Quickstart

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See code examples on GitHub​
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See results in Neptune​
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Run example in Google Colab​
Introduction
This guide will show you how to:
Install the Neptune client library for Python
Connect Neptune to your script and create a run
Log metrics and view the results in the app
By the end of it, you'll see metadata tracked to your first run in Neptune!
If you're using R, see the R setup guide.​
Before you start
Have Python 3.7+ installed
Have pip or conda installed
If you'd rather not install anything, you can follow the guide in Google Colab with zero setup.
Step 1: Install the Neptune client library
pip
conda
pip install neptune-client
conda install -c conda-forge neptune-client
For more help, see Installation and setup.
Step 2: Create hello_neptune.py
Create a Python script called hello_neptune.py and copy-paste the following code to it:
hello_neptune.py
import neptune.new as neptune
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# Start a run
run = neptune.init(
    project="common/quickstarts",  # anyone can log to this public project
    api_token="ANONYMOUS",  # enables logging without registration
)
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# Track metadata and hyperparameters of your run
run["JIRA"] = "NPT-952"
run["algorithm"] = "ConvNet"
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params = {
    "batch_size": 64,
    "dropout": 0.2,
    "learning_rate": 0.001,
    "optimizer": "Adam",
}
run["parameters"] = params
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# 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)
    # this just creates a couple of series so we can view them as charts
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# Log the final results
run["f1_score"] = 0.66
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# Stop the connection and sync the data with Neptune
run.stop()
Step 3: Run your script and explore the results
Now that you have your Hello Neptune script ready, run it from your terminal, Jupyter Lab, or other environments.
python hello_neptune.py
Click on the link in the console output to open the run.
Example link: https://app.neptune.ai/common/quickstarts/e/QUI-80914​
In the left pane, check out the following sections:
All metadata - the logged metadata displayed in a folder-like structure.
Charts - metrics visualized as charts.
Monitoring - hardware consumption during the run execution.
Source code - the script that was used for the run.
Viewing all metadata of a run
Conclusion
Congrats! You've learned how to track some model-building metadata with Neptune.
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See code examples on GitHub​
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See results in Neptune​
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Run example in Google Colab​
What’s next?
Rerun the script with different parameters to track a few more runs, then click Compare runs in the very left pane to compare and visualize them.
When you move on from Hello Neptune, complete the authentication part of installation and setup:
Save your API token as an environment variable and omit the api_token parameter from the code. You can then log metadata to any Neptune project you have access to and your API token is kept secure.
Set the project argument to your own project name, or export that as an environment variable too.
Other resources to check out:
How to add Neptune to your code
What can you log and display?
Best practices
Examples
How-to guides
Integrations and Supported Tools

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Examples

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Outline

Introduction

Before you start

Step 1: Install the Neptune client library

Step 2: Create hello_neptune.py

Step 3: Run your script and explore the results

Conclusion

What’s next?