python - 挂细胞 : running multiple jupyter notebooks in parallel with papermill

Question

我正在尝试通过从另一个笔记本启动它们来并行运行 jupyter 笔记本。我正在使用 papermill保存笔记本的输出。
在我的 scheduler.ipynb 中，我使用了 multiprocessing这是什么some people取得了成功。我从基本笔记本创建进程，这似乎总是在第一次运行时工作。我可以用 sleep 10 运行 3 个笔记本在 13 秒内。如果我有一个后续单元尝试运行完全相同的东西，它产生的进程(多个笔记本)会无限期地挂起。我尝试添加代码以确保生成的进程具有退出代码并已完成，甚至在完成后调用终止它们 - 不走运，我的第二次尝试永远不会完成。
如果我做:

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 个正在运行的笔记本。这似乎在 a few kernels can service multiple notebooks 的文档中得到支持。 . “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，所以在没有获得衍生进程的地方发生了一些不好的事情？ Some say it’s not possible使用多处理。 Others have described the same problem .

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}'\n\n")
          
      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.")

我正在使用 python==3.6.12，papermill==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

Answer 1

您是否尝试过使用 subprocess 模块？对您来说，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，这可用于调用命令和程序，就像您使用 shell 一样。我发现编写 python 脚本而不是 bash 脚本真的很有用。
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.