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HǺH5UO  H5NHIH5NLL\$PLISI3HǺH5|N Ht$ fHnD$0fHnD|$flD$HD$8)\$@EH|$PHT$Ht$H|$PL HǺH5TO83 H5NHI,H5"NLH|$PHWH7LHǺH5N LL$ fLnD$0fMnD|$fElD$HD$8D)d$@EFH|$PHT$Ht$H|$P&u HǺH5SOa\ H5NHIUH5KNLALT$PLIRI2-HǺH5N LT$PfMn1LfMnfElD)$L$LL$TLD$PfHn1LfInfl)$L$LL$oNL\$PfIn1LfInӺfl)$$L$LL$YH|$8LT$@Ht$P1DH=4Hl$XLT$PLl$`H|$8Ht$@1DLt$XHt$PH=GHt$PLl$`EpH|$8HT$@Ht$P1L|$XHT$PH=NDLl$` HL$@Ht$PDLt$XHL$PH=1Ll$`Ld$@Ht$P1DH=Hl$XLd$PLl$`'Ht$@1DHl$XHt$PH=\oHt$PLl$`}HH|$8L\$@Ht$P1DH=fHl$XL\$PLl$`B LD$@Ht$P1DH=W@L|$XLD$PLl$`HL$@Ht$PDLt$XHL$PH=91Ll$`H|$8L\$@Ht$P1DH=Lt$XL\$PLl$`tLl$pLGD\$0fEHDŽ$Dt$xD$AEH|$8 Lt$XHt$P1DLt$@H=WLl$`Lt$P#H|$pLd$PLL觟LLl$pL蛠DD$0fEHDŽ$DD$xD$AEH|$8 Hl$XHt$P1DHl$@H=V%Ll$`Hl$PwH|$pKLd$PLLLA#HL$@Ht$PDLt$XHL$PH=Xh1Ll$`AuLd$@Ht$P1DH= 9Hl$XLd$PLl$`kLl$pL腟D$0fHDŽ$D$x$hH|$8 Ld$XHt$P1DLd$@H=Ll$`Ld$PgH|$p;Ld$PLLLLl$pLDL$0fEHDŽ$DT$xD$AeSEH|$8b Ld$@Ht$P1DH=Lt$XLd$PLl$`H|$pLd$PLLCL;Ll$pL;DT$0fEHDŽ$Dd$xD$AEVH|$8H\$@Ht$P1DH={Hl$XH\$PLl$`H|$pLd$PLL蛜LLl$pL蓝t$0fHDŽ$\$x$H|$8 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|L|$`L\$01DL|$@fMnHt$PLl$hfMnH=DWfElD)L$PT Ht$@HD$01DfHnfLnHt$PLt$`fDlH=`YLl$hD)|$P Ht$0H|$@1DfHnfHnHt$PLd$`flH= #Ll$h)L$P bHL$@LL$0DLd$`fHnfInHt$P1flH=Ll$h)d$P HT$0Ht$@1Lt$`fLnfLnHt$PDfElH=Ll$hD)l$PH LT$0L\$@1DfMnfMnHt$PLt$`fElH=Ll$hD)\$P @HL$@HD$0DHl$`fLnfLnHt$P1fElH=Ll$hD)\$PDLL$0LT$@1DfMnfMnHt$PHl$`fElH=,(Ll$hD)L$P|LD$0Ld$@1DfInfInHt$PLt$`flH=NLl$h)L$P:Ld$PLLL[WHPH\HaHUH H=HH%HHHHHHHHHuHHkHMHlhH#HAWAVAUATUSH(dH%(H$1H$hD$0L$pL$xHH$H$HD$PL$L$H|$@HT$0Ht$P|H|$@tyHǺQH5RO蘜H5*NHIH5NOLxHL$@LHQH1dHǺH5NPK$ $HTOHcLl$pLDd$0fHDŽ$d$x$AyEH|$8,!HL$@Ht$PDHl$XHL$PH=/1Ll$`fH|$p(@L.H$dH3%((H([]A\A]A^A_H7fInfHnfInLflflL$L$L|$(HDŽ$DŽ$LD$ $)$L LSL[okAL{(LkL$[0L$L$L$$$M9tL$LLl$Ld$ Ll$fMnfMnD$0fElDt$HD$8D$D)\$@E*H|$PHT$Ht$fH|$P HǺH5n>OH5NHIH5NLHD$PLHPH0HǺH5`N@H fMnfHnfMnL$flfElLHDŽ$L$L|$XDŽ$Ll$P$D)$ůLLsL[DoSLc(HkL$[0L$L$L$$D$M9tL$L"H|$@HT$P1Hl$@fLnfLnLfElD),$L$LL$Of.H_ fLnfInfInLfElflH$L|$XHt$PHDŽ$DŽ$L$D$)$襮LHkokL{(Ƌ{0HCL$H$LcH$L$$$L9tH$HLL$@HL$PLfHnfIn1Ld$@fl)<$L$LL$TfDHG fMnfMnfHnLflfElL$L|$XHDŽ$DŽ$L$Ld$P$D)$荭H;LCLKDoSL{(S0H$L$HKL$L$$D$M9tL$LH\$@Lt$PHL$@LfLnfMn1fElD),$L$LL$WYfH/ fInfLnfInLfElH$flL$Ht$PL|$XHDŽ$DŽ$D$)$uH HSL{okLS(LkH$[0H$L$L$$$M9tL$L"Hl$@HD$P1LfHnfHnغLl$@fl)<$L$LL$#^fDH fMnfHnfMnLflfElL$L$L|$XHDŽ$DŽ$LT$P$D)$]L;LkL[DoSHk(C0L$L$LsL$H$$D$I9tL$L7 LD$@LL$P1HfMnfMnLt$@fElD),$L$LL$bfHfMnfLnfMnL$fElfElLL|$(L$HDŽ$DŽ$Ll$ D$D)$CL3HkLKDokALc(HKL$[0H$L$L$$D$M9tH$HHT$ H\$PDLfHnfHnHL$Pfl1)<$L$LL$g~fDHfMnfLnfMnLfElfElH$L$L|$(HDŽ$DŽ$Hl$ D$D)$#L+HSLCDo[ALs(LcL$[0H$L$L$$D$M9tH$HLl$H\$ Ld$fInfHnD$0flD|$HD$8D$)T$@EH|$PHT$Ht$ H|$PHǺH56O蕑H5'NHHH5NHuLL$PHIQI1aHǺH5NMHHfMnfHnfInLflfAlL$L$L|$(HDŽ$DŽ$Ld$ $)$fL+LsH{DoKAHk(HKL$[0L$H$H$$D$H9tH$Hu Ll$HT$ HL$fInfHnD$0flD|$HD$8D$)L$@EH|$PHT$Ht$MH|$PHǺH5U4O؏H5jNHIH5¹NLLD$PLIPI0HǺH5GNHgfInfHnfInL$flflLL|$(L$$HDŽ$DŽ$Ll$ )$该H+HSHsosALs(LcH$[0H$H$L$$$L9tL$L H\$Ll$ Ld$fLnfInD$0fDlD|$HD$8D$D)|$@EBH|$PHT$Ht$薟H|$P"5HǺH52O!H5NHIH5 NLH|$PHWH7LHǺH5N@HfInfHnfInL$flflLL|$(L$$HDŽ$DŽ$Ll$ )$L#Lso{Hk(AHKHCL$[0L$H$H$$$H9tH$HUH\$HT$ HL$fLnfLnD$0fElD|$HD$8D$D)l$@E:H|$PHT$Ht$ޝH|$P}HǺH50OidH5NHI]H5SNLIHt$PLHVH65HǺH5N!@wLl$pLl$0fEHDŽ$Dl$xD$&  H|$8L\$@Ht$P1DH=L|$XL\$PLl$`fH|$pLd$PLLTLL@AHD$@Ht$P1DH=@Hl$XHD$PLl$`tLl$pL DD$0fHDŽ$L$x$A Ek H|$8vHt$@1DLt$XHt$PH=Ht$PLl$`f.H|$pLd$PLLd~L\@Ll$pL[|$0fEHDŽ$D|$xD$H|$8BHL$@Ht$PDHl$XHL$PH=1Ll$`:f.H|$pTLd$PLL}L@Ll$pL~DT$0fHDŽ$\$x$AE H|$8Ld$@Ht$P1DH=Lt$XLd$PLl$`f.H|$pLd$PLL}L@AnHD$@Ht$P1DH=Lt$XHD$PLl$`$?FH|$@Ht$P1DH|$PH=6L|$XLl$`L\$@Ht$P1DH=Hl$XL\$PLl$`A%H|$@Ht$P1DH|$PH=JLt$XLl$`{HD$PfMn1LfLnfElD)$L$LL$5BxHD$ fInD$0fHnDt$flD$HD$8)T$@Ea H|$PHT$Ht$H|$PA HǺH5+OxsH5 NHHlH5bNHXHL$PHHQH1DHǺH5N0+H|$PfMnܺ1fLnHfElD)$L$LL$XmLD$ fLnD$0fMnD|$fElD$HD$8D)T$@E H|$PHT$Ht$H|$P HǺH5*OkfH5NHI_H5UNLKLL$PLIQI17HǺH5N#H|$ fMnD$0fLnD|$fElD$HD$8D)D$@E-H|$PHT$Ht$ H|$P HǺH5*O蔅H5&NHIH5~NLtLD$PLIPI0`HǺH5NLGLT$ fLnD$0fMnD|$fElD$HD$8D)t$@EH|$PHT$Ht$2H|$PHǺH5:)O轄H5ONHIH5NLL\$PLISI3HǺH5,NupHt$ fHnD$0fHnD|$flD$HD$8)\$@EH|$PHT$Ht$]H|$PHǺH5e(OH5zNHIH5ҭNLH|$PHWH7LHǺH5WNLL$ fLnD$0fMnD|$fElD$HD$8D)d$@EFH|$PHT$Ht$膔H|$P&%HǺH5'O H5NHIH5NLLT$PLIRI2HǺH5NLT$PfMn1LfMnfElD)$L$LL$HLD$PfHn1LfInfl)$L$LL$oBL\$PfIn1LfInӺfl)$$L$LL$MH|$8LT$@Ht$P1DH=dHl$XLT$PLl$`0H|$8Ht$@1DLt$XHt$PH=Ht$PLl$`pH|$8HT$@Ht$P1L|$XHT$PH=DLl$`HL$@Ht$PDLt$XHL$PH=r1Ll$`Ld$@Ht$P1DH=PHl$XLd$PLl$`\'Ht$@1DHl$XHt$PH=, Ht$PLl$`-HH|$8L\$@Ht$P1DH=%Hl$XL\$PLl$` LD$@Ht$P1DH=L|$XLD$PLl$`HL$@Ht$PDLt$XHL$PH=1Ll$`H|$8L\$@Ht$P1DH=}Lt$XL\$PLl$`YtLl$pLtD\$0fEHDŽ$Dt$xD$AEH|$8 Lt$XHt$P1DLt$@H=Ll$`Lt$PH|$pLd$PLLWsLOLl$pLKtDD$0fEHDŽ$DD$xD$AEH|$8 Hl$XHt$P1DHl$@H=vLl$`Hl$P'H|$pKLd$PLLrL裵A#HL$@Ht$PDLt$XHL$PH=X1Ll$`AuLd$@Ht$P1DH=kHl$XLd$PLl$`kLl$pL5sD$0fHDŽ$D$x$hH|$8 Ld$XHt$P1DLd$@H=Ll$`Ld$PH|$p;Ld$PLLqL蓴Ll$pLrDL$0fEHDŽ$DT$xD$AeSEH|$8b Ld$@Ht$P1DH=CLt$XLd$PLl$`oH|$pLd$PLLpLLl$pLqDT$0fEHDŽ$Dd$xD$AEVH|$8H\$@Ht$P1DH=BHl$XH\$PLl$`H|$pLd$PLLKpLCLl$pLCqt$0fHDŽ$\$x$H|$8 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LMQMi01IQ8L@EIMYIA(L0H`L)IYH@LPMi@LFHXL8LhHHȠL0L LLcH8HhL(AIM4MM M]8HULAoEAoMAoU(AE@RLhHPLMMHHLXEAU7H@L])E)MU趕XH ZL;`tFH(E1H HH;HSH)IIt 1ICH M9wIL; iHHLH9IHLFLH+LL LLH9vHHfHEdH3%(uHe[A\A]A^A_]iHHtHl#HHHLI9uNLpLxLgLpHLHDžpLxLLH=f+tHD"HHHZlԡ9f.DUHHAWAVAUATSHHL`HLrdH<%(H}1LLM=8MHc흫HLK >L9IGH M9 LLHHHHIHHHfDLMHIHMi I1HMa0MQIQ8IY@L0D.MYLPEIA(MqL(L`M)HXLEH@LhH8LLLLhL(HcMAIhL@H8LXII8Ao AoYIA Aoi(Aq@HMIIN`LuHEAVAULPLu7AUH0)M)])@mLhXZML9`t(HIH;HSH)IIt D.CIL; HHHH9ILFL LLHHH+H9vHH2f.HEdH3%(uHe[A\A]A^A_]eHHtHHHIILI9uNLpLxLfcLpHLHDžpLx>LvLnH=+tL@g=HHI躼l49f.DUHHAWAVAUATSHHL`HLrdH<%(H}1LLM5蘹MHcMHLK >L9IGH M9LLHHHHIHHHfDLMHIHMi I1HMa0MQIQ8IY@L0D.MYLPEIA(MqL(L`M)HXLEH@LhH8pLLLLhL(HcMAIL@H8LXII8Ao AoYIA Aoi(Aq@HMIILuHEAVAULPLu7AUH0)M)])@m^LhXZML9`t(HIH;HSH)IIt *CIL; HHHH9ILFL LLHHH+H9vHH3HEdH3%(uHe[A\A]A^A_]ebHHtHi4HHI豹LI9uNLpLxL_LpHLHDžpLxL޵LֵH=.+tLd=ϚHHI"l蜚9UHHAWAVAUATSHHLpHLzdH<%(H}1LLMHcLHLJ ?L9IGH M9HIHHLHHHLHfDLDA@HMQIyHhIZ8I2HMbMj IR0MZHPIB(L`MrL8HXH)Mb@H@HHIGL0H@ؕL`LHHL0HhIM H(IAMOAT$@IMLuHHH@Ao$AoL$M\$ AoT$(ID$8LhAVSLPUL6H8L]HE)E)MU蹊H`LXXZLM9tYH(E1H IIDIRI:MHhH)III I&AFML9wIL; ?LHLL9MLLFLL+LL LLL9vSHHLIABH IL9,IL; nfDHEdH3%(uHe[A\A]A^A_]E^HHtHI HHI葵LI;uNLpLxL[HpHLHDžpHxL辱L趱H=+tLD9诖HHIl|9UHHAWAVAUATSHHLpHLzdH<%(H}1LLM豫Hc蠒LHLJ ?L9IGH M9HIHHLHHHLHfDLDA@HMQIyHhIZ8I2HMbMj IR0MZHPIB(L`MrL8HXH)Mb@H@HHIGL0H@踑L`LHHL0HhIM H(IAMAT$@IN`MLuHHH@Ao$AoL$M\$ AoT$(ID$8LhAVSLPUL6H8L]HE)E)MU蘆H`LXXZLM9tXH(E1H II@IRI:MHhH)III I"AFML9wIL; ?LHLL9MLLFLL+LL LLL9vSHHLIABH IL9,IL; nfDHEdH3%(uHe[A\A]A^A_]%ZHHtH)HHIqLI;uNLpLxLWHpHLHDžpHxf L螭L薭H=+tL$5菒eHHIⰫl\9U1AHAWIAVL`AUATSHHWdH%(HE1HhLPH>SY^u?HEdH3%(He[A\A]A^A_]ÐHHݳtHhL`H@MMoIM`@LIHIXD(IxL EIp MH(HMP0MX8LPM)LEHHXH8L@LHL0虍LXHLHcHMILH HUH@L0o oYLI oi(LY8A@RLAULHE7AUH8LMIL])M)]) m茂LXXZIL9Pt(HHH;HSH)IIt CIL9YWHHtHHHHPHH>uNLpLxLmTLpHLHDžpLxEL}LuH=+tH2n:DHHHl;9fDU1AHAWIAVL`AUATSHHWdH%(HE1HhLPH{Y^u?HEdH3%(He[A\A]A^A_]ÐHHM7tHhL`H@MMoIM`@LIHIXD(IxL EIp MH(HMP0MX8LPM)LEHHXH8L@LHL0yLXHLHcHMIL~H HUH@L0o oYLI oi(LY8A@RLAULHE7AUH8LMIL])M)]) mlLXXZIL9Pt(HHH;HSH)IIt CIL9YSHHtH HHH0HH>uNLpLxLMQLpHLHDžpLx%L]LUH=+tH.N:$ HHH衪l9fDU1HAWIAVHhL`AUATSHHWdH%(HE1HHLL@QLY^u>HEdH3%(He[A\A]A^A_]HH蝭tHhL`H@MGIGMh@IHM`8IXIpIxL D)MH L0EMP(MX0HLPM)LEHHXL8L@LHXLXHLHcHMIL]H HUIH@L0oo_LW oo(HG8O@RAULHMH8LAU6LUHE)M)]) mK|LXXZI8L9Pt(HHH;HSH)IIt CIL9XPHHtH HHHLI9uNLpLxL,NLpHLHDžpLxLHEdH3%(He[A\A]A^A_]HHHHJLI;uNLpLxLgEHpHLHDžpHx?LwLoH=+l@IL9w;HHuHhL`HMGDMw@HIXILIpIxHXMH M`8HMP(H MX0HPHHH)H@L(HGL@M`@L0L8!|LPHHHH IMHIHXMML$8Ao$L]AoL$I|$ AoT$(ET$@ASH8LXLMML0L@SDULH}A6H()E)MUqHPLHXZHM9\HE1H H2IIRI:MHXH)IIt+I I! AFML9wLIABH IL9wIL9%EH-Ht.HHHv'}Hv pU1AHAWAVL`AUATSHHHWdH%(HE1HhLPHiY^uCHEdH3%(He[A\A]A^A_]DHH%tHhL`H@HLLkIM`@LIHIxD(Ip L EMH(MP0HMX8LPM)MpLEHXH8L@LHL0!yLXLLHHcMI؋LiH H@L0o oYLI oi(LY8A@)MILuLMIAVAULHLE7AUH8L])]) mnXZHXLL9Pt(HIH;HSH)IIt [ CIL9YBHHtH[HHIؙHH>uNLpLxL?LpHLHDžpLxLLH=U+tLz:HHIIlz9f.@U1AHAWAVL`AUATSHHHWdH%(HE1HhLPH;Y^uC_HEdH3%(He[A\A]A^A_]DHH5tHhL`H@HLLkIM`@LIHIxD(Ip L EMH(MP0HMX8LPM)MpLEHXH8L@LHL0uLXLLHHcMI؋LzfH H@L0o oYLI oi(LY8A@)MILuLMIAVAULHLE7AUH8L])]) mjXZHXLL9Pt(HIH;HSH)IIt +CIL9Y\?HHtH`+HHI訖HH>uNLpLxLuNLpLxL9LpHLHDžpLxlL褏L蜏H=+tL*t:kHHI蒫lbt9f.U1AHAWAVL`AUATSHHHWdH%(HE1HhLPH:`Y^uCHEdH3%(He[A\A]A^A_]DHHetHhL`H@HLLkIM`@LIHIxD(Ip L EMH(MP0HMX8LPM)MpLEHXH8L@LHL0oLXLLHHcMI؋LH H@L0o oYLI oi(LY8A@)MIN`LuLMIAVAULHLE7AUH8L])]) mdXZHXLL9Pt(HIH;HSH)IIt CIL9X8HHtHHHIGHH>uNLpLxLd6LpHLHDžpLxuNLpLxLLpHLHDžpLxӪLuLuH=3+ZIL9w3HH|HhL`HLD@HMPMhHXIZILIzIr MJ(Mb8H MZ0HPMrH(HHH)H@L0HGL@Mb@L8VLPLHHL HXIMHIAMءI|$ It$8EL$@IN`LuL8H0Ao$AoL$H}AoT$(LXAVL@H(SDMALMHu0)E)MUoKHPLHXZHM9cHE1H H IIRI:MHXH)IIt+I IAFML9wLIABH IL9wIL9H)Ht̪a٪HHIv#XXLlfAWAVIAUATUHSHHXdH%(H$H1 ǪfH="+ffH_H8HPHUHJPH; 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LH[-H-I-I-H;-H-H-H-L$ILD$05-I.I.I?-f.AWAVAUATUHSH(Hw(HGHHO Lg0LnHD$H_8H $LHt$L9vk@MuIHT$I~@MFPL9t$I~ MN0L9t|$I>MVL9<g$I`L9t$uIL9l$wH|$H9|$9L;<$tFI@IOPH9t&$I Iw0H9t$I?MoL9#I`L9<$uI9tBH{@L{PL9t#H{ Ls0L9t#H;HSH9D#H`I9uH}HCLeHH](Ml$I9IH;HIy#MIAI9Mt}ItfItTItBIt0ItIt H;H0#H;H$#H;H#H;H #H;H#H;H"H;H"I9vPH;H@"H{"H{"H{"H{"H{"H{"H{"I9wH}H([]A\A]A^A_|"@H`I9{H}HH([]A\A]A^A_I`L9<$@DI`L9t$IL9l$ZDM9QI@M_PL9t!I I_0H9t!I?IGH9t!I`M9uDI`M9ufIUUUUUUUAWAVAUATIUSHH(LoHHw(dH%(HD$1HW0H+W8HLLC HL+CH)HIHLHHDHIM9LLSMM)LIL)H%Mt$IEHk0LULUM<$LT$MMt M.Lt$IRIAHD$ML}0LuB0Ml$(L} Mt$ LLt M Ll$IIELDE0Lm(B/H}PH}@Mt$@Md$HMMt MLd$IIE>D}PLeHB'HkHHEHfHnfHnHfHnflflK0[@HD$dH3%(6H([]A\A]A^A_fM>HD$DM0L+MhbfLqK 6I9oMҾHIEMl2I9J<#LCHLHs(L)IHIPL4H9t H)LBfH;L;LkMA~M,.Ls(LkHIfInflCImHk8HHk@Ht$H} 1yLL$HE HLM0LLLl$H} Ht$1HxHt$HEHHuLLVLt$HEHt$H}@1xL\$HE@HL]PLLLd$H}@M)IILO4H)L9vL9LeL9H}H)LdH= H=K,H= H= H|$LIH(IH} I9t H}H9|$tLH[HH{QI eLruHHbasic_string::_M_construct null not validSaving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified./__w/ml-builds/ml-builds/xgboost/src/c_api/c_api.ccDMatrix/Booster has not been initialized or has already been disposed.XGDMatrixSetGroup is deprecated, use `XGDMatrixSetUIntInfo` instead.Check failed: type >= 1 && type <= 4binary saving only supported by SimpleDMatrixCurrent DMatrix type does not support set data./__w/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.static_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structureXGQuantileDMatrixCreateFromCallbackXGDeviceQuantileDMatrixCreateFromCallbackp_m->Info().num_col_ <= std::numeric_limits::max()/__w/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/c_api/../common/threading_utils.hUnknown field for Booster feature info:XGDMatrixCreateFromArrowCallbackDMatrix has not been initialized or has already been disposed.Check failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.Invalid input type for inplace predict.Check failed: std::string(schema->format) == "+s"Column data type not supported by XGBoostCheck failed: !schema_->columns.empty()Cannot import record batch without a schemaThe quantile cut hasn't been generated yet. Unless this is a `QuantileDMatrix`, quantile cut is generated during training.n_numeric == n_features - n_categoriesindptr.back() == vals.size() + mins.size() - n_categoriesFeatureMap feature index exceed boundfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()Check failed: ec == std::errc()%02d:%02d:%02d] basic_string::appendOnly %d dimensional array is valid.XGDMatrixCreateFromCSR2.0.0XGDMatrixCreateFromCSRExDMLC_LOG_STACK_TRACE_DEPTH%zuInvalid pointer argument: Check failed: p_outbufout_dptrout_lenXGBoosterSaveModelToBuffer1.6.0XGBoosterGetModelRawjson_parametersfnameXGDMatrixCreateFromCSCXGDMatrixCreateFromCSCExCheck failed: mc_interface_strname_strCheck failed: out_strjsonubj1.7.0dtraingradhessout_indptrout_indicesout_dataCheck failed: page.pagen_threads >= 1feature_namefeature_typeout_resultdeprecatedUnknown format: `XGDMatrixCreateFromURIsilentdata_split_modeXGDMatrixCreateFromCallbackcache_prefixnthreadnextresetCheck failed: _refInvalid handle to ref.max_binnbatchout_featuresdmatsevnamesc_json_configXGBoosterPredictFromDMatrixiteration_beginiteration_endntree_limittrainingstrict_shapeout_dimout_shapeInplacePredictImplCheck failed: p_predtp_predt->Size() >= n_samplesCheck failed: proxyarray_interfacestr.size() >= 3str[0] == '{'ftypefmap+sCheck failed: columns.empty()Check failed: format_strFormat string cannot be emptyCheck failed: falsecpui < data.size()end <= data.size()cudaXGBoosterFeatureScoreimportance_typefeature_mapfeature_namestree_idxidx < names_.size()out_n_featuresout_scoresCheck failed: ejson_strfloat32float64bool8int32int8int16int64Unknown data table type.%ldUnknown global parameters: { cannot create std::vector larger than max_size()%s: __pos (which is %zu) > this->size() (which is %zu)Stack trace: _Z +basic_string::basic_string [bt] () /__w/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCannot reset RecordBatchesIterAdapterCheck failed: at_first_Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaDatetimeObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. Or open an issue./__w/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.h Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.htmll ,Ll̞잓 ,LCannot reset IteratorAdapter/__w/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Slicing DMatrix columns is not supported for Proxy DMatrix./__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/utils.hread can not have position excceed buffer length vs. /__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/io.hcurr_ptr_ + size <= buffer_size_/__w/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from /__w/ml-builds/ml-builds/xgboost/src/c_api/../data/../c_api/c_api_utils.hCheck failed: pp_mInvalid DMatrix handleCheck failed: p_mProxy DMatrix cannot return data batch.vector::_M_realloc_insertvector::_M_default_appendchunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast(1), std::multiplies<>{}) == chunksize * rowslearnergradient_boostergblineargbtree_model_paramnum_parallel_treestoiUnknown booster:Missing `version' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.Missing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4`typestr' should be of format .Missing `shape' field for array interfaceMissing `data' field for array interfacetypestrmaskEmpty data passed in.map::atj_shape.size() == 1type_length == 1stridesstrides.size() == 1Memory should be contiguous.vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)basic_string::at: __n (which is %zu) >= this->size() (which is %zu)mask with bitfield type should be of 1 byte per bitfield.mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.get(strides.at(0)) == type_lengthInvalid type for: ``, expecting one of the: {`}, got: `Argument `` is required for ``.`, Argument `missing` is required.stream%lu</__w/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size())unknown feature type, use i for indicator and q for quantityfloatcustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.feature_map.Size() == n_featuresout_modelsi < Dj_strides.size() == j_shape.size()stride and shape don't match.Half type is not supported.ptr % alignment == 0Input pointer misalignment.Empty data with non-zero shape.Check failed: mask_it == array.cend() || IsA(mask_it->second)Masked array is not yet supported.1 뒒&Check failed: m == 1 || n == 1n_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.get(array).size() == 1Column: __Cannot find argument '', Possible Arguments: ---------------- : vector::_M_range_insertCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEN4dmlc5ErrorEN4dmlc10ParamErrorEN4dmlc9parameter16FieldAccessEntryEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIiEEiEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIiEEiEEN4dmlc9parameter10FieldEntryIiEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIbEEbEEN4dmlc9parameter10FieldEntryIbEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIfEEfEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIfEEfEEN4dmlc9parameter10FieldEntryIfEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIdEEdEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIdEEdEEN4dmlc9parameter10FieldEntryIdEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIlEElEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIlEElEEN4dmlc9parameter10FieldEntryIlEEN4dmlc6StreamEN4dmlc10SeekStreamEN4dmlc7istream5InBufEN4dmlc7istreamEN5rabit5utils19MemoryFixSizeBufferEN5rabit5utils18MemoryBufferStreamEN4dmlc8DataIterIN7xgboost4data15CSRAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSRAdapterBatchEEEN7xgboost4data10CSRAdapterEN4dmlc8DataIterIN7xgboost4data17DenseAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17DenseAdapterBatchEEEN7xgboost4data12DenseAdapterEN4dmlc8DataIterIN7xgboost4data17ArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_17ArrayAdapterBatchEEEN7xgboost4data12ArrayAdapterEN4dmlc8DataIterIN7xgboost4data20CSRArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSRArrayAdapterBatchEEEN7xgboost4data15CSRArrayAdapterEN4dmlc8DataIterIN7xgboost4data15CSCAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_15CSCAdapterBatchEEEN7xgboost4data10CSCAdapterEN4dmlc8DataIterIN7xgboost4data20CSCArrayAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_20CSCArrayAdapterBatchEEEN7xgboost4data15CSCArrayAdapterEN4dmlc8DataIterIN7xgboost4data21DataTableAdapterBatchEEEN7xgboost4data6detail19SingleBatchDataIterINS0_21DataTableAdapterBatchEEEN7xgboost4data16DataTableAdapterEN4dmlc8DataIterIN7xgboost4data16FileAdapterBatchEEEN7xgboost4data18ArrowColumnarBatchEN4dmlc8DataIterISt6vectorISt10unique_ptrIN7xgboost4data18ArrowColumnarBatchESt14default_deleteIS5_EESaIS8_EEEEN7xgboost4data24RecordBatchesIterAdapterEN7xgboost4data12DMatrixProxyEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIjEEjEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryIjEEjEEN4dmlc9parameter10FieldEntryIjEEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryImEEmEEN4dmlc9parameter17FieldEntryNumericINS0_10FieldEntryImEEmEEN4dmlc9parameter10FieldEntryImEEN7xgboost4data15IteratorAdapterIPvFiS2_PFiS2_15XGBoostBatchCSRES2_ES3_EESt15_Sp_counted_ptrIPN7xgboost7DMatrixELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data12DMatrixProxyELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_11EllpackPageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_16GHistIndexMatrixEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13ExtSparsePageEEELN9__gnu_cxx12_Lock_policyE2EE/__w/ml-builds/ml-builds/xgboost/src/c_api/c_apiBUILTIN_PREFETCHMM_PREFETCH_PRES?0C?CXGBOOST_COMMUNICATORrabitfederatedin-memoryin-memory-ncclUnknown communicator type /__w/ml-builds/ml-builds/xgboost/src/collective/communicator.hUnknown data type./__w/ml-builds/ml-builds/xgboost/src/collective/rabit_communicator.hFloating point types do not support bitwise operations.Unknown allreduce operationUnknown data typeh8IN_MEMORY_WORLD_SIZEIN_MEMORY_RANKin_memory_world_sizein_memory_rankFederated rank must be set.World size is less than 1.Rank is less than 0./__w/ml-builds/ml-builds/xgboost/src/collective/in_memory_communicator.hFederated world size must be set. is greater than world_size - 1: =1=0Failed to initialize Rabit/__w/ml-builds/ml-builds/xgboost/src/collective/communicator.ccXGBoost is not compiled with Federated Learning support.N7xgboost10collective12CommunicatorEN7xgboost10collective20InMemoryCommunicatorEN7xgboost10collective16NoOpCommunicatorEN7xgboost10collective17RabitCommunicatorEXGBOOST_COMMUNICxgboost_communic/__w/ml-builds/ml-builds/xgboost/src/collective/in_memory_handler.ccCheck failed: world_size_ < world_sizeIn memory handler already initialized.In memory handler already shutdown.Check failed: world_size_ > 0Invalid reduce operationInvalid data type+04`8;<&8): handling requestbasic_string::replace: all requests received: waiting for all clients: sending reply: all replies sent: waiting for current sequence number/src/collective/in_memory_handle/__w/ml-builds/ml-builds/xgboost/src/collective/socket.ccgetsockopt(handle_, SOL_SOCKET, SO_DOMAIN, reinterpret_cast(&domain), &len)static_cast(socket.Domain()) == static_cast(addr.Domain())Check failed: !this->IsClosed()str.size() < std::numeric_limits::max()this->SendAll(&len, sizeof(len)) == sizeof(len)this->RecvAll(&len, sizeof(len)) == sizeof(len)Failed to get addr info for: 127.0.0.10.0.0.0::1::Unknown IA family.system::CloseSocket(handle_)sendFailed to send string length.bytes == str.size()Failed to send string.recvFailed to recv string length.bytes == lenFailed to recv string./__w/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.h): Failed to call ``: @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/__w/ml-builds/ml-builds/xgboost/src/common/../data/../common/io.h/__w/ml-builds/ml-builds/xgboost/src/common/column_matrix.ccgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_valw_n_bytes == n_bytesw_n_bytes == remainingbad_malloc: Failed to allocate /__w/ml-builds/ml-builds/xgboost/src/common/../data/../common/ref_resource_view.h bytes.mem_->Size() >= nreinterpret_cast(ptr) % std::alignment_of_v == 0St19_Sp_make_shared_tagN7xgboost6common14MallocResourceESt23_Sp_counted_ptr_inplaceIN7xgboost6common14MallocResourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE?\\\"\b\f\n\r\t\u%04xYou have manually specified the `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.The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead. E.g. tree_method = "hist", device = "cuda" Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: /__w/ml-builds/ml-builds/xgboost/include/xgboost/context.h, while the input data is on: Potential solutions: - Use a data structure that matches the device ordinal in the booster. - Set the device for booster before call to inplace_predict. This warning will only be shown once. Empty dataset at worker: Unknown device.` is deprecated since, use `` instead.device E.g. device=cpu/cuda/cuda:0/src/common/erro/__w/ml-builds/ml-builds/xgboost/src/common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/common/quantile.hentries_per_columns.size() == static_cast(n_columns)column_sizes_tloc[i].size() == static_cast(n_columns)entries_per_column.size() == info.num_col_Check failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))vector::_M_fill_insertSortedSketchContainerinvalid init parameter/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.ccCheck failed: !offsetsCheck failed: offsets/__w/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachableSt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_10SparsePageEEELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_13SortedCSCPageEEELN9__gnu_cxx12_Lock_policyE2EE)A@/__w/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()basic_string::substrCheck failed: outFaled to call munmap: close(handle_->fd) != -1Faled to close: pointer_ <= buffer_.size()file://Opening fd >= 0Failed to open:ptr != ((void *) -1)Failed to map: /__w/ml-builds/ml-builds/xgboost/src/common/io.ccmunmap(handle_->base_ptr, handle_->base_size) != -1/__w/ml-builds/ml-builds/xgboost/src/common/io.hNot implementedN7xgboost6common16PeekableInStreamEN7xgboost6common15FixedSizeStreamEN7xgboost6common15ResourceHandlerEN7xgboost6common12MmapResourceEN7xgboost6common25AlignedResourceReadStreamEN7xgboost6common22PrivateMmapConstStreamEN7xgboost6common18AlignedWriteStreamEN7xgboost6common22AlignedFileWriteStreamEN7xgboost6common21AlignedMemWriteStreamE4TtԾ4jjjjjjjjjjjjjjjjjjjjsjCX 0 x0h   Object of type , around character position: , "beg <= size_\0 f64 is not supported.ch != -1cursor_.Pos(): raw_str_.size():/__w/ml-builds/ml-builds/xgboost/src/common/json.cc can not be indexed by Integer. can not be indexed by string./__w/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hHigh precision number is not supported.Check failed: ret.ec == std::errc()` is not supported for typed array.", got: "EOF"\0"N7xgboost5ValueEN7xgboost10JsonStringEN7xgboost9JsonArrayEN7xgboost10JsonObjectEN7xgboost10JsonNumberEN7xgboost11JsonIntegerEN7xgboost8JsonNullEN7xgboost11JsonBooleanEN7xgboost10JsonReaderEN7xgboost10JsonWriterEN7xgboost9UBJReaderEN7xgboost9UBJWriterEN7xgboost14JsonTypedArrayIfLNS_5Value9ValueKindE7EEEN7xgboost14JsonTypedArrayIhLNS_5Value9ValueKindE8EEEN7xgboost14JsonTypedArrayIiLNS_5Value9ValueKindE9EEEN7xgboost14JsonTypedArrayIlLNS_5Value9ValueKindE10EEEUnknown construcExpecting null v/__w/ml-builds/ml-builds/xgboost/src/common/common.h optional, default=, requiredvalue 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='/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h*p == ')'Invalid NAN literalOut of range valuepos <= value.length()Out of range value for , value='No conversion could be performed/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hSome trailing characters could not be parsed: 'key has already been registered in Y@v? L APesudoHuberParamThe delta term in Pseudo-Huber lgroup_ptr.size() >= 2PushColPagemax_cat + 1 >= n_categoriessketch->temp.size <= max_size, stemp.size=HostSketchContainer^^`___0` `H`_p__``aaapa`aHa0a aa`0|0|X}8} }}|||||x|}}~~~p~`~P~@~0~ ~~pX@(聛ȁ؃pXІІ؇x`P8```@(Ј@@`Ы@h౛౛P@pб`б/__w/ml-builds/ml-builds/xgboost/src/common/quantile.cchessian.size() == info.num_row_group_ptr.back() == hessian.size()weights.size() == info.num_row_/__w/ml-builds/ml-builds/xgboost/src/common/categorical.hInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than Maximum cateogry should not be lesser than the total number of categories.Finalize: invalid maximum size, max_size=worker_segments.size() >= 1info.weights_.Size() == n_groupsSize of weight must equal to the number of query groups when ranking group is used.Invalid query group structure. The number of rows obtained from group doesn't equal to the number of rows from the data.group_ptr.back() == n_samplesfeature_ptr.front() == 0rank_size == totalbase_rowid < group_ptr.back()Row: is not found in any group.src.size == 0this->size == 0Check failed: a.dataCheck failed: a.data && reduced[fidx].data/__w/ml-builds/ml-builds/xgboost/src/common/../data/adapter.hP؄mingap=, maxgap=, wgap=bug in combineCheck failed: size <= sa.size + sb.size check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=] rmin=, rmax=, wmin=, v=Check failed: nbig < nquantile: too many large chunksketch->temp.size < max_size, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=invalid maximum size max_size=Check failed: worker_feature.data()count <= total_entriesh_weights.size() == batch.Size()Invalid size of sample weight.PushRowPagen_threads_ >= 1sketches_.size() == n_columns |l\l\D $LLl\L$,<$<<<|\TdL4 AllReducen_columns == sketches_.size()num_cuts.size() == 0Number of columns differs across workersMakeCutsp_cuts->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > p_cuts->cut_ptrs_.HostVector().back()SketchContainerImplcolumns_size_.size() != 0軞軞轞hXȺ@8HȺȺȺؼpX8 ໞXX@(`8 连пxPԞpԞXԞ8ԞԞҞԞ՞՞՞՞|՞Ԟ֞Check failed: reduced[i].dataK_Ao;o:>N4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryIN7xgboost6common15ParamFloatArrayEEES5_EEN4dmlc9parameter10FieldEntryIN7xgboost6common15ParamFloatArrayEEEQuantileLossParaList of quantiles for quantile l/__w/ml-builds/ml-builds/xgboost/src/common/algorithm.hCheck failed: ctx->IsCPU()/__w/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()/__w/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0Check failed: ctx_St23_Sp_counted_ptr_inplaceIN7xgboost16HostDeviceVectorIjEESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE75//__w/ml-builds/ml-builds/xgboost/src/common/ranking_utils.h can only be used with binary labels.Check failed: label_is_integerWhen using relevance degree as target, label must be either 0 or positive integer.Relevance degress must be lesser than or equal to when the exponential NDCG gain function is used. Set `ndcg_exp_gain` to false to use custom DCG gain./__w/ml-builds/ml-builds/xgboost/src/common/ranking_utils.ccg_rank.size() == sorted_idx.size()%u[-]?Check failed: is_binaryCheck failed: label_is_validrank.size() == predt.size()enum_back_map_.count(value) != 0UValue not found in enum declaredInvalid Input: '', valid values are: Enum exisit!) Enums: ), long (non-negative)booleandoubleԶ809`9 :9N4dmlc9parameter10FieldEntryIN7xgboost3ltr10PairMethodEEElambdarank_pair_Method for constlambdarank_num_pNumber of pairs for each sample lambdarank_unbiaUnbiased lambda mart. Use extended IPW to debiaslambdarank_bias_Lp regularization for unbiased lWhen set to true, the label gain is 2^rel - 1, otherwise it's reKHgY?@חA$@/__w/ml-builds/ml-builds/xgboost/src/common/stats.cct_v.Shape(1) != 0/__w/ml-builds/ml-builds/xgboost/src/common/stats.hCheck failed: alpha >= 0 && alpha <= 1k >= 0\89h+14d$?/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././strtonum.hN4dmlc9parameter10FieldEntryIN7xgboost6common27ProbabilityDistributionTypeEEEaft_loss_distribChoice of distribution for the noise term in Accelerated FailureScaling factor used to scale the distribution in Accelerated FaiInvalid thread limit for OpenMP./sys/fs/cgroup/cpu/cpu.cfs_quota_us/sys/fs/cgroup/cpu/cpu.cfs_period_uslimit >= 1Timer for calls @ us======== Monitor ( ======== did not get stopped properly..A/src/common/time/__w/ml-builds/ml-builds/xgboost/src/common/version.ccfi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch)Invaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it agaibad optional access/__w/ml-builds/ml-builds/xgboost/src/context.ccBoth `device` and `gpu_id` are specified. Use `device` instead.Check failed: opt_id.has_value()Invalid value for `gpu_id`. Got:Invalid argument for `device`. Expected to be one of the following: - cpu - cuda - cuda: # e.g. cuda:0 - gpu - gpu: # e.g. gpu:0 gpu(:[0-9]+)?|cuda(:[0-9]+)?|cpuopt_id.value() <= std::numeric_limits::max()new_d.ordinal == this->device_.ordinalthis->device_.ordinal == kCpuIdthis->device_.ordinal > kCpuIdUnexpected end of regex when escaping.Unexpected end of regex when reading control code.Unexpected end of regex when ascii character.Got: `ContextgpuOrdinal value too large.NULSOHSTXETXEOTENQACKalertbackspacenewlinevertical-tabform-feedcarriage-returnSOSIDLEDC1DC2DC3DC4NAKSYNETBCANEMSUBESCIS4IS3IS2IS1exclamation-markquotation-marknumber-signdollar-signpercent-signampersandapostropheleft-parenthesisright-parenthesisasteriskplus-signcommahyphenperiodzeroonetwothreefourfivesixsevenninesemicolonless-than-signequals-signgreater-than-signquestion-markcommercial-atAGJPSVWleft-square-bracketbackslashright-square-bracketcircumflexunderscoregrave-accentzleft-curly-bracketvertical-lineright-curly-brackettildeDELalnumblankcntrlgraphlowerprintpunctupperxdigit has not been registered in Alias seedlongrandom_stateseed_per_iterationDevice ordinal.Number of threads to use.n_jobsfail_on_invalid_gpu_idvalidate_parametersRandom number seed during training.Seed PRNG determnisticly via iterator number.Fail with error when gpu_id is invalid.Enable checking whether parameters are used or not.Unexpected escape character.Unexpected end of regex when in an open parenthesis.Invalid special open parenthesis.Unexpected end of regex when in brace expression.Unexpected character in brace expression.Unexpected end of character class.Unexpected end of regex when in bracket expression.Unexpected character class open bracket.^$\.*+?()[]{}|.[\()*+?{|^$.[\*^$.[\()*+?{|^$ .[\*^$ Number of NFA states exceeds limit. Please use shorter regex string, or use smaller brace expression, or make _GLIBCXX_REGEX_STATE_LIMIT larger.Unexpected back-reference in polynomial mode.Back-reference index exceeds current sub-expression count.Back-reference referred to an opened sub-expression.cannot create std::deque larger than max_size()0X0Pp@0 \\D(X0hInvalid range in bracket expression.Nothing to repeat before a quantifier.Unexpected token in brace expression.Unexpected end of brace expression.Invalid range in brace expression.Invalid collate element.Invalid equivalence class.Invalid character class.Unexpected dash in bracket expression. For POSIX syntax, a dash is not treated literally only when it is at beginning or end.Character is expected after a dash.Unexpected character in bracket expression.Parenthesis is not closed.St19bad_optional_accessN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEES8_EEN4dmlc9parameter10FieldEntryINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEEESt23_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_traitsIcEELb1ELb1EEEww""//\\abf n r bad any_castauto/__w/ml-builds/ml-builds/xgboost/src/data/data.ccInvalid device ordinal. Data is associated with a different device ordinal than the booster. The device ordinal of the data is: ; the device ordinal of the Booster is: Unknown data type for getting meta info./__w/ml-builds/ml-builds/xgboost/src/data/../common/common.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.hself_offset.size() == other_offset.size()XGBoost federated mode detected, not splitting data among workersXGBoost distributed mode detected, will split data among workerscache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification.Column-wise data split is not support for external memory.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector.group_ptr_.size() == weights_.Size() + 1the 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.feature_weights.Size() == num_col_Size of feature_weights must equal to number of columns.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.All feature_types must be one of {int, float, i, q, c}. must be equal to number of columns.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)Check failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)Number of columns is expected to be 1.this->num_col_ == that.num_col_Number of columns must be consistent across batches.transpose.offset.Size() == num_columns + 1Meta info like label or weight can not have missing value.Check failed: t_out.CContiguous()Check failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.this->labels.Size() % this->num_row_ == 0Label contains NaN, infinity or a value too large./__w/ml-builds/ml-builds/xgboost/src/data/validation.hCheck failed: valid_query_group`qid` must be sorted in non-decreasing order along with data.Weights must be positive values.Feature weight must be greater than 0.Check failed: strm->Read(&(shape[i]))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.p`P8 `pP8 0 p !!x!"!P@0 PpX@0Hp%p%0%$$$$X%$"$X% $ $$#####p#P#"0$++ ,,p,-`-p(--8-p(p+p+P++***+*)*+P*P*8***))))))`*000@000 000/.00///X/@/ ///.../std::future_error: base_marginlabel_lower_boundlabel_upper_boundfeature_weightsUnknown float field name: group_ptrUnknown uint32 field name: self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size().r matrix with entries loaded from Load part of data partsSplitting data by columnUnknown feature info: array.valid.Capacity() == 0group_ptr_.back() == num_row_weights_.Size() == num_row_labels.Shape(0) == num_row_field_cnt == kNumFieldWrong number of fieldssize == this->num_col_Length of Check failed: infoUnknown feature info name: MetaInfo: Invalid format for name == expected_name Expected field: , got: Expected field of type: got field type: Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: !is_scalar to be a vector; got a scalarshape.second == 1that.group_ptr_.size() != 0Incorrect size for labels.Invalid group structure.Check failed: non_decUnknown key for MetaInfo: Check failed: keyCheck failed: t.CContiguous() to be a tensor; got a scalar is no longer supported. again.major >= 1minor >= 6MetaInfo: invalid formatnum_field >= expectedfields.)/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_source.hCheck failed: page_/__w/ml-builds/ml-builds/xgboost/src/data/../data/array_interface.h/__w/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hD\EDEEDDECheck failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix.$H$H4HDHdH|HHHHHH I/__w/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hthis->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification.shape[i] == r.Shape(i)^_P^^^__<___/__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.hCheck failed: parser_qidpUWX Zx[\^H_`bdUȃ0`ȫЮ诰0Pȷ/*Okey >= builder_base_row_offset@@Ŵ0ĴĴHôxôôĴô迴迴´@´0´ô´ ´´P0p@ѴѴҴҴҴҴӴ0ӴPӴpӴӴӴPp @@x0h X0'AYCheck failed: !cache_info_->written/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_writer.hUnknown format type format.front() == '.'wb MB written in seconds.Check failed: fmt->Read(page.get(), fi.get())Check failed: !at_end_n_prefetch_batches > 0total batches:Check failed: curr_exce_fetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.Check failed: !(*ring_)[count_].valid()Check failed: validInput data contains `inf` or a value too large, while `missing` is not set to `inf`/__w/ml-builds/ml-builds/xgboost/src/data/../data/proxy_dmatrix.hUnknown type: cache_info_->offset.size() == n_batches_ + 1count_ == n_batches_Check failed: proxy_this->count_ >= 1source_->Iter() == this->count_St19__shared_ptr_accessIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt12__shared_ptrIN7xgboost4data15CSRArrayAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data12ArrayAdapterELN9__gnu_cxx12_Lock_policyE2EEN7xgboost10SparsePageEN7xgboost7DMatrixEN7xgboost4data11FileAdapterESt12bad_any_castNSt13__future_base13_State_baseV2ENSt13__future_base21_Async_state_commonV2ESt10shared_ptrIN7xgboost4data15CSRArrayAdapterEESt10shared_ptrIN7xgboost4data12ArrayAdapterEEN7xgboost17BatchIteratorImplINS_11EllpackPageEEEN7xgboost4data20SparsePageSourceImplINS_11EllpackPageEEEN7xgboost4data18PageSourceIncMixInINS_11EllpackPageEEEN7xgboost4data17EllpackPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt23_Sp_counted_ptr_inplaceIN7xgboost6common12MmapResourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost10SparsePageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost10SparsePageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_10SparsePageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE OOOOAAGGGG/src/data/./sparse_page_source.hlabels_lower_boulabels_upper_bouP?eA/__w/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/__w/ml-builds/ml-builds/xgboost/src/data/file_iterator.ccOnly one `#` is allowed in file path for cachefile specificationURI parameter `format` is required for loading text data: filename?format=csvCheck failed: std::getline(is, kv.first, '=')Check failed: std::getline(is, kv.second)name_args_cache.size() <= 2name_args.size() == 2Invalid uri argument format for key in arg for value in arg /__w/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.h/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.ccCheck failed: this->data.empty()Check failed: resource->Type() == common::ResourceHandler::kMalloc/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index.hhit_count.size() == n_bins_totalCheck failed: p_fmat->SingleColBlock()Check failed: columns_n_bytes >= this->data.size()Check failed: malloc_resourcerow_ptr.size() == 0Check failed: !sorted_sketch/__w/ml-builds/ml-builds/xgboost/src/data/../common/io.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/ref_resource_view.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/column_matrix.hCheck failed: storage.Resource()->Type() == ResourceHandler::kMalloc[Internal Error]: Cannot grow the vector when external memory is used.m_size >= storage.size()Check failed: resourcenum_nonzeros_.size() == n_featuresf<,,4 | D|\$ l< \\\l|Iter()cuts_.Values().size() != 0/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.h/__w/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.h/__w/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.ccN7xgboost17BatchIteratorImplINS_16GHistIndexMatrixEEEN7xgboost4data20SparsePageSourceImplINS_16GHistIndexMatrixEEEN7xgboost4data18PageSourceIncMixInINS_16GHistIndexMatrixEEEN7xgboost4data23GradientIndexPageSourceENSt13__future_base7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost16GHistIndexMatrixELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost16GHistIndexMatrixESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost16GHistIndexMatrixEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_16GHistIndexMatrixEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE/src/data/sparsepp` H ЋpP0芹 ุhP0๸๸໸лxX@ оp`H(pP@0ƸƸ@ȸǸǸpǸPǸ¸Ǹȸ0Ǹ¸øøhŸĸĸPŸ@Ÿø ŸŸ0ŸøøøƸŸŸŸŸxŸPƸ0Ƹ`ŸƸϸϸ@ѸииpиPи0̸иϸ0и0̸X͸X͸θ͸͸Pθ0θ(͸͸x͸@θ(͸ʸʸϸθθθθθpθPθ8θ ϸ׸׸0ٸظظ`ظ@ظ Ըظ׸ ظ ԸHոHոոոո@ָ ָոոhո0ָոҸҸ׸ָָָָxָ`ָ@ָ(ָ׸߸߸0`@(ܸ߸ (ܸHݸHݸݸݸݸ@޸ ޸ݸݸhݸ0޸ݸڸڸ߸޸޸޸޸x޸`޸@޸(޸߸x`@(x8`8 x芹00pP0،@pXȏhP0ؑpГГ P8p`pH0ЖxP hpP0`@ Йp ȜȜpP0؝h袹`pP80P(ࡹȡ`ȦhH(Х誹`pP80P(੹ȩ`ȮhH(Э貹`pP80P(౹ȱ`ȶhH(е/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.ccpage.row_ptr.size() == h_offset.size()/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/hist_util.href->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.param.max_bin == batch_.max_binInconsistent `max_bin`. `max_bin` should be the same across different QuantileDMatrix, and consistent with the Booster being trained.Check failed: !param.regen && param.hess.empty()Check failed: !detail::RegenGHist(param, batch_)`QuantileDMatrix` not initialized.`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`.Inconsistent number of columns.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Something went wrong during iteration.Data must has at least 1 column.accumulated_rows == Info().num_row_this->ghist_->Features() == Info().num_col_proxy->Info().labels.Size() == 0Iterative DMatrix must have at least 1 batch.((H(赹x`ȵ෹ȷpX@ ععغ(@x8P88xX8 л@̹@̹͹͹p͹P͹8͹͹̹̹̹̹8ι8ιϹϹhϹHϹ(ϹϹιιιιCheck failed: refCheck failed: p_cutsn_features == num_cols()n_features >= 1h_ft.size() == n_ftCheck failed: p_sketchh_ft.size() == n_featuresrbegin == Info().num_row_/__w/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptr/__w/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hNot implemented.Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`.Slicing DMatrix is not supported for Quantile DMatrix.Slicing DMatrix columns is not supported for Quantile DMatrix./__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.hCheck failed: proxy_handleInvalid proxy handle.Check failed: typedCheck failed: !std::isnan(sparse_thresh)$$$$###t#d#$D#l T#$"""""l"T"<"$""t ";;;D;4;$;;;:8;;::t:T:<:::999$8:SSLSRRRRtSRORtS= 2Generating new Ellpack page./__w/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: in_stream->Read(&tmagic)invalid format, magic number mismatch/__w/ml-builds/ml-builds/xgboost/src/data/batch_utils.hBatch parameter is not initialized.batch_param_.max_bin == param.max_binCheck failed: !detail::RegenGHist(batch_param_, param)Inconsistent sparse threshold.Generating new Gradient Index.batch_param_.hess.data() == param.hess.data()/__w/ml-builds/ml-builds/xgboost/src/data/../common/error_msg.hSample size too large for the current updater. Maximum number of samples:. Consider using a different updater or tree_method.offset_vec.back() == info_.num_nonzero_0lPll@mmn`nn ooo/__w/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h/__w/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: data_Column is emptyCheck failed: data_ && row_idx < length_Column is empty or out-of-bound index of the columnCheck failed: (std::is_same::value || std::is_same::value)Expecting CSCAdapterN7xgboost7CSCPageEN7xgboost13SortedCSCPageEN7xgboost4data6ColumnEN7xgboost4data15PrimitiveColumnIaEEN7xgboost4data15PrimitiveColumnIhEEN7xgboost4data15PrimitiveColumnIsEEN7xgboost4data15PrimitiveColumnItEEN7xgboost4data15PrimitiveColumnIiEEN7xgboost4data15PrimitiveColumnIjEEN7xgboost4data15PrimitiveColumnIlEEN7xgboost4data15PrimitiveColumnImEEN7xgboost4data15PrimitiveColumnIfEEN7xgboost4data15PrimitiveColumnIdEEN7xgboost4data13SimpleDMatrixEN7xgboost17BatchIteratorImplINS_10SparsePageEEEN7xgboost4data23SimpleBatchIteratorImplINS_10SparsePageEEEN7xgboost17BatchIteratorImplINS_7CSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_7CSCPageEEEN7xgboost17BatchIteratorImplINS_13SortedCSCPageEEEN7xgboost4data23SimpleBatchIteratorImplINS_13SortedCSCPageEEESt15_Sp_counted_ptrIPN7xgboost7CSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost13SortedCSCPageELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost11EllpackPageELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIaEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIhEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIsEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnItEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIiEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIjEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIlEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnImEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIfEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data15PrimitiveColumnIdEESaIS3_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost17BatchIteratorImplINS0_7CSCPageEEELN9__gnu_cxx12_Lock_policyE2EE/__w/ml-builds/ml-builds/xgboost/src/data/./../common/common.h/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccCheck failed: sparse_page_source_Check failed: ghist_index_source_Couldn't remove external memory cache file ; you may want to remove it manuallyCheck failed: iter_.Next()Must have at least 1 batch.-rinfo_.num_col_ != 0this->Info().num_col_ != 0.sorted.col.page.gradient_index.pagecuts.Values().size() != 0/__w/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hSlicing DMatrix is not supported for external memory.Slicing DMatrix columns is not supported for external memory.Can not obtain a single CSR page for external memory DMatrix/__w/ml-builds/ml-builds/xgboost/src/data/./proxy_dmatrix.hCheck failed: kv.secondWaitthis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size()Make cache:N7xgboost4data20SparsePageSourceImplINS_10SparsePageEEEN7xgboost4data16SparsePageSourceEN7xgboost4data20SparsePageSourceImplINS_7CSCPageEEEN7xgboost4data18PageSourceIncMixInINS_7CSCPageEEEN7xgboost4data13CSCPageSourceEN7xgboost4data20SparsePageSourceImplINS_13SortedCSCPageEEEN7xgboost4data18PageSourceIncMixInINS_13SortedCSCPageEEEN7xgboost4data19SortedCSCPageSourceEN7xgboost4data17SparsePageDMatrixENSt13__future_base7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENSt13__future_base7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEEN7xgboost16GHistIndexMatrixENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEENSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS4_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS7_EEESt15_Sp_counted_ptrIPN7xgboost4data5CacheELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost4data23GradientIndexPageSourceELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data16SparsePageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data13CSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost4data19SortedCSCPageSourceESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost7CSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost13SortedCSCPageESaIS1_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base17_Async_state_implINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceINSt13__future_base15_Deferred_stateINSt6thread8_InvokerISt5tupleIJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEESaISF_ELN9__gnu_cxx12_Lock_policyE2EENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost7CSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_7CSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEENSt13__future_base12_Task_setterISt10unique_ptrINS_7_ResultISt10shared_ptrIN7xgboost13SortedCSCPageEEEENS_12_Result_base8_DeleterEENSt6thread8_InvokerISt5tupleIJZNS4_4data20SparsePageSourceImplIS5_E9ReadCacheEvEUlvE_EEEES6_EENSt6thread11_State_implINS_8_InvokerISt5tupleIJZNSt13__future_base17_Async_state_implINS1_IS2_IJZN7xgboost4data20SparsePageSourceImplINS5_13SortedCSCPageEE9ReadCacheEvEUlvE_EEEESt10shared_ptrIS8_EEC4EOSC_EUlvE_EEEEEE/__w/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccCheck failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()Raw binary data format.page->offset.Size() != 0UInvalid SparsePage fileN7xgboost4data16SparsePageFormatINS_10SparsePageEEEN7xgboost4data19SparsePageRawFormatINS_10SparsePageEEEN7xgboost4data16SparsePageFormatINS_7CSCPageEEEN7xgboost4data19SparsePageRawFormatINS_7CSCPageEEEN7xgboost4data16SparsePageFormatINS_13SortedCSCPageEEEN7xgboost4data19SparsePageRawFormatINS_13SortedCSCPageEEE*N7xgboost4dataUlvE_E*N7xgboost4dataUlvE0_E*N7xgboost4dataUlvE1_Egpu_coord_descentLinear booster does not support prediction range.Linear booster, implement generalized linear model.layer_begin == 0GBLinearTrainParam/__w/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by the current booster.Inplace predict is not supported by the current booster./__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear.ccgblinear does not support prediction of leaf indexmodel/__w/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(¶m_, sizeof(param_)) == sizeof(param_)get(in["name"]) == "gblinear"/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hCheck failed: !model_.weight.empty()gblinear doesn't support number of trees for feature importance.gblinear only has `weight` defined for feature importance.Model is not initializedCheck failed: trees.empty()importance_type == "weight"PredictBatchInternalbase_margin.Size() == nsize * ngroup { "bias": [ ], "weight": [ ] }bias: weight: shotguntolerancemax_row_perbatchMaximum rows per batch.Update algorithm for linear model. One of shotgun/coord_descentStop if largest weight update is smaller than this number.gblinear_train_paramCheck failed: this->updater_/__w/ml-builds/ml-builds/xgboost/src/gbm/../common/common.hdevice="cuda", updater="coord_descent"GBLinearN7xgboost5ModelEN7xgboost12ConfigurableEN7xgboost15GradientBoosterEN7xgboost3gbm8GBLinearE*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_7ContextEE_Eboosted_roundsN7xgboost3gbm13GBLinearModelEDeprecatedGBLinedeprecated_num_fint (non-negativdeprecated_num_oUnknown gbm type /__w/ml-builds/ml-builds/xgboost/src/gbm/gbm.cc/__w/ml-builds/ml-builds/xgboost/src/gbm/../tree/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/gbm/../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccCheck failed: !updaters_.empty()model_.param.num_parallel_tree == trees.size()model_.param.num_parallel_tree == 1Boosting random forest is not supported for current objective.Check failed: !trees.front()->IsMultiTarget() support for multi-target tree is not yet implemented.trees.size() == model_.param.num_parallel_treetree_end <= model_.trees.size()Inplace predict accepts only DMatrixProxy as input.Unknown data type for inplace prediction:get(in["name"]) == "gbtree"Check failed: this->model_.learner_model_param->Initialized()this->model_.trees_to_update.size() == this->model_.trees.size() trees remain. Slice the model before making update if you only want to update a portion of trees.out_model.iteration_indptr.front() == 0Number of features used for training and prediction.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.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 roundsMismatching size between number of rows from input data and size of gradient vector.Check failed: tparam_.tree_method == TreeMethod::kHist || tparam_.tree_method == TreeMethod::kAutoOnly the hist tree method is supported for building multi-target trees with vector leaf.GPU is not yet supported for vector leaf.Current objective doesn't support external memory.in_gpair->Size() % n_groups == 0Umust have exactly ngroup * nrow gpairsThe `exact` tree method is not supported on GPU. Loading from a raw memory buffer (like pickle in Python, RDS in R) on a CPU-only machine. Consider using `save_model/load_model` instead. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more details about differences between saving model and serializing. Changing `tree_method` to `hist`./__w/ml-builds/ml-builds/xgboost/src/gbm/../common/error_msg.hIf you are loading a serialized model (like pickle in Python, RDS in R) or configuration generated by an older version of XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. See: https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html for more details about differences between saving model and serializing. Changing updater from `grow_gpu_hist` to `grow_quantile_histmaker`.Tree booster, gradient boosted trees.Check failed: cpu_predictor_Update tree leafout_preds->version == 0Invalid number of trees.Unknown device type:Check failed: p_gbtreeend != beginEmpty slice is not allowed.Not all trees are updated, GBTreeModelParamnum_treesGBTreeTrainParamDartTrainParamgbtree_train_paramspecified_updaterUpdater: `` n_out % gpair->Size() == 0gpair->Size() == n_outCheck failed: ctx_->IsCPU()BoostNewTreesn_groups != 0cpu_predictorUsing tree method: grow_quantile_histmakergrow_histmakergrow_colmaker,pruneUnknown tree_method: `grow_gpu_histTree booster, dart.weight_drop/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.hiteration_indptr.size() == 1/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree.hCheck failed: !model.iteration_indptr.empty()end <= model.BoostedRounds()Out of range for tree layers.tree_begin <= tree_endtree_begin == 0Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.step >= 1out_l == n_layersget(in["name"]) == "dart"Check failed: !this->model_.learner_model_param->IsVectorLeaf()p_out_preds->predictions.Size() == predts.predictions.Size()Check failed: predictori >= p_out_preds->versionCheck failed: successUnsupported data type for inplace predict.predts.predictions.Size() == p_out_preds->predictions.Size()Check failed: tree_param_.GetInitialised()drop trees, weight = Check failed: p_dartCheck failed: p_dart->weight_drop_.empty()vector::reserveupdater_seqTree updater sequence.process_typedefaultupdatetree_methodapproxexactWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.Choice of tree construction method.sample_typeuniformweightednormalize_typeforestrate_dropone_dropskip_dropDifferent 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.dart_train_param3443=585*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_7ContextEE_E*N7xgboost3gbmUlPKNS_17LearnerModelParamEPKNS_7ContextEE0_Eidx <= total_n_treesInvalid tree index.total_gaintotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: N4dmlc9parameter10FieldEntryIN7xgboost10TreeMethodEEEN4dmlc9parameter10FieldEntryIN7xgboost15TreeProcessTypeEEEN7xgboost3gbm6GBTreeEN7xgboost3gbm4DartE?A;layer_trees != 0GBTree: invalid model fileiteration_indptr/__w/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast(trees.size())model.trees.size() == model.param.num_treesmodel.tree_info.size() == model.param.num_treesmodel.iteration_indptr.back() == model.param.num_treesCheck failed: !iteration_indptr.empty()iteration_indptr.back() == param.num_treesparam.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_treestrees_json.size() == param.num_treestree_info_json.size() == param.num_treesN7xgboost3gbm11GBTreeModelEgbtree_model_pariteration_indptrFlag to print out detailed breakdown of runtime.Whether to use RAPIDS Memory Manager to allocate GPU memory in XGlobalConfigurat/__w/ml-builds/ml-builds/xgboost/src/learner.ccmulti-target-multi-class is not yet supported. Output classes:Check failed: std::as_const(base_score_).Data()->HostCanRead()Model is not yet initialized (not fitted).Check failed: base_score_.Data()->HostCanRead()Check failed: base_score_.Data()->DeviceCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead(), output targets:base_score_.Size() == 1LearnerModelParamLegacyLearnerTrainParamCheck failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0Check failed: !this->need_configuration_/__w/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by current objective function/__w/ml-builds/ml-builds/xgboost/include/xgboost/cache.hqueue_.size() == container_.size()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_/__w/ml-builds/ml-builds/xgboost/src/collective/aggregator.hCheck failed: ret.ec == std::errc{}num_featurenum_classnum_targetboost_from_averageCheck failed: ptrCall Configure before saving model.learner_model_paramobjectiveattributesfeature_typesModelConfig/__w/ml-builds/ml-builds/xgboost/src/common/error_msg.hGlobal bias of the model.int (non-negative)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.Number of output targets. Can be set automatically if not specified.Whether we should calculate the base score from training data.disable_default_eval_metricreg:squarederrormulti_strategyone_output_per_treemulti_output_treeFlag to disable default metric. Set to >0 to disableGradient booster used for training.Objective function used for obtaining gradient.Strategy used for training multi-target models. `multi_output_tree` means building one single tree for all targets.multi:softprobnum_output_groupmulti:softmaxcount:poisson0.7learner_train_parammetricsgeneric_paramCheck failed: !this->learner_model_param_.IsVectorLeaf()Please use JSON/UBJ format for model serialization with multi-output models.;binfUnsupported prediction type:Check failed: it != container_.cend()remained.size() + expired.size() == container_.size()Check failed: matrix.first.ptrCheck failed: !matrix.second.ref.expired()Unfortunately, XGBoost does not support data matrices with 0 feature is supplied. Are you using raw Booster interface?n_features <= max_n_features features or greatermparam_.num_feature != 0Check failed: this->obj_Inconsistent labels.Check failed: n_targets == t || 1 == tCheck failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:Check failed: tparam_.GetInitialised()learner_model_param_.BaseScore(Ctx()).Size() != 0base_score.Size() == 1Check failed: !std::isnan(mparam_.base_score)Check failed: std::equal(data.cbegin(), data.cend(), sync.cbegin())Different model parameter across workers.Check failed: !std::isinf(mparam_.base_score)Check failed: IsA(in)Invalid version string in configget(objective_fn["name"]) == tparam_.objectiveFound JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.Check failed: that.lock_.try_lock()container_.size() < max_size__Map_base::atmultiple_predictions <= 1Check failed: gbm_ != nullptrPerform one kind of prediction at a time.Predict must happen after Load or configurationBoostOneIterUpdateOneIterPredictRawPredictionsGetGradientGradientsLearnerCONFIG-offset:this->learner_model_param_.num_feature != 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0begin >= 0best_iterationbest_scoreEvalOneIterInvalid parameter "" contains whitespace. Parameters: { ", } are not used. Configurefixed_size->Read(&buffer[0], size) == sizebs64header != "bs64"fp.Read(&header[0], 4) == 4UInvalid model formatcount_poisson_max_delta_stepmulti:%uBase64 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)data_.Size() == detail::CalcSize(shape_)Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityInvalid serialization file.sz > 0fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)N4dmlc12SerializableEN7xgboost7LearnerEN4dmlc9parameter10FieldEntryIN7xgboost13MultiStrategyEEEN7xgboost20LearnerConfigurationEN7xgboost9LearnerIOEN7xgboost11LearnerImplESt23_Sp_counted_ptr_inplaceIN7xgboost20PredictionCacheEntryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EEUnknown linear updater /__w/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccLearning rate ofL2 regularizatioL1 regularizatiofeature_selectorFeature selection or ordering meLinearTrainParamUpdate linear model according to coordinate descent algorithm./__w/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeatureCoordinateParam/__w/ml-builds/ml-builds/xgboost/src/linear/updater_coordinate.ccSave config for CPU updater.coordinate_param/__w/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: (XPN7xgboost13LinearUpdaterEN7xgboost6linear15FeatureSelectorEN7xgboost6linear21CyclicFeatureSelectorEN7xgboost6linear22ShuffleFeatureSelectorEN7xgboost6linear21RandomFeatureSelectorEN7xgboost6linear21GreedyFeatureSelectorEN7xgboost6linear22ThriftyFeatureSelectorEN7xgboost6linear17CoordinateUpdaterE*N7xgboost6linearUlvE_EThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the featurlinear_train_parcoordinate_paramCoordinateUpdateUpdate linear model according to shotgun coordinate descent algorithm./__w/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. Supported options are: {cyclic, shuffle}4_T__^_N7xgboost6linear14ShotgunUpdaterE*N7xgboost6linearUlvE_EWARNING: DEBUG: INFO: aucaucpr/__w/ml-builds/ml-builds/xgboost/src/metric/../common/common.hlabels.Size() == predts.size()Receiver Operating Characteristic Area Under the Curve.Area under PR curve for both classification and rank.Invalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples.labels.Size() != 0auc <= 1.0f/__w/ml-builds/ml-builds/xgboost/src/metric/../collective/aggregator.h/__w/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/metric/auc.ccinfo.group_ptr_.size() >= 2n_classes != 0AUC doesn't support multi-target model./__w/ml-builds/ml-builds/xgboost/src/metric/../common/algorithm.hinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()Dataset is empty, or contains only positive or negative samples.auc <= 1Total AUC across groups: , valid groups: auc <= 1.0/__w/ml-builds/ml-builds/xgboost/src/metric/auc.hPR-AUC supports only binary relevance for learning to rank.N7xgboost6MetricEN7xgboost13MetricNoCacheEN7xgboost6metric7EvalAUCINS0_10EvalROCAUCEEEN7xgboost6metric10EvalROCAUCEN7xgboost6metric7EvalAUCINS0_9EvalPRAUCEEEN7xgboost6metric9EvalPRAUCE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E????/src/metric/auc.??mphequantilegamma-nloglikgamma-deviancepoisson-nloglikmapemaermslermse/__w/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cuCheck failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)sscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metricMean absolute percentage error.Negative 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.tweedie-nloglik@%fRooted mean square error.Rooted mean square log error.Mean absolute error.Mean Pseudo-huber error.Binary classification error.tweedie-nloglikQuantile regression error.info.labels.Shape(0) == info.num_row_slope for pseudo huber cannot be 0.slope != 0.0preds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0Check failed: !alpha_.Empty()dat[1] > 0n_targets != 0quantile_loss_parampseudo_huber_paramname == "quantile"/__w/ml-builds/ml-builds/xgboost/src/metric/../common/quantile_loss_utils.hCheck failed: GetInitialised()Check failed: !quantile_alpha.Get().empty()quantile alpha must be in the range [0.0, 1.0].N7xgboost6metric15PseudoErrorLossEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowRMSEEEEN7xgboost6metric13EvalEWiseBaseINS0_12EvalRowRMSLEEEEN7xgboost6metric13EvalEWiseBaseINS0_10EvalRowMAEEEEN7xgboost6metric13EvalEWiseBaseINS0_11EvalRowMAPEEEEN7xgboost6metric13EvalEWiseBaseINS0_14EvalRowLogLossEEEN7xgboost6metric13EvalEWiseBaseINS0_20EvalPoissonNegLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_17EvalGammaDevianceEEEN7xgboost6metric13EvalEWiseBaseINS0_16EvalGammaNLogLikEEEN7xgboost6metric13EvalEWiseBaseINS0_9EvalErrorEEEN7xgboost6metric13EvalEWiseBaseINS0_18EvalTweedieNLogLikEEEN7xgboost6metric13QuantileErrorE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_E*N7xgboost6metricUlPKcE4_E*N7xgboost6metricUlPKcE5_E*N7xgboost6metricUlPKcE6_E*N7xgboost6metricUlPKcE7_E*N7xgboost6metricUlPKcE8_E*N7xgboost6metricUlPKcE9_E*N7xgboost6metricUlPKcE10_E@$tI$tɕ$pseudo_huber_parư>/__w/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function merrormloglossMulticlass classification error.Multiclass negative loglikelihood./__w/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),preds.Size() == 0nclass >= 1U num_class= but found in labelN7xgboost6metric14EvalMClassBaseINS0_14EvalMatchErrorEEEN7xgboost6metric14EvalMatchErrorEN7xgboost6metric14EvalMClassBaseINS0_16EvalMultiLogLossEEEN7xgboost6metric16EvalMultiLogLossE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E]Bcox-nloglikInvalid output score, might be caused by invalid query group weight.Negative log partial likelihood of Cox proportional hazards model.score <= 1.0 + kRtEpsAMS metric for higgs.preprecision@k for rank.map@k for ranking.ndcg@k for ranking./__w/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccCheck failed: !collective::IsDistributed()Cox metric does not support distributed evaluationAMS must be in format ams@kams@/__w/ml-builds/ml-builds/xgboost/src/metric/../common/ranking_utils.hCheck failed: param_.GetInitialised()Groups() == info.weights_.Size()the size of label.weight.weights.size() == p_cache->Groups()metric AMS do not support distributed evaluationbest-ams-ratio= LDLParam() == param_St23_Sp_counted_ptr_inplaceIN7xgboost3ltr8MAPCacheESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost3ltr9NDCGCacheESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN7xgboost3ltr8PreCacheESaIS2_ELN9__gnu_cxx12_Lock_policyE2EEN7xgboost6metric7EvalAMSEN7xgboost6metric7EvalCoxEN7xgboost6metric17EvalRankWithCacheINS_3ltr8PreCacheEEEN7xgboost6metric13EvalPrecisionEN7xgboost6metric17EvalRankWithCacheINS_3ltr9NDCGCacheEEEN7xgboost6metric8EvalNDCGEN7xgboost6metric17EvalRankWithCacheINS_3ltr8MAPCacheEEEN7xgboost6metric12EvalMAPScoreE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E*N7xgboost6metricUlPKcE1_E*N7xgboost6metricUlPKcE2_E*N7xgboost6metricUlPKcE3_Elambdarank_paramA@@zo ?aft-nloglikinterval-regression-accuracyNegative log likelihood of Accelerated Failure Time model./__w/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuCheck failed: metric_AFT metric must be configured first, with distribution type and scaleUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()N7xgboost6metric20AFTNLogLikDispatcherEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common18NormalDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common20LogisticDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_14EvalAFTNLogLikINS_6common19ExtremeDistributionEEEEEN7xgboost6metric21EvalEWiseSurvivalBaseINS0_30EvalIntervalRegressionAccuracyEEE*N7xgboost6metricUlPKcE_E*N7xgboost6metricUlPKcE0_E-q=' @;f?/__w/ml-builds/ml-builds/xgboost/src/objective/../common/common.h/__w/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.cc/__w/ml-builds/ml-builds/xgboost/src/objective/../common/stats.hCheck failed: h_row_set.empty()begin_pos <= sorted_pos.size()Check failed: !position.empty()h_node_ptr.back() <= info.num_row_/__w/ml-builds/ml-builds/xgboost/src/objective/../collective/aggregator.h/__w/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf()k + 1 < h_node_ptr.size()y_idx <= info.labels.Shape(1)nptr.size() > 0n_unique == n_leaf/__w/ml-builds/ml-builds/xgboost/src/objective/../common/algorithm.h8]HQpQ C(I$4\ +0,`,-,/__w/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /__w/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.info.weights_.Size() == ndataUnrecognized distributionsurvival:aftAFT loss functionN7xgboost3obj6AFTObjE*N7xgboost3objUlvE_E..@ؗҜ<@@pApHinge loss. Expects labels to be in [0,1f]/__w/ml-builds/ml-builds/xgboost/src/objective/hinge.culabels are not correctly providedinfo.labels.Size() != 0Ulabel set cannot be emptypreds.size=, label.size=binary:hingeN7xgboost3obj8HingeObjE*N7xgboost3objUlvE_E/__w/ml-builds/ml-builds/xgboost/src/objective/init_estimation.hinfo.weights_.Size() == info.num_row_Invalid shape of labels.N7xgboost3obj12FitInterceptE/__w/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.cch_n_rel.size() == label.Size()LambdaRank with NDCG loss as objectiveLambdaRank with RankNet loss as objectiveLambdaRank with MAP loss as objective.h_n_rel.size() == gptr.back()rank:ndcgrank:pairwiserank:map/__w/ml-builds/ml-builds/xgboost/src/objective/../common/ranking_utils.hUnreachable.Check failed: param_.ndcg_exp_gainNDCG gain can not be set for the MAP objective.NDCG gain can not be set for the pairwise objective./__w/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.hg_rank.size() == g_label.Size()@@K L(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. SoftmaxMultiClassObj: label must be in [0, num_class).label.Size() * num_class: preds.Size(): N7xgboost3obj20SoftmaxMultiClassObjE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_ENumber of output class in the multi-class classiSoftmaxMultiClassoftmax_multiclaҜ= n_alphasget(in["name"]) == Name()/__w/ml-builds/ml-builds/xgboost/src/objective/../common/quantile_loss_utils.hh_weights.size() == h_labels.Size(),\NN(OONN7xgboost3obj18QuantileRegressionE*N7xgboost3objUlvE_E/__w/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cureg:linear is now deprecated in favor of reg:squarederror.info.labels.Size() == preds.Size()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.Logistic regression for binary classification task.Logistic regression for classification, output score before logistic transformation.Regression Pseudo Huber error.Poisson 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.RegLossParamscale_pos_weightPoissonRegressionParamTweedieRegressionParamtweedie_variance_powerreg:squaredlogerrorreg:logisticbinary:logisticbinary:logitrawreg:linearMean absoluate error.reg:absoluteerrorStringView{get(in["name"])} == StringView{"reg:absoluteerror"}Check failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!/__w/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: base_margin->Size() == 1Sum of weights is close to 0.0, skipping base score estimation.reg:gammasurvival:coxreg:pseudohubererrorpoisson_regression_paramreg:tweedietweedie_regression_paramPoissonRegression: label must be nonnegativeGammaRegression: label must be positive.TweedieRegression: label must be nonnegativereg_loss_paramlabel must be in [0,1] for logistic regressionlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.N7xgboost3obj10RegLossObjINS0_16LinearSquareLossEEEN7xgboost3obj10RegLossObjINS0_15SquaredLogErrorEEEN7xgboost3obj10RegLossObjINS0_18LogisticRegressionEEEN7xgboost3obj10RegLossObjINS0_22LogisticClassificationEEEN7xgboost3obj10RegLossObjINS0_11LogisticRawEEEN7xgboost3obj21PseudoHuberRegressionEN7xgboost3obj17PoissonRegressionEN7xgboost3obj13CoxRegressionEN7xgboost3obj15GammaRegressionEN7xgboost3obj17TweedieRegressionEN7xgboost3obj17MeanAbsoluteErrorE*N7xgboost3objUlvE_E*N7xgboost3objUlvE0_E*N7xgboost3objUlvE1_E*N7xgboost3objUlvE2_E*N7xgboost3objUlvE3_E*N7xgboost3objUlvE4_E*N7xgboost3objUlvE5_E*N7xgboost3objUlvE6_E*N7xgboost3objUlvE7_E*N7xgboost3objUlvE8_E*N7xgboost3objUlvE9_E*N7xgboost3objUlvE10_Epoisson_regressitweedie_regressiư>%(%p$$$&&\%%%p:p:0;;(>pCPC0CCBBBBB`BDDEpEPE0EEDDDDpDCheck failed: IsMultiTarget()Check failed: IsLeaf(nidx)Make predictions using CPU./__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccbase_margin.Shape(1) == ngroupCheck failed: !model.learner_model_param->IsVectorLeaf()Predict interaction contributionCheck failed: !p_fmat->Info().IsColumnSplit()Predict interaction contribution support for column-wise data split is not yet implemented./__w/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/__w/ml-builds/ml-builds/xgboost/src/predictor/../data/proxy_dmatrix.h/__w/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.h/__w/ml-builds/ml-builds/xgboost/include/xgboost/multi_target_tree_model.hCheck failed: xgboost::collective::IsDistributed()column-split prediction is only supported for distributed trainingout_preds->size() == p_fmat->Info().num_row_ * (tree_end_ - tree_begin_)out_preds->size() == p_fmat->Info().num_row_ * model_.learner_model_param->num_output_groupPredict contributionngroup != 0ncolumns != 0Predict contribution support for column-wise data split is not yet implemented.predict instancePredict instance with column splitPredict leaf with column splitm->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.Check failed: !p_m->Info().IsColumnSplit()Inplace predict support for column-wise data split is not yet implemented.Predict DMatrix with column splitout_preds->size() == n_samples * n_groupsSt19__shared_ptr_accessIN7xgboost4data12DenseAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt19__shared_ptr_accessIN7xgboost4data10CSRAdapterELN9__gnu_cxx12_Lock_policyE2ELb0ELb0EESt12__shared_ptrIN7xgboost4data12DenseAdapterELN9__gnu_cxx12_Lock_policyE2EESt12__shared_ptrIN7xgboost4data10CSRAdapterELN9__gnu_cxx12_Lock_policyE2EEN7xgboost9predictor12CPUPredictorESt10shared_ptrIN7xgboost4data12DenseAdapterEESt10shared_ptrIN7xgboost4data10CSRAdapterEE*N7xgboost9predictorUlPKNS_7ContextEE_E?/__w/ml-builds/ml-builds/xgboost/src/predictor/cpu_treeshap.ccunique_path[i].pweight == 0Unique path must have zero weightUnknown predictor type /__w/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccmodel.learner_model_param->num_output_group != 0margin.Shape(0) == n_samplesmargin.Shape(1) == n_groupsInvalid shape of base_margin. Expected: (N7xgboost9PredictorE/__w/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: newsize != 0With error: _/__w/ml-builds/ml-builds/xgboost/src/tree/fit_stump.cc/__w/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.hCheck failed: h_sum.CContiguous()/__w/ml-builds/ml-builds/xgboost/src/tree/../common/common.hn_targets == gpair.Shape(1)ư>ư>/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.ccnodes_to_build.size() == valid_candidates.size()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/param.ccCheck failed: *local_tree == ref_treedebug_synchronizCheck if all distributed tree are identical after tree constructmax_cached_hist_long (non-negatiMaximum number of nodes in CPU histogram cache. Only for internaHistMakerTrainPa/__w/ml-builds/ml-builds/xgboost/src/tree/multi_target_tree_model.ccCheck failed: this->IsLeaf(nidx)Collapsing a split node to leaf weight.Size() == this->NumTarget()weights_.size() >= next_nidx * weight.Size()parent_.size() == left_.size()split_idx < this->param_->num_featureparent_[nidx] != InvalidNodeId()p_weight.Size() == base_weight.Size()l_weight.Size() == left_weight.Size()r_weight.Size() == right_weight.Size()in_weight.Size() == weight_out.size()param_->size_leaf_vector > 1parent_.size() >= 1left_.size() == right_.size()nidx < left_.size()nidx < right_.size()nidx < parent_.size()nidx < split_index_.size()loss_changessum_hessianbase_weightssplit_indicessplit_conditionsdefault_leftparentsleft_childrenright_childrenN7xgboost15MultiTargetTreeE/__w/ml-builds/ml-builds/xgboost/src/tree/param.ccFound floating point number in interaction constraintsUnknown value type for interaction constraint:std::floor(d) == d/__w/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccparam_.num_nodes == static_cast(nodes_.size())param_.num_nodes == static_cast(stats_.size())param_.deprecated_num_roots == 1Check failed: !IsMultiTarget()Please use JSON/UBJSON for saving models with multi-target trees.Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.split_index < this->param_.num_featureCheck failed: this->p_mt_tree_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_deletedthis->split_types_.size() == this->Size()this->GetSplitCategoriesPtr().size() == this->Size()get(out["tree_param"]["num_nodes"]) == std::to_string(param_.num_nodes)this->split_types_.size() == param_.num_nodesstatic_cast(deleted_nodes_.size()) == param_.num_deletedthis->split_categories_segments_.size() == param_.num_nodesDump text representation of treeDump json representation of treeDump graphviz representation of tree{param_.num_nodes != 0mean_values->size() > 0UCheck failed: node.IsLeaf()param_.size_leaf_vector > 1split_typecategories_segmentscategories_sizescategories_nodesUnknown Model Builder:GraphvizParamTreeParamtree_paramnum_nodesdot Line , around ^`/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/json.h, Expect '"' but reach end of line ch == '\"'Error at, Expect '"' but get 'unknown string escape \ch == ','ch == ':', Expect ':' but get ', JSON object expect '}' or ',' 'Check failed: is_numerical in feature map is categorical but tree node is numerical.Check failed: is_categoricalUnknown feature map type. in feature map is numerical but tree node is categorical.=k==k=>pos != std::string::nposparam_.num_nodes < std::numeric_limits::max()number of nodes in the tree exceed 2^31ch == '{', Expect '{' but get 'j_end - j_begin > 0std::numeric_limits::min() != max_catn_nodes != 0sum_hessian.size() == n_nodeslefts.size() == n_nodesrights.size() == n_nodesparents.size() == n_nodesindices.size() == n_nodesconds.size() == n_nodesloss_changes.size() == n_nodesbase_weights.size() == n_nodesdefault_left.size() == n_nodesyes_color#0000FFno_color#FF0000rankdircondition_node_paramsleaf_node_paramsLeaf node configurationgraph_attrsEdge 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`.learning_ratemin_split_lossmax_depthmax_leavesgrow_policydepthwiselossguidemax_cat_to_onehotmax_cat_thresholdmin_child_weightreg_lambdareg_alphasubsamplesampling_methodgradient_basedcolsample_bynodecolsample_bylevelcolsample_bytreesketch_ratiocache_optrefresh_leafmonotone_constraintsinteraction_constraintssparse_thresholdetaLearning rate(step size) of update.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)Maximum number of categories to use one-hot encoding based split.Maximum number of categories considered for split. Used only by partition-basedsplits.Minimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightMaximum 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 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 informationpercentage threshold for treating a feature as sparse, "gain": {loss_chg}, "cover": {sum_hess}{parent}{stat} {left} {right}{parent}{stat}{left}{tabs}{nid}{cond}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing}{tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{tabs}{nid}:[f{fname}<{cond}] yes={left},no={right},missing={missing}{fname} "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": {right}, "no": {left}, "missing": {missing} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{yes}{no}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties}{indent}{parent} {left} {right},gain={loss_chg},cover={sum_hess} {nid} [ label="leaf={leaf-value}" {params}] digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{newline}{indent}{nodes}{ "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {leaf}{sum_hess}{nid}:[{fname}] yes={yes},no={no}, missingnoyes{color}{branch} {nid} -> {child} [label="{branch}" color="{color}"] {<}{params} {nid} [ label="{fname}{<}{cond}" {params}] {tabs}{nid}:leaf={leaf}{stats},cover={cover} {nid} [ label="{fname}:{cond}" {params}] {nodes} " edge graph [ {key}="{value}" ] The following parameters for graphviz are not recognized: Failed to parse graphviz parameters: N7xgboost7RegTreeEN4dmlc9parameter14FieldEntryBaseINS0_10FieldEntryISt6vectorIiSaIiEEEES5_EEN4dmlc9parameter10FieldEntryISt6vectorIiSaIiEEEEN7xgboost13TreeGeneratorEN7xgboost13TextGeneratorEN7xgboost13JsonGeneratorEN7xgboost17GraphvizGeneratorE*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE_E*N7xgboostUlRKNS_10FeatureMapERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE0_E*N7xgboostUlRKNS_10FeatureMapENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEbE1_ENumber of features used in tree size_leaf_vectorSize of leaf vector, reserved fo?Unknown tree updater /__w/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccTree constructor that uses approximate histogram construction for each node./__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/io.h/__w/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hpartition > 0/__w/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.hid != kMax/__w/ml-builds/ml-builds/xgboost/src/tree/../common/row_set.helem_of_each_node_.size() == 0Ui < first_dimension_.size()/__w/ml-builds/ml-builds/xgboost/src/tree/hist/sampler.hexponent <= mod/__w/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hCheck failed: tree.IsLeaf(node.node_id)ptr_offset <= row_set.Data()->size()i < ranges_.size()end > beginnid < nodes_begin < end/__w/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.hCheck failed: !nodes_to_build.empty()n_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endtid < nthreads_/__w/ml-builds/ml-builds/xgboost/src/tree/../data/../common/ref_resource_view.hnidx != kRootvector::_M_fill_insert/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../split_evaluator.hp.monotone_constraints.size() <= n_featuresThe size of monotone constraint should be less or equal to the number of features.cut_ptr[fidx] <= static_cast(std::numeric_limits::max())cut_ptr[fidx + 1] <= static_cast(std::numeric_limits::max())out_preds.DeviceIdx() == Context::kCpuIdpart.Size() == n_nodesout_preds.Size() > 0UCheck failed: p_last_treeout_preds.Shape(1) == n_targets/__w/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccout_preds.Size() == data->Info().num_row_/__w/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.hupper_bound < static_cast(std::numeric_limits::max())p_tree->LeftChild(nidx) + 1 == p_tree->RightChild(nidx)nodes == targeted_hists.size()row_ptr_[nid] == kMaxCheck failed: !features.empty()current_size_ == new_size/__w/ml-builds/ml-builds/xgboost/src/tree/hist/hist_cache.hCheck failed: p_nodes_to_buildCheck failed: !this->hist_.HasExceeded()Check failed: p_nodes_to_subgpair.Shape(1) == n_targetsp_fmat->Info().num_row_ == gpair.Shape(0)target_builders_.size() == n_targetsCheck failed: dummy_sub.empty()Check failed: gpair.Contiguous()InitRootnodes_to_build.size() >= nodes_to_sub.size()nodes_to_sub.size() + nodes_to_build.size() == valid_candidates.size() * 2gpair.Shape(1) == p_tree->NumTargets()t_gpair.Shape(0) == p_fmat->Info().num_row_/__w/ml-builds/ml-builds/xgboost/src/tree/hist/../../common/transform.hInitDatan_targets >= 1n_total_bins == page.cut.TotalBins()base_rowid == gmat.base_rowidgidx > -1any_missing == trueLeafPartitionCheck failed: hist_param_.GetInitialised()Check failed: out.Contiguous()param.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.FbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost11TreeUpdaterEN7xgboost4tree19GlobalApproxUpdaterE*N7xgboost4treeUlPKNS_7ContextEPKNS_7ObjInfoEE_EPFbRKN7xgboost4tree14CPUExpandEntryES3_EN7xgboost6common6ColumnIhEEN7xgboost6common15DenseColumnIterIhLb1EEEN7xgboost6common16SparseColumnIterIhEEN7xgboost6common6ColumnItEEN7xgboost6common15DenseColumnIterItLb1EEEN7xgboost6common16SparseColumnIterItEEN7xgboost6common6ColumnIjEEN7xgboost6common15DenseColumnIterIjLb1EEEN7xgboost6common16SparseColumnIterIjEESt23_Sp_counted_ptr_inplaceIN7xgboost6common13ColumnSamplerESaIS2_ELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN7xgboost6common16PartitionBuilderILm2048EE9BlockInfoELN9__gnu_cxx12_Lock_policyE2EEGlobalApproxUpdaUpdatePredictionhist_train_paramctx_ridx < position_.size()ridx exceed bound ridx= pos=opt_dense_coldefault_directionlearnrightEXP Param: speed optimization for dense column.Default direction choice when encountering a missing valuefmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.param_.max_depth > 0exact tree method doesn't support unlimited depth./__w/ml-builds/ml-builds/xgboost/src/tree/../common/transform.hUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training./__w/ml-builds/ml-builds/xgboost/src/tree/split_evaluator.hN7xgboost4tree8ColMaker7BuilderEN7xgboost4tree8ColMakerE*N7xgboost4treeUlPKNS_7ContextET_E_Ecolmaker_train_pPruner that prune the tree according to statistics./__w/ml-builds/ml-builds/xgboost/src/tree/updater_prune.ccCheck failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()nid >= 1tree pruning end, extra nodes, pruned nodes, max_depth=N7xgboost4tree10TreePrunerE*N7xgboost4treeUlPKNS_7ContextEPKNS_7ObjInfoEE_E/src/tree/updateGrow tree using quantized histogram.Check failed: p_tree->IsMultiTarget()left_child > candidate.nidright_child > candidate.nidstats_.Shape(1) == n_targetsUpdatePredictionCache/__w/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccout_preds.Size() == data->Info().num_row_ * p_last_tree_->NumTargets()hist_train_paramroot_sum_tloc.Shape(1) == gpair.Shape(1)Check failed: root_sum.CContiguous()row_ptr.size() >= 2EvaluateSplitsUpdateTreeUpdatePositionBuildHistogramp_tree->LeftChild(candidate.nid) > candidate.nidCheck failed: param->monotone_constraints.empty()monotone constraintMultiTargetHistBuilderHistUpdaterFbRKN7xgboost4tree16MultiExpandEntryES3_EN7xgboost4tree17QuantileHistMakerE*N7xgboost4treeUlPKNS_7ContextEPKNS_7ObjInfoEE_EPFbRKN7xgboost4tree16MultiExpandEntryES3_EN7xgboost6common15DenseColumnIterIhLb0EEEN7xgboost6common15DenseColumnIterItLb0EEEN7xgboost6common15DenseColumnIterIjLb0EEErefresh/__w/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.N7xgboost4tree13TreeRefresherE*N7xgboost4treeUlPKNS_7ContextET_E_EsyncSyncher that synchronize the tree in all distributed nodes.N7xgboost4tree11TreeSyncherE*N7xgboost4treeUlPKNS_7ContextET_E_E/__w/ml-builds/ml-builds/xgboost/rabit/src/allreduce_base.ccrabit_reduce_ring_mincount should be greater than 0invalid format for %s,shhould be {integer}{unit}, unit can be {B, KB, MB, GB}rabit_timeout_sec should be non negative second Retry connecting to IP(retry time: tracker.SendAll(&magic, sizeof(magic)) == sizeof(magic)tracker.RecvAll(&magic, sizeof(magic)) == sizeof(magic)sync::Invalid tracker message, init failuretracker.Send(xgboost::StringView{task_id}) == task_id.size()must keep rank to same if the node already have onetracker got overwhelmed and not able to assign correct rankbind(handle_, handle, sizeof(std::remove_reference_t))getsockname(handle_, reinterpret_cast(&res_addr), &addrlen)ReConnectLink fail to bind the ports specifiedgetsockopt(handle_, SOL_SOCKET, SO_ERROR, reinterpret_cast(&error), &len)ReConnectLink failure, link rank inconsistentOverride a link that is activesetsockopt(handle_, SOL_SOCKET, SO_KEEPALIVE, reinterpret_cast(&keepalive), sizeof(keepalive))setsockopt(handle_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&tcp_no_delay), sizeof(tcp_no_delay))cannot 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 workergethostname(&buf[0], HOST_NAME_MAX)too large type_nbytes=%lu, buffer_size=%luReadToRingBuffer: buffer not allocatedReadToRingBuffer: max_size_read checkBroadcast: root should be smaller than world sizerabit_tracker_urirabit_tracker_portrabit_task_idDMLC_TRACKER_URIDMLC_TRACKER_PORTDMLC_TASK_IDDMLC_ROLErabit_world_sizerabit_hadoop_moderabit_tree_reduce_minsizerabit_reduce_ring_mincountrabit_reduce_buffer%lu%cinvalid format for %sDMLC_WORKER_CONNECT_RETRYrabit_timeoutrabit_timeout_secrabit_enable_tcp_no_delayConnecting to (failed): [] ReConnectLink failure 3shutdownFailed to shutdown due toworker256MBDMLC_NUM_ATTEMPTtask connected to the trackerReConnectLink failure 4 got new rank listen(handle_, backlog)ReConnectLink failure 5ReConnectLink failure 6ReConnectLink failure 7ReConnectLink failure 8ReConnectLink failure 9ReConnectLink failure 10ReConnectLink failure 12ReConnectLink failure 13ReConnectLink failure 14acceptReConnectLink failure 15ReConnectLink: bad socketfcntlfailed in ReconnectLink mapred_tip_id%[^=]=%smapreduce_task_idmapred_task_idrabit_num_trialmapred_map_tasksmapreduce_job_mapscan only call Init oncestartneed to assume rank structurewrite ptr boundary checkAllreduce: boundary check[%d] read_ptr boundary checkbuffer size inconsistentmust assign buffer_sizeAllreduce: size checkAllreduce: boundary errorPoll timeoutFailed to poll./__w/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hAllgatherRing failedBroadcast failedAllreduce failedN5rabit6engine7IEngineEN5rabit6engine13AllreduceBaseE/rabit/src/allreDMLC_NUM_ATTEMPTDMLC_TRACKER_URIDMLC_TRACKER_PORDMLC_WORKER_CONNFailed to shutdown RABIT worker.Failed to initialize RABIT.unknown data_typeunknown enum_oppp00X`hpHPX8(HxHx(XDataType does not support bitwise AND operation8hDataType does not support bitwise OR operation ( X DataType does not support bitwise XOR operation !H!x!!! the rabit has not been initialized/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hLibSVMParserParamLibFMParserParamCSVParserParamCheck failed: TryLoadCache()failed to build cache file finish reading at .split.partname_cache.size() == 1Uname_args.size() == 1U/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.honly one `#` is allowed in file path for cachefile specificationlibsvmUnknown data type /__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data.cc/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: out_data_ != NULLCalling Value at beginning or end?/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./././strtonum.hCheck failed: base <= 10 && base >= 2sign == true/__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/././parameter.hFile formatindexing_modeIf >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.libfmIf >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.csvFile format.label_columndelimiterweight_columnColumn index (0-based) that will put into label.Delimiter used in the csv file.Column index that will put into instance weights./__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hlabel.size() + 1 == offset.size()Check failed: offset.back() == value.size() || value.size() == 0offset.back() == index.size()No threadparam_.format == "csv"/__w/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: 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) != kDestroy/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"Check 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))Bad RowBlock formatCheck failed: fi->Read(&qid)MB read,finish reading at %g MB/sec/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)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)Delimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->label.size() == 0 || out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()Check failed: out->field.size() == out->index.size()Check failed: out->label.size() + 1 == out->offset.size()qid:/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/data/././text_parser.hchunk.size != 0UNSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImlE8FillDataEPSt6vectorINS4_17RowBlockContainerImlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjlE8FillDataEPSt6vectorINS4_17RowBlockContainerIjlEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImiE8FillDataEPSt6vectorINS4_17RowBlockContainerImiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjiE8FillDataEPSt6vectorINS4_17RowBlockContainerIjiEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseImfE8FillDataEPSt6vectorINS4_17RowBlockContainerImfEESaIS9_EEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc4data14TextParserBaseIjfE8FillDataEPSt6vectorINS4_17RowBlockContainerIjfEESaIS9_EEEUlvE_EEEEEEN4dmlc12ScopedThreadEN4dmlc8DataIterINS_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_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlPPSt6vectorINS0_17RowBlockContainerImfEESaIS8_EEE_ZN4dmlc4data14ThreadedParserImfEC4EPNS0_10ParserImplImfEEEUlvE0_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjfEEE_ZN4dmlc4data11DiskRowIterIjfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImfEEE_ZN4dmlc4data11DiskRowIterImfE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjiEEE_ZN4dmlc4data11DiskRowIterIjiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImiEEE_ZN4dmlc4data11DiskRowIterImiE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerIjlEEE_ZN4dmlc4data11DiskRowIterIjlE12TryLoadCacheEvEUlvE0_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlPPNS0_17RowBlockContainerImlEEE_ZN4dmlc4data11DiskRowIterImlE12TryLoadCacheEvEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerIjfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterISt6vectorINS3_4data17RowBlockContainerImfEESaIS8_EEE4InitESt8functionIFbPPSA_EESC_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImfEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImiEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerIjlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEENSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_4data17RowBlockContainerImlEEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEEhdfs://viewfs://s3://http://https://azure://unknown filesystem protocol stdinCheck failed: fp_ != NULLCheck failed: part < nsplitindexed_recordiounknown input split type rbBeforeFirst is not supported/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hResetPartition is not supported in CachedInputSplit/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: part_index == 0 && num_parts == 1InputSplit do not support write/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfsPlease compile with DMLC_USE_S3=1 to use S3Please compile with DMLC_USE_AZURE=1 to use AzureSingleFileSplit: fail to open invalid input parameter for InputSplit::Createneed to pass index file to use IndexedRecordIOCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeCheck failed: this->InitCachedIter()Failed to initialize CachedIterFvvEN4dmlc10InputSplitEN4dmlc2io15SingleFileSplitEN4dmlc8DataIterINS_2io14InputSplitBase5ChunkEEEN4dmlc12ThreadedIterINS_2io14InputSplitBase5ChunkEEEN4dmlc2io16CachedInputSplitEN4dmlc2io18ThreadedInputSplitEZN4dmlc2io16CachedInputSplit15InitPreprocIterEvEUlPPNS0_14InputSplitBase5ChunkEE_PFvvEZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlPPNS0_14InputSplitBase5ChunkEE_ZN4dmlc2io16CachedInputSplit14InitCachedIterEvEUlvE0_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlPPNS2_5ChunkEE_ZN4dmlc2io18ThreadedInputSplitC4EPNS0_14InputSplitBaseEmEUlvE0_NSt6thread11_State_implINS_8_InvokerISt5tupleIJZN4dmlc12ThreadedIterINS3_2io14InputSplitBase5ChunkEE4InitESt8functionIFbPPS7_EES9_IFvvEEEUlvE_EEEEEE /__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != endN4dmlc2io12LineSplitterE/__w/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::kMagicinvalid record io formatCheck failed: p >= pbegin + 2Invalid RecordIO FormatCheck failed: cflag == 1UN4dmlc2io16RecordIOSplitterE/__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccIndexedRecordIOSplitter does not support multiple index filesexpanded_list.size() == 1ulN4dmlc2io23IndexedRecordIOSplitterE/__w/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 bytesN4dmlc2io14InputSplitBaseE error: LocalFileSystem.GetPathInfo: stdoutCheck failed: allow_null LocalFileSystem::Open "": ../__w/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink LocalFileSystem.ListDirectory Check failed: !std::fseek(fp_, static_cast(pos), SEEK_SET)Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteN4dmlc2io15LocalFileSystemEN4dmlc2io10FileStreamElstat(path.c_str(), &sb) == 0Couldn't remove file ~TemporaryDirectory(): /__w/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hdmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/__w/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;t Xppdp0rstuwwhx7zm{|,}|~x h҄텍 t I 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