181K_gloQ | 1 | 3 | NaN | 2025-05-29 10:19:50.674000+00:00 | 1 | None | 1 |
4 | 2 | yB5yjZ1ML2NvBn-JzBSGLA | 1 | 4 | 1.0 | 2025-05-29 10:19:52.352000+00:00 | 1 | None | 1 |
5 | 2 | yB5yjZ1ML2NvBn-JzBSGLA | 1 | 5 | 1.0 | 2025-05-29 10:19:52.354000+00:00 | 1 | None | 1 |
6 | my_analysis/dataset.parquet | 1ImgyYl4KlCl3XCd-aQE9Q | 1 | 6 | 1.0 | 2025-05-29 10:19:52.357000+00:00 | 1 | None | 1 |
7 | my_analysis/dataset_subsetted.parquet | G9luXJ51Hi4-Csrifos0Lw | 1 | 7 | 1.0 | 2025-05-29 10:19:52.359000+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 | iLouzfX8BtU2 | My project | False | None | None | None | None | 0 | 1 | None | None | 2025-05-29 10:19:45.045000+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 | uvUULVsxq1nLvvaS | None | 2025-05-29 10:19:45.924069+00:00 | NaT | None | None | None | 0 | 1 | 1 | NaN | None | NaN | NaN | 2025-05-29 10:19:45.924000+00:00 | 1 | None | 1 |
2 | ywkultucvUzn6UdO | None | 2025-05-29 10:19:50.653376+00:00 | 2025-05-29 10:19:51.745997+00:00 | None | None | True | 0 | 1 | 2 | 3.0 | None | 2.0 | NaN | 2025-05-29 10:19:50.654000+00:00 | 1 | None | 1 |
3 | tZUhOLq62659MKuT | None | 2025-05-29 10:19:52.335063+00:00 | 2025-05-29 10:19:52.387657+00:00 | None | None | None | 0 | 1 | 3 | NaN | None | NaN | 1.0 | 2025-05-29 10:19:52.335000+00:00 | 1 | None | 1 |
4 | 24AT5Jq51dbCiiw5 | None | 2025-05-29 10:19:52.997163+00:00 | 2025-05-29 10:19:53.048450+00:00 | None | None | None | 0 | 1 | 3 | NaN | None | NaN | 1.0 | 2025-05-29 10:19:52.997000+00:00 | 1 | None | 1 |
5 | X6Ok6k5KLWCH3YVi | None | 2025-05-29 10:19:56.290934+00:00 | 2025-05-29 10:19:57.201598+00:00 | None | None | True | 0 | 1 | 4 | 3.0 | None | 2.0 | NaN | 2025-05-29 10:19:56.291000+00:00 | 1 | None | 1 |
6 | NlWF9g0ls2s4kJs5 | None | 2025-05-29 10:19:57.157657+00:00 | 2025-05-29 10:19:57.197733+00:00 | None | None | None | 0 | 1 | 5 | NaN | None | NaN | 5.0 | 2025-05-29 10:19:57.158000+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 | EWfiiSeyG5xo | /home/runner/work/lamindb/lamindb/docs/test-track | None | local | None | 73KPGC58ahU9 | 1 | None | 2025-05-29 10:19:41.976000+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 | ZzhWkBHdVJk30000 | run-workflow.py/subset_dataframe.py | None | function | @ln.tracked()\ndef subset_dataframe(\n arti... | Dqbr_hMfHs17EhbPXP_PyQ | None | None | 1 | None | None | True | 2025-05-29 10:19:57.155000+00:00 | 1 | None | 1 |
4 | mJyK6DMmFSjh0000 | run-workflow.py | run-workflow.py | script | import argparse\nimport lamindb as ln\n\nln.Pa... | yqr8j5hTUulVRzv4J-o9SQ | None | None | 1 | None | None | True | 2025-05-29 10:19:56.288000+00:00 | 1 | None | 1 |
3 | YrSTlpY0RCk00000 | track.ipynb/subset_dataframe.py | None | function | @ln.tracked()\ndef subset_dataframe(\n inpu... | F_wwrfFs6zmzMGVilG2Prg | None | None | 1 | None | None | True | 2025-05-29 10:19:52.330000+00:00 | 1 | None | 1 |
2 | i5F0Ukhy6xrg0000 | 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 | 2025-05-29 10:19:50.650000+00:00 | 1 | None | 1 |
1 | TKQXkTqKXPCp0000 | track.ipynb | Track notebooks, scripts & functions | notebook | None | None | None | None | 1 | None | None | True | 2025-05-29 10:19:45.913000+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>
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
Show code cell content
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