Weights & Biases

We show how LaminDB can be integrated with W&B to track the training process and associate datasets & parameters with models.

# !pip install 'lamindb[jupyter]' torchvision lightning wandb
!lamin init --storage ./lamin-mlops
!wandb login

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wandb: WARNING Using legacy-service, which is deprecated. If this is unintentional, you can fix it by ensuring you do not call `wandb.require('legacy-service')` and do not set the WANDB_X_REQUIRE_LEGACY_SERVICE environment variable.

wandb: Currently logged in as: felix_lamin (lamin-mlops-demo) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin

import lamindb as ln
import wandb
import lightning

from torch import utils
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from autoencoder import LitAutoEncoder

ln.track()

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→ connected lamindb: anonymous/lamin-mlops

→ created Transform('l9wDUJONa7h50000'), started new Run('FBSaV65t...') at 2025-05-29 10:22:14 UTC

→ notebook imports: autoencoder lamindb==1.5.3 lightning==2.5.1.post0 torch==2.7.0 torchvision==0.22.0 wandb==0.19.11

• recommendation: to identify the notebook across renames, pass the uid: ln.track("l9wDUJONa7h5")

Define a model

We use a basic PyTorch Lightning autoencoder as an example model.

Code of LitAutoEncoder

Simple autoencoder model

import torch
import lightning

from torch import optim, nn


class LitAutoEncoder(lightning.LightningModule):
    def __init__(self, hidden_size: int, bottleneck_size: int) -> None:
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(28 * 28, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, bottleneck_size),
        )
        self.decoder = nn.Sequential(
            nn.Linear(bottleneck_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, 28 * 28),
        )
        self.save_hyperparameters()

    def training_step(
        self, batch: tuple[torch.Tensor, torch.Tensor], batch_idx: int
    ) -> torch.Tensor:
        x, y = batch
        x = x.view(x.size(0), -1)
        z = self.encoder(x)
        x_hat = self.decoder(z)
        loss = nn.functional.mse_loss(x_hat, x)
        self.log("train_loss", loss)
        return loss

    def configure_optimizers(self) -> optim.Optimizer:
        optimizer = optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

Query & download the MNIST dataset

We saved the MNIST dataset in curation notebook which now shows up in the Artifact registry:

ln.Artifact.filter(kind="dataset").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
1	9i3q1WyQmax9iqFB0000	testdata/mnist	None		dataset	None	54950048	amFx_vXqnUtJr0kmxxWK2Q	4	None	md5-d	True	True	1	1	None	None	True	1	2025-05-29 10:21:52.966000+00:00	1	None	1

You can also find it on lamin.ai if you were connected your instance.

Let’s get the dataset:

artifact = ln.Artifact.get(key="testdata/mnist")
artifact

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Artifact(uid='9i3q1WyQmax9iqFB0000', is_latest=True, key='testdata/mnist', suffix='', kind='dataset', size=54950048, hash='amFx_vXqnUtJr0kmxxWK2Q', n_files=4, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-05-29 10:21:52 UTC)

And download it to a local cache:

path = artifact.cache()
path

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PosixUPath('/home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/9i3q1WyQmax9iqFB')

Create a PyTorch-compatible dataset:

dataset = MNIST(path.as_posix(), transform=ToTensor())
dataset

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Dataset MNIST
    Number of datapoints: 60000
    Root location: /home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/9i3q1WyQmax9iqFB
    Split: Train
    StandardTransform
Transform: ToTensor()

Monitor training with wandb

Train our example model and track the training progress with wandb.

from lightning.pytorch.loggers import WandbLogger

MODEL_CONFIG = {"hidden_size": 32, "bottleneck_size": 16, "batch_size": 32}

# create the data loader
train_loader = utils.data.DataLoader(
    dataset, batch_size=MODEL_CONFIG["batch_size"], shuffle=True
)

# init model
autoencoder = LitAutoEncoder(
    MODEL_CONFIG["hidden_size"], MODEL_CONFIG["bottleneck_size"]
)

# initialize the logger
wandb_logger = WandbLogger(project="lamin")

# add batch size to the wandb config
wandb_logger.experiment.config["batch_size"] = MODEL_CONFIG["batch_size"]

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wandb: Currently logged in as: felix_lamin (lamin-mlops-demo) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin

wandb: Tracking run with wandb version 0.19.11

wandb: Run data is saved locally in ./wandb/run-20250529_102215-6qh7toz1
wandb: Run `wandb offline` to turn off syncing.

wandb: Syncing run divine-snow-225

wandb: ⭐️ View project at https://wandb.ai/lamin-mlops-demo/lamin

wandb: 🚀 View run at https://wandb.ai/lamin-mlops-demo/lamin/runs/6qh7toz1

from lightning.pytorch.callbacks import ModelCheckpoint

# store checkpoints to disk and upload to LaminDB after training
checkpoint_callback = ModelCheckpoint(
    dirpath=f"model_checkpoints/{wandb_logger.version}",
    filename="last_epoch",
    save_top_k=1,
    monitor="train_loss",
)

# train model
trainer = lightning.Trainer(
    accelerator="cpu",
    limit_train_batches=3,
    max_epochs=2,
    logger=wandb_logger,
    callbacks=[checkpoint_callback],
)
trainer.fit(model=autoencoder, train_dataloaders=train_loader)

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GPU available: False, used: False

TPU available: False, using: 0 TPU cores

HPU available: False, using: 0 HPUs

  | Name    | Type       | Params | Mode 
-----------------------------------------------
0 | encoder | Sequential | 25.6 K | train
1 | decoder | Sequential | 26.4 K | train
-----------------------------------------------
52.1 K    Trainable params
0         Non-trainable params
52.1 K    Total params
0.208     Total estimated model params size (MB)
8         Modules in train mode
0         Modules in eval mode

/opt/hostedtoolcache/Python/3.13.3/x64/lib/python3.13/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.
/opt/hostedtoolcache/Python/3.13.3/x64/lib/python3.13/site-packages/lightning/pytorch/loops/fit_loop.py:310: The number of training batches (3) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.

Training: |          | 0/? [00:00<?, ?it/s]

Training:   0%|          | 0/3 [00:00<?, ?it/s]

Epoch 0:   0%|          | 0/3 [00:00<?, ?it/s] 

Epoch 0:  33%|███▎      | 1/3 [00:00<00:00, 46.24it/s]

Epoch 0:  33%|███▎      | 1/3 [00:00<00:00, 44.76it/s, v_num=toz1]

Epoch 0:  67%|██████▋   | 2/3 [00:00<00:00, 68.75it/s, v_num=toz1]

Epoch 0:  67%|██████▋   | 2/3 [00:00<00:00, 67.59it/s, v_num=toz1]

Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 83.54it/s, v_num=toz1]

Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 82.30it/s, v_num=toz1]

Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 79.82it/s, v_num=toz1]

Epoch 0:   0%|          | 0/3 [00:00<?, ?it/s, v_num=toz1]        

Epoch 1:   0%|          | 0/3 [00:00<?, ?it/s, v_num=toz1]

Epoch 1:  33%|███▎      | 1/3 [00:00<00:00, 79.18it/s, v_num=toz1]

Epoch 1:  33%|███▎      | 1/3 [00:00<00:00, 75.24it/s, v_num=toz1]

Epoch 1:  67%|██████▋   | 2/3 [00:00<00:00, 91.55it/s, v_num=toz1]

Epoch 1:  67%|██████▋   | 2/3 [00:00<00:00, 89.29it/s, v_num=toz1]

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 102.68it/s, v_num=toz1]

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 100.51it/s, v_num=toz1]

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 98.14it/s, v_num=toz1] 

`Trainer.fit` stopped: `max_epochs=2` reached.

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 82.62it/s, v_num=toz1]

wandb_logger.experiment.name

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'divine-snow-225'

wandb_logger.version

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'6qh7toz1'

wandb.finish()

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wandb:                                                                                

wandb: 🚀 View run divine-snow-225 at: https://wandb.ai/lamin-mlops-demo/lamin/runs/6qh7toz1
wandb: ⭐️ View project at: https://wandb.ai/lamin-mlops-demo/lamin
wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)

wandb: Find logs at: ./wandb/run-20250529_102215-6qh7toz1/logs

See the training progress in the wandb UI:

Save model in LaminDB

# save checkpoint as a model in LaminDB
artifact = ln.Artifact(
    f"model_checkpoints/{wandb_logger.version}",
    key="testmodels/wandb/litautoencoder",  # is automatically versioned
    type="model",
).save()

# create a label with the wandb experiment name
experiment_label = ln.ULabel(
    name=wandb_logger.experiment.name, description="wandb experiment name"
).save()

# annotate the model artifact
artifact.ulabels.add(experiment_label)

# define the associated model hyperparameters in ln.Param
for k, v in MODEL_CONFIG.items():
    ln.Param(name=k, dtype=type(v).__name__).save()
artifact.params.add_values(MODEL_CONFIG)

# look at Artifact annotations
artifact.describe()
artifact.params

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! `type` will be removed soon, please use `kind`

Artifact 
├── General
│   ├── .uid = 'eBRdbeCJE0ecjeLl0000'
│   ├── .key = 'testmodels/wandb/litautoencoder'
│   ├── .size = 636736
│   ├── .hash = 'uPV7AbXGnwGvq19fUvkozg'
│   ├── .n_files = 1
│   ├── .path = /home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/eBRdbeCJE0ecjeLl
│   ├── .created_by = anonymous
│   ├── .created_at = 2025-05-29 10:22:17
│   └── .transform = 'Weights & Biases'
└── Labels
    └── .ulabels                    ULabel                     divine-snow-225                          

Artifact 
└── Params
    └── batch_size                  int                        32                                       
        bottleneck_size             int                        16                                       
        hidden_size                 int                        32                                       

See the checkpoints:

If later on, you want to re-use the checkpoint, you can download it like so:

ln.Artifact.get(key="testmodels/wandb/litautoencoder").cache()

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PosixUPath('/home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/eBRdbeCJE0ecjeLl')

Or on the CLI:

lamin get artifact --key 'testmodels/litautoencoder'

ln.finish()

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! cells [(10, 12)] were not run consecutively

→ finished Run('FBSaV65t') after 3s at 2025-05-29 10:22:17 UTC

! calling anonymously, will miss private instances