Find similar content

Source channel @githubtrending · Post #15440 · Jan 27

#go#config#config_loader#configuration#configuration_file#configuration_management#etcd_client#go#golang#golang_package#s3_bucket#toml#viper#yaml koanf is a lightweight Go library to load config from files (JSON, YAML, TOML), env vars, flags, S3, Vault and more, merging them easily with dot-path keys like "app.server.port". Install core with `go get github.com/knadh/koanf/v2`, add providers/parsers as needed. It's a cleaner Viper alternative with fewer dependencies and better extensibility. This saves you time by simplifying config in apps, letting you override values flexibly without bloat or forced orders. https://github.com/knadh/koanf

Hashtags

#go #config #config_loader #configuration #configuration_file #configuration_management #etcd_client #golang #golang_package #s3_bucket #toml #viper #yaml

Results

1 similar post found

Search: #tfdeploy

当前筛选 #tfdeploy清除筛选

djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

Find similar View

https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

Hashtags

#tfdeploy #numpy #machine_learning