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diff --git a/tensorflow/docs_src/about/roadmap.md b/tensorflow/docs_src/about/roadmap.md index 3ee825ed40..1f934acab6 100644 --- a/tensorflow/docs_src/about/roadmap.md +++ b/tensorflow/docs_src/about/roadmap.md @@ -1,37 +1,86 @@ # Roadmap -**Last updated: January 23, 2017** +**Last updated: Feb 15, 2018** -TensorFlow is a fast moving project. In order for the community to better -understand what the near future will bring, this document shares what we are -working on internally. Many of these features were requested by the community, -and we welcome -[contributions](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome). +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 +developers and about functionality that can be expected in the upcoming releases of +TensorFlow. Many of these areas are driven by community use cases, and we welcome +further +[contributions](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md) +to TensorFlow. -The features on this list are targeted for the next few months. At this point, -we do not have timelines for these features. +The features below do not have concrete release dates. However, the majority can be +expected in the next one to two releases. -### Improve non-Python language support +### APIs +#### High Level APIs: +* Easy multi-GPU utilization with Estimators +* Easy-to-use high-level pre-made estimators for Gradient Boosted Trees, Time Series, and other models -* Support for adding gradient computation for graphs constructed in other - languages (C++, Java, Go etc.) +#### Eager Execution: +* Efficient utilization of multiple GPUs +* Distributed training (multi-machine) +* Performance improvements +* Simpler export to a GraphDef/SavedModel -### Making TensorFlow easier to use -* High-level APIs -* Well-maintained models showing best practices +#### Keras API: +* Better integration with tf.data (ability to call `model.fit` with data tensors) +* Full support for Eager Execution (both Eager support for the regular Keras API, and ability +to create Keras models Eager- style via Model subclassing) +* Better distribution/multi-GPU support and TPU support (including a smoother model-to-estimator workflow) -### Performance -* Speed and memory benchmarks -* Distributed full model benchmarks -* Performance and memory usage improvements +#### Official Models: +* A set of +[reference 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. +* The tf.contrib module will eventually be discontinued in its current form, experimental development will in future happen in other repositories. -### Core Features -* Automatic op placement ([#2126](https://github.com/tensorflow/tensorflow/issues/2126)) -* Support for graph-level functions + +#### Probabilistic Reasoning and Statistical Analysis: +* Rich set of tools for probabilistic and statistical analysis in tf.distributions + and tf.probability. These include new samplers, layers, optimizers, losses, and structured models +* Statistical tools for hypothesis testing, convergence diagnostics, and sample statistics +* Edward 2.0: High-level API for probabilistic programming ### Platforms -* OpenCL support ([#22](https://github.com/tensorflow/tensorflow/issues/22)) +#### TensorFlow Lite: +* Increased 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) + +### Performance +#### Distributed TensorFlow: +* Multi-GPU support optimized for a variety of GPU topologies +* Improved mechanisms for distributing computations on several machines + +#### Optimizations: +* Mixed precision training support with initial example model and guide +* Native TensorRT support +* Int8 support for SkyLake via MKL +* Dynamic loading of SIMD-optimized kernels + +### 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 +* [tf-distribute](https://groups.google.com/a/tensorflow.org/forum/#!forum/tf-distribute) +: build and packaging of TensorFlow +* More to be identified and launched -### Community -* More educational resources -* Better integration of TensorFlow into the opensource big data ecosystem (e.g. -[#2655](https://github.com/tensorflow/tensorflow/issues/2655)) +#### 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 |