How to pickle or store Jupyter (IPython) notebook session for later
Question:
Let’s say I am doing a larger data analysis in Jupyter/Ipython notebook with lots of time consuming computations done. Then, for some reason, I have to shut down the jupyter local server I, but I would like to return to doing the analysis later, without having to go through all the time-consuming computations again.
What I would like love to do is pickle or store the whole Jupyter session (all pandas dataframes, np.arrays, variables, …) so I can safely shut down the server knowing I can return to my session in exactly the same state as before.
Is it even technically possible? Is there a built-in functionality I overlooked?
EDIT: based on this answer there is a %store magic which should be “lightweight pickle”. However you have to store the variables manually like so:
#inside a ipython/nb session
foo = "A dummy string"
%store foo
closing seesion, restarting kernel
%store -r foo # r for refresh
print(foo) # "A dummy string"
which is fairly close to what I would want, but having to do it manually and being unable to distinguish between different sessions makes it less useful.
Answers:
(I’d rather comment than offer this as an actual answer, but I need more reputation to comment.)
You can store most data-like variables in a systematic way. What I usually do is store all dataframes, arrays, etc. in pandas.HDFStore. At the beginning of the notebook, declare
backup = pd.HDFStore('backup.h5')
and then store any new variables as you produce them
backup['var1'] = var1
At the end, probably a good idea to do
backup.close()
before turning off the server. The next time you want to continue with the notebook:
backup = pd.HDFStore('backup.h5')
var1 = backup['var1']
Truth be told, I’d prefer built-in functionality in ipython notebook, too. You can’t save everything this way (e.g. objects, connections), and it’s hard to keep the notebook organized with so much boilerplate codes.
This question is related to: How to cache in IPython Notebook?
To save the results of individual cells, the caching magic comes in handy.
%%cache longcalc.pkl var1 var2 var3
var1 = longcalculation()
....
When rerunning the notebook, the contents of this cell is loaded from the cache.
This is not exactly answering your question, but it might be enough to when the results of all the lengthy calculations are recovered fast. This in combination of hitting the run-all button on top of the notebook is for me a workable solution.
The cache magic cannot save the state of a whole notebook yet. To my knowledge there is no other system yet to resume a “notebook”. This would require to save all the history of the python kernel. After loading the notebook, and connecting to a kernel, this information should be loaded.
I think Dill answers your question well.
pip install dill
Save a Notebook session:
import dill
dill.dump_session('notebook_env.db')
Restore a Notebook session:
import dill
dill.load_session('notebook_env.db')
Let’s say I am doing a larger data analysis in Jupyter/Ipython notebook with lots of time consuming computations done. Then, for some reason, I have to shut down the jupyter local server I, but I would like to return to doing the analysis later, without having to go through all the time-consuming computations again.
What I would like love to do is pickle or store the whole Jupyter session (all pandas dataframes, np.arrays, variables, …) so I can safely shut down the server knowing I can return to my session in exactly the same state as before.
Is it even technically possible? Is there a built-in functionality I overlooked?
EDIT: based on this answer there is a %store magic which should be “lightweight pickle”. However you have to store the variables manually like so:
#inside a ipython/nb session
foo = "A dummy string"
%store foo
closing seesion, restarting kernel
%store -r foo # r for refresh
print(foo) # "A dummy string"
which is fairly close to what I would want, but having to do it manually and being unable to distinguish between different sessions makes it less useful.
(I’d rather comment than offer this as an actual answer, but I need more reputation to comment.)
You can store most data-like variables in a systematic way. What I usually do is store all dataframes, arrays, etc. in pandas.HDFStore. At the beginning of the notebook, declare
backup = pd.HDFStore('backup.h5')
and then store any new variables as you produce them
backup['var1'] = var1
At the end, probably a good idea to do
backup.close()
before turning off the server. The next time you want to continue with the notebook:
backup = pd.HDFStore('backup.h5')
var1 = backup['var1']
Truth be told, I’d prefer built-in functionality in ipython notebook, too. You can’t save everything this way (e.g. objects, connections), and it’s hard to keep the notebook organized with so much boilerplate codes.
This question is related to: How to cache in IPython Notebook?
To save the results of individual cells, the caching magic comes in handy.
%%cache longcalc.pkl var1 var2 var3
var1 = longcalculation()
....
When rerunning the notebook, the contents of this cell is loaded from the cache.
This is not exactly answering your question, but it might be enough to when the results of all the lengthy calculations are recovered fast. This in combination of hitting the run-all button on top of the notebook is for me a workable solution.
The cache magic cannot save the state of a whole notebook yet. To my knowledge there is no other system yet to resume a “notebook”. This would require to save all the history of the python kernel. After loading the notebook, and connecting to a kernel, this information should be loaded.
I think Dill answers your question well.
pip install dill
Save a Notebook session:
import dill
dill.dump_session('notebook_env.db')
Restore a Notebook session:
import dill
dill.load_session('notebook_env.db')