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diff --git a/tensorflow/docs_src/performance/index.md b/tensorflow/docs_src/performance/index.md deleted file mode 100644 index a0f26a8c3a..0000000000 --- a/tensorflow/docs_src/performance/index.md +++ /dev/null @@ -1,52 +0,0 @@ -# Performance - -Performance is an important consideration when training machine learning -models. Performance speeds up and scales research while -also providing end users with near instant predictions. This section provides -details on the high level APIs to use along with best practices to build -and train high performance models, and quantize models for the least latency -and highest throughput for inference. - - * [Performance Guide](../performance/performance_guide.md) contains a collection of best - practices for optimizing your TensorFlow code. - - * [Data input pipeline guide](../performance/datasets_performance.md) describes the tf.data - API for building efficient data input pipelines for TensorFlow. - - * [Benchmarks](../performance/benchmarks.md) contains a collection of - benchmark results for a variety of hardware configurations. - - * For improving inference efficiency on mobile and - embedded hardware, see - [How to Quantize Neural Networks with TensorFlow](../performance/quantization.md), which - explains how to use quantization to reduce model size, both in storage - and at runtime. - - * For optimizing inference on GPUs, refer to [NVIDIA TensorRT™ - integration with TensorFlow.]( - https://medium.com/tensorflow/speed-up-tensorflow-inference-on-gpus-with-tensorrt-13b49f3db3fa) - - -XLA (Accelerated Linear Algebra) is an experimental compiler for linear -algebra that optimizes TensorFlow computations. The following guides explore -XLA: - - * [XLA Overview](../performance/xla/index.md), which introduces XLA. - * [Broadcasting Semantics](../performance/xla/broadcasting.md), which describes XLA's - broadcasting semantics. - * [Developing a new back end for XLA](../performance/xla/developing_new_backend.md), which - explains how to re-target TensorFlow in order to optimize the performance - of the computational graph for particular hardware. - * [Using JIT Compilation](../performance/xla/jit.md), which describes the XLA JIT compiler that - compiles and runs parts of TensorFlow graphs via XLA in order to optimize - performance. - * [Operation Semantics](../performance/xla/operation_semantics.md), which is a reference manual - describing the semantics of operations in the `ComputationBuilder` - interface. - * [Shapes and Layout](../performance/xla/shapes.md), which details the `Shape` protocol buffer. - * [Using AOT compilation](../performance/xla/tfcompile.md), which explains `tfcompile`, a - standalone tool that compiles TensorFlow graphs into executable code in - order to optimize performance. - - - |