Repository files navigation

neptune.ai

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 afree account

Step 2: Install the Neptune client library

pip install neptune

Step 3: Add an experiment tracking snippet to your code

importneptunerun=neptune.init_run(project="workspace-name/project-name")run["parameters"]= {"lr":0.1,"dropout":0.4}run["test_accuracy"]=0.84

neptune.ai integrates with25+ frameworks: PyTorch, Lightning, TensorFlow/Keras, LightGBM, scikit-learn, XGBoost, Optuna, Kedro, 🤗 Transformers, fastai, Prophet, detectron2, Airflow, and more.

Example:

frompytorch_lightningimportTrainerfromlightning.pytorch.loggersimportNeptuneLogger# Create NeptuneLogger instancefromneptuneimportANONYMOUS_API_TOKENneptune_logger=NeptuneLogger(api_key=ANONYMOUS_API_TOKEN,project="common/pytorch-lightning-integration",tags=["training","resnet"],# optional)# Pass the logger to the Trainertrainer=Trainer(max_epochs=10,logger=neptune_logger)# Run the Trainertrainer.fit(my_model,my_dataloader)

Read how various customers use Neptune toimprove their workflow. 

If you get stuck or simply want to talk to us about something, here are your options:

• Check ourFAQ page.
• Chat! In the app, click theblue 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 atsupport@neptune.ai. 

Created with ❤️ by theneptune.ai team