Invalid trace from uni_a -- issue when uni_a is the last switch on the topology ordered list

How to reproduce:

Get kytos running with latest docker version (amlight/kytos) and Mininet with linear topology tmux new-sess -d -s mn mn --topo linear,3 --controller=remote,ip=<IP_ADDRESS>
enable all switches, interfaces and links
create an EVC with UNI A being the last switch in the ordered list of switch's dpid

vlan=101; curl -H 'Content-type: application/json' -X POST http://<IP_ADDRESS>:8181/api/kytos/mef_eline/v2/evc/ -d '{"name": "evc-vlan-'$vlan'", "dynamic_backup_path": true,  "uni_z": {"tag": {"value": '$vlan', "tag_type": 1}, "interface_id": "00:00:00:00:00:00:00:01:1"}, "uni_a": {"tag": {"value": '$vlan', "tag_type": 1}, "interface_id": "00:00:00:00:00:00:00:03:1"}}'

restart Kytos

Expected behavior: Kytos should smoothly activate the EVC again, i.e., all traces should be valid.
Actual behavior:
Sep 15 18:56:27 6a7f9ddec698 uvicorn.access:INFO httptools_impl:506:  <IP_ADDRESS>:41232 - "GET /api/kytos/flow_manager/v2/stored_flows/?state=installed HTTP/1.1" 200
Sep 15 18:56:27 6a7f9ddec698 uvicorn.access:INFO httptools_impl:506:  <IP_ADDRESS>:41228 - "PUT /api/amlight/sdntrace_cp/v1/traces HTTP/1.1" 200
Sep 15 18:56:27 6a7f9ddec698 kytos.napps.kytos/mef_eline:WARNING evc:1250:  Invalid trace from uni_a: [{'dpid': '00:00:00:00:00:00:00:03', 'port': 1, 'time': '2023-09-15 18:56:27.411135', 'type': 'starting', 'vlan': 101}, {'dpid': '00:00:00:00:00:00:00:02', 'port': 3, 'time': '2023-09-15 18:56:27.411195', 'type': 'intermediary', 'vlan': 1}, {'dpid': '00:00:00:00:00:00:00:01', 'port': 2, 'time': '2023-09-15 18:56:27.411224', 'type': 'last', 'vlan': 1, 'out': {'port': 1, 'vlan': 101}}]

PS: you may eventually need to repeat the step 4 a few times, depending on the case of mef_eline loading the EVCs before the link_up events (in order to see the issue, the EVCs have to be loaded after the link up events, leaving the activation of the EVC for the consistency routine)
Adding the following debug information helps understand the issue:
diff --git a/models/evc.py b/models/evc.py
index 335187d..6879890 100644
--- a/models/evc.py
+++ b/models/evc.py
@@ -1247,6 +1247,7 @@ class EVCDeploy(EVCBase):
                                         metadata_vlan,
                                         trace2
                                     ) is False:
+                log.warning(f"Invalid trace from uni_a: link.endpoint_a={link.endpoint_a} metadata_vlan={metadata_vlan} trace2={trace2}")
                 log.warning(f"Invalid trace from uni_a: {trace_a}")
                 return False
             if compare_endpoint_trace(

The log now becomes:
Sep 15 19:29:27 6a7f9ddec698 kytos.napps.kytos/mef_eline:WARNING evc:1250:  Invalid trace from uni_a: link.endpoint_a=Interface('s2-eth3', 3, Switch('00:00:00:00:00:00:00:02')) metadata_vlan=8 trace2={'dpid': '00:00:00:00:00:00:00:03', 'port': 2, 'time': '2023-09-15 19:29:27.553224', 'type': 'last', 'vlan': 8, 'out': {'port': 1, 'vlan': 101}}
Sep 15 19:29:27 6a7f9ddec698 kytos.napps.kytos/mef_eline:WARNING evc:1251:  Invalid trace from uni_a: [{'dpid': '00:00:00:00:00:00:00:03', 'port': 1, 'time': '2023-09-15 19:29:27.553015', 'type': 'starting', 'vlan': 101}, {'dpid': '00:00:00:00:00:00:00:02', 'port': 3, 'time': '2023-09-15 19:29:27.553104', 'type': 'intermediary', 'vlan': 8}, {'dpid': '00:00:00:00:00:00:00:01', 'port': 2, 'time': '2023-09-15 19:29:27.553150', 'type': 'last', 'vlan': 8, 'out': {'port': 1, 'vlan': 101}}]

Here is the EVC data:
# curl http://<IP_ADDRESS>:8181/api/kytos/mef_eline/v2/evc/
{"4a7e03f20ddb42":{"active":false,"archived":false,"backup_path":[],"bandwidth":0,"circuit_scheduler":[],"current_path":[{"id":"4d42dc0852278accac7d9df15418f6d921db160b13d674029a87cef1b5f67f30","endpoint_a":{"id":"00:00:00:00:00:00:00:02:3","name":"s2-eth3","port_number":3,"mac":"c6:64:21:17:33:ad","switch":"00:00:00:00:00:00:00:02","type":"interface","nni":true,"uni":false,"speed":1250000000.0,"metadata":{},"lldp":true,"active":false,"enabled":true,"status":"DOWN","status_reason":["deactivated"],"link":"4d42dc0852278accac7d9df15418f6d921db160b13d674029a87cef1b5f67f30"},"endpoint_b":{"id":"00:00:00:00:00:00:00:03:2","name":"s3-eth2","port_number":2,"mac":"62:6c:53:60:b8:bf","switch":"00:00:00:00:00:00:00:03","type":"interface","nni":true,"uni":false,"speed":1250000000.0,"metadata":{},"lldp":true,"active":false,"enabled":true,"status":"DOWN","status_reason":["deactivated"],"link":"4d42dc0852278accac7d9df15418f6d921db160b13d674029a87cef1b5f67f30"},"metadata":{"s_vlan":{"tag_type":1,"value":8}},"active":false,"enabled":false,"status":"DISABLED","status_reason":["deactivated","disabled"]},{"id":"cf0f4071be426b3f745027f5d22bc61f8312ae86293c9b28e7e66015607a9260","endpoint_a":{"id":"00:00:00:00:00:00:00:01:2","name":"s1-eth2","port_number":2,"mac":"b2:e1:26:b8:ea:42","switch":"00:00:00:00:00:00:00:01","type":"interface","nni":true,"uni":false,"speed":1250000000.0,"metadata":{},"lldp":true,"active":false,"enabled":true,"status":"DOWN","status_reason":["deactivated"],"link":"cf0f4071be426b3f745027f5d22bc61f8312ae86293c9b28e7e66015607a9260"},"endpoint_b":{"id":"00:00:00:00:00:00:00:02:2","name":"s2-eth2","port_number":2,"mac":"1a:e5:27:62:22:10","switch":"00:00:00:00:00:00:00:02","type":"interface","nni":true,"uni":false,"speed":1250000000.0,"metadata":{},"lldp":true,"active":false,"enabled":true,"status":"DOWN","status_reason":["deactivated"],"link":"cf0f4071be426b3f745027f5d22bc61f8312ae86293c9b28e7e66015607a9260"},"metadata":{"s_vlan":{"tag_type":1,"value":8}},"active":false,"enabled":false,"status":"DISABLED","status_reason":["deactivated","disabled"]}],"dynamic_backup_path":true,"enabled":true,"failover_path":[],"id":"4a7e03f20ddb42","metadata":{},"name":"evc-vlan-101","primary_path":[],"service_level":0,"uni_a":{"tag":{"tag_type":1,"value":101},"interface_id":"00:00:00:00:00:00:00:03:1"},"uni_z":{"tag":{"tag_type":1,"value":101},"interface_id":"00:00:00:00:00:00:00:01:1"},"sb_priority":null,"execution_rounds":0,"owner":null,"queue_id":-1,"primary_constraints":{},"secondary_constraints":{},"primary_links":[],"backup_links":[],"start_date":"2023-09-15T18:53:32","creation_time":"2023-09-15T18:53:32","request_time":"2023-09-15T18:53:32","end_date":null,"flow_removed_at":"2023-09-15T19:25:46","updated_at":"2023-09-15T19:28:27"}}

And the stored flows:
# curl "http://<IP_ADDRESS>:8181/api/kytos/flow_manager/v2/stored_flows/?state=installed"
{"00:00:00:00:00:00:00:01":[{"flow":{"owner":"coloring","cookie":12393906174523604993,"match":{"dl_src":"ee:ee:ee:ee:ee:02"},"actions":[{"action_type":"output","port":4294967293}],"table_id":0,"table_group":"base","priority":50000,"idle_timeout":0,"hard_timeout":0},"flow_id":"1def4287eab3ab24897296657de64520","id":"914c314cfe8591090073a14bec4e659f","inserted_at":"2023-09-15T18:52:42.025000","state":"installed","switch":"00:00:00:00:00:00:00:01","updated_at":"2023-09-15T19:28:36.326000"},{"flow":{"owner":"of_lldp","cookie":12321848580485677057,"match":{"dl_type":35020,"dl_vlan":3799},"actions":[{"action_type":"output","port":4294967293}],"table_id":0,"table_group":"base","priority":50000,"idle_timeout":0,"hard_timeout":0},"flow_id":"2ee616117c8ce044e9f8f1f4f8e266ad","id":"b33f354ca83bfc990537e1d8d0c964e4","inserted_at":"2023-09-15T18:52:41.558000","state":"installed","switch":"00:00:00:00:00:00:00:01","updated_at":"2023-09-15T19:28:36.327000"},{"flow":{"owner":"mef_eline","cookie":12270758690135333698,"match":{"in_port":1,"dl_vlan":101},"actions":[{"action_type":"set_vlan","vlan_id":101},{"action_type":"push_vlan","tag_type":"s"},{"action_type":"set_vlan","vlan_id":8},{"action_type":"output","port":2}],"table_id":0,"table_group":"evpl","priority":20000,"idle_timeout":0,"hard_timeout":0},"flow_id":"65393385aaa6df94c92927ab22d2fb2b","id":"c86b59bcd70ee091a04947e93e670377","inserted_at":"2023-09-15T18:53:32.908000","state":"installed","switch":"00:00:00:00:00:00:00:01","updated_at":"2023-09-15T19:25:46.893000"},{"flow":{"owner":"mef_eline","cookie":12270758690135333698,"match":{"in_port":2,"dl_vlan":8},"actions":[{"action_type":"pop_vlan"},{"action_type":"output","port":1}],"table_id":0,"table_group":"evpl","priority":20000,"idle_timeout":0,"hard_timeout":0},"flow_id":"ee5b52b68bd63dcefa9c0eb42f1beb29","id":"f08c70f8246563bf4a93855dae710eff","inserted_at":"2023-09-15T19:25:46.868000","state":"installed","switch":"00:00:00:00:00:00:00:01","updated_at":"2023-09-15T19:25:46.893000"}],"00:00:00:00:00:00:00:02":[{"flow":{"owner":"of_lldp","cookie":12321848580485677058,"match":{"dl_type":35020,"dl_vlan":3799},"actions":[{"action_type":"output","port":4294967293}],"table_id":0,"table_group":"base","priority":50000,"idle_timeout":0,"hard_timeout":0},"flow_id":"355c284b2260fe897f3fb912e23e5b5d","id":"666b779f61fb741c943be13013dbe210","inserted_at":"2023-09-15T18:52:41.569000","state":"installed","switch":"00:00:00:00:00:00:00:02","updated_at":"2023-09-15T19:28:36.334000"},{"flow":{"owner":"coloring","cookie":12393906174523604994,"match":{"dl_src":"ee:ee:ee:ee:ee:03"},"actions":[{"action_type":"output","port":4294967293}],"table_id":0,"table_group":"base","priority":50000,"idle_timeout":0,"hard_timeout":0},"flow_id":"c8a22029a15f4669f35ffe2d24f05539","id":"0c72c8ba69a06905ee3c83ac6082efd7","inserted_at":"2023-09-15T18:52:42.029000","state":"installed","switch":"00:00:00:00:00:00:00:02","updated_at":"2023-09-15T19:28:36.364000"},{"flow":{"owner":"coloring","cookie":12393906174523604994,"match":{"dl_src":"ee:ee:ee:ee:ee:01"},"actions":[{"action_type":"output","port":4294967293}],"table_id":0,"table_group":"base","priority":50000,"idle_timeout":0,"hard_timeout":0},"flow_id":"68f45f0c241bb50ecce17dae4250bf27","id":"d36602f48f45949ee3197e542cca3e7e","inserted_at":"2023-09-15T18:52:42.062000","state":"installed","switch":"00:00:00:00:00:00:00:02","updated_at":"2023-09-15T19:28:36.364000"},{"flow":{"owner":"mef_eline","cookie":12270758690135333698,"match":{"in_port":3,"dl_vlan":8},"actions":[{"action_type":"set_vlan","vlan_id":8},{"action_type":"output","port":2}],"table_id":0,"table_group":"evpl","priority":20000,"idle_timeout":0,"hard_timeout":0},"flow_id":"d0374e0491fd6436843c40893d2f76d1","id":"adf4ced8d08896aaeaabf2e1dc92804c","inserted_at":"2023-09-15T19:25:46.727000","state":"installed","switch":"00:00:00:00:00:00:00:02","updated_at":"2023-09-15T19:25:46.792000"},{"flow":{"owner":"mef_eline","cookie":12270758690135333698,"match":{"in_port":2,"dl_vlan":8},"actions":[{"action_type":"set_vlan","vlan_id":8},{"action_type":"output","port":3}],"table_id":0,"table_group":"evpl","priority":20000,"idle_timeout":0,"hard_timeout":0},"flow_id":"bd65f70d00bd3beeb93ab40a4c3aa086","id":"82e91897febf39a7b86c120800e90beb","inserted_at":"2023-09-15T19:25:46.727000","state":"installed","switch":"00:00:00:00:00:00:00:02","updated_at":"2023-09-15T19:25:46.792000"}],"00:00:00:00:00:00:00:03":[{"flow":{"owner":"of_lldp","cookie":12321848580485677059,"match":{"dl_type":35020,"dl_vlan":3799},"actions":[{"action_type":"output","port":4294967293}],"table_id":0,"table_group":"base","priority":50000,"idle_timeout":0,"hard_timeout":0},"flow_id":"d7954dbf011826f4bc0e2e3ab0c90f1c","id":"4d4697b07ceccc1e1d616e9690beaabd","inserted_at":"2023-09-15T18:52:41.563000","state":"installed","switch":"00:00:00:00:00:00:00:03","updated_at":"2023-09-15T19:28:36.335000"},{"flow":{"owner":"coloring","cookie":12393906174523604995,"match":{"dl_src":"ee:ee:ee:ee:ee:02"},"actions":[{"action_type":"output","port":4294967293}],"table_id":0,"table_group":"base","priority":50000,"idle_timeout":0,"hard_timeout":0},"flow_id":"3cadb140ad2138141f50dc5d3794c84b","id":"c69ee7566ada1922d511d184ad35e516","inserted_at":"2023-09-15T18:52:42.068000","state":"installed","switch":"00:00:00:00:00:00:00:03","updated_at":"2023-09-15T19:28:36.384000"},{"flow":{"owner":"mef_eline","cookie":12270758690135333698,"match":{"in_port":1,"dl_vlan":101},"actions":[{"action_type":"set_vlan","vlan_id":101},{"action_type":"push_vlan","tag_type":"s"},{"action_type":"set_vlan","vlan_id":8},{"action_type":"output","port":2}],"table_id":0,"table_group":"evpl","priority":20000,"idle_timeout":0,"hard_timeout":0},"flow_id":"c11957fac5c529215069c82d92e924a0","id":"a7a96bccbec6216fbbcf374bb74303be","inserted_at":"2023-09-15T18:53:32.858000","state":"installed","switch":"00:00:00:00:00:00:00:03","updated_at":"2023-09-15T19:25:46.856000"},{"flow":{"owner":"mef_eline","cookie":12270758690135333698,"match":{"in_port":2,"dl_vlan":8},"actions":[{"action_type":"pop_vlan"},{"action_type":"output","port":1}],"table_id":0,"table_group":"evpl","priority":20000,"idle_timeout":0,"hard_timeout":0},"flow_id":"143dacb69b8a1d124299d5dfa05e23d3","id":"52ab46472173feaefb612e8706bf49ce","inserted_at":"2023-09-15T19:25:46.827000","state":"installed","switch":"00:00:00:00:00:00:00:03","updated_at":"2023-09-15T19:25:46.856000"}]}

@italovalcy thanks for catching and reporting this.
@gretelliz I've assigned this to you. I've initially set the priority as priority_medium and scheduled for 2023.2, @italovalcy feel free to update the priority accordingly if needed.

@gretelliz when you're back, check out this issue that has been left behind. The workaround seems simpler, but sdntrace_cp should be reliable to also handle this case when comparing the trace result.

Hi team
The most updated version of Kytos does not run sdntrace on the startup anymore (sdntrace was helping to see if the flows were still there to re-activate the EVC -- now that EVCs are activated by default sdntrace wont run anymore)
Anyway, to reproduce the issue now the approach would be:

start Kytos with the console (kytosd -f) -- I usually do this by leveraging tmux.

Then, after following the guidelines presented on this issue, you can connect to Kytos console and run the following test:
kytos $> a = EVCDeploy.check_list_traces(controller.napps[('kytos', 'mef_eline')].circuits.values())
2023-11-13 19:51:58,562 - INFO [uvicorn.access] (MainThread) <IP_ADDRESS>:57420 - "GET /api/kytos/flow_manager/v2/stored_flows/?state=installed HTTP/1.1" 200
2023-11-13 19:51:58,566 - INFO [uvicorn.access] (MainThread) <IP_ADDRESS>:57406 - "PUT /api/amlight/sdntrace_cp/v1/traces HTTP/1.1" 200

kytos $> 2023-11-13 19:51:58,568 - WARNING [kytos.napps.kytos/mef_eline] (ThreadPoolExecutor-1_0) Invalid trace from uni_a: [{'dpid': '00:00:00:00:00:00:00:03', 'port': 1, 'time': '2023-11-13 19:51:58.565206', 'type': 'starting', 'vlan': 999}, {'dpid': '00:00:00:00:00:00:00:02', 'port': 3, 'time': '2023-11-13 19:51:58.565262', 'type': 'intermediary', 'vlan': 3}, {'dpid': '00:00:00:00:00:00:00:01', 'port': 2, 'time': '2023-11-13 19:51:58.565320', 'type': 'last', 'vlan': 3, 'out': {'port': 1, 'vlan': 999}}]
kytos $> a
Out[18]: {'23f4ff3f61ab42': True, '82ff02a96a4d44': False}


[SPARK-14161] [SQL] Native Parsing for DDL Comand Drop Database

What changes were proposed in this pull request?
Based on the Hive DDL document https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL
The syntax of DDL command for Drop Database is
DROP (DATABASE|SCHEMA) [IF EXISTS] database_name [RESTRICT|CASCADE];

If IF EXISTS is not specified, the default behavior is to issue a warning message if database_name does't exist
RESTRICT is the default behavior.

This PR is to provide a native parsing support for DROP DATABASE.
How was this patch tested?
Added a test case DDLCommandSuite

Test build #54199 has started for PR 11962 at commit 8f20e5a.

cc @yhuai @hvanhovell @viirya Thanks!

Test build #54199 has finished for PR 11962 at commit 8f20e5a.

This patch fails from timeout after a configured wait of `250m`.
This patch merges cleanly.
This patch adds the following public classes (experimental):
case class DropDatabase(

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test this please

Test build #54231 has started for PR 11962 at commit 8f20e5a.

Test build #54233 has started for PR 11962 at commit dd0d82b.

Test build #54231 has finished for PR 11962 at commit 8f20e5a.

This patch passes all tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
case class DropDatabase(

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This patch merges cleanly.
This patch adds no public classes.

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LGTM

Test build #54256 has started for PR 11962 at commit 598a6b6.

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Merging to master. Thanks!


[bug] when alt-tabbed out of game, can't tab back eldenring.exe process

Thanks for the great mod, I love it. After playing some days with it, I noticed that when alt-tabbed out of game, I can't tab back eldenring.exe process. When I press alt-tab, switching between processes, the elden ring process does not come active when selecting it. This happens everytime I tab out the game.
I'm on windows 11 (Atlas OS), playing with steam, but replaced the start_protected_game.exe with eldenring.exe to play offline. Using EldenModLoader-117-3-2.

I've got the same issue :/

@s-vivien I thought it might be related to EldenModLoader, but I have the same issue using the standalone .exe or ModEngine2.

I think this is resolved in later version, I have also fixed the display issue while change the display resolution in options.
Closing this, feel free to reopen this if you meet this problem again.


Static Images

Hi,
It would be nice to add a feature of "Static Images" in the same genre as Google Maps (https://developers.google.com/maps/documentation/maps-static/intro) or Mapbox (https: // docs. mapbox.com/api/maps/#static-images).
Thank you :)

Thanks for this suggestion.
This is actually planned: it should be possible in a couple of... weeks


Possible to add local http server?

This is a brilliant plugin. I'm really excited to finally be able to build the Alfred Workflow I want.
Would it be possible to present the action-uri endpoints on a local https-server, like the Omnisearch plugin can? This would make it much easier to test the api from Postman.
Screen shot from the Omnisearch plugin settings:

The x-success, x-error, or x-cancel parameters can be HTTP URLs, yes.
Or do you mean you're talking about the incoming calls?

Yes, I mean "incoming calls". The Omnisearch plugin manages tho solve the following:

expose urls via an http-endpoint (i.e. not obsidian://)
return the resulting data directly in the response

I see. It's definitely in-scope for Actions URI as of right now. Sorry.


istioctl x metrice support to specify resource type, support type [workload]

Please provide a description of this PR:

refactor istioctl x metricscode  of  add the advance features
user-facing:

# Retrieve resource metrics by their type and name, support type [workload]
istioctl experimental metrics workload/productpage-v1

more info:
issue: https://github.com/istio/istio/issues/36428
doc:  https://docs.google.com/document/d/1ATp4qCHktncfAFjH_S9yWm9dGxI_aXHN2cw1AD4kveQ/edit?usp=sharing

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not stale

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[0] Error while mirroring css content to document.

    **Version:**
    1.4.2

    **OS:**
    <!-- Add your hosting platform e.g Microsoft Windows, WSL, MacOs or Linux -->

    **Stack :**
    Could not mirror css changes to document. No workspace mapping was found for 'inspector:///C:/Users/S258149/Downloads/New%20folder%20(6)/web/Responsive%20Portfolio%20Website/Responsive%20Portfolio%20Website/inspector-stylesheet'.

    **Additional context:**
    <!-- Add any other context or about the problem or screenshots here. -->

Closing this one as a duplicate of #748.


refactor webdav subdirectory, fixes #4967

What problem are we solving?
#4967
How are we solving the problem?
Adding a fs wrapper to properly handle path renaming
How is the PR tested?
Manually by starting
weed server -filer
weed webdav -port=11111
# create folder `/subfolder` in filer
weed webdav -port=11112 -filer.path=/subfolder
# try renaming, deleting, creating folders via localhost:11112 WebDAV server

Checks

[ ] I have added unit tests if possible.
[x] I will add related wiki document changes and link to this PR after merging.

hi @chrislusf are you expecting anything more to merge this?


Write overview on the transformation from Quads to Blacklight Index

To facilitate the use of Blacklight as the discovery interface we needed to convert the graph data for each Berenson entity into a single Solr document . This is effectively flattening the graph model into a single document composed of a number of properties. This task is complicated by two factors: we store the data across four named graphs, one for each edition and a project data graph and the general recursive nature of working with triples.
The first problem is solved by querying each edition graph separately and then combining the data together at the end of the process under year labeled fields, for example each solr document had three verso title field:

verso_title_1903_t
verso_title_1938_t
verso_title_1961_t

Which are all indexed separately and included in the fields tied to a "Title" search.
We also added a number of new fields to the index to support the data model from notes, techniques, provenance data, image assets, etc, that appear on each record page.
To quickly index the data we do not execute thousands of SPARQL queries to load all the data for the Berenson entities we simply download all the triples from the endpoint for each graph and process them in bulk. The recursiveness problem, for example having to dereference the label of an institution which is stored in another statement, is done by building internal lookup tables at process time for the literals. This does require that you pre-process all the triples to build those lookup tables before constructing Solr documents, basically processing the dataset twice. This approach works at the scale of our dataset, being able to convert the triples over into a Solr documents and push them into the index in around 5 to 10 minutes depending the server specs. If the dataset was much larger however it would necessitate constructing more detailed SPARQL queries for each Solr document.
The opportunity for enrichment is enabled at the time of indexing by dereferencing external URIs such as VIAF, ULAN or AAT for their full data (and then cache it for later use) and choosing to included that new data in the index. For example if we wanted to include the alt-terms used for techniques in addition to its pref-label we could push those literals into the Solr index only, without having to store them in the our triple store.

Customizations to the interface focused on displaying and organizing the new data fields introduced with work mainly focused on the item display page. Through a number of design iterations and feedback from domain specialists we settled on the layout which accommodates displaying data for a single record but also able to convey the historic data from three different editions. We added an asset viewer using open Seadragon powered by an IIIF server as well as some other static pages and customization to theme the site to the current Itatti website.
On the backend a number of new fields were added to the Solr config with corresponding relevancy boots. And we introduced a couple of new search methods that required some more advanced tokenizing such as the search by Inventory Number feature.

Okay here is the writeup, let me know if you would like parts expanded or other pieces covered.

Sorry for my delay--this is great! Could you expand a little bit on the last paragraph about new fields and new search methods?
It'd also be great to have a few screenshots--I can do these if you tell me which files would be best. I was thinking an image of the lookup tables and also the main Solr document.


CSS Fixes for Sidebar and Landing Page Subtitle

@juliantoledo PTAL
Preview here

LGTM, blue grey looks much better.


Allow hostname instead of IP only for ping again

In recent builds (since https://github.com/louislam/uptime-kuma/commit/3a0bc80016380a5b56617e15b496d0839b9c42bb) there is a requirement to put an IP or a specific hostname into the TCP ping field.
https://github.com/louislam/uptime-kuma/blob/6aaf984f29b6bef6bb9fb8f859744aa60a97ef59/src/pages/EditMonitor.vue#L53
@Ponkhy Is there any technical reason why this is done? Or is there something funky with the regex?
Allowing only IP addresses as ping type seems a bit restrictive.
The host name regex seems to work fine when testing it on its own, but the validation seems to fail nonetheless when a hostname is entered.
Using example.com results in a failed input validation

The commit ID I built my docker image with:
Checking out Revision 6aaf984f29b6bef6bb9fb8f859744aa60a97ef59 (refs/remotes/origin/master)
 > git config core.sparsecheckout # timeout=10
 > git checkout -f 6aaf984f29b6bef6bb9fb8f859744aa60a97ef59 # timeout=10
 > git branch -a -v --no-abbrev # timeout=10
 > git branch -D master # timeout=10
 > git checkout -b master 6aaf984f29b6bef6bb9fb8f859744aa60a97ef59 # timeout=10
Commit message: "fix retryInterval = 0 if it is an old monitor. (#380 https://github.com/louislam/uptime-kuma/pull/380#issuecomment-922618356)"

It's a bug, it was intended to accept hostnames and ip addresses, we want to restrict it because we had a couple cases where ppl put a port in the hostname field too.

Have to concat two regex rules in string.
https://stackoverflow.com/questions/4947561/can-i-combine-2-regex-with-a-logic-or/4947608
But my attempt is failed, since I am not familiar with regex.


missing changelog for rc.2

Version info

Angular:
Firebase:
AngularFire:
Other (e.g. Ionic/Cordova, Node, browser, operating system):
How to reproduce these conditions
Failing test unit, Plunkr, or JSFiddle demonstrating the problem

Steps to set up and reproduce

Sample data and security rules
<-- include/attach/link to some json sample data (or provide credentials to a sanitized, test Firebase project) -->
Debug output
** Errors in the JavaScript console **
** Output from firebase.database().enableLogging(true); **
** Screenshots **
Expected behavior

Actual behavior

Releasing it today. No API changes only library packaging changes.


designing the publish command

there are a few considerations in designing the publish command that largely center around overwriting preexisting scripts and routes:

publishing a script is always a put and always overwrites
publishing a route is a post, and will fail if a route already exists with the same pattern
there is no guarantee that a route with an id has the same pattern as it previously had

i have stopped work on route in #65 by allowing a single publish. if the route is changed in the wrangler.toml, multiple publishes will work, but attempts to publish to a route that has a pattern that has already been published will currently throw an error.
what is the behavior we want to see here? what API do we need to do to improve this situation?

can we codify these considerations into the CLI? for instance, can we look at what the script/route config is pre-publish, and try to determine how successful the publish command will be?
i'm not at all an expert on CLI design/best practices, but intuitively, what feels right to me is having all of the publish-related commands default to bailing if there's any chance that an error will come back – if a user publishes and the API errors out, the routes are going to be left in an unknown state, which kinda sucks! if they want to continue ahead, maybe the best way is to indicate how things are going to change, and then having them confirm – if that happens, can we just delete the already-published route and create the new one as per local config?

@signalnerve thanks for weighing in here!
one previously unspoken constraint i have here is that i anticipate that some folks will want to use this tool in an automated way- CI, etc. as a result, i prefer to avoid interactive commands, aiming that a command should either succeed or error out with a suggestion of the command to call to proceed.
given your feedback, i'd see the design being that we would not want to overwrite unless a force flag is present. here's how i would see that working:

user writes a script, user defines a route pattern in their wrangler.toml
user publishes, they have no previous scripts or routes so this publish succeeds
a user updates their script, leaves route pattern defined
user attempts to publish, this fails because it attempts to create a new route with the same pattern and to overwrite a previous script**
user publishes with force to overwrite the script and the route

** creating a new route feels like overkill on every publish. it's hard tho for several reasons:

users should be able define routes in their wrangler.toml based on pattern, NOT id
id does not guarantee the same pattern

as a result the checking we'll have to do is as follow:

script name => does it already exist (api call), does the user want to overwrite (force flag)
route pattern =>

does a route with this pattern already exist? (api call)
if yes? is it the route that is associated with the script we are currently trying to push? (api call)
if yes, leave route, override script

the tricky thing is... what to do if the answers are no. we can just bail, but what the user should do to fix it is unclear to me.

i solved this in #65


Installation does not work

Had it working last night in VMs and on my actual machine, but now it will not install. It will get up to the point of choosing installation location, and after that it reboots and goes back to the Arch ISO boot screen. Trying to install on /dev/sdc on my actual machine and /dev/sda on a VM
Ironically, currently posting this from a working install
Similar to #102 but this also happens on a real machine

UPDATE: .....nevermind.


Async submodule support

Well, I split the submodule work out into its own PR hoping that'd make it more easily reviewable, but then I went an added a new fixture with submodules so this PR is back to being unreviewable :cry:

That test failure is a race that's over on dh-async-repo too. I'll fix it over there.


Suggest scoped providers first, if retrieving with missing scope [Feature request]

There is one feature I like most: helpful error messages. However, one error message is not very helpful. I have a quite large dependency graph (and growing fast) with scoped singleton somewhere in the middle of it:
database <-- scoped
  - repositoryA
    - controllerA
  - repositoryB
    - controllerB
    - controllerC

I am passing the scope while retrieving only leafs. So, sometimes I am getting a message:
com.github.salomonbrys.kodein.Kodein$NotFoundException: No provider found for bind<MongoDatabase>() with ? { ? }
Registered in Kodein:
        bind<Phrases>() with provider { Phrases }
        bind<MongoClient>() with provider { MongoClient }
        bind<PasswordHash>() with provider { PasswordHash }
        bind<Logger>() with provider { Logger }
        bind<MongoCollection<User>>() with provider { MongoCollection<User> }
        bind<MongoDatabase>() with scopedSingleton(DatabaseScope.Companion) { DatabaseScope -> MongoDatabase }
........

with a full list of providers.
I would like to see in the first place all MongoDatabase binds, whose informs me what exactly scope is missing:
Registered in Kodein:
        bind<MongoDatabase>() with scopedSingleton(DatabaseScope.Companion) { DatabaseScope -> MongoDatabase }
        bind<Phrases>() with provider { Phrases }
        bind<MongoClient>() with provider { MongoClient }
        bind<PasswordHash>() with provider { PasswordHash }
        bind<Logger>() with provider { Logger }
        bind<MongoCollection<User>>() with provider { MongoCollection<User> }
.....

Also, it could be the same with tags.

Kodein 5.0 beta2 has been released, it contains this feature. Could you check ?

It works!

Kodein 5.0 stable has been released :)


Allow Highlight Styling of Individual Warnings/Errors

I like to be able to define individual highlighting styles for individual warnings/errors.
Taking python as an example, I like to have individual highlighting for warnings from MyPy.
Such type warnings represent their own class and hence it would be highly useful to visually differentiate them from formatting or other warnings types.
A different appearance could be achieved by defining a custom color and highlighting style.
Also whether a symbol in the Gutter is shown or not and if Sublime's capabilities allow it which one to display in the first place could be important for discerning different warnings/errors.

Would also be useful to mark TODO comments in a different style and color, e.g. blue.

@FichteFoll #666 will allow each linter to have its own color. So I guess it will depend on what’s highlighting your todo’s (I don’t have experience with that myself).

Sounds nice. I was catching something that seemed related in #665 (or maybe it was #666), but the PRs have grown so much that I just couldn't bother going through the actual details. Instead, I made a related comment on the related issue and am glad to here that something like this is indeed being considered.

Sure, it's going to be a pretty big update. What it entails I'll document later. In any case, if you're using SublimeLinter-annotations to highlight TODO's etc, what you're proposing will be supported.


Save/Get User Credentials

How can I save/get user credentials from the Keychain (iOS) and Keystore (Android) like this plugin: capacitor-native-biometric

https://github.com/aparajita/capacitor-secure-storage


Metadata field SENSING_TIME differs between L30

This issue is derived from a comment posted by @collinss-jpl in https://github.com/opera-adt/PROTEUS/issues/21#issuecomment-1144089908_
There is a discrepancy in the DSWx-HLS metadata regarding the SENSING_TIME field between L30 and S30 datasets.
For L30 datasets the field is set with both a beginning and end time, delimited by semi-colon:
>>> from osgeo import gdal
>>> ds = gdal.Open(".../output_dir/OPERA_L3_DSWx_HLS_LANDSAT-8_T22VEQ_20210905T143156_v2.0_001_B01_WTR.tif")
>>> ds.GetMetadata()['SENSING_TIME']
'2021-09-05T14:31:56.9300799Z; 2021-09-05T14:32:20.8126470Z'

This is fine, as we want a begin/end time to put into the output ISO XML.
However, for an S30 dataset, only a single time is available:
>>> ds = gdal.Open(".../output_dir/OPERA_L3_DSWx_HLS_SENTINEL-2A_T15SXR_20210907T163901_v2.0_001_B01_WTR.tif")
>>> ds.GetMetadata()['SENSING_TIME']
'2021-09-07T16:54:47.751044Z'

Commit 602fc35 fixes this issue.


Issue/1877 security vulnerabilities (#2063)

Pull request opened by the merge tool on behalf of #2063

Merged in dd38824476bba97977ab456e1ef0def43403d010


Is quadrat an instrument?

A quadrat is defined as a transportable frame to sample a unit of area. Currently quadrat belongs to site type (http://linked.data.gov.au/def/tern-cv/74aa68d3-28fd-468d-8ff5-7e791d9f7159). Given the definition it seems that quadrat should be used as an instrument (see GCMD example https://gcmd.earthdata.nasa.gov/kms/concept/1474e6ed-cce2-4f24-980a-68f03eda0194?format=rdf).

Action: to remove the class tern:Quadrat and remove the feature type "quadrat" from the site types vocabulary.
Background
The tern:Quadrat class was originally created to represent some example data provided by The University of Sydney where observations were made within sites that were divided into plots and further divided into quadrats.
The below illustration is taken from Modelling sites, transects and quadrats.

To keep the information model simple, the tern:Quadrat class will be removed. Use tern:Site or some other FeatureOfInterest to represent quadrants or sub-sites within a site.
tern:Quadrat was originally created to represent "sub-sites" that were named quadrats in a University of Sydney dataset back when tern:Site required site visits to be mandatory.
This was unnecessarily complex to model a "site-visit" for each quadrant. Most surveys or site visits, including the sub-sites were performed on one single day.
This requirement for having mandatory site-visits no longer exist to support for opportunistic observations where a site visit to a site does not necessarily exist, at least in the source data. However, this is still up for debate as per the issue https://github.com/ternaustralia/ontology_tern/issues/186.


Change live preview page to features section

Live preview page is moved under features section in docs
new url: http://.../docs/features/live-preview/
#16

LGTM Merging


How to handle cases where OpenAPI is not enough?

Related to #11
Currently we have two actual real world use cases where we use JSON Schema:

Query API (Shop Search)
Event Schema Registry (Nakadi)

We should discuss and agree on:

When is it OK to use JSON Schema?
What are some known shortcomings of OpenAPI that we know of (e.g. anyOf)?
How should APIs be documented that use JSON Schema?
How to embed JSON Schema in OpenAPI?
 Sample JSON files + Schema YAML files?

Embedding a JSON schema could look like this:
Entity:
  x-schema:
    anyOf:
      - type: string
      - type: object

Nitpick: The Query API is part of Search (Matchmaking), not Shop

Maybe we could have a section where we compare the same API, defined once with OpenAPI and once with OpenAPI+JSON Schema and showcase how they differ and what the benefits are.

@mostlymagic Can we discuss the search API (or its JSON schema part) publicly (for a potential comparison of JSON schema and same-featured OpenAPI schema), or should we rather take an imaginary example?

In guild meeting we've decided to leave the decision up to api developers and we expect that it's clear those extensions in api definition have to be documented anyway.


simplify snippets

@aslakhellesoy
cucumber-js supports multiple snippet interfaces

@aslakhellesoy bump on this and the other PRs in this repo

Closing as appears to have been updated separately


Add support for WMTS tile map service

WMTS is a service for georeferenced maps, served as image tiles. It's very similar to the TMS specification, which is already supported.
It seems that the only change to the TMS tilesource implementation that's needed to make it a WMTS implementation is this:
// Original TMS implementation (tmstilesource.js, line 128)
getTileUrl: function( level, x, y ) {
  var yTiles = this.getNumTiles( level ).y - 1;
  return this.tilesUrl + level + "/" + x + "/" + (yTiles - y) + ".png";
}

// Changed to...
getTileUrl: function( level, x, y ) {
  return this.tilesUrl + level + "/" + x + "/" + y + ".png";
}

Therefore I wonder if WMTS could be made a config option to the TMS tilesource?

Sounds great! What do you think would be a good API for it?
According to our demo (which needs a new demo tile source, by the way, if you know of one!), here's an example TMS:
{
  type:       'tiledmapservice',
  tilesUrl:   'http://tilecache.osgeo.org/wms-c/tilecache.py/1.0.0/basic/',
  width: 256 * 65534,
  height: 256 * 32767
}

So maybe we support WMTS like so?
{
  type:       'webmaptileservice',
  tilesUrl:   'http://tilecache.osgeo.org/wms-c/tilecache.py/1.0.0/basic/',
  width: 256 * 65534,
  height: 256 * 32767
}

I suppose we could also do something like this:
{
  type:       'tiledmapservice',
  tilesUrl:   'http://tilecache.osgeo.org/wms-c/tilecache.py/1.0.0/basic/',
  width: 256 * 65534,
  height: 256 * 32767,
  flipY: false
}

… But I think that could be confusing for people just coming looking for WMTS without knowing the implementation details.
Either way, we can lean heavily on the TmsTileSource for implementation.
Sound good? Are you up for making a pull request around this? If so, I'm happy to provide guidance. Otherwise, hopefully someone else will pick up the torch.

I thought about this a little. How about a property called protocol which could either be tms or wmts? If we default to tms, everything stays compatible to the way things are now.
Incidentally, 'TMS' is the acronym of Tile Map Service,  not tiled (i.e. the current tile source key tiledmapservice is - if you're nitpicky - wrong). Therefore, we can argue that both TMS and WMTS are protocols for tiled map delivery. So the name can stay as an appropriate umbrella term, and even though we wouldn't change anything, the naming would actually be correcter than before :-)

Nice. Well, that sounds reasonable to me.
 Sounds like it'll just be a small change to TmsTileSource, plus some documention.
Also, do you know of any sample files out there in these formats that we can use for the demo page?

Yes, more than happy to attempt a pull request for that. (Might take a week or so until I can start looking into this though.)
I'll also look for good example layers to post on the website.

Awesome, thank you! No worries on the timing, of course :-)

Hi. Finally revisited this after a long time. In the end, I think it's not possible to implement useful WMTS support purely with a tilesource.

WMTS have an XML "manifest" which needs to be loaded before, and which defines the properties of the tilesource.
there's also the issue of dealing with geo-coordinates.

The first issue, I assume, could be handled somehow (after all, IIIF manifests work in a similar way). But because of the second issue, I thought this might be easier/better to handle via a plugin.
I made a first version here:
https://github.com/recogito/openseadragon-wmts
Would be interested to hear your feedback!

Even DZI images have an XML “manifest” so I wouldn’t think that’s an issue...
I personally think all tile sources should be plugins so developers can just pull in the ones they use.
Very cool!


New Models and Whatnot

Merge Vowgan's replacement assets into trunk.

May wanna resolve Cue Ball, Black Ball, 9-ball, and 4-ball + and - meshes not being set in the PoolStateManager in the prefab as uploading without it will cause them to not render at all.

@Vowgan Please pull latest and fix on your end.

@Vowgan Looks like you'll need to apply the marker mesh fix on your end, I can't push to this.


xvfb-run: mark as linux-only

Motivation for this change
ZHF: #122042
cc @NixOS/nixos-release-managers
I came across this via a failing build of flent on Darwin: https://hydra.nixos.org/build/142500397/nixlog/1
It looks like the xvfb-run script uses mcookie, which is part of util-linux. For laughs, I tried building util-linux on Darwin, but it has dependencies that are also marked broken on Darwin, so not sure following that path is much use.
I think the proper solution here is to somehow package mcookie separately, as a small build of util-linux? NetBSD appears to be doing this as well: https://ftp.netbsd.org/pub/pkgsrc/current/pkgsrc/x11/mcookie/index.html
Quick resolution is to mark this as linux-only for now.
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[ ] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Ensured that relevant documentation is up to date
[ ] Fits CONTRIBUTING.md.

Just realized this package has no maintainers and nobody was pinged. This file also has very little git history, so I can't tell who would be knowledgable on the issue here.
cc @marsam, because I'm essentially reverting part of #95491


Script to generate DC CSV files from MARC (Preservation metadata)

Scan directories from https://github.com/crkn-rcdr/cihm-metadatabus/issues/58 and generate CSV files.
This tool will only be useful for Preservation, which will be DC only as part of the move to Archivematica.
Rules in https://www.loc.gov/marc/marc2dc.html will be used, with any adjustments requested by the Heritage Services team before we initiate any migration.

Script created. Other work to be done will be decided later.


It is possible to add rust?

as the title says, it is possible to add "rust dedicated server", see https://developer.valvesoftware.com/wiki/Rust_Dedicated_Server

https://github.com/dgibbs64/linuxgameservers/issues/155
Dev's are lazy and wont fix the Linux SteamCMD download

Closing as duplicate.
https://github.com/dgibbs64/linuxgameservers/issues/155


ModuleNotFoundError: No module named 'flash_attn.models.falcon'

I ran the bash scripts/setup_flash.sh without error （but it cost just a few minute)

But I got a wrong message when I run the bash scripts/run_pile.sh
Traceback (most recent call last):
  File "doremi/train.py", line 56, in <module>
    import doremi.models as doremi_models
  File "/storage/home/lanzhenzhongLab/zhaoyu/doremi/doremi/models.py", line 8, in <module>
    from flash_attn.models.gpt import GPTLMHeadModel as GPTLMHeadModelFlash
  File "/storage/home/lanzhenzhongLab/zhaoyu/.conda/envs/zy_doremi/lib/python3.8/site-packages/flash_attn-2.0.4-py3.8-linux-x86_64.egg/flash_attn/models/gpt.py", line 31, in <module>
    from flash_attn.models.falcon import remap_state_dict_hf_falcon
ModuleNotFoundError: No module named 'flash_attn.models.falcon'

what wrong with this?
also,I found something wrong with bash scripts/run_preprocess_pile.sh until I update the packages datasets to 2.15.0 , but  version in setup.py is 2.10.1.
Is something wrong in my operate?

Just pushed an update that should fix the import issue. Could you add more details on the preprocess issue?

tks for ur help! I will try it later.
The preprocessing issue is as follows: it can be successfully run  by updating datasets to 2.15.0

Hello, another error when I run the lastest code.
Original Traceback (most recent call last):
  File "xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
    data = fetcher.fetch(index)
  File "/xxxxu/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
    data.append(next(self.dataset_iter))
  File "/xxxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer_pt_utils.py", line 807, in __iter__
    for element in self.dataset:
  File "/xxxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 1358, in __iter__
    yield from self._iter_pytorch()
  File "/sxxxxu/.conda/envs/zy_doremi/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 1293, in _iter_pytorch
    for key, example in ex_iterable:
  File "/xxxxu/.conda/envs/zy_doremi/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 233, in __iter__
    yield from self.generate_examples_fn(**self.kwargs)
  File "/xxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/datasets/packaged_modules/generator/generator.py", line 30, in _generate_examples
    for idx, ex in enumerate(self.config.generator(**gen_kwargs)):
  File "/sxxxx/doremi/doremi/dataloader.py", line 339, in take_data_generator
    for ex in ds:
  File "/xxxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 1358, in __iter__
    yield from self._iter_pytorch()
  File "/sxxxxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 1274, in _iter_pytorch
    ex_iterable = ex_iterable.shard_data_sources(worker_id=worker_info.id, num_workers=worker_info.num_workers)
TypeError: shard_data_sources() got an unexpected keyword argument 'worker_id'

Wandb has been init ,and have this error. I run the code with a 8*A100 GPUs machine.  It something wrong with my config? or the wrong version I used.

Yes it's because of the different version of datasets. You could try doing pip install fsspec==2023.9.2 with the original datasets version.
The num_epochs should be ignored - the training terminates on steps.

tksss for ur help!!  I can run the run_pile_baseline120M.sh  now.
But I found that something wrong when I run the run_pile_doremi120M.sh  seem like something wrong in backward.
File "doremi/train.py", line 405, in <module>
    main()
  File "doremi/train.py", line 342, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "xxx/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1633, in train
    return inner_training_loop(
  File "xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1902, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/xxx/doremi/doremi/trainer.py", line 386, in training_step
    loss.backward()
  File "/xxxu/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/_tensor.py", line 487, in backward
    torch.autograd.backward(
  File "/xxxx.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/autograd/__init__.py", line 200, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [34,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.

Sorry for asking so many run-related questions. I really can't resolve these issues.😭

All package version are same to the setup.py

I'm not totally sure, but sometimes these errors can be mitigated by uninstalling and doing a fresh install of torch / flash-attn.  You could also try running on CPU or with CUDA_LAUNCH_BLOCKING=1 to see if it will give a better trace. It could also be worth checking your CUDA version vs. which CUDA version your pytorch is compiled with.

The code can't run with CPU mode. And I have rebuilt the conda environment for several times.

Is this right?
My cuda  version  is 11.7 and torch.cuda.is_available is True

Do you have a more detailed trace? What part of the code raises this error?

sure, this is the detail error.
Traceback (most recent call last):
  File "doremi/train.py", line 405, in <module>
    main()
  File "doremi/train.py", line 82, in main
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
  File "/xxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/hf_argparser.py", line 332, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 121, in __init__
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/training_args.py", line 1227, in __post_init__
    and (self.device.type != "cuda")
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/training_args.py", line 1659, in device
    return self._setup_devices
  File "/xxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/utils/generic.py", line 54, in __get__
    cached = self.fget(obj)
  File "/xxxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/training_args.py", line 1649, in _setup_devices
    torch.cuda.set_device(device)
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/cuda/__init__.py", line 350, in set_device
    torch._C._cuda_setDevice(device)
RuntimeError: CUDA error: invalid device ordinal
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

sure，there are the detail error message.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fb982d8c4d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fb982d5636b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fb982e30fa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x137bb (0x7fb982e017bb in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0x22d80 (0x7fb982e10d80 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #5: <unknown function> + 0x4ccb86 (0x7fb9c22c2b86 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x3ee77 (0x7fb982d71e77 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x1be (0x7fb982d6a69e in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7fb982d6a7b9 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x752188 (0x7fb9c2548188 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x325 (0x7fb9c2548535 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #11: /xxx/.conda/envs/zy_doremi/bin/python() [0x50243d]
frame #12: /xxx/.conda/envs/zy_doremi/bin/python() [0x4e0970]
frame #13: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1828]
frame #14: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #15: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #16: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #17: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #18: /xxx/.conda/envs/zy_doremi/bin/python() [0x4c9310]
frame #19: PyDict_SetItemString + 0x52 (0x581a82 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #20: PyImport_Cleanup + 0x93 (0x5a6cb3 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #21: Py_FinalizeEx + 0x71 (0x5a5de1 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #22: Py_RunMain + 0x112 (0x5a1ab2 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #23: Py_BytesMain + 0x39 (0x579e89 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #24: __libc_start_main + 0xf3 (0x7fba0928a6a3 in /lib64/libc.so.6)
frame #25: /xxx/.conda/envs/zy_doremi/bin/python() [0x579d3d]

Traceback (most recent call last):
  File "doremi/train.py", line 405, in <module>
    main()
  File "doremi/train.py", line 342, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1633, in train
    return inner_training_loop(
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1902, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/xxx/code/opensource/doremi/doremi/trainer.py", line 351, in training_step
    curr_domain_weights = train_domain_weights[inputs['domain_ids']].unsqueeze(-1).expand_as(pertoken_loss).detach()
RuntimeError: CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fc421b4e4d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fc421b1836b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fc421bf2fa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x137bb (0x7fc421bc37bb in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0x22d80 (0x7fc421bd2d80 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #5: <unknown function> + 0x4ccb86 (0x7fc461084b86 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x3ee77 (0x7fc421b33e77 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x1be (0x7fc421b2c69e in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7fc421b2c7b9 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x752188 (0x7fc46130a188 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x325 (0x7fc46130a535 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #11: /xxx/.conda/envs/zy_doremi/bin/python() [0x50243d]
frame #12: /xxx/.conda/envs/zy_doremi/bin/python() [0x4e0970]
frame #13: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1828]
frame #14: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #15: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #16: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #17: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #18: /xxx/.conda/envs/zy_doremi/bin/python() [0x4c9310]
frame #19: PyDict_SetItemString + 0x52 (0x581a82 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #20: PyImport_Cleanup + 0x93 (0x5a6cb3 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #21: Py_FinalizeEx + 0x71 (0x5a5de1 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #22: Py_RunMain + 0x112 (0x5a1ab2 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #23: Py_BytesMain + 0x39 (0x579e89 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #24: __libc_start_main + 0xf3 (0x7fc4a804f6a3 in /lib64/libc.so.6)
frame #25: /xxx/.conda/envs/zy_doremi/bin/python() [0x579d3d]

../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [35,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [44,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [50,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [56,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [57,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [58,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [12,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [16,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [23,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [27,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [30,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [0,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [14,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [25,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [26,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [36,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [37,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [48,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [53,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [0,0,0], thread: [59,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
Traceback (most recent call last):
  File "doremi/train.py", line 405, in <module>
    main()
  File "doremi/train.py", line 342, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1633, in train
    return inner_training_loop(
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1902, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/xxx/code/opensource/doremi/doremi/trainer.py", line 351, in training_step
    curr_domain_weights = train_domain_weights[inputs['domain_ids']].unsqueeze(-1).expand_as(pertoken_loss).detach()
RuntimeError: CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f19ad7564d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f19ad72036b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f19ad7fafa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x137bb (0x7f19ad7cb7bb in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0x22d80 (0x7f19ad7dad80 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #5: <unknown function> + 0x4ccb86 (0x7f19ecc8cb86 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x3ee77 (0x7f19ad73be77 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x1be (0x7f19ad73469e in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f19ad7347b9 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x752188 (0x7f19ecf12188 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x325 (0x7f19ecf12535 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #11: /xxx/.conda/envs/zy_doremi/bin/python() [0x50243d]
frame #12: /xxx/.conda/envs/zy_doremi/bin/python() [0x4e0970]
frame #13: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1828]
frame #14: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #15: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #16: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #17: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #18: /xxx/.conda/envs/zy_doremi/bin/python() [0x4c9310]
frame #19: PyDict_SetItemString + 0x52 (0x581a82 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #20: PyImport_Cleanup + 0x93 (0x5a6cb3 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #21: Py_FinalizeEx + 0x71 (0x5a5de1 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #22: Py_RunMain + 0x112 (0x5a1ab2 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #23: Py_BytesMain + 0x39 (0x579e89 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #24: __libc_start_main + 0xf3 (0x7f1a33c516a3 in /lib64/libc.so.6)
frame #25: /xxx/.conda/envs/zy_doremi/bin/python() [0x579d3d]

Traceback (most recent call last):
  File "doremi/train.py", line 405, in <module>
    main()
  File "doremi/train.py", line 342, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1633, in train
    return inner_training_loop(
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1902, in _inner_training_loop
terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7fb1eefcb4d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7fb1eef9536b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7fb1ef06ffa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x137bb (0x7fb1ef0407bb in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0x22d80 (0x7fb1ef04fd80 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #5: <unknown function> + 0x4ccb86 (0x7fb22e501b86 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x3ee77 (0x7fb1eefb0e77 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x1be (0x7fb1eefa969e in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7fb1eefa97b9 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x752188 (0x7fb22e787188 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x325 (0x7fb22e787535 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #11: /xxx/.conda/envs/zy_doremi/bin/python() [0x50243d]
frame #12: /xxx/.conda/envs/zy_doremi/bin/python() [0x4e0970]
frame #13: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1828]
frame #14: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #15: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #16: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #17: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #18: /xxx/.conda/envs/zy_doremi/bin/python() [0x4c9310]
frame #19: PyDict_SetItemString + 0x52 (0x581a82 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #20: PyImport_Cleanup + 0x93 (0x5a6cb3 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #21: Py_FinalizeEx + 0x71 (0x5a5de1 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #22: Py_RunMain + 0x112 (0x5a1ab2 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #23: Py_BytesMain + 0x39 (0x579e89 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #24: __libc_start_main + 0xf3 (0x7fb2754c66a3 in /lib64/libc.so.6)
frame #25: /xxx/.conda/envs/zy_doremi/bin/python() [0x579d3d]

    tr_loss_step = self.training_step(model, inputs)
  File "/xxx/code/opensource/doremi/doremi/trainer.py", line 351, in training_step
    curr_domain_weights = train_domain_weights[inputs['domain_ids']].unsqueeze(-1).expand_as(pertoken_loss).detach()
RuntimeError: CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f545cb834d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f545cb4d36b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f545cc27fa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x80 (0x7f545db0f410 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x58 (0x7f545db129e8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::workCleanupLoop() + 0x227 (0x7f545db13f37 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #6: <unknown function> + 0xc2b73 (0x7f54db71bb73 in /lib64/libstdc++.so.6)
frame #7: <unknown function> + 0x82de (0x7f54e3bb32de in /lib64/libpthread.so.0)
frame #8: clone + 0x43 (0x7f54e315ae83 in /lib64/libc.so.6)

Traceback (most recent call last):
  File "doremi/train.py", line 405, in <module>
    main()
  File "doremi/train.py", line 342, in main
    train_result = trainer.train(resume_from_checkpoint=checkpoint)
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1633, in train
    return inner_training_loop(
  File "/xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/transformers/trainer.py", line 1902, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/xxx/code/opensource/doremi/doremi/trainer.py", line 351, in training_step
    curr_domain_weights = train_domain_weights[inputs['domain_ids']].unsqueeze(-1).expand_as(pertoken_loss).detach()
RuntimeError: CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f44b65cf4d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f44b659936b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f44b6673fa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x80 (0x7f44b755b410 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x58 (0x7f44b755e9e8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::workCleanupLoop() + 0x227 (0x7f44b755ff37 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #6: <unknown function> + 0xc2b73 (0x7f4535197b73 in /lib64/libstdc++.so.6)
frame #7: <unknown function> + 0x82de (0x7f453d62f2de in /lib64/libpthread.so.0)
frame #8: clone + 0x43 (0x7f453cbd6e83 in /lib64/libc.so.6)

wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.
terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f6106bf04d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f6106bba36b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f6106c94fa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x80 (0x7f6107b7c410 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x58 (0x7f6107b7f9e8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::workCleanupLoop() + 0x227 (0x7f6107b80f37 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_cuda.so)
frame #6: <unknown function> + 0xc2b73 (0x7f618578ab73 in /lib64/libstdc++.so.6)
frame #7: <unknown function> + 0x82de (0x7f618dc222de in /lib64/libpthread.so.0)
frame #8: clone + 0x43 (0x7f618d1c9e83 in /lib64/libc.so.6)

terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f2cdf7224d7 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f2cdf6ec36b in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f2cdf7c6fa8 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x137bb (0x7f2cdf7977bb in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #4: <unknown function> + 0x22d80 (0x7f2cdf7a6d80 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10_cuda.so)
frame #5: <unknown function> + 0x4ccb86 (0x7f2d1ec58b86 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x3ee77 (0x7f2cdf707e77 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #7: c10::TensorImpl::~TensorImpl() + 0x1be (0x7f2cdf70069e in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #8: c10::TensorImpl::~TensorImpl() + 0x9 (0x7f2cdf7007b9 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #9: <unknown function> + 0x752188 (0x7f2d1eede188 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #10: THPVariable_subclass_dealloc(_object*) + 0x325 (0x7f2d1eede535 in /xxx/.conda/envs/zy_doremi/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #11: /xxx/.conda/envs/zy_doremi/bin/python() [0x50243d]
frame #12: /xxx/.conda/envs/zy_doremi/bin/python() [0x4e0970]
frame #13: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1828]
frame #14: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #15: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #16: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #17: /xxx/.conda/envs/zy_doremi/bin/python() [0x4f1811]
frame #18: /xxx/.conda/envs/zy_doremi/bin/python() [0x4c9310]
frame #19: PyDict_SetItemString + 0x52 (0x581a82 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #20: PyImport_Cleanup + 0x93 (0x5a6cb3 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #21: Py_FinalizeEx + 0x71 (0x5a5de1 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #22: Py_RunMain + 0x112 (0x5a1ab2 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #23: Py_BytesMain + 0x39 (0x579e89 in /xxx/.conda/envs/zy_doremi/bin/python)
frame #24: __libc_start_main + 0xf3 (0x7f2d65c1e6a3 in /lib64/libc.so.6)
frame #25: /xxx/.conda/envs/zy_doremi/bin/python() [0x579d3d]

wandb: - 0.017 MB of 0.017 MB uploaded (0.000 MB deduped)

I face the same issue, but it worked when I lowered batch_size.


Learner can erroneously change myArray by hand

Challenge Modify Array Data With Indexes has an issue. This code passes the challenge when it shouldn't:
var myArray = [3,2,3]; // erroneously changed by the learner from [1,2,3]
myArray[0] = 3;

Ah I see, are you saying that if the number before the modification is already 3, the challenge should not pass

Ah I see, are you saying that if the number before the modification is already 3, the challenge should not pass

Yes.
Just as the learner did, you changed code above "Only change code below this line".

Right but since the challenge does not pass, it gives the coder the opportunity to Read-Search and Ask, why?

A possible solution could be to move // Only change code below this line. up one line.

@willnwhite great observation I agree -  the comment can be moved up to indicate, write new lines only

@willnwhite @tommygebru sounds good to me. There should be another test that checks for var myArray = [45, 64, 99] (the numbers are changed in the beta/staging). The test message could be "The initial myArray line shouldn't be changed."

@erictleung I thought this too, but then I thought the learner could write:
var myArray = [45, 64, 99];
// Only change code below this line.
var myArray = [3, 64, 99];
...

So where do the checks end?
I noticed that @tommygebru wrote code above the line, as did the learner who prompted this issue, indicating they hadn't read this instruction. So I think that moving the line up may be all that is necessary.

@erictleung @willnwhite The instructions are rather bare, usually we have a lot of good reading material in this panel, this will be a good opportunity to update... will try a new issue soon!

@willnwhite
var myArray = [45, 64, 99];
// Only change code below this line.
var myArray = [3, 64, 99];

This could wouldn't pass all the tests currently in place. This would fail test: "You should be using correct index to modify the value in myArray."
The camper still needs to use the assign operator myArray[0]= to pass that test. I will add a test to check the initial myArray line hasn't been changed.


Support renameLocation for TS/JS refactorings

From https://github.com/Microsoft/TypeScript/issues/17852
Expected refactor flow should trigger rename after applying the refactoring

I still get newFunction, that doesn't seem right?

@roblourens Sorry, TS doesn't actually return the rename location yet so it is expected that this would not actually work. I should have marked this issue as a work item since it just was tracking our support for the rename location value once TS returns it.
I'm planning to create a new feature request next month to track fully supporting the rename flow


Tally Wallet Support

Tally Wallet support
Following an ETHDenver Bounty to integrate Tally wallet into an existing Dapp.
Changes 👷♀

add tally wallet to Injected provider list
set injectedConnector if Tally wallet since Tally only supports mainnet
add support for custom injected icons

Dependencies

depends on https://github.com/makerdao/dai-ui/pull/88

How to test 🧪

In a fresh browser install Tally wallet
click connect on oasis app

Preview

Definition of done ✔

[ ] Acceptance criteria for each issue met
[ ] Unit tests written where needed and passing
[ ] End-to-end tests for happy path
[ ] Documentation updated
[ ] Project builds without errors
[ ] Non-functional requirements met
[ ] Code reviewed and functionality tested by the reviewer (locally, Heroku, etc.)
[ ] Feature verified and accepted by Product Owner
[ ] Project deployed to production environment

Closed, we switched to the blocknative connection method.


New benchmark exec_prefix attribute

Coverage increased (+0.4%) to 78.717% when pulling 924c46f51d5b20ba327371b9721837892db61dbc on feat/exec_prefix into 17a7f2f9a852686661738d692207dd461a86a67c on master.


[2010-12-15] IIS file uploads give "URL Rewrite Module Error"

created by: @halkyon (sharvey)
assigned to: @halkyon (sharvey)
created at: 2010-12-15
original ticket: http://open.silverstripe.org/ticket/6269

Uploads are not viewable directly in the URL, causing TinyMCE to show a broken image when you insert a new image.
Steps to reproduce:

Upload a file using Files & Images in CMS to a folder
Go back to the Pages tab in CMS and open a page
Insert that image onto the page [broken image box appears or nothing at all]
Click Save and Publish [image appears on refresh]

PHP is able to create the file with the correct permissions, but when you view the URL you get a "URL Rewrite Module Error".
Interestingly enough, resampling the file works fine. I've checked permssions and they appear to be correct.
If you remove the  section of the web.config file, the file can then be viewed.
However, that doesn't appear to be the cause of the issue.
The cause I can see is newly uploaded files are missing the IIS_IUSRS group. This essentially means the file wasn't created inside the Uploads folder to inherit the parent permissions, but instead moved directly from temp which files inherit from very limited permissions in the system temp.
Reference: http://silverstripe.org/installing-silverstripe/show/15172#post296738

According to the post above, this issue was resolved via server reconfiguration. While it may still be necessary to update our windows configuration guide, the entire assets store has been radically rewritten since 2.4. Closing.


Fix kdm/data.json cache issue

This commit moves data.json from dockerfile.dapper into scripts/package,
also use dockerfile ARG to disable dockerfile caching.

Fix https://github.com/rancher/rancher/issues/26222.

https://github.com/rancher/rancher/issues/26200


codec(ticdc): refactor avro decoder by split the decode row method into small pieces

What problem does this PR solve?

Issue Number: close #9231
What is changed and how it works?
Check List
Tests

Unit test
Integration test

Questions
Will it cause performance regression or break compatibility?
Do you need to update user documentation, design documentation or monitoring documentation?
Release note
`None`

/test all

/test all

/test verify

/test dm-integration-test

/test cdc-integration-kafka-test

/test cdc-integration-kafka-test

/test dm-integration-test