gistfile1.txt

#option 2, obtain token by username/password

username = 'jupyter'
password = 'jupyter'

# step 1: login with username + password
r = requests.post(login_url, data={'username': username, 'password': password}, allow_redirects=False)
r.raise_for_status()
cookies = r.cookies

# 2. request token
r = requests.post(token_url,
    headers={'Referer': login_url},
    cookies=cookies,
)
r.raise_for_status()
token = r.json()['token']

auth_headers = {'Authorization': 'token %s' % token}

notebook_memory.py

import sys
import re

import pandas as pd
import psutil
import requests
from notebook.notebookapp import list_running_servers

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, columns=["user", "pid", "memory_GB", "kernel_id"])
    return df_mem


df_mem = get_proc_info()

token = ""
auth_headers = {"Authorization": "token %s" % token}

base_url = "https://127.0.0.1:8443/"

r = requests.get(
    base_url + "api/sessions", headers=auth_headers, allow_redirects=False, verify=False
)
r.raise_for_status()
# print(r.json())

if r.status_code == 200 and r.json():
    df_notebooks = pd.DataFrame.from_dict(r.json())
    df_notebooks["kernel_id"] = df_notebooks["kernel"].apply(lambda x: x.get("id"))
    df_notebooks = df_notebooks[["path", "kernel_id"]]
else:
    df_notebooks = pd.DataFrame(columns=["path", "kernel_id"])

pd.set_option("display.max_colwidth", 1000)
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
pd.set_option("display.width", None)


df_final = (
    df_mem.merge(df_notebooks, on="kernel_id")
    .groupby(["path", "kernel_id"])
    .agg(
        pids=("pid", lambda col: str(list(col))),
        count=("memory_GB", "count"),
        memory_GB=("memory_GB", "sum"),
    )
    .sort_values("memory_GB")
)

df_final