Pause training, mine live loss signals to surface mislabels, class imbalance & outliers,
then curate your image, video & LiDAR data, without restarting.
Website · Docs · Demo (Images) · Demo (VLA) · Demo (LiDAR)
WeightsLab hooks into your existing PyTorch training loop and exposes a live UI where you can inspect per-sample signals, edit the dataset, and steer training. Without restarting.
1. Install & Launch
pip install weightslabweightslab start # launch the UI2. Start in the cloud
OR Wrap your training script locally
# wrap the objects in your training script
import weightslab as wl
...
model = wl.watch_or_edit(model, flag='model')
optim = wl.watch_or_edit(optim, flag='opt')
loss = wl.watch_or_edit(loss, flag='signal', name="loss", per_sample=True, log=True)
loader = wl.watch_or_edit(dataset, flag='data', loader_name="train")
...
wl.serve(serving_grpc=True, serving_cli=False)
...Tip
Quick examples to get started
weightslab start example # classification (default)
weightslab start example --cls # classification
weightslab start example --seg # segmentation
weightslab start example --det # detection
weightslab start example --clus # clusteringExplore our sandbox.
For a detailed installation guide and advanced configuration → Documentation.
Pause training mid-run, sort samples by loss, spot mislabels and outliers before they impact your model. Relabel or drop samples live, then continue training from the same checkpoint. Analyze per-sample loss trajectories to see exactly where the model is struggling.
Training script with Weightslab - Step-by-Step Integration
- Add the import at the top of your script:
import weightslab as wl- 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- Run your script, then launch the UI in a separate terminal:
python train.py
weightslab start- Open your browser at the URL printed by
weightslab startand inspect your training in real time.
Training script with Weightslab - Full Example
#!/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()Migrating from 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
+from weightslab.components.global_monitoring import (
+ guard_training_context, guard_testing_context)
+
+@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 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 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()Contributing & Onboarding
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.
Community
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