我正试图通过从另一个笔记本启动jupyter笔记本来并行运行它们。我正在使用造纸机来保存笔记本电脑的输出。
在我的scheduler.ipynb中，我使用multiprocessing，这是一些人成功使用的。我用一个基本的笔记本创建流程，这似乎总是在它第一次运行时起作用。我可以在13秒内用sleep 10运行3台笔记本电脑。如果我有一个后续的单元试图运行完全相同的东西，它产生的进程(多个笔记本(将无限期挂起。我尝试添加代码以确保派生的进程具有退出代码并已完成，甚至在完成后对它们调用terminate——运气不好，我的第二次尝试永远不会完成。
如果我这样做：
sean@server:~$ ps aux | grep ipython
root 2129 0.1 0.2 1117652 176904 ? Ssl 19:39 0:05 /opt/conda/anaconda/bin/python /opt/conda/anaconda/bin/ipython kernel -f /root/.local/share/jupyter/runtime/kernel-eee374ff-0760-4490-8ed0-db03fed84f0c.json
root 3418 0.1 0.2 1042076 173652 ? Ssl 19:42 0:03 /opt/conda/anaconda/bin/python /opt/conda/anaconda/bin/ipython kernel -f /root/.local/share/jupyter/runtime/kernel-3e2f09e8-969f-41c9-81cc-acd2ec4e3d54.json
root 4332 0.1 0.2 1042796 174896 ? Ssl 19:44 0:04 /opt/conda/anaconda/bin/python /opt/conda/anaconda/bin/ipython kernel -f /root/.local/share/jupyter/runtime/kernel-bbd4575c-109a-4fb3-b6ed-372beb27effd.json
root 17183 0.2 0.2 995344 145872 ? Ssl 20:26 0:02 /opt/conda/anaconda/bin/python /opt/conda/anaconda/bin/ipython kernel -f /root/.local/share/jupyter/runtime/kernel-27c48eb1-16b4-4442-9574-058283e48536.json
我看到似乎有4个正在运行的内核(4个进程(。当我查看正在运行的笔记本时，我看到有6个正在运行的笔记。文档中似乎支持这一点，即几个内核可以为多个笔记本提供服务。"一个内核进程可以同时连接到多个前端">
但是，我怀疑，因为ipython内核继续运行，在没有收获派生进程的地方发生了什么不好的事情？有人说，使用多处理是不可能的。其他人也描述了同样的问题。
import re
import os
import multiprocessing
from os.path import isfile
from datetime import datetime
import papermill as pm
import nbformat
# avoid "RuntimeError: This event loop is already running"
# it seems papermill used to support this but it is now undocumented:
# papermill.execute_notebook(nest_asyncio=True)
import nest_asyncio
nest_asyncio.apply()
import company.config
# # Supporting Functions
# In[ ]:
def get_papermill_parameters(notebook,
notebook_prefix='/mnt/jupyter',
notebook_suffix='.ipynb'):
if isinstance(notebook, list):
notebook_path = notebook[0]
parameters = notebook[1]
tag = '_' + notebook[2] if notebook[2] is not None else None
else:
notebook_path = notebook
parameters = None
tag = ''
basename = os.path.basename(notebook_path)
dirpath = re.sub(basename + '$', '', notebook_path)
this_notebook_suffix = notebook_suffix if not re.search(notebook_suffix + '$', basename) else ''
input_notebook = notebook_prefix + notebook_path + this_notebook_suffix
scheduler_notebook_dir = notebook_prefix + dirpath + 'scheduler/'
if not os.path.exists(scheduler_notebook_dir):
os.makedirs(scheduler_notebook_dir)
output_notebook = scheduler_notebook_dir + basename
return input_notebook, output_notebook, this_notebook_suffix, parameters, tag
# In[ ]:
def add_additional_imports(input_notebook, output_notebook, current_datetime):
notebook_name = os.path.basename(output_notebook)
notebook_dir = re.sub(notebook_name, '', output_notebook)
temp_dir = notebook_dir + current_datetime + '/temp/'
results_dir = notebook_dir + current_datetime + '/'
if not os.path.exists(temp_dir):
os.makedirs(temp_dir)
if not os.path.exists(results_dir):
os.makedirs(results_dir)
updated_notebook = temp_dir + notebook_name
first_cell = nbformat.v4.new_code_cell("""
import import_ipynb
import sys
sys.path.append('/mnt/jupyter/lib')""")
metadata = {"kernelspec": {"display_name": "PySpark", "language": "python", "name": "pyspark"}}
existing_nb = nbformat.read(input_notebook, nbformat.current_nbformat)
cells = existing_nb.cells
cells.insert(0, first_cell)
new_nb = nbformat.v4.new_notebook(cells = cells, metadata = metadata)
nbformat.write(new_nb, updated_notebook, nbformat.current_nbformat)
output_notebook = results_dir + notebook_name
return updated_notebook, output_notebook
# In[ ]:
# define this function so it is easily passed to multiprocessing
def run_papermill(input_notebook, output_notebook, parameters):
pm.execute_notebook(input_notebook, output_notebook, parameters, log_output=True)
# # Run All of the Notebooks
# In[ ]:
def run(notebooks, run_hour_utc=10, scheduler=True, additional_imports=False,
parallel=False, notebook_prefix='/mnt/jupyter'):
"""
Run provided list of notebooks on a schedule or on demand.
Args:
notebooks (list): a list of notebooks to run
run_hour_utc (int): hour to run notebooks at
scheduler (boolean): when set to True (default value) notebooks will run at run_hour_utc.
when set to False notebooks will run on demand.
additional_imports (boolean): set to True if you need to add additional imports into your notebook
parallel (boolean): whether to run the notebooks in parallel
notebook_prefix (str): path to jupyter notebooks
"""
if not scheduler or datetime.now().hour == run_hour_utc: # Only run once a day on an hourly cron job.
now = datetime.today().strftime('%Y-%m-%d_%H%M%S')
procs = []
notebooks_base_url = company.config.cluster['resources']['daedalus']['notebook'] + '/notebooks'
if parallel and len(notebooks) > 10:
raise Exception("You are trying to run {len(notebooks)}. We recommend a maximum of 10 be run at once.")
for notebook in notebooks:
input_notebook, output_notebook, this_notebook_suffix, parameters, tag = get_papermill_parameters(notebook, notebook_prefix)
if is_interactive_notebook(input_notebook):
print(f"Not running Notebook '{input_notebook}' because it's marked interactive-only.")
continue
if additional_imports:
input_notebook, output_notebook = add_additional_imports(input_notebook, output_notebook, now)
else:
output_notebook = output_notebook + tag + '_' + now + this_notebook_suffix
print(f"Running Notebook: '{input_notebook}'")
print(" - Parameters: " + str(parameters))
print(f"Saving Results to: '{output_notebook}'")
print("Link: " + re.sub(notebook_prefix, notebooks_base_url, output_notebook))
if not os.path.isfile(input_notebook):
print(f"ERROR! Notebook file does not exist: '{input_notebook}'")
else:
try:
if parameters is not None:
parameters.update({'input_notebook':input_notebook, 'output_notebook':output_notebook})
if parallel:
# trailing comma in args is in documentation for multiprocessing- it seems to matter
proc = multiprocessing.Process(target=run_papermill, args=(input_notebook, output_notebook, parameters,))
print("starting process")
proc.start()
procs.append(proc)
else:
run_papermill(input_notebook, output_notebook, parameters)
except Exception as ex:
print(ex)
print(f"ERROR! See full error in: '{output_notebook}'nn")
if additional_imports:
temp_dir = re.sub(os.path.basename(input_notebook), '', input_notebook)
if os.path.exists(temp_dir):
os.system(f"rm -rf '{temp_dir}'")
if procs:
print("joining")
for proc in procs:
proc.join()
if procs:
print("terminating")
for proc in procs:
print(proc.is_alive())
print(proc.exitcode)
proc.terminate()
print(f"Done: Processed all {len(notebooks)} notebooks.")
else:
print(f"Waiting until {run_hour_utc}:00:00 UTC to run.")
我使用的是python==3.6.12，造纸厂==2.2.2
jupyter core : 4.7.0
jupyter-notebook : 5.5.0
ipython : 7.16.1
ipykernel : 5.3.4
jupyter client : 6.1.7
ipywidgets : 7.2.1
您尝试过使用subprocess模块吗？这似乎是一个更好的选择，而不是多处理。它允许您异步生成将并行运行的子进程，这可以像使用shell一样用于调用命令和程序。我发现写python脚本而不是bash脚本非常有用。
因此，您可以使用主笔记本将其他笔记本作为独立的子进程与subprocesses.run(your_function_with_papermill)并行运行。
解决方案
我使用ProcessPoolExecutor(在后台使用多处理(实现了一个并行Jupyter笔记本执行器。如果您想使它适应您的代码，下面是实现。这是一个通用的执行器，所以有很多事情不适用于您的用例。
如果你想使用这个库，这里有一个你可以使用的片段：
from pathlib import Path
from ploomber import DAG
from ploomber.tasks import NotebookRunner
from ploomber.products import File
from ploomber.executors import Parallel
dag = DAG(executor=Parallel())
engine = None
NotebookRunner(
Path('input.ipynb'),
File('output-1.ipynb'),
dag=dag,
name='one',
papermill_params={'engine': engine})
NotebookRunner(
Path('input.ipynb'),
File('output-2.ipynb'),
dag=dag,
name='two',
papermill_params={'engine': engine})
注意：从Ploomber 0.20开始，笔记本必须有一个"；参数"；单元格(您可以添加一个空单元格(。请参阅此处的说明。
解释
并行执行笔记本(或笔记本内的笔记本(的这些问题来自造纸厂执行它们的方式。它启动内核，内核进程是运行代码的进程；造纸厂进程只发送消息并等待响应。
这在最近的一个项目中成为了一个问题(我需要监控资源使用情况(，所以我编写了一个自定义的造纸机引擎，在同一过程中执行笔记本电脑。这是你可以尝试的另一个选项：
pip install papermill ploomber-engine
papermill input.ipynb output.ipynb --engine profiling
或者来自Python：
import papermill as pm
pm.execute_notebook('input.ipynb', 'output.ipynb', engine='profiling')
(或者，您可以在第一个示例中将engine=None更改为engine='profiling'(