H20 无人驾驶 AI，无法加载自定义配方

问题描述

我使用的是 H2O DAI 1.9.0.6。我正在尝试在专家设置上加载自定义配方（使用自定义配方的 BERT 预存模型）。我正在使用本地文件上传。但是上传没有发生。没有错误，没有任何进展。在那次活动之后，我无法在“食谱”选项卡下看到此模型。

从以下 URL 获取示例食谱并根据我的需要进行修改。感谢创建此食谱的人。

https://github.com/h2oai/driverlessai-recipes/blob/master/models/nlp/portuguese_bert.py

自定义配方

import os
import shutil
from urllib.parse import urlparse

import requests

from h2oaicore.models import TextBERTModel,CustomModel
from h2oaicore.systemutils import make_experiment_logger,temporary_files_path,atomic_move,loggerinfo


def is_url(url):
    try:
        result = urlparse(url)
        return all([result.scheme,result.netloc,result.path])
    except:
        return False


def maybe_download_language_model(logger,save_directory,model_link,config_link,vocab_link):
    model_name = "pytorch_model.bin"
    if isinstance(model_link,str):
        model_name = model_link.split('/')[-1]
        if '.bin' not in model_name:
            model_name = "pytorch_model.bin"

    maybe_download(url=config_link,dest=os.path.join(save_directory,"config.json"),logger=logger)
    maybe_download(url=vocab_link,"vocab.txt"),logger=logger)
    maybe_download(url=model_link,model_name),logger=logger)


def maybe_download(url,dest,logger=None):
    if not is_url(url):
        loggerinfo(logger,f"{url} is not a valid URL.")
        return

    dest_tmp = dest + ".tmp"
    if os.path.exists(dest):
        loggerinfo(logger,f"already downloaded {url} -> {dest}")
        return

    if os.path.exists(dest_tmp):
        loggerinfo(logger,f"Download has already started {url} -> {dest_tmp}. "
        f"Delete {dest_tmp} to download the file once more.")
        return

    loggerinfo(logger,f"Downloading {url} -> {dest}")
    url_data = requests.get(url,stream=True)
    if url_data.status_code != requests.codes.ok:
        msg = "Cannot get url %s,code: %s,reason: %s" % (
            str(url),str(url_data.status_code),str(url_data.reason))
        raise requests.exceptions.RequestException(msg)
    url_data.raw.decode_content = True
    if not os.path.isdir(os.path.dirname(dest)):
        os.makedirs(os.path.dirname(dest),exist_ok=True)
    with open(dest_tmp,'wb') as f:
        shutil.copyfileobj(url_data.raw,f)

    atomic_move(dest_tmp,dest)


def check_correct_name(custom_name):
    allowed_pretrained_models = ['bert','openai-gpt','gpt2','transfo-xl','xlnet','xlm-roberta','xlm','roberta','distilbert','camembert','ctrl','albert']
    assert len([model_name for model_name in allowed_pretrained_models
                if model_name in custom_name]),f"{custom_name} needs to contain the name" \
        " of the pretrained model architecture (e.g. bert or xlnet) " \
        "to be able to process the model correctly."


class CustomBertModel(TextBERTModel,CustomModel):
    """
    Custom model class for using pretrained transformer models.
    The class inherits :
      - CustomModel that really is just a tag. It's there to make sure DAI knows it's a custom model.
      - TextBERTModel so that the custom model inherits all the properties and methods.
    Supported model architecture:
    'bert','albert'
    How to use:
        - You have already downloaded the weights,the vocab and the config file:
            - Set _model_path as the folder where the weights,the vocab and the config file are stored.
            - Set _model_name according to the pretrained architecture (e.g. bert-base-uncased).
        - You want to to download the weights,the vocab and the config file:
            - Set _model_link,_config_link and _vocab_link accordingly.
            - _model_path is the folder where the weights,the vocab and the config file will be saved.
            - Set _model_name according to the pretrained architecture (e.g. bert-base-uncased).
        - Important:
          _model_path needs to contain the name of the pretrained model architecture (e.g. bert or xlnet)
          to be able to load the model correctly.
        - Disable genetic algorithm in the expert setting.
    """

    # _model_path is the full path to the directory where the weights,vocab and the config will be saved.
    _model_name = NotImplemented  # Will be used to create the MOJO
    _model_path = NotImplemented

    _model_link = NotImplemented
    _config_link = NotImplemented
    _vocab_link = NotImplemented
    _booster_str = "pytorch-custom"

    # Requirements for MOJO creation:
    # _model_name needs to be  one of
    # bert-base-uncased,bert-base-multilingual-cased,xlnet-base-cased,roberta-base,distilbert-base-uncased
    # vocab.txt needs to be the same as vocab.txt used in _model_name (no custom vocabulary yet).
    _mojo = False

    @staticmethod
    def is_enabled():
        return False  # Abstract Base model should not show up in models.

    def _set_model_name(self,language_detected):
        self.model_path = self.__class__._model_path
        self.model_name = self.__class__._model_name
        check_correct_name(self.model_path)
        check_correct_name(self.model_name)

    def fit(self,X,y,sample_weight=None,eval_set=None,sample_weight_eval_set=None,**kwargs):
        logger = None
        if self.context and self.context.experiment_id:
            logger = make_experiment_logger(experiment_id=self.context.experiment_id,tmp_dir=self.context.tmp_dir,experiment_tmp_dir=self.context.experiment_tmp_dir)
        maybe_download_language_model(logger,save_directory=self.__class__._model_path,model_link=self.__class__._model_link,config_link=self.__class__._config_link,vocab_link=self.__class__._vocab_link)
        super().fit(X,sample_weight,eval_set,sample_weight_eval_set,**kwargs)


class GermanBertModel(CustomBertModel):
    _model_name = "bert-base-german-dbmdz-uncased"
    _model_path = os.path.join(temporary_files_path,"german_bert_language_model/")

                  
    _model_link = "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/pytorch_model.bin"
    _config_link = "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json"
    _vocab_link = "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"

    _mojo = True

    @staticmethod
    def is_enabled():
        return True

解决方法

检查您的自定义配方是否有 is_enabled() 返回 True。

    def is_enabled():
        return True