23、Ray AI 库入门与大语言模型训练部署实践

Ray AI 库入门与大语言模型训练部署实践

1. 模型调优与检查点获取

在使用 Ray AI 库时，我们可以通过以下代码进行模型调优并获取检查点：

trainer = XGBoostTrainer( 
    label_column="target", 
    params={}, 
    datasets={"train": get_dataset()},
)
param_space = {
    "scaling_config": ScalingConfig( 
        num_workers=tune.grid_search([2, 4]), 
        resources_per_worker={
            "CPU": tune.grid_search([1, 2]),
        },
    ),
    "params": {
        "objective": "binary:logistic",
        "tree_method": "approx",
        "eval_metric": ["logloss", "error"],
        "eta": tune.loguniform(1e-4, 1e-1),
        "subsample": tune.uniform(0.5, 1.0),
        "max_depth": tune.randint(1, 9),
    },
}
tuner = Tuner(trainable=trainer, param_space=param_space, 
              run