Tutorial: Artifacts#

Biology is measured in samples that generate batches of data.

LaminDB provides a framework to transform these batches into more useful representations: validated, queryable datasets, machine learning models, and analytical insights.

The tutorial has two parts, each is a Jupyter notebook:

  1. Tutorial: Artifacts - register & access

  2. Tutorial: Features & labels - validate & annotate

Setup#

Install the lamindb Python package:

pip install 'lamindb[jupyter,aws]'

Init a LaminDB instance with a directory ./lamin-tutorial for storing data:

!lamin init --storage ./lamin-tutorial  # or "s3://my-bucket" or "gs://my-bucket"
Hide code cell output 
💡 connected lamindb: anonymous/lamin-tutorial

import lamindb as ln

ln.settings.verbosity = "hint"
Hide code cell output 
💡 connected lamindb: anonymous/lamin-tutorial
What else can I configure during setup?

  1. Instead of the default SQLite database, use PostgreSQL:

    db=postgresql://<user>:<pwd>@<hostname>:<port>/<dbname>

  2. Instead of a default instance name derived from storage, provide a custom name:

    name=myinstance

  3. Beyond the core schema, use bionty and other schemas:

    schema=bionty,custom1,template1

For more, see Install & setup.

Track a data source#

The code that generates a dataset is a transform (Transform). It could be a script, a notebook, a pipeline or a UI interaction like an upload.

Let’s track the notebook that’s being run:

ln.settings.transform.stem_uid = "NJvdsWWbJlZS"
ln.settings.transform.version = "0"
ln.track()
Hide code cell output 
💡 notebook imports: lamindb==0.71.2

💡 saved: Transform(version='0', uid='NJvdsWWbJlZS6K79', name='Tutorial: Artifacts', key='tutorial', type='notebook', updated_at=2024-05-07 21:17:45 UTC, created_by_id=1)

💡 saved: Run(uid='OsQC0bKGs8ipRQQA2rwd', transform_id=1, created_by_id=1)

💡 tracked pip freeze > /home/runner/.cache/lamindb/run_env_pip_OsQC0bKGs8ipRQQA2rwd.txt

By calling track(), the notebook is automatically linked as the source of all data that’s about to be saved!

What happened under the hood?

  1. Imported package versions of current notebook were detected

  2. Notebook metadata was detected and stored in a Transform record

  3. Run metadata was detected and stored in a Run record

The Transform class registers data transformations: a notebook, a pipeline or a UI operation.

The Run class registers executions of transforms. Several runs can be linked to the same transform if executed with different context (time, user, input data, etc.).

How do I track a pipeline instead of a notebook?
transform = ln.Transform(name="My pipeline", version="1.2.0")
ln.track(transform)
Why should I care about tracking notebooks?

If you can, avoid interactive notebooks: Anything that can be a deterministic pipeline, should be a pipeline.

Just: much insight generated from biological data is driven by computational biologists interacting with it.

A notebook that’s run a single time on specific data is not a pipeline: it’s a (versioned) document that produced insight or some other form of data representation (with parallels to an ELN in the wetlab).

Because humans are in the loop, most mistakes happen when using notebooks: track() helps avoiding some.

(An early blog post on this is here.)

Manage artifacts#

We’ll work with a toy collection of image files and transform them into higher-level features for downstream analysis.

(For other data types: see Data types.)

Consider 3 directories storing images & metadata of Iris flowers, generated in 3 subsequent studies:

ln.UPath("s3://lamindb-dev-datasets/iris_studies").view_tree()
Hide code cell output 
3 sub-directories & 151 files with suffixes '.csv', '.jpg'
s3://lamindb-dev-datasets/iris_studies
├── study0_raw_images
│   ├── iris-0337d20a3b7273aa0ddaa7d6afb57a37a759b060e4401871db3cefaa6adc068d.jpg
│   ├── iris-0797945218a97d6e5251b4758a2ba1b418cbd52ce4ef46a3239e4b939bd9807b.jpg
│   ├── iris-0f133861ea3fe1b68f9f1b59ebd9116ff963ee7104a0c4200218a33903f82444.jpg
│   ├── iris-0fec175448a23db03c1987527f7e9bb74c18cffa76ef003f962c62603b1cbb87.jpg
│   ├── iris-125b6645e086cd60131764a6bed12650e0f7f2091c8bbb72555c103196c01881.jpg
│   ├── iris-13dfaff08727abea3da8cfd8d097fe1404e76417fefe27ff71900a89954e145a.jpg
│   ├── iris-1566f7f5421eaf423a82b3c1cd1328f2a685c5ef87d8d8e710f098635d86d3d0.jpg
│   ├── iris-1804702f49c2c385f8b30913569aebc6dce3da52ec02c2c638a2b0806f16014e.jpg
│   ├── iris-318d451a8c95551aecfde6b55520f302966db0a26a84770427300780b35aa05a.jpg
│   ├── iris-3dec97fe46d33e194520ca70740e4c2e11b0ffbffbd0aec0d06afdc167ddf775.jpg
│   ├── iris-3eed72bc2511f619190ce79d24a0436fef7fcf424e25523cb849642d14ac7bcf.jpg
│   ├── iris-430fa45aad0edfeb5b7138ff208fdeaa801b9830a9eb68f378242465b727289a.jpg
│   ├── iris-4cc15cd54152928861ecbdc8df34895ed463403efb1571dac78e3223b70ef569.jpg
│   ├── iris-4febb88ef811b5ca6077d17ef8ae5dbc598d3f869c52af7c14891def774d73fa.jpg
│   ├── iris-590e7f5b8f4de94e4b82760919abd9684ec909d9f65691bed8e8f850010ac775.jpg
│   ├── iris-5a313749aa61e9927389affdf88dccdf21d97d8a5f6aa2bd246ca4bc926903ba.jpg
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│   ├── iris-5f4e8fffde2404cc30be275999fddeec64f8a711ab73f7fa4eb7667c8475c57b.jpg
│   ├── iris-68d83ad09262afb25337ccc1d0f3a6d36f118910f36451ce8a6600c77a8aa5bd.jpg
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│   ├── iris-7cf1ebf02b2cc31539ed09ab89530fec6f31144a0d5248a50e7c14f64d24fe6e.jpg
│   ├── iris-7dcc69fa294fe04767706c6f455ea6b31d33db647b08aab44b3cd9022e2f2249.jpg
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│   ├── iris-eb01666d4591b2e03abecef5a7ded79c6d4ecb6d1922382c990ad95210d55795.jpg
│   ├── iris-f6e4890dee087bd52e2c58ea4c6c2652da81809603ea3af561f11f8c2775c5f3.jpg
│   └── meta.csv
├── study1_raw_images
│   ├── iris-0879d3f5b337fe512da1c7bf1d2bfd7616d744d3eef7fa532455a879d5cc4ba0.jpg
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│   ├── iris-12adb3a8516399e27ff1a9d20d28dca4674836ed00c7c0ae268afce2c30c4451.jpg
│   ├── iris-17ac8f7b5734443090f35bdc531bfe05b0235b5d164afb5c95f9d35f13655cf3.jpg
│   ├── iris-2118d3f235a574afd48a1f345bc2937dad6e7660648516c8029f4e76993ea74d.jpg
│   ├── iris-213cd179db580f8e633087dcda0969fd175d18d4f325cb5b4c5f394bbba0c1e0.jpg
│   ├── iris-21a1255e058722de1abe928e5bbe1c77bda31824c406c53f19530a3ca40be218.jpg
│   ├── iris-249370d38cc29bc2a4038e528f9c484c186fe46a126e4b6c76607860679c0453.jpg
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│   ├── iris-5691cad06fe37f743025c097fa9c4cec85e20ca3b0efff29175e60434e212421.jpg
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│   ├── iris-66eee9ff0bfa521905f733b2a0c6c5acad7b8f1a30d280ed4a17f54fe1822a7e.jpg
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│   ├── iris-9e3208f4f9fedc9598ddf26f77925a1e8df9d7865a4d6e5b4f74075d558d6a5e.jpg
│   ├── iris-a7e13b6f2d7f796768d898f5f66dceefdbd566dd4406eea9f266fc16dd68a6f2.jpg
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│   ├── iris-fac5f8c23d8c50658db0f4e4a074c2f7771917eb52cbdf6eda50c12889510cf4.jpg
│   └── meta.csv
└── study2_raw_images
    ├── iris-01cdd55ca6402713465841abddcce79a2e906e12edf95afb77c16bde4b4907dc.jpg
    ├── iris-02868b71ddd9b33ab795ac41609ea7b20a6e94f2543fad5d7fa11241d61feacf.jpg
    ├── iris-0415d2f3295db04bebc93249b685f7d7af7873faa911cd270ecd8363bd322ed5.jpg
    ├── iris-0c826b6f4648edf507e0cafdab53712bb6fd1f04dab453cee8db774a728dd640.jpg
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    ├── iris-186fe29e32ee1405ddbdd36236dd7691a3c45ba78cc4c0bf11489fa09fbb1b65.jpg
    ├── iris-1b0b5aabd59e4c6ed1ceb54e57534d76f2f3f97e0a81800ff7ed901c35a424ab.jpg
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    ├── iris-bfcf79b3b527eb64b78f9a068a1000042336e532f0f44e68f818dd13ab492a76.jpg
    ├── iris-c156236fb6e888764485e796f1f972bbc7ad960fe6330a7ce9182922046439c4.jpg
    ├── iris-d99d5fd2de5be1419cbd569570dbb6c9a6c8ec4f0a1ff5b55dc2607f6ecdca8f.jpg
    ├── iris-d9aae37a8fa6afdef2af170c266a597925eea935f4d070e979d565713ea62642.jpg
    ├── iris-dbc87fcecade2c070baaf99caf03f4f0f6e3aa977e34972383cb94d0efe8a95d.jpg
    ├── iris-e3d1a560d25cf573d2cbbf2fe6cd231819e998109a5cf1788d59fbb9859b3be2.jpg
    ├── iris-ec288bdad71388f907457db2476f12a5cb43c28cfa28d2a2077398a42b948a35.jpg
    ├── iris-ed5b4e072d43bc53a00a4a7f4d0f5d7c0cbd6a006e9c2d463128cedc956cb3de.jpg
    ├── iris-f3018a9440d17c265062d1c61475127f9952b6fe951d38fd7700402d706c0b01.jpg
    ├── iris-f47c5963cdbaa3238ba2d446848e8449c6af83e663f0a9216cf0baba8429b36f.jpg
    ├── iris-fa4b6d7e3617216104b1405cda21bf234840cd84a2c1966034caa63def2f64f0.jpg
    ├── iris-fc4b0cc65387ff78471659d14a78f0309a76f4c3ec641b871e40b40424255097.jpg
    └── meta.csv

Our goal is to turn these directories into a validated & queryable dataset that can be used alongside many other datasets.

Register an artifact#

LaminDB uses the Artifact class to manage datasets & models that are stored as files, folders, or arrays. Artifact is a registry to manage search, queries, validation & storage access.

Let’s create a Artifact object from one of the files:

artifact = ln.Artifact(
    "s3://lamindb-dev-datasets/iris_studies/study0_raw_images/meta.csv"
)
artifact
Hide code cell output 
💡 path in storage 's3://lamindb-dev-datasets' with key 'iris_studies/study0_raw_images/meta.csv'

Artifact(uid='QvdXQFVkt7vujQiADwNC', key='iris_studies/study0_raw_images/meta.csv', suffix='.csv', size=4355, hash='ZpAEpN0iFYH6vjZNigic7g', hash_type='md5', visibility=1, key_is_virtual=False, storage_id=2, transform_id=1, run_id=1, created_by_id=1)
Which fields are populated when creating an artifact record?

Basic fields:

  • uid: universal ID

  • key: storage key, a relative path of the artifact in storage

  • description: an optional string description

  • storage: the storage location (the root, say, an S3 bucket or a local directory)

  • suffix: an optional file/path suffix

  • size: the artifact size in bytes

  • hash: a hash useful to check for integrity and collisions (is this artifact already stored?)

  • hash_type: the type of the hash (usually, an MD5 or SHA1 checksum)

  • created_at: time of creation

  • updated_at: time of last update

Provenance-related fields:

  • created_by: the User who created the artifact

  • transform: the Transform (pipeline, notebook, instrument, app) that was run

  • run: the Run of the transform that created the artifact

For a full reference, see Artifact.

Upon .save(), artifact metadata is written to the database:

artifact.save()
Hide code cell output 
Artifact(uid='QvdXQFVkt7vujQiADwNC', key='iris_studies/study0_raw_images/meta.csv', suffix='.csv', size=4355, hash='ZpAEpN0iFYH6vjZNigic7g', hash_type='md5', visibility=1, key_is_virtual=False, updated_at=2024-05-07 21:17:48 UTC, storage_id=2, transform_id=1, run_id=1, created_by_id=1)
What happens during save?

In the database: A artifact record is inserted into the artifact registry. If the artifact record exists already, it’s updated.

In storage:

  • If the default storage is in the cloud, .save() triggers an upload for a local artifact.

  • If the artifact is already in a registered storage location, only the metadata of the record is saved to the artifact registry.

We can get an overview of all artifacts in the database by calling df():

ln.Artifact.df()
Hide code cell output
version uid storage_id key suffix accessor description size hash hash_type n_objects n_observations transform_id run_id visibility key_is_virtual created_at updated_at created_by_id
id
1 None QvdXQFVkt7vujQiADwNC 2 iris_studies/study0_raw_images/meta.csv .csv None None 4355 ZpAEpN0iFYH6vjZNigic7g md5 None None 1 1 1 False 2024-05-07 21:17:48.209773+00:00 2024-05-07 21:17:48.209804+00:00 1

View data lineage#

Visualize data lineage with view_lineage():

artifact.view_lineage()
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_images/6a55bf5eeaf94510bd31c8937965da9fbb1b36fdb72fcc0c976868cf65f11254.svg

Or directly access its linked Transform & Run records:

artifact.transform
Hide code cell output 
Transform(version='0', uid='NJvdsWWbJlZS6K79', name='Tutorial: Artifacts', key='tutorial', type='notebook', updated_at=2024-05-07 21:17:45 UTC, created_by_id=1)

artifact.run
Hide code cell output 
Run(uid='OsQC0bKGs8ipRQQA2rwd', started_at=2024-05-07 21:17:45 UTC, is_consecutive=True, transform_id=1, created_by_id=1)

(For a comprehensive example with data lineage through UI uploads, pipelines & notebooks of multiple data types, see Project flow.)

Access an artifact#

path gives you the file path, a UPath object:

artifact.path
Hide code cell output 
S3Path('s3://lamindb-dev-datasets/iris_studies/study0_raw_images/meta.csv')

Typically, your artifact is in cloud storage - to cache it locally, call cache():

artifact.cache()
Hide code cell output 
PosixUPath('/home/runner/.cache/lamindb/lamindb-dev-datasets/iris_studies/study0_raw_images/meta.csv')

Many artifacts have default in-memory representations. To load artifacts into memory with a default loader, call load():

df = artifact.load(index_col=0)
df.head()
Hide code cell output
0 1
0 iris-0797945218a97d6e5251b4758a2ba1b418cbd52ce... setosa
1 iris-0f133861ea3fe1b68f9f1b59ebd9116ff963ee710... versicolor
2 iris-9ffe51c2abd973d25a299647fa9ccaf6aa9c8eecf... versicolor
3 iris-83f433381b755101b9fc9fbc9743e35fbb8a1a109... setosa
4 iris-bdae8314e4385d8e2322abd8e63a82758a9063c77... virginica

If the data is large, you’ll likely want to query it via backed() or shard the adata across many array-like artifacts. For more on this, see: Query arrays.

How do I update an artifact?

If you’d like to update metadata:

artifact.description = "My new description"
artifact.save()  # save the change to the database

If you’d like to replace the underlying stored object, use replace().

Register directories as artifacts#

Register an entire folder as an artifact:

study0_data = ln.Artifact(f"s3://lamindb-dev-datasets/iris_studies/study0_raw_images")
study0_data.save()
ln.Artifact.df()
Hide code cell output 
💡 path in storage 's3://lamindb-dev-datasets' with key 'iris_studies/study0_raw_images'
version uid storage_id key suffix accessor description size hash hash_type n_objects n_observations transform_id run_id visibility key_is_virtual created_at updated_at created_by_id
id
2 None 7yZB6EZBeqRiXISHOLWN 2 iris_studies/study0_raw_images None None 656692 wVYKPpEsmmrqSpAZIRXCFg md5-d 51.0 None 1 1 1 False 2024-05-07 21:17:48.682467+00:00 2024-05-07 21:17:48.682495+00:00 1
1 None QvdXQFVkt7vujQiADwNC 2 iris_studies/study0_raw_images/meta.csv .csv None None 4355 ZpAEpN0iFYH6vjZNigic7g md5 NaN None 1 1 1 False 2024-05-07 21:17:48.209773+00:00 2024-05-07 21:17:48.209804+00:00 1

Filter & search artifacts#

You can search artifacts directly based on the Artifact registry:

ln.Artifact.search("meta").head()
Hide code cell output
key description score
uid
QvdXQFVkt7vujQiADwNC iris_studies/study0_raw_images/meta.csv 60.0
7yZB6EZBeqRiXISHOLWN iris_studies/study0_raw_images 48.9

You can also query & search the artifact by any metadata combination.

For instance, look up a user with auto-complete from the User registry:

users = ln.User.lookup()
users.anonymous
Hide code cell output 
User(uid='00000000', handle='anonymous', updated_at=2024-05-07 21:17:44 UTC)
How do I act non-anonymously?

  1. Sign up for a free account (see more info) and copy the API key.

  2. Log in on the command line:

    lamin login <email> --key <API-key>

Filter the Transform registry for a name:

transform = ln.Transform.filter(
    name__icontains="Artifacts"
).one()  # get exactly one result
transform
Hide code cell output 
Transform(version='0', uid='NJvdsWWbJlZS6K79', name='Tutorial: Artifacts', key='tutorial', type='notebook', updated_at=2024-05-07 21:17:45 UTC, created_by_id=1)
What does a double underscore mean?

For any field, the double underscore defines a comparator, e.g.,

  • name__icontains="Martha": name contains "Martha" when ignoring case

  • name__startswith="Martha": name starts with "Martha

  • name__in=["Martha", "John"]: name is "John" or "Martha"

For more info, see: Query & search registries.

Use these results to filter the Artifact registry:

ln.Artifact.filter(
    created_by=users.anonymous,
    transform=transform,
    suffix=".csv",
).df().head()
Hide code cell output
version uid storage_id key suffix accessor description size hash hash_type n_objects n_observations transform_id run_id visibility key_is_virtual created_at updated_at created_by_id
id
1 None QvdXQFVkt7vujQiADwNC 2 iris_studies/study0_raw_images/meta.csv .csv None None 4355 ZpAEpN0iFYH6vjZNigic7g md5 None None 1 1 1 False 2024-05-07 21:17:48.209773+00:00 2024-05-07 21:17:48.209804+00:00 1

You can also query for directories using key__startswith (LaminDB treats directories like AWS S3, as the prefix of the storage key):

ln.Artifact.filter(key__startswith="iris_studies/study0_raw_images/").df().head()
Hide code cell output
version uid storage_id key suffix accessor description size hash hash_type n_objects n_observations transform_id run_id visibility key_is_virtual created_at updated_at created_by_id
id
1 None QvdXQFVkt7vujQiADwNC 2 iris_studies/study0_raw_images/meta.csv .csv None None 4355 ZpAEpN0iFYH6vjZNigic7g md5 None None 1 1 1 False 2024-05-07 21:17:48.209773+00:00 2024-05-07 21:17:48.209804+00:00 1

Note

You can look up, filter & search any registry (Registry).

You can chain filter() statements and search(): ln.Artifact.filter(suffix=".jpg").search("my image")

An empty filter returns the entire registry: ln.Artifact.filter()

For more info, see: Query & search registries.

Filter & search on LaminHub

Describe artifacts#

Get an overview of what happened:

artifact.describe()
Hide code cell output 
Artifact(uid='QvdXQFVkt7vujQiADwNC', key='iris_studies/study0_raw_images/meta.csv', suffix='.csv', size=4355, hash='ZpAEpN0iFYH6vjZNigic7g', hash_type='md5', visibility=1, key_is_virtual=False, updated_at=2024-05-07 21:17:48 UTC)

Provenance:
  📎 storage: Storage(uid='EzvEnPnH', root='s3://lamindb-dev-datasets', type='s3', region='us-east-1', instance_uid='pZ1VQkyD3haH')
  📎 transform: Transform(version='0', uid='NJvdsWWbJlZS6K79', name='Tutorial: Artifacts', key='tutorial', type='notebook')
  📎 run: Run(uid='OsQC0bKGs8ipRQQA2rwd', started_at=2024-05-07 21:17:45 UTC, is_consecutive=True)
  📎 created_by: User(uid='00000000', handle='anonymous')

artifact.view_lineage()
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_images/e10905f0f9b44bcdc93aaa6f954eca7979c65d11d0e4001dac723dfa612f03ca.svg

Version artifacts#

If you’d like to version an artifact or transform, either provide the version parameter when creating it or create new versions through is_new_version_of.

For instance:

new_artifact = ln.Artifact(data, is_new_version_of=old_artifact)

If you’d like to add a registered artifact to a version family, use add_to_version_family.

For instance:

new_artifact.add_to_version_family(old_artifact)

Are there remaining questions about storing artifacts? If so, see: Storage FAQ.

Collections#

Often times, several artifacts together represent a collection.

Let’s seed a growing Collection of artifacts:

collection = ln.Collection(
    study0_data,
    name="Iris collection",
    version="1",
    description="Iris study 0",
)
collection.save()

Now, we collect more data in subsequent studies.

We want to keep track of their data as part of a growing versioned collection:

artifacts = [study0_data]
for folder_name in ["study1_raw_images", "study2_raw_images"]:
    # create an artifact for the folder
    artifact = ln.Artifact(f"s3://lamindb-dev-datasets/iris_studies/{folder_name}").save()
    artifacts.append(artifact)
    # create a new version of the collection
    collection = ln.Collection(
        artifacts, is_new_version_of=collection, description=f"Now includes {folder_name}"
    )
    collection.save()
Hide code cell output 
💡 path in storage 's3://lamindb-dev-datasets' with key 'iris_studies/study1_raw_images'

💡 path in storage 's3://lamindb-dev-datasets' with key 'iris_studies/study2_raw_images'

See all artifacts:

ln.Artifact.df()
Hide code cell output
version uid storage_id key suffix accessor description size hash hash_type n_objects n_observations transform_id run_id visibility key_is_virtual created_at updated_at created_by_id
id
4 None ppbZb9wyzB33NCeFSNtJ 2 iris_studies/study2_raw_images None None 665518 PX8Vt9T28y-uCEJO1tKm7A md5-d 51.0 None 1 1 1 False 2024-05-07 21:17:49.275718+00:00 2024-05-07 21:17:49.275749+00:00 1
3 None yoLbtb5j36cpm5Jx6TUr 2 iris_studies/study1_raw_images None None 640617 j61W__GgImA18CKrIf7FVg md5-d 49.0 None 1 1 1 False 2024-05-07 21:17:49.072111+00:00 2024-05-07 21:17:49.072142+00:00 1
2 None 7yZB6EZBeqRiXISHOLWN 2 iris_studies/study0_raw_images None None 656692 wVYKPpEsmmrqSpAZIRXCFg md5-d 51.0 None 1 1 1 False 2024-05-07 21:17:48.682467+00:00 2024-05-07 21:17:48.682495+00:00 1
1 None QvdXQFVkt7vujQiADwNC 2 iris_studies/study0_raw_images/meta.csv .csv None None 4355 ZpAEpN0iFYH6vjZNigic7g md5 NaN None 1 1 1 False 2024-05-07 21:17:48.209773+00:00 2024-05-07 21:17:48.209804+00:00 1

See all collections:

ln.Collection.df()
Hide code cell output
version uid name description hash reference reference_type transform_id run_id artifact_id visibility created_at updated_at created_by_id
id
3 3 CuNAZ1pvU6HfySuYutGY Iris collection Now includes study2_raw_images T-U8z2Zi5rFYdAD9pzmS None None 1 1 None 1 2024-05-07 21:17:49.289704+00:00 2024-05-07 21:17:49.289739+00:00 1
2 2 CuNAZ1pvU6HfySuYaBX9 Iris collection Now includes study1_raw_images 5cCK6ZLOPB0cV3tyeZup None None 1 1 None 1 2024-05-07 21:17:49.086148+00:00 2024-05-07 21:17:49.086185+00:00 1
1 1 CuNAZ1pvU6HfySuYLwvU Iris collection Iris study 0 WwFLpNFmK8GMC2dSGj1W None None 1 1 None 1 2024-05-07 21:17:48.874894+00:00 2024-05-07 21:17:48.874926+00:00 1

Most functionality that you just learned about artifacts - e.g., queries & provenance - also applies to Collection.

Collections become powerful if you directly leverage them for training models: Train a machine learning model on a collection.

View changes#

With view(), you can see the latest changes to the database:

ln.view()  # link tables in the database are not shown
Hide code cell output 
Artifact
version uid storage_id key suffix accessor description size hash hash_type n_objects n_observations transform_id run_id visibility key_is_virtual created_at updated_at created_by_id
id
4 None ppbZb9wyzB33NCeFSNtJ 2 iris_studies/study2_raw_images None None 665518 PX8Vt9T28y-uCEJO1tKm7A md5-d 51.0 None 1 1 1 False 2024-05-07 21:17:49.275718+00:00 2024-05-07 21:17:49.275749+00:00 1
3 None yoLbtb5j36cpm5Jx6TUr 2 iris_studies/study1_raw_images None None 640617 j61W__GgImA18CKrIf7FVg md5-d 49.0 None 1 1 1 False 2024-05-07 21:17:49.072111+00:00 2024-05-07 21:17:49.072142+00:00 1
2 None 7yZB6EZBeqRiXISHOLWN 2 iris_studies/study0_raw_images None None 656692 wVYKPpEsmmrqSpAZIRXCFg md5-d 51.0 None 1 1 1 False 2024-05-07 21:17:48.682467+00:00 2024-05-07 21:17:48.682495+00:00 1
1 None QvdXQFVkt7vujQiADwNC 2 iris_studies/study0_raw_images/meta.csv .csv None None 4355 ZpAEpN0iFYH6vjZNigic7g md5 NaN None 1 1 1 False 2024-05-07 21:17:48.209773+00:00 2024-05-07 21:17:48.209804+00:00 1 
Collection
version uid name description hash reference reference_type transform_id run_id artifact_id visibility created_at updated_at created_by_id
id
3 3 CuNAZ1pvU6HfySuYutGY Iris collection Now includes study2_raw_images T-U8z2Zi5rFYdAD9pzmS None None 1 1 None 1 2024-05-07 21:17:49.289704+00:00 2024-05-07 21:17:49.289739+00:00 1
2 2 CuNAZ1pvU6HfySuYaBX9 Iris collection Now includes study1_raw_images 5cCK6ZLOPB0cV3tyeZup None None 1 1 None 1 2024-05-07 21:17:49.086148+00:00 2024-05-07 21:17:49.086185+00:00 1
1 1 CuNAZ1pvU6HfySuYLwvU Iris collection Iris study 0 WwFLpNFmK8GMC2dSGj1W None None 1 1 None 1 2024-05-07 21:17:48.874894+00:00 2024-05-07 21:17:48.874926+00:00 1 
Run
uid transform_id started_at finished_at created_by_id json report_id environment_id is_consecutive reference reference_type created_at
id
1 OsQC0bKGs8ipRQQA2rwd 1 2024-05-07 21:17:45.925913+00:00 None 1 None None None True None None 2024-05-07 21:17:45.926056+00:00 
Storage
uid root description type region instance_uid created_at updated_at created_by_id
id
2 EzvEnPnH s3://lamindb-dev-datasets None s3 us-east-1 pZ1VQkyD3haH 2024-05-07 21:17:48.173455+00:00 2024-05-07 21:17:48.173489+00:00 1
1 Gtx1UEiStING /home/runner/work/lamindb/lamindb/docs/lamin-t... None local None 5WuFt3cW4zRx 2024-05-07 21:17:44.036529+00:00 2024-05-07 21:17:44.036557+00:00 1 
Transform
version uid name key description type latest_report_id source_code_id reference reference_type created_at updated_at created_by_id
id
1 0 NJvdsWWbJlZS6K79 Tutorial: Artifacts tutorial None notebook None None None None 2024-05-07 21:17:45.911589+00:00 2024-05-07 21:17:45.911634+00:00 1 
User
uid handle name created_at updated_at
id
1 00000000 anonymous None 2024-05-07 21:17:44.032026+00:00 2024-05-07 21:17:44.032056+00:00

Save notebook & scripts#

When you’ve completed the work on a notebook or script, you can save the source code and, for notebooks, an execution report to your storage location like so:

ln.finish()

This enables you to query execution report & source code via transform.latest_report and transform.source_code.

If you registered the instance on LaminHub, you can share it like here.

Get notebooks & scripts#

If you want to cache a notebook or script, call:

lamin get https://lamin.ai/laminlabs/lamindata/transform/NJvdsWWbJlZSz8

Read on#

Now, you already know about 6 out of 9 LaminDB core classes! The two most central are:

And the four registries related to provenance:

  • Transform: transforms of artifacts

  • Run: runs of transforms

  • User: users

  • Storage: storage locations like S3/GCP buckets or local directories

If you want to validate data, label artifacts, and manage features, read on: Tutorial: Features & labels.