将正在运行的ipython笔记本与进程匹配 [英] Match a running ipython notebook to a process

问题描述

我的服务器运行着许多长时间运行的笔记本电脑，我想监视笔记本电脑的内存.

My server runs many long running notebooks, and I'd like to monitor the notebooks memory.

是否可以在pid或进程名称与笔记本之间进行匹配?

Is there a way to match between the pid or process name and a notebook?

推荐答案

由于问题是关于监视笔记本的内存，因此，我编写了一个完整的示例来显示正在运行的笔记本的内存消耗.它基于出色的@ jcb91 答案和其他一些答案( 2 ，3 ， 4 ).

Since the question is about monitoring notebooks' memory, I've written a complete example showing the memory consumption of the running notebooks. It is based on the excellent @jcb91 answer and a few other answers (1, 2, 3, 4).

import json
import os
import os.path
import posixpath
import subprocess
import urllib2

import pandas as pd
import psutil


def show_notebooks_table(host, port):
    """Show table with info about running jupyter notebooks.

    Args:
        host: host of the jupyter server.
        port: port of the jupyter server.

    Returns:
        DataFrame with rows corresponding to running notebooks and following columns:
            * index: notebook kernel id.
            * path: path to notebook file.
            * pid: pid of the notebook process.
            * memory: notebook memory consumption in percentage.
    """
    notebooks = get_running_notebooks(host, port)
    prefix = long_substr([notebook['path'] for notebook in notebooks])
    df = pd.DataFrame(notebooks)
    df = df.set_index('kernel_id')
    df.index.name = prefix
    df.path = df.path.apply(lambda x: x[len(prefix):])
    df['pid'] = df.apply(lambda row: get_process_id(row.name), axis=1)
    # same notebook can be run in multiple processes
    df = expand_column(df, 'pid')
    df['memory'] = df.pid.apply(memory_usage_psutil)
    return df.sort_values('memory', ascending=False)


def get_running_notebooks(host, port):
    """Get kernel ids and paths of the running notebooks.

    Args:
        host: host at which the notebook server is listening. E.g. 'localhost'.
        port: port at which the notebook server is listening. E.g. 8888.
        username: name of the user who runs the notebooks.

    Returns:
        list of dicts {kernel_id: notebook kernel id, path: path to notebook file}.
    """
    # find which kernel corresponds to which notebook
    # by querying the notebook server api for sessions
    sessions_url = posixpath.join('http://%s:%d' % (host, port), 'api', 'sessions')
    response = urllib2.urlopen(sessions_url).read()
    res = json.loads(response)
    notebooks = [{'kernel_id': notebook['kernel']['id'],
                  'path': notebook['notebook']['path']} for notebook in res]
    return notebooks


def get_process_id(name):
    """Return process ids found by (partial) name or regex.

    Source: https://stackoverflow.com/a/44712205/304209.
    >>> get_process_id('kthreadd')
    [2]
    >>> get_process_id('watchdog')
    [10, 11, 16, 21, 26, 31, 36, 41, 46, 51, 56, 61]  # ymmv
    >>> get_process_id('non-existent process')
    []
    """
    child = subprocess.Popen(['pgrep', '-f', name], stdout=subprocess.PIPE, shell=False)
    response = child.communicate()[0]
    return [int(pid) for pid in response.split()]


def memory_usage_psutil(pid=None):
    """Get memory usage percentage by current process or by process specified by id, like in top.

    Source: https://stackoverflow.com/a/30014612/304209.

    Args:
        pid: pid of the process to analyze. If None, analyze the current process.

    Returns:
        memory usage of the process, in percentage like in top, values in [0, 100].
    """
    if pid is None:
        pid = os.getpid()
    process = psutil.Process(pid)
    return process.memory_percent()


def long_substr(strings):
    """Find longest common substring in a list of strings.

    Source: https://stackoverflow.com/a/2894073/304209.

    Args:
        strings: list of strings.

    Returns:
        longest substring which is found in all of the strings.
    """
    substr = ''
    if len(strings) > 1 and len(strings[0]) > 0:
        for i in range(len(strings[0])):
            for j in range(len(strings[0])-i+1):
                if j > len(substr) and all(strings[0][i:i+j] in x for x in strings):
                    substr = strings[0][i:i+j]
    return substr


def expand_column(dataframe, column):
    """Transform iterable column values into multiple rows.

    Source: https://stackoverflow.com/a/27266225/304209.

    Args:
        dataframe: DataFrame to process.
        column: name of the column to expand.

    Returns:
        copy of the DataFrame with the following updates:
            * for rows where column contains only 1 value, keep them as is.
            * for rows where column contains a list of values, transform them
                into multiple rows, each of which contains one value from the list in column.
    """
    tmp_df = dataframe.apply(
        lambda row: pd.Series(row[column]), axis=1).stack().reset_index(level=1, drop=True)
    tmp_df.name = column
    return dataframe.drop(column, axis=1).join(tmp_df)

这是show_notebooks_table('localhost', 8888)的示例输出:

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