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Neptune in 3 minutes

What does Neptune do?

Neptune is a metadata store for MLOps, built for teams that run a lot of experiments.

It gives you a single place to log, store, display, organize, compare, and query all your model building metadata.

Neptune Metadata Store for MLOps: Client + Database + Dashboard

Neptune has three main use cases:

  • Experiment tracking: Log, display, organize, and compare ML experiments in a single place.

  • Model registry: Version, store, manage, and query trained models, and model building metadata.

  • Monitoring ML runs live: Record and monitor model training, evaluation, or production runs live.

How does Neptune work?

Source code on GitHub

Results in Neptune

Notebook in Colab

See example code

See results

Run example

Step 1: Installation

Install neptune-client. Check the docs for more help.

pip install neptune-client

Step 2: Prepare your training script

Create a new file main.py, then copy and paste the following script into it:

import neptune.new as neptune
run = neptune.init(project="your_workspace/your_project")
# Track metadata and hyperparameters of your run
run["JIRA"] = "NPT-952"
run["parameters"] = {"learning_rate": 0.001,
"optimizer": "Adam"}
# Track the training process by logging your training metrics
for epoch in range(100):
run["train/accuracy"].log(epoch * 0.6)
run["train/loss"].log(epoch * 0.4)
run["f1_score"] = 0.66

Step 3: Run your training script

Go to the terminal and run:

python main.py

You will get a link to Neptune Web UI, open it!

It will look like the one below:

https://app.neptune.ai/o/common/org/colab-test-run/e/COL-72/all

Step 4: See everything in the UI

Source code on GitHub

Results in Neptune

Notebook in Colab

See example code

See results

Run example

What is next?