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diff --git a/tensorflow/docs_src/community/roadmap.md b/tensorflow/docs_src/community/roadmap.md deleted file mode 100644 index d11b6ed467..0000000000 --- a/tensorflow/docs_src/community/roadmap.md +++ /dev/null @@ -1,123 +0,0 @@ -# Roadmap -**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 -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 below do not have concrete release dates. However, the majority can be -expected in the next one to two releases. - -### APIs -#### High Level APIs: -* 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 support (multi-machine) -* Performance improvements -* Simpler export to a GraphDef/SavedModel - -#### 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) - -#### Official Models: -* A set of -[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: -* 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. - - -#### 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 -#### 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 -* 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: -* Continue to expand support for importing TensorFlow SavedModels and Keras models into browser with unified APIs supporting retraining in browser -* Improve inference and training performance in both browser and Node.js environments -* Widen the collection of pre-built models in [tfjs-models](https://github.com/tensorflow/tfjs-models), - including but not limited to audio- and speech-oriented models -* Release tfjs-data API for efficient data input pipelines -* Integration with [TF-Hub](https://www.tensorflow.org/hub/) - -#### 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: -* 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. - - -#### 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 - - -### Community and Partner Engagement -#### Special Interest Groups: -* 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 -* 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 |