yukw777

yukw777

yukw777

/*
This server simply bombards each websocket connection with a message every millisecond up to 20k messages.

How to run.

1. Run pushpin locally with the default publish port and backend port as 8080.
ex: pushpin --merge-output --port=17999 --route="* localhost:8080,over_http" --config pushpin.conf

2. Start this server in another terminal
ex: node loadtest.js

yukw777

    def training_step(self, batch: DataPoint, batch_idx: int) -> pl.TrainResult:
        planes, target_move, target_val = batch
        pred_move, pred_val = self(planes)
        mse_loss, cross_entropy_loss, loss = self.loss(
            pred_move, pred_val, target_move, target_val
        )
        result = pl.TrainResult(minimize=loss)
        result.log("train_loss", loss, prog_bar=True)
        result.log_dict(
            {

yukw777

    def training_step(self, batch: DataPoint, batch_idx: int) -> pl.TrainResult:
        planes, target_move, target_val = batch
        pred_move, pred_val = self(planes)
        mse_loss, cross_entropy_loss, loss = self.loss(
            pred_move, pred_val, target_move, target_val
        )
        result = pl.TrainResult(minimize=loss)
        result.log("train_loss", loss, prog_bar=True)
        result.log_dict(
            {

yukw777

class DataModule(pl.LightningDataModule):
    def __init__(
        self,
        train_data_dir: str,
        val_data_dir: str,
        test_data_dir: str,
        train_dataloader_conf: Optional[DictConfig] = None,
        val_dataloader_conf: Optional[DictConfig] = None,
        test_dataloader_conf: Optional[DictConfig] = None,
    ):

yukw777

# @package _group_
_target_: leela_zero_pytorch.network.NetworkLightningModule
network_conf:
  residual_channels: 32
  residual_layers: 8

yukw777

# @package _group_
_target_: leela_zero_pytorch.network.NetworkLightningModule
network_conf:
  residual_channels: 128
  residual_layers: 6

yukw777

@hydra.main(config_path="conf", config_name="config")
def main(cfg: DictConfig) -> Trainer:
    logger.info(f"Training with the following config:\n{OmegaConf.to_yaml(cfg)}")

    network = instantiate(cfg.network, cfg.train)

yukw777

@pytest.mark.parametrize("network_size", ["small", "big", "huge"])
def test_train_network_size(monkeypatch, tmp_path, capsys, network_size):
    with initialize(config_path="../leela_zero_pytorch/conf"):
        cfg = compose(
            config_name="config",
            overrides=[
                f"+network={network_size}",
                "data.train_data_dir=tests/test-data",
                "data.train_dataloader_conf.batch_size=2",
                "data.val_data_dir=tests/test-data",