I am running a fairly large model in OPL, it has 576723 constraints, 1132515 variables, 3855 binary, 27150711 Non zero co-efficients.
At about 12 minutes the optimisation stops, it says 1 solution but displays no solution. In the profiler tab I get the Overflow occurred, please use oplrun -profile message.
The Engine log looks as below ( Updated on 24th Sep):
Found incumbent of value 0.000000 after 0.02 sec. (30.57 ticks)
Presolve has eliminated 65039 rows and 117138 columns...
Presolve has improved bounds 1277962 times...
Aggregator has done 20701 substitutions...
Aggregator has done 42701 substitutions...
Aggregator has done 65901 substitutions...
Aggregator has done 89601 substitutions...
Aggregator has done 114601 substitutions...
Aggregator has done 141901 substitutions...
Aggregator has done 172001 substitutions...
Aggregator has done 205101 substitutions...
Aggregator has done 242201 substitutions...
Aggregator has done 285501 substitutions...
Aggregator has done 339801 substitutions...
Aggregator has done 425001 substitutions...
Tried aggregator 2 times.
MIP Presolve eliminated 65049 rows and 119516 columns.
MIP Presolve modified 3304560 coefficients.
Aggregator did 505533 substitutions.
Reduced MIP has 6138 rows, 507466 columns, and 15507869 nonzeros.
Reduced MIP has 2761 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 52.98 sec. (140577.29 ticks)
Tried aggregator 1 time.
Reduced MIP has 6138 rows, 507466 columns, and 15507869 nonzeros.
Reduced MIP has 2761 binaries, 0 generals, 0 SOSs, and 0 indicators.
Presolve time = 4.59 sec. (4115.32 ticks)
Probing time = 0.33 sec. (193.08 ticks)
Clique table members: 674.
MIP emphasis: balance optimality and feasibility.
MIP search method: dynamic search.
Parallel mode: deterministic, using up to 16 threads.
Root relaxation solution time = 5983.52 sec. (4525135.08 ticks)
Nodes Cuts/
Node Left Objective IInf Best Integer Best Bound ItCnt Gap
* 0+ 0 0.0000 4585.0158 ---
0 0 1414.4727 839 0.0000 1414.4727 74713 ---
0 0 cutoff 0.0000 5409203 ---
Elapsed time = 19950.47 sec. (18809991.19 ticks, tree = 0.01 MB, solutions = 1)
Clique cuts applied: 2
Cover cuts applied: 57
Implied bound cuts applied: 91
Flow cuts applied: 121
Mixed integer rounding cuts applied: 236
Gomory fractional cuts applied: 4
Root node processing (before b&c):
Real time = 19950.63 sec. (18810086.10 ticks)
Parallel b&c, 16 threads:
Real time = 0.00 sec. (0.00 ticks)
Sync time (average) = 0.00 sec.
Wait time (average) = 0.00 sec.
------------
Total (root+branch&cut) = 19950.63 sec. (18810086.10 ticks)
<<< solve
OBJECTIVE: 0
<<< post process
<<< done
Profiler Report
Time PeakMemory SelfTime LocalMem Count Nodes Description
20,190.282 100% 9.902 G 100% 0.753 0% 879.507 M 9% 1 126 TOTAL
0.000 0% 0 B 0% 0.000 0% 256 B 0% 1 1 READING MODEL DEFINITION Ashes200_data
38.626 0% 840.113 M 8% 0.128 0% 721.418 M 7% 1 97 LOADING MODEL Ashes200_data-0000025C59804DD8
7.277 0% 103.191 M 1% 2.750 0% 84.547 M 1% 1 52 LOADING DATA D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data 3yr.dat
0.005 0% 28 K 0% 0.005 0% 400 B 0% 1 1 INIT TimePeriods at 13:1-24 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.003 0% 8 K 0% 0.003 0% 54.047 K 0% 1 1 INIT PitBlocks at 14:1-25 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 16 K 0% 0.001 0% 35.641 K 0% 1 1 INIT DumpBlocks at 15:1-25 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 576 B 0% 1 1 INIT Stockpiles at 17:1-25 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 424 B 0% 1 1 INIT Plants at 19:1-21 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.329 0% 22.695 M 0% 0.329 0% 18.362 M 0% 1 1 INIT Pathid at 21:1-22 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 904 B 0% 1 1 INIT AverageGrade at 48:1-37 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 864 B 0% 1 1 INIT DensityGradeBins at 49:1-42 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 8 K 0% 0.002 0% 5.531 K 0% 1 1 INIT grade at 26:1-30 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 5.516 K 0% 1 1 INIT oreTons at 27:1-32 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 5.562 K 0% 1 1 INIT density at 28:1-32 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 5.523 K 0% 1 1 INIT wasteVolume at 29:1-36 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.068 0% 0 B 0% 0.068 0% 5.523 K 0% 1 1 INIT totalVolume at 30:1-36 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 8 K 0% 0.002 0% 3.773 K 0% 1 1 INIT dumpVolume at 32:1-35 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 872 B 0% 1 1 INIT resourceMaxCap at 35:1-40 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 840 B 0% 1 1 INIT resourceMinCap at 36:1-40 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.013 0% 0 B 0% 0.013 0% 1.484 K 0% 1 1 INIT processMinCap at 37:1-46 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 1.516 K 0% 1 1 INIT processMaxCap at 38:1-46 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 1.477 K 0% 1 1 INIT GradeMin at 39:1-42 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 936 B 0% 1 1 INIT SellPrice at 41:1-35 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 840 B 0% 1 1 INIT wasteMiningCost at 42:1-41 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 840 B 0% 1 1 INIT coalMiningCost at 43:1-40 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 840 B 0% 1 1 INIT washCost at 44:1-34 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 840 B 0% 1 1 INIT HaulageCost at 45:1-37 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 848 B 0% 1 1 INIT StockPileRehandlingCost at 46:1-49 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 128 B 0% 1 1 INIT SwellFactor at 52:1-24 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 56 K 0% 0.001 0% 2.031 K 0% 1 1 INIT StockPileMaxCap at 56:1-52 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 2.094 K 0% 1 1 INIT StockPileMinCap at 55:1-52 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 128 B 0% 1 1 INIT DisountRate at 58:1-24 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 128 B 0% 1 1 INIT DumpCapacity at 60:1-25 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.323 0% 0 B 0% 0.323 0% 130.461 K 0% 1 2 INIT PitBlocksType at 287:1-27 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 976 B 0% 1 1 INIT ijk at 278:1-284:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.003 0% 0 B 0% 0.003 0% 57.203 K 0% 1 2 INIT DumpBlocksType at 273:1-34 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 872 B 0% 1 1 INIT blockType at 263:1-268:3 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.273 0% 48 K 0% 0.273 0% 90.812 K 0% 1 2 INIT PitLagInfoXYB at 79:1-25 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 872 B 0% 1 1 INIT xyz at 64:1-69:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.172 0% 0 B 0% 0.172 0% 55.266 K 0% 1 1 INIT DumpLagInfoXYB at 78:1-26 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.165 0% 0 B 0% 0.165 0% 20.453 K 0% 1 1 INIT DumpXYZ at 72:1-29 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 2.555 K 0% 1 1 INIT PlantXYZ at 73:1-26 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 2.742 K 0% 1 1 INIT StockpilesXYZ at 74:1-35 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.003 0% 40 K 0% 0.003 0% 30.953 K 0% 1 1 INIT PitXYZ at 71:1-27 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
2.463 0% 56.422 M 1% 2.463 0% 45.421 M 0% 1 2 INIT rawPbd at 131:1-20 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 1.117 K 0% 1 1 INIT Raw at 121:1-130:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.011 0% 188 K 0% 0.011 0% 174.133 K 0% 1 1 INIT rawPbm at 132:1-20 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.024 0% 652 K 0% 0.024 0% 414.375 K 0% 1 1 INIT rawPbs at 133:1-20 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.625 0% 21.031 M 0% 0.625 0% 19.388 M 0% 1 2 INIT sourceDestD at 108:1-37 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 744 B 0% 1 1 INIT sourceDestination at 103:1-106:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.005 0% 292 K 0% 0.005 0% 61.859 K 0% 1 1 INIT sourceDestM at 109:1-37 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.006 0% 240 K 0% 0.006 0% 177.469 K 0% 1 1 INIT sourceDestS at 110:1-37 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 12.562 K 0% 1 2 INIT NullVariablesSet at 450:1-40 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 744 B 0% 1 1 INIT nullVariables at 445:1-448:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
28.810 0% 463.848 M 5% 0.000 0% 416.204 M 4% 1 29 PRE PROCESSING
0.410 0% 640 K 0% 0.345 0% 649.023 K 0% 1 4 EXECUTE anonymous#1 at 90:1-8 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.065 0% 640 K 0% 0.065 0% 647.672 K 0% 1 3 INIT OntopDumpLag at 85:6-87:52 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 8 K 0% 0.000 0% 280 B 0% 1 1 INIT D at 81:11-14 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 296 B 0% 1 1 INIT BottomPitBenNo at 82:24-25 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
5.935 0% 229.957 M 2% 5.935 0% 211.206 M 2% 1 8 EXECUTE anonymous#2 at 158:1-8 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 624 B 0% 1 1 INIT emptysetd at 153:22-24 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 5.641 K 0% 1 2 INIT Pbd at 148:12-14 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 1.117 K 0% 1 1 INIT Path at 136:1-145:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 840 B 0% 1 1 INIT emptysetm at 154:22-24 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 2.875 K 0% 1 1 INIT Pbm at 149:13-15 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 792 B 0% 1 1 INIT emptysets at 155:22-24 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 2.875 K 0% 1 1 INIT Pbs at 150:12-14 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.902 0% 51.145 M 1% 0.788 0% 47.271 M 0% 1 2 EXECUTE anonymous#3 at 237:1-8 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.114 0% 51.145 M 1% 0.114 0% 47.271 M 0% 1 1 INIT hc at 233:1-31 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
1.678 0% 2.129 M 0% 1.029 0% 1.958 M 0% 1 2 EXECUTE anonymous#4 at 303:1-8 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.649 0% 2.129 M 0% 0.649 0% 1.957 M 0% 1 1 INIT OntopPit at 290:7-299:28 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
5.335 0% 117.746 M 1% 5.163 0% 106.703 M 1% 1 6 EXECUTE anonymous#5 at 367:1-8 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 624 B 0% 1 1 INIT MaxS at 364:10-12 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.061 0% 42.777 M 0% 0.061 0% 39.647 M 0% 1 1 INIT splitPitBlocksPath at 353:1-34 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 0 B 0% 0.001 0% 121.359 K 0% 1 1 INIT splitPitBlocksPathM at 354:1-35 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.001 0% 536 K 0% 0.001 0% 361.719 K 0% 1 1 INIT splitPitBlocksPathS at 355:1-35 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.109 0% 43.75 M 0% 0.109 0% 43.522 M 0% 1 1 INIT splitDumpBlocksPath at 356:1-35 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
14.550 0% 62.246 M 1% 14.436 0% 48.431 M 0% 1 6 EXECUTE anonymous#6 at 470:1-8 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 268 K 0% 0.000 0% 263.789 K 0% 1 1 INIT capBMT at 453:1-46 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.112 0% 50.91 M 1% 0.112 0% 47.161 M 0% 1 1 INIT capBDT at 455:1-50 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 536 K 0% 0.002 0% 555.375 K 0% 1 1 INIT capBST at 457:1-50 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 268 K 0% 0.000 0% 143.125 K 0% 1 1 INIT capBT at 459:1-35 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 147.5 K 0% 1 1 INIT capschedulePit at 461:1-44 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
2.101 0% 256.062 M 3% 0.009 0% 218.874 M 2% 1 10 INIT npv at 699:19-703:108 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 5.156 K 0% 1 1 INIT Dfbmt at 684:1-103 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.006 0% 0 B 0% 0.006 0% 419.82 K 0% 1 1 INIT Xbmt at 672:1-89 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 3.914 K 0% 1 1 INIT Dfbdt at 687:1-59 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
1.350 0% 108.723 M 1% 1.350 0% 106.255 M 1% 1 1 INIT Xbdt at 673:1-91 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 3.914 K 0% 1 1 INIT Dfbst at 690:1-58 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.012 0% 1.051 M 0% 0.012 0%1,020.648 K 0% 1 1 INIT Xbst at 674:1-91 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 4.805 K 0% 1 1 INIT Dfsmt at 694:1-87 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 3.758 K 0% 1 1 INIT Xsmt at 663:1-51 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.724 0% 109.227 M 1% 0.724 0% 106.847 M 1% 1 1 INIT ypt at 677:1-47 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.018 0% 16.035 M 0% 0.018 0% 315.047 K 0% 1 1 INIT schedulePit at 676:1-87 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.287 0% 0 B 0% 0.287 0% 875.367 K 0% 1 1 INIT OnBelowDump at 313:6-323:47 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.002 0% 0 B 0% 0.002 0% 202.047 K 0% 1 1 INIT scheduleDump at 668:1-52 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 2.156 K 0% 1 1 INIT StockPileVol at 54:1-45 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.003 0% 272 K 0% 0.003 0% 280.82 K 0% 1 1 INIT zbt at 675:1-70 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
177.026 1% 9.063 G 92% 10.546 0% 158.001 M 2% 1 2 EXTRACTING Ashes200_data-0000025C59804DD8
166.480 1% 8.179 G 83% 166.480 1% 17.213 M 0% 1 1 OBJECTIVE at 714:1-716:4 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
19,951.910 99% 4.281 G 43%5,989.816 30% 319.668 M 3% 1 13 CPLEX MIP Optimization
52.990 0% 389.746 M 4% 52.990 0% 389.746 M 4% 1 1 CPLEX Pre Solve
4.589 0% 256.008 M 3% 4.589 0% 256.008 M 3% 1 1 CPLEX Pre Solve
0.000 0% 0 B 0% 0.000 0% 0 B 0% 1 1 CPLEX Solve LP Relaxation
13,904.515 69% 1.05 G 11% 23.882 0% 467.602 M 5% 1 9 CPLEX Generating Cuts for Root Node
13,714.446 68% 52 K 0% 2.292 0% 78.424 M 1% 7 3 CPLEX Solve LP Relaxation
13,711.520 68% 0 B 0%13,711.520 68% 110.169 M 1% 4 1 CPLEX Solve LP Relaxation
0.634 0% 52 K 0% 0.634 0% 225.727 M 2% 1 1 CPLEX Pre Solve
165.425 1% 604.797 M 6% 0.170 0% 604.797 M 6% 1 3 CPLEX Heuristics
165.255 1% 324.707 M 3% 0.289 0% 81.177 M 1% 4 2 CPLEX Solve LP Relaxation
164.966 1% 309.051 M 3% 164.966 1% 152.584 M 2% 2 1 CPLEX Solve LP Relaxation
0.130 0% 0 B 0% 0.130 0% 0 B 0% 1 1 CPLEX Probing
0.632 0% 225.676 M 2% 0.632 0% 225.676 M 2% 1 1 CPLEX Pre Solve
21.967 0% 8.656 M 0% 0.009 0% 35.43 K 0% 1 12 POST PROCESSING
21.958 0% 8.656 M 0% 17.082 0% 39.516 K 0% 1 11 EXECUTE anonymous#7 at 1300:1-1301:0 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.011 0% 8.656 M 0% 0.011 0% 9.328 K 0% 1 2 INIT solXbmt at 1252:21-112 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 1,000 B 0% 1 1 INIT SolXbmt at 1245:1-1250:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
2.327 0% 0 B 0% 2.327 0% 7.258 K 0% 1 2 INIT solXbdt at 1263:24-118 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 1,000 B 0% 1 1 INIT SolXbdt at 1255:1-1260:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.036 0% 0 B 0% 0.036 0% 7.352 K 0% 1 2 INIT solXbst at 1275:22-117 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 1,000 B 0% 1 1 INIT SolXbst at 1267:1-1272:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 7.258 K 0% 1 2 INIT solXsmt at 1284:21-111 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 1,000 B 0% 1 1 INIT SolXsmt at 1277:1-1282:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
2.502 0% 0 B 0% 2.502 0% 6.18 K 0% 1 2 INIT solPath at 1297:21-84 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
0.000 0% 0 B 0% 0.000 0% 944 B 0% 1 1 INIT SolPath at 1291:1-1295:2 D:\PhD\Minex_Data\FINAL_PAPER2022\AshesPit200\Ashes_Pit200\Ashes200_data.mod
<<< profile
Kindly suggest how to overcome this problem.
Use better units. An objective value of 8.95478e+11 indicates you are using cents instead of billions of dollars. Also, make sure any big-M constants are not larger than needed.
Related
I am stuck at the following, I want to check the below site in an automate way if there are new packages/shares available (new boxes with a buy option essentially):
https://staking.pocketfives.com/staking/market-place
Given You don't need to log in, it should be okay to scrape afaik.
So far I have read a dozen questions and tried all the methods in the title, however I was unable to make it work. When I use Beautifulsoup or requests_html it just gives me back only the title and one other line of text, not the text in the boxes that I need. The closest I got is this:
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import pyperclip
from webdriver_manager.chrome import ChromeDriverManager
import time
import random
link='https://staking.pocketfives.com/staking/market-place'
CD=ChromeDriverManager().install()
driver = webdriver.Chrome(CD)
driver.get(link)
element=driver.find_element_by_tag_name('body')
time.sleep(random.uniform(6, 12))
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
time.sleep(random.uniform(3, 6))
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
time.sleep(random.uniform(3, 6))
element.send_keys(Keys.CONTROL,'a')
element.send_keys(Keys.CONTROL,'c')
driver.quit()
alltext=pyperclip.paste()
print(alltext)
I then check every so many minutes in a loop and compare the text from iteration x+1 to iteration x to see if anything has changed.
However this has one big problem, even when scrolling through the website, this does not copy all text! Just the last part of the site.
This is obviously not the most elegant way either, so I am very open to other solutions. I tried making it work with other packages, I just can't seem to run the dynamic scripts on the site correctly, even using render etc. I went into the HTML code but I have trouble understanding it and it consists of a lot of scripts.
Help is much appreciated!
I'm not really sure what you are after, but they do have an api. Does this get you what you want?
import requests
import pandas as pd
url = 'https://api2.pocketfives.com/FrontOfficeStake/GetStakeListRequest'
payload= {
'currentPage': '1',
'pageSize': '9999'}
jsonData = requests.post(url, json=payload).json()
df = pd.DataFrame(jsonData['stakeList'])
Output:
print(df.to_string())
stakeId sellerId sellerName sellerImageUrl venueId venueName venueImageUrl tournamentId startTime title buyIn guarantee markup offeredPercent offeredPrice thresholdPercent thresholdPrice purchaseCapPercent purchaseCapPrice soldPercent soldPrice availablePercent availablePrice note hasPassword myTransactionSummary replyCount
0 108 481 RobinPoker //cdn.pocketfives.com/v72/monthly_2021_10/6A79114C-C8C4-4EB1-8476-D1E9E01B1D83.thumb.jpeg.f629f2b49f04b8c9d2c7c272e04cd973.jpeg 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1234 2021-11-01T18:00:00 Event #61: Deepstack Championship No-Limit Hold'em 600.0 0.0 1.3 50.0 390.00 0.0 0.0 5.00 39.00 50.000 390.00 0.000 0.0 <p><span style="background-color: rgb(246, 248, 249);">*You are purchasing action for FIRST BULLET ONLY (if player get knocked out and re-enters, you DO NOT have action.)</span></p> False None 0
1 39 480 Pamsi //cdn.pocketfives.com/v72/monthly_2021_09/pam_gg.thumb.jpg.ce2c1f50b5bc3c805fa4545ab9a3227d.jpg 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1238 2021-11-03T18:00:00 Event #65: MINI Main Event No-Limit Hold’em (freezeout) 1000.0 0.0 1.1 15.0 165.00 0.0 0.0 1.00 11.00 15.000 165.00 0.000 0.0 <p>Pamela Balzano</p> False None 0
2 42 23 JonathanLittle //cdn.pocketfives.com/v72/monthly_2021_09/1004.thumb.JPG.10a21649c3a3cfef8d0ebc5d6f133ec9.JPG 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1240 2021-11-04T18:00:00 Event #67A: MAIN EVENT No-Limit Hold'em World Championship - Day 1A 10000.0 0.0 1.0 10.0 1000.00 0.0 0.0 0.10 10.00 10.000 1000.00 0.000 0.0 None False None 0
3 109 481 RobinPoker //cdn.pocketfives.com/v72/monthly_2021_10/6A79114C-C8C4-4EB1-8476-D1E9E01B1D83.thumb.jpeg.f629f2b49f04b8c9d2c7c272e04cd973.jpeg 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1244 2021-11-08T20:00:00 Event #68A: LITTLE ONE FOR ONE DROP No-Limit Hold’em 1111.0 0.0 1.2 50.0 666.60 0.0 0.0 5.00 66.66 50.000 666.60 0.000 0.0 <p><span style="background-color: rgb(246, 248, 249);">*You are purchasing action for FIRST BULLET ONLY (if player get knocked out and re-enters, you DO NOT have action.)</span></p> False None 0
4 40 480 Pamsi //cdn.pocketfives.com/v72/monthly_2021_09/pam_gg.thumb.jpg.ce2c1f50b5bc3c805fa4545ab9a3227d.jpg 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1244 2021-11-08T20:00:00 Event #68A: LITTLE ONE FOR ONE DROP No-Limit Hold’em 1111.0 0.0 1.1 15.0 183.32 0.0 0.0 1.00 12.22 15.000 183.32 0.000 0.0 <p>Pamela Balzano</p> False None 0
5 41 480 Pamsi //cdn.pocketfives.com/v72/monthly_2021_09/pam_gg.thumb.jpg.ce2c1f50b5bc3c805fa4545ab9a3227d.jpg 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1248 2021-11-11T20:00:00 Event #70A: CRAZY EIGHTS No-Limit Hold'em 8-Handed 888.0 0.0 1.1 15.0 146.52 0.0 0.0 1.00 9.77 15.000 146.52 0.000 0.0 <p>Pamela Balzano</p> False None 0
6 59 23 JonathanLittle //cdn.pocketfives.com/v72/monthly_2021_09/1004.thumb.JPG.10a21649c3a3cfef8d0ebc5d6f133ec9.JPG 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1257 2021-11-15T23:00:00 Event #76: Super Turbo Bounty No-Limit Hold'em (freezeout) 10000.0 0.0 1.0 10.0 1000.00 0.0 0.0 0.10 10.00 10.000 1000.00 0.000 0.0 <p>*You are purchasing action for FIRST BULLET ONLY (if player gets knocked out and re-enters, you DO NOT have action.)</p><p><br></p><blockquote><br></blockquote><p><br></p> False None 0
7 34 56 Daniel Negreanu //cdn.pocketfives.com/v72/monthly_2021_09/239657270_ScreenShot2021-09-10at10_04_11AM.thumb.png.fdefefe1ee4ccfa9148e72b34c59bdf0.png 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1263 2021-11-18T23:00:00 Event #82: Super High Roller No-Limit Hold'em 250000.0 0.0 1.0 25.0 62500.00 0.0 0.0 0.20 500.00 25.000 62500.00 0.000 0.0 <p>*You are purchasing action for FIRST BULLET ONLY (if player get knocked out and re-enters, you DO NOT have action.</p><p><br></p><p>Are you guys as ready for the WSOP as I am? This is my first public offering for a piece of me in some WSOP action! Ive opened up 25% of of my action at NO MARKUP! Lets make some money and have some fun!</p> False None 0
8 43 23 JonathanLittle //cdn.pocketfives.com/v72/monthly_2021_09/1004.thumb.JPG.10a21649c3a3cfef8d0ebc5d6f133ec9.JPG 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1267 2021-11-20T23:00:00 Event #85: HIGH ROLLER No-Limit Hold’em 50000.0 0.0 1.0 20.0 10000.00 0.0 0.0 0.20 100.00 15.700 7850.00 4.300 2150.0 None False None 0
9 52 23 JonathanLittle //cdn.pocketfives.com/v72/monthly_2021_09/1004.thumb.JPG.10a21649c3a3cfef8d0ebc5d6f133ec9.JPG 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1269 2021-11-21T23:00:00 Event #87: High Roller No-Limit Hold’em 100000.0 0.0 1.0 70.0 70000.00 69.0 69000.0 0.00 0.00 64.852 64852.00 5.148 5148.0 <p>Please be aware that if this package does not sell out in its entirety, all investors will get a full refund and the package is canceled.</p> False None 0
10 36 56 Daniel Negreanu //cdn.pocketfives.com/v72/monthly_2021_09/239657270_ScreenShot2021-09-10at10_04_11AM.thumb.png.fdefefe1ee4ccfa9148e72b34c59bdf0.png 1 WSOP https://cdn.pocketfives.com/staking/common/venue/wsop.png 1269 2021-11-21T23:00:00 Event #87: High Roller No-Limit Hold’em 100000.0 0.0 1.0 25.0 25000.00 0.0 0.0 0.25 250.00 25.000 25000.00 0.000 0.0 <p><span style="background-color: rgb(246, 248, 249);">*You are purchasing action for FIRST BULLET ONLY (if player get knocked out and re-enters, you DO NOT have action.</span></p> False None 0
Using Keras with Tensorflow backend, I am trying to train an LSTM network and it is taking much longer to run it on a GPU than a CPU.
I am training an LSTM network using the fit_generator function. It takes CPU ~250 seconds per epoch while it takes GPU ~900 seconds per epoch. The packages in my GPU environment include
keras-applications 1.0.8 py_0 anaconda
keras-base 2.2.4 py36_0 anaconda
keras-gpu 2.2.4 0 anaconda
keras-preprocessing 1.1.0 py_1 anaconda
...
tensorflow 1.13.1 gpu_py36h3991807_0 anaconda
tensorflow-base 1.13.1 gpu_py36h8d69cac_0 anaconda
tensorflow-estimator 1.13.0 py_0 anaconda
tensorflow-gpu 1.13.1 pypi_0 pypi
My Cuda compilation tools are of version 9.1.85 and my CUDA and Driver version are
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.67 Driver Version: 418.67 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2080 On | 00000000:0A:00.0 Off | N/A |
| 0% 39C P8 5W / 225W | 7740MiB / 7952MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce RTX 2080 On | 00000000:42:00.0 Off | N/A |
| 0% 33C P8 19W / 225W | 142MiB / 7951MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 49251 C .../whsu014/.conda/envs/whsuphd/bin/python 7729MiB |
| 1 1354 G /usr/lib/xorg/Xorg 16MiB |
| 1 49251 C .../whsu014/.conda/envs/whsuphd/bin/python 113MiB |
+-----------------------------------------------------------------------------+
When I insert this line of code
tf.Session(config = tf.configProto(log_device_placement = True)):
I see the below in my terminal
...
ining_1/Adam/Const_10: (Const)/job:localhost/replica:0/task:0/device:GPU:0
training_1/Adam/Const_11: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2019-06-25 11:27:31.720653: I tensorflow/core/common_runtime/placer.cc:1059] training_1/Adam/Const_11: (Const)/job:localhost/replica:0/task:0/device:GPU:0
training_1/Adam/add_15/y: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2019-06-25 11:27:31.720666: I tensorflow/core/common_runtime/placer.cc:1059] training_1/Adam/add_15/y: (Const)/job:localhost/replica:0/task:0/device:GPU:0
...
So it seems that Tensorflow is using GPU.
When I profile the code,
on GPU this is the first 10 lines
10852017 function calls (10524203 primitive calls) in 184.768 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
16200 173.827 0.011 173.827 0.011 {built-in method _pywrap_tensorflow_internal.TF_SessionRunCallable}
6 0.926 0.154 0.926 0.154 {built-in method _pywrap_tensorflow_internal.TF_SessionMakeCallable}
62 0.813 0.013 0.813 0.013 {built-in method _pywrap_tensorflow_internal.TF_SessionRun_wrapper}
156954 0.414 0.000 0.415 0.000 {built-in method numpy.array}
16200 0.379 0.000 1.042 0.000 training.py:643(_standardize_user_data)
24300 0.338 0.000 0.338 0.000 {method 'partition' of 'numpy.ndarray' objects}
68 0.301 0.004 0.301 0.004 {built-in method _pywrap_tensorflow_internal.ExtendSession}
32458 0.223 0.000 2.122 0.000 tensorflow_backend.py:156(get_session)
3206 0.212 0.000 0.238 0.000 tf_stack.py:31(extract_stack)
76024 0.210 0.000 0.702 0.000 ops.py:5246(get_controller)
...
on CPU this is the first 10 lines
22123473 function calls (21647174 primitive calls) in 60.173 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
16269 42.491 0.003 42.491 0.003 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_Run}
16269 0.568 0.000 48.964 0.003 session.py:1042(_run)
56 0.532 0.010 0.532 0.010 {built-in method time.sleep}
153641 0.458 0.000 0.460 0.000 {built-in method numpy.core.multiarray.array}
183148/125354 0.447 0.000 1.316 0.000 python_message.py:469(init)
1226659 0.362 0.000 0.364 0.000 {built-in method builtins.getattr}
2302110/2301986 0.339 0.000 0.358 0.000 {built-in method builtins.isinstance}
8 0.285 0.036 0.285 0.036 {built-in method tensorflow.python._pywrap_tensorflow_internal.TF_ExtendGraph}
12150 0.267 0.000 0.271 0.000 callbacks.py:211(on_batch_end)
147026/49078 0.264 0.000 1.429 0.000 python_message.py:1008(ByteSize)
...
This is my code.
def train_generator(x_list, y_list):
# 0.1 validatioin split
train_length = (len(x_list)//10)*9
while True:
for i in range(train_length):
train_x = np.array([x_list[i]])
train_y = np.array([y_list[i]])
yield train_x, train_y
def val_generator(x_list, y_list):
# 0.1 validation split
val_length = len(x_list)//10
while True:
for i in range(-val_length, 0, 1):
val_x = np.array([x_list[i]])
val_y = np.array([y_list[i]])
yield val_x, val_y
with tf.Session(config = tf.ConfigProto(log_device_placement = True)):
model = Sequential()
model.add(LSTM(64, return_sequences=False,
input_shape=(None, 24)))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
checkpointer = ModelCheckpoint(filepath="weights.hdf5",
monitor='val_loss', verbose=1,
save_best_only=True)
history = model.fit_generator(generator=train_generator(train_x,
train_y),
steps_per_epoch=(len(train_x)//10)*9,
epochs=5,
validation_data=val_generator(train_x,
train_y),
validation_steps=len(train_x)//10,
callbacks=[checkpointer],
verbose=2, shuffle=False)
# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='validation')
pyplot.legend()
pyplot.show()
I expect a significant speed up when using GPU for training. How can I fix this? Can someone help me to understand what is causing the slowdown? Thank you.
Couple of observations:
Use CuDNNLSTM instead of LSTM to train on GPU, you will see considerable increase in speed.
Sometimes, for very small networks, the overhead of transferring between CPU and GPU outweighs the parallel computations made on GPU; in other words, there is more time lost on transferring the data than time gained by training on GPU.
GPUs should be used for highly intensive tasks and computations (very big LSTM/heavy CNN networks). Nevertheless, for very small MLPs and even small LSTMs you might observe that the network trains equally fast on CPU and GPU or that in some particular cases the speed on CPU is even better (very particular cases with super small networks).
UPDATE FOR TENSORFLOW >= 2.0
The imports default to using CuDNNLSTM/CuDNNGRU if the video card is detected; therefore it is not needed explicitly to import them.
Can someone please help me. I'm trying to get values greater than 40, but when it's at 100 it doesn't get it.
[root#localhost home]# df -Pk --block-size=1M
Filesystem 1048576-blocks Used Available Capacity Mounted on
/dev/mapper/rhel-root 22510 13135 9375 59% /
devtmpfs 905 0 905 0% /dev
tmpfs 920 1 920 1% /dev/shm
tmpfs 920 9 911 1% /run
tmpfs 920 0 920 0% /sys/fs/cgroup
/dev/sda1 1014 178 837 18% /boot
Linux_DB2 240879 96794 144086 41% /media/sf_Linux_DB2
tmpfs 184 1 184 1% /run/user/42
tmpfs 184 1 184 1% /run/user/0
*/dev/sr0 56 56 0 100% /run/media/root/VBox_GAs_5.2.20*
[root#localhost home]# df -Pk --block-size=1M | awk '$5 > 40'
Filesystem 1048576-blocks Used Available Capacity Mounted on
/dev/mapper/rhel-root 22510 13135 9375 59% /
Linux_DB2 240879 96794 144086 41% /media/sf_Linux_DB2
The /dev/sr0 56 56 0 100% /run/media/root/VBox_GAs_5.2.20 doesn't come out.
Could you please try following once.
df -hP | awk '$5+0>40'
Explanation: Since 5th field of disk usage is having string with digits added, so by adding a zero +0 with $5 it tells awk to keep only digits in comparison and it will NOT have strings in it. Then this condition will considered like digits are getting compared, will show the right output then. Here -P option with df command is also crucial since it gives the output of df in a single line and it makes awk command's life easy to get its calculations done.
I've attached the text(s) file I'm working with below:
using sed I can get a result which adds the text but it's being added to every line that contained my variable which happened to be "33" in this case. I would only like the text "SLOTS" added to the first occurrence in the file and basically stop once the first is encountered. sed adds the text "SLOTS" to every line that contains my variable in my text file there are other columns that happen to have the same value as my variable. I've searched through numerous websites to get the desired results but haven't had any luck trying awk or other sed examples. any help would be greatly appreciated.
here's my current sed line:
sed '/\b'$slot_drill'\b/s/$/SLOTS/' $slot_card > $new_slot_card
what my current sed line outputs:
d25104-1.dr -- PANEL SIZE: 18x24
Drilled Slots: Yes
Tool Tool Spindle Feed Hits Max Bits Path Time
Size Speed Rate Hits (Min)
T01 126 1.0 1.0 58 1600 0.0 182.8 1.7
T02 250 1.0 1.0 9 1600 0.0 67.5 0.7
T03 12 1.0 1.0 3965 1600 2.5 514.4 4.6
T04 31 1.0 1.0 65 1600 0.0 62.0 0.6
T05 33 1.0 1.0 569 1600 0.4 46.6 0.4 SLOTS
T06 35 1.0 1.0 33 1600 0.0 45.3 0.4 SLOTS
T07 41 1.0 1.0 97 1600 0.1 79.6 0.7
T08 42 1.0 1.0 135 1600 0.1 104.6 0.9
T09 43 1.0 1.0 33 1600 0.0 53.0 0.5 SLOTS
T10 49.2 1.0 1.0 65 1600 0.0 44.2 0.4
T11 52 1.0 1.0 17 1600 0.0 40.7 0.4
T12 63.5 1.0 1.0 33 1600 0.0 55.6 0.5 SLOTS
T13 98 1.0 1.0 25 1600 0.0 52.0 0.5
Total 5104 3.2 1348.2 12.5
output that is desired:
d25104-1.dr -- PANEL SIZE: 18x24
Drilled Slots: Yes
Tool Tool Spindle Feed Hits Max Bits Path Time
Size Speed Rate Hits (Min)
T01 126 1.0 1.0 58 1600 0.0 182.8 1.7
T02 250 1.0 1.0 9 1600 0.0 67.5 0.7
T03 12 1.0 1.0 3965 1600 2.5 514.4 4.6
T04 31 1.0 1.0 65 1600 0.0 62.0 0.6
T05 33 1.0 1.0 569 1600 0.4 46.6 0.4 SLOTS
T06 35 1.0 1.0 33 1600 0.0 45.3 0.4
T07 41 1.0 1.0 97 1600 0.1 79.6 0.7
T08 42 1.0 1.0 135 1600 0.1 104.6 0.9
T09 43 1.0 1.0 33 1600 0.0 53.0 0.5
T10 49.2 1.0 1.0 65 1600 0.0 44.2 0.4
T11 52 1.0 1.0 17 1600 0.0 40.7 0.4
T12 63.5 1.0 1.0 33 1600 0.0 55.6 0.5
T13 98 1.0 1.0 25 1600 0.0 52.0 0.5
Total 5104 3.2 1348.2 12.5
desired output
It is difficult to tell what you are asking. A good minimal example would probably omit the shell variables and take the line you are working with as completely independent so that other people can run it on their own systems.
However, it sounds as if you are just asking how to limit sed's s command to working only for the first line it finds that is a match. In general, I think the way is to prefix a range to the command and use 0 for the beginning of the range. For example: sed '0,/foo/ {s/foo/bar}' will replace only the first instance of foo that it finds with bar.
This is an answer to this sort of problem given at https://unix.stackexchange.com/questions/188264/want-to-substitute-only-first-occurence-with-sed
awk to the rescue!
on a simplified file
$ cat file
1
2
3
1
2
3
append SLOT to the first occurrence of the variable slot, let's say 3.
$ awk -v value="$slot" '$1==value && !c++{$0=$0 "SLOT"} 1' file
1
2
3SLOT
1
2
3
Looking at your example you'll need to use `$2==value``
I am loosing about 18k every five minutes in the Unknown Field. Any ideas what this could be and how to debug it?
Thanks!
Applications Memory Usage (kB):
Uptime: 444373583 Realtime: 444373583
** MEMINFO in pid 4758 [org.domain.activity] **
Pss Private Private Swapped Heap Heap Heap
Total Dirty Clean Dirty Size Alloc Free
------ ------ ------ ------ ------ ------ ------
Native Heap 0 0 0 0 13492 12878 613
Dalvik Heap 6983 6912 0 0 8932 7183 1749
Dalvik Other 1761 1620 0 0
Stack 192 192 0 0
Ashmem 7 0 0 0
Other dev 192 188 4 0
.so mmap 2996 2284 88 0
.apk mmap 53 0 12 0
.ttf mmap 7 0 0 0
.dex mmap 896 8 544 0
Other mmap 7 4 0 0
Unknown 12018 11992 0 0
TOTAL 25112 23200 648 0 22424 20061 2362
Objects
Views: 26 ViewRootImpl: 1
AppContexts: 3 Activities: 1
Assets: 3 AssetManagers: 3
Local Binders: 7 Proxy Binders: 16
Death Recipients: 0
OpenSSL Sockets: 0
SQL
MEMORY_USED: 0
PAGECACHE_OVERFLOW: 0 MALLOC_SIZE: 0
Asset Allocations
zip:/data/app/org.domain.activity.apk:/assets/DS-DIGIB.TTF: 24K