Track notebooks & scripts¶

This guide explains how to use track() & finish() to track notebook & scripts along with their inputs and outputs.

For tracking data lineage in pipelines, see Pipelines – workflow managers.

# !pip install 'lamindb[jupyter]'
!lamin init --storage ./test-track

Track data lineage¶

Call track() to register a data transformation and start tracking inputs & outputs of a run. You will find your notebook or script in the Transform registry along with pipelines & functions. Run stores executions.

import lamindb as ln

# --> `ln.track()` generates a uid for your code
# --> `ln.track(uid)` initiates a tracked run
ln.track("9priar0hoE5u0000")

# your code

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

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

Query & load a notebook or script¶

In the API, filter Transform to obtain a transform record:

transform = ln.Transform.get(name="Track notebooks & scripts")
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

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/13VINnFk89PE0004

Sync scripts with GitHub¶

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 run parameters¶

LaminDB’s validation dialogue auto-generates code for run parameters. Here is an example:

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()

run-track-with-params.py¶

import argparse
import lamindb as ln

if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument("--input-dir", type=str)
    p.add_argument("--downsample", action="store_true")
    p.add_argument("--learning-rate", type=float)
    args = p.parse_args()
    params = {
        "input_dir": args.input_dir,
        "learning_rate": args.learning_rate,
        "preprocess_params": {
            "downsample": args.downsample,
            "normalization": "the_good_one",
        },
    }
    ln.track("JjRF4mACd9m00001", params=params)
    # your code
    ln.finish()

Run the script.

!python scripts/run-track-with-params.py  --input-dir ./mydataset --learning-rate 0.01 --downsample

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()

Show code cell output Hide code cell output

	uid	started_at	finished_at	is_consecutive	reference	reference_type	transform_id	report_id	environment_id	parent_id	created_at	created_by_id
id
1	8RfhDp7Ei3WOq7DYcbcy	2024-10-18 15:55:52.162899+00:00	2024-10-18 15:55:53.457895+00:00	True	None	None	1	None	1	None	2024-10-18 15:55:52.162943+00:00	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

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()

Or on the hub.

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

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

Show code cell output Hide code cell output

	param__name	created_by__handle	value	param_id	created_at	created_by_id
id
1	input_dir	testuser1	./mydataset	1	2024-10-18 15:55:52.174895+00:00	1
2	learning_rate	testuser1	0.01	2	2024-10-18 15:55:52.174939+00:00	1
3	preprocess_params	testuser1	{'downsample': True, 'normalization': 'the_goo...	3	2024-10-18 15:55:52.174975+00:00	1

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()