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 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?

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

What is next?