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Run sweeps from Jupyter notebooks

Sweep from Jupyter Notebook

Initialize a sweep

import wandb

sweep_config = {
  "name": "My Sweep",
  "method": "grid",
  "parameters": {
        "parameter1": {
            "values": [1, 2, 3]

sweep_id = wandb.sweep(sweep_config)

{% hint style="info" %} Use the following methods in order to specify the entity or project for the sweep:

  • Arguments to wandb.sweep() For example: wandb.sweep(sweep_config, entity="user", project="my_project")
  • Environment Variables WANDB_ENTITY and WANDB_PROJECT
  • Command Line Interface using the wandb init command {% endhint %}

Run an agent

When running an agent from python, the agent runs a specified function instead of using the program key from the sweep configuration file.

import wandb
import time

def train():
    run = wandb.init()
    print("config:", dict(run.config))
    for epoch in range(35):
        print("running", epoch)
        wandb.log({"metric": run.config.param1, "epoch": epoch})

wandb.agent(sweep_id, function=train)

Quick overview: Run in colab

Complete walkthrough of using sweeps in a project: Run in colab

Run a local controller

If you want to develop your own parameter search algorithms you can run your controller from python.

The simplest way to run a controller:

sweep = wandb.controller(sweep_id)

If you want more control of the controller loop:

import wandb
sweep = wandb.controller(sweep_id)
while not sweep.done():

Or even more control over the parameters being served:

import wandb
sweep = wandb.controller(sweep_id)
while not sweep.done():
    params =

If you want to specify your sweep entirely with code you can do something like this:

import wandb
from wandb.sweeps import GridSearch,RandomSearch,BayesianSearch
from wandb.sweeps import HyperbandEarlyTerminate

sweep = wandb.controller()
sweep.configure_parameter('param1', value=3)
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