Call wandb.init() at the top of your script to start a new run

Call wandb.init() once at the beginning of your script to initialize a new job. This creates a new run in W&B and launches a background process to sync data.

  • On Prem: If you need a private cloud or local instance of W&B, see our Self Hosted offerings.

  • Automated Environments: Most of these settings can also be controlled via Environment Variables. This is often useful when you're running jobs on a cluster.

Reference Docs

Check the reference docs for arguments.

Common Questions

How do I launch multiple runs from one script?

If you're trying to start multiple runs from one script, add two things to your code:

  1. run = wandb.init(reinit=True): Use this setting to allow reinitializing runs

  2. run.finish(): Use this at the end of your run to finish logging for that run

import wandb
for x in range(10):
run = wandb.init(project="runs-from-for-loop", reinit=True)
for y in range (100):
wandb.log({"metric": x+y})

Alternatively you can use a python context manager which will automatically finish logging:

import wandb
for x in range(10):
run = wandb.init(reinit=True)
with run:
for y in range(100):
run.log({"metric": x+y})

LaunchError: Permission denied

If you're getting a LaunchError: Launch exception: Permission denied error, you don't have permissions to log to the project you're trying to send runs to. This might be for a few different reasons.

  1. You aren't logged in on this machine. Run wandb login on the command line.

  2. You've set an entity that doesn't exist. "Entity" should be your username or the name of an existing team. If you need to create a team, go to our Subscriptions page.

  3. You don't have project permissions. Ask the creator of the project to set the privacy to Open so you can log runs to this project.

Get the readable run name

Get the nice, readable name for your run.

import wandb
run_name = wandb.run.name

Set the run name to the generated run ID

If you'd like to overwrite the run name (like snowy-owl-10) with the run ID (like qvlp96vk) you can use this snippet:

import wandb
wandb.run.name = wandb.run.id

Save the git commit

When wandb.init() is called in your script, we automatically look for git information to save a link to your repo the SHA of the latest commit. The git information should show up on your run page. If you aren't seeing it appear there, make sure that your script where you call wandb.init() is located in a folder that has git information.

The git commit and command used to run the experiment are visible to you but are hidden to external users, so if you have a public project, these details will remain private.

Save logs offline

By default, wandb.init() starts a process that syncs metrics in real time to our cloud hosted app. If your machine is offline or you don't have internet access, here's how to run wandb using the offline mode and sync later.

Set two environment variables:

  1. WANDB_API_KEY: Set this to your account's API key, on your settings page

  2. WANDB_MODE: dryrun

Here's a sample of what this would look like in your script:

import wandb
import os
os.environ["WANDB_MODE"] = "dryrun"
config = {
"dataset": "CIFAR10",
"machine": "offline cluster",
"model": "CNN",
"learning_rate": 0.01,
"batch_size": 128,
for i in range(100):
wandb.log({"accuracy": i})

Here's a sample terminal output:

And once I have internet, I run a sync command to send that folder to the cloud.

wandb sync wandb/dryrun-folder-name