The operations included in ev-demo
are shown on the following flowchart:
The demo is completely automatic and can be executed directly from your shell, as well as from the platform container.
We will open a shell into the platform container and execute the demo script from there against aws-do-pm, running as a local docker-compose deployment. This is recommended in order to avoid any variability due to the users's local shell environment. The commands would be identical when running the demo against a local or distributed Kubernetes instance.
./exec.sh
Output:
root@18aa4d472ec5:/app/pm#
Within the platform shell, execute pm config set
and review the current settings. If you are running on Docker Compose, the values will already be set. If you are running on Kubernetes, then you will need to set the same values as the initial setup.
The demo comes with defaults that work. Settings can be adjusted as needed by editing the CONFIGURATION section of the ev-demo
script.
vi ./ev-demo
############################################################################################
# CONFIGURATION:
# Import system settings
source .env
# Enable QUIET mode to allow command output parsing
export QUIET=true
# Number of vehicles and routes to generate. Only one vehicle will be randomly selected for the demo.
export NUM_VEHICLES=10
export NUM_ROUTES=10
# Number of routes to predict. Route 0 is used for training, route 1 is used for update, predictions start from route 2.
# NUM_PREDICTED_ROUTES<=NUM_ROUTES-2
export NUM_PREDICTED_ROUTES=3
# Model update threshold. If the prediction error is greater than this value, the model for the current vehicle will be udpated.
export MODEL_UPDATE_THRESHOLD=0.05
############################################################################################
./ev-demo
Expand log
================================================================================= Executing EV demo for a single vehicle, randomly selected from a fleet of 10 electric vehicles driving 10 routes each =================================================================================Launch time: 20220224-023854
================================================================================= Clearing system ... ================================================================================= Removing services ... Clearing graph database ... Clearing local storage ... Clearing cloud storage ... fatal error: An error occurred (NoSuchBucket) when calling the ListObjectsV2 operation: The specified bucket does not exist remove_bucket failed: Unable to delete all objects in the bucket, bucket will not be deleted.
================================================================================= Initializing system ... ================================================================================= { "Location": "http://aws-do-pm-alex.s3.amazonaws.com/" } Creating Vertex Collection: technique Creating Vertex Collection: data Creating Vertex Collection: model Creating Vertex Collection: service Creating Vertex Collection: asset Creating Vertex Collection: task Creating Vertex Collection: error Creating Vertex Collection: trash Creating Vertex Collection: user Creating Edge Collection: link Registering technique data_registration ... task/mrup05ph52bjno3dnltkk7plp39bpg3i Executing task from wd/task/mrup05ph52bjno3dnltkk7plp39bpg3i/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/mrup05ph52bjno3dnltkk7plp39bpg3i/task.json'] technique/c03623kizy06twxajbt2wgr7bs7ghumg Return code: 0 Registering technique model_registration ... task/qb7sv1h92ab9n0rbmloa3o4dwwh1j2zx Executing task from wd/task/qb7sv1h92ab9n0rbmloa3o4dwwh1j2zx/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/qb7sv1h92ab9n0rbmloa3o4dwwh1j2zx/task.json'] technique/vyeadt2bivrpfbgw66h7r8oq4f4dp1wf Return code: 0 Registering technique model_build_ann ... task/sr27lkyzsjnsksn741z97rsb8etrr64y Executing task from wd/task/sr27lkyzsjnsksn741z97rsb8etrr64y/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/sr27lkyzsjnsksn741z97rsb8etrr64y/task.json'] technique/fyd77r2f504mux7lqyz16upt0ynbti9k Return code: 0 Registering technique model_serve_grpc ... task/jb2c4uzgh2ciu8am1j1hc8ahulh8powb Executing task from wd/task/jb2c4uzgh2ciu8am1j1hc8ahulh8powb/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/jb2c4uzgh2ciu8am1j1hc8ahulh8powb/task.json'] technique/ghzrgcwia8984k57fvtza1e5sl6mud26 Return code: 0 Registering technique model_service_configure ... task/mxbqaaeo0hkz2859ey3sg6ym2ldsc0yi Executing task from wd/task/mxbqaaeo0hkz2859ey3sg6ym2ldsc0yi/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/mxbqaaeo0hkz2859ey3sg6ym2ldsc0yi/task.json'] technique/lcm7kriknfgy9xxlyofhzhfru9g97le0 Return code: 0 Registering technique model_service_destroy ... task/ml1f1hjbxlsn1cqmxleqyd58642x5oyo Executing task from wd/task/ml1f1hjbxlsn1cqmxleqyd58642x5oyo/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/ml1f1hjbxlsn1cqmxleqyd58642x5oyo/task.json'] technique/l90obo0myv0ko7iyrvq69p88p9pkn8qv Return code: 0 Registering technique model_update_ukf_grpc ... task/ske70in52c6nalp68i8mi99s1ahau4fr Executing task from wd/task/ske70in52c6nalp68i8mi99s1ahau4fr/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/ske70in52c6nalp68i8mi99s1ahau4fr/task.json'] technique/phbcrp1maka7v11bdhyqx0tdvpm3h2fd Return code: 0 Registering technique model_predict_grpc ... task/x8l58tujkhmx4ews4he5ps390h88v204 Executing task from wd/task/x8l58tujkhmx4ews4he5ps390h88v204/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/x8l58tujkhmx4ews4he5ps390h88v204/task.json'] technique/gs0efg1ggemccochnci9gvbn6436u7dz Return code: 0 Registering technique model_sensitivity_grpc ... task/srqmoies40gbr2wuhletoh3gm6ge688u Executing task from wd/task/srqmoies40gbr2wuhletoh3gm6ge688u/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/srqmoies40gbr2wuhletoh3gm6ge688u/task.json'] technique/ew1lqmb6osxepmieb5ne4fs3yiz69trg Return code: 0
================================================================================= Generating Electric Vehicle data for 10 vehicles, 10 routes each ... ================================================================================= Number of electrical vehicles: 10 Number of routes per vehicle: 10 Logging in to ecr ************.dkr.ecr.us-west-2.amazonaws.com/ ... Login Succeeded 100% 10/10 [00:00<00:00, 24.95it/s] Shape of Overall Pandas DataFrame: (23208, 9) 100% 10/10 [00:01<00:00, 8.09it/s] 100% 10/10 [00:00<00:00, 1187.92it/s] Exported: wd/generated_ev_data/metadata.json Task status: wd/generated_ev_data/task_ev_data_generate_status.json
================================================================================= Registering raw data set ... ================================================================================= Registering data from local path: wd/generated_ev_data Temporary destination path: wd/tmp/1/generated_ev_data-20220224023918566 task/dbgrcpuzykme4ef72n8tv7hkpoh4h8n2 Executing task from wd/task/dbgrcpuzykme4ef72n8tv7hkpoh4h8n2/task.json ... Executing PM task: ['python', '/src/python/pm/data/task_executor_register_data.py', '--config', 'wd/task/dbgrcpuzykme4ef72n8tv7hkpoh4h8n2/task.json'] data/vxm9pykrwrdt401m22dbmqqn8x98oeme Return code: 0 upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_0/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_0/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_3/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_3/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_1/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_1/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_2/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_2/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_6/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_6/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V1_0/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V1_0/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V1_1/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V1_1/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V1_2/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V1_2/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V1_3/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V1_3/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_5/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_5/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_7/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_7/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V0_8/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V0_8/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V1_4/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V1_4/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V1_6/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V1_6/input_output.json upload: wd/tmp/1/generated_ev_data-20220224023918566/generated_ev_data/V1_7/input_output.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/V1_7/input_output.json upload: 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s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/task_ev_data_generate_status.json to wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/task_ev_data_generate_status.json download: s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/task_ev_data_generate.json to wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/task_ev_data_generate.json download: s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/trip_pd.csv to wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/trip_pd.csv download: s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/plots/SOC_V_Dist.png to wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/plots/SOC_V_Dist.png download: s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/plots/cumDist.png to wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/plots/cumDist.png download: s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/train_data/input_output.json to wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/train_data/input_output.json download: s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/overall_pd.csv to wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/overall_pd.csv upload: wd/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/metadata.json to s3://aws-do-pm-alex/data/vxm9pykrwrdt401m22dbmqqn8x98oeme/metadata.json RAW_EV_DATA_ID=vxm9pykrwrdt401m22dbmqqn8x98oeme
================================================================================= Registering data for model training ... ================================================================================= Registering data from local path: wd/generated_ev_data/train_data Temporary destination path: wd/tmp/1/train_data-20220224023941545 task/q4oxfd7ngrb87v4r8j8figo44xte3oog Executing task from wd/task/q4oxfd7ngrb87v4r8j8figo44xte3oog/task.json ... Executing PM task: ['python', '/src/python/pm/data/task_executor_register_data.py', '--config', 'wd/task/q4oxfd7ngrb87v4r8j8figo44xte3oog/task.json'] data/n1hf1cv4nift5iy92q7smpuqt8n75rr2 Return code: 0 upload: wd/tmp/1/train_data-20220224023941545/train_data/input_output.json to s3://aws-do-pm-alex/data/n1hf1cv4nift5iy92q7smpuqt8n75rr2/input_output.json download: s3://aws-do-pm-alex/data/n1hf1cv4nift5iy92q7smpuqt8n75rr2/input_output.json to wd/data/n1hf1cv4nift5iy92q7smpuqt8n75rr2/input_output.json upload: wd/data/n1hf1cv4nift5iy92q7smpuqt8n75rr2/metadata.json to s3://aws-do-pm-alex/data/n1hf1cv4nift5iy92q7smpuqt8n75rr2/metadata.json TRAIN_DATA_ID=n1hf1cv4nift5iy92q7smpuqt8n75rr2
================================================================================= Building Neural Network model using DATA_ID n1hf1cv4nift5iy92q7smpuqt8n75rr2 ... ================================================================================= Task: inserting edge from data/n1hf1cv4nift5iy92q7smpuqt8n75rr2 to task/iy4wcnmxcs9o8qhon2tk2fn5mkrf99o0 task/iy4wcnmxcs9o8qhon2tk2fn5mkrf99o0 Executing task from wd/task/iy4wcnmxcs9o8qhon2tk2fn5mkrf99o0/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_build_ann:latest', 'python', '/src/python/technique/model_build_ann/train_torch_model.py', '--config', 'wd/task/iy4wcnmxcs9o8qhon2tk2fn5mkrf99o0/task.json'] model/w30if15bemzn5fvl1bbu1dbh30bltyb7 Return code: 0 Host GPU Check Mode: False Requested GPU Mode: True Target GPU Mode: False Epoch 1 loss: 2012.093017578125, val_loss: 501.5348815917969 Epoch 2 loss: 2008.2371826171875, val_loss: 500.5689392089844 Epoch 3 loss: 2004.3736572265625, val_loss: 499.60107421875 Epoch 4 loss: 2000.501953125, val_loss: 498.6311340332031 Epoch 5 loss: 1996.622314453125, val_loss: 497.6591796875 Epoch 6 loss: 1992.7344970703125, val_loss: 496.6850891113281 Epoch 7 loss: 1988.838134765625, val_loss: 495.7088928222656 Epoch 8 loss: 1984.933349609375, val_loss: 494.73052978515625 Epoch 9 loss: 1981.0198974609375, val_loss: 493.7500305175781 Epoch 10 loss: 1977.097900390625, val_loss: 492.7672119140625 Epoch 11 loss: 1973.1666259765625, val_loss: 491.7822570800781 Epoch 12 loss: 1969.2266845703125, val_loss: 490.794921875 Epoch 13 loss: 1965.27734375, val_loss: 489.8052673339844 Epoch 14 loss: 1961.3189697265625, val_loss: 488.8133239746094 Epoch 15 loss: 1957.35107421875, val_loss: 487.8189697265625 Epoch 16 loss: 1953.37353515625, val_loss: 486.82220458984375 Epoch 17 loss: 1949.386474609375, val_loss: 485.8229675292969 Epoch 18 loss: 1945.3896484375, val_loss: 484.8212585449219 Epoch 19 loss: 1941.3828125, val_loss: 483.81707763671875 Epoch 20 loss: 1937.3662109375, val_loss: 482.81036376953125 Epoch 21 loss: 1933.339111328125, val_loss: 481.8011169433594 Epoch 22 loss: 1929.3021240234375, val_loss: 480.78924560546875 Epoch 23 loss: 1925.254638671875, val_loss: 479.7747802734375 Epoch 24 loss: 1921.19677734375, val_loss: 478.7576904296875 Epoch 25 loss: 1917.12841796875, val_loss: 477.7379455566406 Epoch 26 loss: 1913.049560546875, val_loss: 476.71551513671875 Epoch 27 loss: 1908.959716796875, val_loss: 475.69036865234375 Epoch 28 loss: 1904.859130859375, val_loss: 474.6625061035156 Epoch 29 loss: 1900.7476806640625, val_loss: 473.6318664550781 Epoch 30 loss: 1896.625244140625, val_loss: 472.59844970703125 Epoch 31 loss: 1892.4915771484375, val_loss: 471.5622863769531 Epoch 32 loss: 1888.346923828125, val_loss: 470.5232849121094 Epoch 33 loss: 1884.19091796875, val_loss: 469.4814758300781 Epoch 34 loss: 1880.0235595703125, val_loss: 468.4367980957031 Epoch 35 loss: 1875.8448486328125, val_loss: 467.38922119140625 Epoch 36 loss: 1871.65478515625, val_loss: 466.3387756347656 Epoch 37 loss: 1867.452880859375, val_loss: 465.28546142578125 Epoch 38 loss: 1863.239501953125, val_loss: 464.2291564941406 Epoch 39 loss: 1859.014404296875, val_loss: 463.16998291015625 Epoch 40 loss: 1854.777587890625, val_loss: 462.1078186035156 Epoch 41 loss: 1850.529052734375, val_loss: 461.0426940917969 Epoch 42 loss: 1846.2685546875, val_loss: 459.974609375 Epoch 43 loss: 1841.9962158203125, val_loss: 458.90350341796875 Epoch 44 loss: 1837.7117919921875, val_loss: 457.82940673828125 Epoch 45 loss: 1833.414794921875, val_loss: 456.75225830078125 Epoch 46 loss: 1829.1068115234375, val_loss: 455.6720886230469 Epoch 47 loss: 1824.7861328125, val_loss: 454.5888671875 Epoch 48 loss: 1820.453125, val_loss: 453.5025634765625 Epoch 49 loss: 1816.10791015625, val_loss: 452.4131774902344 Epoch 50 loss: 1811.7506103515625, val_loss: 451.3207702636719 ... Epoch 100 loss: 1468.67236328125, val_loss: 364.0300598144531 ... Epoch 150 loss: 523.628173828125, val_loss: 120.9228286743164 ... Epoch 200 loss: 263.3040771484375, val_loss: 63.20686340332031 ... Epoch 250 loss: 241.77708435058594, val_loss: 58.02519989013672 ... Epoch 300 loss: 222.97677612304688, val_loss: 53.493011474609375 ... Epoch 350 loss: 205.6614532470703, val_loss: 49.394378662109375 ... Epoch 400 loss: 189.7362518310547, val_loss: 45.713802337646484 ... Epoch 450 loss: 175.96153259277344, val_loss: 42.6832389831543 ... Epoch 500 loss: 164.2222900390625, val_loss: 40.050376892089844 ... Epoch 550 loss: 153.41661071777344, val_loss: 37.65180206298828 ... Epoch 600 loss: 143.66873168945312, val_loss: 35.435848236083984 ... Epoch 650 loss: 133.2382354736328, val_loss: 33.02635192871094 ... Epoch 700 loss: 119.98348236083984, val_loss: 30.069091796875 ... Epoch 750 loss: 107.97725677490234, val_loss: 27.13340950012207 ... Epoch 800 loss: 97.11802673339844, val_loss: 24.065200805664062 ... Epoch 850 loss: 88.97050476074219, val_loss: 21.764673233032227 ... Epoch 900 loss: 83.25723266601562, val_loss: 20.275564193725586 ... Epoch 950 loss: 78.12405395507812, val_loss: 18.877120971679688 ... Epoch 1000 loss: 71.22881317138672, val_loss: 17.132665634155273 ... Epoch 1050 loss: 65.61013793945312, val_loss: 15.783658981323242 ... Epoch 1100 loss: 61.719764709472656, val_loss: 14.854129791259766 ... Epoch 1150 loss: 58.021263122558594, val_loss: 14.052042961120605 ... Epoch 1200 loss: 54.56269454956055, val_loss: 13.259014129638672 ... Epoch 1250 loss: 51.136077880859375, val_loss: 12.449197769165039 ... Epoch 1300 loss: 48.34165954589844, val_loss: 11.695030212402344 ... Epoch 1350 loss: 46.169368743896484, val_loss: 11.146100997924805 ... Epoch 1400 loss: 43.895957946777344, val_loss: 10.615802764892578 ... Epoch 1450 loss: 42.121803283691406, val_loss: 10.186094284057617 ... Epoch 1500 loss: 40.1564826965332, val_loss: 9.80024242401123 ... Epoch 1550 loss: 38.75495529174805, val_loss: 9.482778549194336 ... Epoch 1600 loss: 37.12869644165039, val_loss: 9.22691535949707 ... Epoch 1650 loss: 35.817012786865234, val_loss: 8.927409172058105 ... Epoch 1700 loss: 34.67178726196289, val_loss: 8.666121482849121 ... Epoch 1750 loss: 33.6358757019043, val_loss: 8.431020736694336 ... Epoch 1800 loss: 32.6888313293457, val_loss: 8.21117877960205 ... Epoch 1850 loss: 31.837568283081055, val_loss: 7.969207763671875 ... Epoch 1900 loss: 31.14990997314453, val_loss: 7.774572372436523 ... Epoch 1950 loss: 33.16026306152344, val_loss: 7.593169689178467 Epoch 1951 loss: 30.469100952148438, val_loss: 7.752513885498047 Epoch 1952 loss: 31.269990921020508, val_loss: 7.711300849914551 Epoch 1953 loss: 30.772985458374023, val_loss: 7.541573524475098 Epoch 1954 loss: 30.341594696044922, val_loss: 7.557385444641113 Epoch 1955 loss: 30.397411346435547, val_loss: 7.709839820861816 Epoch 1956 loss: 30.726852416992188, val_loss: 7.650208473205566 Epoch 1957 loss: 30.833709716796875, val_loss: 7.529184818267822 Epoch 1958 loss: 30.271907806396484, val_loss: 7.787655353546143 Epoch 1959 loss: 31.01612091064453, val_loss: 7.71023416519165 Epoch 1960 loss: 31.169626235961914, val_loss: 7.547399997711182 Epoch 1961 loss: 30.40171241760254, val_loss: 8.1809663772583 Epoch 1962 loss: 32.267520904541016, val_loss: 7.50651741027832 Epoch 1963 loss: 30.45893669128418, val_loss: 7.612319469451904 Epoch 1964 loss: 30.819934844970703, val_loss: 7.692008018493652 Epoch 1965 loss: 30.7268009185791, val_loss: 7.501777648925781 Epoch 1966 loss: 30.188003540039062, val_loss: 7.52454948425293 Epoch 1967 loss: 30.258968353271484, val_loss: 7.689920425415039 Epoch 1968 loss: 30.628429412841797, val_loss: 7.566681861877441 Epoch 1969 loss: 30.51046371459961, val_loss: 7.46189546585083 Epoch 1970 loss: 30.121971130371094, val_loss: 7.717378616333008 Epoch 1971 loss: 30.88013458251953, val_loss: 7.573777675628662 Epoch 1972 loss: 30.748859405517578, val_loss: 7.487414836883545 Epoch 1973 loss: 30.239776611328125, val_loss: 7.986697196960449 Epoch 1974 loss: 31.55860710144043, val_loss: 7.515182018280029 Epoch 1975 loss: 30.221763610839844, val_loss: 7.550901412963867 Epoch 1976 loss: 30.44923973083496, val_loss: 7.6617536544799805 Epoch 1977 loss: 30.568843841552734, val_loss: 7.4305830001831055 Epoch 1978 loss: 30.083568572998047, val_loss: 7.402945518493652 Epoch 1979 loss: 30.026958465576172, val_loss: 7.595363616943359 Epoch 1980 loss: 30.442155838012695, val_loss: 7.506576061248779 Epoch 1981 loss: 30.326671600341797, val_loss: 7.454261302947998 Epoch 1982 loss: 29.977453231811523, val_loss: 7.756158351898193 Epoch 1983 loss: 30.78609275817871, val_loss: 7.509827136993408 Epoch 1984 loss: 30.37646484375, val_loss: 7.438156604766846 Epoch 1985 loss: 30.104475021362305, val_loss: 7.794588565826416 Epoch 1986 loss: 31.091533660888672, val_loss: 7.403245449066162 Epoch 1987 loss: 29.963275909423828, val_loss: 7.448260307312012 Epoch 1988 loss: 30.14856719970703, val_loss: 7.619695663452148 Epoch 1989 loss: 30.610374450683594, val_loss: 7.419189929962158 Epoch 1990 loss: 29.85854148864746, val_loss: 7.422970771789551 Epoch 1991 loss: 29.910219192504883, val_loss: 7.557424068450928 Epoch 1992 loss: 30.252426147460938, val_loss: 7.399770736694336 Epoch 1993 loss: 29.94264030456543, val_loss: 7.369198322296143 Epoch 1994 loss: 29.734962463378906, val_loss: 7.503969669342041 Epoch 1995 loss: 30.048994064331055, val_loss: 7.451272487640381 Epoch 1996 loss: 30.021629333496094, val_loss: 7.389279365539551 Epoch 1997 loss: 29.693683624267578, val_loss: 7.537532806396484 Epoch 1998 loss: 30.133331298828125, val_loss: 7.417536735534668 Epoch 1999 loss: 30.07450294494629, val_loss: 7.351589679718018 Epoch 2000 loss: 29.668468475341797, val_loss: 7.697206020355225 Initializing: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/mlp_train.pth Exported: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/model.json Exported: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/metadata.json Export task status: wd/task/iy4wcnmxcs9o8qhon2tk2fn5mkrf99o0/task_status.json upload: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/metadata.json to s3://aws-do-pm-alex/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/metadata.json upload: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/model.json to s3://aws-do-pm-alex/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/model.json upload: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/mlp_train.pth to s3://aws-do-pm-alex/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/mlp_train.pth upload: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/Actual-Vs_Predicted.png to s3://aws-do-pm-alex/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/Actual-Vs_Predicted.png upload: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/mlp_train_epoch.png to s3://aws-do-pm-alex/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/mlp_train_epoch.png upload: wd/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/model_history.pkl to s3://aws-do-pm-alex/model/w30if15bemzn5fvl1bbu1dbh30bltyb7/model_history.pkl MODEL_ID_RAW=model/w30if15bemzn5fvl1bbu1dbh30bltyb7 MODEL_ID=w30if15bemzn5fvl1bbu1dbh30bltyb7
================================================================================= Registering custom technique for EV data selection ... ================================================================================= task/l6qjhi1gwcgyl18kfl3p3sj3wq2wkk6b Executing task from wd/task/l6qjhi1gwcgyl18kfl3p3sj3wq2wkk6b/task.json ... Executing PM task: ['python', '/src/python/pm/task/task_technique_register.py', '--config', 'wd/task/l6qjhi1gwcgyl18kfl3p3sj3wq2wkk6b/task.json'] technique/ghfiudgg2mdl6uoxkg0uf2rrpbojmgkd Return code: 0
================================================================================= Selecting new data for vehicle # 5, route # 1 ... ================================================================================= Task: inserting edge from data/vxm9pykrwrdt401m22dbmqqn8x98oeme to task/lyahsc6nx8wj22w1dezkhxdk1omlwa0u task/lyahsc6nx8wj22w1dezkhxdk1omlwa0u Executing task from wd/task/lyahsc6nx8wj22w1dezkhxdk1omlwa0u/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-ev_datagen_em:latest', 'python', '/src/python/example/ev/technique/ev_data_select/extract_filter_data.py', '--config', 'wd/task/lyahsc6nx8wj22w1dezkhxdk1omlwa0u/task.json'] data/dq78ldot5trw530pby6mahuhzeda6a38 Return code: 0 Executing data selection task: wd/task/lyahsc6nx8wj22w1dezkhxdk1omlwa0u/task.json ... Len of Data sets: 101 {'vehicle_id': 'V5', 'route_id': '1'} Len of Filtered Data: 1 Exported Data: wd/data/dq78ldot5trw530pby6mahuhzeda6a38/data.json Exported Data: wd/data/dq78ldot5trw530pby6mahuhzeda6a38/metadata.json NEW_DATA_ID=dq78ldot5trw530pby6mahuhzeda6a38
================================================================================= Updating model w30if15bemzn5fvl1bbu1dbh30bltyb7 using data dq78ldot5trw530pby6mahuhzeda6a38 ... ================================================================================= Task: inserting edge from data/dq78ldot5trw530pby6mahuhzeda6a38 to task/d3fv5kf96fubn2rpniek3rb7zbsfjs50 Task: inserting edge from model/w30if15bemzn5fvl1bbu1dbh30bltyb7 to task/d3fv5kf96fubn2rpniek3rb7zbsfjs50 Task: inserting edge from service/axovwtvhqbyo1epbso9xzsid3ceokocl to task/d3fv5kf96fubn2rpniek3rb7zbsfjs50 task/d3fv5kf96fubn2rpniek3rb7zbsfjs50 Executing task from wd/task/d3fv5kf96fubn2rpniek3rb7zbsfjs50/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_update_ukf_grpc:latest', 'python', '/src/python/technique/model_update_ukf_grpc/update_model_data.py', '--config', 'wd/task/d3fv5kf96fubn2rpniek3rb7zbsfjs50/task.json'] model/edlkgt40ngr4pddtu0xx519s8i9bjg91 Return code: 0 Initial State after UKF initialization [-2.96143502e-01 3.44804436e-01 -2.92074047e-02 5.57845056e-01 1.07957297e-14] Starting Iterative UKF update Iteration: 1 Initial State at this iteration [-2.96143502e-01 3.44804436e-01 -2.92074047e-02 5.57845056e-01 1.07957297e-14] Updated State at this iteration [-5.59501139e-01 4.60381532e-01 -2.92074047e-02 6.52937788e-01 1.07951842e-14] Iteration: 2 Initial State at this iteration [-5.59501139e-01 4.60381532e-01 -2.92074047e-02 6.52937788e-01 1.07951842e-14] Updated State at this iteration [-7.03956110e-01 5.35912368e-01 -2.92074047e-02 7.04991549e-01 1.07951842e-14] Iteration: 3 Initial State at this iteration [-7.03956110e-01 5.35912368e-01 -2.92074047e-02 7.04991549e-01 1.07951842e-14] Updated State at this iteration [-7.59547927e-01 5.75430619e-01 -2.92074047e-02 7.16080624e-01 1.07951842e-14] Iteration: 4 Initial State at this iteration [-7.59547927e-01 5.75430619e-01 -2.92074047e-02 7.16080624e-01 1.07951842e-14] Updated State at this iteration [-7.79122469e-01 6.02991392e-01 -2.92074047e-02 7.08255767e-01 1.07969187e-14] Iteration: 5 Initial State at this iteration [-7.79122469e-01 6.02991392e-01 -2.92074047e-02 7.08255767e-01 1.07969187e-14] Updated State at this iteration [-7.85134534e-01 6.28757648e-01 -2.92074047e-02 6.90980919e-01 1.07969189e-14] Iteration: 6 Initial State at this iteration [-7.85134534e-01 6.28757648e-01 -2.92074047e-02 6.90980919e-01 1.07969189e-14] Updated State at this iteration [-7.85978895e-01 6.56425776e-01 -2.92074047e-02 6.67882821e-01 1.07977863e-14] Iteration: 7 Initial State at this iteration [-7.85978895e-01 6.56425776e-01 -2.92074047e-02 6.67882821e-01 1.07977863e-14] Updated State at this iteration [-7.84747504e-01 6.87234578e-01 -2.92074047e-02 6.40402650e-01 1.07977863e-14] Iteration: 8 Initial State at this iteration [-7.84747504e-01 6.87234578e-01 -2.92074047e-02 6.40402650e-01 1.07977863e-14] Updated State at this iteration [-7.82582734e-01 7.21515496e-01 -2.92074047e-02 6.09181432e-01 1.07977863e-14] Iteration: 9 Initial State at this iteration [-7.82582734e-01 7.21515496e-01 -2.92074047e-02 6.09181432e-01 1.07977863e-14] Updated State at this iteration [-7.79913464e-01 7.59247010e-01 -2.92074047e-02 5.74585403e-01 1.07986537e-14] Iteration: 10 Initial State at this iteration [-7.79913464e-01 7.59247010e-01 -2.92074047e-02 5.74585403e-01 1.07986537e-14] Updated State at this iteration [-7.76908388e-01 8.00275700e-01 -2.92074047e-02 5.36882975e-01 1.07986537e-14] Iteration: 11 Initial State at this iteration [-7.76908388e-01 8.00275700e-01 -2.92074047e-02 5.36882975e-01 1.07986537e-14] Updated State at this iteration [-7.73639823e-01 8.44391542e-01 -2.92074047e-02 4.96313401e-01 1.07986537e-14] Iteration: 12 Initial State at this iteration [-7.73639823e-01 8.44391542e-01 -2.92074047e-02 4.96313401e-01 1.07986537e-14] Updated State at this iteration [-7.70146602e-01 8.91356970e-01 -2.92074047e-02 4.53112336e-01 1.07977864e-14] Iteration: 13 Initial State at this iteration [-7.70146602e-01 8.91356970e-01 -2.92074047e-02 4.53112336e-01 1.07977864e-14] Updated State at this iteration [-7.66454691e-01 9.40925137e-01 -2.92074047e-02 4.07513024e-01 1.07951848e-14] Iteration: 14 Initial State at this iteration [-7.66454691e-01 9.40925137e-01 -2.92074047e-02 4.07513024e-01 1.07951848e-14] Updated State at this iteration [-7.62586527e-01 9.92839614e-01 -2.92074047e-02 3.59754061e-01 1.07934495e-14] Iteration: 15 Initial State at this iteration [-7.62586527e-01 9.92839614e-01 -2.92074047e-02 3.59754061e-01 1.07934495e-14] Updated State at this iteration [-7.58561769e-01 1.04684710e+00 -2.92074047e-02 3.10069073e-01 1.07917147e-14] Iteration: 16 Initial State at this iteration [-7.58561769e-01 1.04684710e+00 -2.92074047e-02 3.10069073e-01 1.07917147e-14] Updated State at this iteration [-7.54399898e-01 1.10269035e+00 -2.92074047e-02 2.58695060e-01 1.07917147e-14] Iteration: 17 Initial State at this iteration [-7.54399898e-01 1.10269035e+00 -2.92074047e-02 2.58695060e-01 1.07917147e-14] Updated State at this iteration [-7.50119772e-01 1.16012006e+00 -2.92074047e-02 2.05861493e-01 1.07917147e-14] Iteration: 18 Initial State at this iteration [-7.50119772e-01 1.16012006e+00 -2.92074047e-02 2.05861493e-01 1.07917147e-14] Updated State at this iteration [-7.45739865e-01 1.21888787e+00 -2.92074047e-02 1.51796941e-01 1.07917145e-14] Iteration: 19 Initial State at this iteration [-7.45739865e-01 1.21888787e+00 -2.92074047e-02 1.51796941e-01 1.07917145e-14] Updated State at this iteration [-7.41277696e-01 1.27876235e+00 -2.92074047e-02 9.67143056e-02 1.07917148e-14] Iteration: 20 Initial State at this iteration [-7.41277696e-01 1.27876235e+00 -2.92074047e-02 9.67143056e-02 1.07917148e-14] Updated State at this iteration [-7.36749773e-01 1.33951171e+00 -2.92074047e-02 4.08265410e-02 1.07899802e-14] Iteration: 21 Initial State at this iteration [-7.36749773e-01 1.33951171e+00 -2.92074047e-02 4.08265410e-02 1.07899802e-14] Updated State at this iteration [-7.32172903e-01 1.40092087e+00 -2.92074047e-02 -1.56682119e-02 1.07917146e-14] Iteration: 22 Initial State at this iteration [-7.32172903e-01 1.40092087e+00 -2.92074047e-02 -1.56682119e-02 1.07917146e-14] Updated State at this iteration [-7.27562604e-01 1.46278142e+00 -2.92074047e-02 -7.25779208e-02 1.07917147e-14] Iteration: 23 Initial State at this iteration [-7.27562604e-01 1.46278142e+00 -2.92074047e-02 -7.25779208e-02 1.07917147e-14] Updated State at this iteration [-7.22932923e-01 1.52490054e+00 -2.92074047e-02 -1.29725619e-01 1.07917147e-14] Iteration: 24 Initial State at this iteration [-7.22932923e-01 1.52490054e+00 -2.92074047e-02 -1.29725619e-01 1.07917147e-14] Updated State at this iteration [-7.18297099e-01 1.58709945e+00 -2.92074047e-02 -1.86946914e-01 1.07899796e-14] Iteration: 25 Initial State at this iteration [-7.18297099e-01 1.58709945e+00 -2.92074047e-02 -1.86946914e-01 1.07899796e-14] Updated State at this iteration [-7.13668332e-01 1.64920823e+00 -2.92074047e-02 -2.44084993e-01 1.07882456e-14] Iteration: 26 Initial State at this iteration [-7.13668332e-01 1.64920823e+00 -2.92074047e-02 -2.44084993e-01 1.07882456e-14] Updated State at this iteration [-7.09057608e-01 1.71107218e+00 -2.92074047e-02 -3.00998022e-01 1.07882453e-14] Iteration: 27 Initial State at this iteration [-7.09057608e-01 1.71107218e+00 -2.92074047e-02 -3.00998022e-01 1.07882453e-14] Updated State at this iteration [-7.04475678e-01 1.77255020e+00 -2.92074047e-02 -3.57555975e-01 1.07882455e-14] Iteration: 28 Initial State at this iteration [-7.04475678e-01 1.77255020e+00 -2.92074047e-02 -3.57555975e-01 1.07882455e-14] Updated State at this iteration [-6.99931956e-01 1.83351421e+00 -2.92074047e-02 -4.13641123e-01 1.07882454e-14] Iteration: 29 Initial State at this iteration [-6.99931956e-01 1.83351421e+00 -2.92074047e-02 -4.13641123e-01 1.07882454e-14] Updated State at this iteration [-6.95435479e-01 1.89384719e+00 -2.92074047e-02 -4.69145588e-01 1.07882453e-14] Iteration: 30 Initial State at this iteration [-6.95435479e-01 1.89384719e+00 -2.92074047e-02 -4.69145588e-01 1.07882453e-14] Updated State at this iteration [-6.90993867e-01 1.95344283e+00 -2.92074047e-02 -5.23971820e-01 1.07882453e-14] Iteration: 31 Initial State at this iteration [-6.90993867e-01 1.95344283e+00 -2.92074047e-02 -5.23971820e-01 1.07882453e-14] Updated State at this iteration [-6.86613648e-01 2.01221569e+00 -2.92074047e-02 -5.78041060e-01 1.07882452e-14] Iteration: 32 Initial State at this iteration [-6.86613648e-01 2.01221569e+00 -2.92074047e-02 -5.78041060e-01 1.07882452e-14] Updated State at this iteration [-6.82300959e-01 2.07007830e+00 -2.92074047e-02 -6.31272976e-01 1.07917146e-14] Iteration: 33 Initial State at this iteration [-6.82300959e-01 2.07007830e+00 -2.92074047e-02 -6.31272976e-01 1.07917146e-14] Updated State at this iteration [-6.78061296e-01 2.12696754e+00 -2.92074047e-02 -6.83609242e-01 1.07899801e-14] Iteration: 34 Initial State at this iteration [-6.78061296e-01 2.12696754e+00 -2.92074047e-02 -6.83609242e-01 1.07899801e-14] Updated State at this iteration [-6.73899295e-01 2.18281346e+00 -2.92074047e-02 -7.34985596e-01 1.07917150e-14] Iteration: 35 Initial State at this iteration [-6.73899295e-01 2.18281346e+00 -2.92074047e-02 -7.34985596e-01 1.07917150e-14] Updated State at this iteration [-6.69818262e-01 2.23757044e+00 -2.92074047e-02 -7.85360494e-01 1.07917149e-14] Iteration: 36 Initial State at this iteration [-6.69818262e-01 2.23757044e+00 -2.92074047e-02 -7.85360494e-01 1.07917149e-14] Updated State at this iteration [-6.65822086e-01 2.29119109e+00 -2.92074047e-02 -8.34689824e-01 1.07917148e-14] Iteration: 37 Initial State at this iteration [-6.65822086e-01 2.29119109e+00 -2.92074047e-02 -8.34689824e-01 1.07917148e-14] Updated State at this iteration [-6.61912481e-01 2.34364568e+00 -2.92074047e-02 -8.82946575e-01 1.07917147e-14] Iteration: 38 Initial State at this iteration [-6.61912481e-01 2.34364568e+00 -2.92074047e-02 -8.82946575e-01 1.07917147e-14] Updated State at this iteration [-6.58091770e-01 2.39490669e+00 -2.92074047e-02 -9.30105334e-01 1.07917146e-14] Iteration: 39 Initial State at this iteration [-6.58091770e-01 2.39490669e+00 -2.92074047e-02 -9.30105334e-01 1.07917146e-14] Updated State at this iteration [-6.54362296e-01 2.44495206e+00 -2.92074047e-02 -9.76145432e-01 1.07917147e-14] Iteration: 40 Initial State at this iteration [-6.54362296e-01 2.44495206e+00 -2.92074047e-02 -9.76145432e-01 1.07917147e-14] Updated State at this iteration [-6.50724417e-01 2.49376214e+00 -2.92074047e-02 -1.02104920e+00 1.07917149e-14] Exported: wd/model/edlkgt40ngr4pddtu0xx519s8i9bjg91/model.json Exported: wd/model/edlkgt40ngr4pddtu0xx519s8i9bjg91/metadata.json Export task status: wd/task/d3fv5kf96fubn2rpniek3rb7zbsfjs50/task_status.json upload: wd/model/edlkgt40ngr4pddtu0xx519s8i9bjg91/metadata.json to s3://aws-do-pm-alex/model/edlkgt40ngr4pddtu0xx519s8i9bjg91/metadata.json upload: wd/model/edlkgt40ngr4pddtu0xx519s8i9bjg91/model.json to s3://aws-do-pm-alex/model/edlkgt40ngr4pddtu0xx519s8i9bjg91/model.json UPDATED_MODEL_ID=edlkgt40ngr4pddtu0xx519s8i9bjg91
================================================================================= Analyzing model sensitivity using model edlkgt40ngr4pddtu0xx519s8i9bjg91 and data dq78ldot5trw530pby6mahuhzeda6a38 ... ================================================================================= Task: inserting edge from data/dq78ldot5trw530pby6mahuhzeda6a38 to task/cc27mp1aacm8lxvsit0ubnmsl66pg4b6 Task: inserting edge from model/edlkgt40ngr4pddtu0xx519s8i9bjg91 to task/cc27mp1aacm8lxvsit0ubnmsl66pg4b6 Task: inserting edge from service/dok9typ60maaw6cdmhuxwuur8vd42fbw to task/cc27mp1aacm8lxvsit0ubnmsl66pg4b6 task/cc27mp1aacm8lxvsit0ubnmsl66pg4b6 Executing task from wd/task/cc27mp1aacm8lxvsit0ubnmsl66pg4b6/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_predict_grpc:latest', 'python', '/src/python/technique/model_sensitivity_grpc/calc_model_sensitivity.py', '--config', 'wd/task/cc27mp1aacm8lxvsit0ubnmsl66pg4b6/task.json'] data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36 Return code: 0 Setting: n_processors = 1 Generating Sample Split Group: 0 / 10 Generating Sample Split Group: 1 / 10 Generating Sample Split Group: 2 / 10 Generating Sample Split Group: 3 / 10 Generating Sample Split Group: 4 / 10 Generating Sample Split Group: 5 / 10 Generating Sample Split Group: 6 / 10 Generating Sample Split Group: 7 / 10 Generating Sample Split Group: 8 / 10 Generating Sample Split Group: 9 / 10 Saltelli Sampling Size: (18000, 8) 100% 18000/18000 [00:33<00:00, 535.39it/s] Evaluation of Samples completed Elapsed Time: 0.009760 secs Global maximum memory usage: 160.40 (mb) Exported: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/sensitivity_results.json Exported: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/metadata.json upload: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/metadata.json to s3://aws-do-pm-alex/model/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/metadata.json upload: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/sensitivity_results.json to s3://aws-do-pm-alex/model/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/sensitivity_results.json upload: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/trip_v_total_first_order.png to s3://aws-do-pm-alex/model/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/trip_v_total_first_order.png upload: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/trip_v_interaction.png to s3://aws-do-pm-alex/model/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/trip_v_interaction.png upload: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/salitelli_eval.csv to s3://aws-do-pm-alex/model/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/salitelli_eval.csv upload: wd/data/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/salitelli_samples.csv to s3://aws-do-pm-alex/model/bxsazyw7ge37ryx0ysdr94tz5gh8ae36/salitelli_samples.csv
================================================================================= Deploying model edlkgt40ngr4pddtu0xx519s8i9bjg91 as a gRPC service ... ================================================================================= Task: inserting edge from model/edlkgt40ngr4pddtu0xx519s8i9bjg91 to task/ysak4szm7pzg0fv0mkryoxsb89ghhchk task/ysak4szm7pzg0fv0mkryoxsb89ghhchk Executing task from wd/task/ysak4szm7pzg0fv0mkryoxsb89ghhchk/task.json ... Executing PM task: ['docker', 'run', '-d', '--hostname', 'service-fmbqlx0fjmx3z454g7qz0vx32gc018pj', '--name', 'service-fmbqlx0fjmx3z454g7qz0vx32gc018pj', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_serve_grpc:latest', 'python', '/src/python/technique/model_serve_grpc/model_server_startup.py', '--config', 'wd/task/ysak4szm7pzg0fv0mkryoxsb89ghhchk/task.json'] service/fmbqlx0fjmx3z454g7qz0vx32gc018pj Task is waiting for wd/service/fmbqlx0fjmx3z454g7qz0vx32gc018pj/metadata.json ... Return code: 0 2d30d9c8fb393001785ba68c81a0d9a814b2c7dd96a219ef40b3573019730125 SERVICE_ID=fmbqlx0fjmx3z454g7qz0vx32gc018pj SERVICE_ID_RAW=SERVICE_ID=fmbqlx0fjmx3z454g7qz0vx32gc018pj SERVICE_ID=fmbqlx0fjmx3z454g7qz0vx32gc018pj SERVICE_ID=fmbqlx0fjmx3z454g7qz0vx32gc018pj vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Predicting route 2 for vehicle 5 ...
================================================================================= Selecting new data for vehicle # 5, route # 2 ... ================================================================================= Task: inserting edge from data/vxm9pykrwrdt401m22dbmqqn8x98oeme to task/ton5opcspfn9jic663eionp0zbdtpw2o task/ton5opcspfn9jic663eionp0zbdtpw2o Executing task from wd/task/ton5opcspfn9jic663eionp0zbdtpw2o/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-ev_datagen_em:latest', 'python', '/src/python/example/ev/technique/ev_data_select/extract_filter_data.py', '--config', 'wd/task/ton5opcspfn9jic663eionp0zbdtpw2o/task.json'] data/bzf1iqbuvxx2e9en0owt5wzt0zzt4coy Return code: 0 Executing data selection task: wd/task/ton5opcspfn9jic663eionp0zbdtpw2o/task.json ... Len of Data sets: 101 {'vehicle_id': 'V5', 'route_id': '2'} Len of Filtered Data: 1 Exported Data: wd/data/bzf1iqbuvxx2e9en0owt5wzt0zzt4coy/data.json Exported Data: wd/data/bzf1iqbuvxx2e9en0owt5wzt0zzt4coy/metadata.json NEW_DATA_ID=bzf1iqbuvxx2e9en0owt5wzt0zzt4coy
================================================================================= Predicting with model service fmbqlx0fjmx3z454g7qz0vx32gc018pj and data bzf1iqbuvxx2e9en0owt5wzt0zzt4coy ... ================================================================================= Task: inserting edge from data/bzf1iqbuvxx2e9en0owt5wzt0zzt4coy to task/gy8b7sxffam6qs9bztqzccyy9gouor6v Task: inserting edge from model/edlkgt40ngr4pddtu0xx519s8i9bjg91 to task/gy8b7sxffam6qs9bztqzccyy9gouor6v Task: inserting edge from service/fmbqlx0fjmx3z454g7qz0vx32gc018pj to task/gy8b7sxffam6qs9bztqzccyy9gouor6v task/gy8b7sxffam6qs9bztqzccyy9gouor6v Executing task from wd/task/gy8b7sxffam6qs9bztqzccyy9gouor6v/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_predict_grpc:latest', 'python', '/src/python/technique/model_predict_grpc/predict_model_data.py', '--config', 'wd/task/gy8b7sxffam6qs9bztqzccyy9gouor6v/task.json'] data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8 Return code: 0 Exported: wd/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/metadata.json upload: wd/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/nominal_pred.csv to s3://aws-do-pm-alex/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/nominal_pred.csv upload: wd/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/prediction.png to s3://aws-do-pm-alex/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/prediction.png upload: wd/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/metadata.json to s3://aws-do-pm-alex/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/metadata.json upload: wd/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/uncertainty_pred.csv to s3://aws-do-pm-alex/data/wby50vqtdixhsb9hhyf4jp9zjmd0tcs8/uncertainty_pred.csv PREDICTED_DATA_ID=wby50vqtdixhsb9hhyf4jp9zjmd0tcs8 PREDICTION_ERROR=0.05026985436366428 vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Updating model for vehicle for vehicle 5 ... Using service id fmbqlx0fjmx3z454g7qz0vx32gc018pj
================================================================================= Updating model fmbqlx0fjmx3z454g7qz0vx32gc018pj using data bzf1iqbuvxx2e9en0owt5wzt0zzt4coy ... ================================================================================= Task: inserting edge from data/bzf1iqbuvxx2e9en0owt5wzt0zzt4coy to task/kpb9q6yhhxda34xc8f2zz1yx4ez4xlcq Task: inserting edge from model/edlkgt40ngr4pddtu0xx519s8i9bjg91 to task/kpb9q6yhhxda34xc8f2zz1yx4ez4xlcq Task: inserting edge from service/fmbqlx0fjmx3z454g7qz0vx32gc018pj to task/kpb9q6yhhxda34xc8f2zz1yx4ez4xlcq task/kpb9q6yhhxda34xc8f2zz1yx4ez4xlcq Executing task from wd/task/kpb9q6yhhxda34xc8f2zz1yx4ez4xlcq/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_update_ukf_grpc:latest', 'python', '/src/python/technique/model_update_ukf_grpc/update_model_data.py', '--config', 'wd/task/kpb9q6yhhxda34xc8f2zz1yx4ez4xlcq/task.json'] model/rfrvsfenoo32z1sbetksuyhymop7ghb5 Return code: 0 Initial State after UKF initialization [-6.50724417e-01 2.49376214e+00 -2.92074047e-02 -1.02104920e+00 1.07917149e-14] Starting Iterative UKF update Iteration: 1 Initial State at this iteration [-6.50724417e-01 2.49376214e+00 -2.92074047e-02 -1.02104920e+00 1.07917149e-14] Updated State at this iteration [-4.82363075e-01 2.42827397e+00 -2.92074047e-02 -1.08053121e+00 1.07917147e-14] Iteration: 2 Initial State at this iteration [-4.82363075e-01 2.42827397e+00 -2.92074047e-02 -1.08053121e+00 1.07917147e-14] Updated State at this iteration [-4.10907412e-01 2.39725706e+00 -2.92074047e-02 -1.11416463e+00 1.07908474e-14] Iteration: 3 Initial State at this iteration [-4.10907412e-01 2.39725706e+00 -2.92074047e-02 -1.11416463e+00 1.07908474e-14] Updated State at this iteration [-3.87493854e-01 2.39080620e+00 -2.92074047e-02 -1.12841773e+00 1.07904135e-14] Iteration: 4 Initial State at this iteration [-3.87493854e-01 2.39080620e+00 -2.92074047e-02 -1.12841773e+00 1.07904135e-14] Updated State at this iteration [-3.79796387e-01 2.39344652e+00 -2.92074047e-02 -1.13718395e+00 1.07899799e-14] Iteration: 5 Initial State at this iteration [-3.79796387e-01 2.39344652e+00 -2.92074047e-02 -1.13718395e+00 1.07899799e-14] Updated State at this iteration [-3.76945737e-01 2.39991888e+00 -2.92074047e-02 -1.14513910e+00 1.07899800e-14] Iteration: 6 Initial State at this iteration [-3.76945737e-01 2.39991888e+00 -2.92074047e-02 -1.14513910e+00 1.07899800e-14] Updated State at this iteration [-3.75531927e-01 2.40853409e+00 -2.92074047e-02 -1.15371659e+00 1.07899798e-14] Iteration: 7 Initial State at this iteration [-3.75531927e-01 2.40853409e+00 -2.92074047e-02 -1.15371659e+00 1.07899798e-14] Updated State at this iteration [-3.74493898e-01 2.41872124e+00 -2.92074047e-02 -1.16332378e+00 1.07899801e-14] Iteration: 8 Initial State at this iteration [-3.74493898e-01 2.41872124e+00 -2.92074047e-02 -1.16332378e+00 1.07899801e-14] Updated State at this iteration [-3.73504842e-01 2.43025826e+00 -2.92074047e-02 -1.17404636e+00 1.07908473e-14] Iteration: 9 Initial State at this iteration [-3.73504842e-01 2.43025826e+00 -2.92074047e-02 -1.17404636e+00 1.07908473e-14] Updated State at this iteration [-3.72466847e-01 2.44302132e+00 -2.92074047e-02 -1.18586096e+00 1.07917148e-14] Iteration: 10 Initial State at this iteration [-3.72466847e-01 2.44302132e+00 -2.92074047e-02 -1.18586096e+00 1.07917148e-14] Updated State at this iteration [-3.71353727e-01 2.45692187e+00 -2.92074047e-02 -1.19871382e+00 1.07925820e-14] Iteration: 11 Initial State at this iteration [-3.71353727e-01 2.45692187e+00 -2.92074047e-02 -1.19871382e+00 1.07925820e-14] Updated State at this iteration [-3.70162021e-01 2.47186879e+00 -2.92074047e-02 -1.21252984e+00 1.07925821e-14] Iteration: 12 Initial State at this iteration [-3.70162021e-01 2.47186879e+00 -2.92074047e-02 -1.21252984e+00 1.07925821e-14] Updated State at this iteration [-3.68894888e-01 2.48778046e+00 -2.92074047e-02 -1.22723631e+00 1.07917147e-14] Iteration: 13 Initial State at this iteration [-3.68894888e-01 2.48778046e+00 -2.92074047e-02 -1.22723631e+00 1.07917147e-14] Updated State at this iteration [-3.67557991e-01 2.50457495e+00 -2.92074047e-02 -1.24275839e+00 1.07917149e-14] Iteration: 14 Initial State at this iteration [-3.67557991e-01 2.50457495e+00 -2.92074047e-02 -1.24275839e+00 1.07917149e-14] Updated State at this iteration [-3.66158385e-01 2.52215557e+00 -2.92074047e-02 -1.25900701e+00 1.07917147e-14] Iteration: 15 Initial State at this iteration [-3.66158385e-01 2.52215557e+00 -2.92074047e-02 -1.25900701e+00 1.07917147e-14] Updated State at this iteration [-3.64702823e-01 2.54043889e+00 -2.92074047e-02 -1.27590512e+00 1.07899794e-14] Iteration: 16 Initial State at this iteration [-3.64702823e-01 2.54043889e+00 -2.92074047e-02 -1.27590512e+00 1.07899794e-14] Updated State at this iteration [-3.63198547e-01 2.55933677e+00 -2.92074047e-02 -1.29337100e+00 1.07882449e-14] Iteration: 17 Initial State at this iteration [-3.63198547e-01 2.55933677e+00 -2.92074047e-02 -1.29337100e+00 1.07882449e-14] Updated State at this iteration [-3.61652360e-01 2.57876305e+00 -2.92074047e-02 -1.31132519e+00 1.07882454e-14] Iteration: 18 Initial State at this iteration [-3.61652360e-01 2.57876305e+00 -2.92074047e-02 -1.31132519e+00 1.07882454e-14] Updated State at this iteration [-3.60070969e-01 2.59863508e+00 -2.92074047e-02 -1.32969110e+00 1.07882454e-14] Iteration: 19 Initial State at this iteration [-3.60070969e-01 2.59863508e+00 -2.92074047e-02 -1.32969110e+00 1.07882454e-14] Updated State at this iteration [-3.58459954e-01 2.61887425e+00 -2.92074047e-02 -1.34839669e+00 1.07882455e-14] Iteration: 20 Initial State at this iteration [-3.58459954e-01 2.61887425e+00 -2.92074047e-02 -1.34839669e+00 1.07882455e-14] Updated State at this iteration [-3.56826003e-01 2.63940263e+00 -2.92074047e-02 -1.36736965e+00 1.07899801e-14] Iteration: 21 Initial State at this iteration [-3.56826003e-01 2.63940263e+00 -2.92074047e-02 -1.36736965e+00 1.07899801e-14] Updated State at this iteration [-3.55175323e-01 2.66014244e+00 -2.92074047e-02 -1.38653769e+00 1.07917148e-14] Iteration: 22 Initial State at this iteration [-3.55175323e-01 2.66014244e+00 -2.92074047e-02 -1.38653769e+00 1.07917148e-14] Updated State at this iteration [-3.53512790e-01 2.68102975e+00 -2.92074047e-02 -1.40584223e+00 1.07917149e-14] Iteration: 23 Initial State at this iteration [-3.53512790e-01 2.68102975e+00 -2.92074047e-02 -1.40584223e+00 1.07917149e-14] Updated State at this iteration [-3.51843985e-01 2.70199912e+00 -2.92074047e-02 -1.42522244e+00 1.07917148e-14] Iteration: 24 Initial State at this iteration [-3.51843985e-01 2.70199912e+00 -2.92074047e-02 -1.42522244e+00 1.07917148e-14] Updated State at this iteration [-3.50173983e-01 2.72298178e+00 -2.92074047e-02 -1.44461506e+00 1.07899797e-14] Iteration: 25 Initial State at this iteration [-3.50173983e-01 2.72298178e+00 -2.92074047e-02 -1.44461506e+00 1.07899797e-14] Updated State at this iteration [-3.48506995e-01 2.74392279e+00 -2.92074047e-02 -1.46396932e+00 1.07882455e-14] Iteration: 26 Initial State at this iteration [-3.48506995e-01 2.74392279e+00 -2.92074047e-02 -1.46396932e+00 1.07882455e-14] Updated State at this iteration [-3.46847309e-01 2.76477558e+00 -2.92074047e-02 -1.48324182e+00 1.07882452e-14] Iteration: 27 Initial State at this iteration [-3.46847309e-01 2.76477558e+00 -2.92074047e-02 -1.48324182e+00 1.07882452e-14] Updated State at this iteration [-3.45198318e-01 2.78548988e+00 -2.92074047e-02 -1.50238671e+00 1.07882452e-14] Iteration: 28 Initial State at this iteration [-3.45198318e-01 2.78548988e+00 -2.92074047e-02 -1.50238671e+00 1.07882452e-14] Updated State at this iteration [-3.43564152e-01 2.80602075e+00 -2.92074047e-02 -1.52136183e+00 1.07882456e-14] Iteration: 29 Initial State at this iteration [-3.43564152e-01 2.80602075e+00 -2.92074047e-02 -1.52136183e+00 1.07882456e-14] Updated State at this iteration [-3.41947366e-01 2.82633009e+00 -2.92074047e-02 -1.54013243e+00 1.07882453e-14] Iteration: 30 Initial State at this iteration [-3.41947366e-01 2.82633009e+00 -2.92074047e-02 -1.54013243e+00 1.07882453e-14] Updated State at this iteration [-3.40351556e-01 2.84638505e+00 -2.92074047e-02 -1.55866739e+00 1.07882456e-14] Iteration: 31 Initial State at this iteration [-3.40351556e-01 2.84638505e+00 -2.92074047e-02 -1.55866739e+00 1.07882456e-14] Updated State at this iteration [-3.38777644e-01 2.86615551e+00 -2.92074047e-02 -1.57693982e+00 1.07882454e-14] Iteration: 32 Initial State at this iteration [-3.38777644e-01 2.86615551e+00 -2.92074047e-02 -1.57693982e+00 1.07882454e-14] Updated State at this iteration [-3.37228672e-01 2.88561659e+00 -2.92074047e-02 -1.59492630e+00 1.07917157e-14] Iteration: 33 Initial State at this iteration [-3.37228672e-01 2.88561659e+00 -2.92074047e-02 -1.59492630e+00 1.07917157e-14] Updated State at this iteration [-3.35706823e-01 2.90473577e+00 -2.92074047e-02 -1.61259667e+00 1.07899799e-14] Iteration: 34 Initial State at this iteration [-3.35706823e-01 2.90473577e+00 -2.92074047e-02 -1.61259667e+00 1.07899799e-14] Updated State at this iteration [-3.34213587e-01 2.92349726e+00 -2.92074047e-02 -1.62993645e+00 1.07917147e-14] Iteration: 35 Initial State at this iteration [-3.34213587e-01 2.92349726e+00 -2.92074047e-02 -1.62993645e+00 1.07917147e-14] Updated State at this iteration [-3.32749842e-01 2.94188525e+00 -2.92074047e-02 -1.64693120e+00 1.07917148e-14] Iteration: 36 Initial State at this iteration [-3.32749842e-01 2.94188525e+00 -2.92074047e-02 -1.64693120e+00 1.07917148e-14] Updated State at this iteration [-3.31316961e-01 2.95988770e+00 -2.92074047e-02 -1.66356947e+00 1.07917148e-14] Iteration: 37 Initial State at this iteration [-3.31316961e-01 2.95988770e+00 -2.92074047e-02 -1.66356947e+00 1.07917148e-14] Updated State at this iteration [-3.29915759e-01 2.97749458e+00 -2.92074047e-02 -1.67984199e+00 1.07917150e-14] Iteration: 38 Initial State at this iteration [-3.29915759e-01 2.97749458e+00 -2.92074047e-02 -1.67984199e+00 1.07917150e-14] Updated State at this iteration [-3.28547249e-01 2.99468664e+00 -2.92074047e-02 -1.69573127e+00 1.07917146e-14] Iteration: 39 Initial State at this iteration [-3.28547249e-01 2.99468664e+00 -2.92074047e-02 -1.69573127e+00 1.07917146e-14] Updated State at this iteration [-3.27211959e-01 3.01146178e+00 -2.92074047e-02 -1.71123537e+00 1.07917149e-14] Iteration: 40 Initial State at this iteration [-3.27211959e-01 3.01146178e+00 -2.92074047e-02 -1.71123537e+00 1.07917149e-14] Updated State at this iteration [-3.25910216e-01 3.02781970e+00 -2.92074047e-02 -1.72635351e+00 1.07917146e-14] Exported: wd/model/rfrvsfenoo32z1sbetksuyhymop7ghb5/model.json Exported: wd/model/rfrvsfenoo32z1sbetksuyhymop7ghb5/metadata.json Export task status: wd/task/kpb9q6yhhxda34xc8f2zz1yx4ez4xlcq/task_status.json upload: wd/model/rfrvsfenoo32z1sbetksuyhymop7ghb5/metadata.json to s3://aws-do-pm-alex/model/rfrvsfenoo32z1sbetksuyhymop7ghb5/metadata.json upload: wd/model/rfrvsfenoo32z1sbetksuyhymop7ghb5/model.json to s3://aws-do-pm-alex/model/rfrvsfenoo32z1sbetksuyhymop7ghb5/model.json UPDATED_MODEL_ID=rfrvsfenoo32z1sbetksuyhymop7ghb5 Configuring service id fmbqlx0fjmx3z454g7qz0vx32gc018pj with model rfrvsfenoo32z1sbetksuyhymop7ghb5 Task: inserting edge from model/rfrvsfenoo32z1sbetksuyhymop7ghb5 to task/sz48ptwjbhwtnwl3vdql0oaay444fcr9 Task: inserting edge from service/fmbqlx0fjmx3z454g7qz0vx32gc018pj to task/sz48ptwjbhwtnwl3vdql0oaay444fcr9 task/sz48ptwjbhwtnwl3vdql0oaay444fcr9 Executing task from wd/task/sz48ptwjbhwtnwl3vdql0oaay444fcr9/task.json ... Executing PM task: ['python', '/src/python/pm/service/service_configure.py', '--config', 'wd/task/sz48ptwjbhwtnwl3vdql0oaay444fcr9/task.json'] service/fmbqlx0fjmx3z454g7qz0vx32gc018pj Return code: 0 Executing plug model into service ... Using settings from specified config file wd/task/sz48ptwjbhwtnwl3vdql0oaay444fcr9/task.json Saving updated service metadata to wd/service/fmbqlx0fjmx3z454g7qz0vx32gc018pj/metadata.json vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Predicting route 3 for vehicle 5 ...
================================================================================= Selecting new data for vehicle # 5, route # 3 ... ================================================================================= Task: inserting edge from data/vxm9pykrwrdt401m22dbmqqn8x98oeme to task/czca2qw0y52j6n1mc885jix629fse6cp task/czca2qw0y52j6n1mc885jix629fse6cp Executing task from wd/task/czca2qw0y52j6n1mc885jix629fse6cp/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-ev_datagen_em:latest', 'python', '/src/python/example/ev/technique/ev_data_select/extract_filter_data.py', '--config', 'wd/task/czca2qw0y52j6n1mc885jix629fse6cp/task.json'] data/av0qmtmqidmgjjdggivv86l46ypn6y74 Return code: 0 Executing data selection task: wd/task/czca2qw0y52j6n1mc885jix629fse6cp/task.json ... Len of Data sets: 101 {'vehicle_id': 'V5', 'route_id': '3'} Len of Filtered Data: 1 Exported Data: wd/data/av0qmtmqidmgjjdggivv86l46ypn6y74/data.json Exported Data: wd/data/av0qmtmqidmgjjdggivv86l46ypn6y74/metadata.json NEW_DATA_ID=av0qmtmqidmgjjdggivv86l46ypn6y74
================================================================================= Predicting with model service fmbqlx0fjmx3z454g7qz0vx32gc018pj and data av0qmtmqidmgjjdggivv86l46ypn6y74 ... ================================================================================= Task: inserting edge from data/av0qmtmqidmgjjdggivv86l46ypn6y74 to task/yzcplt54sz3mq0ay7fk61kcgr0ftmmm6 Task: inserting edge from model/edlkgt40ngr4pddtu0xx519s8i9bjg91 to task/yzcplt54sz3mq0ay7fk61kcgr0ftmmm6 Task: inserting edge from service/fmbqlx0fjmx3z454g7qz0vx32gc018pj to task/yzcplt54sz3mq0ay7fk61kcgr0ftmmm6 task/yzcplt54sz3mq0ay7fk61kcgr0ftmmm6 Executing task from wd/task/yzcplt54sz3mq0ay7fk61kcgr0ftmmm6/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_predict_grpc:latest', 'python', '/src/python/technique/model_predict_grpc/predict_model_data.py', '--config', 'wd/task/yzcplt54sz3mq0ay7fk61kcgr0ftmmm6/task.json'] data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw Return code: 0 Exported: wd/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/metadata.json upload: wd/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/metadata.json to s3://aws-do-pm-alex/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/metadata.json upload: wd/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/nominal_pred.csv to s3://aws-do-pm-alex/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/nominal_pred.csv upload: wd/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/prediction.png to s3://aws-do-pm-alex/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/prediction.png upload: wd/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/uncertainty_pred.csv to s3://aws-do-pm-alex/data/ji66d8wwh7mqy79r591dgtyvpe7p7ujw/uncertainty_pred.csv PREDICTED_DATA_ID=ji66d8wwh7mqy79r591dgtyvpe7p7ujw PREDICTION_ERROR=0.015177638688635916 vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Predicting route 4 for vehicle 5 ...
================================================================================= Selecting new data for vehicle # 5, route # 4 ... ================================================================================= Task: inserting edge from data/vxm9pykrwrdt401m22dbmqqn8x98oeme to task/tjjx2ot1fcvvso08p3mvi5kfk3nvmsrk task/tjjx2ot1fcvvso08p3mvi5kfk3nvmsrk Executing task from wd/task/tjjx2ot1fcvvso08p3mvi5kfk3nvmsrk/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-ev_datagen_em:latest', 'python', '/src/python/example/ev/technique/ev_data_select/extract_filter_data.py', '--config', 'wd/task/tjjx2ot1fcvvso08p3mvi5kfk3nvmsrk/task.json'] data/ra8imryxhun7xjn61vnb862szodnada0 Return code: 0 Executing data selection task: wd/task/tjjx2ot1fcvvso08p3mvi5kfk3nvmsrk/task.json ... Len of Data sets: 101 {'vehicle_id': 'V5', 'route_id': '4'} Len of Filtered Data: 1 Exported Data: wd/data/ra8imryxhun7xjn61vnb862szodnada0/data.json Exported Data: wd/data/ra8imryxhun7xjn61vnb862szodnada0/metadata.json NEW_DATA_ID=ra8imryxhun7xjn61vnb862szodnada0
================================================================================= Predicting with model service fmbqlx0fjmx3z454g7qz0vx32gc018pj and data ra8imryxhun7xjn61vnb862szodnada0 ... ================================================================================= Task: inserting edge from data/ra8imryxhun7xjn61vnb862szodnada0 to task/yzsohcz054lu9qp99uoc5ahx0jk4que7 Task: inserting edge from model/edlkgt40ngr4pddtu0xx519s8i9bjg91 to task/yzsohcz054lu9qp99uoc5ahx0jk4que7 Task: inserting edge from service/fmbqlx0fjmx3z454g7qz0vx32gc018pj to task/yzsohcz054lu9qp99uoc5ahx0jk4que7 task/yzsohcz054lu9qp99uoc5ahx0jk4que7 Executing task from wd/task/yzsohcz054lu9qp99uoc5ahx0jk4que7/task.json ... Executing PM task: ['docker', 'run', '-it', '--rm', '-v', '/Users/iankouls/Desktop/Alex/wd/projects/gitlab.aws.dev/pm/aws-do-pm/wd:/wd', '--network', 'aws-do-pm_default', 'aws-do-pm-model_predict_grpc:latest', 'python', '/src/python/technique/model_predict_grpc/predict_model_data.py', '--config', 'wd/task/yzsohcz054lu9qp99uoc5ahx0jk4que7/task.json'] data/cgde10q3y7iqsrv2vysi6pe8at13mkhb Return code: 0 Exported: wd/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/metadata.json upload: wd/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/metadata.json to s3://aws-do-pm-alex/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/metadata.json upload: wd/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/nominal_pred.csv to s3://aws-do-pm-alex/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/nominal_pred.csv upload: wd/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/prediction.png to s3://aws-do-pm-alex/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/prediction.png upload: wd/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/uncertainty_pred.csv to s3://aws-do-pm-alex/data/cgde10q3y7iqsrv2vysi6pe8at13mkhb/uncertainty_pred.csv PREDICTED_DATA_ID=cgde10q3y7iqsrv2vysi6pe8at13mkhb PREDICTION_ERROR=0.022176428830930028
================================================================================= Destroying gRPC service fmbqlx0fjmx3z454g7qz0vx32gc018pj ... ================================================================================= Task: inserting edge from service/fmbqlx0fjmx3z454g7qz0vx32gc018pj to task/kewqcgofqeycge4fkcxh6wihu23ymm8g task/kewqcgofqeycge4fkcxh6wihu23ymm8g Executing task from wd/task/kewqcgofqeycge4fkcxh6wihu23ymm8g/task.json ... Executing PM task: ['python', '/src/python/pm/service/service_destroy.py', '--config', 'wd/task/kewqcgofqeycge4fkcxh6wihu23ymm8g/task.json'] trash/lng05ehzixhu0n6305azkjw764cz1yi0 Return code: 0 ['docker', 'rm', '-f', 'service-fmbqlx0fjmx3z454g7qz0vx32gc018pj'] service-fmbqlx0fjmx3z454g7qz0vx32gc018pj
================================================================================= EV demo completed. Browse model and data results at http://localhost:8501 See resulting graph at http://localhost:8529 ================================================================================= Launch end time: 20220224-024302 Total elapsed time: 4 minutes
The `ev-demo` script takes about 4 minutes to complete. Data is stored in the configured S3 Bucket and in the graph database.
Results from ev-demo
will be available both in the platform UI and the graphdb UI.
Visit http://localhost:8501/ to open the platform UI. You will be able to see data.
Expand the menu from the upper right corner of the app and select Settings, then under APPEARANCE, select "Wide mode". Then click the "x" to close the settings dialog box. This makes the app screen wider and allows a better view of the data.
Pull down the "Select Data" menu and choose the second dataset with description "Selected EV data with filter...", then pull down the "Select File Type" menu and choose "png_files".
This plot shows the input data for trip velocity, trip distance, and trip load.
Pull down the "Select File" menu and choose "output_data_plot.png".
This plot shows the output data for battery voltage.
Next, pull down the "Select Data" menu again, and choose the dataset following the one you selected previously, named "metadata.json".
This plot shows the predicted output with a band of uncertainty around the predicted values.
To view model sensitivity analysis plots, pull down the "Select Data" menu and choose the data set with description "Sensitivity Analysis"
This plot shows interactions between model inputs and parameters.
Pull down the "Select File" menu and choose "trip_v_total_first_order.png".
Next, choose the "model" page from the left navigation pane. Under "Select Model" we will select the option for "Artificial Neural Network model".
This plot shows plots of actual vs. predicted values for the ANN model. Lastly, select the "mlp_train_epoch.png" file.
This plot shows the loss convergeance for the model training over 2000 epochs.
You may choose to browse the emperical data by selecting the "json_files" file Type. All of the data and models can also be accessed through the configured S3 bucket.
To view the execution graph, open the graph database UI by visiting http://localhost:8529. Log in using the credentials you configured for the graphdb user account and select the pm database.
Once you log in, you will be able to see that the database hosts a nubmer of collections.
Vertex collections are the nodes in the graph, and edge collections are the links between them. Select "GRAPHS" from the left navigation pane.
Then open the graph named "pm"
By default the last few nodes of the graph are shown with their unique IDs.
Click on the menu icon in the upper rigth corner to open the graph settings. Enter "node_name" under the "Nodes->Labels" section, and click the "Save" button.
The nodes in the graph now have read-friendly labels. Select the "share" icon from the top right menu and click on the "Load full graph" button.
The full execution graph of the ev-demo
run is displayed.
To make the graph easier to understand the nodes can be rearranged as the following.
The root node of the graph is a method for technique registration in the graph database. Several "register" tasks are spawned using this technique during the initialization phase and all system (embedded) techniques are registered. EV data is generated outside of the system and the raw dara set is imported/registered in aws-do-pm. Similarly the model training dataset is registered. A model is built, using the training dataset and the ann model building technique. A custom technique for EV data selection is registered and then used to select data for route 1 of some vehicle from the raw dataset. This data is used to update the model. A model is deployed as a gRPC service and the service is updated as needed. Using the EV data selection technique Data from the raw dataset is extracted for routes 2,3, and 4. The extracted data is used for prediction. The prediction error is compared with a threshold and the model is updated as needed. Once all the predictions are completed and the model services are no longer needed, they are destroyed. The output of the run are predicted data artifacts and an updated model.
An execution graph like this one, helps explain the operation of the system and the sequence of events that occurred during a particular run. The metadata contained within the graph can be used to extract valuable insights about the workload.
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