1.MLflow实践与Project Hydrogen最新进展 阿⾥里里云-E-MapReduce 江宇 2019.6.19 

2.内容 1 2 MLflow Project Hydrogen 

3.MLflow Part I 

4.MLflow 机器器学习流程和挑战 MLflow解决⽅方式 MLflow组件构成 MLflow系统演示 

5.MLflow 机器器学习整体流程⾮非常复杂 Data Prepare Row Data Trainging Deploy 

6.MLflow 机器器学习整体流程⾮非常复杂 Data Prepare Aliyun EMR Row Data Trainging Deploy 

7.MLflow Scale 机器器学习整体流程⾮非常复杂 Data Prepare Aliyun EMR Model Row Data Trainging Exchange Deploy Scale Scale Governance Scale 

8.MLflow 机器器学习整体流程⾮非常复杂 hidden technical debt in machine learning systems 

9.MLflow 成熟的ML平台 Facebook FBLearner, Uber Michelangelo, Google TFX + 成熟的流程、包含了了从数据处理理、训练、部署的全流程 - 与公司的基础架构紧密结合，有限的算法 - 闭源 

10.MLflow MLflow介绍 开源的机器器学习平台 ‣ 兼容各种ML library和各种编程语⾔言 Python, Java, R ‣ 统⼀一的运⾏行行环境，可以运⾏行行任何地⽅方(⽐比如阿⾥里里云中) ‣ 设计时考虑到各种规模⽤用户，1或者1000+⼈人公司 发展历史 ‣ 2018年年6⽉月开源 ‣ 2019年年4⽉月 Spark AI⼤大会发布 1.0 

11.MLflow MLflow介绍 

12.MLflow MLflow — Tracking MLflow import mlflow with mlflow.start_run(): Tracking mlflow.log_param("x", 1) mlflow.log_metric("y", 2) with mlflow.start_run(): Record and Query for epoch in range(0, 3): experiments:code, configs, mlflow.log_metric(key="quality", value=2*epoch, step=epoch) results, parameters …etc mlflow.log_artifact('/root/mlflow_tracking_example.py') 

13.MLflow MLflow — Tracking 基本概念 • Code Version • Start Time • Source • Parameters • Metrics • Artifacts 

14.MLflow MLflow — Tracking DEMO 

15.MLflow MLflow — Tracking 存储 Backend Store • File Store • Database-backend Store Artifact Store • Aws S3 • Azure Blob Storage • Google Cloud Storage • FTP Server • SFTP Server • NFS • HDFS 

16.MLflow MLflow — Project Local Execution MLflow Project Packaging format Aliyun EMR for reproducible runs Remote Execution on any platform Code Config Data 

17.MLflow MLflow — Project 定义ml项⽬目的⽬目录结构 • 本地⽬目录或者Github仓库 • 可选的MLproject⽂文件⽤用于整个项⽬目配置 定义项⽬目的环境依赖 • Conda(Docker..)等依赖 • 可以在⼏几乎所有环境进⾏行行重复运⾏行行 通过API在本地或者远程运⾏行行 • mlflow run (cli) • mlflow.projects.run() (API) 

18. MLproject MLflow name: tutorial MLflow — Project conda_env: conda.yaml User project/ entry_points: main: parameters: MLproject alpha: float l1_ratio: {type: float, default: 0.1} conda.yaml command: "python train.py {alpha} {l1_ratio}" train.py conda.yaml tuil.py name: tutorial channels: …… - defaults dependencies: - python=3.6 - scikit-learn=0.19.1 - pip: - mlflow>=1.0 

19.MLflow MLflow — Project DEMO 

20.MLflow MLflow — Models Inference Batch & Stream Scoring Cloud Serving Tools ML framework 

21.MLflow MLflow — Models Inference Mlflow Model Format Flavor 1 Flavor 1 Batch & Stream Scoring sklearn TF Cloud Serving Tools ML framework 

22.MLflow MLflow — Models 定义模型存储格式 • 根据MLmodel⽂文件来定义各种 • 不不同框架具有不不同flavors(格式) 定义项⽬目的环境依赖 • Conda(Docker..)等依赖 • 可以在⼏几乎所有环境进⾏行行重复运⾏行行 通过API在本地或者远程运⾏行行 • mlflow server(cli) • mlflow.sklearn.load() (API) 

23.MLflow MLmodel flavors: MLflow — Model python_function: data: model.pkl env: conda.yaml loader_module: mlflow.sklearn User_model / python_version: 3.6.5 sklearn: pickled_model: model.pkl serialization_format: cloudpickle MLmodel sklearn_version: 0.19.1 utc_time_created: '2019-06-17 02:46:05.185724' conda.yaml model.pkl conda.yaml channels: …… - defaults dependencies: - python=3.6.5 - scikit-learn=0.19.1 - pip: - mlflow - cloudpickle==0.5.3 name: mlflow-env 

24.MLflow MLflow — Model • Python Function (generic) • R Function • H20 • Keras • MLeap • PyTorch • SKlearn • Spark MLlib • TensorFlow • ONNX 

25.MLflow MLflow — Model DEMO 

26.Project Hydrogen Part II 

27.Project Hydrogen Project Hydrogen aims at enabling first-class support for all distributed machine learning frameworks on Apache SparkTM, by substantially improving the performance and fault-recovery of distributed deep learning and machine learning frameworks on Spark Barrier Optimized Accelerator Execution Data Aware Mode Exchange Scheduling 

28.Project Hydrogen Data Prepare Aliyun EMR Row Data Trainging Deploy 

29.Project Hydrogen 数据科学家需要 分布式训练 ETL Databricks Delta Data warehouse load fit model kafka Aliyun EMR Data Source