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Troubleshooting","\u002Fconnector-development\u002Fguides\u002Fserial-troubleshooting","20.connector-development\u002F40.guides\u002F70.serial-troubleshooting",{"title":741,"path":742,"stem":743},"Testing","\u002Fconnector-development\u002Fguides\u002Ftesting","20.connector-development\u002F40.guides\u002F80.testing",{"title":745,"path":746,"stem":747,"children":748},"References","\u002Fconnector-development\u002Freference","20.connector-development\u002F50.reference\u002Findex",[749],{"title":745,"path":746,"stem":747},{"id":751,"title":460,"body":752,"description":1326,"extension":1327,"links":1328,"meta":1329,"navigation":879,"path":461,"seo":1330,"stem":462,"__hash__":1331},"docs\u002F07.automate\u002F10.concepts\u002F10.workflow.md",{"type":753,"value":754,"toc":1319},"minimark",[755,763,771,796,801,1013,1027,1031,1064,1068,1071,1174,1178,1181,1290,1294,1315],[756,757,758,759,762],"p",{},"A ",[760,761,447],"strong",{}," is the top-level executable process designed to achieve a specific scientific outcome. It workflow engine phases, manages data flow between them, and owns the state of all samples and resources for the duration of the run.",[756,764,765,766,770],{},"You define a workflow as a Python function decorated with ",[767,768,769],"code",{},"@workflow",". Inside it, you call phases like regular functions — the workflow engine handles scheduling, parallelism, error recovery, and setting a checkpoint .",[772,773,775],"callout",{"icon":774},"i-heroicons-information-circle",[756,776,777,778,781,782,785,786,789,790,792,793,795],{},"For ",[767,779,780],{},"unitelabs-sdk\u003C 0.10.0",", import ",[767,783,784],{},"flow"," and ",[767,787,788],{},"task"," from Prefect instead. Use ",[767,791,784],{}," in place of both workflow and phase decorators, and ",[767,794,788],{}," as step decorators.",[797,798,800],"h2",{"id":799},"example","Example",[802,803,809],"pre",{"className":804,"code":805,"filename":806,"language":807,"meta":808,"style":808},"language-python shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","from unitelabs.sdk.automate import workflow\nfrom .phases import sample_preparation, agitation, washing_cycle, detection\n\n@workflow(name=\"ELISA\")\nasync def main_workflow():\n    plate = await sample_preparation()\n    await agitation(plate)\n\n    for _ in range(3):\n        await washing_cycle()\n\n    await detection()\n","workflows\u002Felisa.py","python","",[767,810,811,842,874,881,912,928,944,959,964,988,998,1003],{"__ignoreMap":808},[812,813,816,820,824,828,831,833,836,839],"span",{"class":814,"line":815},"line",1,[812,817,819],{"class":818},"s7zQu","from",[812,821,823],{"class":822},"sTEyZ"," unitelabs",[812,825,827],{"class":826},"sMK4o",".",[812,829,830],{"class":822},"sdk",[812,832,827],{"class":826},[812,834,835],{"class":822},"automate ",[812,837,838],{"class":818},"import",[812,840,841],{"class":822}," workflow\n",[812,843,845,847,850,853,855,858,861,864,866,869,871],{"class":814,"line":844},2,[812,846,819],{"class":818},[812,848,849],{"class":826}," .",[812,851,852],{"class":822},"phases ",[812,854,838],{"class":818},[812,856,857],{"class":822}," sample_preparation",[812,859,860],{"class":826},",",[812,862,863],{"class":822}," agitation",[812,865,860],{"class":826},[812,867,868],{"class":822}," washing_cycle",[812,870,860],{"class":826},[812,872,873],{"class":822}," detection\n",[812,875,877],{"class":814,"line":876},3,[812,878,880],{"emptyLinePlaceholder":879},true,"\n",[812,882,884,887,890,893,897,900,903,907,909],{"class":814,"line":883},4,[812,885,886],{"class":826},"@",[812,888,447],{"class":889},"s2Zo4",[812,891,892],{"class":826},"(",[812,894,896],{"class":895},"sHdIc","name",[812,898,899],{"class":826},"=",[812,901,902],{"class":826},"\"",[812,904,906],{"class":905},"sfazB","ELISA",[812,908,902],{"class":826},[812,910,911],{"class":826},")\n",[812,913,915,919,922,925],{"class":814,"line":914},5,[812,916,918],{"class":917},"spNyl","async",[812,920,921],{"class":917}," def",[812,923,924],{"class":889}," main_workflow",[812,926,927],{"class":826},"():\n",[812,929,931,934,936,939,941],{"class":814,"line":930},6,[812,932,933],{"class":822},"    plate ",[812,935,899],{"class":826},[812,937,938],{"class":818}," await",[812,940,857],{"class":889},[812,942,943],{"class":826},"()\n",[812,945,947,950,952,954,957],{"class":814,"line":946},7,[812,948,949],{"class":818},"    await",[812,951,863],{"class":889},[812,953,892],{"class":826},[812,955,956],{"class":889},"plate",[812,958,911],{"class":826},[812,960,962],{"class":814,"line":961},8,[812,963,880],{"emptyLinePlaceholder":879},[812,965,967,970,973,976,979,981,985],{"class":814,"line":966},9,[812,968,969],{"class":818},"    for",[812,971,972],{"class":822}," _ ",[812,974,975],{"class":818},"in",[812,977,978],{"class":889}," range",[812,980,892],{"class":826},[812,982,984],{"class":983},"sbssI","3",[812,986,987],{"class":826},"):\n",[812,989,991,994,996],{"class":814,"line":990},10,[812,992,993],{"class":818},"        await",[812,995,868],{"class":889},[812,997,943],{"class":826},[812,999,1001],{"class":814,"line":1000},11,[812,1002,880],{"emptyLinePlaceholder":879},[812,1004,1006,1008,1011],{"class":814,"line":1005},12,[812,1007,949],{"class":818},[812,1009,1010],{"class":889}," detection",[812,1012,943],{"class":826},[756,1014,1015,1016,1019,1020,1019,1023,1026],{},"The workflow drives the scientific narrative. Phases represent the meaningful stages — ",[767,1017,1018],{},"sample_preparation",", ",[767,1021,1022],{},"agitation",[767,1024,1025],{},"detection"," — while the workflow defines the order and control flow between them.",[797,1028,1030],{"id":1029},"key-properties","Key properties",[1032,1033,1034,1046,1052,1058],"ul",{},[1035,1036,1037,1040,1041,1045],"li",{},[760,1038,1039],{},"Scientific goal",": a workflow is defined by ",[1042,1043,1044],"em",{},"what result it produces"," (e.g., \"ELISA\"), not the hardware it uses",[1035,1047,1048,1051],{},[760,1049,1050],{},"Control flow",": supports loops, conditionals, and branching; not limited to a linear sequence",[1035,1053,1054,1057],{},[760,1055,1056],{},"State ownership",": is the source of truth for sample identity, lineage, and all consumed resources across phases",[1035,1059,1060,1063],{},[760,1061,1062],{},"Versioned",": workflows are deployed and versioned; each run is linked to a specific version",[797,1065,1067],{"id":1066},"non-linear-control-flow","Non-linear control flow",[756,1069,1070],{},"Because a workflow is just a Python function, you can use standard control flow:",[802,1072,1075],{"className":804,"code":1073,"filename":1074,"language":807,"meta":808,"style":808},"@workflow(name=\"Repeat Until Clean\")\nasync def repeat_workflow():\n    result = await initial_wash()\n\n    while not result.is_clean:\n        result = await additional_wash()\n\n    await final_rinse()\n","workflows\u002Frepeat_until_clean.py",[767,1076,1077,1098,1109,1123,1127,1147,1161,1165],{"__ignoreMap":808},[812,1078,1079,1081,1083,1085,1087,1089,1091,1094,1096],{"class":814,"line":815},[812,1080,886],{"class":826},[812,1082,447],{"class":889},[812,1084,892],{"class":826},[812,1086,896],{"class":895},[812,1088,899],{"class":826},[812,1090,902],{"class":826},[812,1092,1093],{"class":905},"Repeat Until Clean",[812,1095,902],{"class":826},[812,1097,911],{"class":826},[812,1099,1100,1102,1104,1107],{"class":814,"line":844},[812,1101,918],{"class":917},[812,1103,921],{"class":917},[812,1105,1106],{"class":889}," repeat_workflow",[812,1108,927],{"class":826},[812,1110,1111,1114,1116,1118,1121],{"class":814,"line":876},[812,1112,1113],{"class":822},"    result ",[812,1115,899],{"class":826},[812,1117,938],{"class":818},[812,1119,1120],{"class":889}," initial_wash",[812,1122,943],{"class":826},[812,1124,1125],{"class":814,"line":883},[812,1126,880],{"emptyLinePlaceholder":879},[812,1128,1129,1132,1135,1138,1140,1144],{"class":814,"line":914},[812,1130,1131],{"class":818},"    while",[812,1133,1134],{"class":826}," not",[812,1136,1137],{"class":822}," result",[812,1139,827],{"class":826},[812,1141,1143],{"class":1142},"swJcz","is_clean",[812,1145,1146],{"class":826},":\n",[812,1148,1149,1152,1154,1156,1159],{"class":814,"line":930},[812,1150,1151],{"class":822},"        result ",[812,1153,899],{"class":826},[812,1155,938],{"class":818},[812,1157,1158],{"class":889}," additional_wash",[812,1160,943],{"class":826},[812,1162,1163],{"class":814,"line":946},[812,1164,880],{"emptyLinePlaceholder":879},[812,1166,1167,1169,1172],{"class":814,"line":961},[812,1168,949],{"class":818},[812,1170,1171],{"class":889}," final_rinse",[812,1173,943],{"class":826},[797,1175,1177],{"id":1176},"parallel-phases","Parallel phases",[756,1179,1180],{},"Phases with no dependency between them can run in parallel. The workflow engine detects independence automatically — you only need to express the data dependency:",[802,1182,1185],{"className":804,"code":1183,"filename":1184,"language":807,"meta":808,"style":808},"@workflow(name=\"Parallel Preparation\")\nasync def parallel_workflow():\n    # These three phases have no shared inputs — the workflow engine runs them concurrently\n    reagent_a = await prepare_reagent_a()\n    reagent_b = await prepare_reagent_b()\n    await wash_plate()\n\n    # This phase depends on both reagents, so it waits for both to complete\n    await combine(reagent_a, reagent_b)\n","workflows\u002Fparallel_example.py",[767,1186,1187,1208,1219,1225,1239,1253,1262,1266,1271],{"__ignoreMap":808},[812,1188,1189,1191,1193,1195,1197,1199,1201,1204,1206],{"class":814,"line":815},[812,1190,886],{"class":826},[812,1192,447],{"class":889},[812,1194,892],{"class":826},[812,1196,896],{"class":895},[812,1198,899],{"class":826},[812,1200,902],{"class":826},[812,1202,1203],{"class":905},"Parallel Preparation",[812,1205,902],{"class":826},[812,1207,911],{"class":826},[812,1209,1210,1212,1214,1217],{"class":814,"line":844},[812,1211,918],{"class":917},[812,1213,921],{"class":917},[812,1215,1216],{"class":889}," parallel_workflow",[812,1218,927],{"class":826},[812,1220,1221],{"class":814,"line":876},[812,1222,1224],{"class":1223},"sHwdD","    # These three phases have no shared inputs — the workflow engine runs them concurrently\n",[812,1226,1227,1230,1232,1234,1237],{"class":814,"line":883},[812,1228,1229],{"class":822},"    reagent_a 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