MLFlow

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

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

import lamindb as ln
import mlflow
import lightning

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

ln.track()

Show code cell output Hide code cell output

→ connected lamindb: anonymous/lamin-mlops

→ created Transform('8bRjGzrd96Vs0000'), started new Run('GJz3fAng...') at 2025-06-03 11:34:13 UTC

→ notebook imports: autoencoder lamindb==1.6.0 lightning==2.5.1.post0 mlflow-skinny==2.22.0 mlflow==2.22.0 torch==2.7.0 torchvision==0.22.0

• recommendation: to identify the notebook across renames, pass the uid: ln.track("8bRjGzrd96Vs")

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()

Show code cell output Hide 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	5M0828i4DPDrHSub0000	testdata/mnist	None		dataset	None	54950048	amFx_vXqnUtJr0kmxxWK2Q	4	None	md5-d	True	True	1	1	None	None	True	1	2025-06-03 11:33:32.756000+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

Show code cell output Hide code cell output

Artifact(uid='5M0828i4DPDrHSub0000', is_latest=True, key='testdata/mnist', suffix='', kind='dataset', size=54950048, hash='amFx_vXqnUtJr0kmxxWK2Q', n_files=4, branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-06-03 11:33:32 UTC)

And download it to a local cache:

path = artifact.cache()
path

Show code cell output Hide code cell output

PosixUPath('/home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/5M0828i4DPDrHSub')

Create a PyTorch-compatible dataset:

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

Show code cell output Hide code cell output

Dataset MNIST
    Number of datapoints: 60000
    Root location: /home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/5M0828i4DPDrHSub
    Split: Train
    StandardTransform
Transform: ToTensor()

Monitor training with MLflow

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

mlflow.pytorch.autolog()

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

# Start MLflow run
with mlflow.start_run() as run:
    train_dataset = MNIST(
        root="./data", train=True, download=True, transform=ToTensor()
    )
    train_loader = utils.data.DataLoader(
        train_dataset, batch_size=MODEL_CONFIG["batch_size"]
    )

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

    # Create checkpoint callback
    from lightning.pytorch.callbacks import ModelCheckpoint

    checkpoint_callback = ModelCheckpoint(
        dirpath="model_checkpoints",
        filename=f"{run.info.run_id}_last_epoch",
        save_top_k=1,
        monitor="train_loss",
    )

    # Train model
    trainer = lightning.Trainer(
        accelerator="cpu",
        limit_train_batches=3,
        max_epochs=2,
        callbacks=[checkpoint_callback],
    )

    trainer.fit(model=autoencoder, train_dataloaders=train_loader)

    # Get run information
    run_id = run.info.run_id
    metrics = mlflow.get_run(run_id).data.metrics
    params = mlflow.get_run(run_id).data.params

    # Access model artifacts path
    model_uri = f"runs:/{run_id}/model"
    artifacts_path = run.info.artifact_uri

Show code cell output Hide code cell output

2025/06/03 11:34:14 WARNING mlflow.utils.autologging_utils: MLflow pytorch autologging is known to be compatible with 1.9.0 <= torch <= 2.6.0, but the installed version is 2.7.0+cu126. If you encounter errors during autologging, try upgrading / downgrading torch to a compatible version, or try upgrading MLflow.

  0%|          | 0.00/9.91M [00:00<?, ?B/s]

  1%|          | 65.5k/9.91M [00:00<00:17, 573kB/s]

  2%|▏         | 229k/9.91M [00:00<00:09, 1.07MB/s]

 10%|▉         | 983k/9.91M [00:00<00:02, 3.54MB/s]

 41%|████      | 4.03M/9.91M [00:00<00:00, 12.5MB/s]

100%|██████████| 9.91M/9.91M [00:00<00:00, 18.6MB/s]

  0%|          | 0.00/28.9k [00:00<?, ?B/s]

100%|██████████| 28.9k/28.9k [00:00<00:00, 506kB/s]

  0%|          | 0.00/1.65M [00:00<?, ?B/s]

  6%|▌         | 98.3k/1.65M [00:00<00:01, 854kB/s]

 24%|██▍       | 393k/1.65M [00:00<00:00, 1.85MB/s]

 91%|█████████▏| 1.51M/1.65M [00:00<00:00, 5.39MB/s]

100%|██████████| 1.65M/1.65M [00:00<00:00, 4.72MB/s]

  0%|          | 0.00/4.54k [00:00<?, ?B/s]

100%|██████████| 4.54k/4.54k [00:00<00:00, 10.7MB/s]

INFO:pytorch_lightning.utilities.rank_zero:GPU available: False, used: False

INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores

INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs

/opt/hostedtoolcache/Python/3.13.3/x64/lib/python3.13/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:76: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
2025/06/03 11:34:17 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "/opt/hostedtoolcache/Python/3.13.3/x64/lib/python3.13/site-packages/mlflow/pytorch/_lightning_autolog.py:465: UserWarning: Autologging is known to be compatible with pytorch-lightning versions between 1.9.0 and 2.5.1 and may not succeed with packages outside this range."

/opt/hostedtoolcache/Python/3.13.3/x64/lib/python3.13/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:654: Checkpoint directory /home/runner/work/lamin-mlops/lamin-mlops/docs/model_checkpoints exists and is not empty.

  | 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, 48.62it/s]

Epoch 0:  33%|███▎      | 1/3 [00:00<00:00, 47.03it/s, v_num=0]

Epoch 0:  67%|██████▋   | 2/3 [00:00<00:00, 70.85it/s, v_num=0]

Epoch 0:  67%|██████▋   | 2/3 [00:00<00:00, 69.51it/s, v_num=0]

Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 85.18it/s, v_num=0]

Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 83.86it/s, v_num=0]

Epoch 0: 100%|██████████| 3/3 [00:00<00:00, 82.18it/s, v_num=0]

2025/06/03 11:34:17 WARNING mlflow.utils.checkpoint_utils: Checkpoint logging is skipped, because checkpoint 'save_best_only' config is True, it requires to compare the monitored metric value, but the provided monitored metric value is not available.

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

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

Epoch 1:  33%|███▎      | 1/3 [00:00<00:00, 138.28it/s, v_num=0]

Epoch 1:  33%|███▎      | 1/3 [00:00<00:00, 128.97it/s, v_num=0]

Epoch 1:  67%|██████▋   | 2/3 [00:00<00:00, 140.10it/s, v_num=0]

Epoch 1:  67%|██████▋   | 2/3 [00:00<00:00, 134.77it/s, v_num=0]

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 140.37it/s, v_num=0]

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 137.01it/s, v_num=0]

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 132.83it/s, v_num=0]

2025/06/03 11:34:17 WARNING mlflow.utils.checkpoint_utils: Checkpoint logging is skipped, because checkpoint 'save_best_only' config is True, it requires to compare the monitored metric value, but the provided monitored metric value is not available.

INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2` reached.

Epoch 1: 100%|██████████| 3/3 [00:00<00:00, 101.40it/s, v_num=0]

2025/06/03 11:34:24 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.

See the training progress in the mlflow UI:

Save model in LaminDB

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

# create a label with the mlflow experiment name
mlflow_run_name = mlflow.get_run(run_id).data.tags.get(
    "mlflow.runName", f"run_{run_id}"
)
experiment_label = ln.ULabel(
    name=mlflow_run_name, description="mlflow 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

Show code cell output Hide code cell output

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

! calling anonymously, will miss private instances

→ returning existing Feature record with same name: 'hidden_size'

→ returning existing Feature record with same name: 'bottleneck_size'

→ returning existing Feature record with same name: 'batch_size'

/tmp/ipykernel_3331/2236147127.py:22: FutureWarning: Use features instead of params, params will be removed in the future.
  artifact.params.add_values(MODEL_CONFIG)

Artifact .ckpt
├── General
│   ├── .uid = '4DSByE7nnqos4Mkf0000'
│   ├── .key = 'testmodels/mlflow/litautoencoder.ckpt'
│   ├── .size = 636736
│   ├── .hash = 'j3PiRncPY0aSNJh7BSUBhA'
│   ├── .path = /home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/4DSByE7nnqos4Mkf0000.ckpt
│   ├── .created_by = anonymous
│   ├── .created_at = 2025-06-03 11:34:25
│   └── .transform = 'MLFlow'
├── Linked features
│   └── batch_size                  int                        32                                       
│       bottleneck_size             int                        16                                       
│       hidden_size                 int                        32                                       
└── Labels
    └── .ulabels                    ULabel                     glamorous-sow-773                        

/tmp/ipykernel_3331/2236147127.py:26: FutureWarning: Use features instead of params, params will be removed in the future.
  artifact.params

Artifact .ckpt
└── Linked features
    └── 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/mlflow/litautoencoder.ckpt").cache()

Show code cell output Hide code cell output

PosixUPath('/home/runner/work/lamin-mlops/lamin-mlops/docs/lamin-mlops/.lamindb/4DSByE7nnqos4Mkf0000.ckpt')

Or on the CLI:

lamin get artifact --key 'testmodels/litautoencoder'

ln.finish()

Show code cell output Hide code cell output

! cells [(9, 11)] were not run consecutively

→ finished Run('GJz3fAng') after 12s at 2025-06-03 11:34:26 UTC

Show code cell content Hide code cell content

!rm -rf ./lamin-mlops
!lamin delete --force lamin-mlops

! calling anonymously, will miss private instances
• deleting instance anonymous/lamin-mlops