＃！/ usr / bin / env python3.7＃版权所有2021，guro狗万app足彩bi优化，llc＃此示例从文件中读取MIP模型，以批处理模式，＃并打印JSON解决方案字符串。##您将需要此示例的群集管理器许可证。导入系统导入时间导入JSON导入GUROBIP作为GUROBIPY导入GUROM导入GUR＃设置批处理模式优化的环境。##函数创建空环境，设置所有必要的参数，＃并将现成的envegy返回给呼叫者。当它没有禁止时，它是＃来电者的责任处理这个环境。def setupbatchenv（）：env = gp.env（空= true）env.setParam（'logfile'，'batchmode.log'）env.setParam（'CsManager'，'http：// localhost：61080'）env.setParam（'用户名'，'gurobi'）env.setParam（'serverpassword'，'pass'）env.setParam（'csbatchMode'，1）＃没有网络通信达到这一点。一旦调用者调用返回的env对象的start（）方法，就会发生＃。返回env＃打印批处理作业错误信息，如果有任何def printbatcherrorinfo（批量）：如果批次为none或batch.batcherrorcode == 0：返回打印（“批处理ID {}：错误代码{}（{}）”。格式（batch.batchid，batch.batcherrcode，batch.batcherrormessage））＃创建给定问题文件def newbatchrequest（文件名）：＃start环境，通过使用env和model的上下文处理程序创建模型对象，从文件##创建模型对象确保使用setupbatchenv（）自动调用＃model.dispose（）和envispose（）。start（）作为env，gp.read（filename，env = env）作为模型：＃设置一些参数model.params。MIPGap = 0.01 model.Params.JSONSolDetail = 1 # Define tags for some variables in order to access their values later for count, v in enumerate(model.getVars()): v.VTag = "Variable{}".format(count) if count >= 10: break # Submit batch request batchID = model.optimizeBatch() return batchID # Wait for the final status of the batch. # Initially the status of a batch is "submitted"; the status will change # once the batch has been processed (by a compute server). def waitforfinalstatus(batchID): # Wait no longer than one hour maxwaittime = 3600 # Setup and start environment, create local Batch handle object with setupbatchenv().start() as env, gp.Batch(batchID, env) as batch: starttime = time.time() while batch.BatchStatus == GRB.BATCH_SUBMITTED: # Abort this batch if it is taking too long curtime = time.time() if curtime - starttime > maxwaittime: batch.abort() break # Wait for two seconds time.sleep(2) # Update the resident attribute cache of the Batch object with the # latest values from the cluster manager. batch.update() # If the batch failed, we retry it if batch.BatchStatus == GRB.BATCH_FAILED: batch.retry() # Print information about error status of the job that processed the batch printbatcherrorinfo(batch) def printfinalreport(batchID): # Setup and start environment, create local Batch handle object with setupbatchenv().start() as env, gp.Batch(batchID, env) as batch: if batch.BatchStatus == GRB.BATCH_CREATED: print("Batch status is 'CREATED'") elif batch.BatchStatus == GRB.BATCH_SUBMITTED: print("Batch is 'SUBMITTED") elif batch.BatchStatus == GRB.BATCH_ABORTED: print("Batch is 'ABORTED'") elif batch.BatchStatus == GRB.BATCH_FAILED: print("Batch is 'FAILED'") elif batch.BatchStatus == GRB.BATCH_COMPLETED: print("Batch is 'COMPLETED'") print("JSON solution:") # Get JSON solution as string, create dict from it sol = json.loads(batch.getJSONSolution()) # Pretty printing the general solution information print(json.dumps(sol["SolutionInfo"], indent=4)) # Write the full JSON solution string to a file batch.writeJSONSolution('batch-sol.json.gz') else: # Should not happen print("Batch has unknown BatchStatus") printbatcherrorinfo(batch) # Instruct the cluster manager to discard all data relating to this BatchID def batchdiscard(batchID): # Setup and start environment, create local Batch handle object with setupbatchenv().start() as env, gp.Batch(batchID, env) as batch: # Remove batch request from manager batch.discard() # Solve a given model using batch optimization if __name__ == '__main__': # Ensure we have an input file if len(sys.argv) < 2: print("Usage: {} filename".format(sys.argv[0])) sys.exit(0) # Submit new batch request batchID = newbatchrequest(sys.argv[1]) # Wait for final status waitforfinalstatus(batchID) # Report final status info printfinalreport(batchID) # Remove batch request from manager batchdiscard(batchID) print('Batch optimization OK')
