#!/usr/bin/env python
# coding: utf-8

# # Interacting with dask workers via IPython
# 
# Experimentation/illustration for https://github.com/dask/distributed/pull/370

# In[1]:


from distributed import Executor

e = Executor('127.0.0.1:8786')
e


# In[2]:


futures = e.map(lambda x: 1/(x-5), range(10))
futures


# `Executor.start_ipython()` starts IPython on workers,
# returning the connection information needed to connect Jupyter clients to them.

# In[3]:


info = e.start_ipython()
info


# If you pass `qtconsole=True`, then dask will spawn a local Juptyer QtConsole, connected to the remote worker(s).
# 
# The worker object is injected into the worker namespace as `worker`.

# In[4]:


workers = list(e.ncores())
info = e.start_ipython(workers[0], qtconsole=True)


# In[8]:


from IPython.display import Image
Image('./qtc.png')


# If we pass magic_names as a list of strings of the same length,
# we will get a line and cell magic that redirects execution to the given worker.

# In[5]:


info = e.start_ipython(workers[:2], magic_names=['w1', 'w2'])


# In[6]:


get_ipython().run_line_magic('w1', 'os.getpid()')
get_ipython().run_line_magic('w2', 'os.getpid()')
os.getpid()


# Since the remote kernel is a full IPython instance,
# we can even do plotting with matplotlib and see the figures:

# In[7]:


get_ipython().run_cell_magic('w1', '', '%matplotlib inline\nimport matplotlib.pyplot as plt\nplt.plot(list(worker.data.values()))\n')