Python调用
上一篇中讲解了如何使用命令行来调用和查询结果。里面的接口都有封装为Python接口，我们可以输入如下指令引入文件，也可以再输入help指令来查看对应的Python API说明：
import mtunerpy as mtuner #help(mtuner)
OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k
这里我们可以如下方式来提交任务：
scenario_dict = { 'solver': 'cbc', 'problem': ['./model/nl_train_1.nl'], 'max_tuning_time': 600 } mtuner.create_task(scenario_dict)
Problem file "nl_train_1.nl" uploaded successfully. Task #438397484918644736 created succesfully.
这里小编得到任务ID：438394637284024320。
类似地，我们还可以提交一组优化问题算例mps_train_oss.txt进行调参。
如下，得到任务ID 438394766997069824。
scenario_dict = { 'solver': 'cbc', 'problem': ['./model/mps_train_oss.txt'], 'max_tuning_time': 3600 } mtuner.create_task(scenario_dict)
Task #438397553394851840 created succesfully.
运行成功后，我们可以通过如下方式获取结果。可以看到和命令行的结果一样，都可以得到比较多的求解效率提升。
import mtunerpy as mtuner #print("----调参状态和文件---") #mtuner.task_status(['438394637284024320','438394766997069824']) print("----nl_train_1.nl调参效果---") mtuner.fetch_result('438394637284024320','result/performance.txt') print("----一组3个问题调参效果---") mtuner.fetch_result('438394766997069824','result/performance.txt', False)
----nl_train_1.nl调参效果--- Tuning finished, the best wallclock_time is 0.09 [** 36.33x improvement **] ----一组3个问题调参效果--- Tuning finished, the best avg_wallclock_time is 3.83 [** 33.32x improvement **]
调参的进阶用法
前面主要讲解调参的基础用法。提交任务的时候，通过选择了求解器后，由MindOpt Tuner来给出调参任务的默认设置来进行调参。比如页面提交，可以不用参数填写的格子，直接使用。
除此之外，我们还可以设置调参的任务。详情我们可以查看用户文档的调参任务配置。
这里我们先-h一下查看接口的说明:
!mindopt-tuner create-task -h
OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k MindOpt Tuner v0.9.0 (Build: 20230404) Usage: mindopt-tuner create-task [-h] --solver {cbc,cplex} --problem <problem> [<problem> ...] [--parameters <parameters> [<parameters> ...]] [--log-level {info,debug,error}] [--verbose {0,1,2}] [--tuning-objective {wallclock_time,cpu_time}] [--max-distinct-para-combos <number>] [--max-tuning-time <seconds>] [--max-eval-time <seconds>] Create a new tuning task, and then start it. Arguments: -h, --help Show help information. --solver {cbc,cplex} (Required) Specify the solver to tune. --problem <problem> [<problem> ...] (Required) Path to the optimization problem file(s), separated by space. --parameters <parameters> [<parameters> ...] Specify the solver's tunable parameters (see documentation), separated by space. --log-level {info,debug,error} Log detail level. --verbose {0,1,2} Set verbosity level. --tuning-objective {wallclock_time,cpu_time} Select the tuning objective. --max-distinct-para-combos <number> Tuning will be terminated after the number of distinct parameter combinations reach this number. --max-tuning-time <seconds> Tuning will be terminated after surpassing this time (in seconds) 1, 2, ..., 3600000. --max-eval-time <seconds> Maximum time allowed for a single evaluation in seconds 1, 2, ..., 144000).
这里我们自定义这些参数。
这里可以举个cbc相关的例子。比如默认参数定义文件内容是test_cbc_params.txt：
cuts {off, on, root, ifmove, forceOn}[on] # Switches all cut generators on or off. cat preprocess {off, on, save, equal, sos, trysos, equalall, strategy, aggregate, forcesos}[sos] # Whether to use integer preprocessing. cat heuristics [0, 1][1]i # Switches most primal heuristics on or off. boolean strongBranching [0, 5][5]i # Number of variables to look at in strong branching. int trustPseudoCosts [-3, 100][10]i # Number of branches before we trust pseudocosts. int
这里是遵循 参数名 取值范围 默认值的格式来，最后面的[10]i指的是整型变量。
特别提醒 ：无特殊需求不需要动这个参数，取值错误等都会导致调参任务异常。
这里，我们可以将这个参数、里面选取部分参数来条，比如：我们可以通过如下指令来自定义自己的调参范围，只调2个参数，还可以减少调参时间。
!mindopt-tuner create-task --solver cbc --problem './model/nl_train_1.nl' --max-tuning-time 400 --parameters cuts preprocess
OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k Problem file "nl_train_1.nl" uploaded successfully. Task #438413495201964032 created succesfully.
小编这里得到taskID438413495201964032。
求解完后，我们可以通过如下方式获取结果，也可以直接访问页面端作业列表。
从结果看，缩小调参的范围，也会有提升结果！提速了32倍！。
#图中结果提速了12.6倍。
#print("-------查看状态和results文件链接--------") #!mindopt-tuner task-status --task-id 438413495201964032 print("-------本地的提交结果--------") ! mindopt-tuner fetch-result --task-id 438413495201964032 --file result/performance.txt
-------本地的提交结果-------- OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k Tuning finished, the best wallclock_time is 0.1 [** 32.00x improvement **]
