Jupyter Lab and Jupyter Notebook

You can execute your runs in Jupyter notebooks and track them in Neptune. You just need to:
In one of the first cells install Neptune client
! pip install neptune-client
Create Neptune run
import neptune.new as neptune
​
run = neptune.init(project='my_workspace/my_project')
To make sure that you set up API token.
Log metrics and other metadata to Neptune (learn what else you can log here).
# 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 score
run["f1_score"] = 0.66
Neptune-Jupyter notebooks integration
Neptune supports keeping track of Jupyter Notebook checkpoints with neptune-notebooks extension. If you do that, your notebook checkpoints will get an auto-snapshot whenever you create a run. Go here to read more about that.
Any IDE
Keeping track of Jupyter Notebooks
Last updated 2 days ago