1. Add the import at the top of your script:

   import weightslab as wl

2. Wrap your parameters, model, optimizer, signals, and dataset:

   parameters      = wl.watch_or_edit(parameters, flag='hp',     ...) # ← WeightsLab monitors your parameters and lets you update them from the UI
   model           = wl.watch_or_edit(model, flag='model', ...) # ← WeightsLab monitors your model state
   optimizer       = wl.watch_or_edit(optim.Adam(...),                         flag='opt',    ...) # ← Tracks optimizer state and lets you update the learning rate from the UI

   train_criterion = wl.watch_or_edit(nn.CrossEntropyLoss(reduction="none"),  flag='signal', name="train_loss/sample", per_sample=True, log=True)   # ← Wrap and plot your signals on the UI
   test_criterion  = wl.watch_or_edit(nn.CrossEntropyLoss(reduction="none"),  flag='signal', name="test_loss/sample",  per_sample=True, log=False)  # ← Per-sample only, plot disabled

   train_loader    = wl.watch_or_edit(train_dataset, flag='data', loader_name="train_loader", ...)  # ← Track your training dataset
   val_loader      = wl.watch_or_edit(val_dataset,   flag='data', loader_name="val_loader",   ...)  # ← Track your validation dataset

3. Run your script, then launch the UI in a separate terminal:

   python train.py
   weightslab start

4. Open your browser at the URL printed by weightslab start and inspect your training in real time.

#!/usr/bin/env python3
"""
Basic PyTorch training script with WeightsLab integration
"""
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import weightslab as wl


class SimpleModel(nn.Module):
    def __init__(self, input_shape=10, output_shape=1):
        super().__init__()
        self.linear = nn.Linear(input_shape, output_shape)

    def forward(self, x):
        return self.linear(x)


def create_data(n_samples=1000):
    X = torch.randn(n_samples, 10)
    y = X.sum(dim=1, keepdim=True) + 0.1 * torch.randn(n_samples, 1)
    return TensorDataset(X, y)


def main():
    parameters = wl.watch_or_edit({}, flag="hyperparameters", poll_interval=1.0) or {}

    model     = wl.watch_or_edit(SimpleModel(), flag='model')
    optimizer = wl.watch_or_edit(optim.Adam(model.parameters(), lr=0.01), flag='optimizer')
    criterion = wl.watch_or_edit(nn.CrossEntropyLoss(reduction="none"), flag="loss", signal_name="train-loss-CE", log=True)
    loader    = wl.watch_or_edit(create_data(), flag="data", loader_name="loader", batch_size=8, is_training=True)

    for epoch in range(parameters.get('n_epochs', 5)):
        total_loss = 0
        for batch_X, batch_y in loader:
            predictions = model(batch_X)
            loss = criterion(predictions, batch_y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            total_loss += loss.item()

            # Write the history of these samples every x steps
            if model.get_age() % 100 == 0:
                print(f'Dump signals history and dataframe at age {model.get_age()}')
                wl.write_history(
                    # path=None,  # Use root_log_dir by default, filename generated from parameters md5 hash
                    type_of_history="all",
                    graph_name=[
                        'train/clsf_instance',
                        'val/clsf_instance'
                    ],
                    # experiment_hash=None,  Default is 'last', i.e., current experiment hash
                    sample_id=['11', '29', '28', '27', '22'],
                    instance_id=[1, 2, 3]
                )

                # Dump the sample dataframe: all signals plus the loss_shape categorical tag,
                wl.write_dataframe(
                    columns=["signals", "tag:loss_shape"],
                    format='csv'
                    # sample_id=['0', '28']
                    # instance_id=[1, 2],
                )

        avg_loss = total_loss / len(loader)
        print(f"Epoch {epoch+1}/5 - Loss: {avg_loss:.4f}")

    print("✅ Training complete!")


if __name__ == "__main__":
    main()

WeightsLab vs Weights & Biases

Weights & Biases (wandb) tracks experiments. WeightsLab connects training signals back to the exact samples causing them — so you can fix your data, not just log it.

--- train_baseline.py
+++ train_wl.py
@@ -1,11 +1,12 @@
 import argparse
 import torch
 import torch.nn as nn
-from torch.utils.data import DataLoader
 from torchvision import datasets, transforms, models
 from torchmetrics.classification import MulticlassAccuracy

-import wandb
+import weightslab as wl
+
+@wl.signal(name="byte_adjusted_loss", subscribe_to="loss/CE")
+def byte_adjusted_loss(ctx): return ctx.subscribed_value / ctx.image_bytes
+
 def main():
@@ -15,29 +16,38 @@
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     parameters = {"batch_size": 128, "lr": 1e-3}

-    wandb.init(project="cifar10")
-
     transform = transforms.Compose([...])
     train_set = datasets.CIFAR10("./data", train=True,  download=True, transform=transform)
     test_set  = datasets.CIFAR10("./data", train=False, download=True, transform=transform)
-    train_loader = DataLoader(train_set, batch_size=parameters["batch_size"], shuffle=True, num_workers=2)
-    test_loader  = DataLoader(test_set,  batch_size=256, num_workers=2)
+    wl.watch_or_edit(parameters, flag="hyperparameters")  # live-editable in UI
+
+    train_loader = wl.watch_or_edit(
+        train_set, flag="data", loader_name="train_loader",
+        batch_size=parameters["batch_size"], shuffle=True, is_training=True)
+    test_loader  = wl.watch_or_edit(
+        test_set,  flag="data", loader_name="test_loader",
+        batch_size=256, shuffle=False, is_training=False)

     model     = models.resnet18(weights=None)
     model.fc  = nn.Linear(model.fc.in_features, 10)
     optimizer = torch.optim.Adam(model.parameters(), lr=parameters["lr"])

-    criterion = nn.CrossEntropyLoss()
-    accuracy  = MulticlassAccuracy(num_classes=10).to(device)
+    criterion = wl.watch_or_edit(nn.CrossEntropyLoss(), flag="loss", signal_name="loss/CE")
+    accuracy  = wl.watch_or_edit(MulticlassAccuracy(num_classes=10).to(device), flag="metric", signal_name="acc")
+
+    wl.serve(serving_grpc=True)

     for epoch in range(1, args.epochs + 1):
         model.train()
         for x, y in train_loader:
+            with wl.guard_training_context:
                 logits = model(x.to(device))
                 loss   = criterion(logits, y.to(device))
                 optimizer.zero_grad(); loss.backward(); optimizer.step()
                 accuracy.update(logits, y)
-            wandb.log({"train/loss": loss.item()})
-        wandb.log({"train/acc": accuracy.compute().item(), "epoch": epoch})
+            wl.save_signals(preds_raw=logits, targets=y,
+                            signals={"metric/accuracy": accuracy.compute().item()})

         model.eval()
         with torch.no_grad():
             for x, y in test_loader:
+                with wl.guard_testing_context:
                     accuracy.update(model(x.to(device)), y)
-        wandb.log({"test/acc": accuracy.compute().item(), "epoch": epoch})
+                wl.save_signals(preds_raw=logits, targets=y,
+                                signals={"metric/accuracy": accuracy.compute().item()})

-    wandb.finish()
+    wl.keep_serving()

Find our documentation online.

WeightsLab ships two distinct agent surfaces — the backend SDK agent for data-manipulation queries, and a local OpenCode-backed agent with a full bash/file toolset that can restart training, edit your code, and run recurring /loop monitoring jobs. See the Agent docs for how the two connect, how to point either one at a local model, and the full /loop reference.

New here (human or AI coding agent)? Start with AGENTS.md — it captures the cross-repo architecture (weightslab backend ↔ weights_studio frontend via the shared proto), the module maps, the integration pattern, where tests live, and the gotchas that aren't obvious from any single file. It's the fastest way to orient before a first change and contribution.

We're building a community of ML engineers around data-centric training tooling. Interested in contributing or just want to say hi? → hello [at] graybx [dot] com