batchmode.py
# !/usr/bin/env python3.7 # Copyright 2019, 狗万app足彩Gurobi Optimization, LLC # This example reads a MIP model from a file, solve it in batch mode， # and print the JSON solution string。# #这个示例需要Cluster Manager许可才能工作。import sys import time import json import gurobipy as gp from gurobipy import GRB #设置批处理模式优化环境。该函数创建一个空的环境，设置所有必要的参数，并将准备启动的Env对象返回给调用者。当不再需要这个环境时，处理它是#调用者的责任。def setupbatchenv(): env = gp.Env(empty=True)setParam(“记录文件”、“batchmode.log”)env。setParam (CSManager, http://localhost: 61080) env。setParam(“用户名”、“gurobi”)env。setParam(“ServerPassword”、“通过”)env。setParam('CSBatchMode'， 1) #目前为止没有发生网络通信。 This will happen # once the caller invokes the start() method of the returned Env object. return env # Print batch job error information, if any def printbatcherrorinfo(batch): if batch is None or batch.BatchErrorCode == 0: return print("Batch ID {}: Error code {} ({})".format( batch.BatchID, batch.BatchErrorCode, batch.BatchErrorMessage)) # Create a batch request for given problem file def newbatchrequest(filename): # Start environment, create Model object from file # # By using the context handlers for env and model, it is ensured that # model.dispose() and env.dispose() are called automatically with setupbatchenv().start() as env, gp.read(filename, env=env) as model: # Set some parameters 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])) quit() # 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')
