Jupyter notebook: memory usage for each notebook

Question

The memory on my lab\'s server (Ubuntu) is constantly filling up due to users never shutting down old notebooks. I would like to get a better idea of how much memory each no

Answer 1

I made some improvements to sharchaea's script for portability and speed.

Mainly, only check ports that notebooks are running on, check different hostname options, improve the kernel process check and check for ipython or jupyter.

import argparse
import re
import subprocess

import pandas as pd
import psutil
import requests
import tabulate

kernel_regex = re.compile(r".+kernel-(.+)\.json")
notebook_regex = re.compile(r"(https?://([^:/]+):?(\d+)?)/?(\?token=([a-z0-9]+))?")


def get_proc_info():
    pids = psutil.pids()

    # memory info from psutil.Process
    df_mem = []

    for pid in pids:
        try:
            proc = psutil.Process(pid)
            cmd = " ".join(proc.cmdline())
        except psutil.NoSuchProcess:
            continue

        if len(cmd) > 0 and ("jupyter" in cmd or "ipython" in cmd) and "kernel" in cmd:
            # kernel
            kernel_ID = re.sub(kernel_regex, r"\1", cmd)

            # memory
            mem = proc.memory_info()[0] / float(1e9)

            uname = proc.username()

            # user, pid, memory, kernel_ID
            df_mem.append([uname, pid, mem, kernel_ID])

    df_mem = pd.DataFrame(df_mem)
    df_mem.columns = ["user", "pid", "memory_GB", "kernel_ID"]
    return df_mem


def get_running_notebooks():
    notebooks = []

    for n in subprocess.Popen(
        ["jupyter", "notebook", "list"], stdout=subprocess.PIPE
    ).stdout.readlines()[1:]:
        match = re.match(notebook_regex, n.decode())
        if match:
            base_url, host, port, _, token = match.groups()
            notebooks.append({"base_url": base_url, "token": token})
        else:
            print("Unknown format: {}".format(n.decode()))

    return notebooks


def get_session_info(password=None):
    df_nb = []
    kernels = []

    for notebook in get_running_notebooks():
        s = requests.Session()
        if notebook["token"] is not None:
            s.get(notebook["base_url"] + "/?token=" + notebook["token"])
        else:
            # do a get to the base url to get the session cookies
            s.get(notebook["base_url"])
        if password is not None:
            # Seems jupyter auth process has changed, need to first get a cookie,
            # then add that cookie to the data being sent over with the password
            data = {"password": password}
            data.update(s.cookies)
            s.post(notebook["base_url"] + "/login", data=data)

        res = s.get(notebook["base_url"] + "/api/sessions")

        if res.status_code != 200:
            raise Exception(res.json())

        for sess in res.json():
            kernel_ID = sess["kernel"]["id"]
            if kernel_ID not in kernels:
                kernel = {
                    "kernel_ID": kernel_ID,
                    "kernel_name": sess["kernel"]["name"],
                    "kernel_state": sess["kernel"]["execution_state"],
                    "kernel_connections": sess["kernel"]["connections"],
                    # "notebook_url": notebook["base_url"] + "/notebook/" + sess["id"],
                    "notebook_path": sess["path"],
                }
                kernel.update(notebook)
                df_nb.append(kernel)
                kernels.append(kernel_ID)

    df_nb = pd.DataFrame(df_nb)
    del df_nb["token"]
    return df_nb


def parse_args():
    parser = argparse.ArgumentParser(description="Find memory usage.")
    parser.add_argument("--password", help="password (only needed if pass-protected)")

    return parser.parse_args()


def main(password=None, print_ascii=False):
    df_mem = get_proc_info()
    df_nb = get_session_info(password)

    # joining tables
    df = pd.merge(df_nb, df_mem, on=["kernel_ID"], how="inner")
    df = df.sort_values("memory_GB", ascending=False).reset_index(drop=True)
    if print_ascii:
        print(tabulate.tabulate(df, headers=(df.columns.tolist())))
    return df


if __name__ == "__main__":
    args = vars(parse_args())
    main(args["password"], print_ascii=True)

I'll probably continue to make updates to this at this gist

edit: Code has been updated to work with newer versions of Jupyter using token authentication, to leverage only psutil making it Windows compatible, and to work on Python 3.