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)
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()
workers = ", ".join(schedule[k])
print(" - {:10} {:>5}: {}".format(day, time, workers))
if __name__ == '__main__':
# Create Cluster Manager environment in batch mode.
env = gp.Env(empty=True)
env.setParam('CSBatchMode', 1)
# env is a context manager; upon leaving, Env.dispose() is called
with env.start():
# Submit the assignment problem to the cluster manager, get batch ID
batchID = submit_assigment_problem(env)
# Create a batch object, wait for batch to complete, query solution JSON
with gp.Batch(batchID, env) as batch:
waitforfinalbatchstatus(batch)
if batch.BatchStatus != GRB.BATCH_COMPLETED:
print("Batch request couldn't be completed")
sys.exit(0)
jsonsol = batch.getJSONSolution()
# Dump JSON solution string into a dict
soldict = json.loads(jsonsol)
# Has the assignment problem been solved as expected?
if soldict['SolutionInfo']['Status'] != GRB.OPTIMAL:
# Shouldn't happen...
print("Assignment problem could not be solved to optimality")