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+## finetune训练(可选) + +由于推荐业务每天都有实时更新的数据,如果用户采用先训练一批历史数据,后面每天finetune更新模型的话,可以利用以上begin调优的最优结果,再在新数据上微调。如果用户每次更新模型都是重新开始训练的话,则不需要此步骤。 + +### 调优经验 + +例如:用户有40天历史数据,可以先利用以上步骤调优30天数据,然后根据搜索出的最优参数,再finetuen剩余10天。 +经验是:根据begin训练得出的最优参数,将learning_rate设置为begin结束时的learning_rate。 +例如: +begin训练时learning_rate如下,begin训练总计为8000步,因此可以设置finetune时initial_learning_rate=1e-6或者1e-7: + +``` +learning_rate { + exponential_decay_learning_rate { + initial_learning_rate: 0.001 + decay_steps: 1000 + decay_factor: 0.1 + min_learning_rate: 1e-07 + } + } +``` + +支持手动修改,也支持代码修改配置,修改效果如下: +![image.png](../../images/automl/modify_lr.jpg) + +#### 使用代码修改配置(可选) + +支持本地上pipeline文件修改 + +```bash +python modify_pipeline_config.py --pipeline_config_path=./samples/pipeline.config --save_path=./samples/pipeline_finetune.config --learning_rate=1e-6 +``` + +也支持oss上pipeline文件直接修改 + +```bash +python modify_pipeline_config.py --pipeline_config_path=oss://easyrec/pipeline889.config --save_path=oss://easyrec/pipeline889-f.config --learning_rate=1e-6 --oss_config=../config/.ossutilconfig +``` + +如果用户想要看是否有更优参数,可以看下级目录启动调优。 + +### 启动调优(可选) + +```bash +nnictl create --config config_finetune.yml --port=8617 +``` + +#### config_finetune.ini + +``` +[platform_config] +name=MaxCompute +{% set date_list = [20220616,20220617] %} +{% set date_begin = 20220616 %} +{% for bizdate in date_list %} +{% set eval_ymd = bizdate +1 %} +{% set predate = bizdate -1 %} +{% if bizdate == date_begin %} +cmd1_{{bizdate}}="PAI -name=easy_rec_ext + -project=algo_public + -Dscript='oss://automl-nni/easyrec/easy_rec_ext_615_res.tar.gz' + -Dtrain_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{bizdate}}' + -Deval_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{eval_ymd}}' + -Dcmd=train + -Deval_method=separate + -Dfine_tune_checkpoint="oss://automl-nni/easyrec/finetune/{{predate}}_finetune_model_nni_622" + -Dconfig='oss://automl-nni/easyrec/config/easyrec_model_${exp_id}_${trial_id}.config' + -Dmodel_dir='oss://automl-nni/easyrec/finetune/{{bizdate}}_finetune_model_nni_622/${exp_id}_${trial_id}' + -Dselected_cols='is_valid_play,ln_play_time,is_like,is_comment,features,content_features' + -Dbuckets='oss://automl-nni/' + -Darn='xxx' + -DossHost='oss-cn-beijing-internal.aliyuncs.com' + -Dcluster={"ps":{"count":1,"cpu":1600,"memory":40000 },"worker":{"count":12,"cpu":1600,"memory":40000}} " + +{% else %} +cmd1_{{bizdate}}="PAI -name=easy_rec_ext + -project=algo_public + -Dscript='oss://automl-nni/easyrec/easy_rec_ext_615_res.tar.gz' + -Dtrain_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{bizdate}}' + -Deval_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{eval_ymd}}' + -Dcmd=train + -Deval_method=separate + -Dfine_tune_checkpoint="oss://automl-nni/easyrec/finetune/{{predate}}_finetune_model_nni_622/${exp_id}_${trial_id}" + -Dconfig='oss://automl-nni/easyrec/config/easyrec_model_${exp_id}_${trial_id}.config' + -Dmodel_dir='oss://automl-nni/easyrec/finetune/{{bizdate}}_finetune_model_nni_622/${exp_id}_${trial_id}' + -Dselected_cols='is_valid_play,ln_play_time,is_like,is_comment,features,content_features' + -Dbuckets='oss://automl-nni/' + -Darn='xxx' + -DossHost='oss-cn-beijing-internal.aliyuncs.com' + -Dcluster={"ps":{"count":1,"cpu":1600,"memory":40000 },"worker":{"count":12,"cpu":1600,"memory":40000}} " +{% endif %} + +{% endfor %} + + +[metric_config] +# metric type is summary/table +metric_type=summary +{% set date_list = [20220616,20220617] %} +{% for bizdate in date_list %} +metric_source_{{bizdate}}=oss://automl-nni/easyrec/finetune/{{bizdate}}_finetune_model_nni_622/${exp_id}_${trial_id}/eval_val/ +{% endfor %} +# best/final/avg,default=best +final_mode=final +source_list_final_mode=avg +metric_dict={'auc_is_like':0.25, 'auc_is_valid_play':0.5, 'auc_is_comment':0.25} +``` + +与begin训练的`差异点`: + +- 每个配置模块支持jinja模版渲染 +- 配置finetune日期{% set date_list = \[20220616,20220617\] %} +- 配置finetune开始日期{% set date_begin = 20220616 %},Dfine_tune_checkpoint开始日期和后续日期采取的model路径不一样 +- 假设每天finetune: + - {bizdate} 必须保留,将会在代码中根据当天日期进行替换 + - {eval_ymd} 必须保留,将会在代码中根据第二天日期进行替换 + - {predate} 必须保留,将会在代码中根据前一天日期进行替换 +- metric_source也是多条路径,每一天训练结果为summary的最终结果,整组参数finetune的结果为这些天的平均值 + +#### 配置超参搜索空间search_space.json + +参考begin训练阶段中想要搜索的参数即可,注意由于是finetune训练,网络结构相关的参数不要进行搜索,经验是搜索LR diff --git a/docs/source/automl/hpo_config.md b/docs/source/automl/hpo_config.md new file mode 100644 index 000000000..5f99e42e6 --- /dev/null +++ b/docs/source/automl/hpo_config.md @@ -0,0 +1,299 @@ +HPO启动配置包含exp.yml. trial.ini, search_space.json三个模块。 + +# exp.yml + +exp.yml是作为NNI的配置文件,将代码和搜索空间进行结合,并使用指定的环境来运行您的训练代码,具体参考此exp.yml文件。在这里,您还可以还提供其他信息,例如并发度、调优算法、最大Trial数量和最大持续时间等参数。https://nni.readthedocs.io/zh/stable/reference/experiment_config.html#experimentconfig + +## 字段内容 + +字段可以直接参考NNI官网,区别在于为了结合PAI,这些字段需保持不变 + +``` +trialCommand: python3 -m hpo_tools.core.utils.run --config=./trial.ini +trainingService: + platform: local +assessor: + name: PAIAssessor +``` + +同时,为了能够停止PAI任务,需要使用PAIAssessor + +## PAIAssessor + +``` +支持将该组中的实验结果和同组中的所有历史进行比较,如果不满足比较标准(例如小于中位数),则停止该组超参数的运行。比如说设置最大运行次数max_trial_num, 实际使用量会显著小于max_trial_num,但具体数量就和实际跑的任务及随机到的超参有关系了。例如max_trial_num=50时,可能最终可能不到 25 次,并且差不多已经是完整探索了50组超参。 +``` + +| PAIAssessor | 描述 | 值 | +| ------------- | ----------------------------- | ----------------- | +| optimize_mode | 最大化优化的方向 | maximize/minimize | +| start_step | 从第几步开始进行早停判定 | 2 | +| moving_avg | 早停判断时,采用所有历史的滑动平均值作为判断标准 | True | +| proportion | 本次超参搜索的最优值和历史记录的proportion值比较 | 0.5 | +| patience | metric指标连续下降几次,就停止 | 10 | + +### 示例 + +``` +experimentWorkingDirectory: ../expdir +searchSpaceFile: search_space.json +trialCommand: python3 -m hpo_tools.core.utils.run --config=./trial.ini +trialConcurrency: 1 +maxTrialNumber: 4 +tuner: + name: TPE + classArgs: + optimize_mode: maximize +debug: true +logLevel: debug +trainingService: + platform: local +assessor: + name: PAIAssessor + classArgs: + platform: MAXCOMPUTE + optimize_mode: maximize + start_step: 1 + moving_avg: true + proportion: 0.5 +``` + +# trial.ini + +## 变量替换原则 + +### 值替换 + +程序会将trial.ini 中以下这些key默认替换成对应的值。参数默认支持值替换、列表替换、字典替换、json替换、文件替换(params_config)、支持嵌套字典的key替换(组合参数例子dlc_mnist_nested_search_space) + +- cmd = cmd.replace('${exp_id}', experment_id.lower()) + +- cmd = cmd.replace('${trial_id}', trial_id.lower()) + +- cmd = cmd.replace('${NNI_OUTPUT_DIR}',os.environ.get('NNI_OUTPUT_DIR', './tmp')) + +- cmd = cmd.replace('${tuner_params_list}', tuner_params_list) + +- cmd = cmd.replace('${tuner_params_dict}', tuner_params_dict) + +- cmd = cmd.replace('${tuner_params_json}', json.dumps(tuner_params)) + +- cmd = cmd.replace('${params}', params)->支持参数标识路径,例如lr0.001_batchsize64 注意其中可能含有浮点数,请确定是否支持用来标识数据/数据表 + +- cmd = cmd.replace(p, str(v)) 将搜索的参数替换为搜索的值,搜索参数可以使用${batch_size}、${lr}来标记,需要和search_space.json中的key匹配使用 + +### jinja渲染 + +每个配置模块支持jinja模版渲染,用于用户在一开始设置变量,具体可以查看案例cross-validation/maxcompute-easyrec + +``` +[metric_config] +# metric type is summary/table +metric_type=summary +{% set date_list = [20220616,20220617] %} +{% for bizdate in date_list %} +metric_source_{{bizdate}}=oss://automl-nni/easyrec/finetune/{{bizdate}}_finetune_model_nni_622/${exp_id}_${trial_id}/eval_val/ +{% endfor %} +``` + +### 字段介绍 + +| 配置模块 | 描述 | 是否可选 | +| --------------- | ----------------------------------------------------------------- | ---- | +| platform_config | 用于标记任务执行的平台以及对应的执行命令 | 必选 | +| metric_config | 用于标记任务metric的获取来源、metric的key以及对应权重、metric类型、最终metric的方式 | 必选 | +| output_config | 如果使用服务版,可以配置output_config用来获取最优模型配置summary_path,用于配制tensorboard路径 | 可选 | +| schedule_config | 如果任务在指定时间内调度任务,则需要配置schedule_config,修改对应的schedule_config的值 | 可选 | +| params_config | 如果用户的参数是保存在文件中,则需要配置params_config, 用于标记需要修改参数的源文件路径和目标路径 | 可选 | +| oss_config | 如果任务需要使用OSS存储,则需要配置OSS config | 可选 | +| odps_config | 如果任务需要使用maxcompute平台执行任务,则需要配置odps config | 可选 | +| ts_config | 如果任务需要使用trainingservice平台执行任务,则需要配置ts config | 可选 | +| paiflow_config | 如果任务需要执行工作流任务,则需要配置paiflow_config,修改对应的paiflow_config的值 | 可选 | +| dlc_config | 如果任务需要执行dlc任务,则需要配置dlc_config,修改对应的dlc_config的值 | 可选 | +| monitor_config | 支持失败告警,最优metric更新时提醒 | 可选 | + +## platform_config + +| platform_config | 描述 | 值 | +| --------------- | ------------------------------------------------------------ | -------------------------------------------------------------- | +| name | 用于标记任务执行的平台 | DLC/MaxCompute/DataScience/LOCAL/PAI/PAIFLOW | +| cmdxx | 用于标记执行的命令,以cmd开头 | dlc submit pytorch --name=test_nni\_${exp_id}\_${trial_id} xxx | +| resume | 1表示开启续跑模式;用于用户一次运行时,比如说第一行任务成功,第二行由于资源不足失败,可以开启续跑,从第二行命令开始运行 | 0/1 | + +## metric_config + +| metric_config | 描述 | 值 | +| ---------------------- | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| metric_type | metric类型 | summary/table/api/json/stdout | +| metric_source | metric来源(可以为多个以metric_source开头的,具体可以看maxcompute_crossvalidation案例) | 对应为具体的路径或者job | +| final_mode | 如果任务运行过程中,存在很多中间metric,那么需要确定最终metric的计算方式 | final/best/avg | +| source_list_final_mode | 可选,默认值为final_mode,可选值为final/best/avg,用于有多个metric_source时最终metric如何计算,具体可以看maxcompute_crossvalidation案例 | final/best/avg | +| metric_dict | 对应查询的key以及对应的权重;可以为负值 | metric_dict={'auc_is_like':0.25, 'auc_is_valid_play':0.5, 'auc_is_comment':0.25, 'loss_play_time':-0.25} metric=val(’auc_is_valid_play’)\*0.5+val(’auc_is_like’)\*0.25+val(’auc_is_comment’)\*0.25-val(’loss_play_time’)\*0.25 | + +- 如果metric_type=stdout类型,则metric_dict对应的key为正则表达式,value为对应的权重 + +``` +[metric_config] +# metric type is summary/table +metric_type=stdout +metric_source=oss://test-nni/examples/search/pai/stdout/stdout_${exp_id}_${trial_id} +# best or final,default=best +final_mode=best +metric_dict={'validation: accuracy=([0-9\\.]+)':1} +``` + +- 如果metric_type=stdout类型,则metric_source支持指定默认任务的日志为来源 + - stdoutmetric:支持指定具体的任务;例如metric_source=cmd1,即使用cmd1输出的任务日志做正则 + - stdoutmetric:支持指定具体的任务,并过滤文件,例如metric_source=cmd,worker;即使用cmd1任务中所有的worker日志做正则 + +``` +[metric_config] +# metric type is summary/table +metric_type=stdout +# default is cmd, cmd->platform job 1,we will get the job1 all default stdout +# if the job is distributed, you can use [cmd,worker] to assign which log has metric or just use [cmd] to choose all stdout +metric_source=cmd,worker +# best or final,default=best +final_mode=best +metric_dict={'validation: accuracy=([0-9\\.]+)':1} +optimize_mode=maximize +``` + +- 如果metric_type=summary类型,则metric_source为对应的summary路径 + +``` +[metric_config] +# metric type is summary/table +metric_type=summary +# the easyrec model_dir/eval_val/ have the events summary file +metric_source=hdfs://123.57.44.211:9000/user/nni/datascience_easyrec/model_nni/${exp_id}_${trial_id}/eval_val/ +``` + +- 如果metric_type=table类型,则metric_source为对应的sql语句 + +``` +[metric_config] +# metric type is summary/table +metric_type=table +metric_source=select * from ps_smart_classification_metrics where pt='${exp_id}_${trial_id}'; +``` + +- 如果metric_type=api类型,则metric支持指定具体的任务;例如metric_source=cmd1 + +``` +[metric_config] +# metric type is summary/table/api +metric_type=api +# default is cmd1,cmd1->platform job 1, we will get the default job1 metric +# if is list,metric_source_1=cm1,metric_source_2=cmd2 +metric_source=cmd1 +``` + +## output_config + +| output_config | 描述 | 值 | +| ------------- | ------------------------------------ | --- | +| model_path | 如果使用服务版,可以配置model_path用来获取最优模型 | 路径 | +| summary_path | 如果使用单机版,可以配置summary用于本地查看TensorBoard | 路径 | + +## schedule_config + +| schedule_config | 描述 | 值 | +| --------------- | -------------------- | ---------------- | +| day | 支持在指定时间范围内调度AutoML任务 | everyday/weekend | +| start_time | 指定调度开始时间 | 00:00-23:59 | +| end_time | 指定调度结束时间 | 00:00-23:59 | + +## params_config + +如果用户的参数是保存在文件中,则需要配置params_config, + +| params_config | 描述 | 值 | +| -------------------------- | ------------------------------------------------------ | ----------------- | +| params_src_dst_filepath1xx | 用于标记需要修改参数的源文件路径和目标路径,可以为多个,以params_src_dst_filepath开头 | src_path,dst_path | +| params_src_dst_filepath2xx | xx | xx | + +## oss_config + +| oss_config | 描述 | 值 | +| --------------- | -------- | -------------------------------------------------------------------------- | +| endpoint | endpoint | [http://oss-cn-shanghai.aliyuncs.com](http://oss-cn-shanghai.aliyuncs.com) | +| accessKeyID | ak | ak | +| accessKeySecret | sk | sk | +| role_arn | role_arn | acs:ram::xxx:role/aliyunserviceroleforpaiautoml | + +## odps_config + +| odps_config | 描述 | 值 | +| ------------- | ------------ | -------------------------------------------------------------------------------------- | +| access_id | ak | ak | +| access_key | sk | ak | +| project_name | project_name | xxx | +| end_point | end_point | 弹外: http://service.odps.aliyun.com/api 弹内:http://service-corp.odps.aliyun-inc.com/api | +| log_view_host | logview host | 弹外:http://logview.odps.aliyun.com 弹内:http://logview.alibaba-inc.com | +| role_arn | role_arn | acs:ram::xxx:role/aliyunserviceroleforpaiautoml | + +## dlc_config + +| dlc_config | 描述 | 值 | +| ---------- | ----------- | ----------------------------------------------------------------- | +| access_id | ak | ak | +| access_key | sk | ak | +| end_point | end_point | 弹外:pai-dlc.cn-shanghai.aliyuncs.com 弹内:pai-dlc-share.aliyuncs.com | +| region | cn-shanghai | cn-shanghai | +| protocol | protocol | http/https | + +## ts_config + +| ts_config | 描述 | 值 | +| ----------------- | --------- | ---------------------------- | +| access_key_id | ak | ak | +| access_key_secret | sk | ak | +| region_id | reigin | xxx | +| endpoint | end_point | pai.cn-hangzhou.aliyuncs.com | + +## paiflow_config + +| paiflow_config | 描述 | 值 | +| ----------------- | ------------ | ------- | +| access_key_id | ak | ak | +| access_key_secret | sk | ak | +| region_id | reigin | xxx | +| workspace_id | workspace_id | 2332411 | + +## monitor_config + +- 参考[阿里钉机器人](https://open.dingtalk.com/document/robots/custom-robot-access)去添加自定义机器人,获取url + - 点击阿里钉头像->机器人管理-自定义机器人->群组选择工作通知 + - 点击阿里钉头像->机器人管理-自定义机器人->群组:选择对应的群号 + +| monitor_config | 描述 | 值 | +| -------------- | -------------------------------------------- | ----------------------------------------------------- | +| url | url为创建自定义机器人对应的Webhook地址 | https://oapi.dingtalk.com/robot/send?access_token=xxx | +| keyword | 添加自定义机器人:自定义关键词 | monitor | +| at_mobiles | 在content里添加@人的手机号,且只有在群内的成员才可被@,非群内成员手机号会被脱敏 | \['11xx'\] | +| at_user_ids | 被@人的用户userid。即工号 | \[\] | +| is_at_all | 是否@所有人 | True/False | + +## search_space.json + +| search_space | 描述 | 值 | +| ------------ | --------------------------------------------------------------------------------------------------------- | --- | +| key | trial.ini中配置的搜索参数变量 | | +| type | nni中定义的搜索类型,相关配置参考[NNI searchSpace参考手册](https://nni.readthedocs.io/en/v2.2/Tutorial/SearchSpaceSpec.html) | | + +- {”\_type”: “choice”, “\_value”: options}:从options中选取一个。 +- {”\_type”: “randint”, “\_value”: \[lower, upper\]}:\[low,upper)之间选择一个随机整数。 +- {”\_type”: “uniform”, “\_value”: \[low, high\]}:\[low,upper\]之间随机采样。 + | + | value | value是根据业务、经验设置相关搜索值 | + | + +### 示例 + +``` +{ + "${batch_size}": {"_type":"choice", "_value": [16, 32, 64, 128]}, + "${lr}":{"_type":"choice","_value":[0.0001, 0.001, 0.01, 0.1]} +} +``` diff --git a/docs/source/automl/hpo_res.md b/docs/source/automl/hpo_res.md new file mode 100644 index 000000000..ef3abe85d --- /dev/null +++ b/docs/source/automl/hpo_res.md @@ -0,0 +1,131 @@ +## 调优结果 + +在运行实验后,可以在命令行界面中找到如下的Web界面地址 :\[Your IP\]:\[Your Port\] +![image.png](../../images/automl/pai_nni_create.jpg) + +### 查看概要页面 + +点击Overview按钮,在这里可以看到实验相关信息,如配置文件、搜索空间、运行时长、日志路径等。NNI 还支持通过 Experiment summary 按钮下载这些信息和参数。 +![image.png](../../images/automl/pai_nni_overview.jpg) + +### 查看Trial详情页面 + +点击Trials detail按钮,您可以在此页面中看到整个实验过程中,每个trial的结果情况。 +其中succeeded代表此次trial成功运行,earlystop表示该组参数运行结果不太好,被提前停止了。停止策略可以查看pai_nni/core/pai_assessor.PaiAssessor,当然也可以根据业务情况去修改。 +![image.png](../../images/automl/pai_nni_detail.jpg) + +### 查看作业日志详情 + +点击每个Trial No,可以看到每个参数Trial的日志、参数详情,报错和输出可以点击以下3个按钮。 +![image.png](../../images/automl/pai_nni_log.jpg) + +### 手动停止某组实验超参 + +如果某些参数的结果不太好,可以进行手动停止。 +例如停止第一组参数。 +![image.png](../../images/automl/nni_stop.png) + +### 多目标default metric查看 + +假设用户配置的metric_config为如下,那么UI中Default metric中显示3项;该组trial最终的metric + +- default=auc\*0.5+accuracy\*0.5 +- auc即为最终的auc值 +- accuracy即为最终的accuracy值 + +``` +auc=0.5 +accuracy=0.5 +``` + +![image.png](../../images/automl/nni_metric.png) + +### 最优模型和参数 + +可以按照metric排序,获取最优精度的参数,这组实验id kfv91xl5 和trial_id zuKwM,代码中默认设置模型保存路径为以下方式,因此可以在对应的路径下找到该模型 +-Dmodel_dir='oss://lcl-bj/eval_dist_test/model\_${exp_id}\_${trial_id}' +![image.png](../../images/automl/best-model.png) + +### 多组参数比较 + +点击Trail NO,选中后,点击compare, 就可以查看对应参数的相关信 +![image.png](../../images/automl/nni-compare.png) + +### 多组实验查看 + +多组实验可以点击All experiments,然后点击具体的实验ID进入对应的实验详情 +![image.png](../../images/automl/exp-list.png) + +### 自定义参数或者失败重试 + +可以使用自定义参数,也可以使用该功能重启失败的trial。 +点击复制这个按钮,然后跳出Customized trial,点击提交/或者修改即可,此处是新增一组参数,应该记得调高MaxTrialNo +注意该功能在2.10目前有问题;需要nni\<=2.9 +![image.png](../../images/automl/retry_trial.jpg) + +### 失败续跑 + +仅实时修改trial.ini,或者初始设置trial.ini 时添加配置resume=1即可将重跑变成断点续跑; +注意续跑时会从上一次该参数失败的cmd开始跑。 + +``` +[platform_config] +name=MaxCompute +resume=1 +cmd=PAI -name xxx +``` + +### 一键重试失败的Trial + +当用户确认失败原因为没有资源,或者算法偶现失败等原因时,想采取重试策略,可以使用该API发起多个失败的Trial一起重试。在内部其实是将NNI最大运行次数增大,并发数保持不变;并且是新增了多个Trial,每个Trial的参数和之前失败的Trial保持一致。 + +注意该功能在2.10目前有问题;需要nni\<=2.9 + +- experiment_id: 重试的实验ID(必选) + +``` +python -m hpo_tools.core.utils.retry_multi_failed_trials --experiment_id=o968matg +``` + +### 一键停止运行的Trial + +当用户得到想要的模型和参数时,由于NNI停止实验时,只会停止本地的进程,不会将dlc/trainingservice等平台的任务停止,目前提供了接口,可以将实验正常运行的作业给停止掉,并且将最大实验次数调至1(最小的正数),避免停止的瞬间起新的作业。 + +- experiment_id: 停止的实验ID(必选) + +``` +python -m hpo_tools.core.utils.kill_multi_running_trials --experiment_id=o968matg +``` + +### 停止实验 + +在停止实验之前,先参考一键停止运行的Trial,再停止实验 + +``` +nnictl stop exp_id +``` + +### 超参数分析 + +可以点击超参数Hyper-parameter,选中关注的指标,就可以看出来最好的参数大概是哪些;对参数进行分析 +![image.png](../../images/automl/hyper.jpg) + +### tensorboard + +需要配置output_config,获取相应的summary文件 +可以参考:https://nni.readthedocs.io/zh/stable/experiment/web_portal/tensorboard.html + +注意目前NNI有个bug,需要先规避一下: + +``` +nni_tensorboard_filepath=$(python3 -c "import nni;import os;print(os.path.join(os.path.dirname((os.path.dirname(nni.__file__))),'nni_node/extensions/nniTensorboardManager.js'))") +echo "nni_tensorboard_filepath:"$nni_tensorboard_filepath +sed -i -e "s/--bind_all/--host 0.0.0.0/g" $nni_tensorboard_filepath +``` + +``` +[output_config] +summary_path=oss://lcl-bj/eval_dist_test/model_${exp_id}_${trial_id}_${params} +``` + +![image.png](../../images/automl/nni_tensorboard.jpg) diff --git a/docs/source/automl/pai_nni_hpo.md b/docs/source/automl/pai_nni_hpo.md index 4ff77de4e..7f08fc470 100644 --- a/docs/source/automl/pai_nni_hpo.md +++ b/docs/source/automl/pai_nni_hpo.md @@ -1,73 +1,60 @@ # PAI-NNI-HPO -## GetStarted +HPO是对模型参数、训练超参数(opt、lr)等进行自动搜索调优的一个工具,从而获取较优参数,提升模型效果。可以大大地节省算法同学调参的时间,聚焦在建模和业务上。我们对NNI、PAI产品和算法等进行集成,支持多平台0代码修改调参,并做了加速、监控、调度、续跑等功能增强。 -注意NNI仅支持python>=3.7,因此请配置python>=3.7的环境 +NNI参考:https://nni.readthedocs.io/en/stable/hpo/overview.html -NNI is tested and supported on Ubuntu >= 18.04, Windows 10 >= 21H2, and macOS >= 11. +# 安装 -### 下载安装easyrec +系统:Ubuntu >= 18.04, Windows 10 >= 21H2, macOS >= 11. -```bash -git clone https://github.com/alibaba/EasyRec.git -cd EasyRec -bash scripts/init.sh -python setup.py install -``` +python环境:注意NNI仅支持python>=3.7,因此请配置python>=3.7的环境 + +java环境:如果需要运行MC的PAI命令,需要java8 -### 下载安装hpo-tools +## 下载安装hpo-tools -#### 安装python3.7+以上环境 (可选) +安装命令为 ``` -wget http://automl-nni.oss-cn-beijing.aliyuncs.com/nni/hpo_tools/Anaconda3-5.3.1-Linux-x86_64.sh -bash Anaconda3-5.3.1-Linux-x86_64.sh -source ~/.bashrc -conda create -n test python=3.7 -source activate test +source install_hpo_tools.sh $1 $2 ``` -#### 安装java8及以上环境(可选) - -如果用户不需要提交maxcompute作业,或者环境已经满足,可以跳过。[odpscmd 参考](https://help.aliyun.com/document_detail/27971.html#section-dje-rvv-jp2) +- 第一个参数为下载examples的位置,默认下载在输入路径下面的examples下; 如果没写目录,默认生成在根目录下。 +- 第二个参数为aliyun/eflops/mac-dlc/mac-arm-dlc,用来控制安装dlc的版本,如果没写,则默认安装aliyun版本的dlc -此处给了一个mac安装java8的教程,一般linux服务器会自带java8 +### Linux ``` -# on mac -brew install --cask homebrew/cask-versions/adoptopenjdk8 - -# 接下来获取jdk安装路径,输入 /usr/libexec/java_home -V.红方框內就是jdk的安装路径,复制备用(/Library/Java/JavaVirtualMachines/adoptopenjdk-8.jdk/Contents/Home) -/usr/libexec/java_home -V - -# 设置环境变量 vi /etc/profile -export JAVA_HOME=/Library/Java/JavaVirtualMachines/adoptopenjdk-8.jdk/Contents/Home -export PATH=$JAVA_HOME/bin:$PATH:. -export CLASS_PATH=$JAVA_HOME/lib -source /etc/profile -java -version +wget https://automl-nni.oss-cn-beijing.aliyuncs.com/nni/hpo_tools/scripts/install_hpo_tools.sh +source install_hpo_tools.sh ./ aliyun +source ~/.bashrc ``` -#### 安装hpo_tools - -第一个参数为下载examples的位置,默认下载在输入路径下面的examples下; 如果没写目录,默认生成在根目录下。 - -安装2选1,install_hpo_tools.sh默认会安装最新版本,最新版本内的代码和案例都是匹配的,可以正常运行,但可能文档配置未更新。因此可以采用安装当前文档匹配的版本。 - -#### 安装最新版本(可选) +### MAC ``` +# 如果是mac系统 将zsh->bash +chsh -s /bin/bash + +# 如果是mac系统,aliyun/eflops/mac-dlc/mac-arm-dlc wget https://automl-nni.oss-cn-beijing.aliyuncs.com/nni/hpo_tools/scripts/install_hpo_tools.sh -source install_hpo_tools.sh ./ aliyun -cd ./examples/search/maxcompute_easyrec +source install_hpo_tools.sh ./ mac-dlc + +source ~/.bashrc ``` -#### 安装当前版本(可选) +### MAC ARM ``` -wget https://automl-nni.oss-cn-beijing.aliyuncs.com/nni/hpo_tools/scripts/install_hpo_tools_0.1.10.sh -source install_hpo_tools_0.1.10.sh ./ aliyun -cd ./examples/search/maxcompute_easyrec +# 如果是mac系统 将zsh->bash +chsh -s /bin/bash + +# 如果是mac系统,aliyun/eflops/mac-dlc/mac-arm-dlc +wget https://automl-nni.oss-cn-beijing.aliyuncs.com/nni/hpo_tools/scripts/install_hpo_tools.sh +source install_hpo_tools.sh ./ mac-arm-dlc + +source ~/.bashrc ``` - 注意如果有旧版本,会先卸载旧版本,升级新版本hpo-tools @@ -76,124 +63,79 @@ cd ./examples/search/maxcompute_easyrec - 默认会安装dlc命令行工具,用于提交dlc作业 - 默认会安装odpscmd命令行工具,用于提交maxcompute作业 -### 卸载hpo-tools(可选) +## 提供镜像包(可选) -如果需要升级,则需要先卸载之前安装包,第一个参数为原始安装的位置;默认会卸载hpo_tools,删除examples/dlc/odpscmd +提供镜像用于用户免安装使用,支持local/dlc/mc/trainingservice/paiflow -``` -bash uninstall_hpo_tools.sh ./ -``` +- 弹外GPU镜像:registry.cn-shanghai.aliyuncs.com/mybigpai/nni:gpu-latest +- 弹外CPU镜像:registry.cn-shanghai.aliyuncs.com/mybigpai/nni:cpu-latest + +### 启动镜像 -## 配置 +``` +mkdir -p ./examples +cd examples +echo $(pwd) -### 配置 config.ini +# 挂载&获取container id +container_id=`docker run -td --network host -v $(pwd):/HpoTools/test registry.cn-shanghai.aliyuncs.com/mybigpai/nni:cpu-latest` +echo $container_id -#### config.ini中参数的自动替换: +# get examples:cp docker examples to local +docker cp $container_id:/HpoTools/examples/search $(pwd) +# 配置案例路径 +ls $(pwd)/search -程序会将config.ini 中以下这些key默认替换成对应的值。参数默认支持值替换、列表替换、字典替换、json替换、文件替换(params_config) +# 运行镜像 +docker exec -ti $container_id /bin/bash +cd /HpoTools/test/search +### 查看具体案例 去本地修改$(pwd)/search下面的实验配置,第2章节 +### 查看具体案例 去容器/HpoTools/test/search 启动调优,第3章节 +### 查看具体案例 去本地UI查看调优结果,第4章节 ``` -def update_default_params(cmd, tuner_params={}, params_only=False): - """update params in cmd.""" - trial_id = str(nni.get_trial_id()) - experment_id = str(nni.get_experiment_id()) - - tuner_params_list = '' - tuner_params_dict = '' - for p, v in tuner_params.items(): - cmd = cmd.replace(p, str(v)) - tuner_params_list += p + ' ' + str(v) + ' ' - tuner_params_dict += p + '=' + str(v) + ' ' - - # params_only used in replace ak,sk in test at the begining - if not params_only: - # lower for k8s meta.name - cmd = cmd.replace('${exp_id}', experment_id.lower()) - cmd = cmd.replace('${trial_id}', trial_id.lower()) - cmd = cmd.replace('${NNI_OUTPUT_DIR}', - os.environ.get('NNI_OUTPUT_DIR', './tmp')) - cmd = cmd.replace('${tuner_params_list}', tuner_params_list) - cmd = cmd.replace('${tuner_params_dict}', tuner_params_dict) - cmd = cmd.replace('${tuner_params_json}', json.dumps(tuner_params)) - - return cmd -``` - -#### params_config(可选) -- 如果用户的参数是保存在文件中,则需要配置params_config, 用于标记需要修改参数的源文件路径和目标路径;可以为多个params_src_dst_filepathxx=src_path,dst_path,注意以,分割;支持OSS/HDFS/NAS/LOCAL -- 如果用户想要生成参数,也可以配置params_config,只需要配置目标路径即可 +# 配置 -#### platform_config(必选) +HPO启动配置包含exp.yml. trial.ini, search_space.json三个模块。 -用于标记任务执行的平台以及对应的执行命令 +HPO的启动命令是: ``` -name=DLC/MaxCompute/DataScience/LOCAL/PAI -cmdxx=xx (执行的命令行) +nnictl create --config exp.yml ``` -#### metric_config(必选) - -用于标记任务metric的获取来源、metric类型、最终metric的方式、metric的key以及对应权重、 -其中 - -- metric_type=summary/table/api/json/stdout(必选) -- metric_source=xxx(必选,可以为多个以metric_source开头的,具体可以看finetune案例) - - metric_source=oss://lcl-bj/eval_dist_test/model\_${exp_id}\_${trial_id}/eval_val/ 为easyrec model_dir/eval_val/下 -- final_mode=final/best/avg(可选,默认值为best,可选值为final/best/avg) -- optimize_mode=maximize/minimize (可选,默认值为maximize, 可选值为maximize/minimize) -- source_list_final_mode=final/best/avg(可选,默认值为final_mode,可选值为final/best/avg,用于有多个metric_source时最终metric如何计算,具体可以看maxcompute_crossvalidation案例) -- metric_dict示例:对应查询的key以及对应的权重 - - 多目标示例:metric=val(’auc_is_valid_play’)\*0.5+val(’auc_is_like’)\*0.25+val(’auc_is_comment’)\*0.25 - ``` - metric_dict={'auc_is_like':0.25, 'auc_is_valid_play':0.5, 'auc_is_comment':0.25} - ``` - - 多目标示例:metric=val(’auc_is_valid_play’)\*0.5+val(’auc_is_like’)\*0.25+val(’auc_is_comment’)\*0.25-val(’loss_play_time’)\*0.25 - 注意:如果config.yml中nni tuner、assessor的配置方式是按metric最大化方式去选择参数的,对于loss这种越小越好的metric,需要定义权重为负值。 - ``` - metric_dict={'auc_is_like':0.25, 'auc_is_valid_play':0.5, 'auc_is_comment':0.25, 'loss_play_time':-0.25} - ``` - - 单目标示例:metric=val(’auc_is_valid_play’)\*1 - ``` - metric_dict={'auc_is_valid_play':1} - ``` - - 如果metric_type=stdout类型,则metric_dict对应的key为正则表达式,value为对应的权重,可以查看dlc_mnist/config_local_stdout.ini示例 - ``` - [metric_config] - # metric type is summary/table - metric_type=stdout - metric_source=oss://test-nni/examples/search/pai/stdout/stdout_${exp_id}_${trial_id} - # best or final,default=best - final_mode=best - metric_dict={'validation: accuracy=([0-9\\.]+)':1} - optimize_mode=maximize - ``` - -#### oss_config (可选) - -如果任务需要使用OSS存储,则需要配置OSS config,修改对应的oss_config的值 - -#### odps_config (可选) - -如果任务需要使用maxcompute平台执行任务,则需要配置odps config,修改对应的odps_config的值 - -#### schedule_config (可选) - -支持在指定时间范围内调度AutoML任务 - -- 天数级:例如:everyday/weekend -- 分钟级:例如:09:00~21:00 +- 启动入口为exp.yml +- 通过trialCommand: python3 -m hpo_tools.core.utils.run --config=./trial.ini 连接用户的具体的启动任务。 +- 通过字段searchSpaceFile: search_space.json 连接 search_space.json; + +配置案例均可以在安装目录examples/search目录下,细节请参考[HPO配置介绍](./hpo_config.md) + +## exp.yml 示例 ``` -[schedule_config] -# everyday/weedend -day=everyday -start_time=15:15 -end_time=21:59 +experimentName: maxcompute_easyrec +experimentWorkingDirectory: ../expdir +searchSpaceFile: search_space.json +trialCommand: python3 -m hpo_tools.core.utils.run --config=./trial.ini +trialConcurrency: 1 +maxTrialNumber: 1 +tuner: + name: TPE + classArgs: + optimize_mode: maximize +trainingService: + platform: local +assessor: + name: PAIAssessor + classArgs: + optimize_mode: maximize + start_step: 1 ``` -#### config.ini 示例 +## trial.ini 示例 + +可以查看安装目录下examples/search/maxcompute_easyrec/trial.ini,执行在PAI MaxCompute平台 ``` [oss_config] @@ -239,15 +181,9 @@ metric_dict={'auc':1} ``` -##### easyrec命令配置 - -相关参数说明参考[MaxCompute Tutorial](../quick_start/mc_tutorial.md): +## trial_local.ini 示例 -注意pai命令中的value需要用引号,例如DossHost='oss-cn-beijing-internal.aliyuncs.com' - -#### config_local.ini 示例 - -其中执行的命令的是在本地的,而不是在PAI MaxCompute平台 +可以查看安装目录下examples/local_easyrec/trial.ini其中执行的命令的是在本地的,而不是在PAI MaxCompute平台 ``` [params_config] @@ -267,7 +203,7 @@ final_mode=final metric_dict={'auc':1} ``` -##### CPU/GPU +### CPU/GPU [NNI Local配置参考手册](https://nni.readthedocs.io/zh/stable/reference/experiment_config.html#localconfig) @@ -275,7 +211,7 @@ metric_dict={'auc':1} - 如果想将任务执行在CPU上,则使用config_local.yml ![image.png](../../images/automl/nni_local.jpg) -### 配置超参搜索空间search_space.json +## 配置超参搜索空间search_space.json - key是Dconfig中的参数名称,相关配置参考[EasyRecConfig参考手册](../reference.md) - type是nni中定义的搜索类型,相关配置参考[NNI searchSpace参考手册](https://nni.readthedocs.io/en/v2.2/Tutorial/SearchSpaceSpec.html) @@ -289,7 +225,7 @@ metric_dict={'auc':1} 常见搜索空间可以参考:samples/hpo/search_space.json -##### key配置注意项 +### key配置注意项 ${initial_learning_rate} 为search_space.json中的key,需要在easyrec pipeline config中提前进行替换,原理是采用变量替换的方式去引入新的超参 @@ -312,7 +248,7 @@ train_config { } ``` -##### type配置注意事项 +### type配置注意事项 [NNI searchSpace参考手册](https://nni.readthedocs.io/en/v2.2/Tutorial/SearchSpaceSpec.html) @@ -320,9 +256,11 @@ train_config { - {"\_type": "randint", "\_value": \[lower, upper\]}:\[low,upper)之间选择一个随机整数。 - {"\_type": "uniform", "\_value": \[low, high\]}:\[low,upper\]之间随机采样。 -## 启动调优 +## 高级 + +finetune搜索高级用法参考[HPO finetune](./finetune_config.md) -### 启动命令 +# 启动调优 ```bash nnictl create --config config.yml --port=8780 @@ -336,332 +274,18 @@ nnictl create --config config.yml --port=8780 启动成功界面: ![image.png](../../images/automl/pai_nni_create.jpg) -### config.yml 参数说明 +如果启动失败,请先查看第6章节FAQ -config.yml是作为NNI的配置文件,将代码和搜索空间进行结合,并使用指定的环境来运行您的训练代码,具体参考此config.yml文件。在这里,您还可以还提供其他信息,例如并发度、调优算法、最大Trial数量和最大持续时间等参数。 - -[NNI参考手册config.yml](https://nni.readthedocs.io/zh/stable/reference/experiment_config.html#experimentconfig) - -``` -experimentWorkingDirectory: ../expdir -searchSpaceFile: search_space.json -trialCommand: python3 -m hpo_tools.core.utils.run --config=./config.ini -trialConcurrency: 1 -maxTrialNumber: 1 -tuner: - name: TPE - classArgs: - optimize_mode: maximize -debug: true -logLevel: debug -trainingService: - platform: local -assessor: - name: PAIAssessor - classArgs: - platform: MAXCOMPUTE - optimize_mode: maximize - start_step: 1 -``` +# HPO调优结果 -### 并发度和最大Trial数量、最大运行时间可以实时调整: - -建议:刚开始设置为1,调测代码成功后,可以先调大最大运行次数Max trial No.,再调大并发度Concurrency。 -![image.png](../../images/automl/pai_nni_modify.jpg) - -## 调优结果 - -在运行实验后,可以在命令行界面中找到如下的Web界面地址 :\[Your IP\]:\[Your Port\] -![image.png](../../images/automl/pai_nni_create.jpg) - -### 查看概要页面 - -点击Overview按钮,在这里可以看到实验相关信息,如配置文件、搜索空间、运行时长、日志路径等。NNI 还支持通过 Experiment summary 按钮下载这些信息和参数。 +点击生成的URL,例如http://127.0.0.1:8780,可以看到webUI ![image.png](../../images/automl/pai_nni_overview.jpg) +查看调优结果没问题后,可以调整最大Trial运行数量MaxTrialNo和并发度Concurrency。 +如果需要更详细的调优结果,可查看[HPO调优结果](./hpo_res.md) -### 查看Trial详情页面 - -点击Trials detail按钮,您可以在此页面中看到整个实验过程中,每个trial的结果情况。 -其中succeeded代表此次trial成功运行,earlystop表示该组参数运行结果不太好,被提前停止了。停止策略可以查看pai_nni/core/pai_assessor.PaiAssessor,当然也可以根据业务情况去修改。 -![image.png](../../images/automl/pai_nni_detail.jpg) - -### 查看作业日志详情 - -点击每个Trial No,可以看到每个参数Trial的日志、参数详情,报错和输出可以点击以下3个按钮。 -![image.png](../../images/automl/pai_nni_log.jpg) - -### 手动停止某组实验超参 - -如果某些参数的结果不太好,可以进行手动停止。 -例如停止第一组参数。 -![image.png](../../images/automl/nni_stop.png) - -### 多目标default metric查看 - -假设用户配置的metric_config为如下,那么UI中Default metric中显示3项;该组trial最终的metric - -- default=auc\*0.5+accuracy\*0.5 -- auc即为最终的auc值 -- accuracy即为最终的accuracy值 - -``` -auc=0.5 -accuracy=0.5 -``` - -![image.png](../../images/automl/nni_metric.png) - -### 最优模型和参数 - -可以按照metric排序,获取最优精度的参数,这组实验id kfv91xl5 和trial_id zuKwM,代码中默认设置模型保存路径为以下方式,因此可以在对应的路径下找到该模型 --Dmodel_dir='oss://lcl-bj/eval_dist_test/model\_${exp_id}\_${trial_id}' -![image.png](../../images/automl/best-model.png) - -### 多组参数比较 - -点击Trail NO,选中后,点击compare, 就可以查看对应参数的相关信 -![image.png](../../images/automl/nni-compare.png) - -### 多组实验查看 - -多组实验可以点击All experiments,然后点击具体的实验ID进入对应的实验详情 -![image.png](../../images/automl/exp-list.png) - -### 自定义参数或者失败重试 - -可以使用自定义参数,也可以使用该功能重启失败的trial。 -点击复制这个按钮,然后跳出Customized trial,点击提交/或者修改即可,此处是新增一组参数,应该记得调高MaxTrialNo -注意该功能在2.10目前有问题;需要nni\<=2.9 -![image.png](../../images/automl/retry_trial.jpg) - -### 一键重试失败的Trial - -当用户确认失败原因为没有资源,或者算法偶现失败等原因时,想采取重试策略,可以使用该API发起多个失败的Trial一起重试。在内部其实是将NNI最大运行次数增大,并发数保持不变;并且是新增了多个Trial,每个Trial的参数和之前失败的Trial保持一致。 - -注意该功能在2.10目前有问题;需要nni\<=2.9 - -- experiment_id: 重试的实验ID(必选) -- trial_begin_id: 默认为0(可选,表明重试的开始为第0个trial); -- trial_end_id: 默认为-1 (可选,表明重试的结束为最后一个trial) - -例如: -实验exp跑了20组,失败5组;最大运行次数为30 - -启动第一次重试,参数为(exp,0,-1);最大运行次数将被修改为35,此时仍有失败2组. - -启动第二次重试时,参数为(exp,20,-1);最大运行次数将被修改为37, 此时全部成功;后续无需重启 - -``` -python -m hpo_tools.core.utils.retry_multi_failed_trials --experiment_id=o968matg --trial_begin_id=0 --trial_end_id=-1 -python -m hpo_tools.core.utils.retry_multi_failed_trials --experiment_id=o968matg --trial_begin_id=20 --trial_end_id=-1 -``` - -### 一键停止运行的Trial - -当用户得到想要的模型和参数时,由于NNI停止实验时,只会停止本地的进程,不会将dlc/trainingservice等平台的任务停止,目前提供了接口,可以将实验正常运行的作业给停止掉,并且将最大实验次数调至1(最小的正数),避免停止的瞬间起新的作业。 - -- experiment_id: 停止的实验ID(必选) -- trial_begin_id: 默认为0(可选,表明停止的开始为第0个trial); -- trial_end_id: 默认为-1 (可选,表明停止的结束为最后一个trial) - -``` -python -m hpo_tools.core.utils.kill_multi_running_trials --experiment_id=o968matg --trial_begin_id=0 --trial_end_id=-1 -``` - -### 停止实验 - -在停止实验之前,先参考一键停止运行的Trial,再停止实验 - -``` -nnictl stop exp_id -``` - -### 超参数分析 - -可以点击超参数Hyper-parameter,选中关注的指标,就可以看出来最好的参数大概是哪些;对参数进行分析 -![image.png](../../images/automl/hyper.jpg) - -## finetune训练(可选) - -由于推荐业务每天都有实时更新的数据,如果用户采用先训练一批历史数据,后面每天finetune更新模型的话,可以利用以上begin调优的最优结果,再在新数据上微调。如果用户每次更新模型都是重新开始训练的话,则不需要此步骤。 - -### 调优经验 - -例如:用户有40天历史数据,可以先利用以上步骤调优30天数据,然后根据搜索出的最优参数,再finetuen剩余10天。 -经验是:根据begin训练得出的最优参数,将learning_rate设置为begin结束时的learning_rate。 -例如: -begin训练时learning_rate如下,begin训练总计为8000步,因此可以设置finetune时initial_learning_rate=1e-6或者1e-7: - -``` -learning_rate { - exponential_decay_learning_rate { - initial_learning_rate: 0.001 - decay_steps: 1000 - decay_factor: 0.1 - min_learning_rate: 1e-07 - } - } -``` - -支持手动修改,也支持代码修改配置,修改效果如下: -![image.png](../../images/automl/modify_lr.jpg) - -#### 使用代码修改配置(可选) - -支持本地上pipeline文件修改 - -```bash -python modify_pipeline_config.py --pipeline_config_path=./samples/pipeline.config --save_path=./samples/pipeline_finetune.config --learning_rate=1e-6 -``` - -也支持oss上pipeline文件直接修改 - -```bash -python modify_pipeline_config.py --pipeline_config_path=oss://easyrec/pipeline889.config --save_path=oss://easyrec/pipeline889-f.config --learning_rate=1e-6 --oss_config=../config/.ossutilconfig -``` - -如果用户想要看是否有更优参数,可以看下级目录启动调优。 - -### 启动调优(可选) - -```bash -nnictl create --config config_finetune.yml --port=8617 -``` - -#### config_finetune.ini - -``` -[platform_config] -name=MaxCompute -{% set date_list = [20220616,20220617] %} -{% set date_begin = 20220616 %} -{% for bizdate in date_list %} -{% set eval_ymd = bizdate +1 %} -{% set predate = bizdate -1 %} -{% if bizdate == date_begin %} -cmd1_{{bizdate}}="PAI -name=easy_rec_ext - -project=algo_public - -Dscript='oss://automl-nni/easyrec/easy_rec_ext_615_res.tar.gz' - -Dtrain_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{bizdate}}' - -Deval_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{eval_ymd}}' - -Dcmd=train - -Deval_method=separate - -Dfine_tune_checkpoint="oss://automl-nni/easyrec/finetune/{{predate}}_finetune_model_nni_622" - -Dconfig='oss://automl-nni/easyrec/config/easyrec_model_${exp_id}_${trial_id}.config' - -Dmodel_dir='oss://automl-nni/easyrec/finetune/{{bizdate}}_finetune_model_nni_622/${exp_id}_${trial_id}' - -Dselected_cols='is_valid_play,ln_play_time,is_like,is_comment,features,content_features' - -Dbuckets='oss://automl-nni/' - -Darn='xxx' - -DossHost='oss-cn-beijing-internal.aliyuncs.com' - -Dcluster={"ps":{"count":1,"cpu":1600,"memory":40000 },"worker":{"count":12,"cpu":1600,"memory":40000}} " - -{% else %} -cmd1_{{bizdate}}="PAI -name=easy_rec_ext - -project=algo_public - -Dscript='oss://automl-nni/easyrec/easy_rec_ext_615_res.tar.gz' - -Dtrain_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{bizdate}}' - -Deval_tables='odps://pai_rec_dev/tables/rec_sv_rebuild_acc_rnk_rank_sample_embedding_modify/dt={{eval_ymd}}' - -Dcmd=train - -Deval_method=separate - -Dfine_tune_checkpoint="oss://automl-nni/easyrec/finetune/{{predate}}_finetune_model_nni_622/${exp_id}_${trial_id}" - -Dconfig='oss://automl-nni/easyrec/config/easyrec_model_${exp_id}_${trial_id}.config' - -Dmodel_dir='oss://automl-nni/easyrec/finetune/{{bizdate}}_finetune_model_nni_622/${exp_id}_${trial_id}' - -Dselected_cols='is_valid_play,ln_play_time,is_like,is_comment,features,content_features' - -Dbuckets='oss://automl-nni/' - -Darn='xxx' - -DossHost='oss-cn-beijing-internal.aliyuncs.com' - -Dcluster={"ps":{"count":1,"cpu":1600,"memory":40000 },"worker":{"count":12,"cpu":1600,"memory":40000}} " -{% endif %} - -{% endfor %} - - -[metric_config] -# metric type is summary/table -metric_type=summary -{% set date_list = [20220616,20220617] %} -{% for bizdate in date_list %} -metric_source_{{bizdate}}=oss://automl-nni/easyrec/finetune/{{bizdate}}_finetune_model_nni_622/${exp_id}_${trial_id}/eval_val/ -{% endfor %} -# best/final/avg,default=best -final_mode=final -source_list_final_mode=avg -metric_dict={'auc_is_like':0.25, 'auc_is_valid_play':0.5, 'auc_is_comment':0.25} -``` - -与begin训练的`差异点`: - -- 每个配置模块支持jinja模版渲染 -- 配置finetune日期{% set date_list = \[20220616,20220617\] %} -- 配置finetune开始日志{% set date_begin = 20220616 %},Dfine_tune_checkpoint开始日期和后续日期采取的model路径不一样 -- 假设每天finetune: - - {bizdate} 必须保留,将会在代码中根据当天日期进行替换 - - {eval_ymd} 必须保留,将会在代码中根据第二天日期进行替换 - - {predate} 必须保留,将会在代码中根据前一天日期进行替换 -- metric_source也是多条路径,每一天训练结果为summary的最终结果,整组参数finetune的结果为这些天的平均值 - -#### 配置超参搜索空间search_space.json - -参考begin训练阶段中想要搜索的参数即可,注意由于是finetune训练,网络结构相关的参数不要进行搜索,经验是搜索LR - -## EarlyStop算法 - -### 算法介绍 - -支持将该组中的实验结果和同组中的所有历史进行比较,如果不满足比较标准(例如小于中位数),则停止该组超参数的运行。比如说设置最大运行次数max_trial_num, 实际使用量会显著小于max_trial_num,但具体数量就和实际跑的任务及随机到的超参有关系了。例如max_trial_num=50时,可能最终可能不到 25 次,并且差不多已经是完整探索了50组超参。 -在config.yml中: - -- optimize_mode: 最大化优化的方向,maximize/minimize -- start_step: 从第2步开始进行早停判定 -- moving_avg: 早停判断时,采用所有历史的滑动平均值作为判断标准 -- proportion: 本次超参搜索的最优值和历史记录的proportion值比较 -- patience:metric指标连续下降几次,就停止 -- platform: 目前支持LOCAL/PAI/DLC/DATASCIENCE/MAXCOMPUTE/TRAININGSERVICE - -``` -assessor: - name: PAIAssessor - classArgs: - platform: MAXCOMPUTE - optimize_mode: maximize - start_step: 1 - moving_avg: true - proportion: 0.5 -``` - -### 算法修改 - -如果您想设置自定义停止策略,例如到达多少步,精度还没到达0.9,可以修改代码,来加速搜索,可以参考NNI CustomizeAssessor -注意继承对应平台的assessor函数: hpo_tools/core/assessor/pai_assessor.PAIAssessor -trial_end函数,该函数是用来当一个实验被停止时,去将平台上运行的任务关闭,同时会去将监听metric的线程给kill掉。 - -``` -def trial_end(self, trial_job_id, success): - logging.info('trial end') - # user_cancelled or early_stopped - if not success: - if self.platform == 'DATASCIENCE': - DataScienceTask(trial_id=trial_job_id).stop_job() - elif self.platform in ['LOCAL', 'PAI']: - logging.info( - "the platform is local or pai, don't need to stop remote job" - ) - elif self.platform == 'DLC': - DLCTask(trial_id=trial_job_id).stop_job() - elif self.platform == 'MAXCOMPUTE': - MaxComputeTask(trial_id=trial_job_id).stop_job() - elif self.platform == 'TRAININGSERVICE': - TrainingServiceTask(trial_id=trial_job_id).stop_job() - else: - raise TypeError( - f"the self.platform {self.platform} not " - f"in DATASCIENCE,DLC,MAXCOMPUTE,LOCAL,PAI,TRAININGSERVICE " - ) - # remove json file - remove_filepath(trial_id=trial_job_id) -``` +参考[NNI WebPortal 相关介绍](https://nni.readthedocs.io/en/stable/experiment/web_portal/web_portal.html) -## FAQ +# FAQ - 如果是用MAC安装,遇到nni启动权限问题,可以手动解决下 diff --git a/easy_rec/python/compat/early_stopping.py b/easy_rec/python/compat/early_stopping.py index fe4c12132..fc850fb62 100644 --- a/easy_rec/python/compat/early_stopping.py +++ b/easy_rec/python/compat/early_stopping.py @@ -21,9 +21,9 @@ import os import threading import time -from distutils.version import LooseVersion import tensorflow as tf +from distutils.version import LooseVersion from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import init_ops diff --git a/easy_rec/python/test/train_eval_test.py b/easy_rec/python/test/train_eval_test.py index 2ae51751f..087c90cd8 100644 --- a/easy_rec/python/test/train_eval_test.py +++ b/easy_rec/python/test/train_eval_test.py @@ -7,11 +7,11 @@ import threading import time import unittest -from distutils.version import LooseVersion import numpy as np import six import tensorflow as tf +from distutils.version import LooseVersion from tensorflow.python.platform import gfile from easy_rec.python.main import predict diff --git a/setup.cfg b/setup.cfg index 2303ef802..9f89eca98 100644 --- a/setup.cfg +++ b/setup.cfg @@ -10,7 +10,7 @@ multi_line_output = 7 force_single_line = true known_standard_library = setuptools known_first_party = easy_rec -known_third_party = absl,common_io,docutils,eas_prediction,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,six,sklearn,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,yaml +known_third_party = absl,common_io,distutils,docutils,eas_prediction,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,six,sklearn,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,yaml no_lines_before = LOCALFOLDER default_section = THIRDPARTY skip = easy_rec/python/protos