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2_Bind_simpleIFZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS3_17RowBlockContainerIjiEESaIS8_EEEUlvE_vEEEE_ZN4dmlc4data14TextParserBaseIjiE9ParseNextEPSt6vectorINS0_17RowBlockContainerIjiEESaIS5_EE_ZN4dmlc4data10ParserImplIjiE4NextEv_ZN4dmlc4data12BasicRowIterIjiE4InitEPNS_6ParserIjiEE_ZN4dmlc4data11DiskRowIterIjiE10BuildCacheEPNS_6ParserIjiEE_ZN4dmlc12RowBlockIterIjiE6CreateEPKcjjS3__ZNSt6vectorIN4dmlc4data17RowBlockContainerImfEESaIS3_EE17_M_default_appendEm_ZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS0_17RowBlockContainerImfEESaIS5_EE_ZTVSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTVNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS3_17RowBlockContainerImfEESaIS8_EEEUlvE_vEEEE_ZN4dmlc4data14TextParserBaseImfE9ParseNextEPSt6vectorINS0_17RowBlockContainerImfEESaIS5_EE_ZNSt6vectorIN4dmlc4data17RowBlockContainerIjfEESaIS3_EE17_M_default_appendEm_ZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS0_17RowBlockContainerIjfEESaIS5_EE_ZTVSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTVNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS3_17RowBlockContainerIjfEESaIS8_EEEUlvE_vEEEE_ZN4dmlc4data14TextParserBaseIjfE9ParseNextEPSt6vectorINS0_17RowBlockContainerIjfEESaIS5_EE_ZTSNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS3_17RowBlockContainerImlEESaIS8_EEEUlvE_vEEEE_ZTINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS3_17RowBlockContainerImlEESaIS8_EEEUlvE_vEEEE_ZTSNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS3_17RowBlockContainerIjlEESaIS8_EEEUlvE_vEEEE_ZTINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS3_17RowBlockContainerIjlEESaIS8_EEEUlvE_vEEEE_ZTSNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS3_17RowBlockContainerImiEESaIS8_EEEUlvE_vEEEE_ZTINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS3_17RowBlockContainerImiEESaIS8_EEEUlvE_vEEEE_ZTSNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS3_17RowBlockContainerIjiEESaIS8_EEEUlvE_vEEEE_ZTINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS3_17RowBlockContainerIjiEESaIS8_EEEUlvE_vEEEE_ZTSNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS3_17RowBlockContainerImfEESaIS8_EEEUlvE_vEEEE_ZTINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS3_17RowBlockContainerImfEESaIS8_EEEUlvE_vEEEE_ZTSNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS3_17RowBlockContainerIjfEESaIS8_EEEUlvE_vEEEE_ZTINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS3_17RowBlockContainerIjfEESaIS8_EEEUlvE_vEEEE_ZTSSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EE_ZTSN4dmlc8DataIterINS_8RowBlockIjfEEEE_ZTIN4dmlc8DataIterINS_8RowBlockIjfEEEE_ZTSN4dmlc12RowBlockIterIjfEE_ZTIN4dmlc12RowBlockIterIjfEE_ZTSN4dmlc8DataIterINS_8RowBlockImfEEEE_ZTIN4dmlc8DataIterINS_8RowBlockImfEEEE_ZTSN4dmlc12RowBlockIterImfEE_ZTIN4dmlc12RowBlockIterImfEE_ZTSN4dmlc8DataIterINS_8RowBlockIjiEEEE_ZTIN4dmlc8DataIterINS_8RowBlockIjiEEEE_ZTSN4dmlc12RowBlockIterIjiEE_ZTIN4dmlc12RowBlockIterIjiEE_ZTSN4dmlc8DataIterINS_8RowBlockImiEEEE_ZTIN4dmlc8DataIterINS_8RowBlockImiEEEE_ZTSN4dmlc12RowBlockIterImiEE_ZTIN4dmlc12RowBlockIterImiEE_ZTSN4dmlc8DataIterINS_8RowBlockIjlEEEE_ZTIN4dmlc8DataIterINS_8RowBlockIjlEEEE_ZTSN4dmlc12RowBlockIterIjlEE_ZTIN4dmlc12RowBlockIterIjlEE_ZTSN4dmlc8DataIterINS_8RowBlockImlEEEE_ZTIN4dmlc8DataIterINS_8RowBlockImlEEEE_ZTSN4dmlc12RowBlockIterImlEE_ZTIN4dmlc12RowBlockIterImlEE_ZTSN4dmlc6ParserIjfEE_ZTIN4dmlc6ParserIjfEE_ZTSN4dmlc6ParserImfEE_ZTIN4dmlc6ParserImfEE_ZTSN4dmlc6ParserIjiEE_ZTIN4dmlc6ParserIjiEE_ZTSN4dmlc6ParserImiEE_ZTIN4dmlc6ParserImiEE_ZTSN4dmlc6ParserIjlEE_ZTIN4dmlc6ParserIjlEE_ZTSN4dmlc6ParserImlEE_ZTIN4dmlc6ParserImlEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEE_ZTSN4dmlc4data11DiskRowIterIjfEE_ZTIN4dmlc4data11DiskRowIterIjfEE_ZTSN4dmlc4data12BasicRowIterIjfEE_ZTIN4dmlc4data12BasicRowIterIjfEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEE_ZTSN4dmlc4data11DiskRowIterImfEE_ZTIN4dmlc4data11DiskRowIterImfEE_ZTSN4dmlc4data12BasicRowIterImfEE_ZTIN4dmlc4data12BasicRowIterImfEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEE_ZTSN4dmlc4data11DiskRowIterIjiEE_ZTIN4dmlc4data11DiskRowIterIjiEE_ZTSN4dmlc4data12BasicRowIterIjiEE_ZTIN4dmlc4data12BasicRowIterIjiEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEE_ZTSN4dmlc4data11DiskRowIterImiEE_ZTIN4dmlc4data11DiskRowIterImiEE_ZTSN4dmlc4data12BasicRowIterImiEE_ZTIN4dmlc4data12BasicRowIterImiEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEE_ZTSN4dmlc4data11DiskRowIterIjlEE_ZTIN4dmlc4data11DiskRowIterIjlEE_ZTSN4dmlc4data12BasicRowIterIjlEE_ZTIN4dmlc4data12BasicRowIterIjlEE_ZTSN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEE_ZTIN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEE_ZTSN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEE_ZTIN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEE_ZTSN4dmlc4data11DiskRowIterImlEE_ZTIN4dmlc4data11DiskRowIterImlEE_ZTSN4dmlc4data12BasicRowIterImlEE_ZTIN4dmlc4data12BasicRowIterImlEE_ZTSN4dmlc4data10ParserImplIjfEE_ZTIN4dmlc4data10ParserImplIjfEE_ZTSN4dmlc4data14TextParserBaseIjfEE_ZTIN4dmlc4data14TextParserBaseIjfEE_ZTSN4dmlc4data12LibSVMParserIjfEE_ZTIN4dmlc4data12LibSVMParserIjfEE_ZTSN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTIN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTSN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTIN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEE_ZTSN4dmlc4data14ThreadedParserIjfEE_ZTIN4dmlc4data14ThreadedParserIjfEE_ZTSN4dmlc4data10ParserImplImfEE_ZTIN4dmlc4data10ParserImplImfEE_ZTSN4dmlc4data14TextParserBaseImfEE_ZTIN4dmlc4data14TextParserBaseImfEE_ZTSN4dmlc4data12LibSVMParserImfEE_ZTIN4dmlc4data12LibSVMParserImfEE_ZTSN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTIN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTSN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEE_ZTIN4dmlc12ThreadedIterISt6vectorINS_4data17Ro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H|$*oHrHHH$H$HH9tp|HH$H$H0H9tQ|ž LHπH$ IL9t)|HHNbH$HIH9t|LlHfDHY(1H$zH$H|$xHD$x^L3 RPRHl$8DH1HfH$H5}_AXHH H!%tHDHJHDщHD$(HH)H袇H5:3H莇H5ߩIHzH5QHf)HyH5ôHEH$H5H)H$(H$ HH5:f$HH$Ht$4D$4 @H$H$H$H$H$IFH$DH$H$H$H$H$IFH$HsPL]H$HH$ H$(H$PH$0ID$H$@fDH$hH$ H$(H$pH$0ID$H$`YfDH{pwNH(Ƅ$ HHDŽ$Ƅ$!1HDŽ$(HDŽ$0HD$HHDŽ$8H$@H(HDŽ$@HhL`H}H$L'[HL(H{@HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HPH@HDŽ$H$@H!(H$HH$H(HsH{pDŽ$HDŽ$(Ƅ$0HH$HC`H$ 荐H$Lt$x1vHLHD$xZHxL/ RPRDH1bAYAZH5HHxH H!%tHDHJHDщHH)Hx袃H5:/H莃H5ߥIHzH5MHf#HyH5ðHEH5(H1H5HH5+H H=.MHtH5.HH1f L$LQH5H譂H$H$H蕂H5ZH聂H$IL9tuL$ VIID$HDŽ$(Ƅ$0H$ H$HEH$H9H$L$`IWHHL$H$`H='eH$ IHL9tDuLeH4HHI?xRxHD$x H$H$H9tJ@H{@HCPH9ttH{ HC0H9ttH;HCH9ttHpH9uH$HtHutHD$H$HPH9tZtHD$H$H0H9t?tHD$H$HH9t$tH$dH3%(kH[]A\A]A^A_H$H|$Hp@ƫH5HH+D$HHHD$xH%HHH$h(IOH$`H$ H92IL$H9OH$ H$hHH$0H$(H$pH$0>H$`H$pHDŽ$hIH$`L9trLLˆHD(H$ IHL9IEtrH(H5((L`HH$L$@HHD$IGHH$@uHt H=$bH+T$HIH$hIMIGYL$HB0IOH$@H$ H9IL$H9H$ H$HHH$0H$(H$PH$0H$@H$PHDŽ$HIH$@L9HHLHH(H$1H$*pH|$xHHD$xxTH$H$L?) H $IιRPRDH1[H$H5XZHߺk}IvH H!%tHDHJHDщHH)H4$H}H5(H }H5ZIH|H5fGH|8HH5>H|H$H5LH|H5՟%H|IWI7H|H5[$Hm|H$HƄ$ P|H<(Lt$x1H$H$xnH$LHD$xRH}L' RPRDH1YH$Y^H5RH{HuH H!%tHDHJHDщHH)Hu{H5'Hk{H5IHW{H5EHC{5HVH5H"{H$H5)H{IWI7HzH$LD$x zwLLMRH$@LL5RH$` 1LL[HH$`HD$xHt$H$pHfeHl$xH$`I1HLHH$@H$H$PHt$LeL$H$@eHD$$PIGM$pHH$HP HsPLSQXH$HH$ H$(H$PH$0IGH$@aH$hH$ H$(H$pH$0IGH$`HHHH$HIH9lvH{IQHELL`HD$H{pHHH$DH=H$Ht$7HD$7 xH$IHL9H$Ht$6HD$6 vxCHH,HH{I胘HELLHD$H{pHHH$@'DHHHҨHHH/>HHHD$H$HPH9tIkHD$H$H0H9t.kHD$H$HH9zkpH^H{H趗ID$LPHD$H{pHHH$\CHH$Ht$5HD$5 #wHfDAWAVAUATUSfHdH%(H$1H$H|$PHt$(1HT$HDŽ$HHD$HHDŽ$)$ )$0)$@)$P?H$1HHDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HDŽ$HD$X?H$HKH$H$H$H$L$HHL$ H$HHL$H$ H$ H$H$(H$(H$H$H$0H$0H$H$8H$8H$H$HHL$H$@H$@H$H$HH$H$PH$H$XH$H$HH$PH$H$H$L$XH$H$H$HH$LhM9scfDMeM$@I|$@ID$PH9tgI|$ ID$0H9tgI<$ID$H9tgI`M9uIM9rL9t$H9\$ Ld$ tB@H{@HCPH9t~gH{ HC0H9tlgH;HCH9t[gH`L9uH;l$H\$tEfDH}@HEPH9t.gH} HE0H9tgH}HEH9t gH`H9uH$Ht;H$XH$8HXH9vH}HfH9wH$fH$H$H`H9BHtmHD$(LcL#L(HhLH M H=VIL$ IMH LsHB2H$H`H$H$H9L$`ID$HD$HD$xHD$0H$HD$8@H|$PHDŽ$HDŽ$HDŽ$ HT$HHPH$H$H`H{@H9HCPH9t}eH{ HC0H9tkeH;HH9tZeH$`H$H$H9HD$HD$H]H$`LmLHMfH=9DU@H$H$H9HD$tQHH{@HCPH9tdH{ HC0H9tdH;HCH9tdHpH9uH$HD$HtHxdH$H9$HH$L$H$HD$H$LhHD$M9vYMeM$I|$@ID$PH9t dI|$ ID$0H9tcI<$ID$H9tcI`M9uIM9wL;t$H9\$ILl$I|$@ID$PH9tcI|$ ID$0H9tcI<$ID$H9tzcI`M9uH9t?H}@HEPH9tZcH} HE0H9tHcH}HEH9t6cH`H9uH$Ht7H$H$HhH9vH;HbH9wH$bH$dH3%( H[]A\A]A^A_H9tbH{ HC0H9tbH;HH9tbH$bH$HPH$H@HH$H$H$H$HpHD$(H$HD$@@HH\$xaHHHD$!H$hID$0H]H$LmLHtMuH= RHH$HLHID$0H$ID$PH]H$LmLHtMuH=Q@HH$HHID$P^H$HEH$E$ HD$HXH;XHHCL$hHL$`HD$ LLOMFH=Pf.IL$nI4MH{LkHB(H$HC`$ChHD$HXHpHXH$ID$PH9t1`H$ID$0H9t`H$`H;|$t`H9l$ HD$H]HpH$`LmLHMHH\$xfDHt$01LHH$`HD$xH$pHLVH\$xH$`s@IL$I$MHD$ Ls0LkB(L$Ls L$LLtMuH=Q\O@IL$IML@H{PLk(B(L$H{@L$LLM H=NfDHt$8I|$ 1ǕHH$H$H$HLUH$H$@Ht$(I|$@1wHH$H$H$HLUH$H$@H$H$H`H9HtoHCL$hHL$`HD$ LLtbMu]H=MIL$IMHLkHB*H$H`H$fDIL$IMHD$ L{0LkB(L$L{ L$LLtMuH=1IVH9tUI`M9uIL9l$wHt$H9t$H;\$@H{@HCPH9tNUH{ HC0H9tIRI9rEtHH/I<$RI$ID$tHH膈fDAWAVAUATIUSHH8HoHHHWdH%(HD$(1HH)HHH)HUHEHk0HtnHEMeHEMuHD$LLMH=OZBf.ILl$ I'MH LmHB(HCHHPHSHH@HHC8HC0HD$(dH3%(HS@cH8[]A\A]A^A_@ILd$IMHD$L}0LeB Me(L} Mu LLtMuH=tAILd$I7MLH}PLe(B H}@Me@MmHLLMH=AfHw(HH)HLpL`K6H9mHHlHH9IH<SIHHs(L)HM4HCHHPH)HHHH;8PL;HkILs(IKl&HkHHCHHC HEHC8HHC@Ht$ H}@1 HHE@HD$ HEPLL5GLl$ HE@Ht$1HHHEHD$HELLFLd$HEHt$H} 1蠆HHE HD$HE0LLFLd$HE 4L)HL4L9HUH)HHHLIz{DAEHD$~AE0LfA$EPH*fDL{SInIYHUIH)HHH8LH)Lz%fHLsX~(H} I9t NH}H9|$tNLH*HCHHxMSoHzHÃHHbasic_string::_M_construct null not validAssertError:%s, shutting down process AssertError:%s, rabit is configured to keep process running read can not have position excceed buffer lengthwrite position exceed fixed buffer size%s: __pos (which is %zu) > this->size() (which is %zu)/home/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix./home/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.h/home/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support./home/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccDMatrix/Booster has not been intialized or has already been disposed.binary saving only supported by SimpleDMatrixXGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.Current DMatrix type does not support set data.[xgboost] Condition %s failed. /home/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size())unknown feature type, use i for indicator and q for quantityXGBoost distributed mode detected, static_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structureCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from %02d:%02d:%02d] Stack trace: _Z +basic_string::basic_string [bt] () DMLC_LOG_STACK_TRACE_DEPTH%zuCheck failed: at_first_Cannot reset IteratorAdapterCheck failed: p_mCheck failed: m_ptr || _count == 0Not implemented. vs. Check failed: cache_id == 0Cache ID is not supported yetCheck failed: p_predtjsonstr.size() > 2str[0] == '{'vector::_M_default_appendfloattextwill split data among workersautovector::_M_range_insertSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data12DenseAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data10CSRAdapterELN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN4dmlc5ErrorEN4dmlc6StreamEN4dmlc10SeekStreamEN4dmlc7istream5InBufEN4dmlc7istreamEN5rabit5utils19MemoryFixSizeBufferEN5rabit5utils18MemoryBufferStreamEN4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEEN7xgboost4data10CSRAdapterEN4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEEN7xgboost4data12DenseAdapterEN4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEEN7xgboost4data10CSCAdapterEN4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_21DataTableAdapterBatchEEEN7xgboost4data16DataTableAdapterEN4dmlc8DataIterIN7xgboost4data16FileAdapterBatchEEEN7xgboost4data12DMatrixProxyEN7xgboost4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EESt15_Sp_counted_ptrIPN7xgboost10JsonObjectELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost7DMatrixELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12DMatrixProxyELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12DenseAdapterELN9__gnu_cxx12_Lock_policyE2EESt10shared_ptrIN7xgboost4data12DenseAdapterEESt15_Sp_counted_ptrIPN7xgboost4data10CSRAdapterELN9__gnu_cxx12_Lock_policyE2EESt10shared_ptrIN7xgboost4data10CSRAdapterEESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_10SparsePageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_7CSCPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13SortedCSCPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_11EllpackPageEEELN9__gnu_cxx12_Lock_policyE2EE@je _vH@0ļ4&k7y؅W4Ngm=`X@xPMJ- yCx(,*42q0/'|!1錠9Y>R$n`F"x|+MraB ydiWC*fJ\)c7]`xxֲK}Yq\Ck 3TgffffffQ~jt*Ǻ"<8b->Nc{-1+x#c^- Knu7X%Y^_GzK`eq]p J&gչ o"4%QttS`؈$C(ӦfHRl ۩#j+vv=IJ+10Q=ѬVZbu)j}և]:1VӾa'miK$!Q㢢P@9j>`KUq݉ [qB1h?(SuYQ!"GMk9X 3D۸N <C.{$~ۃ6/l##ՂF!00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899/home/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)/home/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.hCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSizebatch_size < offset_vec.size()/home/runner/work/ml-builds/ml-builds/xgboost/src/common/./quantile.hCheck failed: size <= sa.size + sb.sizefi.Read(&this->size, sizeof(this->size)) == sizeof(this->size)fi.Read(this->data, this->size * sizeof(Entry)) == this->size * sizeof(Entry)/home/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptrpage.Size() <= dmat->Info().num_col_/home/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.hsizeof(BinIdxType) == bins_type_size_bins_type_size == kUint32BinsTypeSizenthread == omp_get_num_threads()Check failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))summary_array.size() == in_sketchs->size()p_cuts_->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > p_cuts_->cut_ptrs_.HostVector().back()Building quantile cut on a sparse dataset.Building quantile cut on a dense dataset or distributed environment.curent_bin_size == kUint32BinsTypeSize%lubase_rowid < group_ptr.back()Row: is not found in any group.ibegin + inst.size() == iendsibling.size() == sizeparent.size() == sizevector::_M_emplace_back_auxmingap=, maxgap=, wgap=bug in combinevector::_M_fill_insertnthreads > 0Load balanceCheck failed: falseindex_ < span_->size()index_ != span_->size()end_col >= beg_collocal_ptrs.size() >= 1invalid init parameterSingleThreadBuild: InitTotal number of hist bins: cut.Values().size() > 0UInitBuildConcatBuildDenseCutsSparseCutsHistogramCutsN7xgboost6common11CutsBuilderEN7xgboost6common10SparseCutsEN7xgboost6common9DenseCutsEN7xgboost6common6ColumnIhEEN7xgboost6common6ColumnItEEN7xgboost6common6ColumnIjEEN7xgboost6common11DenseColumnIhEEN7xgboost6common12SparseColumnIhEEN7xgboost6common11DenseColumnItEEN7xgboost6common12SparseColumnItEEN7xgboost6common11DenseColumnIjEEN7xgboost6common12SparseColumnIjEEC?h㈵> A?A_o:Size() == other.Size()Size() == other.size()/home/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccbasic_string::substrCheck failed: outNot implementedpointer_ <= buffer_.size()file://basic_string::appendOpening /home/runner/work/ml-builds/ml-builds/xgboost/src/common/io.cc/home/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hN7xgboost6common16PeekableInStreamEN7xgboost6common15FixedSizeStreamE/home/runner/work/ml-builds/ml-builds/xgboost/src/common/json.cc can not be indexed by Integer. can not be indexed by string. Please try obtaining std::string first./home/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hCheck failed: ret.ec == std::errc()StringNumberObjectArrayBooleanNullIntegerObject of type , around character position: , "beg <= size_\n\0Expecting: "", got: "EOF"\0"%dExpecting null value "null"Expecting digit Unknown escapeInvalid cast, from \\\"\b\f\r\t\u%04xch != -1cursor_.Pos(): raw_str_.size():Ppз0N7xgboost5ValueEN7xgboost10JsonStringEN7xgboost9JsonArrayEN7xgboost10JsonObjectEN7xgboost10JsonNumberEN7xgboost11JsonIntegerEN7xgboost8JsonNullEN7xgboost11JsonBooleanEN7xgboost10JsonReaderEN7xgboost10JsonWriterESt15_Sp_counted_ptrIPN7xgboost8JsonNullELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost10JsonNumberELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost11JsonIntegerELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost9JsonArrayELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost10JsonStringELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost11JsonBooleanELN9__gnu_cxx12_Lock_policyE2EE75value for Parameter exceed bound [ should be greater equal to should be smaller equal to Required parameter is not presentedInvalid Parameter format for expect but value=' optional, default=, requiredmap::atInvalid Input: '', valid values are: *p == ')'Invalid NAN literalpos <= value.length()Out of range value for , value='Out of range valueEnum exisit!) Enums: ), key /home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.henum_back_map_.count(value) != 0UValue not found in enum declared/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././strtonum.hNo conversion could be performedSome trailing characters could not be parsed: ' has already been registered in N4dmlc10ParamErrorEN4dmlc9parameter16FieldAccessEntryEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEEN4dmlc9parameter10FieldEntryIiEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEEN4dmlc9parameter10FieldEntryIfEEN4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEEY@v? LTimer for calls @ CollectFromOtherRanks======== Monitor: ========From rank: did not get stopped properly./home/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.cc.AIncorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again./home/runner/work/ml-builds/ml-builds/xgboost/src/common/version.ccfi->Read(&read[0], verstr.size()) == verstr.size()fi->Read(&major, sizeof(major)) == sizeof(major)fi->Read(&minor, sizeof(major)) == sizeof(minor)fi->Read(&patch, sizeof(major)) == sizeof(patch)Invaid version format in loaded JSON object: _idx < size()Device that data is on: `gpu_id` for XGBoost: float32float64bool8int32int8int16int64Unknown data table type.Cache file num_rownum_colnum_nonzerolabelsgroup_ptrlabels_lower_boundlabels_upper_boundfeature_namesfeature_typeslabel_lower_boundlabel_upper_boundUnknown float field name: Unknown uint32 field name: group_ptr_.back() == num_row_weights_.Size() == num_row_labels_.Size() == num_row_self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()size == this->num_col_Length of feature_typefeature_nameUnknown feature info name: Unknown format type Unknown data typeUnknown key for MetaInfo: Unknown feature info: cache_shards.size() != 0U.fmt-that.group_ptr_.size() != 0MetaInfo: Invalid format. name == expected_name Expected field: , got: Expected field of type: got field type: Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: !is_scalar to be a vector; got a scalarshape.second == 1major == 1 is no longer supported. again.MetaInfo: invalid formatnum_field >= expectedfields.)info.name_shards.size() != 0Utmagic == kMagicInvalid page formatNo thread.row.pageWriting MB/s, written__base_ctor .rXGBOOST_TEST_NPARTLoad part of data parts matrix with entries loaded from Load.group groups are loaded from .base_margin base_margin are loaded from .weight weights are loaded from csvlibsvm . Encountered parser error: /home/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: v.DeviceIdx() == GenericParameter::kCpuId || device == GenericParameter::kCpuId || v.DeviceIdx() == deviceData is resided on a different device than `gpu_id`. /home/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCheck failed: mutex_.try_lock()Multiple threads attempting to use prefetcher/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: out_data_ != NULLCalling Value at beginning or end?/home/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h/home/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hCouldn't remove external memory cache file ; you may want to remove it manually exists already; Is there another DMatrix with the same cache prefix? Otherwise please remove it manually.All feature_types must be {int, float, i, q}Unknown data type for getting meta info.group_ptr_.size() == weights_.Size() + 1Size of weights must equal to number of groups when ranking group is used.Invalid group structure. Number of rows obtained from groups doesn't equal to actual number of rows given by data.Size of weights must equal to number of rows.Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.self_offset.size() == other_offset.size() must be equal to number of columns./home/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hname_shards.size() == format_shards.size()this->num_col_ == that.num_col_Number of columns must be consistent across batches.Check failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Check failed: *out_page == nullptrCheck failed: qrecycle_.Pop(out_page)Check failed: strm->Read(&name)Check failed: strm->Read(&type_val)Check failed: type == expected_typeCheck failed: strm->Read(&is_scalar)Check failed: strm->Read(field, sizeof(T))Check failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)Number of columns is expected to be 1.Check failed: strm->Read(field)Binary DMatrix generated by XGBoost: Please process and save your data in current version: Check failed: fi->Read(&num_field)MetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains MetaInfo: the given binary file contains extra fields which will be ignored.finfo->Read(&tmagic, sizeof(tmagic)) == sizeof(tmagic)invalid format, magic number mismatchCheck failed: fi->Read(&format)Check failed: !producer_sig_processed_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroySparsePageWriter Finished writing to SparsePageSource Finished writing to Check failed: qids.empty() || qids.size() == info.num_row_key >= builder_base_row_offsetcache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification.No format parameter is provided in input uri, but found file extension: Consider providing a uri parameter: filename?format=No format parameter is provided in input uri. Choosing default parser in dmlc-core. Consider providing a uri parameter like: filename?format=csv uXuuuutt|d,N7xgboost7DMatrixENSt6thread10_Impl_baseEN7xgboost4data11FileAdapterEN4dmlc12ScopedThreadEN4dmlc8DataIterIN7xgboost10SparsePageEEEN7xgboost4data24ExternalMemoryPrefetcherINS_10SparsePageEEEN4dmlc8DataIterIN7xgboost7CSCPageEEEN7xgboost4data24ExternalMemoryPrefetcherINS_7CSCPageEEEN4dmlc8DataIterIN7xgboost13SortedCSCPageEEEN7xgboost4data24ExternalMemoryPrefetcherINS_13SortedCSCPageEEEN4dmlc12ThreadedIterIN7xgboost7CSCPageEEEN4dmlc12ThreadedIterIN7xgboost13SortedCSCPageEEEN4dmlc8DataIterIN7xgboost11EllpackPageEEEN7xgboost4data24ExternalMemoryPrefetcherINS_11EllpackPageEEEN4dmlc12ThreadedIterIN7xgboost10SparsePageEEENSt6thread5_ImplISt12_Bind_simpleIFZN7xgboost4data16SparsePageWriterINS2_10SparsePageEEC4ERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISD_EESH_mEUlvE_vEEEEN4dmlc12ThreadedIterIN7xgboost11EllpackPageEEESt19_Sp_make_shared_tagSt15_Sp_counted_ptrIPN7xgboost10SparsePageELN9__gnu_cxx12_Lock_policyE2EEZN7xgboost4data24ExternalMemoryPrefetcherINS_10SparsePageEEC4ERKNS0_9CacheInfoEEUlPPS2_E_ZN7xgboost4data24ExternalMemoryPrefetcherINS_10SparsePageEEC4ERKNS0_9CacheInfoEEUlvE0_NSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterIN7xgboost10SparsePageEE4InitESt8functionIFbPPS5_EES7_IFvvEEEUlvE_vEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN7xgboost4data16SparsePageWriterINS3_10SparsePageEEC4ERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISE_EESI_mEUlvE_vEEEESaISM_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterIN7xgboost10SparsePageEE4InitESt8functionIFbPPS6_EES8_IFvvEEEUlvE_vEEEESaISI_ELN9__gnu_cxx12_Lock_policyE2EE GGGGOOOOAAeA/home/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/home/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page_source.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPageSource is requiredinvalid input file formatparam.gpu_id >= 0param.max_bin >= 2Expecting CSCAdapter/home/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.hCheck failed: page_ != nullptr/home/runner/work/ml-builds/ml-builds/xgboost/src/data/adapter.h/home/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: in_stream->Read(&tmagic, sizeof(tmagic)) == sizeof(tmagic)Batch parameter is not initialized.Check failed: param != BatchParam{}Check failed: (std::is_same::value)N7xgboost4data13SimpleDMatrixEN7xgboost17BatchIteratorImplINS_10SparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_10SparsePageEEEN7xgboost17BatchIteratorImplINS_7CSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_7CSCPageEEEN7xgboost17BatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13SortedCSCPageEEEN7xgboost17BatchIteratorImplINS_11EllpackPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_11EllpackPageEEE/home/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory.Check failed: source_ != nullptr/home/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_writer.hCSCPageSource: Finished writing to SortedCSCPageSource: Finished writing to /home/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccWriting to .sorted.col.pageN7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_10SparsePageEEES3_EEN7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_7CSCPageEEES3_EEN7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_13SortedCSCPageEEES3_EEN7xgboost4data23SparseBatchIteratorImplINS0_24ExternalMemoryPrefetcherINS_11EllpackPageEEES3_EEN7xgboost4data17SparsePageDMatrixEZN7xgboost4data24ExternalMemoryPrefetcherINS_7CSCPageEEC4ERKNS0_9CacheInfoEEUlPPS2_E_ZN7xgboost4data24ExternalMemoryPrefetcherINS_7CSCPageEEC4ERKNS0_9CacheInfoEEUlvE0_NSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterIN7xgboost7CSCPageEE4InitESt8functionIFbPPS5_EES7_IFvvEEEUlvE_vEEEEZN7xgboost4data24ExternalMemoryPrefetcherINS_13SortedCSCPageEEC4ERKNS0_9CacheInfoEEUlPPS2_E_ZN7xgboost4data24ExternalMemoryPrefetcherINS_13SortedCSCPageEEC4ERKNS0_9CacheInfoEEUlvE0_NSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterIN7xgboost13SortedCSCPageEE4InitESt8functionIFbPPS5_EES7_IFvvEEEUlvE_vEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterIN7xgboost7CSCPageEE4InitESt8functionIFbPPS6_EES8_IFvvEEEUlvE_vEEEESaISI_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterIN7xgboost13SortedCSCPageEE4InitESt8functionIFbPPS6_EES8_IFvvEEEUlvE_vEEEESaISI_ELN9__gnu_cxx12_Lock_policyE2EE/home/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccoffset_vec.back() == page.data.Size()Check failed: page.offset.Size() != 0 && offset_vec[0] == 0disk_offset_[fid] > curr_offsetfi->Read(dmlc::BeginPtr(data_vec) + offset_vec[i], size_to_read * sizeof(Entry)) == size_to_read * sizeof(Entry)fi->Read(dmlc::BeginPtr(data_vec), (page->data).Size() * sizeof(Entry)) == (page->data).Size() * sizeof(Entry)Raw binary data format.fid + 1 < disk_offset_.size()Invalid SparsePage filepage->offset.Size() != 0UN7xgboost4data16SparsePageFormatINS_10SparsePageEEEN7xgboost4data19SparsePageRawFormatINS_10SparsePageEEEN7xgboost4data16SparsePageFormatINS_7CSCPageEEEN7xgboost4data19SparsePageRawFormatINS_7CSCPageEEEN7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEEN7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE*N7xgboost4dataUlvE_E*N7xgboost4dataUlvE0_E*N7xgboost4dataUlvE1_Egpu_coord_descentDoBoostntree_limit == 0UPredictBatchInternalPredictBatchgblinearmodel { "bias": [ ], "weight": [ ] }bias: weight: shotguntoleranceMaximum rows per batch.max_row_perbatchlong (non-negative)GBLinearTrainParamGBLineargblinear_train_paramCheck failed: this->updater_/home/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccgblinear does not support prediction of leaf index/home/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hInplace predict is not supported by current booster./home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.h/home/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(¶m_, sizeof(param_)) == sizeof(param_)GBLinear::PredictContribution: ntrees is only valid for gbtree predictorbase_margin.size() == nsize * ngroupGBLinear::Predict ntrees is only valid for gbtree predictorget(in["name"]) == "gblinear"Update algorithm for linear model. One of shotgun/coord_descentStop if largest weight update is smaller than this number./home/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.hLinear booster, implement generalized linear model.N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEES8_EEN4dmlc9parameter10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEEN7xgboost5ModelEN7xgboost12ConfigurableEN7xgboost15GradientBoosterEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryImEEmEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryImEEmEEN4dmlc9parameter10FieldEntryImEEN7xgboost3gbm8GBLinearE*N7xgboost3gbmUlPKNS_17LearnerModelParamEE_EN7xgboost3gbm13GBLinearModelEUnknown gbm type /home/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.cc/home/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccTree method is automatically selected to be 'approx' for distributed training.Tree method is automatically set to 'approx' since external-memory data matrix is used.Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'./home/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.hTree method is selected to be 'hist', which uses a single updater grow_quantile_histmaker.Data is on CUDA device, but XGBoost is not compiled with CUDA support.num_group == model_.learner_model_param->num_output_groupmodel_.param.size_leaf_vector == 0size_leaf_vector is enforced to 0 so farpreds.size() == p_fmat->Info().num_row_ * num_groupups.size() == updaters_.size() mismatched updater sequence. get(in["name"]) == "gbtree"get(in["name"]) == "dart"Number of features used for training and prediction.Reserved option for vector tree.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.Whether to run the normal boosting process that creates new trees, or to update the trees in an existing model.Choice of tree construction method.Different types of sampling algorithm.Different types of normalization algorithm.Fraction of trees to drop during the dropout.Whether at least one tree should always be dropped during the dropout.Probability of skipping the dropout during a boosting iteration.Learning rate(step size) of update.Check failed: !updaters_.front()->CanModifyTree()can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsgpair->Size() == p_fmat->Info().num_row_Mismatching size between number of rows from input data and size of gradient vector.Check failed: this->configured_Check failed: tparam_.GetInitialised()in_gpair->Size() % ngroup == 0Umust have exactly ngroup * nrow gpairsDANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using`tree_method` parameter instead.Loading from a raw memory buffer on CPU only machine. Changing predictor to auto.Loading from a raw memory buffer on CPU only machine. Changing tree_method to hist.Tree booster, gradient boosted trees.Using tree method: Check failed: configured_grow_histmaker,prunegrow_colmaker,prunegrow_quantile_histmakergrow_gpu_histUnknown tree_method () detectedCheck failed: cpu_predictor_gbtreedartweight_dropCommitModelInternal Error: Specified updaters: Actual updaters: drop trees, weight = num_treessize_leaf_vectorGBTreeModelParam has not been registered in Alias num_parallel_treeTree updater sequence.updater_sequpdatedefaultprocess_typePredictor algorithm typegpu_predictorcpu_predictorexactapproxtree_methodGBTreeTrainParamweighteduniformsample_typeforestnormalize_typerate_dropone_dropbooleanskip_droplearning_rateetaDartTrainParamUpdater: `` Using updaters: BoostNewTreesngroup != 0GBTreegbtree_train_paramspecified_updaterdart_train_paramvector::reserveout_preds.size() == nTree booster, dart.(*N7xgboost3gbmUlPKNS_17LearnerModelParamEE_E*N7xgboost3gbmUlPKNS_17LearnerModelParamEE0_EN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEEN4dmlc9parameter10FieldEntryIbEEN4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEEN4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEEN4dmlc9parameter10FieldEntryIN7xgboost13PredictorTypeEEEN7xgboost3gbm6GBTreeEN7xgboost3gbm4DartEA;?GBTree: invalid model file/home/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast(trees.size())fi->Read(¶m, sizeof(param)) == sizeof(param)fi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treesparam.num_trees == static_cast(trees.size())N7xgboost3gbm11GBTreeModelE/home/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccCheck failed: tparam_.dsplit != DataSplitMode::kAutoPrecondition violated; dsplit cannot be 'auto' in distributed modeColumn-wise data split is currently not supported.learner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Perform one kind of prediction at a time.Predict must happen after Load or configurationCheck failed: !this->need_configuration_Call Configure before saving model.Number of features in training data, this parameter will be automatically detected by learner.Number of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.Random number seed during training.Seed PRNG determnisticly via iterator number, this option will be switched on automatically on distributed mode.The primary GPU device ordinal.GPU page size when running in external memory mode.Enable using JSON for memory serialization (Python Pickle, rabit checkpoints etc.).enable_experimental_json_serializationEnable checking whether parameters are used or not. Deprecated. Single process multi-GPU training is no longer supported. Please switch to distributed training with one process per GPU. This can be done using Dask or Spark. See documentation for details./home/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/generic_parameters.hData split mode for distributed training.Flag to disable default metric. Set to >0 to disableGradient booster used for training.Objective function used for obtaining gradient.Check failed: !matrix.second.ref.expired()num_col <= static_cast(std::numeric_limits::max())Unfortunately, XGBoost does not support data matrices with 0 feature is supplied. Are you using raw Booster interface? } might not be used. This may not be accurate due to some parameters are only used in language bindings but passed down to XGBoost core. Or some parameters are not used but slip through this verification. Please open an issue if you find above cases. fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_ If you are loading a serialized model (like pickle in Python) generated by older XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. There's a simple script for helping the process. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for reference to the script, and more details about differences between saving model and serializing. fp.Read(&sz, sizeof(sz)) == sizeof(sz)Base64 format is no longer supported in brick.fi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility : multiple_predictions <= 1Check failed: gbm_ != nullptrUpdateOneIterPredictRawPredictionsGetGradientGradientsConfigEvalOneIterbase_scorenum_featurenum_classlearnerlearner_model_paramgradient_boosterobjectiveattributesGlobal bias of the model.int (non-negative)LearnerModelParamLegacyseedlongrandom_stateseed_per_iterationNumber of threads to use.nthreadn_jobsgpu_idgpu_page_sizevalidate_parametersn_gpusGenericParameter n_gpus: dsplitdisable_default_eval_metricreg:squarederrorLearnerTrainParamConfigureNumFeaturesCheck failed: matrix.first features or greatermparam_.num_feature != 0multi:softprobnum_output_groupmulti:softmaxcount:poisson0.7learner_train_parammetricsgeneric_paramBoostOneIter__Cannot find argument '', Possible Arguments: ---------------- Check failed: IsA(in)rabit_bootstrap_cacheSAVED_PARAM_;binfLearnernum_roundCONFIG-offset:Unsupported prediction type:verbosity Parameters: { Configuresz > 0bs64header != "bs64"fp.Read(&header[0], 4) == 4Ucount_poisson_max_delta_stepmulti:N4dmlc12SerializableEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIlEElEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIlEElEEN4dmlc9parameter10FieldEntryIlEEN7xgboost7LearnerEN4dmlc9parameter10FieldEntryIN7xgboost13DataSplitModeEEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEEN4dmlc9parameter10FieldEntryIjEEN7xgboost20LearnerConfigurationEN7xgboost9LearnerIOEN7xgboost11LearnerImplEUnknown linear updater /home/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUpdateFeaturetop_kCoordinateParamunknown coordinate selector: CoordinateUpdatercoordinate_paramThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features./home/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hUpdate linear model according to coordinate descent algorithm.(EzB N7xgboost13LinearUpdaterEN7xgboost6linear15FeatureSelectorEN7xgboost6linear21CyclicFeatureSelectorEN7xgboost6linear22ShuffleFeatureSelectorEN7xgboost6linear21RandomFeatureSelectorEN7xgboost6linear21GreedyFeatureSelectorEN7xgboost6linear22ThriftyFeatureSelectorEN7xgboost6linear17CoordinateUpdaterE*N7xgboost6linearUlvE_E>/home/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. Supported options are: {cyclic, shuffle}Update linear model according to shotgun coordinate descent algorithm.B\(N7xgboost6linear14ShotgunUpdaterE*N7xgboost6linearUlvE_EWARNING: DEBUG: INFO: gamma-nloglikgamma-deviancepoisson-nloglikmphemaermslermsetweedie-nloglik@%flabel set is emptyEvalRooted mean square error.Rooted mean square log error.Mean absolute error.Mean Pseudo Huber error.Binary classification error.tweedie-nloglik/home/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cusscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)preds.Size() == info.labels_.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationNegative loglikelihood for logistic regression.Negative loglikelihood for poisson regression.Residual deviance for gamma regression.Negative log-likelihood for gamma regression.tweedie-nloglik@rho for tweedie regression.N7xgboost6MetricEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowRMSEEEEN7xgboost6metric13EvalEWiseBaseINS0_12EvalRowRMSLEEEEN7xgboost6metric13EvalEWiseBaseINS0_10EvalRowMAEEEEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowMPHEEEEN7xgboost6metric13EvalEWiseBaseINS0_14EvalRowLogLossEEEN7xgboost6metric13EvalEWiseBaseINS0_20EvalPoissonNegLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_17EvalGammaDevianceEEEN7xgboost6metric13EvalEWiseBaseINS0_16EvalGammaNLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_9EvalErrorEEEN7xgboost6metric13EvalEWiseBaseINS0_18EvalTweedieNLogLikEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E*N7xgboost6metricUlPKcE6_E*N7xgboost6metricUlPKcE7_E*N7xgboost6metricUlPKcE8_E$]_p0@?Unknown metric function /home/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.ccCannot find a GPU metric builder for metric . Resorting to the CPU buildermerrormloglosspreds.Size() == 0nclass >= 1U num_class= but found in label/home/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cuCheck failed: preds.Size() % info.labels_.Size() == 0label and prediction size not matchmlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast(n_class)MultiClassEvaluation: label must be in [0, num_class),Multiclass classification error.Multiclass negative loglikelihood.N7xgboost6metric14EvalMClassBaseINS0_14EvalMatchErrorEEEN7xgboost6metric14EvalMatchErrorEN7xgboost6metric14EvalMClassBaseINS0_16EvalMultiLogLossEEEN7xgboost6metric16EvalMultiLogLossE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E]Bauccox-nloglikaucpr%u[-]?preAMS must be in format ams@kams@gptr.size() != 0Ugptr.back() == preds.Size()info.labels_.Size() != 0Ulabel set cannot be emptydat[1] > 0.0fdat[0] <= dat[1]AUC-PR: AUC > 1.0AMS metric for higgs.precision@k for rank.ndcg@k for rank.map@k for rank.Check failed: !distributedbest-ams-ratio=Check failed: !auc_errorAUC-PR: error in calculation/home/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.cclabel size predict size not matchmust specify group when constructing rank fileEvalRank: group structure must match number of predictiongptr.back() == info.labels_.Size()EvalAucPR: group structure must match number of predictionAUC-PR: the dataset only contains pos or neg samplesEvalAuc: group structure must match number of predictionAUC: the dataset only contains pos or neg samplesArea under curve for both classification and rank.Area under PR curve for both classification and rank.Negative log partial likelihood of Cox proportioanl hazards model.Cox metric does not support distributed evaluationmetric AMS do not support distributed evaluationqplq4qLqXN7xgboost6metric14EvalRankConfigEN7xgboost6metric7EvalAMSEN7xgboost6metric7EvalAucEN7xgboost6metric8EvalRankEN7xgboost6metric13EvalPrecisionEN7xgboost6metric8EvalNDCGEN7xgboost6metric7EvalMAPEN7xgboost6metric7EvalCoxEN7xgboost6metric9EvalAucPRE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E@$@aft-nloglikinterval-regression-accuracyCheck failed: metric_/home/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuAFT metric must be configured first, with distribution type and scaleUnknown probability distributioninfo.labels_lower_bound_.Size() != 0Ulabels_lower_bound cannot be emptyinfo.labels_upper_bound_.Size() != 0Ulabels_upper_bound cannot be emptypreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()Negative log likelihood of Accelerated Failure Time model.N7xgboost6metric20AFTNLogLikDispatcherEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common18NormalDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common20LogisticDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common19ExtremeDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_30EvalIntervalRegressionAccuracyEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E-q=' @;f?info.weights_.Size() == ndataUnrecognized distributionAFT loss functionsurvival:aft/home/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /home/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndataNumber of weights should be equal to number of data points.N7xgboost11ObjFunctionEN7xgboost3obj6AFTObjE*N7xgboost3objUlvE_E..@@ؗҜ<@ppApreds.size=, label.size=binary:hinge/home/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedHinge loss. Expects labels to be in [0,1f]N7xgboost3obj8HingeObjE*N7xgboost3objUlvE_Elabel.Size() * num_class: preds.Size(): SoftmaxMultiClassParamsoftmax_multiclass_param/home/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast(param_.num_class) * info.labels_.Size())SoftmaxMultiClassObj: label size and pred size does not match. SoftmaxMultiClassObj: label must be in [0, num_class).(_count == dynamic_extent) ? (_offset <= size()) : (_offset + _count <= size())Number of output class in the multi-class classification.Softmax for multi-class classification, output class index.Softmax for multi-class classification, output probability distribution.N7xgboost3obj20SoftmaxMultiClassObjE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_EҜ 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: PoissonRegression: label must be nonnegativeGammaRegression: label must be nonnegativeTweedieRegression: label must be nonnegativelabel must be in [0,1] for logistic regressionlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.Scale the weight of positive examples by this factorMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.Tweedie variance power. Must be between in range [1, 2).Regression with squared error.Regression with root mean squared logarithmic error.Logistic regression for probability regression task.Regression Pseudo Huber error.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.Possion regression for count data.Cox regression for censored survival data (negative labels are considered censored).Gamma regression for severity data.Tweedie regression for insurance data.Check failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!N7xgboost3obj10RegLossObjINS0_16LinearSquareLossEEEN7xgboost3obj10RegLossObjINS0_15SquaredLogErrorEEEN7xgboost3obj10RegLossObjINS0_18LogisticRegressionEEEN7xgboost3obj10RegLossObjINS0_16PseudoHuberErrorEEEN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEEEN7xgboost3obj10RegLossObjINS0_11LogisticRawEEEN7xgboost3obj17PoissonRegressionEN7xgboost3obj13CoxRegressionEN7xgboost3obj15GammaRegressionEN7xgboost3obj17TweedieRegressionE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_E*N7xgboost3objUlvE2_E*N7xgboost3objUlvE3_E*N7xgboost3objUlvE4_E*N7xgboost3objUlvE5_E*N7xgboost3objUlvE6_E*N7xgboost3objUlvE7_E*N7xgboost3objUlvE8_E*N7xgboost3objUlvE9_E/home/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccmodel.learner_model_param->num_output_group != 0Ignoring the base margin, since it has incorrect length. The base margin must be an array of length [num_class] * [number of data points], i.e. Instead, all data points will use [number of data points], i.e. model.param.size_leaf_vector == 0/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/any.hCheck failed: type_ != nullptrCheck failed: *(type_->ptype_info) == typeid(T)m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.Data type is not supported by CPU Predictor.out_preds->size() == p_fmat->Info().num_row_ * model.learner_model_param->num_output_groupCheck failed: out_preds->Size() == output_groups * dmat->Info().num_row_ || out_preds->Size() == dmat->Info().num_row_out_preds->Size() == n * base_score = ncolumns != 0The any container is empty requested=The stored type mismatch stored=tree_begin == 0predts->version >= tree_beginpredts->version == 0output_groups != 0ntree_limit != 0delta <= model.trees.size()Make predictions using CPU.N7xgboost9predictor12CPUPredictorE*N7xgboost9predictorUlPKNS_16GenericParameterEE_EUnknown predictor type _Map_base::at[Internal error]: DMatrix: has expired./home/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccCheck failed: container_.find(m) != container_.cend()Check failed: container_.at(m).ref.lock()N7xgboost9PredictorEnewsize != 0With error: /home/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: _std::floor(d) == d/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.ccFound floating point number in interaction constraintsUnknown value type for interaction constraint:/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/split_evaluator.ccElasticNet does not accept an inner SplitEvaluatorMonotonicConstraint must be given an inner evaluatorCheck failed: !std::isnan(mid)Check failed: nodeid < upper_.size()Use an elastic net regulariserEnforces that the tree is monotonically increasing/decreasing w.r.t. specified featuresUnknown SplitEvaluator Check failed: r->params_Check failed: c->params_elastic_netN7xgboost4tree14SplitEvaluatorEN7xgboost4tree10ElasticNetEN7xgboost4tree19MonotonicConstraintE*N7xgboost4treeUlSt10unique_ptrINS0_14SplitEvaluatorESt14default_deleteIS2_EEE_E*N7xgboost4treeUlSt10unique_ptrINS0_14SplitEvaluatorESt14default_deleteIS2_EEE0_E/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccFeatureMap feature index exceed boundthis->node_mean_values_.size() > 0Uparam.num_nodes == static_cast(nodes_.size())param.num_nodes == static_cast(stats_.size())param.deprecated_num_roots == 1/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././json.h, Expect '"' but reach end of line , JSON object expect '}' or ',' 'fi->Read(¶m, sizeof(TreeParam)) == sizeof(TreeParam)fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast(deleted_nodes_.size()) == param.num_deletedNumber of features used in tree construction.Size of leaf vector, reserved for vector treeEdge color when meets the node condition.Edge color when doesn't meet the node condition.Passed to graphiz via graph_attr.Conditional node configurationAny other extra attributes for graphviz `graph_attr`.Minimum loss reduction required to make a further partition.Maximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policyMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featureTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)Minimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightDefault direction choice when encountering a missing valueMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintRow subsample ratio of training instance.Sampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)Subsample ratio of columns, resample on each node (split).Subsample ratio of columns, resample on each level.Subsample ratio of columns, resample on each tree construction.EXP Param: Sketch accuracy of approximate algorithm.EXP Param: Sketch accuracy related parameter of approximate algorithm.EXP Param: Cache aware optimization.Whether the refresh updater needs to update leaf values.Constraint of variable monotonicityConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationThe criteria to use for ranking splitspercentage threshold for treating a feature as sparseif >0, enable feature grouping to ameliorate work imbalance among worker threadswhen grouping features, how many "conflicts" to allow.conflict is when an instance has nonzero values for two or more features.default is 0, meaning features should be strictly complementary.when grouping features, how much effort to expend to prevent singleton groups. We'll try to insert each feature into existing groups before creating a new group for that feature; to save time, only up to (max_search_group) of existing groups will be considered. If set to zero, ALL existing groups will be examined.loss_changes.size() == n_nodesbase_weights.size() == n_nodesleaf_child_counts.size() == n_nodesdefault_left.size() == n_nodesstatic_cast(deleted_nodes_.size()) == param.num_deletedget(out["tree_param"]["num_nodes"]) == std::to_string(param.num_nodes){{properties} {stat}, "children": [{left}, {right} {indent}]} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing} "nodeid": {nid}, "depth": {depth}, "split": {fname}, "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}{ "nodeid": {nid}, "leaf": {leaf} {stat}} {nid} [ label="leaf={leaf-value}" {params}] digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{tabs}{nid}:leaf={leaf}{stats},gain={loss_chg},cover={sum_hess}, "gain": {loss_chg}, "cover": {sum_hess} {nid} [ label="{fname}{<}{cond}" {params}] {nid} -> {child} [label="{is_missing}" color="{color}"] {nid}:[{fname}] yes={yes},no={no}Dump text representation of treeDump json representation of treeDump graphviz representation of tree graph [ {key}="{value}" ] The following parameters for graphviz are not recognized: Failed to parse graphviz parameters: basic_string::replaceUnknown Model Builder: Line , around ^`idx < names_.size()pos != std::string::nposunique_path[i].pweight == 0Unique path must have zero weightUnknown feature map type.param.num_nodes != 0ch == '\"'Error at, Expect '"' but get 'unknown string escape \ch == ','ch == ':', Expect ':' but get 'ch == '{', Expect '{' but get 'num_nodesnum_deletedTreeParamdouble#0000FFyes_color#FF0000no_colorrankdircondition_node_paramsLeaf node configurationleaf_node_paramsgraph_attrsGraphvizParammin_split_lossmax_depthmax_leavesmax_binlossguidedepthwisegrow_policymin_child_weightreg_lambdareg_alpharightlearndefault_directionsubsamplegradient_basedsampling_methodcolsample_bynodecolsample_bylevelcolsample_bytreesketch_epssketch_ratiocache_optrefresh_leafmonotone_constraintsinteraction_constraintselastic_net,monotonicsplit_evaluatorsparse_thresholdenable_feature_groupingmax_conflict_ratemax_search_grouptree_paramn_nodes != 0loss_changessum_hessiansum_hessian.size() == n_nodesbase_weightsleaf_child_countsleft_childrenlefts.size() == n_nodesright_childrenrights.size() == n_nodesparentsparents.size() == n_nodessplit_indicesindices.size() == n_nodessplit_conditionsconds.size() == n_nodesdefault_left{nodes} {properties}{stat}{left}{indent}{nid}%u{depth}{fname}{yes}{no}{cond}{missing}{tabs}, "cover": {sum_hess} {leaf}{sum_hess}{newline}{indent}{nodes}{newline}{leaf-value}{params}{parent} {left} {right}{parent}{rankdir}{graph_attrs},cover={cover}{stats}{parent}{stat} {left} {right}{loss_chg}yes, missingno{child}{color}{is_missing}<{<}dot" edge{key}{value}N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter10FieldEntryIdEEN7xgboost7RegTreeEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryISt6vectorIiSaIiEEEES5_EEN4dmlc9parameter10FieldEntryISt6vectorIiSaIiEEEEN7xgboost13TreeGeneratorEN7xgboost13TextGeneratorEN7xgboost13JsonGeneratorEN7xgboost17GraphvizGeneratorE*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE_E*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE0_E*N7xgboostUlRKNS_10FeatureMapENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE1_EHgY?@חA?Unknown tree updater /home/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccgrow_colmakerridx < position_.size()ridx exceed bound ridx= pos=opt_dense_colColMakerTrainParamcolmaker_train_paramfeatures.size() > 0Check failed: node.IsLeaf()/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccCheck failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0/home/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hparam.num_nodes < std::numeric_limits::max()number of nodes in the tree exceed 2^31EXP Param: speed optimization for dense column./home/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/random.hfmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.Updater `grow_colmaker` or `exact` tree method doesn't support distributed training.Grow tree with parallelization over columns.N7xgboost11TreeUpdaterEN7xgboost4tree8ColMaker7BuilderEN7xgboost4tree8ColMakerE*N7xgboost4treeUlvE_ESt23_Sp_counted_ptr_inplaceIN7xgboost16HostDeviceVectorIjEESaIS2_ELN9__gnu_cxx12_Lock_policyE2EEgrow_histmakergrow_local_histmakerret > 0Usketch->temp.size < max_size, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=fv=, hist.size=hist[]=, hist.last=fid * 2 + 1 < fminmax_.size() check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=] rmin=, rmax=, wmin=, v=Check failed: nbig < nfid * 2 < fminmax_.size()istart != hist.sizeSyncInfoSampleColCreateHistsketch->temp.size <= max_size, stemp.size=offset == -2ResetPosAndProposethis->qexpand_.size() == 1U/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_histmaker.cc/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_basemaker-inl.hCheck failed: tree[nid].IsLeaf()node2workindex_[nid] == static_cast(wid)/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./param.hinvalid maximum size max_size=FeatHelper fid exceed query bound /home/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/quantile.hquantile: too many large chunkCheck failed: param_.max_depth > 0max_depth must be larger than 0Finalize: invalid maximum size, max_size=summary_array_.size() == sketchs_.size()this->wspace_.rptr.size() == (fset.size() + 1) * this->qexpand_.size() + 1this->wspace_.rptr.back() == this->wspace_.cut.size()Tree constructor that uses approximate histogram construction.Tree constructor that uses approximate global of histogram construction.tN7xgboost4tree9BaseMakerEN7xgboost4tree9HistMakerEN7xgboost4tree11CQHistMakerEN7xgboost4tree23GlobalProposalHistMakerEZN7xgboost4tree11CQHistMaker10CreateHistERKSt6vectorINS_6detail20GradientPairInternalIfEESaIS5_EEPNS_7DMatrixERKS2_IjSaIjEERKNS_7RegTreeEEUlvE_*N7xgboost4treeUlvE_E*N7xgboost4treeUlvE0_Eư>o;o:nid >= 1PrunerUpdatetree pruning end, extra nodes, pruned nodes, max_depth=/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.ccCheck failed: !tree[pid].IsLeaf()Pruner that prune the tree according to statistics.N7xgboost4tree10TreePrunerE*N7xgboost4treeUlvE_ESubtractionTrickbegin < endi < ranges_.size()i < first_dimension_.size()SyncHistogramsdata_ || size_ == 0n_left + n_right == e.Size()begin + n_left <= e.endvector::_M_fill_insertQuantile::BuilderQuantileHistMakerrow_ptr_[nid] != kMaxSyncHistogramsAllreducenid < nodes_tid < nthreads_SplitSiblingssingle_precision_histogramCPUHistMakerTrainParamUpdatePredictionCacheout_preds.size() > 0UInitNewNodeBuildNodeStatscpu_hist_train_paramspan_ == rhs.span_EvaluateSplitsInitDatamin_nbins_per_feature > 0UApplySplitrow_ptr_[nid] == kMaxAddHistRowsBuildLocalHistogramsExpandWithLossGuideGmatInitializationend > begingrow_fast_histmaker/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./../common/hist_util.h/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./../common/threading_utils.h/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccCheck failed: (*p_last_tree_)[nid].IsLeaf()/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./../common/row_set.helem_of_each_node_.size() == 0Ucut_ptr[fid] <= static_cast(std::numeric_limits::max())cut_ptr[fid + 1] <= static_cast(std::numeric_limits::max())Check failed: e.begin != nullptrbegin + n_left + n_right == e.end/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./../common/column_matrix.hgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_valgmat_bin_size == kUint32BinsTypeSizebins_type_size_ == kUint32BinsTypeSizegrow_fast_histmaker is deprecated, use grow_quantile_histmaker instead.upper_bound < static_cast(std::numeric_limits::max())Use single precision to build histograms./home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./updater_quantile_hist.hAttempted to load interal configuration for a model file that was generated by a previous version of XGBoost. A likely cause for this warning is that the model was saved with saveRDS() in R or pickle.dump() in Python. We strongly ADVISE AGAINST using saveRDS() or pickle.dump() so that the model remains accessible in current and upcoming XGBoost releases. Please use xgb.save() instead to preserve models for the long term. For more details and explanation, see https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/./../common/random.hCheck failed: (param_.max_depth > 0 || param_.max_leaves > 0)max_depth or max_leaves cannot be both 0 (unlimited); at least one should be a positive quantity.max_depth cannot be 0 (unlimited) when grow_policy is depthwise.nodes == targeted_hists.size()hist_total == hist_memory_.size()(Deprecated, use grow_quantile_histmaker instead.) Grow tree using quantized histogram.Grow tree using quantized histogram.FbN7xgboost4tree17QuantileHistMaker7BuilderIdE11ExpandEntryES4_EFbN7xgboost4tree17QuantileHistMaker7BuilderIfE11ExpandEntryES4_EN7xgboost4tree17QuantileHistMakerEN7xgboost4tree17QuantileHistMaker7BuilderIfEEN7xgboost4tree17QuantileHistMaker7BuilderIdEEN7xgboost4tree16HistSynchronizerIfEEN7xgboost4tree27DistributedHistSynchronizerIfEEN7xgboost4tree13HistRowsAdderIfEEN7xgboost4tree24DistributedHistRowsAdderIfEEN7xgboost4tree21BatchHistSynchronizerIfEEN7xgboost4tree18BatchHistRowsAdderIfEEN7xgboost4tree16HistSynchronizerIdEEN7xgboost4tree27DistributedHistSynchronizerIdEEN7xgboost4tree13HistRowsAdderIdEEN7xgboost4tree24DistributedHistRowsAdderIdEEN7xgboost4tree21BatchHistSynchronizerIdEEN7xgboost4tree18BatchHistRowsAdderIdEE*N7xgboost4treeUlvE_E*N7xgboost4treeUlvE0_EPFbN7xgboost4tree17QuantileHistMaker7BuilderIfE11ExpandEntryES4_EPFbN7xgboost4tree17QuantileHistMaker7BuilderIdE11ExpandEntryES4_Erefresh/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits::max()Refresher that refreshes the weight and statistics according to data.N7xgboost4tree13TreeRefresherEZN7xgboost4tree13TreeRefresher6UpdateEPNS_16HostDeviceVectorINS_6detail20GradientPairInternalIfEEEEPNS_7DMatrixERKSt6vectorIPNS_7RegTreeESaISC_EEEUlvE_*N7xgboost4treeUlvE_Egrow_skmakerqexpand_.size() != 0USyncNodeStatsBuildSketchApproximate sketching maker./home/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_skmaker.ccN7xgboost4tree11SketchMakerE*N7xgboost4treeUlvE_EOsync/home/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_sync.ccSyncher that synchronize the tree in all distributed nodes.N7xgboost4tree11TreeSyncherE*N7xgboost4treeUlvE_E%s, shutting down process rabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_reduce_ring_mincountrabit_reduce_buffer%lu%cinvalid format for %sDMLC_WORKER_CONNECT_RETRYrabit_debugrabit_timeoutrabit_timeout_secrabit_enable_tcp_no_delaySocket %s Error:%sCreatecannot obtain address of %sDoes not support IPv6connect to (failed): [%s] ConnectSendAllReConnectLink failure 1RecvAllReConnectLink failure 2ReConnectLink failure 3error during send SendStrfailed to shutdown due to %s PollAllreduce: boundary checkbuffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorneed to assume rank structureAllgatherRing failedwrite ptr boundary check[%d] read_ptr boundary checkAllreduce failedBroadcast failedworker256MBReConnectLink failure 4task %s got new rank %d 0.0.0.0TryBindHostGetSockErrorReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8error during send RecvStrReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure 14AcceptReConnectLink failure 15ReConnectLink: bad socketSetNonBlock-1SetNonBlock-2SetKeepAlivefailed in ReconnectLink %s mapred_tip_id%[^=]=%smapreduce_task_idmapred_task_idmapred_map_tasksmapreduce_job_mapscan only call Init oncefail to get host namestartrabit_num_trial%s, rabit is configured to keep process running rabit_reduce_ring_mincount should be greater than 0invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}DMLC_WORKER_STOP_PROCESS_ON_ERRORinvalid value of DMLC_WORKER_STOP_PROCESS_ON_ERRORrabit_timeout_sec should be non negative secondInitAfterException: not implementedretry connect to ip(retry time %d): [%s] sync::Invalid tracker message, init failureSocket::Close double close the socket or close without createtoo large type_nbytes=%lu, buffer_size=%luReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkBroadcast: root should be smaller than world sizetask %s connected to the tracker must keep rank to same if the node already have oneReConnectLink fail to bind the ports specifiedReConnectLink failure, link rank inconsistentOverride a link that is activecannot find parent in the linkcannot find prev ring in the linkcannot find next ring in the linkhadoop_mode is set but cannot find mapred_task_idhadoop_mode is set but cannot find mapred_map_tasksRabit Module currently only work with dmlc worker, quit this program by exit 0 N5rabit6engine7IEngineEN5rabit6engine13AllreduceBaseEcannot initialize reduce handle twicethe rabit has not been initializedmust intialize handle to call AllReduce/home/runner/work/ml-builds/ml-builds/xgboost/rabit/src/c_api.ccDataType does not support bitwise or operationRead pickle stringnot implementedAllreduce_unknown data_typeunknown enum_opAllgatherRabitBroadcastX''&&8&%hh! x 0 XH!""x"8"!!p!8#%$x$0$##X#H%8((((()h)X''N5rabit5c_api11ReadWrapperEN5rabit5c_api12WriteWrapperErabit_global_replicarabit_local_replicafailed to get timestd::future_error: recovermust know the error linkcannot allocate 0 size memoryTryDecideRoutingrecv_link must be activeTryRecoverDataparent must be 0invalie stage idMsgPassing invalid stageRingPassing: boundary check1RingPassing: boundary check2RingPassing: boundary check3must already gone badwrong oob msgshould already read past markBUGwrong Ack MSGinvalid local replicaPushTemp seqid inconsistentPushTemp inconsistentcan't set empty cacherequest result inconsistentTryGetResult::Checkpoint pass negative seqno flag version min seq bugcheckpoint reqcheckpoint actcheckpoint with kSpecialOploadcache reqloadcache actShutdownexpect timeout task return GetBootstrapCache::#LoadCheckPointread in version numbercheck ack must return trueno need to load cache [%d] loadcheckpoint reset CheckPoint_check point must return true[EXPERIMENTAL] bootstrap cache has been enabled [%d] timeout thread %ld starts [%d] timeout task thread %ld exits [%d] exit due to time out %d s Can only call Checkpoint/LoadCheckPoint always withor without local_model, but not mixed case[EXPERIMENTAL] timeout thread expires in %d second(s) there is nothing to be droppedTryCheckinLocalState must have exactly 1 statelocal chkpt space inconsistent[%d] Allreduce size inconsistent, distin=%lu, size=%lu, reporting=%lu Too many nodes went down and we cannot recover..cannot get and receive requestrequester is expected to have fewer cache entriesLoadCheckPoint: too many nodes fails, cannot recover local statezero size check point is not allowedAllreduce Recovered data size do not match the specification of function call. Please check if calling sequence of recovered program is the same the original one in current VersionNumbermust only set seqno for normal operationsreporter:status:Rabit Phase[%03d] Operation %03d check ack & check pt cannot occur together with normal ops[%d] %s - |%lu|%d|%d|%d|%d| - |%lu|%d| checkpoint node should be KHaveData rolebuf should have data from resbufbuf size should be greater than 0load cache state expect no nodes doing load checkpointload cache state expect no nodes doing checkpointload cache state expect no nodes doing checkpoint ackload cache not interacte with rest statesShutdown: check point must return trueShutdown: check ack must return truecur_cache_seq is smaller than lookup cache seq indexcache size stored expected to be same as requestedcache size should be greater than 0[%d] allgather (%s) finished version %d, seq %d, take %f seconds [%d] allreduce (%s) finished version %d, seq %d, take %f seconds [%d] broadcast (%s) root %d finished version %d,seq %d, take %f seconds need to set rabit_local_replica larger than 1 to checkpoint local_model[%d] local model inconsistent, nlocal=%dlocal model inconsistent, nlocal=%d[%d] loadcheckpoint size %ld finished version %d, seq %d, take %f seconds [%d] checkpoint finished version %d,seq %d, take %f seconds [%d] checkpoint ack finished version %d, take %f seconds NSt13__future_base13_State_baseV2ENSt13__future_base21_Async_state_commonV2EN5rabit6engine15AllreduceRobustENSt13__future_base7_ResultIbEE*NSt13__future_base17_Async_state_implISt12_Bind_simpleIFZN5rabit6engine15AllreduceRobust15CheckAndRecoverENS3_13AllreduceBase10ReturnTypeEEUlvE_vEEbEE*NSt13__future_base15_Deferred_stateISt12_Bind_simpleIFZN5rabit6engine15AllreduceRobust15CheckAndRecoverENS3_13AllreduceBase10ReturnTypeEEUlvE_vEEbEE*St23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implISt12_Bind_simpleIFZN5rabit6engine15AllreduceRobust15CheckAndRecoverENS4_13AllreduceBase10ReturnTypeEEUlvE_vEEbEESaISB_ELN9__gnu_cxx12_Lock_policyE2EE*NSt6thread5_ImplISt12_Bind_simpleIFZNSt13__future_base17_Async_state_implIS1_IFZN5rabit6engine15AllreduceRobust15CheckAndRecoverENS5_13AllreduceBase10ReturnTypeEEUlvE_vEEbEC4EOSB_EUlvE_vEEEE*NSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultIbEENS_12_Result_base8_DeleterEESt12_Bind_simpleIFZN5rabit6engine15AllreduceRobust15CheckAndRecoverENS9_13AllreduceBase10ReturnTypeEEUlvE_vEEbEE*St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZNSt13__future_base17_Async_state_implIS2_IFZN5rabit6engine15AllreduceRobust15CheckAndRecoverENS6_13AllreduceBase10ReturnTypeEEUlvE_vEEbEC4EOSC_EUlvE_vEEEESaISI_ELN9__gnu_cxx12_Lock_policyE2EE& .>cannot call ParseNextsign == true.split.partname_cache.size() == 1UInvalid uri argument format for key in arg for value in arg name_args.size() == 1UUnknown data type File formatindexing_modeLibSVMParserParamlibfmLibFMParserParamFile format.label_columndelimiterweight_columnCSVParserParamoffset.back() == index.size()param_.format == "libsvm"param_.format == "libfm"param_.format == "csv"Bad RowBlock formatCheck failed: fi->Read(&qid)Delimiter '' is not found in the line. Expected 'MB read,finish reading at %g MB/secCheck failed: TryLoadCache()failed to build cache file finish reading at qid:chunk.size != 0U/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.h/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././parameter.h/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././strtonum.hCheck failed: base <= 10 && base >= 2/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.honly one `#` is allowed in file path for cachefile specificationCheck failed: std::getline(is, kv.first, '=')Check failed: std::getline(is, kv.second)/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.If >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.Column index (0-based) that will put into label.Delimiter used in the csv file.Column index that will put into instance weights./home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hlabel.size() + 1 == offset.size()Check failed: offset.back() == value.size() || value.size() == 0/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.h/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.h/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hCheck failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsCheck failed: fi->Read(&label)Check failed: fi->Read(&weight)Check failed: fi->Read(&field)Check failed: fi->Read(&index)Check failed: fi->Read(&value)Check failed: fi->Read(&max_field, sizeof(IndexType))Check failed: fi->Read(&max_index, sizeof(IndexType))batch.field[i] <= std::numeric_limits::max()field exceed numeric bound of current typebatch.index[i] <= std::numeric_limits::max()index exceed numeric bound of current type' as the delimiter to separate fields.Check failed: out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.h/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hCheck failed: out->field.size() == out->index.size()/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/././text_parser.hNSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS3_17RowBlockContainerImlEESaIS8_EEEUlvE_vEEEENSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS3_17RowBlockContainerIjlEESaIS8_EEEUlvE_vEEEENSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS3_17RowBlockContainerImiEESaIS8_EEEUlvE_vEEEENSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS3_17RowBlockContainerIjiEESaIS8_EEEUlvE_vEEEENSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS3_17RowBlockContainerImfEESaIS8_EEEUlvE_vEEEENSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS3_17RowBlockContainerIjfEESaIS8_EEEUlvE_vEEEESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_vEEEESaISG_ELN9__gnu_cxx12_Lock_policyE2EEN4dmlc8DataIterINS_8RowBlockIjfEEEEN4dmlc12RowBlockIterIjfEEN4dmlc8DataIterINS_8RowBlockImfEEEEN4dmlc12RowBlockIterImfEEN4dmlc8DataIterINS_8RowBlockIjiEEEEN4dmlc12RowBlockIterIjiEEN4dmlc8DataIterINS_8RowBlockImiEEEEN4dmlc12RowBlockIterImiEEN4dmlc8DataIterINS_8RowBlockIjlEEEEN4dmlc12RowBlockIterIjlEEN4dmlc8DataIterINS_8RowBlockImlEEEEN4dmlc12RowBlockIterImlEEN4dmlc6ParserIjfEEN4dmlc6ParserImfEEN4dmlc6ParserIjiEEN4dmlc6ParserImiEEN4dmlc6ParserIjlEEN4dmlc6ParserImlEEN4dmlc8DataIterINS_4data17RowBlockContainerIjfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjfEEEEN4dmlc4data11DiskRowIterIjfEEN4dmlc4data12BasicRowIterIjfEEN4dmlc8DataIterINS_4data17RowBlockContainerImfEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImfEEEEN4dmlc4data11DiskRowIterImfEEN4dmlc4data12BasicRowIterImfEEN4dmlc8DataIterINS_4data17RowBlockContainerIjiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjiEEEEN4dmlc4data11DiskRowIterIjiEEN4dmlc4data12BasicRowIterIjiEEN4dmlc8DataIterINS_4data17RowBlockContainerImiEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImiEEEEN4dmlc4data11DiskRowIterImiEEN4dmlc4data12BasicRowIterImiEEN4dmlc8DataIterINS_4data17RowBlockContainerIjlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerIjlEEEEN4dmlc4data11DiskRowIterIjlEEN4dmlc4data12BasicRowIterIjlEEN4dmlc8DataIterINS_4data17RowBlockContainerImlEEEEN4dmlc12ThreadedIterINS_4data17RowBlockContainerImlEEEEN4dmlc4data11DiskRowIterImlEEN4dmlc4data12BasicRowIterImlEEN4dmlc4data10ParserImplIjfEEN4dmlc4data14TextParserBaseIjfEEN4dmlc4data12LibSVMParserIjfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerIjfEESaIS4_EEEEN4dmlc4data14ThreadedParserIjfEEN4dmlc4data10ParserImplImfEEN4dmlc4data14TextParserBaseImfEEN4dmlc4data12LibSVMParserImfEEN4dmlc8DataIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc12ThreadedIterISt6vectorINS_4data17RowBlockContainerImfEESaIS4_EEEEN4dmlc4data14ThreadedParserImfEEN4dmlc4data11LibFMParserIjfEEN4dmlc4data11LibFMParserImfEEN4dmlc4data9CSVParserIjfEEN4dmlc4data9CSVParserImfEEN4dmlc4data10ParserImplIjiEEN4dmlc4data14TextParserBaseIjiEEN4dmlc4data9CSVParserIjiEEN4dmlc4data10ParserImplImiEEN4dmlc4data14TextParserBaseImiEEN4dmlc4data9CSVParserImiEEN4dmlc4data10ParserImplIjlEEN4dmlc4data14TextParserBaseIjlEEN4dmlc4data9CSVParserIjlEEN4dmlc4data10ParserImplImlEEN4dmlc4data14TextParserBaseImlEEN4dmlc4data9CSVParserImlEEZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlPPSt6vectorINS0_17RowBlockContainerIjfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserIjfEC4EPNS0_10ParserImplIjfEEEUlvE0_NSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterISt6vectorINS2_4data17RowBlockContainerIjfEESaIS7_EEE4InitESt8functionIFbPPS9_EESB_IFvvEEEUlvE_vEEEEZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlPPSt6vectorINS0_17RowBlockContainerImfEESaIS8_EEE_ZN4dmlc4data14Threa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compile with DMLC_USE_AZURE=1 to use AzurePlease compile with DMLC_USE_HDFS=1 to use hdfsPlease compile with DMLC_USE_S3=1 to use S3/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: part_index == 0 && num_parts == 1/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeResetPartition is not supported in CachedInputSplitInputSplit do not support writeSingleFileSplit: fail to open invalid input parameter for InputSplit::Createneed to pass index file to use IndexedRecordIOFailed to initialize CachedIterCheck failed: this->InitCachedIter()viewfs://s3://http://https://azure://unknown filesystem protocol hdfs://BeforeFirst is not supportedstdinCheck failed: fp_ != NULLCheck failed: part < nsplitindexed_recordiounknown input split type rbFvvEN4dmlc10InputSplitEN4dmlc2io15SingleFileSplitEN4dmlc8DataIterINS_2io14InputSplitBase5ChunkEEEN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEEEN4dmlc2io16CachedInputSplitEN4dmlc2io18ThreadedInputSplitEZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_PFvvENSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterINS2_2io14InputSplitBase5ChunkEE4InitESt8functionIFbPPS6_EES8_IFvvEEEUlvE_vEEEEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE0_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlPPNS2_5ChunkEE_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlvE0_St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFZN4dmlc12ThreadedIterINS3_2io14InputSplitBase5ChunkEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_vEEEESaISJ_ELN9__gnu_cxx12_Lock_policyE2EECheck failed: begin != end/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccN4dmlc2io12LineSplitterEinvalid record io formatCheck failed: p >= pbegin + 2Invalid RecordIO FormatCheck failed: cflag == 1U/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0(reinterpret_cast(begin) & 3UL) == 0U(reinterpret_cast(end) & 3UL) == 0UCheck failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->end(reinterpret_cast(chunk->begin) & 3UL) == 0U(reinterpret_cast(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: p[0] == RecordIOWriter::kMagicN4dmlc2io16RecordIOSplitterEexpanded_list.size() == 1ul/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccIndexedRecordIOSplitter does not support multiple index filesN4dmlc2io23IndexedRecordIOSplitterE/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size()file offset not calculated correctlyThis could due to compiler version, g++-4.9 is neededCannot find any files that matches the URI pattern Check failed: files_[i].size % align_bytes == 0curr=,begin=,end=,fileptr=,fileoffset=offset[^$\.*+?()[]{}|.[\*^$.[\()*+?{|^$.[\*^$ .[\()*+?{|^$ bad regex files_.size() != 0Ufile do not align by bytesNULSOHSTXETXEOTENQACKalertbackspacenewlinevertical-tabform-feedcarriage-returnSOSIDLEDC1DC2DC3DC4NAKSYNETBCANEMSUBESCIS4IS3IS2IS1exclamation-markquotation-marknumber-signdollar-signpercent-signampersandapostropheleft-parenthesisright-parenthesisasteriskplus-signcommahyphenperiodonetwothreefourfivesixsevenninesemicolonless-than-signequals-signgreater-than-signquestion-markcommercial-atAJPRSVWleft-square-bracketbackslashright-square-bracketcircumflexunderscoregrave-accentjzleft-curly-bracketvertical-lineright-curly-brackettildeDELalnumblankcntrlgraphlowerprintpunctupperxdigit[\8\h\x]_0_]8^h^[^[N4dmlc2io14InputSplitBaseESt23_Sp_counted_ptr_inplaceINSt8__detail4_NFAINSt7__cxx1112regex_traitsIcEEEESaIS5_ELN9__gnu_cxx12_Lock_policyE2EENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0ELb1EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1ELb0EEENSt8__detail11_AnyMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1ELb1EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0EEENSt8__detail12_CharMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb0ELb0EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb0ELb1EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb1ELb0EEENSt8__detail15_BracketMatcherINSt7__cxx1112regex_traitsIcEELb1ELb1EEEwFileStream.Write incompletestdoutwbCheck failed: allow_null LocalFileSystem::Open "": error: LocalFileSystem.GetPathInfo: ../home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccCheck failed: std::fwrite(ptr, 1, size, fp_) == sizeCheck failed: !std::fseek(fp_, static_cast(pos), SEEK_SET)LocalFileSystem.GetPathInfo: detected symlink LocalFileSystem.ListDirectory N4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamElstat(path.c_str(), &sb) == 0Couldn't remove file ~TemporaryDirectory(): /home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hdmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/home/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectorySuccessfully deleted temporary directory Could not remove temporary directory N4dmlc2io10FileSystemE;$#@[]pup{{F{dqXȮX8hp  %(P3h8;pwx8`yhh0h@hX"`"H"8#@$X!X&$&H,' 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