This package adds %cache
line-magic to ipython kernels in jupyter notebooks.
you can an example-notebook using this magic here
- 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
- open jupyter notebook
- create new cell
- enter
!pip install cache-magic
- execute
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
%cache
shows all variables in cache as html-table
%cache -r
%cache --reset
deletes all cached values for all variables
In the directory where the kernel was started (usually where the notebook is located) in a subfolder called .cache_magic
prepare environment:
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
test installation
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
requires the bash with latest anaconda on path
bash
mkdir test && cd test
conda skeleton pypi ipython-cache
conda config --set anaconda_upload yes
conda-build ipython-cache -c conda-forge
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".