| Tasks at hand on joblib, in increasing order of difficulty. |
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| * Add a changelog! |
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| * In parallel: need to deal with return arguments that don't pickle. |
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| * Improve test coverage and documentation |
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| * Store a repr of the arguments for each call in the corresponding |
| cachedir |
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| * Try to use Mike McKerns's Dill pickling module in Parallel: |
| Implementation idea: |
| * Create a new function that is wrapped and takes Dillo pickles as |
| inputs as output, feed this one to multiprocessing |
| * pickle everything using Dill in the Parallel object. |
| http: |
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| * Make a sensible error message when wrong keyword arguments are given, |
| currently we have:: |
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| from joblib import Memory |
| mem = Memory(cachedir='cache') |
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| def f(a=0, b=2): |
| return a, b |
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| g = mem.cache(f) |
| g(c=2) |
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| /home/varoquau/dev/joblib/joblib/func_inspect.pyc in filter_args(func, |
| ignore_lst, *args, **kwargs), line 168 |
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| TypeError: Ignore list for diffusion_reorder() contains and |
| unexpected keyword argument 'cachedir' |
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| * add a 'depends' keyword argument to memory.cache, to be able to |
| specify that a function depends on other functions, and thus that the |
| cache should be cleared. |
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| * add a 'argument_hash' keyword argument to Memory.cache, to be able to |
| replace the hashing logic of memory for the input arguments. It should |
| accept as an input the dictionary of arguments, as returned in |
| func_inspect, and return a string. |
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| * add a sqlite db for provenance tracking. Store computation time and usage |
| timestamps, to be able to do 'garbage-collection-like' cleaning of |
| unused results, based on a cost function balancing computation cost and |
| frequency of use. |
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