Neptune is an experiment tracker purpose-built for foundation model training.
With Neptune, you can monitor thousands of per-layer metrics—losses, gradients, and activations—at any scale. Visualize them with no lag and no missed spikes. Drill down into logs and debug training issues fast. Keep your model training stable while reducing wasted GPU cycles.
Watch a 3min explainer video →
Play with a live example project in the Neptune app →
Step 1: Create a free account
Step 2: Install the Neptune client library
pip install neptuneStep 3: Add an experiment tracking snippet to your code
import neptune
run = neptune.init_run(project="workspace-name/project-name")
run["parameters"] = {"lr": 0.1, "dropout": 0.4}
run["test_accuracy"] = 0.84
neptune.ai integrates with 25+ frameworks: PyTorch, Lightning, TensorFlow/Keras, LightGBM, scikit-learn, XGBoost, Optuna, Kedro, 🤗 Transformers, fastai, Prophet, detectron2, Airflow, and more.
Example:
from pytorch_lightning import Trainer
from lightning.pytorch.loggers import NeptuneLogger
# Create NeptuneLogger instance
from neptune import ANONYMOUS_API_TOKEN
neptune_logger = NeptuneLogger(
api_key=ANONYMOUS_API_TOKEN,
project="common/pytorch-lightning-integration",
tags=["training", "resnet"], # optional
)
# Pass the logger to the Trainer
trainer = Trainer(max_epochs=10, logger=neptune_logger)
# Run the Trainer
trainer.fit(my_model, my_dataloader)
Read how various customers use Neptune to improve their workflow.
If you get stuck or simply want to talk to us about something, here are your options:
- Check our FAQ page.
- Chat! In the app, click the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP).
- You can just shoot us an email at [email protected].
Created with ❤️ by the neptune.ai team