Quickstart to logging
Start by installing Neptune and configuring your Neptune API token and project, as described in Get started.
Then, you can use the following script to log some mocked metadata:
from random import random
from neptune_scale import Run
custom_id = random()
offset = custom_id / 5
def hello_neptune():
run = Run(
api_token="eyJhcGlfYWRkcmVzcyh0...0In0=", # replace with your Neptune API token
project="team-alpha/project-x", # replace with your workspace and project name
experiment_name="seabird-flying-skills",
run_id=f"seagull-{custom_id}",
)
run.log_configs(
{
"parameters/use_preprocessing": True,
"parameters/learning_rate": 0.002,
"parameters/batch_size": 64,
"parameters/optimizer": "Adam",
}
)
for step in range(20):
acc = 1 - 2**-step - random() / (step + 1) - offset
loss = 2**-step + random() / (step + 1) + offset
run.log_metrics(
data={
"accuracy": acc,
"loss": loss,
},
step=step,
)
run.add_tags(["purple", "blue", "client 0.6.3"])
run.close()
if __name__ == "__main__":
hello_neptune()
The line
if __name__ == "__main__":ensures safe importing of the main module. For details, see the Python documentation.
To inspect the logged metadata in the web app:
- In the Neptune project, click on the run to explore all its metadata.
- If you log multiple runs, enable compare mode by toggling eye icons ().
To get a link to the run in the Neptune web app:
from neptune_scale import Run
run = Run(...)
run.get_run_url()
For details, see Construct Neptune URLs.