EMR Tutorial¶

输入数据:¶

输入一般是csv格式的文件。如下所示，列之间用,分割

示例数据:
- train: dwd_avazu_ctr_deepmodel_train.csv
- test: dwd_avazu_ctr_deepmodel_test.csv
- 示例:

1,10,1005,0,85f751fd,c4e18dd6,50e219e0,0e8e4642,b408d42a,09481d60,a99f214a,5deb445a, f4fffcd0,1,0,2098,32,5,238,0,56,0,5

Note: csv文件不需要有header!!!

创建DataScience集群:¶

DataScience集群参考

Copy data to HDFS¶

hadoop fs -mkdir -p hdfs://emr-header-1:9000/user/easy_rec/data/
hadoop fs -put dwd_avazu_ctr_deepmodel_train.csv hdfs://emr-header-1:9000/user/easy_rec/data/
hadoop fs -put dwd_avazu_ctr_deepmodel_test.csv hdfs://emr-header-1:9000/user/easy_rec/data/

训练:¶

配置文件: dwd_avazu_ctr_deepmodel.config ** ** 配置文件采用prototxt格式，内容解析见配置文件
使用el_submit提交训练任务，el_submit相关参数请参考tf_on_yarn

开源TF模式¶

el_submit -yaml train.tf.yaml 配置文件内容如下

app:
    app_type: tensorflow-ps
    app_name: easyrec_tf_train
    mode: local
    exit_mode: true
    verbose: true
    files: dwd_avazu_ctr_deepmodel.config
    command: python -m easy_rec.python.train_eval --pipeline_config_path dwd_avazu_ctr_deepmodel.config --continue_train
    wait_time: 8
    hook: /usr/local/dstools/bin/hooks.sh

resource:
    ps_num: 1
    ps_cpu: 1
    ps_memory: 10g
    ps_mode_arg:
    worker_num: 2
    worker_cpu: 6
    worker_gpu: 1
    worker_memory: 10g
    worker_mode_arg:

Paitf模式¶

使用Paitf需要token授权，请联系产品架构团队索取token el_submit -yaml train.paitf.yaml配置文件内容如下

app:
    app_type: tensorflow-ps
    app_name: easyrec_paitf_train
    mode: docker-pai
    mode_arg: paitf:1.12-gpu
    token: AAAAAAAAAAAAAAABBBBBBBBBBBBBBB==
    exit_mode: true
    verbose: true
    files: dwd_avazu_ctr_deepmodel.config
    command: python -m easy_rec.python.train_eval --pipeline_config_path dwd_avazu_ctr_deepmodel.config --continue_train
    hook: /usr/local/dstools/bin/hooks.sh
    wait_time: 8

resource:
    ps_num: 1
    ps_cpu: 1
    ps_memory: 10g
    ps_mode_arg:  paitf:1.12-cpu
    worker_num: 1
    worker_cpu: 6
    worker_gpu: 1
    worker_memory: 10g
    worker_mode_arg: paitf:1.12-gpu

查看任务日志

评估:¶

使用el_submit提交评估任务，el_submit相关参数请参考tf_on_yarn
Note: 本示例仅仅展示流程，效果无参考价值。

开源TF模式¶

el_submit -yaml eval.tf.yaml配置文件内容如下

app:
    app_type: standalone
    app_name: easyrec_tf_eval
    mode: local
    exit_mode: true
    verbose: true
    files: dwd_avazu_ctr_deepmodel.config
    command: python -m easy_rec.python.eval --pipeline_config_path dwd_avazu_ctr_deepmodel.config
    wait_time: 8
    hook: /usr/local/dstools/bin/hooks.sh

resource:
    worker_num: 1
    worker_cpu: 6
    worker_gpu: 1
    worker_memory: 10g
    worker_mode_arg:

Paitf模式¶

使用Paitf需要token授权，请联系产品架构团队索取token el_submit -yaml eval.paitf.yaml配置文件内容如下

app:
    app_type: standalone
    app_name: easyrec_paitf_eval
    mode: docker-pai
    mode_arg: paitf:1.12-gpu
    token: AAAAAAAAAAAAAAABBBBBBBBBBBBBBB==
    exit_mode: true
    verbose: true
    files: dwd_avazu_ctr_deepmodel.config
    command: python -m easy_rec.python.eval --pipeline_config_path dwd_avazu_ctr_deepmodel.config
    wait_time: 8
    hook: /usr/local/dstools/bin/hooks.sh

resource:
    worker_num: 1
    worker_cpu: 6
    worker_gpu: 1
    worker_memory: 10g
    worker_mode_arg: paitf:1.12-gpu

导出:¶

使用el_submit提交导出任务, el_submit相关参数请参考tf_on_yarn

–pipeline_config_path: EasyRec配置文件 –export_dir: 导出模型目录 –checkpoint_path: 指定checkpoint，默认不指定，不指定则使用model_dir下面最新的checkpoint

开源TF模式¶

el_submit -yaml export.tf.yaml配置文件内容如下

app:
    app_type: standalone
    app_name: easyrec_tf_export
    mode: local
    exit_mode: true
    verbose: true
    files: dwd_avazu_ctr_deepmodel.config
    command: python -m easy_rec.python.export --pipeline_config_path dwd_avazu_ctr_deepmodel.config --export_dir hdfs://emr-header-1:9000/user/easy_rec/experiment/export
    wait_time: 8
    hook: /usr/local/dstools/bin/hooks.sh

resource:
    worker_num: 1
    worker_cpu: 6
    worker_gpu: 1
    worker_memory: 10g
    worker_mode_arg:

Paitf模式¶

使用Paitf需要token授权，请联系产品架构团队索取token el_submit -yaml export.paitf.yaml配置文件内容如下

app:
    app_type: standalone
    app_name: easyrec_paitf_export
    mode: docker-pai
    mode_arg: paitf:1.12-gpu
    token: AAAAAAAAAAAAAAABBBBBBBBBBBBBBB==
    exit_mode: true
    verbose: true
    files: dwd_avazu_ctr_deepmodel.config
    command: python -m easy_rec.python.export --pipeline_config_path dwd_avazu_ctr_deepmodel.config --export_dir hdfs://emr-header-1:9000/user/easy_rec/experiment/export
    wait_time: 8
    hook: /usr/local/dstools/bin/hooks.sh

resource:
    worker_num: 1
    worker_cpu: 6
    worker_gpu: 1
    worker_memory: 10g
    worker_mode_arg: paitf:1.12-gpu

查看导出结果¶

hadoop fs -ls hdfs://emr-header-1:9000/user/easy_rec/experiment/export

部署到Pai-EAS服务¶

1. 导出模型到savedmodel，并压缩成tar包¶

hadoop fs -get /user/easy_rec/experiment/export/mazeng/1606721697 savedmodel
tar zcvf savedmodel.tar.gz savedmodel

2. 配置AK，部署eas服务需要¶

eascmd64 config -i AAAAAAAAAA -k BBBBBBBBBBB -e pai-eas.cn-beijing.aliyuncs.com

3. 上传模型压缩包，获取oss url¶

eascmd64 upload savedmodel.tar.gz

4. 部署到线上服务（慎行）¶

eascmd64 create pmml.json

pmml.json配置文件内容如下, easyrec是基于tensorflow/paitf的，因此processor需选择tensorflow相关的

{
  "name": "demo0",
  "generate_token": "true",
  "model_path": "oss://eas-model-beijing/166408185111/savedmodel.tar.gz",
  "processor": "tensorflow_cpu_1.14",
  "metadata": {
    "instance": 1,
    "eas.enabled_model_verification": false,
    "cpu": 1
  }
}

5. 构造服务请求¶

参考 https://help.aliyun.com/document_detail/111055.html

1) 获取模型input output信息¶

curl http://pai-eas-vpc.cn-beijing.aliyuncs.com/api/predict/mnist_saved_model_example | python -mjson.tool

2) python版¶

参考 https://github.com/pai-eas/eas-python-sdk

#!/usr/bin/env python

from eas_prediction import PredictClient
from eas_prediction import StringRequest
from eas_prediction import TFRequest

if __name__ == '__main__':
    client = PredictClient('http://1828488879222746.cn-beijing.pai-eas.aliyuncs.com', 'mnist_saved_model_example')
    client.set_token('AAAAAAAAAAAAAAABBBBBBBBBBBBBBB==')
    client.init()

    #request = StringRequest('[{}]')
    req = TFRequest('predict_images')
    req.add_feed('images', [1, 784], TFRequest.DT_FLOAT, [1] * 784)
    for x in range(0, 1000000):
        resp = client.predict(req)
        print(resp)

3) 其他语言版¶

参考 https://help.aliyun.com/document_detail/111055.html

配置文件:¶

输入输出¶

# 训练表和测试数据
train_input_path: "hdfs://emr-header-1:9000/user/easy_rec/data/dwd_avazu_ctr_deepmodel_train.csv"
eval_input_path: "hdfs://emr-header-1:9000/user/easy_rec/data/dwd_avazu_ctr_deepmodel_test.csv"
# 模型保存路径
model_dir: "hdfs://emr-header-1:9000/user/easy_rec/experiment/"

数据相关¶

# 数据相关的描述
data_config {
  separator: ","
  input_fields: {
    input_name: "label"
    input_type: FLOAT
    default_val:""
  }
  input_fields: {
    input_name: "hour"
    input_type: STRING
    default_val:""
  }
  input_fields: {
    input_name: "c1"
    input_type: STRING
    default_val:""
  }
  ...
  input_fields: {
    input_name: "c20"
    input_type: STRING
    default_val:""
  }
  input_fields: {
    input_name: "c21"
    input_type: STRING
    default_val:""
  }

  label_fields: "label"

  batch_size: 1024
  prefetch_size: 32
  input_type: CSVInput
}

特征相关¶

feature_config:{
  features: {
    input_names: "hour"
    feature_type: IdFeature
    embedding_dim: 16
    hash_bucket_size: 50
  }
  features: {
    input_names: "c1"
    feature_type: IdFeature
    embedding_dim: 16
    hash_bucket_size: 10
  }
  ...
  features: {
    input_names: "c20"
    feature_type: IdFeature
    embedding_dim: 16
    hash_bucket_size: 500
  }
  features: {
    input_names: "c21"
    feature_type: IdFeature
    embedding_dim: 16
    hash_bucket_size: 500
  }
}

训练相关¶

# 训练相关的参数
train_config {
  # 每200轮打印一行log
  log_step_count_steps: 200
  # 优化器相关的参数
  optimizer_config: {
    adam_optimizer: {
      learning_rate: {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.0001
          decay_steps: 100000
          decay_factor: 0.5
          min_learning_rate: 0.0000001
        }
      }
    }
    use_moving_average: false
  }
  # 使用SyncReplicasOptimizer进行分布式训练(同步模式)
  sync_replicas: true
  # num_steps = total_sample_num * num_epochs / batch_size / num_workers
  num_steps: 2000
}

评估相关¶

eval_config {
  # 仅仅评估1000个样本，这里是为了示例速度考虑，实际使用时需要删除
  num_examples: 1000
  metrics_set: {
    # metric为auc
    auc {}
  }
}

模型相关¶

model_config:{
  model_class: "MultiTower"
  feature_groups: {
    group_name: "item"
    feature_names: "c1"
    feature_names: "banner_pos"
    feature_names: "site_id"
    feature_names: "site_domain"
    feature_names: "site_category"
    feature_names: "app_id"
    feature_names: "app_domain"
    feature_names: "app_category"
    wide_deep:DEEP
  }
  feature_groups: {
    group_name: "user"
    feature_names: "device_id"
    feature_names: "device_ip"
    feature_names: "device_model"
    feature_names: "device_type"
    feature_names: "device_conn_type"
    wide_deep:DEEP
  }
  feature_groups: {
    group_name: "user_item"
    feature_names: "hour"
    feature_names: "c14"
    feature_names: "c15"
    feature_names: "c16"
    feature_names: "c17"
    feature_names: "c18"
    feature_names: "c19"
    feature_names: "c20"
    feature_names: "c21"
    wide_deep:DEEP
  }

  multi_tower {
    towers {
      input: "item"
      dnn {
        hidden_units: [384, 320, 256, 192, 128]
      }
    }

    towers {
      input: "user"
      dnn {
        hidden_units: [384, 320, 256, 192, 128]
      }
    }

    towers {
      input: "user_item"
      dnn {
        hidden_units: [384, 320, 256, 192, 128]
      }
    }

    final_dnn {
      hidden_units: [256, 192, 128, 64]
    }
    l2_regularization: 0.0
  }
  embedding_regularization: 0.0
}

EMR Tutorial¶

输入数据:¶

创建DataScience集群:¶

Copy data to HDFS¶

训练:¶

开源TF模式¶

Paitf模式¶

评估:¶

开源TF模式¶

Paitf模式¶

导出:¶

开源TF模式¶

Paitf模式¶

查看导出结果¶

部署到Pai-EAS服务¶

1. 导出模型到savedmodel，并压缩成tar包¶

2. 配置AK，部署eas服务需要¶

3. 上传模型压缩包，获取oss url¶

4. 部署到线上服务（慎行）¶

5. 构造服务请求¶

1) 获取模型input output信息¶

2) python版¶

3) 其他语言版¶

配置文件:¶

输入输出¶

数据相关¶

特征相关¶

训练相关¶

评估相关¶

模型相关¶

Config下载¶

ExcelConfig下载¶

参考手册¶

EMR Tutorial¶

输入数据:¶

创建DataScience集群:¶

Copy data to HDFS¶

训练:¶

开源TF模式¶

Paitf模式¶

评估:¶

开源TF模式¶

Paitf模式¶

导出:¶

开源TF模式¶

Paitf模式¶

查看导出结果¶

部署到Pai-EAS服务¶

1. 导出模型到savedmodel， 并压缩成tar包¶

2. 配置AK， 部署eas服务需要¶

3. 上传模型压缩包，获取oss url¶

4. 部署到线上服务（慎行）¶

5. 构造服务请求¶

1) 获取模型input output信息¶

2) python版¶

3) 其他语言版¶

配置文件:¶

输入输出¶

数据相关¶

特征相关¶

训练相关¶

评估相关¶

模型相关¶

Config下载¶

ExcelConfig下载¶

参考手册¶

1. 导出模型到savedmodel，并压缩成tar包¶

2. 配置AK，部署eas服务需要¶