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Revised roadmap #18939

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Apr 27, 2018
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revised roadmap
sandeepngupta Apr 18, 2018
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Revised roadmap
sandeepngupta Apr 27, 2018
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Revised roadmap to fix tense
sandeepngupta Apr 27, 2018
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74 changes: 55 additions & 19 deletions tensorflow/docs_src/community/roadmap.md
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# Roadmap
**Last updated: Feb 15, 2018**
**Last updated: Apr 27, 2018**

TensorFlow is a rapidly moving, community supported project. This document is intended
to provide guidance about priorities and focus areas of the core set of TensorFlow
Expand All @@ -14,12 +14,12 @@ expected in the next one to two releases.

### APIs
#### High Level APIs:
* Easy multi-GPU utilization with Estimators
* Easy multi-GPU and TPU utilization with Estimators
* Easy-to-use high-level pre-made estimators for Gradient Boosted Trees, Time Series, and other models

#### Eager Execution:
* Efficient utilization of multiple GPUs
* Distributed training (multi-machine)
* Distributed training support (multi-machine)
* Performance improvements
* Simpler export to a GraphDef/SavedModel

Expand All @@ -31,14 +31,14 @@ to create Keras models Eager- style via Model subclassing)

#### Official Models:
* A set of
[reference models](https://github.com/tensorflow/models/tree/master/official)
[models](https://github.com/tensorflow/models/tree/master/official)
across image recognition, speech, object detection, and
translation that demonstrate best practices and serve as a starting point for
high-performance model development.

#### Contrib:
* Deprecation notices added to parts of tf.contrib where preferred implementations exist outside of tf.contrib.
* As much as possible, large projects inside tf.contrib moved to separate repositories.
* Deprecate parts of tf.contrib where preferred implementations exist outside of tf.contrib.
* As much as possible, move large projects inside tf.contrib to separate repositories.
* The tf.contrib module will eventually be discontinued in its current form, experimental development will in future happen in other repositories.


Expand All @@ -50,36 +50,72 @@ across image recognition, speech, object detection, and

### Platforms
#### TensorFlow Lite:
* Increased coverage of supported ops in TensorFlow Lite
* Increase coverage of supported ops in TensorFlow Lite
* Easier conversion of a trained TensorFlow graph for use on TensorFlow Lite
* Support for GPU acceleration in TensorFlow Lite (iOS and Android)
* Support for hardware accelerators via Android NeuralNets API
* Improved CPU performance by quantization and other network optimizations (eg. pruning, distillation)
* Increased support for devices beyond Android and iOS (eg. RPi, Cortex-M)
* Improve CPU performance by quantization and other network optimizations (eg. pruning, distillation)
* Increase support for devices beyond Android and iOS (eg. RPi, Cortex-M)

#### TensorFlow.js:
* Release package for Node.js bindings to the TensorFlow C API through the TensorFlow.js backend interface
* Expand support for importing TensorFlow SavedModels and Keras models into browser with unified APIs supporting retraining in browser
* Improve Layers API and allow model exporting/saving
* Release tfjs-data API for efficient data input pipelines

#### TensorFlow with Swift:
* Establish open source project including documentation, open design, and code availability.
* Continue implementing and refining implementation and design through 2018.
* Aim for implementation to be solid enough for general use later in 2018.

### Performance
#### Distributed TensorFlow:
* Multi-GPU support optimized for a variety of GPU topologies
* Improved mechanisms for distributing computations on several machines
* Optimize Multi-GPU support for a variety of GPU topologies
* Improve mechanisms for distributing computations on several machines

#### GPU Optimizations:
* Simplify mixed precision API with initial example model and guide.
* Finalize TensorRT API and move to core.
* CUDA 9.2 and NCCL 2.x default in TensorFlow builds.
* Optimizations for DGX-2.
* Remove support for CUDA less than 8.x and cuDNN less than 6.x.

#### Optimizations:
* Mixed precision training support with initial example model and guide
* Native TensorRT support

#### CPU Optimizations
* Int8 support for SkyLake via MKL
* Dynamic loading of SIMD-optimized kernels
* MKL for Linux and Windows

### End-to-end ML systems:
#### TensorFlow Hub:
* Expand support for module-types in TF Hub with TF Eager integration, Keras layers integration, and TensorFlow.js integration
* Accept variable-sized image input
* Improve multi-GPU estimator support
* Document and improve TPU integration

#### TensorFlow Extended:
* Open source more of the TensorFlow Extended platform to facilitate adoption of TensorFlow in production settings.
* Release TFX libraries for Data Validation

### Documentation and Resources:
* Update documentation, tutorials and Getting Started guides on all features and APIs
* Update [Youtube Tensorflow channel](https://youtube.com/tensorflow) weekly with new content:
Coding TensorFlow - where we teach folks coding with tensorflow
TensorFlow Meets - where we highlight community contributions
Ask TensorFlow - where we answer community questions
Guest and Showcase videos
* Update [Official TensorFlow blog](https://blog.tensorflow.org) with regular articles from Google team and the Community

### Documentation and Usability:
* Updated documentation, tutorials and Getting Started guides
* Process to enable external contributions to tutorials, documentation, and blogs showcasing best practice use-cases of TensorFlow and high-impact applications

### Community and Partner Engagement
#### Special Interest Groups:
* Mobilizing the community to work together in focused domains
* Mobilize the community to work together in focused domains
* [tf-distribute](https://groups.google.com/a/tensorflow.org/forum/#!forum/tf-distribute): build and packaging of TensorFlow
* More to be identified and launched
* SIG TensorBoard, SIG Rust, and more to be identified and launched

#### Community:
* Incorporate public feedback on significant design decisions via a Request-for-Comment (RFC) process
* Formalize process for external contributions to land in TensorFlow and associated projects
* Grow global TensorFlow communities and user groups
* Collaborate with partners to co-develop and publish research papers
* Process to enable external contributions to tutorials, documentation, and blogs showcasing best practice use-cases of TensorFlow and high-impact applications