Cachephant is a small python library which caches function output to disk to avoid unnecessary recomputation. It's aimed for use in Jupyter notebooks.
There's already joblib.Memory and diskcache.memoize. However, they didn't provide the behavior I desired (see table below):
- Joblib is very conservative and it's cache cannot be reused after restarting the Jupyter kernel. Moreover, unrelated code changes lead to cache invalidation.
- In contrast, diskcache is very liberal in its cache reuse and doesn't notice function code or signature changes. (This may be great for running a stable web app for weeks, but less idea for data science development.)
| uses cache if... | joblib | diskcache | cachephant |
|---|---|---|---|
| Jupyter kernel restarts | n | y | y |
| same "resolved" arguments | n | n ! | y |
| some unrelated code changes | n | y | y |
| some related code changes | n | y | y (n would be ideal but difficult) |
| function code changes | n | y ! | n |
| function signature changes | raises error | y ! | n |
import cachephant
cache = cachephant.get_default_cache("/path/to/dir")
@cache
def slow_function():
time.sleep(10)
return 3You can also instantiate cachephant.Cache() and easily pass custom database-, file-, hash-, and evictor-classes.
This library is not meant for high frequency use cases (think hundreds of cache reads/writes per second) and you'll likely see performance issues.