Question

我刚刚使用了本文档中的示例（https://media.readthedocs.org/pdf/portainer/1.19.1/portainer.pdf）配置portainer的代理，但不断收到错误“无法通过Docker套接字代理请求”。在这种情况下，堆栈显示为“向下”。

Portainer在没有代理和挂载docker.sock的情况下也可以正常工作。

这是docker-compose片段

def CleanTranscripts(transcript_dataframe):
        """
        Cleans up transcript dataframe by splitting 'text' column with delimiter
        Data is as follows: RecordID | _(unused) | StartTime | EndTime | Content | _(unused) | _(unused)
        Split data is put into the following columns:
                * RecordID : removed byte order mark (BOM - \ufeff)
                * Content : removed blank or | characters
                * call_time_seconds : uses EndTime converted into seconds - StartTime converted into seconds
                * count_calls : each unique call identified by RecordID will be 1, used in future dataframe merge with prospect table

        Arguments:
                * transcript_dataframe (pandas.DataFrame) -- the raw transcript dataframe acquired from ImportTranscripts() method

        Returns:
                * transcrips_df (pandas.DataFrame) -- cleaned up transcript dataframe with columns of interest, consolidated on a per call level
        """
        tqdm.pandas(desc="Cleaning Transcripts")
        delimiter = r'\|'  # delimiter defined as an escaped pipe character

        transcripts_df = transcript_dataframe['text'].str.split(pat = delimiter, expand = True) # expands transcript dataframe separated by commma
        transcripts_df.columns = ['RecordID', 1, 'start_time', 'end_time', 'Content', 7, 8]     # rename column names
        transcripts_df = transcripts_df[transcripts_df['RecordID'].progress_apply(lambda x: bool(re.match('M', str(x)))) != True]  # remove rows with RecordID that has 'M'
        transcripts_df['RecordID'] = transcripts_df['RecordID'].progress_apply(lambda x: re.sub('(\ufeff)','',str(x)[0:8]))        # remove BOM, take substring 0:8
        transcripts_df['Content'] = transcripts_df['Content'].progress_apply(lambda x: re.sub(r'( )|(\|)','',str(x)))     # remove blank or pipe characters
        transcripts_df.loc[:,'call_time_seconds'] = pd.to_numeric(transcripts_df['end_time'], errors='coerce') - pd.to_numeric(transcripts_df['start_time'], errors='coerce')   # convert end time into seconds, subtract start time converted into seconds
        transcripts_df = transcripts_df.groupby('RecordID').agg({'Content': 'sum', 'call_time_seconds': 'max'}).reset_index()   # group records by RecordID, aggregate rows with same RecordID by summing the contents, and taking max call time
        transcripts_df['count_calls'] = 1       # assign 1 to count_calls columns (each RecordID is one call, used for future merging)
        return transcripts_df   # return consolidated and cleaned transcripts dataframe

如何配置portainer代理？

0 个答案: