# Wandb integration using class and sweep running twice under the same name

**URL:** <https://community.wandb.ai/t/wandb-integration-using-class-and-sweep-running-twice-under-the-same-name/2433>\
**Category:** W&B Help\
**Tags:** sweeps\
**Created:** [May 16, 2022, 4:44pm UTC](https://community.wandb.ai/t/wandb-integration-using-class-and-sweep-running-twice-under-the-same-name/2433 "2022-05-16T16:44:47Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![tschug](https://avatars.discourse-cdn.com/v4/letter/t/a5b964/32.png) [@tschug](https://community.wandb.ai/u/tschug)\
**Post date:** [May 16, 2022, 4:44pm UTC](https://community.wandb.ai/t/wandb-integration-using-class-and-sweep-running-twice-under-the-same-name/2433/1 "2022-05-16T16:44:47Z")

</div>

Hi all,

I’m implementing W&B into an existing project in which Agent, Model creation and Environment are constructed in classes. The code structure in the Python file (`AIAgent.py`) looks like this:

```auto
import wandb

config = {
    'layer_sizes': [17, 16, 12, 4],
    'batch_minsize': 32,
    'max_memory': 100_000,
    'episodes': 2,
    'epsilon': 1.0,
    'epsilon_decay': 0.998,
    'epsilon_min': 0.01,
    'gamma': 0.9,
    'learning_rate': 0.001,
    'weight_decay': 0,
    'optimizer': 'sgd',
    'activation': 'relu',
    'loss_function': 'mse'
}

class AIAgent:
    def __init__ (self):
        self.config = config
        self.pipeline(self.config)

    def pipeline(self, config):
        wandb.init()
        config = wandb.config

        model, criterion, optimizer = self.make(config)
        self.train(model, criterion, optimizer, config) 

    def make(self, config):
        model = LinearQNet(config).to(device)

        if config['loss_function'] == 'mse':
            criterion = nn.MSELoss()

        if config['optimizer'] == 'adam':
            optimizer = torch.optim.Adam(model.parameters(), lr=config['learning_rate'], betas=(0.9, 0.999), eps=1e-08, weight_decay=config['weight_decay'], amsgrad=False)
 
        wandb.watch(model, criterion, log='all', log_freq=1)
        summary(model)

        return model, criterion, optimizer

    def train(self, model, criterion, optimizer, config):
        for episode in range(1, config['episodes'] + 1):
            while True:
                # Where the training is performed

                if done:
                    if (episode % 1) == 0:
                        wandb.log({'episode': episode, 'epsilon': epsilon, 'score': score, 'loss': loss_mean, 'reward': reward_mean, 'score_mean': score_mean, 'images': [wandb.Image(img) for img in env_images]}, step=episode})
                    break

            if episode < config['episodes']:
                game.game_reset()
            else:
                wandb.finish()
                break

class LinearQNet(nn.Module):
    def __init__ (self, config):
        super(LinearQNet, self). __init__ ()
        self.config = config
        # Where the NN is configured

if __name__ == ' __main__':
    AIAgent. __init__ (AIAgent())

```

I’m currently initializing the sweep configuration via a .yaml file calling `wandb sweep sweep.yaml`. The sweep.yaml file looks like this:

```auto
program: AIAgent.py
project: evaluation-sweep-1
method: random
metric:
  name: score_mean
  goal: maximize
command:
  - ${env}
  - python3
  - ${program}
  - ${args}
parameters:
  layer_sizes:
    distribution: constant
    value: [17, 16, 512, 4]
  batch_minsize:
    distribution: int_uniform
    max: 1024
    min: 32
  max_memory:
    distribution: constant
    value: 100_000
  episodes:
    distribution: constant
    value: 50
  epsilon:
    distribution: constant
    value: 1.0
  epsilon_decay:
    distribution: constant
    value: 0.995
  epsilon_min:
    distribution: constant
    value: 0.01
  gamma:
    distribution: uniform
    max: 0.99
    min: 0.8
  learning_rate:
    distribution: uniform
    max: 0.1
    min: 0.0001  
  weight_decay:
    distribution: constant
    value: 0
  optimizer:
    distribution: categorical
    values: ['sgd', 'adam', 'adamw']
  activation:
    distribution: categorical
    values: ['relu', 'sigmoid', 'tanh', 'leakyrelu']
  loss_function:
    distribution: constant
    value: 'mse'
early_terminate:
  type: hyperband
  min_iter: 5

```

Besides general feedback on the implementation I’m a bit dumbfounded with a current bug. The sweeps run fine and show up in the W&B interface but every sweep is performed twice under the same name of which only the loffing of the first is displayed and the second runs ‘silently’ in the environment without update of wandb.log. Does anybody have an idea what the reason for this might be?

Thanks,  
Tobias

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<div class="post-metadata">

**Author:** ![ramit\_goolry](https://avatars.discourse-cdn.com/v4/letter/r/85e7bf/32.png) [@ramit\_goolry](https://community.wandb.ai/u/ramit_goolry)\
**Post date:** [May 18, 2022, 2:52pm UTC](https://community.wandb.ai/t/wandb-integration-using-class-and-sweep-running-twice-under-the-same-name/2433/2 "2022-05-18T14:52:27Z")

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Hi Tobias,

Looks like the source of this bug is this line: `AIAgent. __init__ (AIAgent())` which is calling 2 constructors: 1 from `AIAgent. __init__ ()` and 1 from `AIAgent()`. This, in turn calls `pipeline` twice, which ends up meaning 2 calls to `wandb.init()` and therefore you see 2 runs.

I would suggest changing that line to just `AIAgent` to prevent this error.

Thanks,  
Ramit

---

<div class="post-metadata">

**Author:** ![tschug](https://avatars.discourse-cdn.com/v4/letter/t/a5b964/32.png) [@tschug](https://community.wandb.ai/u/tschug)\
**Post date:** [May 19, 2022, 8:22am UTC](https://community.wandb.ai/t/wandb-integration-using-class-and-sweep-running-twice-under-the-same-name/2433/3 "2022-05-19T08:22:06Z")

</div>

Oh mann. Thanks a lot Ramit.  
Much appreciated!

Best regards,  
Tobias

---

<div class="post-metadata">

**Author:** ![system](https://us1.discourse-cdn.com/flex020/uploads/wandb/original/1X/366b649231631dbab896843020da0056074ac79d.png) [@system](https://community.wandb.ai/u/system)\
**Post date:** [July 18, 2022, 8:22am UTC](https://community.wandb.ai/t/wandb-integration-using-class-and-sweep-running-twice-under-the-same-name/2433/4 "2022-07-18T08:22:24Z")

</div>

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