Neptune in 3 minutes

Try Neptune on Colab with zero setup and see results in the UI

Get a quick feel of how monitoring and keeping track of runs can look like.

What does Neptune do?

Neptune is a lightweight run management tool that helps you keep track of your machine learning runs.

Some of the most common Neptune use cases:

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["algorithm"] = "ConvNet"
​
params = {
    "batch_size": 64,
    "dropout": 0.2,
    "learning_rate": 0.001,
    "optimizer": "Adam"
}
run["parameters"] = params
​
​
# 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)
​
# Log the final results
run["f1_score"] = 0.67

Step 3: Run this example in your terminal

python main.py

You will get a link to Neptune Web UI, open it and take a look at how your runs are visualized.

Step 4: See everything in the UI

Discover Neptune

• ​Example project: See how example project looks in Neptune

• ​YouTube channel: Provides hands-on videos that showcase key Neptune features.

• ​Neptune blog: Provides in-depth articles about best practices in machine learning experimentation (among other things)

• ​neptune-client: Neptune client is an open source Python library that lets you integrate your Python scripts with Neptune.

• Questions? Send an email to [email protected] by email or click the chat icon in the bottom right corner.