Using Weights & Biases with Tune#

Weights & Biases (Wandb) is a tool for experiment tracking, model optimizaton, and dataset versioning. It is very popular in the machine learning and data science community for its superb visualization tools.

Weights & Biases

Ray Tune currently offers two lightweight integrations for Weights & Biases. One is the WandbLoggerCallback, which automatically logs metrics reported to Tune to the Wandb API.

The other one is the setup_wandb() function, which can be used with the function API. It automatically initializes the Wandb API with Tune’s training information. You can just use the Wandb API like you would normally do, e.g. using wandb.log() to log your training process.

Running A Weights & Biases Example#

In the following example we’re going to use both of the above methods, namely the WandbLoggerCallback and the setup_wandb function to log metrics.

As the very first step, make sure you’re logged in into wandb on all machines you’re running your training on:

wandb login

We can then start with a few crucial imports:

import numpy as np

import ray
from ray import air, tune
from ray.air import session
from ray.air.integrations.wandb import setup_wandb
from ray.air.integrations.wandb import WandbLoggerCallback

Next, let’s define an easy train_function function (a Tune Trainable) that reports a random loss to Tune. The objective function itself is not important for this example, since we want to focus on the Weights & Biases integration primarily.

def train_function(config):
    for i in range(30):
        loss = config["mean"] + config["sd"] * np.random.randn()
        session.report({"loss": loss})

You can define a simple grid-search Tune run using the WandbLoggerCallback as follows:

def tune_with_callback():
    """Example for using a WandbLoggerCallback with the function API"""
    tuner = tune.Tuner(
        train_function,
        tune_config=tune.TuneConfig(
            metric="loss",
            mode="min",
        ),
        run_config=air.RunConfig(
            callbacks=[
                WandbLoggerCallback(project="Wandb_example")
            ]
        ),
        param_space={
            "mean": tune.grid_search([1, 2, 3, 4, 5]),
            "sd": tune.uniform(0.2, 0.8),
        },
    )
    tuner.fit()

To use the setup_wandb utility, you simply call this function in your objective. Note that we also use wandb.log(...) to log the loss to Weights & Biases as a dictionary. Otherwise, this version of our objective is identical to its original.

def train_function_wandb(config):
    wandb = setup_wandb(config)

    for i in range(30):
        loss = config["mean"] + config["sd"] * np.random.randn()
        session.report({"loss": loss})
        wandb.log(dict(loss=loss))

With the train_function_wandb defined, running a Tune experiment is as simple as providing this objective and passing some settings to the wandb key of your Tune config:

def tune_with_setup():
    """Example for using the setup_wandb utility with the function API"""
    tuner = tune.Tuner(
        train_function_wandb,
        tune_config=tune.TuneConfig(
            metric="loss",
            mode="min",
        ),
        param_space={
            "mean": tune.grid_search([1, 2, 3, 4, 5]),
            "sd": tune.uniform(0.2, 0.8),
            "wandb": {"project": "Wandb_example"},
        },
    )
    tuner.fit()

Finally, you can also define a class-based Tune Trainable by using the setup_wandb in the setup() method and storing the run object as an attribute. Please note that with the class trainable, you have to pass the trial id, name, and group separately:

class WandbTrainable(tune.Trainable):
    def setup(self, config):
        self.wandb = setup_wandb(
            config, trial_id=self.trial_id, trial_name=self.trial_name, group="Example"
        )

    def step(self):
        for i in range(30):
            loss = self.config["mean"] + self.config["sd"] * np.random.randn()
            self.wandb.log({"loss": loss})
        return {"loss": loss, "done": True}
    
    def save_checkpoint(self, checkpoint_dir: str):
        pass
    
    def load_checkpoint(self, checkpoint_dir: str):
        pass

Running Tune with this WandbTrainable works exactly the same as with the function API. The below tune_trainable function differs from tune_decorated above only in the first argument we pass to Tuner():

def tune_trainable():
    """Example for using a WandTrainableMixin with the class API"""
    tuner = tune.Tuner(
        WandbTrainable,
        tune_config=tune.TuneConfig(
            metric="loss",
            mode="min",
        ),
        param_space={
            "mean": tune.grid_search([1, 2, 3, 4, 5]),
            "sd": tune.uniform(0.2, 0.8),
            "wandb": {"project": "Wandb_example"},
        },
    )
    results = tuner.fit()

    return results.get_best_result().config

Since you may not have an API key for Wandb, we can mock the Wandb logger and test all three of our training functions as follows. If you are logged in into wandb, you can set mock_api = False to actually upload your results to Weights & Biases.

import os

mock_api = True

if mock_api:
    os.environ.setdefault("WANDB_MODE", "disabled")
    os.environ.setdefault("WANDB_API_KEY", "abcd")
    ray.init(
        runtime_env={
            "env_vars": {"WANDB_MODE": "disabled", "WANDB_API_KEY": "abcd"}
        }
    )

tune_with_callback()
tune_with_setup()
tune_trainable()
2022-11-02 16:02:45,355	INFO worker.py:1534 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8266 
2022-11-02 16:02:46,513	INFO wandb.py:282 -- Already logged into W&B.

Tune Status

Current time:2022-11-02 16:03:13
Running for: 00:00:27.28
Memory: 10.8/16.0 GiB

System Info

Using FIFO scheduling algorithm.
Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/3.44 GiB heap, 0.0/1.72 GiB objects

Trial Status

Trial name status loc mean sd iter total time (s) loss
train_function_7676d_00000TERMINATED127.0.0.1:14578 10.411212 30 0.2361370.828527
train_function_7676d_00001TERMINATED127.0.0.1:14591 20.756339 30 5.57185 3.13156
train_function_7676d_00002TERMINATED127.0.0.1:14593 30.436643 30 5.50237 3.26679
train_function_7676d_00003TERMINATED127.0.0.1:14595 40.295929 30 5.60986 3.70388
train_function_7676d_00004TERMINATED127.0.0.1:14596 50.335292 30 5.61385 4.74294

Trial Progress

Trial name date done episodes_total experiment_id experiment_tag hostname iterations_since_restore lossnode_ip pid time_since_restore time_this_iter_s time_total_s timestamp timesteps_since_restoretimesteps_total training_iterationtrial_id warmup_time
train_function_7676d_000002022-11-02_16-02-53True a9f242fa70184d9dadd8952b16fb0ecc0_mean=1,sd=0.4112Kais-MBP.local.meter 300.828527127.0.0.114578 0.236137 0.00381589 0.236137 1667430173 0 307676d_00000 0.00366998
train_function_7676d_000012022-11-02_16-03-03True f57118365bcb4c229fe41c5911f05ad61_mean=2,sd=0.7563Kais-MBP.local.meter 303.13156 127.0.0.114591 5.57185 0.00627518 5.57185 1667430183 0 307676d_00001 0.0027349
train_function_7676d_000022022-11-02_16-03-03True 394021d4515d4616bae7126668f73b2b2_mean=3,sd=0.4366Kais-MBP.local.meter 303.26679 127.0.0.114593 5.50237 0.00494576 5.50237 1667430183 0 307676d_00002 0.00286222
train_function_7676d_000032022-11-02_16-03-03True a575e79c9d95485fa37deaa86267aea43_mean=4,sd=0.2959Kais-MBP.local.meter 303.70388 127.0.0.114595 5.60986 0.00689816 5.60986 1667430183 0 307676d_00003 0.00299597
train_function_7676d_000042022-11-02_16-03-03True 91ce57dcdbb54536b1874666b711350d4_mean=5,sd=0.3353Kais-MBP.local.meter 304.74294 127.0.0.114596 5.61385 0.00672579 5.61385 1667430183 0 307676d_00004 0.00323987
2022-11-02 16:03:13,913	INFO tune.py:788 -- Total run time: 28.53 seconds (27.28 seconds for the tuning loop).

Tune Status

Current time:2022-11-02 16:03:22
Running for: 00:00:08.49
Memory: 9.9/16.0 GiB

System Info

Using FIFO scheduling algorithm.
Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/3.44 GiB heap, 0.0/1.72 GiB objects

Trial Status

Trial name status loc mean sd iter total time (s) loss
train_function_wandb_877eb_00000TERMINATED127.0.0.1:14647 10.738281 30 1.613190.555153
train_function_wandb_877eb_00001TERMINATED127.0.0.1:14660 20.321178 30 1.724472.52109
train_function_wandb_877eb_00002TERMINATED127.0.0.1:14661 30.202487 30 1.8159 2.45412
train_function_wandb_877eb_00003TERMINATED127.0.0.1:14662 40.515434 30 1.715 4.51413
train_function_wandb_877eb_00004TERMINATED127.0.0.1:14663 50.216098 30 1.728275.2814
(train_function_wandb pid=14647) 2022-11-02 16:03:17,149	INFO wandb.py:282 -- Already logged into W&B.

Trial Progress

Trial name date done episodes_total experiment_id experiment_tag hostname iterations_since_restore lossnode_ip pid time_since_restore time_this_iter_s time_total_s timestamp timesteps_since_restoretimesteps_total training_iterationtrial_id warmup_time
train_function_wandb_877eb_000002022-11-02_16-03-18True 7b250c9f31ab484dad1a1fd29823afdf0_mean=1,sd=0.7383Kais-MBP.local.meter 300.555153127.0.0.114647 1.61319 0.00232315 1.61319 1667430198 0 30877eb_00000 0.00391102
train_function_wandb_877eb_000012022-11-02_16-03-22True 5172868368074557a3044ea3a91466731_mean=2,sd=0.3212Kais-MBP.local.meter 302.52109 127.0.0.114660 1.72447 0.0152011 1.72447 1667430202 0 30877eb_00001 0.00901699
train_function_wandb_877eb_000022022-11-02_16-03-22True b13d9bccb1964b4b95e1a858a3ea64c72_mean=3,sd=0.2025Kais-MBP.local.meter 302.45412 127.0.0.114661 1.8159 0.00437403 1.8159 1667430202 0 30877eb_00002 0.00844812
train_function_wandb_877eb_000032022-11-02_16-03-22True 869d7ec7a3544a8387985103e626818f3_mean=4,sd=0.5154Kais-MBP.local.meter 304.51413 127.0.0.114662 1.715 0.00247812 1.715 1667430202 0 30877eb_00003 0.00282907
train_function_wandb_877eb_000042022-11-02_16-03-22True 84d3112d66f64325bc469e44b8447ef54_mean=5,sd=0.2161Kais-MBP.local.meter 305.2814 127.0.0.114663 1.72827 0.00517201 1.72827 1667430202 0 30877eb_00004 0.00272107
(train_function_wandb pid=14660) 2022-11-02 16:03:20,600	INFO wandb.py:282 -- Already logged into W&B.
(train_function_wandb pid=14661) 2022-11-02 16:03:20,600	INFO wandb.py:282 -- Already logged into W&B.
(train_function_wandb pid=14663) 2022-11-02 16:03:20,628	INFO wandb.py:282 -- Already logged into W&B.
(train_function_wandb pid=14662) 2022-11-02 16:03:20,723	INFO wandb.py:282 -- Already logged into W&B.
2022-11-02 16:03:22,565	INFO tune.py:788 -- Total run time: 8.60 seconds (8.48 seconds for the tuning loop).

Tune Status

Current time:2022-11-02 16:03:31
Running for: 00:00:09.28
Memory: 9.9/16.0 GiB

System Info

Using FIFO scheduling algorithm.
Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/3.44 GiB heap, 0.0/1.72 GiB objects

Trial Status

Trial name status loc mean sd iter total time (s) loss
WandbTrainable_8ca33_00000TERMINATED127.0.0.1:14718 10.397894 1 0.0001871590.742345
WandbTrainable_8ca33_00001TERMINATED127.0.0.1:14737 20.386883 1 0.0001518732.5709
WandbTrainable_8ca33_00002TERMINATED127.0.0.1:14738 30.290693 1 0.00014019 2.99601
WandbTrainable_8ca33_00003TERMINATED127.0.0.1:14739 40.33333 1 0.00015831 3.91276
WandbTrainable_8ca33_00004TERMINATED127.0.0.1:14740 50.645479 1 0.0001509195.47779
(WandbTrainable pid=14718) 2022-11-02 16:03:25,742	INFO wandb.py:282 -- Already logged into W&B.

Trial Progress

Trial name date done episodes_total experiment_id hostname iterations_since_restore lossnode_ip pid time_since_restore time_this_iter_s time_total_s timestamp timesteps_since_restoretimesteps_total training_iterationtrial_id warmup_time
WandbTrainable_8ca33_000002022-11-02_16-03-27True 3adb4d0ae0d74d1c9ddd07924b5653b0Kais-MBP.local.meter 10.742345127.0.0.114718 0.000187159 0.000187159 0.000187159 1667430207 0 18ca33_00000 1.31382
WandbTrainable_8ca33_000012022-11-02_16-03-31True f1511cfd51f94b3d9cf192181ccc08a9Kais-MBP.local.meter 12.5709 127.0.0.114737 0.000151873 0.000151873 0.000151873 1667430211 0 18ca33_00001 1.31668
WandbTrainable_8ca33_000022022-11-02_16-03-31True a7528ec6adf74de0b73aa98ebedab66dKais-MBP.local.meter 12.99601 127.0.0.114738 0.00014019 0.00014019 0.00014019 1667430211 0 18ca33_00002 1.32008
WandbTrainable_8ca33_000032022-11-02_16-03-31True b7af756ca586449ba2d4c44141b53b06Kais-MBP.local.meter 13.91276 127.0.0.114739 0.00015831 0.00015831 0.00015831 1667430211 0 18ca33_00003 1.31879
WandbTrainable_8ca33_000042022-11-02_16-03-31True 196624f42bcc45c18a26778573a43a2cKais-MBP.local.meter 15.47779 127.0.0.114740 0.000150919 0.000150919 0.000150919 1667430211 0 18ca33_00004 1.31945
(WandbTrainable pid=14739) 2022-11-02 16:03:30,360	INFO wandb.py:282 -- Already logged into W&B.
(WandbTrainable pid=14740) 2022-11-02 16:03:30,393	INFO wandb.py:282 -- Already logged into W&B.
(WandbTrainable pid=14737) 2022-11-02 16:03:30,454	INFO wandb.py:282 -- Already logged into W&B.
(WandbTrainable pid=14738) 2022-11-02 16:03:30,510	INFO wandb.py:282 -- Already logged into W&B.
2022-11-02 16:03:31,985	INFO tune.py:788 -- Total run time: 9.40 seconds (9.27 seconds for the tuning loop).
{'mean': 1, 'sd': 0.3978937765393781, 'wandb': {'project': 'Wandb_example'}}

This completes our Tune and Wandb walk-through. In the following sections you can find more details on the API of the Tune-Wandb integration.

Tune Wandb API Reference#

WandbLoggerCallback#

class ray.air.integrations.wandb.WandbLoggerCallback(project: Optional[str] = None, group: Optional[str] = None, api_key_file: Optional[str] = None, api_key: Optional[str] = None, excludes: Optional[List[str]] = None, log_config: bool = False, upload_checkpoints: bool = False, save_checkpoints: bool = False, **kwargs)[source]

Weights and biases (https://www.wandb.ai/) is a tool for experiment tracking, model optimization, and dataset versioning. This Ray Tune LoggerCallback sends metrics to Wandb for automatic tracking and visualization.

Example

import random

from ray import tune
from ray.air import session, RunConfig
from ray.air.integrations.wandb import WandbLoggerCallback


def train_func(config):
    offset = random.random() / 5
    for epoch in range(2, config["epochs"]):
        acc = 1 - (2 + config["lr"]) ** -epoch - random.random() / epoch - offset
        loss = (2 + config["lr"]) ** -epoch + random.random() / epoch + offset
        session.report({"acc": acc, "loss": loss})


tuner = tune.Tuner(
    train_func,
    param_space={
        "lr": tune.grid_search([0.001, 0.01, 0.1, 1.0]),
        "epochs": 10,
    },
    run_config=RunConfig(
        callbacks=[WandbLoggerCallback(project="Optimization_Project")]
    ),
)
results = tuner.fit()
Parameters
  • project – Name of the Wandb project. Mandatory.

  • group – Name of the Wandb group. Defaults to the trainable name.

  • api_key_file – Path to file containing the Wandb API KEY. This file only needs to be present on the node running the Tune script if using the WandbLogger.

  • api_key – Wandb API Key. Alternative to setting api_key_file.

  • excludes – List of metrics and config that should be excluded from the log.

  • log_config – Boolean indicating if the config parameter of the results dict should be logged. This makes sense if parameters will change during training, e.g. with PopulationBasedTraining. Defaults to False.

  • upload_checkpoints – If True, model checkpoints will be uploaded to Wandb as artifacts. Defaults to False.

  • **kwargs – The keyword arguments will be pased to wandb.init().

Wandb’s group, run_id and run_name are automatically selected by Tune, but can be overwritten by filling out the respective configuration values.

Please see here for all other valid configuration settings: https://docs.wandb.ai/library/init

setup_wandb#

ray.air.integrations.wandb.setup_wandb(config: Optional[Dict] = None, api_key: Optional[str] = None, api_key_file: Optional[str] = None, rank_zero_only: bool = True, **kwargs) None[source]

Set up a Weights & Biases session.

This function can be used to initialize a Weights & Biases session in a (distributed) training or tuning run.

By default, the run ID is the trial ID, the run name is the trial name, and the run group is the experiment name. These settings can be overwritten by passing the respective arguments as kwargs, which will be passed to wandb.init().

In distributed training with Ray Train, only the zero-rank worker will initialize wandb. All other workers will return a disabled run object, so that logging is not duplicated in a distributed run. This can be disabled by passing rank_zero_only=False, which will then initialize wandb in every training worker.

The config argument will be passed to Weights and Biases and will be logged as the run configuration.

If no API key or key file are passed, wandb will try to authenticate using locally stored credentials, created for instance by running wandb login.

Keyword arguments passed to setup_wandb() will be passed to wandb.init() and take precedence over any potential default settings.

Parameters
  • config – Configuration dict to be logged to Weights and Biases. Can contain arguments for wandb.init() as well as authentication information.

  • api_key – API key to use for authentication with Weights and Biases.

  • api_key_file – File pointing to API key for with Weights and Biases.

  • rank_zero_only – If True, will return an initialized session only for the rank 0 worker in distributed training. If False, will initialize a session for all workers.

  • kwargs – Passed to wandb.init().

Example

PublicAPI (alpha): This API is in alpha and may change before becoming stable.