DeepRec-AI/DeepRec - Githubissues

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Introduction

DeepRec is a high-performance recommendation deep learning framework based on TensorFlow 1.15, Intel-TensorFlow and NVIDIA-TensorFlow. It is hosted in incubation in LF AI & Data Foundation.

Background

Recommendation models have huge commercial values for areas such as retailing, media, advertisements, social networks and search engines. Unlike other kinds of models, recommendation models have large amount of non-numeric features such as id, tag, text and so on which lead to huge parameters.

DeepRec has been developed since 2016, which supports core businesses such as Taobao Search, recommendation and advertising. It precipitates a list of features on basic frameworks and has excellent performance in recommendation models training and inference. So far, in addition to Alibaba Group, dozens of companies have used DeepRec in their business scenarios.

Key Features

DeepRec has super large-scale distributed training capability, supporting recommendation model training of trillion samples and over ten trillion parameters. For recommendation models, in-depth performance optimization has been conducted across CPU and GPU platform. It contains list of features to improve usability and performance for super-scale scenarios.

Embedding & Optimizer

Embedding Variable.
Dynamic Dimension Embedding Variable.
Adaptive Embedding Variable.
Multiple Hash Embedding Variable.
Multi-tier Hybrid Embedding Storage.
Group Embedding.
AdamAsync Optimizer.
AdagradDecay Optimizer.

Training

Asynchronous Distributed Training Framework (Parameter Server), such as grpc+seastar, FuseRecv, StarServer etc.
Synchronous Distributed Training Framework (Collective), such as HybridBackend, Sparse Operation Kits (SOK) etc.
Runtime Optimization, such as Graph Aware Memory Allocator (GAMMA), Critical-path based Executor etc.
Runtime Optimization (GPU), GPU Multi-Stream Engine which support multiple CUDA compute stream and CUDA Graph.
Operator level optimization, such as BF16 mixed precision optimization, embedding operator optimization and EmbeddingVariable on PMEM and GPU, new hardware feature enabling, etc.
Graph level optimization, such as AutoGraphFusion, SmartStage, AutoPipeline, Graph Template Engine, Sample-awared Graph Compression, MicroBatch etc.
Compilation optimization, support BladeDISC, XLA etc.

Deploy and Serving

Delta checkpoint loading and exporting.
Super-scale recommendation model distributed serving.
Multi-tier hybrid storage and multi backend supported.
Online deep learning with low latency.
High performance inference framework SessionGroup (share-nothing), with multiple threadpool and multiple CUDA stream supported.
Model Quantization.

Installation

Prepare for installation

CPU Platform

alideeprec/deeprec-build:deeprec-dev-cpu-py38-ubuntu20.04

GPU Platform

alideeprec/deeprec-build:deeprec-dev-gpu-py38-cu116-ubuntu20.04

How to Build

Configure

$ ./configure

Compile for CPU and GPU defaultly

$ bazel build -c opt --config=opt //tensorflow/tools/pip_package:build_pip_package

Compile for CPU and GPU: ABI=0

$ bazel build --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --host_cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" -c opt --config=opt //tensorflow/tools/pip_package:build_pip_package

Compile for CPU optimization: oneDNN + Unified Eigen Thread pool

$ bazel build -c opt --config=opt --config=mkl_threadpool //tensorflow/tools/pip_package:build_pip_package

Compile for CPU optimization and ABI=0

$ bazel build --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --host_cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" -c opt --config=opt --config=mkl_threadpool //tensorflow/tools/pip_package:build_pip_package

Create whl package

$ ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

Install whl package

$ pip3 install /tmp/tensorflow_pkg/tensorflow-1.15.5+${version}-cp38-cp38m-linux_x86_64.whl

Latest Release Images

Image for CPU

alideeprec/deeprec-release:deeprec2402-cpu-py38-ubuntu20.04

Image for GPU CUDA11.6

alideeprec/deeprec-release:deeprec2402-gpu-py38-cu116-ubuntu20.04

Continuous Build Status

Official Build

Build Type	Status
Linux CPU
Linux GPU
Linux CPU Serving
Linux GPU Serving

Official Unit Tests

Unit Test Type	Status
Linux CPU C
Linux CPU CC
Linux CPU Contrib
Linux CPU Core
Linux CPU Examples
Linux CPU Java
Linux CPU JS
Linux CPU Python
Linux CPU Stream Executor
Linux GPU C
Linux GPU CC
Linux GPU Contrib
Linux GPU Core
Linux GPU Examples
Linux GPU Java
Linux GPU JS
Linux GPU Python
Linux GPU Stream Executor
Linux CPU Serving UT
Linux GPU Serving UT