Heroku is a cloud platform for Ruby, Node.js, Python, Go, PHP, and JVM-based applications. It features Git-based, GitHub, and API deployment strategies, a large number of services offered as add-ons, and a full API.
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Heroku (pronounced her-OH-koo) is a cloud platform for ruby, nodejs, python,php and jvm-based (java, scala, clojure, etc.) applications. It features, among other things:
Cloud-based web and worker processes ('dynos')
Hosted datastores (both shared and dedicated)
A Git-based deployment strategy
A number of services offered as add-ons
Support for other languages through buildpacks
Secure, environment-specific configuration
A fully managed, multi-tenant architecture
A full API
Git-based deployment strategy
Applications on Heroku are managed with Git. Simply pushing your codebase to Heroku is all it takes to deploy your application.
Heroku offers a growing number of add-ons via its add-on provider program. Additional services, such as error tracking and reporting, incoming and outgoing email services, hosted no-SQL databases, full-text search and more, are available via a few clicks or commands on the prompt.
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Heroku Dev Center contains official guides for platform, languages and add-ons etc.,
Any language not supported by default can be enabled by creating a custom 'buildpack'.
For greater safety and portability, Heroku allows you to manage environment-specific configuration separately from your source code.
Fully Managed, Multi-Tenant Architecture
Heroku's architecture is designed to keep your app running smoothly with minimal interaction on your part. The Heroku site has a detailed explanation of its architecture.
Full API
All of Heroku's functionality can be accessed from the command line (via the Heroku gem), including managing SSH keys, increasing or decreasing the number of dynos, managing SSL certificates, adding or removing add-ons, and more.
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Rails 4
Prior to Rails 4, Heroku uses the plugin system to inject some code into your application when you deploy. Plugins are no longer supported in Rails 4, so Heroku has provided some gems. Please see the Rails 4 Documentation
Reference Links:
TensorFlow Addons is a repository of contributions that conform towell-established API patterns, but implement new functionalitynot available in core TensorFlow. TensorFlow natively supportsa large number of operators, layers, metrics, losses, and optimizers.However, in a fast moving field like ML, there are many interesting newdevelopments that cannot be integrated into core TensorFlow(because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).
Installation
Stable Builds
To install the latest version, run the following:
To use addons:
Nightly Builds
There are also nightly builds of TensorFlow Addons under the pip packagetfa-nightly, which is built against the latest stable version of TensorFlow. Nightly buildsinclude newer features, but may be less stable than the versioned releases.
Installing from Source
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You can also install from source. This requires the Bazel build system.
Core Concepts
Standardized API within Subpackages
User experience and project maintainability are core concepts inTF-Addons. In order to achieve these we require that our additionsconform to established API patterns seen in core TensorFlow.
GPU/CPU Custom-Ops
A major benefit of TensorFlow Addons is that there are precompiled ops. Should a CUDA 10 installation not be found then the op will automatically fall back to a CPU implementation.
Proxy Maintainership
Addons has been designed to compartmentalize subpackages and submodules so that they can be maintained by users who have expertise and a vested interest in that component.
Subpackage maintainership will only be granted after substantial contribution has been made in order to limit the number of users with write permission. Contributions can come in the form of issue closings, bug fixes, documentation, new code, or optimizing existing code. Submodule maintainership can be granted with a lower barrier for entry as this will not include write permissions to the repo.
For more information see the RFC on this topic.
Periodic Evaluation of Subpackages
Given the nature of this repository, subpackages and submodules may become less and less useful to the community as time goes on. In order to keep the repository sustainable, we'll be performing bi-annual reviews of our code to ensure everything still belongs within the repo. Contributing factors to this review will be:
Number of active maintainers
Amount of OSS use
Amount of issues or bugs attributed to the code
If a better solution is now available
Functionality within TensorFlow Addons can be categorized into three groups:
Suggested: well-maintained API; use is encouraged.
Discouraged: a better alternative is available; the API is kept for historic reasons; or the API requires maintenance and is the waiting period to be deprecated.
Deprecated: use at your own risk; subject to be deleted.
The status change between these three groups is: Suggested <-> Discouraged -> Deprecated.
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The period between an API being marked as deprecated and being deleted will be 90 days. The rationale being:
In the event that TensorFlow Addons releases monthly, there will be 2-3 releases before an API is deleted. The release notes could give user enough warning.
90 days gives maintainers ample time to fix their code.
Contributing
TF-Addons is a community led open source project. As such, the projectdepends on public contributions, bug-fixes, and documentation. Please see contribution guidelines for a guide on how to contribute. This project adheres to TensorFlow's code of conduct.By participating, you are expected to uphold this code.
Community
SIG Monthly Meeting Notes
Join our mailing list and receive calendar invites to the meeting