├── .gitignore ├── .idea ├── .gitignore ├── vcs.xml ├── misc.xml ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml └── cplexrunonwml.iml ├── model.tar.gz ├── README.md ├── log.txt ├── solution.json ├── diet.lp ├── cplexrunonwml.py ├── cplexrunonwmlv2.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | log.txt 3 | model.tar.gz 4 | -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /workspace.xml 3 | -------------------------------------------------------------------------------- /model.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBMDecisionOptimization/cplexrunonwml/master/model.tar.gz -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # cplexrunonwml 2 | 3 | This is a sample python code to run CPLEX models (.lp, .mps, etc) on WML. 4 | 5 | The v2 versions is to be used with WML v2 instances. 6 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/cplexrunonwml.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /log.txt: -------------------------------------------------------------------------------- 1 | [2020-09-17T09:17:04Z, INFO] CPLEX version 12100000 2 | 3 | [2020-09-17T09:17:04Z, WARNING] Changed parameter CPX_PARAM_THREADS from 0 to 1 4 | 5 | [2020-09-17T09:17:04Z, INFO] Param[1,067] = 1 6 | 7 | [2020-09-17T09:17:04Z, INFO] Param[1,130] = UTF-8 8 | 9 | [2020-09-17T09:17:04Z, INFO] Param[1,132] = -1 10 | 11 | [2020-09-17T09:17:04Z, INFO] Version identifier: 12.10.0.0 | 2020-01-09 | 0d94640 12 | 13 | [2020-09-17T09:17:04Z, INFO] CPXPARAM_Threads 1 14 | 15 | [2020-09-17T09:17:04Z, INFO] CPXPARAM_Output_CloneLog -1 16 | 17 | [2020-09-17T09:17:04Z, INFO] CPXPARAM_Read_APIEncoding "UTF-8" 18 | 19 | [2020-09-17T09:17:04Z, INFO] Tried aggregator 1 time. 20 | 21 | [2020-09-17T09:17:04Z, INFO] LP Presolve eliminated 0 rows and 1 columns. 22 | 23 | [2020-09-17T09:17:04Z, INFO] Reduced LP has 7 rows, 15 columns, and 63 nonzeros. 24 | 25 | [2020-09-17T09:17:04Z, INFO] Presolve time = 0.00 sec. (0.01 ticks) 26 | 27 | [2020-09-17T09:17:04Z, INFO] Initializing dual steep norms . . . 28 | 29 | [2020-09-17T09:17:04Z, INFO] 30 | 31 | [2020-09-17T09:17:04Z, INFO] Iteration log . . . 32 | 33 | [2020-09-17T09:17:04Z, INFO] Iteration: 1 Dual objective = 1.860884 34 | 35 | [2020-09-17T09:17:04Z, INFO] There are no bound infeasibilities. 36 | 37 | [2020-09-17T09:17:04Z, INFO] There are no reduced-cost infeasibilities. 38 | 39 | [2020-09-17T09:17:04Z, INFO] Max. unscaled (scaled) Ax-b resid. = 1.13687e-13 (4.44089e-16) 40 | 41 | [2020-09-17T09:17:04Z, INFO] Max. unscaled (scaled) c-B'pi resid. = 1.17961e-16 (1.17961e-16) 42 | 43 | [2020-09-17T09:17:04Z, INFO] Max. unscaled (scaled) |x| = 41481.6 (10.1273) 44 | 45 | [2020-09-17T09:17:04Z, INFO] Max. unscaled (scaled) |pi| = 0.0266733 (0.321274) 46 | 47 | [2020-09-17T09:17:04Z, INFO] Max. unscaled (scaled) |red-cost| = 0.486119 (4.71716) 48 | 49 | [2020-09-17T09:17:04Z, INFO] Condition number of scaled basis = 4.5e+00 50 | 51 | [2020-09-17T09:17:04Z, INFO] optimal (1) 52 | 53 | -------------------------------------------------------------------------------- /solution.json: -------------------------------------------------------------------------------- 1 | {"CPLEXSolution":{"version":"1.2","header":{"primalFeasible":true,"solutionTypeValue":1,"solutionMethodString":"dual","writeLevel":1,"objectiveValue":"2.6904091716962637","solutionTypeString":"basic","problemName":"diet.lp","solutionStatusString":"optimal","solutionStatusValue":1,"simplexIterations":3,"dualFeasible":true },"quality":{"maxPi":"0.026673346086024577","maxRedCost":"0.48611933803276086","epRHS":"9.9999999999999995e-07","maxDualResidual":"1.1796119636642288e-16","maxX":"41481.567457514182","maxPrimalResidual":"1.1368683772161603e-13","maxPrimalInfeas":"0","maxSlack":"0","kappa":"4.5217531406433773","epOpt":"9.9999999999999995e-07","maxDualInfeas":"0" },"linearConstraints":[{"dual":"0.0012549782286529975","slack":"0","name":"c1","index":0,"status":"LL" },{"dual":"0.0002625434401323112","slack":"0","name":"c2","index":1,"status":"LL" },{"dual":"-0","slack":"0","name":"c3","index":2,"status":"LL" },{"dual":"-0","slack":"0","name":"c4","index":3,"status":"LL" },{"dual":"0.026673346086024577","slack":"0","name":"c5","index":4,"status":"LL" },{"dual":"-0","slack":"0","name":"c6","index":5,"status":"LL" },{"dual":"-0","slack":"0","name":"c7","index":6,"status":"LL" } ],"variables":[{"reducedCost":"0.48611933803276086","name":"Roasted_Chicken","index":0,"value":"0","status":"LL" },{"reducedCost":"0","name":"Spaghetti_W__Sauce","index":1,"value":"2.1551724137931036","status":"BS" },{"reducedCost":"0.19865110785149795","name":"Tomato,Red,Ripe,Raw","index":2,"value":"0","status":"LL" },{"reducedCost":"0.036606720300071643","name":"Apple,Raw,W_Skin","index":3,"value":"0","status":"LL" },{"reducedCost":"0.29482251183368496","name":"Grapes","index":4,"value":"0","status":"LL" },{"reducedCost":"-0.069641568986619426","name":"Chocolate_Chip_Cookies","index":5,"value":"10","status":"UL" },{"reducedCost":"0","name":"Lowfat_Milk","index":6,"value":"1.8311671008899097","status":"BS" },{"reducedCost":"0.08547181116023489","name":"Raisin_Brn","index":7,"value":"0","status":"LL" },{"reducedCost":"0","name":"Hotdog","index":8,"value":"0.92969759913859251","status":"BS" },{"reducedCost":"0.0012549782286529975","name":"Rgc1","index":9,"value":"-500","status":"LL" },{"reducedCost":"0.0002625434401323112","name":"Rgc2","index":10,"value":"-800","status":"LL" },{"reducedCost":"0","name":"Rgc3","index":11,"value":"-18.721682260168105","status":"BS" },{"reducedCost":"0","name":"Rgc4","index":12,"value":"-41481.567457514182","status":"BS" },{"reducedCost":"0.026673346086024577","name":"Rgc5","index":13,"value":"-75","status":"LL" },{"reducedCost":"0","name":"Rgc6","index":14,"value":"-43.194236410955455","status":"BS" },{"reducedCost":"0","name":"Rgc7","index":15,"value":"-48.82627765864693","status":"BS" } ] }} -------------------------------------------------------------------------------- /diet.lp: -------------------------------------------------------------------------------- 1 | \ This file has been generated by DOcplex 2 | \ ENCODING=ISO-8859-1 3 | \Problem name: diet 4 | 5 | Minimize 6 | obj: 0.840000000000 Roasted_Chicken + 0.780000000000 Spaghetti_W__Sauce 7 | + 0.270000000000 Tomato,Red,Ripe,Raw + 0.240000000000 Apple,Raw,W_Skin 8 | + 0.320000000000 Grapes + 0.030000000000 Chocolate_Chip_Cookies 9 | + 0.230000000000 Lowfat_Milk + 0.340000000000 Raisin_Brn 10 | + 0.310000000000 Hotdog 11 | Subject To 12 | c1: 277.400000000000 Roasted_Chicken + 358.200000000000 Spaghetti_W__Sauce 13 | + 25.800000000000 Tomato,Red,Ripe,Raw + 81.400000000000 Apple,Raw,W_Skin 14 | + 15.100000000000 Grapes + 78.100000000000 Chocolate_Chip_Cookies 15 | + 121.200000000000 Lowfat_Milk + 115.100000000000 Raisin_Brn 16 | + 242.100000000000 Hotdog- Rgc1 = 2500 17 | c2: 21.900000000000 Roasted_Chicken + 80.200000000000 Spaghetti_W__Sauce 18 | + 6.200000000000 Tomato,Red,Ripe,Raw + 9.700000000000 Apple,Raw,W_Skin 19 | + 3.400000000000 Grapes + 6.200000000000 Chocolate_Chip_Cookies 20 | + 296.700000000000 Lowfat_Milk + 12.900000000000 Raisin_Brn 21 | + 23.500000000000 Hotdog- Rgc2 = 1600 22 | c3: 1.800000000000 Roasted_Chicken + 2.300000000000 Spaghetti_W__Sauce 23 | + 0.600000000000 Tomato,Red,Ripe,Raw + 0.200000000000 Apple,Raw,W_Skin 24 | + 0.100000000000 Grapes + 0.400000000000 Chocolate_Chip_Cookies 25 | + 0.100000000000 Lowfat_Milk + 16.800000000000 Raisin_Brn 26 | + 2.300000000000 Hotdog- Rgc3 = 30 27 | c4: 77.400000000000 Roasted_Chicken + 3055.200000000000 Spaghetti_W__Sauce 28 | + 766.300000000000 Tomato,Red,Ripe,Raw + 73.100000000000 Apple,Raw,W_Skin 29 | + 24 Grapes + 101.800000000000 Chocolate_Chip_Cookies 30 | + 500.200000000000 Lowfat_Milk + 1250.200000000000 Raisin_Brn- Rgc4 = 50000 31 | c5: 11.600000000000 Spaghetti_W__Sauce + 1.400000000000 Tomato,Red,Ripe,Raw 32 | + 3.700000000000 Apple,Raw,W_Skin + 0.200000000000 Grapes + 4 Raisin_Brn- 33 | Rgc5 = 100 34 | c6: 58.300000000000 Spaghetti_W__Sauce + 5.700000000000 Tomato,Red,Ripe,Raw 35 | + 21 Apple,Raw,W_Skin + 4.100000000000 Grapes 36 | + 9.300000000000 Chocolate_Chip_Cookies + 11.700000000000 Lowfat_Milk 37 | + 27.900000000000 Raisin_Brn + 18 Hotdog- Rgc6 = 300 38 | c7: 42.200000000000 Roasted_Chicken + 8.200000000000 Spaghetti_W__Sauce 39 | + Tomato,Red,Ripe,Raw + 0.300000000000 Apple,Raw,W_Skin 40 | + 0.200000000000 Grapes + 0.900000000000 Chocolate_Chip_Cookies 41 | + 8.100000000000 Lowfat_Milk + 4 Raisin_Brn + 10.400000000000 Hotdog- Rgc7 42 | = 100 43 | 44 | Bounds 45 | Roasted_Chicken <= 10 46 | Spaghetti_W__Sauce <= 10 47 | Tomato,Red,Ripe,Raw <= 10 48 | Apple,Raw,W_Skin <= 10 49 | Grapes <= 10 50 | Chocolate_Chip_Cookies <= 10 51 | Lowfat_Milk <= 10 52 | Raisin_Brn <= 10 53 | Hotdog <= 10 54 | -500 <= Rgc1 <= 0 55 | -800 <= Rgc2 <= 0 56 | -20 <= Rgc3 <= 0 57 | -45000 <= Rgc4 <= 0 58 | -75 <= Rgc5 <= 0 59 | -300 <= Rgc6 <= 0 60 | -50 <= Rgc7 <= 0 61 | End 62 | -------------------------------------------------------------------------------- /cplexrunonwml.py: -------------------------------------------------------------------------------- 1 | import sys, getopt 2 | 3 | try: 4 | sys.modules['sklearn.externals.joblib'] = __import__('joblib') 5 | from watson_machine_learning_client import WatsonMachineLearningAPIClient 6 | except ImportError: 7 | from watson_machine_learning_client import WatsonMachineLearningAPIClient 8 | 9 | 10 | # THIS IS THE USER CREDENTIALS 11 | wml_credentials = { 12 | "apikey": "xxxxxxxxxxxxxxxxxxxxxxxxx", 13 | "instance_id": "xxxxxxxxxxxxxxxxxxxxxxxxx", 14 | "url": "https://us-south.ml.cloud.ibm.com", 15 | } 16 | # END OF THE USER CREDENTIALS 17 | 18 | def main(argv): 19 | cplex_file = "diet.lp" 20 | try: 21 | opts, args = getopt.getopt(argv,"hf:",["ffile="]) 22 | except getopt.GetoptError: 23 | print('cplexrunonwml.py -f ') 24 | sys.exit(2) 25 | for opt, arg in opts: 26 | if opt == '-h': 27 | print('cplexrunonwml.py -f ') 28 | sys.exit() 29 | elif opt in ("-f", "--ffile"): 30 | cplex_file = arg 31 | print('CPLEX file is', cplex_file) 32 | 33 | basename = cplex_file.split('.')[0] 34 | model_name = basename + "_model" 35 | deployment_name = basename + "_deployment" 36 | 37 | print("Creating WML Client") 38 | client = WatsonMachineLearningAPIClient(wml_credentials) 39 | 40 | print("Getting deployment") 41 | deployments = client.deployments.get_details() 42 | 43 | deployment_uid = None 44 | for res in deployments['resources']: 45 | if res['entity']['name'] == deployment_name: 46 | deployment_uid = res['metadata']['guid'] 47 | print("Found deployment", deployment_uid) 48 | break 49 | 50 | if deployment_uid == None: 51 | print("Creating model") 52 | import tarfile 53 | 54 | 55 | def reset(tarinfo): 56 | tarinfo.uid = tarinfo.gid = 0 57 | tarinfo.uname = tarinfo.gname = "root" 58 | return tarinfo 59 | 60 | 61 | tar = tarfile.open("model.tar.gz", "w:gz") 62 | tar.add(cplex_file, arcname=cplex_file, filter=reset) 63 | tar.close() 64 | 65 | print("Storing model") 66 | model_metadata = { 67 | client.repository.ModelMetaNames.NAME: model_name, 68 | client.repository.ModelMetaNames.DESCRIPTION: model_name, 69 | client.repository.ModelMetaNames.TYPE: "do-cplex_12.10", 70 | client.repository.ModelMetaNames.RUNTIME_UID: "do_12.10" 71 | } 72 | 73 | model_details = client.repository.store_model(model='./model.tar.gz', meta_props=model_metadata) 74 | 75 | model_uid = client.repository.get_model_uid(model_details) 76 | 77 | print(model_uid) 78 | 79 | print("Creating deployment") 80 | deployment_props = { 81 | client.deployments.ConfigurationMetaNames.NAME: deployment_name, 82 | client.deployments.ConfigurationMetaNames.DESCRIPTION: deployment_name, 83 | client.deployments.ConfigurationMetaNames.BATCH: {}, 84 | client.deployments.ConfigurationMetaNames.COMPUTE: {'name': 'S', 'nodes': 1} 85 | } 86 | 87 | deployment_details = client.deployments.create(model_uid, meta_props=deployment_props) 88 | 89 | deployment_uid = client.deployments.get_uid(deployment_details) 90 | 91 | print('deployment_id:', deployment_uid) 92 | 93 | print("Creating job") 94 | import pandas as pd 95 | 96 | with open(cplex_file, 'r') as file: 97 | model = file.read(); 98 | import base64 99 | 100 | model = model.encode("UTF-8") 101 | model = base64.b64encode(model) 102 | model = model.decode("UTF-8") 103 | df_model = pd.DataFrame(columns=['___TEXT___'], data=[[model]]) 104 | solve_payload = { 105 | client.deployments.DecisionOptimizationMetaNames.SOLVE_PARAMETERS: { 106 | 'oaas.logAttachmentName': 'log.txt', 107 | 'oaas.logTailEnabled': 'true', 108 | 'oaas.includeInputData': 'false', 109 | 'oaas.resultsFormat': 'JSON' 110 | }, 111 | client.deployments.DecisionOptimizationMetaNames.INPUT_DATA: [ 112 | { 113 | "id": cplex_file, 114 | "values": df_model 115 | } 116 | ], 117 | client.deployments.DecisionOptimizationMetaNames.OUTPUT_DATA: [ 118 | { 119 | "id": ".*\.json" 120 | }, 121 | { 122 | "id": ".*\.txt" 123 | } 124 | ] 125 | } 126 | 127 | job_details = client.deployments.create_job(deployment_uid, solve_payload) 128 | job_uid = client.deployments.get_job_uid(job_details) 129 | 130 | print('job_id', job_uid) 131 | 132 | from time import sleep 133 | 134 | while job_details['entity']['decision_optimization']['status']['state'] not in ['completed', 'failed', 'canceled']: 135 | print(job_details['entity']['decision_optimization']['status']['state'] + '...') 136 | sleep(5) 137 | job_details = client.deployments.get_job_details(job_uid) 138 | 139 | print(job_details['entity']['decision_optimization']['status']['state']) 140 | 141 | for output_data in job_details['entity']['decision_optimization']['output_data']: 142 | if output_data['id'].endswith('csv'): 143 | print('Solution table:' + output_data['id']) 144 | solution = pd.DataFrame(output_data['values'], 145 | columns=output_data['fields']) 146 | solution.head() 147 | else: 148 | print(output_data['id']) 149 | output = output_data['values'][0][0] 150 | output = output.encode("UTF-8") 151 | output = base64.b64decode(output) 152 | output = output.decode("UTF-8") 153 | print(output) 154 | with open(output_data['id'], 'wt') as file: 155 | file.write(output) 156 | 157 | # print ("Deleting deployment") 158 | # client.deployments.delete(deployment_uid) 159 | 160 | 161 | if __name__ == '__main__': 162 | main(sys.argv[1:]) 163 | -------------------------------------------------------------------------------- /cplexrunonwmlv2.py: -------------------------------------------------------------------------------- 1 | import sys, getopt 2 | 3 | try: 4 | sys.modules['sklearn.externals.joblib'] = __import__('joblib') 5 | from ibm_watson_machine_learning import APIClient 6 | except ImportError: 7 | from ibm_watson_machine_learning import APIClient 8 | 9 | 10 | # THIS IS THE USER CREDENTIALS 11 | wml_credentials = { 12 | "apikey": "xxxxxxxxxxxxxxxxxxxxxxxxx", 13 | "url": "https://us-south.ml.cloud.ibm.com" 14 | } 15 | 16 | 17 | import base64 18 | def getfileasdata(filename): 19 | with open(filename, 'r') as file: 20 | data = file.read(); 21 | 22 | data = data.encode("UTF-8") 23 | data = base64.b64encode(data) 24 | data = data.decode("UTF-8") 25 | 26 | return data 27 | 28 | def main(argv): 29 | cplex_file = "diet.lp" 30 | try: 31 | opts, args = getopt.getopt(argv,"hf:",["ffile="]) 32 | except getopt.GetoptError: 33 | print('cplexrunonwml.py -f ') 34 | sys.exit(2) 35 | for opt, arg in opts: 36 | if opt == '-h': 37 | print('cplexrunonwml.py -f ') 38 | sys.exit() 39 | elif opt in ("-f", "--ffile"): 40 | cplex_file = arg 41 | print('CPLEX file is', cplex_file) 42 | 43 | basename = cplex_file.split('.')[0] 44 | model_name = basename + "_model" 45 | deployment_name = basename + "_deployment" 46 | space_name = basename + "_space" 47 | 48 | print("Creating WML Client") 49 | client = APIClient(wml_credentials) 50 | 51 | 52 | def guid_from_space_name(client, name): 53 | space = client.spaces.get_details() 54 | for item in space['resources']: 55 | if item['entity']["name"] == name: 56 | return item['metadata']['id'] 57 | return None 58 | 59 | space_id = guid_from_space_name(client, space_name) 60 | 61 | if space_id == None: 62 | print("Creating space") 63 | cos_resource_crn = 'xxxxxxxxxxxxxxxxxxxxxxxxx' 64 | instance_crn = 'xxxxxxxxxxxxxxxxxxxxxxxxx' 65 | 66 | metadata = { 67 | client.spaces.ConfigurationMetaNames.NAME: space_name, 68 | client.spaces.ConfigurationMetaNames.DESCRIPTION: space_name + ' description', 69 | client.spaces.ConfigurationMetaNames.STORAGE: { 70 | "type": "bmcos_object_storage", 71 | "resource_crn": cos_resource_crn 72 | }, 73 | client.spaces.ConfigurationMetaNames.COMPUTE: { 74 | "name": "existing_instance_id", 75 | "crn": instance_crn 76 | } 77 | } 78 | space = client.spaces.store(meta_props=metadata) 79 | space_id = client.spaces.get_id(space) 80 | 81 | print("space_id:", space_id) 82 | 83 | client.set.default_space(space_id) 84 | 85 | print("Getting deployment") 86 | deployments = client.deployments.get_details() 87 | 88 | deployment_uid = None 89 | for res in deployments['resources']: 90 | if res['entity']['name'] == deployment_name: 91 | deployment_uid = res['metadata']['id'] 92 | print("Found deployment", deployment_uid) 93 | break 94 | 95 | if deployment_uid == None: 96 | print("Creating model") 97 | import tarfile 98 | 99 | 100 | def reset(tarinfo): 101 | tarinfo.uid = tarinfo.gid = 0 102 | tarinfo.uname = tarinfo.gname = "root" 103 | return tarinfo 104 | 105 | 106 | tar = tarfile.open("model.tar.gz", "w:gz") 107 | tar.add(cplex_file, arcname=cplex_file, filter=reset) 108 | tar.close() 109 | 110 | print("Storing model") 111 | model_metadata = { 112 | client.repository.ModelMetaNames.NAME: model_name, 113 | client.repository.ModelMetaNames.DESCRIPTION: model_name, 114 | client.repository.ModelMetaNames.TYPE: "do-cplex_12.10", 115 | client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: client.software_specifications.get_uid_by_name( 116 | "do_12.10") 117 | } 118 | 119 | model_details = client.repository.store_model(model='./model.tar.gz', meta_props=model_metadata) 120 | 121 | model_uid = client.repository.get_model_uid(model_details) 122 | 123 | print(model_uid) 124 | 125 | print("Creating deployment") 126 | deployment_props = { 127 | client.deployments.ConfigurationMetaNames.NAME: deployment_name, 128 | client.deployments.ConfigurationMetaNames.DESCRIPTION: deployment_name, 129 | client.deployments.ConfigurationMetaNames.BATCH: {}, 130 | client.deployments.ConfigurationMetaNames.HARDWARE_SPEC: {'name': 'S', 'nodes': 1} 131 | } 132 | 133 | deployment_details = client.deployments.create(model_uid, meta_props=deployment_props) 134 | 135 | deployment_uid = client.deployments.get_uid(deployment_details) 136 | 137 | print('deployment_id:', deployment_uid) 138 | 139 | print("Creating job") 140 | import pandas as pd 141 | 142 | solve_payload = { 143 | client.deployments.DecisionOptimizationMetaNames.SOLVE_PARAMETERS: { 144 | 'oaas.logAttachmentName': 'log.txt', 145 | 'oaas.logTailEnabled': 'true', 146 | 'oaas.includeInputData': 'false', 147 | 'oaas.resultsFormat': 'JSON' 148 | }, 149 | client.deployments.DecisionOptimizationMetaNames.INPUT_DATA: [ 150 | { 151 | "id": cplex_file, 152 | "content": getfileasdata(cplex_file) 153 | } 154 | ], 155 | client.deployments.DecisionOptimizationMetaNames.OUTPUT_DATA: [ 156 | { 157 | "id": ".*\.json" 158 | }, 159 | { 160 | "id": ".*\.txt" 161 | } 162 | ] 163 | } 164 | 165 | job_details = client.deployments.create_job(deployment_uid, solve_payload) 166 | job_uid = client.deployments.get_job_uid(job_details) 167 | 168 | print('job_id', job_uid) 169 | 170 | from time import sleep 171 | 172 | while job_details['entity']['decision_optimization']['status']['state'] not in ['completed', 'failed', 'canceled']: 173 | print(job_details['entity']['decision_optimization']['status']['state'] + '...') 174 | sleep(5) 175 | job_details = client.deployments.get_job_details(job_uid) 176 | 177 | print(job_details['entity']['decision_optimization']['status']['state']) 178 | 179 | for output_data in job_details['entity']['decision_optimization']['output_data']: 180 | if output_data['id'].endswith('csv'): 181 | print('Solution table:' + output_data['id']) 182 | solution = pd.DataFrame(output_data['values'], 183 | columns=output_data['fields']) 184 | solution.head() 185 | else: 186 | print(output_data['id']) 187 | if "values" in output_data: 188 | output = output_data['values'][0][0] 189 | else: 190 | if "content" in output_data: 191 | output = output_data['content'] 192 | output = output.encode("UTF-8") 193 | output = base64.b64decode(output) 194 | output = output.decode("UTF-8") 195 | print(output) 196 | with open(output_data['id'], 'wt') as file: 197 | file.write(output) 198 | 199 | # print ("Deleting deployment") 200 | # client.deployments.delete(deployment_uid) 201 | 202 | 203 | if __name__ == '__main__': 204 | main(sys.argv[1:]) 205 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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