cachey | Caching based on computation time and storage space | Storage library

by dask Python Version: 0.2.1 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | cachey Summary

cachey is a Python library typically used in Storage, Amazon S3 applications. cachey has no bugs, it has no vulnerabilities, it has build file available and it has low support. However cachey has a Non-SPDX License. You can install using 'pip install cachey' or download it from GitHub, PyPI.

Caching based on computation time and storage space

Support

Quality

Security

License

Reuse

Support

cachey has a low active ecosystem.

It has 109 star(s) with 19 fork(s). There are 22 watchers for this library.

It had no major release in the last 12 months.

There are 7 open issues and 6 have been closed. On average issues are closed in 51 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cachey is 0.2.1

Quality

cachey has no bugs reported.

Security

cachey has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

cachey has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

cachey releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed cachey and discovered the below as its top functions. This is intended to give you an instant insight into cachey implemented functionality, and help decide if they suit your requirements.

Memoizes a function .
Return the number of bytes of an object .
Calculate a time for a given key .
Get a value from the cache .
Compute the key for the given arguments .
Return a numpy array .
Calculate cost .

Get all kandi verified functions for this library.

cachey Key Features

No Key Features are available at this moment for cachey.

cachey Examples and Code Snippets

No Code Snippets are available at this moment for cachey.

Community Discussions

Trending Discussions on cachey

Trouble installing turbodbc

Caching ignores available memory argument

QUESTION

Trouble installing turbodbc

Asked 2021-Jan-11 at 20:49

I am attempting to install turbodbc on my Ubuntu 20.10 machine.
My specs are as follows: pip 20.2.4, Python 3.8.5 , gcc (Ubuntu 10.2.0-13ubuntu1) 10.2.0

I have attempted the solutions provided in the previous posts here and and here.

I am getting this error message

...

ANSWER

Answered 2021-Jan-11 at 20:49

Boost is not installed. You can try this

Source https://stackoverflow.com/questions/65674126

QUESTION

Caching ignores available memory argument

Asked 2018-Feb-16 at 12:27

I'm processing a big dataset that does not fit into memory therefore caching is the only option.

Firstly, Dask's documentation is quite confusing when it comes to caching. There are 2 different sections about the topic suggesting similar solutions.

Opportunistic Caching, still flagged as an experimental feature introduced in version 0.6.2 (latest is 0.17.0)
"How do I spill to disk?" in the FAQs, based on Chest

Secondly, the caching to disk seems not working in my code. I try both approaches listed above. I instantiate a Cache/Chest object and register it globally, setting a synchronous or threaded or multiprocessing scheduler. The caching mechanisms fails: it fills up the whole ram+swap memory, ignoring the available_memory parameter. Whenever I specify a cache path, I do not see the .keys file growing in terms of size.

How do I effectively spill to disk?

Using Dask v0.17.0, cachey v0.1.1, cloudpickle 0.5.2

...

ANSWER

Answered 2018-Feb-16 at 12:27

The easiest way to spill to disk to day is just to use the newer dask.distributed scheduler (which works well on a single machine). Try running the following:

Source https://stackoverflow.com/questions/48825884

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install cachey

Cachey is on PyPI and Conda-forge:. Or install from source.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: