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Search and stop algorithms locally

Search and stop algorithms locally instead of using the Weights & Biases cloud-hosted service.
The hyper-parameter controller is hosted by Weights & Biased as a cloud service by default. W&B agents communicate with the controller to determine the next set of parameters to use for training. The controller is also responsible for running early stopping algorithms to determine which runs can be stopped.
The local controller feature allows the user to commence search and stop algorithms locally. The local controller gives the user the ability to inspect and instrument the code in order to debug issues as well as develop new features which can be incorporated into the cloud service.
This feature is offered to support faster development and debugging of new algorithms for the Sweeps tool. It is not intended for actual hyperparameter optimization workloads.
Before you get start, you must install the Weights & Biases SDK(wandb). Type the following code snippet into your command line:
pip install wandb sweeps
The following examples assume you already have a configuration file and a training loop defined in a python script or Jupyter Notebook. For more information about how to define a configuration file, see Define sweep configuration.

Run the local controller from the command line

Initialize a sweep similarly to how you normally would when you use hyper-parameter controllers hosted by W&B as a cloud service. Specify the controller flag (controller) to indicate you want to use the local controller for W&B sweep jobs:
wandb sweep --controller config.yaml
Alternatively, you can separate initializing a sweep and specifying that you want to use a local controller into two steps.
To separate the steps, first add the following key-value to your sweep's configuration file:
sweep-config.yaml
controller:
type: local
Next, initialize the sweep:
wandb sweep config.yaml
After you initialized the sweep, start a controller with wandb controller:
# wandb sweep command will print a sweep_id
wandb controller {user}/{entity}/{sweep_id}
Once you have specified you want to use a local controller, start one or more Sweep agents to execute the sweep. Start a W&B Sweep similar to how you normally would. See Start sweep agents, for more information.
wandb sweep swepp_ID

Run a local controller with W&B Python SDK

The following code snippets demonstrate how to specify and use a local controller with the Weights & Biases Python SDK.
The simplest way to use a controller with the Python SDK is to pass the sweep ID to the wandb.controller method. Next, use the return objects run method to start the sweep job:
sweep = wandb.controller(sweep_id)
sweep.run()
If you want more control of the controller loop:
import wandb
sweep = wandb.controller(sweep_id)
while not sweep.done():
sweep.print_status()
sweep.step()
time.sleep(5)
Or even more control over the parameters served:
import wandb
sweep = wandb.controller(sweep_id)
while not sweep.done():
params = sweep.search()
sweep.schedule(params)
sweep.print_status()
If you want to specify your sweep entirely with code you can do something like this:
import wandb
sweep = wandb.controller()
sweep.configure_search("grid")
sweep.configure_program("train-dummy.py")
sweep.configure_controller(type="local")
sweep.configure_parameter("param1", value=3)
sweep.create()
sweep.run()