versatile cache line magic for ipython
This package adds %cache line-magic to ipython kernels in jupyter notebooks.
The pip-package is called ipython-cache
The python module is called cache_magic
The magic is called %cache
So you can run the magic by entering this into an ipython-cell:
!pip install ipython-cache import cache_magic %cache a = 1+1 %cache
install directly from notebook
open jupyter notebook
create new cell
enter !pip install cache-magic
install into conda-environment
conda create -n test source activate test conda install -c juergens ipython-cache jupyter notebook
Activate the magic by loading the module like any other module. Write into a cell import cache_magic and excecute it.
When you want to apply the magic to a line, just prepend the line with %cache
%cache myVar = someSlowCalculation(some, "parameters")
This will calculate someSlowCalculation(some, "parameters") once. And in subsequent calls it restores myVar from storage.
The magic turns this example into something like this (if there was no ipython-kernel and no versioning):
try: with open("myVar.txt", 'rb') as fp: myVar = pickle.loads(fp.read()) except: myVar = someSlowCalculation(some, "parameters") with open("myVar.txt", 'wb') as fp: pickle.dump(myVar, fp)
%cache <variable> = <expression>
Variable: This Variable’s value will be fetched from cache.
Expression: This will only be excecuted once and the result will be stored to disk.
%cache [--version <version>] [--reset] [--debug] variable [= <expression>]
-v or –version: either a variable name or an integer. Whenever this changes, a new value is calculated (instead of returning an old value from the cache).
if version is ‘*’ or omitted, the hashed expression is used as version, so whenever the expression changes, a new value is cached.
-r or –reset: delete the cached value for this variable. Forces recalculation, if <expression> is present
-d or –debug: additional logging
shows all variables in cache as html-table
%cache -r %cache --reset
deletes all cached values for all variables
where is the cache stored?
In the directory where the kernel was started (usually where the notebook is located) in a subfolder called .cache_magic
push to pypi
gedit ~/.pypirc chmod 600 ~/.pypirc sudo apt install pandoc
upload changes to test and production:
pandoc -o README.rst README.md restview --pypi-strict README.rst # update version in setup.py rm -r dist python setup.py sdist twine upload dist/* -r testpypi firefox https://testpypi.python.org/pypi/ipython-cache twine upload dist/*
test install from testpypi
pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple ipython_cache --no-cache-dir --user
sudo pip install ipython_cache --no-cache-dir --user
Install into environment with -e:
!pip install -e .
reload after each change:
import cache_magic from imp import reload reload(cache_magic)
Alternatively (if you don’t want to install python, jupyter & co), you can use the docker-compose.yml for development:
cd ipython-cache docker-compose up
create Conda Packet
requires the bash with latest anaconda on path
bash mkdir test && cd test conda skeleton pypi ipython-cache conda-build ipython-cache -c conda-forge anaconda upload /home/juergens/anaconda3/conda-bld/linux-64/ipython-cache-*
bash conda remove --name test --all conda env create -f test/environment.yml source activate test conda remove ipython-cache pip uninstall ipython_cache pip install -e . ./test/run_example.py
If there is any error, it will be printed to stderr and the script fails.
the output can be found in “test/temp”.
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