text:
gateway/wallet cannot retrieve all objects owned by address

Observe the following session (this is on a freshly-genesised devnet):

I list all objects owned by the target address
I query an object that was not listed
That object reveals that it is owned by the address I specified in step 1.
I repeat step 1 and now the missing object is listed.

;  RUST_LOG="trace" SUI_CONFIG_DIR=/$HOME/.sui/devnet_config ./target/debug/wallet objects --address 813ec1ab1e1797c79d1e1fcbebebc27a1fd21f07
Showing 3 results.
(39A8E4EE9A566364BE5455F827B906367B793529, SequenceNumber(0), o#62fb90b41715b42beaa10c0b3906199d49ba583f649d673106e3d7dbbbfacf11)
(59931DCAC57BA20D75321ACAF55E8EB5A2C47E9E, SequenceNumber(0), o#8a7449c6a6b44ba23662a6326e14e796f7d7d3e1d3b55515b85036369feaab41)
(7AA4C8173D7C02916CC1FB8E71588628C8FCF735, SequenceNumber(1), o#5968d28e78f6cb3558bd95b3270d11381193d995afa87d0c1256e3986c9cc695)
[ marklogan@MarksMynMacbook ( 3:51PM) ~/dev/sui 51ccd424 ]
;  RUST_LOG="trace" SUI_CONFIG_DIR=/$HOME/.sui/devnet_config ./target/debug/wallet object --id c11f3c55cd1040e6fb3726298e82610394b0d0b8
Owner: AddressOwner(k#813ec1ab1e1797c79d1e1fcbebebc27a1fd21f07)
Version: 0
ID: C11F3C55CD1040E6FB3726298E82610394B0D0B8
Readonly: false
Type: 0x2::Coin::Coin<0x2::SUI::SUI>
[ marklogan@MarksMynMacbook ( 3:52PM) ~/dev/sui 51ccd424 ]
;  RUST_LOG="trace" SUI_CONFIG_DIR=/$HOME/.sui/devnet_config ./target/debug/wallet objects --address 813ec1ab1e1797c79d1e1fcbebebc27a1fd21f07
Showing 4 results.
(39A8E4EE9A566364BE5455F827B906367B793529, SequenceNumber(0), o#62fb90b41715b42beaa10c0b3906199d49ba583f649d673106e3d7dbbbfacf11)
(59931DCAC57BA20D75321ACAF55E8EB5A2C47E9E, SequenceNumber(0), o#8a7449c6a6b44ba23662a6326e14e796f7d7d3e1d3b55515b85036369feaab41)
(7AA4C8173D7C02916CC1FB8E71588628C8FCF735, SequenceNumber(1), o#5968d28e78f6cb3558bd95b3270d11381193d995afa87d0c1256e3986c9cc695)
(C11F3C55CD1040E6FB3726298E82610394B0D0B8, SequenceNumber(0), o#788c4ea8f2db93c2d68e5fc46d65a7e11f3b5656917f7800fd970b3b2fe595d8)

Possibly relevant: the owning address above is the faucet service, it owns 100 objects. The other genesis addresses own 5 objects each, and listing those works fine.

It works for me
➜ SUI_CONFIG_DIR=$HOME/.sui/devnet_config wallet gas
                Object ID                 |  Version   |  Gas Value
----------------------------------------------------------------------
 003E1D8B91A4CB1538CDB50C3CAE8E62E1960B1C |     0      | 18446744073709551615
 04AC8CC18CEDFF8D0D92800AA0B93CA3598CF35E |     0      | 18446744073709551615
 064E3A82A0F1FB2CC26C89C2C671F23D48CE5EEB |     0      | 18446744073709551615
 077F682D84A0406A21F20034BCA4C74DE8AD5827 |     0      | 18446744073709551615
 0A3051A9FE0B7B2A0A5B9C35497697719E1F17A9 |     0      | 18446744073709551615
 0DCE9190D54CDE842D39537BF94EFE128181B8A6 |     0      | 18446744073709551615
 0F5318719EBE45231204B4D239AF99633FB0CCDB |     0      | 18446744073709551615
 103EDCD1D60EAC5359C3696E3130051D5741036B |     0      | 18446744073709551615
 110DDD927E825BF50EEC53E9A6692C43669317FC |     0      | 18446744073709551615
 11E9321FE959C6C2622E8BFE0AD8276B54BD6C8B |     0      | 18446744073709551615
 1280285CF466720D895280A30B85E66D4F9D3C88 |     0      | 18446744073709551615
 134D4714B853D2BEE670DD3F4012AEF074E493FF |     0      | 18446744073709551615
 152042214BAAF88722BC35676DA6A61A9BA1788C |     0      | 18446744073709551615
 19FE2164DC234D4F50CE20713CB26BE859E0E966 |     0      | 18446744073709551615
 1C5A26F0E946D665209389CF6F860FD6B26A030F |     0      | 18446744073709551615
 1E8C0800CB3E5D532BEE14E8AA28F940827121BB |     0      | 18446744073709551615
 1EBEA782AA9C070B79EE03637CC73F587BA83B5E |     0      | 18446744073709551615
 1F614CFF8953BFC1196AF08B09DC4C44A5C63BC8 |     0      | 18446744073709551615
 1FA80479AE228D6D344459741C042FA14EFA35A6 |     0      | 18446744073709551615
 21D71E077F5EDCCADCD45A27DF74702C98650EBB |     0      | 18446744073709551615
 28EC9E04CBB5F6E5A4CD3E03AFD3C6845AA4B257 |     0      | 18446744073709551615
 28FE58F2BD3E878EED420302F31A8EBB788F72DE |     0      | 18446744073709551615
 294F47C25D6898157EA545BACAA6A47A95006033 |     0      | 18446744073709551615
 2E84971FFFF75E2C0136B9AA8BBDA3819CB97F9D |     0      | 18446744073709551615
 2F3124668B2AF4593669569314B258C969ACA44B |     0      | 18446744073709551615
 2F940978EDDC5921EB93874EE5F0499707E94CD9 |     0      | 18446744073709551615
 39A8E4EE9A566364BE5455F827B906367B793529 |     0      | 18446744073709551615
 3EEB892F4CED5D535802F08BC927DCE84E5759C1 |     0      | 18446744073709551615
 3FEBBB3E2614C2938EB66EE614EAF7E221C0AD22 |     0      | 18446744073709551615
 43022DD9108168C5F8E2220E9DDBF819924C5662 |     0      | 18446744073709551615
 43CD135E11F48D0AAE1421C12F81094140F85118 |     0      | 18446744073709551615
 474546BEFF5DC67454FEB673DA44175E47813849 |     0      | 18446744073709551615
 47F81B764B6B3FC007F9D5FE2B197B6ECA5E5165 |     0      | 18446744073709551615
 4F5561EC6F4DEF25096222E1DE344D4FBDFF0A4F |     0      | 18446744073709551615
 50FE28A5DBD008B71AB4CE35733B21E4594342BD |     0      | 18446744073709551615
 526D948B5705F31C5D59E15B09E67700425D0C08 |     0      | 18446744073709551615
 54EA4729C216C79738651A4902F38B014A555519 |     0      | 18446744073709551615
 57917DD603D307F84F9DBCAD3914F8E4BB7BACD3 |     0      | 18446744073709551615
 57D845215413343B1FB4F66767543D26E5191675 |     0      | 18446744073709551615
 59931DCAC57BA20D75321ACAF55E8EB5A2C47E9E |     0      | 18446744073709551615
 5B2260FF9982CD7408CC8F0F0C95BF7A4FD1E30B |     0      | 18446744073709551615
 5EA9742F988C2323AFB977EB9B250F1F053D7CF9 |     0      | 18446744073709551615
 5F1C673A1AF106C340CE86D751C5F60A3A35A0E1 |     0      | 18446744073709551615
 5F80AD72C9F25B7C9F05CA2426F833837B1AF532 |     0      | 18446744073709551615
 6E2619E72D363EF7DF5E5A8F26035B665342E18C |     0      | 18446744073709551615
 75AD157B5004051C37878FE1409582C54D259C2B |     0      | 18446744073709551615
 76E4B9656C39EDE6BF5041CD9D48268D8E45E547 |     0      | 18446744073709551615
 784E8C981A3A83E7E72B9E65B5B13F1BE0D0A107 |     0      | 18446744073709551615
 78C5199EF393E53532126BB26C8A5793740A5774 |     0      | 18446744073709551615
 796E5E05AADBE097F0B37476AED4CA8F07B155E3 |     0      | 18446744073709551615
 79CF13310F732EBA242E65BA6B02142921D5A6CE |     0      | 18446744073709551615
 7AA4C8173D7C02916CC1FB8E71588628C8FCF735 |     1      |   100000
 8274AD42DF2242CE95E05F2A864F99EDBCE0C230 |     0      | 18446744073709551615
 82D7B0CF0A02548C2F3F9B176D241883AA5A7C53 |     0      | 18446744073709551615
 8690AC5EA62081C2BC724E21394DE448CB39F613 |     0      | 18446744073709551615
 889276B1771C6F15EA4A125438DA0002B323476A |     0      | 18446744073709551615
 8C869CE3233D200D42B03E708B97398D11B35799 |     0      | 18446744073709551615
 8E3D2F721509D41A5C06B7C55C1897270C0ACCEF |     0      | 18446744073709551615
 8E76F1B65D4874753615E866AA112C70D0AC2234 |     0      | 18446744073709551615
 8E87B191E6C028C9C6A260556DE9231D655AD821 |     0      | 18446744073709551615
 9346C51D2D5EA430697F32491EB837A908971303 |     0      | 18446744073709551615
 962F58DC6C53584921C97C4430F2B6E38CE265E1 |     0      | 18446744073709551615
 969E38BEFFA7C01D364196A11AB4BE18EBB47FD7 |     0      | 18446744073709551615
 9AFB0F130C683D94D527DE92C4D1D6C4230AB918 |     0      | 18446744073709551615
 9E465BA1B0D79E0698C029EF777BE9B317270830 |     0      | 18446744073709551615
 9E5DCAAD3B98DA56D5C5876FB2D6382C29D62D2A |     0      | 18446744073709551615
 9EBA6E93A619EF3862D3294054B5E73DBF489135 |     0      | 18446744073709551615
 A3C4692A42DB800608103A87E542B23AE05700A7 |     0      | 18446744073709551615
 A8F532E7ACBB3E7D60CE06A9EDF8E0FC4E37A3A4 |     0      | 18446744073709551615
 AF132F5D2CD060E109A5D37EA4CDEC25216C6D42 |     0      | 18446744073709551615
 AFCE1599DEEA13A7E2569E7B1672B86F0BA0CD8C |     0      | 18446744073709551615
 B3B0766B161AFD574B3A35794338E1359BFEF1DE |     0      | 18446744073709551615
 B60AB3A23E23F8F25E04496857E0E56865B45800 |     0      | 18446744073709551615
 BBD2BF59742EC72F3F2A3B963A276CFBE54B659E |     0      | 18446744073709551615
 BF2FEDFCAF56BFD71834952C881E9855B9B545A4 |     0      | 18446744073709551615
 BFFF8B1D60390633E4C578B63357000156F3F358 |     0      | 18446744073709551615
 C05C6D9808856BF0F9C1E061023A65109E16144B |     0      | 18446744073709551615
 C11F3C55CD1040E6FB3726298E82610394B0D0B8 |     0      | 18446744073709551615
 C6AAF34AFB86A0B2FC2B30C93327BF5928CF0F24 |     0      | 18446744073709551615
 CDE6AE8072987CD604B556621CD124184AF88D39 |     0      | 18446744073709551615
 CE8B8CF6231AB8C64BAF35E9F8ACBB1C7F613B4F |     0      | 18446744073709551615
 D11C08DCDF87BFB4A76E9773DEE6973D3965B8B8 |     0      | 18446744073709551615
 D3FA788FBA5AAE07FD8069C467E7EAD032C62C00 |     0      | 18446744073709551615
 D8D6D89FCA7B99A26D7C97DB34314CD5AD24487D |     0      | 18446744073709551615
 D8F7FFEC361EE83A9F833E07F73F8C9A1BC63301 |     0      | 18446744073709551615
 DA287B1B745BB522F0DF865C6C4DBEE2B6335826 |     0      | 18446744073709551615
 DB9CCD40F91801F693B2BE5BB8AF0680C8967D25 |     0      | 18446744073709551615
 DC727C13AD3AEBBBE916A752189DB5F1AD9EA139 |     0      | 18446744073709551615
 DF1B361E3D1D86A6E472B69C401C0C0E1A15326D |     0      | 18446744073709551615
 E16CF070A1761649849E5B9BEE24108043B45995 |     0      | 18446744073709551615
 E67B3C1C9A763A63BA4F5BD40F0B2A973204E6DF |     0      | 18446744073709551615
 E7D50AA63733D21CECBF9A7CF89F9ADB7212A3DA |     0      | 18446744073709551615
 EB89227A45AB191C6D2D4BD68CA55455BC80E742 |     0      | 18446744073709551615
 EBDF1FA9FACF9229B75B367ADE8F1633625F4A38 |     0      | 18446744073709551615
 ED306A843097E6C887E429A037B1B8824F4688BB |     0      | 18446744073709551615
 EF3BF894329B533495A36E6BF4FC33E195FBA2CD |     0      | 18446744073709551615
 F217F564369714819363B2E9E612FD70860C2428 |     0      | 18446744073709551615
 FA8AD56E2492D85B21AA45D9C003A1B13ACF1ECB |     0      | 18446744073709551615
 FD66C3A98AE36F74B4741FCBCF8E80CF939B05B8 |     0      | 18446744073709551615
 FD6BD9290F69879DB94ED73EFB6B0C805A6CA961 |     0      | 18446744073709551615
 FDD602DDCC6076A8881022ADEDC768A81750AD46 |     0      | 18446744073709551615

devnet on  main [⇣?] using ☁  default/sui-devnet took 3s
➜ SUI_CONFIG_DIR=$HOME/.sui/devnet_config wallet objects --address 813ec1ab1e1797c79d1e1fcbebebc27a1fd21f07
Showing 103 results.
(003E1D8B91A4CB1538CDB50C3CAE8E62E1960B1C, SequenceNumber(0), o#1c64b165a681172455496832bf3c57c3d5bb92ab16790095ae93b599d447ddbb)
(04AC8CC18CEDFF8D0D92800AA0B93CA3598CF35E, SequenceNumber(0), o#0412a2b031d0a907053547ffca17822f7d83ae5bc90db1ccda2dfbca8548641f)
(064E3A82A0F1FB2CC26C89C2C671F23D48CE5EEB, SequenceNumber(0), o#f5f951b7e8d5445d8bf09bd9363d591b9feb7d241ba2c7640e1414960af992fe)
(077F682D84A0406A21F20034BCA4C74DE8AD5827, SequenceNumber(0), o#4a63548aa00673600d21cb81cb2f2fb85b9637e99919aab9069c7ca593e21078)
(0A3051A9FE0B7B2A0A5B9C35497697719E1F17A9, SequenceNumber(0), o#0fb35978632aee5cf9de134a0a8a0c00b0bb07937597bb0b09a1ddca4e82e43c)
(0DCE9190D54CDE842D39537BF94EFE128181B8A6, SequenceNumber(0), o#fb54ca96a10546b90bac319f930abaa3cd24faa91a32099baf181b4cd405b77e)
(0F5318719EBE45231204B4D239AF99633FB0CCDB, SequenceNumber(0), o#bc737e04570c91ed6bad27513aa8aa0d9d2f855f26e62661d951c20fcc8a71f3)
(103EDCD1D60EAC5359C3696E3130051D5741036B, SequenceNumber(0), o#be25ecf94f92eb7adb714250b87d28008cae78ddc62a421dd97515ea4e2ad122)
(110DDD927E825BF50EEC53E9A6692C43669317FC, SequenceNumber(0), o#a344aafb738089de2df69bbf9224c0649f640ee4c6cc806436fe8ce6127d6af8)
(11E9321FE959C6C2622E8BFE0AD8276B54BD6C8B, SequenceNumber(0), o#9019f646284840eda23fe3560b4adab83d59afe71fc869737ee54969d2d64b61)
(1280285CF466720D895280A30B85E66D4F9D3C88, SequenceNumber(0), o#94f6c6894ba91036899e7e55e091c67b615ae2ce01efc0d5b70230475bdc8037)
(134D4714B853D2BEE670DD3F4012AEF074E493FF, SequenceNumber(0), o#4e951dc00a496e80775cf3606d243440a2bac24767dcdf6a0b030c0631b9ad24)
(152042214BAAF88722BC35676DA6A61A9BA1788C, SequenceNumber(0), o#e4ed0b279d83da6d43ecb06e1fb00354f2d194e9c866a588b434895802f70448)
(19FE2164DC234D4F50CE20713CB26BE859E0E966, SequenceNumber(0), o#c960457093147f56d86cf2b40ace0c0a88ef8747e9e8d8df4a88a41101562bbe)
(1C5A26F0E946D665209389CF6F860FD6B26A030F, SequenceNumber(0), o#5f85a76e29f4ce0401998401f69a6f403c2c388d358ec990d430102699f3875d)
(1E8C0800CB3E5D532BEE14E8AA28F940827121BB, SequenceNumber(0), o#566f941891c5e4b88e92bee9b9de99e38c46363297aff670bde4ddbc8ce69fb2)
(1EBEA782AA9C070B79EE03637CC73F587BA83B5E, SequenceNumber(0), o#a7ea186b800e10143fe4cae187451a9a61324becbd29fc1b961bdccdeacd0b20)
(1F614CFF8953BFC1196AF08B09DC4C44A5C63BC8, SequenceNumber(0), o#291463b9175029581b57cb8eea0450a84583d9ef8bf3574ca2e426065d4c0696)
(1FA80479AE228D6D344459741C042FA14EFA35A6, SequenceNumber(0), o#39c141c0b282652f169874918f08fe8a56decc7a15467864513b9792a67952c7)
(21D71E077F5EDCCADCD45A27DF74702C98650EBB, SequenceNumber(0), o#649b3c88552fe06588b9f3cc8464089936c2002d8e958494a656037c5b58c7d0)
(28EC9E04CBB5F6E5A4CD3E03AFD3C6845AA4B257, SequenceNumber(0), o#8b19b47a67f416b860409e510be9ab9ce2b131376d80ddbf8460db0caa8ba18a)
(28FE58F2BD3E878EED420302F31A8EBB788F72DE, SequenceNumber(0), o#cbebb1f06fa81d4105a07b3f8f40ec24ca5469857cc077e87b5573c99c6cfae2)
(294F47C25D6898157EA545BACAA6A47A95006033, SequenceNumber(0), o#1ceea57e8f83054458b8d6cf3f437589cef7c863873bbe39539ac9b7b6cb6f3d)
(2E84971FFFF75E2C0136B9AA8BBDA3819CB97F9D, SequenceNumber(0), o#5b41408c762c31d7a966962f2c3da2d6043d84d528f44ff287c3d24235e4a88e)
(2F3124668B2AF4593669569314B258C969ACA44B, SequenceNumber(0), o#bb8bfc395f355a24ad1297c2efb1252e007e0dcb5c7973916dba8c78921bac78)
(2F940978EDDC5921EB93874EE5F0499707E94CD9, SequenceNumber(0), o#b6f697a09cffd00f31563a5dfb51146376fc6d0fffcb58d1ae9ea05125441458)
(39A8E4EE9A566364BE5455F827B906367B793529, SequenceNumber(0), o#62fb90b41715b42beaa10c0b3906199d49ba583f649d673106e3d7dbbbfacf11)
(3EEB892F4CED5D535802F08BC927DCE84E5759C1, SequenceNumber(0), o#eeae4e602ff99ccdc7b70ed88106c211751bcd3bb0cb86ffa2af4bbe66b577d7)
(3FEBBB3E2614C2938EB66EE614EAF7E221C0AD22, SequenceNumber(0), o#eaddd8050c57c35856006a255e2afa80bd352f3ac4d125bc954bb785edc2613e)
(43022DD9108168C5F8E2220E9DDBF819924C5662, SequenceNumber(0), o#cb661d9c8b2e051ac5bf58c2982495b3e7c6479bcbc9f22a83e937cb678e03c8)
(43CD135E11F48D0AAE1421C12F81094140F85118, SequenceNumber(0), o#41aeaed132c4b88fadc5c6ac8a9a4820d0da1a9ff8b27874b356318800e728ed)
(474546BEFF5DC67454FEB673DA44175E47813849, SequenceNumber(0), o#a80802250b37f7962e4601c97409c6b99e7802699841590b3bfdef58ae189ce9)
(47F81B764B6B3FC007F9D5FE2B197B6ECA5E5165, SequenceNumber(0), o#697b815ebf05ae155d3d8d61760883d39b323545f04cde18a2394534133c3fa5)
(4F5561EC6F4DEF25096222E1DE344D4FBDFF0A4F, SequenceNumber(0), o#1be2cde165efbd2e524871f409c51fbfab61931a0bcf728c5b5d6c2d78f27fbb)
(50FE28A5DBD008B71AB4CE35733B21E4594342BD, SequenceNumber(0), o#1fc9f5719ae1b7e96135117e314624f460296d1fa0a162cabe65e3041841950a)
(526D948B5705F31C5D59E15B09E67700425D0C08, SequenceNumber(0), o#c54022debab05ed1056c284bd911b1bcfe5f8438d4fa670176c2c70396be2c33)
(54EA4729C216C79738651A4902F38B014A555519, SequenceNumber(0), o#49f9bf25e7216ff4941e196c9ac93959555b0123d3cd498fd761ae834f7cc1be)
(57917DD603D307F84F9DBCAD3914F8E4BB7BACD3, SequenceNumber(0), o#4b4001d3231868cd577822320ec73b5cc3eb5de2362e929ee0be6756cd9bf184)
(57D845215413343B1FB4F66767543D26E5191675, SequenceNumber(0), o#98181bb2e49c85f44edb722102c3ef2b2b80608bc166a593135b04a1b11cdaee)
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also works for me, now. Hurray, a heisenbug.

This seems to have resolved itself (probably out of sync versions of client / infra?)

authors: 666lcz gdanezis mystenmark


text:
[pruner] disable active rocksdb iterator for normal flow

it'd still be the case for indirect objects, but we have this feature disabled for now
For indirect objects we need a sync version of mutex table

@andll: actually I can rewrite the loop itself to avoid it for indirect objects as well. I'll do that in separate PR, meanwhile feel free to accept this one to immediately disable open iterators cross await points

Just to clarify - yes we do need to change the loop of the pruner so that it does not possibly hold iterator across await points. I am planning to make a change that will prohibit doing so during compile time (and current code throws compile error, because it might hold iterator across await point, since compiler does not know indirect objects is always empty)

oh yeah, you're right, I'll close this one. Will change the loop in a different PR

source: gharchive/pull-request

authors: andll phoenix-o

repo: MystenLabs/sui

license_type: permissive

license_source: github-api


text:
关于ssm_d_state

Thank you for your outstanding work! Setting all the model parameters ssm_d_state to 1, isn't that a bit too aggressive for state compression? It should have a significant impact on the model's performance. Could you please share your thoughts on how you decided on these parameter settings?

I set the d_state = 1 for the consideration of speed. With poorer support of hardware for selective-scan, the speed can be really a problem.
Also, in the ablation study in appendix, we found that when d_state is small, the impact of d_state on the model' performance is not that significant.

source: gharchive/issue

repo: MzeroMiko/VMamba

license: MIT


text:
syntax error

File "dmm_util.py", line 93
    match len(name):
            ^
SyntaxError: invalid syntax

What python version do you use?
3.10+ is needed.
I use 3.10.2

3.6.8 :/ - it may be a good idea to add some initial check and message

I bet many electronics engines, even they have the latest and greatest Python version, do not know how to import "serial module".
Probably after 5 minutes of Googling most most of will them will give up. I am still Googling.

I have changed the README.
It's not possible to add check, syntax one is done first.
It would be necessary to add a wrapper.
It's much simpler this way

I did not mean syntax check but Python runtime version check.

I have well understood, but this check is not possible.
Python interpreter checks syntax first.  It runs the script, and thus the test in a second time.
It never gets to that stage.

I see.


text:
Fix user count not being updated before the first 30 second tick

Just like @melonmanchan would fix it

:+1:

authors: melonmanchan tanelih

license: mit

license_source: bigquery


text:
trouble installing with conda

I am trying to install IgDiscover with conda according to the IgDiscover documentation, and have tried several versions of both conda and python, but the environment always fails to solve. Is IgDiscover still supported by conda? Is there a trick to solving the installation?
Thanks,
John

Can you be more specific? Which exact command(s) are you using and which error messages do you get?

This repository is outdated and is going to be archived. Please see the new repository at https://gitlab.com/gkhlab/igdiscover22/ or the homepage at https://www.igdiscover.se/ for the most recent and maintained IgDiscover version.

authors: john-dingus marcelm

repo: NBISweden/IgDiscover


text:
dev_1

Closes #190

@clairefio @michaelavs I'm having issues calculating the deviation from the zonal mean. I have calculated the mean using xarray, but a 1-D DataArray is returned instead of a 2-D array. I'm trying to make an array that is the same shape as the original data so I can subtract the mean from the data using an element wise operation. Do either of you know how to expand the dimension of an array so that I am left with an array where the rows are filled with the zonal mean for that longitude?

@clairefio @michaelavs I'm having issues calculating the deviation from the zonal mean. I have calculated the mean using xarray, but a 1-D DataArray is returned instead of a 2-D array. I'm trying to make an array that is the same shape as the original data so I can subtract the mean from the data using an element wise operation. Do either of you know how to expand the dimension of an array so that I am left with an array where the rows are filled with the zonal mean for that longitude?

I'm not sure if you have already found out an answer to this, but I think you can do this with numpy (np.reshape()). If the data is correct in your array, this should work as I think it's meant to just reformat the data array. However, if the data is missing the variable of interest, I don't know if this method will work out for you.

I'm not sure if you have already found out an answer to this, but I think you can do this with numpy (np.reshape()). If the data is correct in your array, this should work as I think it's meant to just reformat the data array. However, if the data is missing the variable of interest, I don't know if this method will work out for you.

@michaelavs I figured out how to do it using numpy.meshgrid but it's not very intuitive. I tried to add clarifying comments. Please let me know if you think it would be unnecessarily difficult for a novice Python user to understand.

I'm not sure if you have already found out an answer to this, but I think you can do this with numpy (np.reshape()). If the data is correct in your array, this should work as I think it's meant to just reformat the data array. However, if the data is missing the variable of interest, I don't know if this method will work out for you.

@michaelavs I figured out how to do it using numpy.meshgrid but it's not very intuitive. I tried to add clarifying comments. Please let me know if you think it would be unnecessarily difficult for a novice Python user to understand.

I think its a good description on why meshgrid is used and what its purpose is! Past that, my only comment before approval of this PR is just adding a couple of extra empty lines between calls/function and the tick marks on the colorbar are a different interval than the original. Other than that, great job so far!

@michaelavs I just made some changes. Please let me know what you think.

The NCL example seems to use only the paler, central values of the BlWhRe colormap, but I'm not sure how to get the same colors in Python. I tried using the geocat-viz function truncate_colormap but it doesn't produce the colors I'm looking for. @jukent @michaelavs @clyne have any of you used that function before, and is it the right approach to solving this problem?

The NCL example seems to use only the paler, central values of the BlWhRe colormap, but I'm not sure how to get the same colors in Python. I tried using the geocat-viz function truncate_colormap but it doesn't produce the colors I'm looking for. @jukent @michaelavs @clyne have any of you used that function before, and is it the right approach to solving this problem?

I figured it out shortly after posting this question. I was using the mininum and maximum values from the data as the minval and maxval arguments instead of using values for the percentage of the colormap I want the truncated version to start and stop at.

authors: hCraker michaelavs

repo: NCAR/GeoCAT-examples


text:
seasonal averages dimension comment

https://ncar.github.io/esds/posts/2022/xarray-groupby-vs-geocat-climatology/#the-incorrect-way-to-compute-seasonal-averages-from-monthly-data

... Notice that the result has a month dimension. ...

The result appears to have the correct season dimension, no month dimension.

Whoops that is my mistake. I will fix this typo shortly

authors: hCraker zmoon

repo: NCAR/esds


text:
Update zio-config, zio-config-magnolia, ... to 1.0.1

Updates

dev.zio:zio-config
dev.zio:zio-config-magnolia
dev.zio:zio-config-typesafe

from 1.0.0-RC29-1 to 1.0.1.
GitHub Release Notes - Version Diff
I'll automatically update this PR to resolve conflicts as long as you don't change it yourself.
If you'd like to skip this version, you can just close this PR. If you have any feedback, just mention me in the comments below.
Configure Scala Steward for your repository with a .scala-steward.conf file.
Have a fantastic day writing Scala!

Ignore future updates
Add this to your .scala-steward.conf file to ignore future updates of this dependency:
updates.ignore = [ { groupId = "dev.zio" } ]

labels: library-update, semver-pre-release

Superseded by #227.

repo: NCATS-Tangerine/cam-kp-api


text:
Feat/rnaseq counts report

added new tool to augment star counts file with fpkm, fpkm-uq, tpm, gene biotype, and chromosome columns

Changed PR base to new develop branch.

Looks good!
After this PR is merged, I will create a release branch which will publish the Dockers you should use for CWL testing.

authors: czyszCTDS tzuni

repo: NCI-GDC/gdc-rnaseq-tool


text:
Flake8 ruins everything!

Flake8 hates the code and fails all the CI when it runs. I've commented it out in the yml files just so that the unit tests will at least run, but obviously we want to get this working. Again, I'm not sure if the problem is just that because our code doesn't work yet, flake8 flags all the errors.

I think this is mainly because I made a typo at the original file as "Link with flake8" when it really had to be "Lint with flake8"! I fixed it and now it seems like it only points out an indent error, nothing else. I'm sorry I made everyone's life harder! Now I'm gonna try fixing a few error that the unit test points out and see if it works.

authors: Sol-sf kyubird

repo: NERC-DTP-Students/pk-project


text:
Elasticsearch suggestion search

This Issues related to NFDI4BIOIMAGE/search_engine#12
This website may be helpful:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-suggesters.html#completion-suggester

The suggestion box should be deleted or hidden after people have searched for something.
It would be great if when you type something into the search box, it is highlighted in the suggestion box.


text:
Locale 国际化中部分链接无效

Version
0.7.0-beta.3
Environment
浏览器版本
Reproduction link
https://github.com/NG-ZORRO/ng-zorro-antd/issues
Steps to reproduce
1.我们目前提供了英语，中文，其他语言将在后续版本中添加，所有语言包可以在 “这里” 找到。“这里”链接无效 404错误
2.如果你找不到你需要的语言包，欢迎你在 “英文语言包 ”的基础上创建一个新的语言包，并给我们 Pull Request。 “英文语言包 ”链接无效 404错误
What is expected?
链接到正确地址
What is actually happening?
链接无效 404错误
Other?

make sure you have read the correct version of doc.
https://ng.ant.design/version/0.7.x/

authors: lulushui vthinkxie

repo: NG-ZORRO/ng-zorro-antd


text:
Table Support Fixed Height

What problem does this feature solve?

场景一：一左一右放置两个表格，此时会需要两个表格对齐，目前表格无法设置最小高度，两个表格数据量不一致时，左右会无法对齐，比较难看
场景二：表格下方还有其他内容时，会由于表格高度不固定，导致下方的内容位置不固定

What does the proposed API look like?
方式一：设置为[nzScroll]="{x:'300px'}"时，表格区域高度固定为300px，不管是否里面有内容能将表格撑开到300px
方式二：增加属性[nzMinHeight]="300px"并结合 [nzScroll]="{x:'300px'}使用

覆盖.ant-table{height:300px} 即可~

后续追加，昨天使用.ant-table{height:300px}虽然解决了问题，但是仅仅是边框看起来是撑到了300px，还有很多元素需要手动去调整高度，还是希望能够考虑加上此特性，所以我重新打开了这个ISSUE~

@HsuanXyz  为何要关闭这个ISSUE?
使用样式来控制固定高度非常的麻烦，还是希望能够考虑在table中加入一个属性来方便的固定高度


text:
build(packaging:cdk): specified @angular/cdk version

close #191
PR Checklist
Please check if your PR fulfills the following requirements:

[x] The commit message follows our guidelines: https://github.com/NG-ZORRO/ng-zorro-antd/blob/master/CONTRIBUTING.md#commit
[ ] Tests for the changes have been added (for bug fixes / features)
[ ] Docs have been added / updated (for bug fixes / features)

PR Type
What kind of change does this PR introduce?

[ ] Bugfix
[ ] Feature
[ ] Code style update (formatting, local variables)
[ ] Refactoring (no functional changes, no api changes)
[x] Build related changes
[ ] CI related changes
[ ] Documentation content changes
[ ] Application (the showcase website) / infrastructure changes
[ ] Other... Please describe:

What is the current behavior?

Issue Number: N/A
What is the new behavior?
Does this PR introduce a breaking change?
[ ] Yes
[ ] No

Other information

Coverage remained the same at 40.348% when pulling a844f6722571ed427bc1c96a5b3b7007c7b08fd5 on vthinkxie:fix-cdk-version into 29a5cbfcdf1dd8935448f385bec18d2b97a85bca on NG-ZORRO:master.

authors: coveralls vthinkxie


text:
Please change click to mousedown in nz-option-item

Blur event happens before click on option
Therefore I suggest changing it to mousedown, so that option selection event('ngModelChange on nz-select') will happen before nzBlur event
PR Checklist
Please check if your PR fulfills the following requirements:

[ ] The commit message follows our guidelines: https://github.com/NG-ZORRO/ng-zorro-antd/blob/master/CONTRIBUTING.md#commit
[ ] Tests for the changes have been added (for bug fixes / features)
[ ] Docs have been added / updated (for bug fixes / features)

PR Type
What kind of change does this PR introduce?

[ ] Bugfix
[ ] Feature
[ ] Code style update (formatting, local variables)
[ ] Refactoring (no functional changes, no api changes)
[ ] Build related changes
[ ] CI related changes
[ ] Documentation content changes
[ ] Application (the showcase website) / infrastructure changes
[ ] Other... Please describe:

What is the current behavior?

Issue Number: N/A
What is the new behavior?
Does this PR introduce a breaking change?
[ ] Yes
[ ] No

Other information

outdated

authors: BohdanChernivtsi Laffery


text:
Question about links

I need to know where the links generated in docker go, when I generate them with link --goes windows for example, it is available in rssh.server/file, but in which folder is it located? Would it be inside Docker or on the machine that is hosted?

/data/cache


text:
SMTP Config - Mauro notifications

Local Mauro SMTP service setup to send notifications to users from Mauro e.g. password reset notification.
Are there any other use cases for SMTP service being setup? If not, how important is this?

Peter has said the email service is only used around user accounts - so registering users, password resets and forgotten passwords. I can't see any other use for it in Mauro.
As we are looking to setup OpenID, the only account that could be potentially affected would be any local Mauro account. In addition, seeing that SMTP currently sends password as clear text when it is reset (highlgihted by security), I would recommend that SMTP is not implemented for now.

Closing - SMTP function won't be setup. We will be using OpenID to login using Single Sign On. In addition, SMTP function stores the email (with password) that is sent to the user, and has been flagged by Cyber as a significant risk.

repo: NHSDigital/DataDictionaryPublication


text:
Document how to run action locally

By running npm dist/main.js with some input options or using act.
Derived from comment in #40

The options are passed as env vars, see https://github.com/actions/toolkit/blob/f915ace0850a6e6f3b802e9d77c59859504cd40c/packages/core/src/core.ts#L88-L89
So action can be run locally with
INPUT_MILLISECONDS=1000 INPUT_REPOSITORIES=repos.txt node dist/index.js
::debug::Waiting 1000 milliseconds ...
::debug::10:13:44 GMT+0200 (Central European Summer Time)
::debug::10:13:45 GMT+0200 (Central European Summer Time)
::set-output name=time::10:13:45 GMT+0200 (Central European Summer Time)

sudo rm -r in out
echo 'https://github.com/tortellini-tools/action' > urls.txt
echo 'https://github.com/fair-software/howfairis' >> urls.txt
INPUT_REPOSITORIES=urls.txt node dist/index.js

Documented in https://github.com/tortellini-tools/action/blob/main/README.dev.md#on-other-repositories

repo: NLeSC/licenseguard


text:
Async / buffering wrapper: Improve performance when blocking

ConcurrentRequestQueue - Improved DequeueBatch performance when blocking
Avoid activating throttle logic, when timer-thread is able to keep up. The throttle logic also affects the timer-thread, so only perform throttle-logic when timer-thread is falling behind.
And unit test for deadlock issue

Codecov Report

Merging #3416 into release/4.6.4 will increase coverage by <1%.
The diff coverage is 100%.

@@              Coverage Diff               @@
##           release/4.6.4   #3416    +/-   ##
==============================================
+ Coverage             80%     80%   +<1%
==============================================
  Files                358     358
  Lines              28373   28373
  Branches            3785    3785
==============================================
+ Hits               22750   22762    +12
+ Misses              4537    4517    -20
- Partials            1086    1094     +8

Apparently hard to make a stable unit-test with multi-threading on the build-engines. One more try.

Hrmm. Don't like how the NetCoreApp2 unit test have become very unstable. Need to be checked.

Yes please check. Unstable tests are really slowing down development and releases. Maybe we should partly mock the unit test.

It's probably now more an integration test, like most "unit tests" in NLog.

Actually think it is a real bug, that has been revealed by the optimization of the dequeue-operation for the timer-thread. But right now the appveyor-build-queue has become very long, so it will have to look at it another day.

Maybe it's easier to run only on your appveyor account for this? Then you could stop/restart it yourself

Think I have found the bug. After we have changed so blocking decrement/increments, then it means that timer-thread no longer automatically reschedules itself because it will not "see" producers by non-zero _count.
It is important now that the producers that wake up will remember to start the consumer-timer-thread, if needed.
Two producers that are blocked will get into TrySpinWaitForLowerCount() and it only performs Interlocked.Read. This means two producers will return with currentCount == 2. But one of them should have returned with currentCount = 1.
This causes both producers to say that they don't need to start the consumer-timer-thread

makes sense!
Thanks for figuring out and fixing this!

authors: 304NotModified codecov-io snakefoot

repo: NLog/NLog


text:
Refactor of module responsible for reading-in gridded inputs from NetCDF file

PURPOSE
The purpose of this PR is to refactor the Noah-OM (gridded version) module that handles tasks associated with reading-in gridded model inputs from a NetCDF file. The previous iteration of this module assumed that each gridded input variable (e.g., soils, slope, etc.) would be provided in a separate input NetCDF file and had one subroutine to read each specific file. Instead, this PR assumes that all gridded input variables are provided in a single NetCDF file and implements a generic type-bound ReadVar subroutine that will be called for all gridded input variables.
ADDITIONS
The NetCDFVarsType module was added and contains the following type definitions:

netcdfvars_type, which is a container for all gridded input variables.
netcdf2dvar_type, which is meant as a generic type with a name attribute
netcdf2dvarINT_type, which extends the netcdf2dvar_type and has the attribute data, which is an allocatable array of integer values
netcdf2dvarREAL_type, which extends the netcdf2dvar_type and has the attribute data, which is an allocatable array of real values
netcdf2dmetadata_type, which is meant to hold all metadata associated with the NetCDF input file and all variables that are meant to be read from the file. A single instance of this type is given as an attribute for netcdfvars_type

The netcdfvars_type has one instance of netcdf2dvar_type (either  netcdf2dvarINT_type or netcdf2dvarREAL_type) for each gridded input variable (e.g., soils, slope).  Each instance of netcdf2dvar_type is given as an argument when calling netcdfvars_type%ReadVar. ReadVar is overloaded based on the argument instance of netcdf2dvar_type being either netcdf2dvarINT_type or netcdf2dvarREAL_type.
This PR adds a new dummy NetCDF input file (/data/netcdf_input.nc) to the repo. This PR also adds a py script (/test/analysis/create_dummy_netcdf_singlefile.py) to create the dummy NetCDF file. The dummy NetCDF file depicts cell (1,1) as having input values equivalent to the values within namelist.input of the main branch of this repo -- thus cell (1,1) should have the same model outputs as the column model (assuming use of same namelist.inpu file).
CHANGES
This PR assumes that all gridded inputs will be given to the model in a single NetCDF file and the path/name of this file must be provided via the namelist.input file. The names of all required variables, dimensions, and attributes within the single NetCDF file are planned to be read-into the model via namelist.input but they are currently hardcoded until I get feedback regarding this PR.
The netcdfvars_type replaces the now removed gridinfo_type and is therefore passed to the Init and/or the InitTransfer subroutines for the gridded derived type members of noahowpgrid_type (i.e. 'the model') -- either to provide access to n_x and n_y for array allocation purposes or to transfer read-in gridded variables.
TESTING
I executed test/analysis/compare_outputnc.py and got this output. As expected, grid cell (1,1) passed all tests while the other grid cells failed for one or more tests (inputs for cell (1,1) are the same as what's listed in namelist.input in the main branch of this repo).
TODO
Read-in variable/dimension/attribute names via namelist.input

@hellkite500, would you be able to provide a critique of this refactor of the NetCDF read-in functionality? Does this jive with what you were aiming for? Things to improve upon?
Note that I did not address your desire to be able to deallocate/reallocate the model's allocatable arrays, which I thought we'd address in a separate PR.

@hellkite500, I know you're a particularly busy person so I requested a review from @nmizukami too. Appreciate you having a look!

One another comment was about save statement in each F90 defining type.  Wondering if there is need for this?  These F90s are to define the type (nothing is instantiated, and nothing to save the values?)

@nmizukami I'll also test your suggestion on the save statements. I'll put more thought into that.

One another comment was about save statement in each F90 defining type. Wondering if there is need for this? These F90s are to define the type (nothing is instantiated, and nothing to save the values?)

You were right, @nmizukami : the save statement was not needed. I removed the save statement from the NetCDFVarsType module, recompiled, re-executed the model, and then re-executed /test/analysis/compare_outputnc.py with expected results. I pushed this change.
I'll also test removal off all other save statements in the type definition modules and put those in a separate PR for you to review.

One another comment was about save statement in each F90 defining type. Wondering if there is need for this? These F90s are to define the type (nothing is instantiated, and nothing to save the values?)

You were right, @nmizukami : the save statement was not needed. I removed the save statement from the NetCDFVarsType module, recompiled, re-executed the model, and then re-executed /test/analysis/compare_outputnc.py with expected results. I pushed this change.
I'll also test removal off all other save statements in the type definition modules and put those in a separate PR for you to review.

Hi @GreyEvenson-NOAA ,  Yes, I agree with separate PR for removing save!  thanks for checking this.

Appreciate your time and input on this, @hellkite500 and @nmizukami. I'll merge this PR now.
@nmizukami: Depending on your availability, I may send one PR regarding the save statements in the type-definition modules and a second PR to read-in the NetCDF variable/dimension/attribute names from namelist.input.
@hellkite500: Sounds good regarding the decoupling. @SnowHydrology explained this as requirement for parallelization, though I admittedly may have misunderstood. Will be cool to talk more on this.

Hi @GreyEvenson-NOAA ,  yes, I am happy to review the next PRs too.

authors: GreyEvenson-NOAA nmizukami

repo: NOAA-OWP/noah-owp-modular


text:
accented letters not showing properly

Made an italian translation for a quest in the shadow rune project, everything works fine except the accented letters. While playing all the accented letters are replaced by a question mark.
Valkyrie Version: 2.0.1

Which accented letters did you try? Letters like: âûôäöüéÈ?

What platform do you use. I tested on Android and accented letter were just fine for both Mom and Descent.
My guess is, that this problem relates to the text encoding. You need to use a more advanced text editor like Notepad++ and save your text as UTF-8 (without BOM; BOM = Byte Order Mask).

I use a windows Vista pc.
In the next couple of days I'll try to convert my files. I'll let you know as soon as I can if anything changes!
Thanks.

Just converted my files and everything works fine, no more question marks.
Thanks a lot!

Glad to help you

repo: NPBruce/valkyrie


text:
Gitlab Omniauth provider

I would like to see the Omniauth Gitlab authentication strategy included by default.
The advantage is that it can be used with gitlab.com as well as on premise Gitlab installations.

omniauth-gitlab has been added to the v0.14.0 release. Documentation: https://api-umbrella.readthedocs.io/en/latest/server/admin-auth.html#gitlab

authors: GUI bachp

repo: NREL/api-umbrella


text:
DEV: name without dot

Exclude dot from key names.

address https://github.com/NSLS-II/bluesky/issues/572

Nice!
I am pretty sure we don't want to set additionalProperties to false. Doesn't that disallow user-specified metadata? The event schema does block additional properties (by design) but all of the other documents are "dynamic," allowing arbitrary extra fields.

I think if you do not set 'additionalProperties' to False, then the format checking (effectively) does not apply.  If I understand it correctly, it is 'may there be properties that are not permitted by some other rule'.
@licode Does this apply recursively? ex,
doc = {...,  # mandatory start doc stuff
       'valid_key': {'invalid.subkey': 1}}

should fail.

Now this can check name recursively.

Nice!
I wrote a test for bluesky verifying that names with dots raise validation errors (when run against this branch of event_model). I found some bugs -- this actually isn't valid JSON. One way to test that is simply to try importing the event_model package.
I opened up a PR against your branch. With those fixes, my bluesky test passes, so this should be good to merge. I'll push my bluesky test once this is merged.

Neat. I just merged that.

Great. Thanks again for diving into jsonschema, @licode.

authors: danielballan licode tacaswell

repo: NSLS-II/event-model


text:
install generator fails in rails 4.1

When trying to run the install generator I get the following error about formtastic:
/Users/kg/.rvm/gems/ruby-2.1.2/gems/surveyor-0.20.0/lib/formtastic/surveyor_builder.rb:3:in <module:Formtastic>': uninitialized constant Formtastic::SemanticFormBuilder (NameError) 	from /Users/kg/.rvm/gems/ruby-2.1.2/gems/surveyor-0.20.0/lib/formtastic/surveyor_builder.rb:2:in <top (required)>'
from /Users/kg/.rvm/gems/ruby-2.1.2/gems/surveyor-0.20.0/lib/surveyor.rb:3:in `require'

Did same
gem 'surveyor', :git => 'git://github.com/NUBIC/surveyor.git', :branch => 'simple_form'

authors: KarlGl TaiwoAyanleye

repo: NUBIC/surveyor


text:
Toggle Groups of Roles

Fixes #651
Adds flags to configure to set and unset groups of roles.

I've had a play around with it - I did expect unset_roles to unset all of them. writing out individual roles might as well use -i. The main thing here is that it's annoying after --clean that everything is turned on. I'd like to be able to do ./b configure [--clean] --unset_roles --set_webots_roles to turn on just the webots roles.

@JesseWilliamson I've had a look at this and I think what I missed last time is that --unset_all_roles is a thing.
If I do
./b configure [--clean] --unset_all_roles --set_webots/_roles
it works as I expect
we'll want to remove the trailing / on the folder names

If you're going to set it up like this maybe use glob rules to set and unset too?

If you're going to set it up like this maybe use glob rules to set and unset too?

Added. I'm curious why you said globbing rather than regex? I thought globbing was mostly just used for file names.
Also this kind of makes the other path-based arguments (--set_all_roles, --set_{dir}_roles) redundant, but I will leave them in so globbing isn't required.

Nobody wants to type out regex on the command line as arguments. And most of the time what you need can be represented with a glob. Another option is just to specify the roles you want to build directly as role file arguments. Then you can use the file system and shell to do globbing etc for you

authors: CMurtagh-LGTM JesseWilliamson TrentHouliston ysims

repo: NUbots/NUbots


text:
[YOLOV7-QAT] Cannot convert onnx to trt engine

Hi @wanghr323 Thank for your Yolov7 QAT.
I follow your tutorial and successful on QAT training.
Loading and preparing results...
pycocotools unable to run: Results do not correspond to current coco set
QAT Finetuning 10 / 10, Loss: 0.67706, LR: 1e-06: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 590/590 [04:10<00:00,  2.36it/s]
               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 597/597 [01:20<00:00,  7.39it/s]
                 all        5963        6888       0.953       0.915       0.942       0.809

Evaluating pycocotools mAP... saving qat_models/trained_qat/pgie/1/_predictions.json...
loading annotations into memory...
Done (t=0.44s)
creating index...
index created!
Loading and preparing results...
pycocotools unable to run: Results do not correspond to current coco set

/usr/src/tensorrt/bin/trtexec --onnx=qat_models/trained_qat/pgie/1/qat.onnx --int8 --fp16 --workspace=1024000 --minShapes=images:1x3x416x416 --optShapes=images:16x3x416x416 --maxShapes=images:32x3x416x416
After that, I convert the qat trained model to onnx then convert the onnx model to trt engine. But I failed on convert onnx to trt engine.
Pls help me to check it
[12/04/2023-09:07:36] [E] [TRT] ModelImporter.cpp:729: --- End node ---
[12/04/2023-09:07:36] [E] [TRT] ModelImporter.cpp:732: ERROR: builtin_op_importers.cpp:1216 In function QuantDequantLinearHelper:
[6] Assertion failed: scaleAllPositive && "Scale coefficients must all be positive"
[12/04/2023-09:07:36] [E] Failed to parse onnx file
[12/04/2023-09:07:36] [I] Finish parsing network model
[12/04/2023-09:07:36] [E] Parsing model failed
[12/04/2023-09:07:36] [E] Failed to create engine from model or file.
[12/04/2023-09:07:36] [E] Engine set up failed
&&&& FAILED TensorRT.trtexec [TensorRT v8502] # /usr/src/tensorrt/bin/trtexec --onnx=qat_models/trained_qat/pgie/1/qat.onnx --int8 --fp16 --workspace=1024000 --minShapes=images:4x3x416x416 --optShapes=images:4x3x416x416 --maxShapes=images:4x3x416x416

Scale coefficients must all be positive occurs when the stored scale value is zero. This is a bug in pytorch_quantization_library. it can be fixed by constraining the value of amax(like, amax.clamp(1e-6)) when export to onnx.
tensor_quantizer.py

also, you can change the scale value using onnx in Python.

@hopef 我也遇到了这个问题，但加上您在上面提到的amax.clap(1e-6)后仍然报错。
[01/12/2024-11:24:30] [E] [TRT] ModelImporter.cpp:771: While parsing node number 175 [QuantizeLinear -> "/model.7/conv/_weight_quantizer/QuantizeLinear_output_0"]:
[01/12/2024-11:24:30] [E] [TRT] ModelImporter.cpp:772: --- Begin node ---
[01/12/2024-11:24:30] [E] [TRT] ModelImporter.cpp:773: input: "model.7.conv.weight"
input: "/model.7/conv/_weight_quantizer/Constant_output_0"
input: "/model.7/conv/_weight_quantizer/Constant_1_output_0"
output: "/model.7/conv/_weight_quantizer/QuantizeLinear_output_0"
name: "/model.7/conv/_weight_quantizer/QuantizeLinear"
op_type: "QuantizeLinear"
attribute {
name: "axis"
i: 0
type: INT
}
[01/12/2024-11:24:30] [E] [TRT] ModelImporter.cpp:774: --- End node ---
[01/12/2024-11:24:30] [E] [TRT] ModelImporter.cpp:777: ERROR: builtin_op_importers.cpp:1197 In function QuantDequantLinearHelper:
[6] Assertion failed: scaleAllPositive && "Scale coefficients must all be positive"
[01/12/2024-11:24:30] [E] Failed to parse onnx file
[01/12/2024-11:24:30] [I] Finished parsing network model. Parse time: 0.0231789
[01/12/2024-11:24:30] [E] Parsing model failed
[01/12/2024-11:24:30] [E] Failed to create engine from model or file.
[01/12/2024-11:24:30] [E] Engine set up failed

authors: Doctor-L-end HoangTienDuc hopef


text:
Patch OSS vulnerabilities

Apply patches for:

CVE-2024-5535 in openssl
CVE-2024-7006 in libtiff

Update following packages to include fixes for reported vulnerabilites:

FFmpeg 7.0.1 -> 7.0.2
curl 8.8.0 -> 8.9.1

17890009, 17890048


text:
Update to support checkout to the cudf release tag [skip ci]

To fix: https://github.com/NVIDIA/spark-rapids-jni/issues/1852
Update ci/submodule-sync.sh to support pinning cudf submodule to the release tag,
then CI job can run the auto release leveraging this change.

Minor suggestion, lgtm.

Good point, it's much simple than the previous change!

build

repo: NVIDIA/spark-rapids-jni


text:
[auto-merge] bot-auto-merge-branch-24.10 to branch-24.12 [skip ci] [bot]

auto-merge triggered by github actions on bot-auto-merge-branch-24.10 to create a PR keeping branch-24.12 up-to-date. If this PR is unable to be merged due to conflicts, it will remain open until manually fix.

SUCCESS - auto-merge


text:
[submodule-sync] bot-submodule-sync-branch-22.08 to branch-22.08 [skip ci] [bot]

submodule-sync to create a PR keeping thirdparty/cudf up-to-date.
HEAD commit SHA: 4116ade544f791e551142faf16a1cb131eb3cc27, cudf commit SHA: https://github.com/rapidsai/cudf/commit/b2ae9a9f9e1b0291ace5066d0b7d2c300c037a99
This PR will be auto-merged if test passed. If failed, it will remain open until test pass or manually fix.

HEAD commit SHA: 4116ade544f791e551142faf16a1cb131eb3cc27, CUDF commit SHA: https://github.com/rapidsai/cudf/commit/b2ae9a9f9e1b0291ace5066d0b7d2c300c037a99
Test passed: True

SUCCESS - auto-merge


text:
[BUG] join_test failed in integration tests

Describe the bug
Failed on Branch-23.06 only
 FAILED ../../src/main/python/join_test.py::test_sortmerge_join_struct_as_key[Left-Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])][INJECT_OOM, IGNORE_ORDER({'local': True})] - TypeError: object of type 'NoneType' has no len()

 FAILED ../../src/main/python/join_test.py::test_sortmerge_join_struct_as_key[Right-Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])][INJECT_OOM, IGNORE_ORDER({'local': True})] - TypeError: object of type 'NoneType' has no len()

 FAILED ../../src/main/python/join_test.py::test_sortmerge_join_struct_mixed_key[Right-Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])][INJECT_OOM, IGNORE_ORDER({'local': True})] - TypeError: object of type 'NoneType' has no len()

 FAILED ../../src/main/python/join_test.py::test_broadcast_join_right_struct_as_key[Left-Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])][INJECT_OOM, IGNORE_ORDER({'local': True})] - TypeError: object of type 'NoneType' has no len()

 FAILED ../../src/main/python/join_test.py::test_broadcast_join_right_struct_as_key[Right-Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])][IGNORE_ORDER({'local': True})] - TypeError: object of type 'NoneType' has no len()

 FAILED ../../src/main/python/join_test.py::test_broadcast_join_right_struct_mixed_key[Right-Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])][INJECT_OOM, IGNORE_ORDER({'local': True})] - TypeError: object of type 'NoneType' has no len()

Detail log
test_sortmerge_join_struct_as_key[Right-Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])] _
 [gw1] linux -- Python 3.8.10 /usr/bin/python

 data_gen = Struct(['child0', String(not_null)],['child1', Byte(not_null)],['child2', Short(not_null)],['child3', Integer(not_null)],['child4', Long(not_null)],['child5', Boolean(not_null)],['child6', Date(not_null)],['child7', Timestamp(not_null)])
 join_type = 'Right'

     @ignore_order(local=True)
     @pytest.mark.parametrize('data_gen', struct_gens, ids=idfn)
     @pytest.mark.parametrize('join_type', ['Inner', 'Left', 'Right', 'Cross', 'LeftSemi', 'LeftAnti'], ids=idfn)
     def test_sortmerge_join_struct_as_key(data_gen, join_type):
         def do_join(spark):
             left, right = create_df(spark, data_gen, 500, 250)
             return left.join(right, left.a == right.r_a, join_type)
 >       assert_gpu_and_cpu_are_equal_collect(do_join, conf=_sortmerge_join_conf)

../../src/main/python/join_test.py 672:
 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../src/main/python/asserts.py 562: in assert_gpu_and_cpu_are_equal_collect
     _assert_gpu_and_cpu_are_equal(func, 'COLLECT', conf=conf, is_cpu_first=is_cpu_first)
../../src/main/python/asserts.py 493: in _assert_gpu_and_cpu_are_equal
     assert_equal(from_cpu, from_gpu)
../../src/main/python/asserts.py 106: in assert_equal
     _assert_equal(cpu, gpu, float_check=get_float_check(), path=[])
../../src/main/python/asserts.py 42: in _assert_equal
     _assert_equal(cpu[index], gpu[index], float_check, path + [index])
../../src/main/python/asserts.py 35: in _assert_equal
     _assert_equal(cpu[field], gpu[field], float_check, path + [field])
 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

 cpu = Row(child0='\x02?i/\xad\x9d\x81è', child1=0, child2=-9690, child3=1021976800, child4=-7292110493237686856, child5=False, child6=datetime.date(4859, 10, 23), child7=datetime.datetime(5205, 4, 22, 0, 17, 58, 529789))
 gpu = None
 float_check = <function get_float_check.<locals>.<lambda> at 0x7fced8812160>
 path = [8, 'a']

     def _assert_equal(cpu, gpu, float_check, path):
         t = type(cpu)
         if (t is Row):
 >           assert len(cpu) == len(gpu), "CPU and GPU row have different lengths at {} CPU: {} GPU: {}".format(path, len(cpu), len(gpu))
E           TypeError: object of type 'NoneType' has no len()

../../src/main/python/asserts.py 31: TypeError
 ----------------------------- Captured stdout call -----------------------------
 ### CPU RUN ###
 ### GPU RUN ###
 ### COLLECT: GPU TOOK 0.5862219333648682 CPU TOOK 0.5391867160797119 ###

I suspect this is caused by a change in cudf.  I rolled back the plugin source by a week and it still fails, implying it's not something in the plugin source that is triggering the failure.

Maybe related to https://github.com/rapidsai/cudf/pull/12787? @jlowe Can you try to reverse this in spark-rapids-jni and test it?

Yep, tests pass with this reverted and fail when it is restored.

Tested https://github.com/rapidsai/cudf/pull/13120 against the failed tests. All these tests pass with that fix.

Close as resolved.

authors: NvTimLiu jlowe ttnghia

repo: NVIDIA/spark-rapids


text:
[auto-merge] branch-21.06 to branch-21.08 [skip ci] [bot]

auto-merge triggered by github actions on branch-21.06 to create a PR keeping branch-21.08 up-to-date. If this PR is unable to be merged due to conflicts, it will remain open until manually fix.

SUCCESS - auto-merge


text:
[auto-merge] branch-21.06 to branch-21.08 [skip ci] [bot]

auto-merge triggered by github actions on branch-21.06 to create a PR keeping branch-21.08 up-to-date. If this PR is unable to be merged due to conflicts, it will remain open until manually fix.

FAILURE - Unable to auto-merge. Manual operation is required.
{'message': 'Pull Request is not mergeable', 'documentation_url': 'https://docs.github.com/rest/reference/pulls#merge-a-pull-request'}

Fixing this.

Fixed by #2565

authors: jlowe nvauto


text:
Refactoring cudf_udf test setup [databricks]

Signed-off-by: Peixin Li pxli@nyu.edu
fixes https://github.com/NVIDIA/spark-rapids/issues/4065

build


text:
[WIP] Implement a tiered projection

Signed-off-by: Jim Brennan jimb@nvidia.com
This is an initial proof-of-concept for https://github.com/NVIDIA/spark-rapids/issues/5085.
I am putting this up as a draft to get some feedback.
The general approach is to provide an alternative to bindGpuReferences, currently called bindGpuReferencesTiered, which returns a case class that contains a tiered list of references, and the corresponding method in that case class that applies the tiered projections in order.  The tiered list of references are created by recursively pulling out common sub-expressions from the initial list of expressions and rewriting expressions to refer to the output of the sub-expressions.
There are some known problems with this initial POC.

It currently depends on EquivalentExpressions.scala in Spark, and the name for the method we are calling changes between 3.1 and 3.2.  This patch currently uses the 3.1 version.  If we were to continue to use this, I think we would need a shim to deal with the differences.
EquivalentExpressions has rules that control which nodes in the expression tree are evaluated, so it can avoid conditional expressions and expressions with side-effects.  These rules don't know anything about GpuExpressions, so for instance, the rules for handling if expressions or case when expressions won't be applied for the GPU equivalents, and we'll end up getting more sub-expressions than we should.  For the POC, I added some checks on the results from EquivalentExpressions to try to simulate this, but I think for the final patch we may need to replicate what EquivalentExpressions is doing instead of using it directly.
Need to add unit tests.
Need to remove debug code.

I put up #6466  with the non-POC version of this.


text:
[WIP] Unit test for local shuffle reader optimization with AQE

This PR builds on https://github.com/NVIDIA/spark-rapids/pull/674.
This ports the "multiple joins" test from Spark and adapts it for GPU. It creates an initial plan consisting of SMJ and then during execution, asserts that these get replaced with BHJ based on statistics and that the shuffle readers get converted to local readers.

The test failed against Spark 3.0.2 in CI, although that works for me locally. I am assuming that this due to either a) order of data being written to Parquet varies, and compressese differently so that we miss a threshold, or b) it is a race condition where the order of query stage completion matters.

build

Closing this since it is mostly superseded by https://github.com/NVIDIA/spark-rapids/pull/781 and we don't have a solution for faking the statistics from the initial plan to match how these tests work in Spark (as per comments in https://github.com/NVIDIA/spark-rapids/pull/781).


text:
Fallback to CPU when reading Delta delta_log parquet checkpoint files

fixes https://github.com/NVIDIA/spark-rapids/issues/7020
In multiple customer queries I see the stages reading this files taking longer on the GPU so simply have them fallback and run the entire query on CPU like we already do for the json files.   These queries end up going from columnar to row and back again multiple times.
This has been tested on Databricks with customer queries for performance
I added a config to be able to turn this fallback off in case we come across some case we haven't seen yet.

build

need to generate configs doc

build

build


text:
Distribute model across multiple GPUs

We have multiple sets of models with each set having over 100 models each. Say the total of Set1 is around 10GiB and the total of another is 20GiB. Up until recently (yesterday) we could put each set of models into their own bucket and run one Triton server per GPU (as the model sets were fitting on one GPU each) but the number and size of the models has increased and so this is no longer possible as the GPU RAM is limited.
I know Triton tries to run all the models on all the GPUs if you give it more than one GPU. What we need is for it to run just 1 copy of each model across all the GPUs we give it. Ideally with some ability to fine tune i.e. round robin, or fill up 1 GPU before moving to the next, or just balance the models so the GPUs are as equally full as possible.
Right now we're going to have to write some complicated script to take all the models group them into buckets and run them across multiple Triton servers, 1 per GPU on the same inference machine. Not fun. The other alternative is to keep buy larger GPUs which is already
I don't know if this is a common problem when doing inference in production.

I don't think you are asking about the mechanics of how to have Triton place certain models only on certain GPUs, but just in case.... you can do that with instance_group setting in the model config: https://docs.nvidia.com/deeplearning/triton-inference-server/master-user-guide/docs/model_configuration.html#instance-groups
I think you are generally asking about what we call "model orchestration". That is the problem of having:

a large number of (potentially changing) models
a latency and throughput requirement for each model (also potentially changing dynamically)
a set of systems with GPUs to run the models

The problem is how to map the models onto the systems/GPUs so that the latency and throughput requirements are met, dynamically changing the mapping as necessary. There are simple, manual solutions for some cases (as you have described) but you are asking for a more general solution. Is that your question?

@deadeyegoodwin what's the general solution to achieve "model orchestration"?
The instance-group setting (as far as I know) are static. How to make those dynamic, changing as per the needs on runtime?

Yeah, a more general solution. we looked at the instance_group solution but we're taking advantage of the auto configuration for models without config.pbtxts since generating and updating these for hundreds of models is rather tedious and error prone (also, how do you automate tests for that?)
Ideally we could just give Triton all the models (around 230) and some scaling schema and it would run at least one of each on a GPU which is what we really need.
A bonus would be if it scaled (up & down) the number of models based on load as we know some models are needed for every inference and are slower than other which are less popular. But at the moment that's a "nice to have" atm. (I guess you'd also need to make sure popular models are not all on the same GPU)

Here's a high-level doc of how Triton thinks about the problem of "model orchestration": https://docs.google.com/document/d/1iM0GMiO_ysAWEjk-9WGIYLxuBDXR2dcy09jI8YgYtsc/edit
Triton itself is not the entire solution. There need to be components around it to manage the models and to decide which models should be mapped to which systems (and therefore GPUs) over time.
You should look at the KFServing project. Triton is active in that project and they are working on a model orchestration system that will address both CPUs and GPUs (as well as other accelerators): https://github.com/kubeflow/kfserving

Looking at building a custom solution now but having to generate a set of config.pbtxt for each model which are going to be site/deployment specific as its based on the number of GPUs available. So on premise there might be 4 but on a cloud instance there might be 1 or 2 or 10 etc. Also, if we want role back of model version we ether have to delete the version(s) we don't want or update the config.pbtxt. This kind of makes deploying from a bucket impossible if the models are shared from there (which is the point of using a bucket).

Depending on your other requirements and restrictions, it is likely better to virtually partition your "datacenter" into 1 GPU nodes and have a Triton running on each of the virtual nodes. That way the default behavior of having 1 model per GPU is what you want and you use node auto-scaling (which can be driven by metrics) to increase of decrease the number of nodes that hold a model. As an example the following setup can be used with a system like Kubernetes (of KFServing which is built on kubernetes). It greatly simplifies the model orchestration problem by not grouping together different models on a GPU (so it can be wasteful of GPUs if there are models that get only a small number of requests):

For each model generating the config.pbtxt is part of preparing the model for deployment and so must be done once. The primary things to consider is the appropriate dynamic batching and instance group settings needed to get the individual model to have the desired performance on the deployment GPU (see optimization section of docs).
Each model is deployed in its own, scale-to-zero cluster composed of single-GPU nodes. Kubernetes (or KFServing) manages the scale-up/down of each cluster based on model demand.
Models are removed from datacenter by removing the corresponding cluster.

authors: deadeyegoodwin whillas yasirofficial

repo: NVIDIA/triton-inference-server


text:
kmod-nvidia-535 package version 535.216 for EL8 4.18.0-553.22.1 missing

Several new packages for the 535.216.01 driver release were pushed to the EL8 CUDA repo on 09/26, except the kmod-nvidia-535 driver package for 535.216.01.
The kmod-nvidia-535 package is still at 535.183.06 for the 4.18.0-553.22.1 kernel.
This is causing errors during system updates because dnf is trying to install the various support packages for version 535.216.01, but there's no matching kmod-nvidia-535 package version to go with them. There are matching DKMS packages, but our workflow prefers the pre-compiled kmod.
Additionally, the repo's pre-compiled status page still lists driver 535.183.06 as the latest 535 branch driver, not 535.216.01.
https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/precompiled/
Examples:
Currently enabled Nvidia modules on an affected EL8 system:
nvidia-driver        535 [e]         default [d], fm, ks, src                 Nvidia driver for 535 branch

Errors when attempting dnf upgrade on an affected EL8 system:
Problem 1: package nvidia-kmod-common-3:535.216.01-1.el8.noarch from cuda-rhel8-x86_64 requires nvidia-kmod = 3:535.216.01, but none of the providers can be installed
  - cannot install the best update candidate for package nvidia-kmod-common-3:535.183.06-1.el8.noarch
  - package kmod-nvidia-latest-dkms-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 is filtered out by modular filtering
  - package kmod-nvidia-open-dkms-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 is filtered out by modular filtering
 Problem 2: package nvidia-driver-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 requires nvidia-kmod-common = 3:535.216.01, but none of the providers can be installed
  - package nvidia-kmod-common-3:535.216.01-1.el8.noarch from cuda-rhel8-x86_64 requires nvidia-kmod = 3:535.216.01, but none of the providers can be installed
  - cannot install the best update candidate for package nvidia-driver-3:535.183.06-1.el8.x86_64
  - package kmod-nvidia-latest-dkms-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 is filtered out by modular filtering
  - package kmod-nvidia-open-dkms-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 is filtered out by modular filtering
 Problem 3: problem with installed package nvidia-driver-3:535.183.06-1.el8.x86_64
  - package nvidia-driver-3:535.183.06-1.el8.x86_64 from @System requires libnvidia-glcore.so.535.183.06()(64bit), but none of the providers can be installed
  - package nvidia-driver-3:535.183.06-1.el8.x86_64 from @System requires libnvidia-tls.so.535.183.06()(64bit), but none of the providers can be installed
  - package nvidia-driver-3:535.183.06-1.el8.x86_64 from @System requires nvidia-driver-libs(x86-64) = 3:535.183.06, but none of the providers can be installed
  - package nvidia-driver-3:535.183.06-1.el8.x86_64 from cuda-rhel8-x86_64 requires libnvidia-glcore.so.535.183.06()(64bit), but none of the providers can be installed
  - package nvidia-driver-3:535.183.06-1.el8.x86_64 from cuda-rhel8-x86_64 requires libnvidia-tls.so.535.183.06()(64bit), but none of the providers can be installed
  - package nvidia-driver-3:535.183.06-1.el8.x86_64 from cuda-rhel8-x86_64 requires nvidia-driver-libs(x86-64) = 3:535.183.06, but none of the providers can be installed
  - package nvidia-driver-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 requires nvidia-kmod-common = 3:535.216.01, but none of the providers can be installed
  - cannot install both nvidia-driver-libs-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 and nvidia-driver-libs-3:535.183.06-1.el8.x86_64 from @System
  - cannot install both nvidia-driver-libs-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 and nvidia-driver-libs-3:535.183.06-1.el8.x86_64 from cuda-rhel8-x86_64
  - package nvidia-kmod-common-3:535.216.01-1.el8.noarch from cuda-rhel8-x86_64 requires nvidia-kmod = 3:535.216.01, but none of the providers can be installed
  - cannot install the best update candidate for package nvidia-driver-libs-3:535.183.06-1.el8.x86_64
  - package kmod-nvidia-latest-dkms-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 is filtered out by modular filtering
  - package kmod-nvidia-open-dkms-3:535.216.01-1.el8.x86_64 from cuda-rhel8-x86_64 is filtered out by modular filtering
=========================================================================================================================================================================================
 Package                                        Architecture                       Version                                           Repository                                     Size
=========================================================================================================================================================================================
Skipping packages with conflicts:
(add '--best --allowerasing' to command line to force their upgrade):
 nvidia-driver-libs                             x86_64                             3:535.216.01-1.el8                                cuda-rhel8-x86_64                             183 M
Skipping packages with broken dependencies:
 nvidia-driver                                  x86_64                             3:535.216.01-1.el8                                cuda-rhel8-x86_64                             157 M
 nvidia-kmod-common                             noarch                             3:535.216.01-1.el8                                cuda-rhel8-x86_64                              11

We have published

build date
package
driver
kernel
tag

2024-11-04
kmod-nvidia
565.57.01
4.18.0-553.27.1
el8_10.x86_64

2024-11-04
kmod-nvidia
560.35.03
4.18.0-553.27.1
el8_10.x86_64

2024-11-04
kmod-nvidia
550.127.05
4.18.0-553.27.1
el8_10.x86_64

2024-11-04
kmod-nvidia
535.216.01
4.18.0-553.27.1
el8_10.x86_64

authors: DLAtWork kmittman

repo: NVIDIA/yum-packaging-precompiled-kmod


text:
Here looks like have lots of memory waste.

I load a simplified model which has almost 50k vertices. And there is an OOM.
Here is the key code. It limits kaolin to rendering large models.
https://github.com/NVIDIAGameWorks/kaolin/blob/bcce7a95506847b3ced48fcfc3207f61488dcb42/kaolin/ops/mesh/mesh.py#L116
Is there any plan for improvement?

Hi @FavorMylikes , Thank you for your interest in Kaolin and good catch!
We might be able to optimize this by using torch.diagonal. I will try to test it on my side but do give it a try for your own use case! :)

authors: Caenorst FavorMylikes

repo: NVIDIAGameWorks/kaolin


text:
[Toolkit feature]: Enhanced Skybox Support

Contact Details (Optional)
No response
What is the context of the feature/improvement?
Many games do not have properly functioning skies or skyboxes, and the current best workaround appears to be anchoring a large sphere to a prop, marking it as a sky element, then going from there. This is a major hassle and isn't ideal.
Please describe the feature/improvement?
This would allow for easy configuration and addition of skyboxes in games that have been Remixed.
What would be your solution? (optional)
I propose the following:

Ability to add a skybox to a world without manually adding and modifying a mesh

Size control for said skybox

An artificial sun and moon that act as light sources

Ideally with custom textures that can be specified

The ability to control rotation speed and axis of the sun/moon and skybox separately

Ideally, also a basic cloud system. essentially these would act as layers on top of each other

Version
2024.3.1

This would be huge, The ability to relight a level is fundamental to the idea behind remix. Interiors work well, we know this from portal. Exteriors are struggling and something like would be a fantastic solution

I second the motion, this would be very useful in a lot of my projects

+1. This would be killer, many older titles also simply have fairly blurry skyboxes that could use a good facelift

I found that i could not select or even find skybox in the captured scene as a mesh to modify. Even tho it was there somehow and had textures.

Tracking internally as REMIX-3590.

repo: NVIDIAGameWorks/rtx-remix


text:
[Data Formatting] pop_change_00_10

This should indicate the + or - change.
Currently works great for negative numbers:

But positive numbers should be prefixed with a "+". They're currently not:

Without the "+", this percentage makes no sense.

@chriswhong Is this easy to fix with Numeral.js?

Easy fix, change numeral template from '0%' to '+0%'

Closed by #157

authors: andycochran chriswhong

repo: NYCPlanning/labs-community-portal


text:
DSD-240: Multiselect Component

Fixes JIRA ticket DSD-240
This PR does the following:

Adds the MultiSelect component
Modifies IconSizes to include xsmall for SelectedItemsCount Button.
Modifies Button component to allow for forwarding refs, which needed for Downshift's getToggleButtonProps
Adds downshift package for Listbox variant of MultiSelect.

How has this been tested?

Unit tests in DS.
Locally through storybook.
Integration with Scout app to replace existing "local" MultiSelect.

Accessibility concerns or updates

Checklist:

[x] I have updated the Storybook documentation accordingly.
[x] I have added relevant accessibility documentation for this pull request.
[x] All new and existing tests passed.

Front End Review:

[ ] View the example in Storybook

Tugboat has finished building the preview for this pull request!
Link:

https://pr987-w0erkzvtsq2sz8oxj0hjr41csw2hsu4j.tugboat.qa

Dashboard:

https://dashboard.tugboat.qa/62731a9c2e91387b17f9009b

Tugboat has finished building the preview for this pull request!
Link:

https://pr987-w0erkzvtsq2sz8oxj0hjr41csw2hsu4j.tugboat.qa

Dashboard:

https://dashboard.tugboat.qa/62731a9c2e91387b17f9009b

@isastettler So a few follow up tasks for this.

Add tests to check for onChange being called, ex on checkbox here: https://github.com/NYPL/nypl-design-system/blob/5edd44e9528ce30e9a9d718b0ff0c6882d317fb1/src/components/Checkbox/Checkbox.test.tsx#L192
Look at the Figma designs and confirm that the styles are correct: https://www.figma.com/file/qShodlfNCJHb8n03IFyApM/Main?node-id=43593%3A24611
Update MultiSelect.stories.mdx file -- there's parts of this that I can help you write, but at least get familiar with how the file is structured and what code does what, as it's a little confusing at first.

This is coming along very nicely!
There are lots of comments so let us know if we should also go over them next week and help update files. One thing Olivia and I were thinking about regarding file structure is to nest all sub-components in a components folder. Right now the structure is:

This is a similar architecture to how we are structuring the Header component:

(the useMultiSelect.ts hook is moved to the hooks folder)
Similarly, we are breaking up all the tests for sub-components into separate test files. All the tests are in the MultiSelect.test.tsx file, but another possibility is to break up relevant tests into MultiSelectDialog.test.tsx, etc. Here is an example:

Let us know what you think about this change!

Some of the styles need to be updated to reflect the figma designs:

@bigfishdesign13 and @EdwinGuzman -- Final round of fixes/change requests are ready for review:

[ ] Wrap MultiSelect components in chakra function to allow chakra props.
[ ] Add ms prefix to IDs.
[ ] Add isOpen as prop to useMultiStyleConfig and render css for open/close in theme file.
[x] Use DS UL and LI components (done already by Marty).
[ ] Replace useWindowSize hook with useNYPLBreakpoints hook.
[ ] General code cleanup (remove @ts-ignore and fix some failing tests).

As far as I can tell, the following wasn't actually completed by @bigfishdesign13 :

Use DS UL and LI components (done already by Marty).

The components still import UnorderedList and ListItem from Chakra. Let's not keep this as a blocker but make it a new ticket to fix in a new PR.

The updates look good. Just remove the unnecessary comment and this can be merged into the new feature branch DSD-225/MultiSelect.
All new updates and fixes should point to this new feature branch. Once we agree the MultiSelect ready for QA, we'll use this branch for QA.

@EdwinGuzman @wluisi @isastettler
Visually, the component looks good.  However, a number of the props are currently not functional in Storybook.  Specifically, I am referring to width, isDefaultOpen and isBlockElement.  What are you all experiencing in your local builds and the Vercel build?

@bigfishdesign13 Further small work on this will happen on the MultiSelectGroup PR. So can you approve this now?

authors: EdwinGuzman bigfishdesign13 nypl-wluisi

repo: NYPL/nypl-design-system


text:
http: multiple response.WriteHeader calls

How would one use the gzip handler with non-200 responses? The following example seems like it should work but logs http: multiple response.WriteHeader calls and the response isn't compressed.
package main

import (
    "fmt"
    "net/http"
    "os"

    "github.com/gorilla/mux"
    "github.com/nytimes/gziphandler"
)

func main() {
    r := mux.NewRouter()
    r.HandleFunc("/", homeHandler)
    r.NotFoundHandler = http.HandlerFunc(notFoundHandler)
    http.Handle("/", gziphandler.GzipHandler(r))
    http.ListenAndServe(":"+os.Getenv("PORT"), r)
}

func homeHandler(w http.ResponseWriter, r *http.Request) {
    fmt.Fprintf(w, "Hello, World!\n")
}

func notFoundHandler(w http.ResponseWriter, r *http.Request) {
    w.WriteHeader(http.StatusNotFound)
    fmt.Fprintf(w, "404 - Not Found!\n")
}

This might be related to #5 but I'm mentioning it because I thought #16 might have fixed it. This also seems to be related to https://github.com/gorilla/handlers/issues/83. Although this handler just logs the error but still returns the response successfully (although uncompressed).
Here's some debug information if this helps:
$GOPATH/src/github.com/nytimes/gziphandler master
❯ git rev-parse HEAD
44668d75e46f05932cf7c1c7a375d0765b324a0b

❯ go version
go version go1.7.1 darwin/amd64

Caveat emptor: I have nothing to do with this library. That being said, the following example works perfectly fine.
The first bug in the above example is that you're not actually using the gzip handler because you are registering it with http.DefaultServeMux but then explicitly passing a handler into http.ListenAndServe (which is very good practice IMO).
The second bug in the above example is that the notFoundHandler doesn't set the Content-Type before it writes the error message. <speculation> This leaves it too late for the GzipHandler to set the Content-Type header but the http.Server does it's own sniffing and infers the incorrect mime-type application/x-gzip. </speculation> The built-in http.Error method does set the Content-Type header before it calls WriteHeader and thus avoids this problem.
Working example:
package main

import (
	"fmt"
	"net/http"

	"github.com/gorilla/mux"
	"github.com/NYTimes/gziphandler"
)

func main() {
	r := mux.NewRouter()
	r.HandleFunc("/", homeHandler)
	r.NotFoundHandler = http.HandlerFunc(notFoundHandler)
	http.ListenAndServe(":3333", gziphandler.GzipHandler(r))
}

func homeHandler(w http.ResponseWriter, r *http.Request) {
	fmt.Fprintf(w, "Hello, World!\n")
}

func notFoundHandler(w http.ResponseWriter, r *http.Request) {
	http.Error(w, "404 - Not Found!", http.StatusNotFound)
}

authors: tmthrgd wayneashleyberry

repo: NYTimes/gziphandler


text:
Feature/#49 add unit tests for x unit generator

close #49 issue

Codecov Report

Merging #55 (076dd4b) into main (eb90784) will decrease coverage by 0.61%.
The diff coverage is 100.00%.

:mega: This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more
@@            Coverage Diff             @@
##             main      #55      +/-   ##
==========================================
- Coverage   90.74%   90.14%   -0.61%
==========================================
  Files          21       20       -1
  Lines         227      213      -14
  Branches       37       37
==========================================
- Hits          206      192      -14
  Misses          9        9
  Partials       12       12

Impacted Files
Coverage Δ

...Extensions/Generators/SignatureWrapperGenerator.cs
100.00% <ø> (ø)

.../Analyzers/TheoryAttributeOverJsonDataAttribute.cs
94.73% <100.00%> (ø)

...ers/TheoryAttributeOverJsonDataAttributeCodeFix.cs
92.59% <100.00%> (ø)

...sExtensions/Analyzers/TextExtensionsDescriptors.cs

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.

authors: codecov-commenter lukasz-strus


text:
🛑 Damien is down

In a62d7b6, Damien (https://damien-doussaud.com/) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Damien is back up in 5820f20.

repo: Namide/upptime


text:
Create directory for image infomation files?

Can I create a folder like "jsons" or "info" and export image information files to there?
i'd like to change to different location from image directory.

set the filenameinfoformat or filenameSeriesJSON with \ as the path separator, e.g. info\%member_id% %member_token%\%R-18% %urlFilename% - %title% in the config.ini

Thank you, it worked!

authors: Nandaka SamsonTKG


text:
mixedModelDE is only for mixed models

[ ] Add documentation about mixedModelDE only being for mixed models
[ ] Consider defaulting to normal LM if something like single slide DE for the user or at least add to documentation since this does not require a custom function
[ ] User currently gets an error about the fixed effect object not being found because of single column structure defaulting to vector here https://github.com/Nanostring-Biostats/GeomxTools/blob/97959d5f38eb07b3bab380e5b82048ae368d3f72/R/NanoStringGeoMxSet-de.R#L57 Consider adding drop=FALSE so they at least receive the no random effect error from lmer.
error without drop: "object 'Group' not found" where Group is fixed effect column
error with drop added: "No random effects terms specified in formula"
[ ] check elt exists in mixedModelDE, otherwise give informative error

Issue #174 is related to this. User receives the "object 'Group' not found" where Group is fixed effect column message. Could we consider adding in a "no random effects present" error message?

If there is still interest in adding support for non-mixed model analyses, I have created a modified version of mixedModelDE to fit simple linear models for a single-slide analysis I was working on: https://github.com/lardenoije/GeomxTools/blob/master/R/modelDE.R
Perhaps it can be of use.

Hi @lardenoije, this could be very useful for me since I'm following this vignette: [https://bioconductor.org/packages/devel/workflows/vignettes/GeoMxWorkflows/inst/doc/GeomxTools_RNA-NGS_Analysis.html#7_Differential_Expression]
However I only have a single GeoMx slide to analyze so the mixed-effect model part is throwing an error. Would you be able to show how your modelDE.R function would substitute into this section of the vignette?
Within Slide Analysis: Glomeruli vs Tubules
One informative exploration is to study differences between morphological structures. In this example, we can study differential expression between glomeruli and tubules. We will focus on the diseased kidney tissues. Because we are comparing structures that co-exist within the a given tissue we will use the LMM model with a random slope. Morphological structure (Region) is our test variable. We control for tissue subsampling with slide name using a random slope and intercept; the intercept adjusts for the multiple regions placed per unique tissue, since we have one tissue per slide. If multiple tissues are placed per slide, we would change the intercept variable to the unique tissue name (ex: tissue name, Block ID, etc).
In this analysis we save log2 fold change estimates and P-values across all levels in the factor of interest. We also apply a Benjamini-Hochberg multiple test correction.
convert test variables to factors
pData(target_demoData)$testRegion <-
factor(pData(target_demoData)$region, c("glomerulus", "tubule"))
pData(target_demoData)[["slide"]] <-
factor(pData(target_demoData)[["slide name"]])
assayDataElement(object = target_demoData, elt = "log_q") <-
assayDataApply(target_demoData, 2, FUN = log, base = 2, elt = "q_norm")
run LMM:
formula follows conventions defined by the lme4 package
results <- c()
for(status in c("DKD", "normal")) {
ind <- pData(target_demoData)$class == status
mixedOutmc <-
mixedModelDE(target_demoData[, ind],
elt = "log_q",
modelFormula = ~ testRegion + (1 + testRegion | slide),
groupVar = "testRegion",
nCores = parallel::detectCores(),
multiCore = FALSE)
# format results as data.frame
r_test <- do.call(rbind, mixedOutmc["lsmeans", ])
tests <- rownames(r_test)
r_test <- as.data.frame(r_test)
r_test$Contrast <- tests

# use lapply in case you have multiple levels of your test factor to
# correctly associate gene name with it's row in the results table
r_test$Gene <-
    unlist(lapply(colnames(mixedOutmc),
                  rep, nrow(mixedOutmc["lsmeans", ][[1]])))
r_test$Subset <- status
r_test$FDR <- p.adjust(r_test$`Pr(>|t|)`, method = "fdr")
r_test <- r_test[, c("Gene", "Subset", "Contrast", "Estimate",
                     "Pr(>|t|)", "FDR")]
results <- rbind(results, r_test)

}
Note that the example uses nCores = parallel:detectCores() and multiCore = FALSE to implement the parallel package clustering of all available cores. If working in a Windows environment use multicore = FALSE. If in a UNIX-based environment setting multicore = TRUE will parallelize using the mcapply package. If you do not want to use all available cores, change the nCores variable to the desired number to use.

authors: NicoleEO ahug030 danielnewhouse lardenoije

repo: Nanostring-Biostats/GeomxTools


text:
无法正常识别到比赛

log：auto_derby.log
debug：https://1drv.ms/u/s!Ahiyss4uSuRDv9ZhmU39p6qYiGaviQ?e=vnc9et

看log一直在重複auto_derby.template:197: no match: tmpl=(tmpl<single_mode_training_confirm.png>,)
這是在腳本要進訓練時，按到比賽那邊了吧
INFO  [2022-09-29 01:20:24]:auto_derby.scenes.scene:40: enter: single-mode-command -> single-mode-training
這句看起來就是要進訓練的樣子

看log一直在重複auto_derby.template:197: no match: tmpl=(tmpl<single_mode_training_confirm.png>,) 這是在腳本要進訓練時，按到比賽那邊了吧 INFO [2022-09-29 01:20:24]:auto_derby.scenes.scene:40: enter: single-mode-command -> single-mode-training 這句看起來就是要進訓練的樣子

其实我也一直这样以为，但是最近几次训练都是在这里卡住了，没有什么对策所以回报了

又出现了类似的情况
log：auto_derby.log
debug：https://1drv.ms/u/s!Ahiyss4uSuRDv9cTq9bvX-zP-G33Lg?e=4U6QRL

类似的情况
log：https://1drv.ms/u/s!Ahiyss4uSuRDv9gcp75biRYXtnSFLw?e=nqQUaP
debug：https://1drv.ms/u/s!Ahiyss4uSuRDv9gbdxWKQH3JyF7-aA?e=LLQnex

repo: NateScarlet/auto-derby


text:
Website Blocking - Hacker Alert!

The blocking of https://ghidra-sre.org/ to Israeli IP address is broken! Some hacker I know was able to bypass that with what he called web proxies. He pointed me to a service called HMA, so I suggest you block it and all of its kind. Also, it seems like Google is conspiring with the enemy and creating a backdoor to the site with their innocent-looking service of Google Web Site Translate, so please block Google as well.

Thanks for the info!

authors: assafnativ ryanmkurtz

repo: NationalSecurityAgency/ghidra


text:
Add support for R_AARCH64_MOVW_UABS_G* relocations

Adding support for the R_AARCH64_MOVW_UABS_G* series of static relocations. Fixes #3545.

Is there a reason this PR has not been merged?

I believe the code is incorrect.  While the instruction often uses the same shift as the relocation, to be fully correct the "shift" value needs to come from the relocation type (G0 is 0, G1 is 1, etc.), not from the instruction.

@ryanmkurtz I addressed the concerns in my pull request, #5292

@ryanmkurtz I addressed the concerns in my pull request, #5292

Does the new pull-request #5292 supercede this one?  Should this one be closed?

@ryanmkurtz I addressed the concerns in my pull request, #5292

@dukesilverrr Does the new pull-request #5292 supercede this one? Should this one be closed?

Based on my read, #5292 is the correct fix and supercedes this incorrect PR.

This PR has been superceded by PR #5292

authors: ZFrichtl dukesilverrr ghidra1 resrever