cargo-udeps: 0.1.21 -> 0.1.22

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://github.com/est31/cargo-udeps/releases.
meta.description for cargo-udeps is: "Find unused dependencies in Cargo.toml"
meta.homepage for cargo-udeps is: "https://github.com/est31/cargo-udeps"
meta.changelog for cargo-udeps is: ""
Updates performed

Rust version update

To inspect upstream changes

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Impact

Checks done (click to expand)

built on NixOS

The tests defined in passthru.tests, if any, passed

/nix/store/h5s4l0i385rns6v3lwkhvfc0mn2670la-cargo-udeps-0.1.22/bin/cargo-udeps passed the binary check.

1 of 1 passed binary check by having a zero exit code.

1 of 1 passed binary check by having the new version present in output.

found 0.1.22 with grep in /nix/store/h5s4l0i385rns6v3lwkhvfc0mn2670la-cargo-udeps-0.1.22

found 0.1.22 in filename of file in /nix/store/h5s4l0i385rns6v3lwkhvfc0mn2670la-cargo-udeps-0.1.22

directory tree listing: https://gist.github.com/2ddc5f74f851e3f493021039be8a52c7

du listing: https://gist.github.com/bd952b1750029ca54a492f2fd300000d

Rebuild report (if merged into master) (click to expand)

4 total rebuild path(s)

1 package rebuild(s)

1 x86_64-linux rebuild(s)
1 i686-linux rebuild(s)
1 x86_64-darwin rebuild(s)
1 aarch64-linux rebuild(s)

First fifty rebuilds by attrpath
cargo-udeps

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/h5s4l0i385rns6v3lwkhvfc0mn2670la-cargo-udeps-0.1.22 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A cargo-udeps https://github.com/r-ryantm/nixpkgs/archive/ebdd686ab99c8f3e15dfd6dcbd2ba35f5cd385ff.tar.gz

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/h5s4l0i385rns6v3lwkhvfc0mn2670la-cargo-udeps-0.1.22
ls -la /nix/store/h5s4l0i385rns6v3lwkhvfc0mn2670la-cargo-udeps-0.1.22/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

cargo-udeps

Maintainer pings
cc @B4dM4n for testing.

Result of nixpkgs-review pr 127286 run on x86_64-darwin 1

1 package failed to build:

cargo-udeps

addressed in https://github.com/NixOS/nixpkgs/pull/127286


[Besu] Duplicate generation of private, public keys even if they are present in vault

Describe the bug
Currently the create/crypto playbook generates the public private key and node address for all nodes, even if their keys are present in the vault. And this happens when we again run the playbook, also this leads to generation of new genesis file will all the new address, even for the older nodes.
To Reproduce
Run the network more than once.
Expected behavior
Before generating the public private key and node address, playbook, should check for the keys in the vault and generate only for the new nodes.
Environment (please complete the following information):

Besu

I'll take this issue

Hi @mgCepeda , I tried to address this issue in PR, maybe you can do a validation check and take a call according.

I'll do validation check

I have done validation check and the code is working fine.


Keyboard navigation

Keyboard navigation in the calender would be great. I think about just moving from day to day with cursor keys.
(I replaced rednotebook with Joplin for it’s markdown abilities and got it nearly completely replaced by using calendar and journal add-on)

Thanks for the suggestion - I do want to add more keyboards in the future so I'll keep this in mind.

Added in 1.0.0-pre-release-4.


Encounter error when run demo on the colab

I run the demo, and open the webui, then upload image and run, but got error below:
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/gradio/routes.py", line 337, in run_predict
    output = await app.get_blocks().process_api(
  File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 1015, in process_api
    result = await self.call_function(
  File "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py", line 833, in call_function
    prediction = await anyio.to_thread.run_sync(
  File "/usr/local/lib/python3.10/dist-packages/anyio/to_thread.py", line 31, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 937, in run_sync_in_worker_thread
    return await future
  File "/usr/local/lib/python3.10/dist-packages/anyio/_backends/_asyncio.py", line 867, in run
    result = context.run(func, *args)
  File "/content/layerdivider/demo.py", line 36, in divide_layer
    return self.segment_divide(input_image, loops, init_cluster, ciede_threshold, blur_size, layer_mode, h_split, v_split, n_cluster, alpha, th_rate, split_bg, area_th)
  File "/content/layerdivider/demo.py", line 46, in segment_divide
    masks = load_masks(output_dir)
  File "/content/layerdivider/ldivider/ld_utils.py", line 77, in load_masks
    with open(pkl_path, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: '/content/layerdivider/output/tmp/seg_layer/sorted_masks.pkl'

Where to find the sorted_masks.pkl?

the same issue.


Update json manifest for 2023 cloudworld

{ "title": "Oracle CloudWorld 2023 - Support", "filename": "https://oracle-livelabs.github.io/common/support/ocwsupportlab/ocwsupportlab.md" }

Completed in latest pull request

Completed in latest pull request


[Device Support Request] Immax Neo Smart Keypad "_TZE200_n9clpsht"

Immax Neo Keypad Zigbee 3.0 / MaxSmart MS-K1AZ, is discovered fine by ZHA, but unfortunately no entities show up.
It seems a Quirk is needed...
Describe the solution you'd like
Entities for armed/disarmed/armed-home, battery, SOS and others if possible to show up in HA

Device signature
{
  "node_descriptor": "NodeDescriptor(logical_type=<LogicalType.EndDevice: 2>, complex_descriptor_available=0, user_descriptor_available=0, reserved=0, aps_flags=0, frequency_band=<FrequencyBand.Freq2400MHz: 8>, mac_capability_flags=<MACCapabilityFlags.AllocateAddress: 128>, manufacturer_code=4098, maximum_buffer_size=82, maximum_incoming_transfer_size=82, server_mask=11264, maximum_outgoing_transfer_size=82, descriptor_capability_field=<DescriptorCapability.NONE: 0>, *allocate_address=True, *is_alternate_pan_coordinator=False, *is_coordinator=False, *is_end_device=True, *is_full_function_device=False, *is_mains_powered=False, *is_receiver_on_when_idle=False, *is_router=False, *is_security_capable=False)",
  "endpoints": {
    "1": {
      "profile_id": 260,
      "device_type": "0x0051",
      "in_clusters": [
        "0x0000",
        "0x0004",
        "0x0005",
        "0xef00"
      ],
      "out_clusters": [
        "0x000a",
        "0x0019"
      ]
    }
  },
  "manufacturer": "_TZE200_n9clpsht",
  "model": "TS0601",
  "class": "zigpy.device.Device"
}

Diagnostic information
{
  "home_assistant": {
    "installation_type": "Home Assistant Container",
    "version": "2022.10.5",
    "dev": false,
    "hassio": false,
    "virtualenv": false,
    "python_version": "3.10.5",
    "docker": true,
    "arch": "armv7l",
    "timezone": "Europe/Madrid",
    "os_name": "Linux",
    "os_version": "5.10.103-v7l+",
    "run_as_root": true
  },
  "custom_components": {
    "hacs": {
      "version": "1.28.2",
      "requirements": [
        "aiogithubapi>=22.2.4"
      ]
    },
    "zha_toolkit": {
      "version": "v0.8.20",
      "requirements": [
        "packaging>=20.8"
      ]
    },
    "imou_life": {
      "version": "1.0.2",
      "requirements": [
        "imouapi==1.0.2"
      ]
    },
    "alarmo": {
      "version": "v1.9.6",
      "requirements": []
    },
    "smartir": {
      "version": "1.17.6",
      "requirements": [
        "aiofiles==0.6.0"
      ]
    }
  },
  "integration_manifest": {
    "domain": "zha",
    "name": "Zigbee Home Automation",
    "config_flow": true,
    "documentation": "https://www.home-assistant.io/integrations/zha",
    "requirements": [
      "bellows==0.34.2",
      "pyserial==3.5",
      "pyserial-asyncio==0.6",
      "zha-quirks==0.0.83",
      "zigpy-deconz==0.19.0",
      "zigpy==0.51.3",
      "zigpy-xbee==0.16.2",
      "zigpy-zigate==0.10.2",
      "zigpy-znp==0.9.1"
    ],
    "usb": [
      {
        "vid": "10C4",
        "pid": "EA60",
        "description": "*2652*",
        "known_devices": [
          "slae.sh cc2652rb stick"
        ]
      },
      {
        "vid": "1A86",
        "pid": "55D4",
        "description": "*sonoff*plus*",
        "known_devices": [
          "sonoff zigbee dongle plus v2"
        ]
      },
      {
        "vid": "10C4",
        "pid": "EA60",
        "description": "*sonoff*plus*",
        "known_devices": [
          "sonoff zigbee dongle plus"
        ]
      },
      {
        "vid": "10C4",
        "pid": "EA60",
        "description": "*tubeszb*",
        "known_devices": [
          "TubesZB Coordinator"
        ]
      },
      {
        "vid": "1A86",
        "pid": "7523",
        "description": "*tubeszb*",
        "known_devices": [
          "TubesZB Coordinator"
        ]
      },
      {
        "vid": "1A86",
        "pid": "7523",
        "description": "*zigstar*",
        "known_devices": [
          "ZigStar Coordinators"
        ]
      },
      {
        "vid": "1CF1",
        "pid": "0030",
        "description": "*conbee*",
        "known_devices": [
          "Conbee II"
        ]
      },
      {
        "vid": "10C4",
        "pid": "8A2A",
        "description": "*zigbee*",
        "known_devices": [
          "Nortek HUSBZB-1"
        ]
      },
      {
        "vid": "0403",
        "pid": "6015",
        "description": "*zigate*",
        "known_devices": [
          "ZiGate+"
        ]
      },
      {
        "vid": "10C4",
        "pid": "EA60",
        "description": "*zigate*",
        "known_devices": [
          "ZiGate"
        ]
      },
      {
        "vid": "10C4",
        "pid": "8B34",
        "description": "*bv 2010/10*",
        "known_devices": [
          "Bitron Video AV2010/10"
        ]
      }
    ],
    "codeowners": [
      "@dmulcahey",
      "@adminiuga",
      "@puddly"
    ],
    "zeroconf": [
      {
        "type": "_esphomelib._tcp.local.",
        "name": "tube*"
      },
      {
        "type": "_zigate-zigbee-gateway._tcp.local.",
        "name": "*zigate*"
      },
      {
        "type": "_zigstar_gw._tcp.local.",
        "name": "*zigstar*"
      }
    ],
    "dependencies": [
      "file_upload"
    ],
    "after_dependencies": [
      "onboarding",
      "usb",
      "zeroconf"
    ],
    "iot_class": "local_polling",
    "loggers": [
      "aiosqlite",
      "bellows",
      "crccheck",
      "pure_pcapy3",
      "zhaquirks",
      "zigpy",
      "zigpy_deconz",
      "zigpy_xbee",
      "zigpy_zigate",
      "zigpy_znp"
    ],
    "is_built_in": true
  },
  "data": {
    "ieee": "**REDACTED**",
    "nwk": 25691,
    "manufacturer": "_TZE200_n9clpsht",
    "model": "TS0601",
    "name": "_TZE200_n9clpsht TS0601",
    "quirk_applied": false,
    "quirk_class": "zigpy.device.Device",
    "manufacturer_code": 4098,
    "power_source": "Battery or Unknown",
    "lqi": 68,
    "rssi": -83,
    "last_seen": "2022-10-21T18:56:12",
    "available": true,
    "device_type": "EndDevice",
    "signature": {
      "node_descriptor": "NodeDescriptor(logical_type=<LogicalType.EndDevice: 2>, complex_descriptor_available=0, user_descriptor_available=0, reserved=0, aps_flags=0, frequency_band=<FrequencyBand.Freq2400MHz: 8>, mac_capability_flags=<MACCapabilityFlags.AllocateAddress: 128>, manufacturer_code=4098, maximum_buffer_size=82, maximum_incoming_transfer_size=82, server_mask=11264, maximum_outgoing_transfer_size=82, descriptor_capability_field=<DescriptorCapability.NONE: 0>, *allocate_address=True, *is_alternate_pan_coordinator=False, *is_coordinator=False, *is_end_device=True, *is_full_function_device=False, *is_mains_powered=False, *is_receiver_on_when_idle=False, *is_router=False, *is_security_capable=False)",
      "endpoints": {
        "1": {
          "profile_id": 260,
          "device_type": "0x0051",
          "in_clusters": [
            "0x0000",
            "0x0004",
            "0x0005",
            "0xef00"
          ],
          "out_clusters": [
            "0x000a",
            "0x0019"
          ]
        }
      }
    },
    "active_coordinator": false,
    "entities": [],
    "neighbors": [],
    "endpoint_names": [
      {
        "name": "SMART_PLUG"
      }
    ],
    "user_given_name": "Alarm Keypad",
    "device_reg_id": "29d983dbf0e41066045e1ef60dcc2199",
    "area_id": null,
    "cluster_details": {
      "1": {
        "device_type": {
          "name": "SMART_PLUG",
          "id": 81
        },
        "profile_id": 260,
        "in_clusters": {
          "0x0000": {
            "endpoint_attribute": "basic",
            "attributes": {
              "0x0004": {
                "attribute_name": "manufacturer",
                "value": "_TZE200_n9clpsht"
              },
              "0x0005": {
                "attribute_name": "model",
                "value": "TS0601"
              }
            },
            "unsupported_attributes": {}
          },
          "0x0004": {
            "endpoint_attribute": "groups",
            "attributes": {},
            "unsupported_attributes": {}
          },
          "0x0005": {
            "endpoint_attribute": "scenes",
            "attributes": {},
            "unsupported_attributes": {}
          },
          "0xef00": {
            "endpoint_attribute": null,
            "attributes": {},
            "unsupported_attributes": {}
          }
        },
        "out_clusters": {
          "0x0019": {
            "endpoint_attribute": "ota",
            "attributes": {},
            "unsupported_attributes": {}
          },
          "0x000a": {
            "endpoint_attribute": "time",
            "attributes": {},
            "unsupported_attributes": {}
          }
        }
      }
    }
  }
}

Additional context

There is a custom component for Zigbee2MQTT that is working for this device, could someone help out converting this to a ZHA quirk?
const fz = require('zigbee-herdsman-converters/converters/fromZigbee');
const tz = require('zigbee-herdsman-converters/converters/toZigbee');
const exposes = require('zigbee-herdsman-converters/lib/exposes');
const reporting = require('zigbee-herdsman-converters/lib/reporting');
const extend = require('zigbee-herdsman-converters/lib/extend');
const ota = require('zigbee-herdsman-converters/lib/ota');
const tuya = require('zigbee-herdsman-converters/lib/tuya');
const e = exposes.presets;
const ea = exposes.access;

const definition = {
    fingerprint: [
        {
            // The model ID from: Device with modelID 'TS0601' is not supported
            // You may need to add \u0000 at the end of the name in some cases
            modelID: 'TS0601',
            // The manufacturer name from: Device with modelID 'TS0601' is not supported.
            manufacturerName: '_TZE200_n9clpsht'
        },
    ],
    model: '07505L', // Vendor model number, look on the device for a model number
    vendor: 'Immax', // Vendor of the device (only used for documentation and startup logging)
    description: 'Immax Neo Smart Keypad', // Description of the device, copy from vendor site. (only used for documentation and startup logging)
    fromZigbee: [
		fz.ignore_basic_report,
		{
		  cluster: "manuSpecificTuya",
		  type: [
			"commandSetDataResponse",
			"commandGetData",
			"commandActiveStatusReport",
			"commandDataResponse"
		  ],
		  convert: (model, msg, publish, options, meta) => {
			const dp = msg.data.dpValues[0].dp;
			const armModeImmax = {
				26: 'disarm',
				27: 'arm_away',
				28: 'arm_home',
				29: 'sos',
			};

			return {action: armModeImmax[dp]};
//			return {data: msg.data};
		  },
		},
	],
    exposes: [e.action(['disarm', 'arm_home', 'arm_away', 'sos'])],
    toZigbee: [],
};

module.exports = definition;

I've requested support for this in DeCONZ as well, and have purchased a Tuya Zigbee Gateway to map out all the DP ID's.
You can find the DP ID's here if it helps with the integration: https://github.com/dresden-elektronik/deconz-rest-plugin/issues/6277#issuecomment-1260484312

I've requested support for this in DeCONZ as well, and have purchased a Tuya Zigbee Gateway to map out all the DP ID's. You can find the DP ID's here if it helps with the integration: dresden-elektronik/deconz-rest-plugin#6277 (comment)

Wow amazing work, I was going to buy a tuya hub just to be able to get those values, that is amazing, thanks for your hard work.

I've requested support for this in DeCONZ as well, and have purchased a Tuya Zigbee Gateway to map out all the DP ID's. You can find the DP ID's here if it helps with the integration: dresden-elektronik/deconz-rest-plugin#6277 (comment)

Wow amazing work, I was going to buy a tuya hub just to be able to get those values, that is amazing, thanks for your hard work.

If I'm missing any values, please tell, and I'll try and get them :)

That wood be amazing, as now I have had to add Zigbee2MQTT just to use this keypad, it would be amazing if i could use it with ZHA, I have been trying with Quirks, but i don't know how to process the messages received from the device in ZHA, I can see them on the debug log but can't process them as in Z2M.

Lets see if we can get something to work with ZHA, and get a working quirk so we can start using this keypad 😊

I got some time to work on the quirk this weekend, but am far from finished.
So far, I am able to control the configuration (enabling/disabling settings, and setting arm delay) of the device using ZHA, and I am also able to reset the device from ZHA.
I still need to figure out how to map the state events, and if it is possible to set the armed/unarmed more using ZHA.
from typing import Dict

from zigpy.profiles import zha
from zigpy.quirks import CustomDevice
import zigpy.types as t
from zigpy.zcl.clusters.general import Basic, Groups, Ota, Scenes, Time
from zigpy.zcl.clusters.security import IasAce
from zigpy.zcl.clusters.general import PowerConfiguration

from zhaquirks.const import (
    DEVICE_TYPE,
    ENDPOINTS,
    INPUT_CLUSTERS,
    MODELS_INFO,
    OUTPUT_CLUSTERS,
    PROFILE_ID,
    SKIP_CONFIGURATION,
)

from zhaquirks.tuya import TuyaLocalCluster, TUYA_CLUSTER_ID
from zhaquirks.tuya.mcu import (
    DPToAttributeMapping,
    TuyaDPType,
    TuyaMCUCluster,
)

TUYA_DP_ID_BATTERY_LEVEL_PERCENTAGE = 3  # Percentage
TUYA_DP_ID_SOS_ALARM = 23
TUYA_DP_ID_ANTI_REMOVE_ALARM = 24
TUYA_DP_ID_BATTERY_LEVEL_ENUM = 25  # "Low", "Middle", "High"
TUYA_DP_ID_DISARMED = 26
TUYA_DP_ID_ARMED = 27
TUYA_DP_ID_ARMED_HOME = 28
TUYA_DP_ID_SOS = 29
TUYA_DP_ID_ARM_DELAY_TIME = 103  # Seconds
TUYA_DP_ID_KEYPAD_BEEPS = 104  # Boolean
TUYA_DP_ID_QUICK_SOS = 105  # Boolean
TUYA_DP_ID_QUICK_DISARM = 106  # Boolean
TUYA_DP_ID_QUICK_ARM = 107  # Boolean
TUYA_DP_ID_ADMIN_CODE = 108
TUYA_DP_ID_USER_CODE = 109
TUYA_DP_ID_RESET = 110
TUYA_DP_ID_ARM_DELAY_BEEPS = 111  # Boolean
TUYA_DP_ID_UNKNOWN = 112

class TuyaPowerConfigurationCluster3AAA(PowerConfiguration, TuyaLocalCluster):
    """PowerConfiguration cluster for devices with 3 AAA."""

    BATTERY_SIZE = 0x0031
    BATTERY_QUANTITY = 0x0033
    BATTERY_RATED_VOLTAGE = 0x0034

    _CONSTANT_ATTRIBUTES = {
        BATTERY_SIZE: PowerConfiguration.BatterySize.AAA,
        BATTERY_QUANTITY: 3,
        BATTERY_RATED_VOLTAGE: 15,
    }

class WirelessZigbeeKeypadManufCluster(TuyaMCUCluster):
    """Tuya manufacturer cluster."""

    attributes = TuyaMCUCluster.attributes.copy()
    attributes.update(
        {
            TUYA_CLUSTER_ID + TUYA_DP_ID_ARM_DELAY_TIME: ("arm_delay_time", t.uint32_t),
            TUYA_CLUSTER_ID + TUYA_DP_ID_KEYPAD_BEEPS: ("keypad_beeps", t.Bool),
            TUYA_CLUSTER_ID + TUYA_DP_ID_QUICK_SOS: ("quick_sos", t.Bool),
            TUYA_CLUSTER_ID + TUYA_DP_ID_QUICK_DISARM: ("quick_disarm", t.Bool),
            TUYA_CLUSTER_ID + TUYA_DP_ID_QUICK_ARM: ("quick_arm", t.Bool),
            TUYA_CLUSTER_ID + TUYA_DP_ID_ARM_DELAY_BEEPS: ("arm_delay_beeps", t.Bool),
            TUYA_CLUSTER_ID + TUYA_DP_ID_RESET: ("reset", t.Bool),
        }  # type: ignore
    )

    dp_to_attribute: Dict[int, DPToAttributeMapping] = {
        TUYA_DP_ID_BATTERY_LEVEL_PERCENTAGE: DPToAttributeMapping(
            TuyaPowerConfigurationCluster3AAA.ep_attribute,
            "battery_percentage_remaining",
            TuyaDPType.VALUE,
            converter=lambda x: x * 2,
        ),
        TUYA_DP_ID_ARM_DELAY_TIME: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "arm_delay_time",
            TuyaDPType.VALUE,
        ),
        TUYA_DP_ID_KEYPAD_BEEPS: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "keypad_beeps",
            TuyaDPType.BOOL,
        ),
        TUYA_DP_ID_QUICK_SOS: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "quick_sos",
            TuyaDPType.BOOL,
        ),
        TUYA_DP_ID_QUICK_DISARM: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "quick_disarm",
            TuyaDPType.BOOL,
        ),
        TUYA_DP_ID_QUICK_ARM: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "quick_arm",
            TuyaDPType.BOOL,
        ),
        TUYA_DP_ID_USER_CODE: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "user_code",
            TuyaDPType.VALUE,
        ),
        TUYA_DP_ID_ARM_DELAY_BEEPS: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "arm_delay_beeps",
            TuyaDPType.BOOL,
        ),
        TUYA_DP_ID_RESET: DPToAttributeMapping(
            TuyaMCUCluster.ep_attribute,
            "reset",
            TuyaDPType.BOOL,
        ),
    }

    data_point_handlers = {
        TUYA_DP_ID_BATTERY_LEVEL_PERCENTAGE: "_dp_2_attr_update",
        TUYA_DP_ID_SOS_ALARM: "_dp_2_attr_update",
        TUYA_DP_ID_ANTI_REMOVE_ALARM: "_dp_2_attr_update",
        TUYA_DP_ID_BATTERY_LEVEL_ENUM: "_dp_2_attr_update",
        TUYA_DP_ID_DISARMED: "_dp_2_attr_update",
        TUYA_DP_ID_ARMED: "_dp_2_attr_update",
        TUYA_DP_ID_ARMED_HOME: "_dp_2_attr_update",
        TUYA_DP_ID_SOS: "_dp_2_attr_update",
        TUYA_DP_ID_ARM_DELAY_TIME: "_dp_2_attr_update",
        TUYA_DP_ID_KEYPAD_BEEPS: "_dp_2_attr_update",
        TUYA_DP_ID_QUICK_SOS: "_dp_2_attr_update",
        TUYA_DP_ID_QUICK_DISARM: "_dp_2_attr_update",
        TUYA_DP_ID_QUICK_ARM: "_dp_2_attr_update",
        TUYA_DP_ID_ADMIN_CODE: "_dp_2_attr_update",
        TUYA_DP_ID_USER_CODE: "_dp_2_attr_update",
        TUYA_DP_ID_RESET: "_dp_2_attr_update",
        TUYA_DP_ID_ARM_DELAY_BEEPS: "_dp_2_attr_update",
        TUYA_DP_ID_UNKNOWN: "_dp_2_attr_update",
    }

class WirelessZigbeeKeypad(CustomDevice):
    """MS-K1AZ Wireless Zigbee Keypad."""

    signature = {
        MODELS_INFO: [("_TZE200_n9clpsht", "TS0601")],
        ENDPOINTS: {
            1: {
                PROFILE_ID: zha.PROFILE_ID,
                DEVICE_TYPE: zha.DeviceType.SMART_PLUG,
                INPUT_CLUSTERS: [
                    Basic.cluster_id,
                    Groups.cluster_id,
                    Scenes.cluster_id,
                    WirelessZigbeeKeypadManufCluster.cluster_id,
                ],
                OUTPUT_CLUSTERS: [
                    Ota.cluster_id,
                    Time.cluster_id,
                ],
            }
        },
    }

    replacement = {
        SKIP_CONFIGURATION: True,
        ENDPOINTS: {
            1: {
                PROFILE_ID: zha.PROFILE_ID,
                DEVICE_TYPE: zha.DeviceType.IAS_ANCILLARY_CONTROL,
                INPUT_CLUSTERS: [
                    WirelessZigbeeKeypadManufCluster,
                    TuyaPowerConfigurationCluster3AAA,
                ],
                OUTPUT_CLUSTERS: [
                    Ota.cluster_id,
                    Time.cluster_id,
                ],
            }
        },
    }

Have these conversations in PR’s just mark them draft until you’re ready

@MrAdam Amazing work 👌🏻 I will give it a try.

I stopped working on this and moved to zigbee2mqtt, as I had to make changes in both home assistant repository, and this repository, but weren't able to get any feedback on which direction to go with the code.
See comment here: https://github.com/zigpy/zha-device-handlers/pull/1908#issuecomment-1453438851


Update README.md with appendix section

Hello, maintainers,
Claude Computer Use is exciting, and many users are eager to try it but find the installation of Docker and environment setup challenging. To address this, we have created a one-click virtual environment using LabEx infrastructure. This solution enables non-developers to easily access the Claude Computer Use demo. I've included a link in the appendix for your evaluation.
Thank you!

https://github.com/user-attachments/assets/9508f98e-c941-47f7-b2f7-0395b9c186a6

@huhuhang thanks for opening your first PR. We're working on formal contribution standards that we hope to release soon; however this PR does not meet the criteria of avoiding self-promotion.


Add scanner.c to sources in ext_modules

Currently the python bindings when installed via pip install -e . in the root of the repo cause a failure when importing the package:
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/anton/tools/matlabdomain/venv/lib/python3.10/site-packages/tree_sitter_matlab/__init__.py", line 3, in <module>
    from ._binding import language
ImportError: /home/anton/tools/matlabdomain/venv/lib/python3.10/site-packages/tree_sitter_matlab/_binding.abi3.so: undefined symbol: tree_sitter_matlab_external_scanner_create

This is due to the scanner not being included in the sources that are included in package.

Thank you for the pull request.


[Automation] ConnectionError while doing GET operation on inventory url

ConnectionError seen on Palmetto with latest openbmc build v1.99.0-108-g5d5f8df-dirty. These errors are seen with inventory related test cases. This seem to be happening as Palmetto's inventory url is changed recently in openbmc fw. Changes need to be taken care in automation framework.
CPU Present                                                           URI:https://<IP_ADDRESS>/org/openbmc/inventory/system/chassis/motherboard/cpu0/attr/present, method:Get, args:{}
FFDC Collection Started 	: 2016-09-20 07:21:13,870742
FFDC Collection Completed 	: 2016-09-20 07:21:57,393340
| FAIL |
ConnectionError: HTTPSConnectionPool(host='<IP_ADDRESS>', port=443): Max retries exceeded with url: /org/openbmc/inventory/system/chassis/motherboard/cpu0/attr/present (Caused by ReadTimeoutError("HTTPSConnectionPool(host='<IP_ADDRESS>', port=443): Read timed out. (read timeout=5.0)",))
Tried running manually GET operation and found that path is not found.
rahulmah@rackspace:~$ curl -c cjar -b cjar -k -H "Content-Type: application/json" -X GET -d "{"data":[]}" https://<IP_ADDRESS>/org/openbmc/inventory/system/chassis/motherboard/cpu0/attr/present
{
"data": {
"description": "org.openbmc.ObjectMapper.Error.NotFound: path or object not found: /org/openbmc/inventory/system/chassis/motherboard/cpu0"
},
"message": "404 Not Found",
"status": "error"

@rahulmah  Check once you get hands on a system booting to HOST_BOOTED state.. I suspect 404 may be due to pre condition state requirement.

@gkeishin : This has nothing to do with system state. System inventory should url which I have tried should work on host power off state (I cross checked it). Anyway my system was booting to HOST_BOOTED state while doing testing.

Code checked for review.


ContinueOn failed is not respected under certain circumstances

Summary
What happened/what you expected to happen?
The following workflow fails, no matter the ContinueOn: failed:
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: build-wf-
spec:
  entrypoint: test-workflow
  templates:
  - name: test-workflow
    dag:
      tasks:
      - name: A
        template: ok
      - name: B
        template: fail
        continueOn:
          failed: true
        dependencies: [A]
      - name: C
        template: ok
  - name: ok
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 0
  - name: fail
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 1

Result:
Name:                build-wf-gfns5
Namespace:           argo
ServiceAccount:      argo
Status:              Failed
Conditions:
 Completed           True
Created:             Wed Oct 28 11:03:29 +0100 (1 minute ago)
Started:             Wed Oct 28 11:03:29 +0100 (1 minute ago)
Finished:            Wed Oct 28 11:04:35 +0100 (now)
Duration:            1 minute 6 seconds
ResourcesDuration:   23s*cpu,23s*memory

STEP               TEMPLATE       PODNAME                    DURATION  MESSAGE
 ✖ build-wf-gfns5  test-workflow
 ├-✔ A             ok             build-wf-gfns5-4101692634  48s
 ├-✔ C             ok             build-wf-gfns5-4068137396  49s
 └-✖ B             fail           build-wf-gfns5-4084915015  14s       failed with exit code 1

workflow controller log:
time="2020-10-28T10:03:29Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="Updated phase  -> Running" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="DAG node build-wf-gfns5 initialized Running" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="All of node build-wf-gfns5.A dependencies [] completed" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="Pod node build-wf-gfns5-4101692634 initialized Pending" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="Created pod: build-wf-gfns5.A (build-wf-gfns5-4101692634)" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="All of node build-wf-gfns5.C dependencies [] completed" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="Pod node build-wf-gfns5-4068137396 initialized Pending" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="Created pod: build-wf-gfns5.C (build-wf-gfns5-4068137396)" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:29Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14009765 workflow=build-wf-gfns5
time="2020-10-28T10:03:30Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:30Z" level=info msg="Updating node build-wf-gfns5-4101692634 message: ContainerCreating"
time="2020-10-28T10:03:30Z" level=info msg="Updating node build-wf-gfns5-4068137396 message: ContainerCreating"
time="2020-10-28T10:03:30Z" level=info msg="Skipped pod build-wf-gfns5.A (build-wf-gfns5-4101692634) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-gfns5
time="2020-10-28T10:03:30Z" level=info msg="Skipped pod build-wf-gfns5.C (build-wf-gfns5-4068137396) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-gfns5
time="2020-10-28T10:03:30Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14009773 workflow=build-wf-gfns5
time="2020-10-28T10:03:31Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:03:31Z" level=info msg="Skipped pod build-wf-gfns5.A (build-wf-gfns5-4101692634) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-gfns5
time="2020-10-28T10:03:31Z" level=info msg="Skipped pod build-wf-gfns5.C (build-wf-gfns5-4068137396) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-gfns5
time="2020-10-28T10:04:16Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:16Z" level=info msg="Updating node build-wf-gfns5-4068137396 status Pending -> Running"
time="2020-10-28T10:04:16Z" level=info msg="Skipped pod build-wf-gfns5.A (build-wf-gfns5-4101692634) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-gfns5
time="2020-10-28T10:04:16Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010000 workflow=build-wf-gfns5
time="2020-10-28T10:04:17Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:17Z" level=info msg="Updating node build-wf-gfns5-4101692634 status Pending -> Running"
time="2020-10-28T10:04:17Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010005 workflow=build-wf-gfns5
time="2020-10-28T10:04:18Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:18Z" level=info msg="Setting node build-wf-gfns5-4101692634 outputs"
time="2020-10-28T10:04:18Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010011 workflow=build-wf-gfns5
time="2020-10-28T10:04:19Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:19Z" level=info msg="Updating node build-wf-gfns5-4101692634 status Running -> Succeeded"
time="2020-10-28T10:04:19Z" level=info msg="Setting node build-wf-gfns5-4068137396 outputs"
time="2020-10-28T10:04:19Z" level=info msg="All of node build-wf-gfns5.B dependencies [A] completed" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:19Z" level=info msg="Pod node build-wf-gfns5-4084915015 initialized Pending" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:19Z" level=info msg="Created pod: build-wf-gfns5.B (build-wf-gfns5-4084915015)" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:19Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010021 workflow=build-wf-gfns5
time="2020-10-28T10:04:20Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:20Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-gfns5-4101692634
time="2020-10-28T10:04:20Z" level=info msg="Updating node build-wf-gfns5-4068137396 status Running -> Succeeded"
time="2020-10-28T10:04:20Z" level=info msg="Updating node build-wf-gfns5-4084915015 message: ContainerCreating"
time="2020-10-28T10:04:20Z" level=info msg="Skipped pod build-wf-gfns5.B (build-wf-gfns5-4084915015) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-gfns5
time="2020-10-28T10:04:20Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010030 workflow=build-wf-gfns5
time="2020-10-28T10:04:21Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:21Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-gfns5-4101692634
time="2020-10-28T10:04:21Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-gfns5-4068137396
time="2020-10-28T10:04:21Z" level=info msg="Skipped pod build-wf-gfns5.B (build-wf-gfns5-4084915015) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-gfns5
time="2020-10-28T10:04:27Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:27Z" level=info msg="Updating node build-wf-gfns5-4084915015 status Pending -> Running"
time="2020-10-28T10:04:27Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010080 workflow=build-wf-gfns5
time="2020-10-28T10:04:28Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:33Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:34Z" level=info msg="Setting node build-wf-gfns5-4084915015 outputs"
time="2020-10-28T10:04:34Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010109 workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-gfns5-4084915015 templateName=fail workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Updating node build-wf-gfns5-4084915015 status Running -> Failed"
time="2020-10-28T10:04:35Z" level=info msg="Updating node build-wf-gfns5-4084915015 message: failed with exit code 1"
time="2020-10-28T10:04:35Z" level=info msg="Outbound nodes of build-wf-gfns5 set to [build-wf-gfns5-4084915015 build-wf-gfns5-4068137396]" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="node build-wf-gfns5 phase Running -> Failed" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="node build-wf-gfns5 finished: 2020-10-28 10:04:35.068847535 +0000 UTC" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Checking daemoned children of build-wf-gfns5" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Updated phase Running -> Failed" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Marking workflow completed" namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Checking daemoned children of " namespace=argo workflow=build-wf-gfns5
time="2020-10-28T10:04:35Z" level=info msg="Workflow update successful" namespace=argo phase=Failed resourceVersion=14010117 workflow=build-wf-gfns5
time="2020-10-28T10:04:36Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-gfns5-4084915015

However, the same workflow succeeds, only by adding dependencies to task C:
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: build-wf-
spec:
  entrypoint: test-workflow
  templates:
  - name: test-workflow
    dag:
      tasks:
      - name: A
        template: ok
      - name: B
        template: fail
        continueOn:
          failed: true
        dependencies: [A]
      - name: C
        template: ok
        dependencies: [B]
  - name: ok
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 0
  - name: fail
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 1

Result:
Name:                build-wf-vd6r6
Namespace:           argo
ServiceAccount:      argo
Status:              Succeeded
Conditions:
 Completed           True
Created:             Wed Oct 28 11:07:25 +0100 (40 seconds ago)
Started:             Wed Oct 28 11:07:25 +0100 (40 seconds ago)
Finished:            Wed Oct 28 11:08:05 +0100 (now)
Duration:            40 seconds
ResourcesDuration:   27s*cpu,27s*memory

STEP               TEMPLATE       PODNAME                    DURATION  MESSAGE
 ✔ build-wf-vd6r6  test-workflow
 ├-✔ A             ok             build-wf-vd6r6-2862112367  13s
 ├-✖ B             fail           build-wf-vd6r6-2878889986  12s       failed with exit code 1
 └-✔ C             ok             build-wf-vd6r6-2895667605  12s

workflow log:
time="2020-10-28T10:07:25Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:25Z" level=info msg="Updated phase  -> Running" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:25Z" level=info msg="DAG node build-wf-vd6r6 initialized Running" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:25Z" level=info msg="All of node build-wf-vd6r6.A dependencies [] completed" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:25Z" level=info msg="Pod node build-wf-vd6r6-2862112367 initialized Pending" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:25Z" level=info msg="Created pod: build-wf-vd6r6.A (build-wf-vd6r6-2862112367)" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:25Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010894 workflow=build-wf-vd6r6
time="2020-10-28T10:07:26Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:27Z" level=info msg="Updating node build-wf-vd6r6-2862112367 message: ContainerCreating"
time="2020-10-28T10:07:27Z" level=info msg="Skipped pod build-wf-vd6r6.A (build-wf-vd6r6-2862112367) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-vd6r6
time="2020-10-28T10:07:27Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010903 workflow=build-wf-vd6r6
time="2020-10-28T10:07:28Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:28Z" level=info msg="Skipped pod build-wf-vd6r6.A (build-wf-vd6r6-2862112367) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-vd6r6
time="2020-10-28T10:07:33Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:33Z" level=info msg="Updating node build-wf-vd6r6-2862112367 status Pending -> Running"
time="2020-10-28T10:07:33Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010935 workflow=build-wf-vd6r6
time="2020-10-28T10:07:34Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:39Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:39Z" level=info msg="Setting node build-wf-vd6r6-2862112367 outputs"
time="2020-10-28T10:07:39Z" level=info msg="Updating node build-wf-vd6r6-2862112367 status Running -> Succeeded"
time="2020-10-28T10:07:39Z" level=info msg="All of node build-wf-vd6r6.B dependencies [A] completed" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:39Z" level=info msg="Pod node build-wf-vd6r6-2878889986 initialized Pending" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:39Z" level=info msg="Created pod: build-wf-vd6r6.B (build-wf-vd6r6-2878889986)" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:39Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010969 workflow=build-wf-vd6r6
time="2020-10-28T10:07:40Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:40Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-vd6r6-2862112367
time="2020-10-28T10:07:40Z" level=info msg="Updating node build-wf-vd6r6-2878889986 message: ContainerCreating"
time="2020-10-28T10:07:40Z" level=info msg="Skipped pod build-wf-vd6r6.B (build-wf-vd6r6-2878889986) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-vd6r6
time="2020-10-28T10:07:40Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14010977 workflow=build-wf-vd6r6
time="2020-10-28T10:07:41Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:41Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-vd6r6-2862112367
time="2020-10-28T10:07:41Z" level=info msg="Skipped pod build-wf-vd6r6.B (build-wf-vd6r6-2878889986) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-vd6r6
time="2020-10-28T10:07:48Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:48Z" level=info msg="Updating node build-wf-vd6r6-2878889986 status Pending -> Running"
time="2020-10-28T10:07:48Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011019 workflow=build-wf-vd6r6
time="2020-10-28T10:07:49Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:52Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:52Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-vd6r6-2878889986 templateName=fail workflow=build-wf-vd6r6
time="2020-10-28T10:07:52Z" level=info msg="Setting node build-wf-vd6r6-2878889986 outputs"
time="2020-10-28T10:07:52Z" level=info msg="Updating node build-wf-vd6r6-2878889986 status Running -> Failed"
time="2020-10-28T10:07:52Z" level=info msg="Updating node build-wf-vd6r6-2878889986 message: failed with exit code 1"
time="2020-10-28T10:07:52Z" level=info msg="All of node build-wf-vd6r6.C dependencies [B] completed" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:52Z" level=info msg="Pod node build-wf-vd6r6-2895667605 initialized Pending" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:52Z" level=info msg="Created pod: build-wf-vd6r6.C (build-wf-vd6r6-2895667605)" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:52Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011044 workflow=build-wf-vd6r6
time="2020-10-28T10:07:53Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:53Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-vd6r6-2878889986
time="2020-10-28T10:07:53Z" level=info msg="Updating node build-wf-vd6r6-2895667605 message: ContainerCreating"
time="2020-10-28T10:07:53Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-vd6r6-2878889986 templateName=fail workflow=build-wf-vd6r6
time="2020-10-28T10:07:53Z" level=info msg="Skipped pod build-wf-vd6r6.C (build-wf-vd6r6-2895667605) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-vd6r6
time="2020-10-28T10:07:53Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011055 workflow=build-wf-vd6r6
time="2020-10-28T10:07:54Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:07:54Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-vd6r6-2878889986 templateName=fail workflow=build-wf-vd6r6
time="2020-10-28T10:07:54Z" level=info msg="Skipped pod build-wf-vd6r6.C (build-wf-vd6r6-2895667605) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-vd6r6
time="2020-10-28T10:08:00Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:00Z" level=info msg="Updating node build-wf-vd6r6-2895667605 status Pending -> Running"
time="2020-10-28T10:08:00Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-vd6r6-2878889986 templateName=fail workflow=build-wf-vd6r6
time="2020-10-28T10:08:00Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011090 workflow=build-wf-vd6r6
time="2020-10-28T10:08:01Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:01Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-vd6r6-2878889986 templateName=fail workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Setting node build-wf-vd6r6-2895667605 outputs"
time="2020-10-28T10:08:05Z" level=info msg="Updating node build-wf-vd6r6-2895667605 status Running -> Succeeded"
time="2020-10-28T10:08:05Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-vd6r6-2878889986 templateName=fail workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Outbound nodes of build-wf-vd6r6 set to [build-wf-vd6r6-2895667605]" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="node build-wf-vd6r6 phase Running -> Succeeded" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="node build-wf-vd6r6 finished: 2020-10-28 10:08:05.169494246 +0000 UTC" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Checking daemoned children of build-wf-vd6r6" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Updated phase Running -> Succeeded" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Marking workflow completed" namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Checking daemoned children of " namespace=argo workflow=build-wf-vd6r6
time="2020-10-28T10:08:05Z" level=info msg="Workflow update successful" namespace=argo phase=Succeeded resourceVersion=14011116 workflow=build-wf-vd6r6
time="2020-10-28T10:08:06Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-vd6r6-2895667605

Nonetheless, when I add a when clause (doesn't really matter which) to task C, the workflow fails again:
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: build-wf-
spec:
  entrypoint: test-workflow
  arguments:
    parameters:
    - name: testparam
      value: nothing
  templates:
  - name: test-workflow
    dag:
      tasks:
      - name: A
        template: ok
      - name: B
        template: fail
        continueOn:
          failed: true
        dependencies: [A]
      - name: C
        template: ok
        dependencies: [B]
        when: "{{workflow.parameters.testparam}} == something"
  - name: ok
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 0
  - name: fail
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 1

Result:
Name:                build-wf-qv9w7
Namespace:           argo
ServiceAccount:      argo
Status:              Failed
Conditions:
 Completed           True
Created:             Wed Oct 28 11:10:54 +0100 (30 seconds ago)
Started:             Wed Oct 28 11:10:54 +0100 (30 seconds ago)
Finished:            Wed Oct 28 11:11:24 +0100 (now)
Duration:            30 seconds
ResourcesDuration:   19s*cpu,19s*memory
Parameters:
  testparam:         nothing

STEP               TEMPLATE       PODNAME                    DURATION  MESSAGE
 ✖ build-wf-qv9w7  test-workflow
 ├-✔ A             ok             build-wf-qv9w7-3547017399  13s
 ├-✖ B             fail           build-wf-qv9w7-3563795018  13s       failed with exit code 1
 └-○ C             ok                                                  when 'nothing == something' evaluated false

workflow log:

time="2020-10-28T10:10:54Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:54Z" level=info msg="Updated phase  -> Running" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:54Z" level=info msg="DAG node build-wf-qv9w7 initialized Running" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:54Z" level=info msg="All of node build-wf-qv9w7.A dependencies [] completed" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:54Z" level=info msg="Pod node build-wf-qv9w7-3547017399 initialized Pending" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:54Z" level=info msg="Created pod: build-wf-qv9w7.A (build-wf-qv9w7-3547017399)" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:54Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011895 workflow=build-wf-qv9w7
time="2020-10-28T10:10:55Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:55Z" level=info msg="Updating node build-wf-qv9w7-3547017399 message: ContainerCreating"
time="2020-10-28T10:10:55Z" level=info msg="Skipped pod build-wf-qv9w7.A (build-wf-qv9w7-3547017399) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-qv9w7
time="2020-10-28T10:10:55Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011904 workflow=build-wf-qv9w7
time="2020-10-28T10:10:56Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:10:56Z" level=info msg="Skipped pod build-wf-qv9w7.A (build-wf-qv9w7-3547017399) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-qv9w7
time="2020-10-28T10:11:02Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:02Z" level=info msg="Updating node build-wf-qv9w7-3547017399 status Pending -> Running"
time="2020-10-28T10:11:02Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011939 workflow=build-wf-qv9w7
time="2020-10-28T10:11:03Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:08Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:08Z" level=info msg="Setting node build-wf-qv9w7-3547017399 outputs"
time="2020-10-28T10:11:08Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011969 workflow=build-wf-qv9w7
time="2020-10-28T10:11:09Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:09Z" level=info msg="Updating node build-wf-qv9w7-3547017399 status Running -> Succeeded"
time="2020-10-28T10:11:09Z" level=info msg="All of node build-wf-qv9w7.B dependencies [A] completed" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:09Z" level=info msg="Pod node build-wf-qv9w7-3563795018 initialized Pending" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:09Z" level=info msg="Created pod: build-wf-qv9w7.B (build-wf-qv9w7-3563795018)" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:09Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011977 workflow=build-wf-qv9w7
time="2020-10-28T10:11:10Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:10Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-qv9w7-3547017399
time="2020-10-28T10:11:10Z" level=info msg="Updating node build-wf-qv9w7-3563795018 message: ContainerCreating"
time="2020-10-28T10:11:10Z" level=info msg="Skipped pod build-wf-qv9w7.B (build-wf-qv9w7-3563795018) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-qv9w7
time="2020-10-28T10:11:10Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14011988 workflow=build-wf-qv9w7
time="2020-10-28T10:11:11Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:11Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-qv9w7-3547017399
time="2020-10-28T10:11:11Z" level=info msg="Skipped pod build-wf-qv9w7.B (build-wf-qv9w7-3563795018) creation: already exists" namespace=argo podPhase=Pending workflow=build-wf-qv9w7
time="2020-10-28T10:11:18Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:18Z" level=info msg="Updating node build-wf-qv9w7-3563795018 status Pending -> Running"
time="2020-10-28T10:11:18Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14012030 workflow=build-wf-qv9w7
time="2020-10-28T10:11:19Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:22Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:23Z" level=info msg="Setting node build-wf-qv9w7-3563795018 outputs"
time="2020-10-28T10:11:23Z" level=info msg="Workflow update successful" namespace=argo phase=Running resourceVersion=14012051 workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Processing workflow" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Pod failed" displayName=B namespace=argo pod=build-wf-qv9w7-3563795018 templateName=fail workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Updating node build-wf-qv9w7-3563795018 status Running -> Failed"
time="2020-10-28T10:11:24Z" level=info msg="Updating node build-wf-qv9w7-3563795018 message: failed with exit code 1"
time="2020-10-28T10:11:24Z" level=info msg="All of node build-wf-qv9w7.C dependencies [B] completed" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Skipped node build-wf-qv9w7-3580572637 initialized Skipped (message: when 'nothing == something' evaluated false)" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Outbound nodes of build-wf-qv9w7 set to [build-wf-qv9w7-3580572637]" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="node build-wf-qv9w7 phase Running -> Failed" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="node build-wf-qv9w7 finished: 2020-10-28 10:11:24.085691078 +0000 UTC" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Checking daemoned children of build-wf-qv9w7" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Updated phase Running -> Failed" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Marking workflow completed" namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Checking daemoned children of " namespace=argo workflow=build-wf-qv9w7
time="2020-10-28T10:11:24Z" level=info msg="Workflow update successful" namespace=argo phase=Failed resourceVersion=14012060 workflow=build-wf-qv9w7
time="2020-10-28T10:11:25Z" level=info msg="Labeled pod completed" namespace=argo pod=build-wf-qv9w7-3563795018
## Diagnostics

What Kubernetes provider are you using?

AKS 1.17.9

What version of Argo Workflows are you running?
v2.11.6
---
<!-- Issue Author: Don't delete this message to encourage other users to support your issue! -->
**Message from the maintainers**:

Impacted by this bug? Give it a 👍. We prioritise the issues with the most 👍.

Note that this issue came up after upgrading from v2.8 to v2.11.6

I think there must be a regression, because the problem still occurs in 2.12.10

@ptemmer I am unable to replicate with the workflow provided in this issue. Can you include a new workflow that reproduces this issue?

Yes. Here you go:
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: build-wf-
spec:
  entrypoint: test-workflow
  templates:
  - name: test-workflow
    dag:
      tasks:
      - name: A
        template: ok
      - name: B
        template: fail
        continueOn:
          failed: true
        dependencies: [A]
      - name: C
        template: ok
        dependencies: [B]
      - name: D
        template: ok
        dependencies: [B]
        when: "{{tasks.B.status}} == Succeeded"
  - name: ok
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 0
  - name: fail
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 1

Still unable to reproduce:

However with this workflow, the DAG does fail:
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: build-wf-
spec:
  entrypoint: test-workflow
  templates:
  - name: test-workflow
    dag:
      tasks:
      - name: A
        template: ok
      - name: B
        template: fail
        continueOn:
          failed: true
        dependencies: [A]
      - name: C
        template: ok
        dependencies: [B]
        when: "{{tasks.B.status}} == Succeeded"
  - name: ok
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 0
  - name: fail
    container:
      image: busybox
      command: [sh, -c]
      args:
      - |
        exit 1

STEP               TEMPLATE       PODNAME                   DURATION  MESSAGE
 ✖ build-wf-bqhmm  test-workflow
 ├─✔ A             ok             build-wf-bqhmm-425295890  6s
 ├─✖ B             fail           build-wf-bqhmm-408518271  6s        Error (exit code 1)
 └─○ C             ok                                                 when 'Failed == Succeeded' evaluated false

Note that is result is expected as we don't consider this to be a Succeeded DAG since the last node to run on a branch (B) fails.

In that case I must have misinterpreted the reasoning behind ContinueOn. It was my understanding that it allowed us to ignore tasks that are bound to fail, but should not affect the overal DAG result. I'm surprised however that this is only true if the final task of the DAG is successful.

I believe continueOn is only meant to do that, continue in case of a failure. I'm not sure that its scope should be beyond that (such as determining DAG) status. If we had a simple DAG such as
    dag:
      tasks:
      - name: A
        template: fail
        continueOn:
          failed: true

I would still expect the DAG itself to be failed as our "target task" (i.e. the leaf nodes of the graph) did not succeed.
Note that it's trivial to add a workaround if you really want this behavior:
    dag:
      tasks:
      - name: A
        template: fail
        continueOn:
          failed: true
      - name: D
        template: ok
        dependencies: [A]
        when: "{{tasks.A.status}} == Failed"

☝ This DAG should never be Failed

What if the last task is conditional? You tried that example before, however I'm getting other results

I'll take a look again


Move "Hardware sizing guidelines" to Reference section

Seems to me this makes more sense here.

[x] Tests pass (no build errors)
[x] Rebased/mergeable

TODO

[ ] update links

As in, links to /enterprise_influxdb/v1.9/guides/hardware_sizing/ would be redirected to /enterprise_influxdb/v1.9/reference/hardware_sizing/? Still, it's a bit confusing to future editors if we don't update the links, IMO.

Ah, I see. I think it's best to move the file as well.


Update deploy_app.sh

Thank you for your submission! We require that all contributors sign our Contributor License Agreement ("CLA") before we can accept the contribution. Read and sign the agreement
Learn more about why HashiCorp requires a CLA and what the CLA includes
Have you signed the CLA already but the status is still pending? Recheck it.

approved

everything is working fine so approve this

ok


wherehows how to config the application.conf

I encounted the problem that configuration the ladap in the application.conf. at the same , when i first visit the web logging page, I cannot find the create the account href.

Dear issue owner,
Thanks for your interest in WhereHows. We have recently announced DataHub which is the rebranding of WhereHows. LinkedIn improved the architecture of WhereHows and rebranded WhereHows into DataHub and replaced its metadata infrastructure in this direction. DataHub is a more advanced and improved metadata management product compared to WhereHows.
Unfortunately, we have to stop supporting WhereHows to better focus on DataHub and offer more help to DataHub users. Therefore, we will drop all issues related to WhereHows and will not accept any contribution for it. Active development for DataHub has already started on datahub branch and will continue to live in there until it's finally merged to master and project is renamed to DataHub.
Please check the datahub branch to get familar with DataHub.
Best,
DataHub team


[otbn] The simulator should get its address map from otbn.hjson

At the moment, otbnsim doesn't know anything about address layout. We should probably teach it to load up otbn.hjson so that it knows the (data) memory size. Any ELF loading code in future will want that information too (how do I interpret an LMA?), but I think the simulator's Model class is probably the right place for this.

Fixed, some time ago (but I forgot about the issue).


Issue from website page Debugging Flutter Apps

From URL: https://flutter.io/debugging.md

If you output too much at once, then Android sometimes discards some log lines. To avoid this, you can use debugPrint(), from Flutter’s services library.

debugPrint() is in foundation, not services

cc @mit-mit @mjohnsullivan


Support for ubuntu 16.04

This would be great to have 👍

Thank you very much for PR and sorry for taking so long with response!
The problem is now resolved with PR #83, please let me know if there any problems.


Error processing pattern properties

patternProperties (https://json-schema.org/understanding-json-schema/reference/object.html#pattern-properties) is listed as supported here but when I try to parse an OAS with it I get an error:
[attribute  is not of type `object`]

Java
Path file = Path.of("openapi.yaml");
OpenAPIParser parser = new OpenAPIParser();
SwaggerParseResult result = parser.readContents(file.toString(), Collections.emptyList(), new ParseOptions());
System.out.println(result.getMessages());

OAS
openapi: 3.1.0
info:
  title: Test API
  version: '1.0'
paths:
  /test:
    post:
      requestBody:
        content:
          application/json:
            schema:
              type: object
              properties:
                static:
                  type: string
              patternProperties:
                dynamic:
                  type: string
      responses:
        '200':
          description: Success!
          content:
            application/json:
              schema:
                type: string

See test.zip for the full example.

Typo in the code.


Rank and/or quantile transform

It’d nice to have a map transform that converts quantitative values into ranks (1, 2, 3, …) or the equivalent p-quantile in [0,1]. This would be an alternative to the quantile scale.

Should this be part of d3-array?
https://observablehq.com/d/c1eb24e6680046d7

I figured we would build it and see if it’s useful before we decide.
One nuance on implementation: if the same value is repeated, then they should have the same rank, and ranks should be skipped. So for example [3, 1, 3, 4, 2, 0] is mapped to [4, 2, 4, 6, 3, 1] (assuming 1-based ranking). Also I think all non-comparable values would need to be replaced with NaN (or undefined?).

Also if we use d3.sort we will need to fix https://github.com/d3/d3-array/issues/217 such that all undefined values (null, NaN, and undefined) are moved to the end.

I've added a "same value" test (with a test on ! a <= b to make sure two Date objects representing the same date have the same rank).
I'm not sold on starting at 1. I see the cosmetic appeal, but it makes this slightly more difficult to, for example, use the rank to lookup a value in another array "🥇🥈🥉"[rank].

I’m not convinced we want 1-based ranking either. That was just my first thought… we can start with 0-based if you like.

The inherent difficulty of writing this "simple" function makes me think it should be in d3-array. And it's already nice to use in Plot:

pull-request for d3.rank https://github.com/d3/d3-array/pull/228

Added in #570.

Added in #570.


Where are assemblies with Strong Name?

Please forgive the boring questions.
I develop small application with Material Design Xaml Toolkit.
But this application must to be signed with string name.
So all referenced assemblies need to be signed.
I can not find signed 'MaterialDesignThemes.Wpf' and 'MaterialDesignThemes.MahApps'.
Will you not provide signed dlls?
If you never provide them, I have to compile source code to add strong name, right?
Thank you for your cooperation.

Hi, I suggest using this to strong name the assemblies as part of your build process:
https://github.com/dsplaisted/strongnamer

Oh my god!!!
Build errors Was removed!! It's miracle!!!
Thank you for telling me good tool!!!

Happy days :)

The solution with strongnamer does not work. I have created a small strong project with just 3 nuggets and the themes applied in application resources and the app will crash at runtime with the following message:
Could not load file or assembly 'MaterialDesignThemes.Wpf, PublicKeyToken=null' or one of its dependencies. The located assembly's manifest definition does not match the assembly reference. (Exception from HRESULT: 0x80131040)":"MaterialDesignThemes.Wpf, PublicKeyToken=null.
Check the sample project to easily reproduce the issue: StrongTesting.zip
Do you have any solution for this?

Put of curiosity, did you sign the Colours assembly too?

Yes, the Colours assembly is also signed.

I've found a different solution:
I've embedded all the unsigned material design dlls into my main assembly using Fody.Costura.
Thanks for your help.

Any update on this ?

We also have the same problem. We need signed assemblies and there is no signed assemblies from Material Design.

@mumarniaz. You can sign the assemblies yourself. https://github.com/MaterialDesignInXAML/MaterialDesignInXamlToolkit/wiki/Strong-Naming

I just ran into this and wanted to leave a note that it's an issue with strongnamer. When it runs it is not properly updating the BAML (the compiled form of XAML). here is what worked for me.
public App()
{
AppDomain.CurrentDomain.AssemblyResolve += CurrentDomainOnAssemblyResolve;
}
private Assembly CurrentDomainOnAssemblyResolve(object sender, ResolveEventArgs args)
{
var assemblyName = new AssemblyName(args.Name);
var assembly = AppDomain.CurrentDomain.GetAssemblies()
.FirstOrDefault(a => a.GetName().Name == assemblyName.Name);
return assembly;
}


Backport "fix: semanticdb symbol creation when targetName set" to LTS

Backports #20950 to the LTS branch.
PR submitted by the release tooling.
[skip ci]

No regressions detected in the community build up to lts-21070.
Reference


Axolotl install and train is broken.

Please check that this issue hasn't been reported before.

[X] I searched previous Bug Reports didn't find any similar reports.

Expected Behavior
Looks like something is broken in the latest axolotl.
Earlier it was working fine with latest torch:
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl
pip install torch
pip3 install packaging ninja wheel
pip3 install -e '.[flash-attn,deepspeed]'
But now it gives error related to dependancies.
Below works :
pip install torch=="2.1.2"
pip install -e git+https://github.com/OpenAccess-AI-Collective/axolotl#egg=axolotl
pip install flash-attn=="2.5.0"
pip install deepspeed=="0.13.1"
But then is fails later during training.
Even colab example notebook is failing.
Current behaviour
During package installation dependancy errors are seen.
Below error is seen during training with downgraded torch.
[2024-04-26 17:29:07,623] [ERROR] [axolotl.load_model:673] [PID:7553] [RANK:0]
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the
quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules
in 32-bit, you need to set load_in_8bit_fp32_cpu_offload=True and pass a custom device_map to
from_pretrained. Check
https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu
for more details.
Traceback (most recent call last):
File "/content/src/axolotl/src/axolotl/utils/models.py", line 630, in load_model
model = getattr(transformers, model_type).from_pretrained(
File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py", line 563, in from_pretrained
return model_class.from_pretrained(
File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 3050, in from_pretrained
hf_quantizer.validate_environment(
File "/usr/local/lib/python3.10/dist-packages/transformers/quantizers/quantizer_bnb_4bit.py", line 86, in validate_environment
raise ValueError(
ValueError:
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the
quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules
in 32-bit, you need to set load_in_8bit_fp32_cpu_offload=True and pass a custom device_map to
from_pretrained. Check
https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu
for more details.
Traceback (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/content/src/axolotl/src/axolotl/cli/train.py", line 59, in
fire.Fire(do_cli)
File "/usr/local/lib/python3.10/dist-packages/fire/core.py", line 143, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/usr/local/lib/python3.10/dist-packages/fire/core.py", line 477, in _Fire
component, remaining_args = _CallAndUpdateTrace(
File "/usr/local/lib/python3.10/dist-packages/fire/core.py", line 693, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "/content/src/axolotl/src/axolotl/cli/train.py", line 35, in do_cli
return do_train(parsed_cfg, parsed_cli_args)
File "/content/src/axolotl/src/axolotl/cli/train.py", line 55, in do_train
return train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
File "/content/src/axolotl/src/axolotl/train.py", line 87, in train
model, peft_config = load_model(cfg, tokenizer, inference=cli_args.inference)
File "/content/src/axolotl/src/axolotl/utils/models.py", line 674, in load_model
raise err
File "/content/src/axolotl/src/axolotl/utils/models.py", line 630, in load_model
model = getattr(transformers, model_type).from_pretrained(
File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py", line 563, in from_pretrained
return model_class.from_pretrained(
File "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py", line 3050, in from_pretrained
hf_quantizer.validate_environment(
File "/usr/local/lib/python3.10/dist-packages/transformers/quantizers/quantizer_bnb_4bit.py", line 86, in validate_environment
raise ValueError(
ValueError:
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the
quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules
in 32-bit, you need to set load_in_8bit_fp32_cpu_offload=True and pass a custom device_map to
from_pretrained. Check
https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu
for more details.
Traceback (most recent call last):
File "/usr/local/bin/accelerate", line 8, in
sys.exit(main())
File "/usr/local/lib/python3.10/dist-packages/accelerate/commands/accelerate_cli.py", line 46, in main
args.func(args)
File "/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py", line 1057, in launch_command
simple_launcher(args)
File "/usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py", line 673, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['/usr/bin/python3', '-m', 'axolotl.cli.train', '/content/test_axolotl.yaml']' returned non-zero exit status 1.
Steps to reproduce
Try Axolotl example notebook.
During training it fails with given error.
It used to work last week perfectly.
Tried Gemma 7b model too and it too is failing.
Config yaml
base_model: google/gemma-7b
#base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: AutoModelForCausalLM  #For Gemma
#model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: /content/test_txt_data-10exmpl.json
    type: completion
    field: text
#datasets:
#  - path: ./mar_alpaca_dataset.json
#    type: alpaca
#    ds_type: json
dataset_prepared_path: /content
dataset_processes: 10
val_set_size: 0
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 700
sample_packing: true
pad_to_sequence_len: true

lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj
#lora_modules_to_save:
  #- embed_tokens
  #- lm_head
lora_target_linear: true
lora_fan_in_fan_out:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: false
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: False

warmup_ratio: 0.1
evals_per_epoch: 1
eval_table_size:
eval_max_new_tokens: 128
eval_sample_packing: False
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

save_safetensors: True
gpu_memory_limit: 14

Possible solution
Fix the errors and dependancies.
Which Operating Systems are you using?

[X] Linux
[ ] macOS
[ ] Windows

Python Version
3.10
axolotl branch-commit
latest
Acknowledgements

[X] My issue title is concise, descriptive, and in title casing.
[X] I have searched the existing issues to make sure this bug has not been reported yet.
[X] I am using the latest version of axolotl.
[X] I have provided enough information for the maintainers to reproduce and diagnose the issue.

@MeDott29 , you may be having FA issues RuntimeError: FlashAttention only supports Ampere GPUs or newer.
@amitagh , did you try other example configs?

Running on Colab produces the following error:
File "/content/src/axolotl/src/axolotl/train.py", line 170, in train
trainer.train(resume_from_checkpoint=resume_from_checkpoint)
File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 1859, in train
return inner_training_loop(
File "/usr/local/lib/python3.10/dist-packages/transformers/trainer.py", line 2249, in inner_training_loop
grad_norm = self.accelerator.clip_grad_norm(
File "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py", line 2269, in clip_grad_norm
self.unscale_gradients()
File "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py", line 2219, in unscale_gradients
self.scaler.unscale_(opt)
File "/usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py", line 307, in unscale_
optimizer_state["found_inf_per_device"] = self.unscale_grads(
File "/usr/local/lib/python3.10/dist-packages/torch/cuda/amp/grad_scaler.py", line 248, in unscale_grads
torch.amp_foreach_non_finite_check_and_unscale(
RuntimeError: "_amp_foreach_non_finite_check_and_unscale_cuda" not implemented for 'BFloat16'

I have met the exactly same error as jaydeepthik's while I was running Axolotl training on Colab notebook with L4 GPU.
File "/usr/local/lib/python3.10/dist-packages/torch/amp/grad_scaler.py", line 278, in _unscale_grads_
    torch._amp_foreach_non_finite_check_and_unscale_(
RuntimeError: "_amp_foreach_non_finite_check_and_unscale_cuda" not implemented for 'BFloat16'

The error occurred in the following code cell.
!accelerate launch -m axolotl.cli.train /content/llama-3-8b-Instruct-bnb-4bit-qlora.yaml
Would you take a look?

@chdaesung @jaydeepthik , sorry, I missed the notifications. Do you have a sample Colab notebook? I was able to run successfully on Colab these past weeks.

Hi, we've had a major overhaul since then and Axolotl installation is now much easier. Here is an updated colab notebook: https://github.com/axolotl-ai-cloud/axolotl/blob/main/examples/colab-notebooks/colab-axolotl-example.ipynb
which details installation.


WMF is planning to remove api_urls field in /page/summary endpoint

I just saw this post in wikitech-l, reposting in its entirety below. Do you know if we are using that endpoint @maskaravivek @ashishkumar468 ?

Message: 1
Date: Sun, 14 Jun 2020 21:00:00 -0500
From: Adam Baso<EMAIL_ADDRESS>To: Wikimedia developers<EMAIL_ADDRESS>Subject: [Wikitech-l] Planned removal of api_urls field in
/page/summary   endpoint
Message-ID:
<EMAIL_ADDRESS>Content-Type: text/plain; charset="UTF-8"
This message is a notice about forthcoming removal of a response field in
the REST API’s /page/summary endpoint. [1] This message is being cross
posted to wikitech-l and mediawiki-api-announce.
The endpoint is used in the Page Previews (“hovercards”) functionality on
the classic web (desktop) and Android & iOS experiences for Wikipedia, in
addition to numerous external experiences. We don't anticipate impacts on
the mainline Wikipedia experiences from this forthcoming field removal, as
the endpoint will still be operational.
The REST API's /page/summary endpoint provides an api_urls field in its
response with links to several other REST API endpoints supported by the
Page Content Service.
Three of the api_urls subfields refer to experimental endpoints that have
been removed from the REST API altogether in favor of newer API endpoints.

media (/page/media)
references (/page/references)
metadata (/page/metadata).

api_urls also contains subfields referring to stable endpoints that
continue to exist in the REST API:

summary (which is self referential)
edit_html (/page/html - the Parsoid HTML)
talk_html (/page/html/Talk: - the Parsoid HTML for the
corresponding Talk page)

Wikimedia’s Product Infrastructure team intends to remove the api_urls
field of the /page/summary response.
A cursory review at https://codesearch.wmflabs.org/ and Wikimedia Git
mirrors suggests api_urls isn’t in use in consuming code.
Review of web logs suggests traffic for the following endpoints:

The media endpoint is at about 5% of its original traffic before its
decommission and it appears to be from old Wikipedia for Android clients.
This is expected.
The references endpoint’s Wikipedia for Android traffic does not seem
present and its other traffic appears to be from non-user application
software based on User-Agent header components.
-The metadata endpoint’s traffic seems to have all but stopped.

This change is being announced in advance of the change because the
endpoint is advertised as stable. [2]
Please update your clients if you rely on the presence of the api_urls
field. If this change poses a problem for your clients, please do let us
know as soon as possible at the tracking task:
https://phabricator.wikimedia.org/T247991
We plan to remove the api_urls field described here on or after 14 July
2020.
Thank you.
Adam Baso
Director of Engineering
Wikimedia Foundation
[1]
https://en.wikipedia.org/api/rest_v1/#/Page content/get_page_summary__title_
[2] Refer to
https://www.mediawiki.org/wiki/API_versioning#End_point_stability and
https://www.mediawiki.org/wiki/Wikimedia_Product/Wikimedia_Product_Infrastructure_team/API_endpoint_stability_policy
for more information on stability designations.

We are not using this endpoint.

Thanks @maskaravivek


fix config file and  document typo for  ttl (#5087)

Please answer these questions before submitting pull request

Why submit this pull request?

[x] Bug fix

[ ] New feature provided

[ ] Improve performance

Related issues
question about TTL configuration #5087

Bug fix

Bug description.

How to fix?

New feature or improvement

Describe the details and related test reports.

@a198720 Why closed? Anything wrong?


AWS: New Cluster Mirror Pod Issues

I'm having issues bringing up a new build from master on AWS. The cluster starts and is able to start services as monitoring and logging services are started. However, when listing pods etcd, controller-manager, and the scheduler are stuck in pending.
I first noticed the issue when trying to create a secret which returns resource not found, which I assume is related to etcd not able to be found. I've attached logs that seem relevant. I've tried killing services and then restarting kubelet to no avail.
Any ideas on how to investigate further would be appreciated!
kube-controller-manager.log
I0603 16:32:54.670054       5 endpoints_controller.go:217] Pod "default/kube-controller-manager-ip-172-20-0-9" was deleted but we don't have a record of its final state, so it will take up to 30s before it will be removed from all endpoint records.
I0603 16:32:54.670102       5 endpoints_controller.go:217] Pod "default/kube-apiserver-ip-172-20-0-9" was deleted but we don't have a record of its final state, so it will take up to 30s before it will be removed from all endpoint records.
E0603 16:34:08.530361       5 reflector.go:168] watch of *api.ServiceAccount ended with: 401: The event in requested index is outdated and cleared (the requested history has been cleared [10483/10347]) [11482]
E0603 16:34:11.580963       5 reflector.go:168] watch of *api.Secret ended with: 401: The event in requested index is outdated and cleared (the requested history has been cleared [10489/10297]) [11488]

kubelet.log:
E0603 16:19:58.984326   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:19:59.177902   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:19:59.179253   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:19:59.185575   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:19:59.186625   14108 kubelet.go:1153] Deleting mirror pod "kube-scheduler-ip-172-20-0-9_default" because it is outdated
E0603 16:19:59.384458   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:19:59.387726   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:19:59.391711   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:19:59.392791   14108 kubelet.go:1153] Deleting mirror pod "kube-apiserver-ip-172-20-0-9_default" because it is outdated
W0603 16:19:59.567436   14108 status_manager.go:60] Failed to updated pod status: error updating status for pod "etcd-server-ip-172-20-0-9": pods "etcd-server-ip-172-20-0-9" not found
E0603 16:19:59.778052   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:19:59.778482   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:19:59.978561   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:19:59.978619   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:20:00.167777   14108 mirror_client.go:68] Failed deleting a mirror pod "kube-scheduler-ip-172-20-0-9_default": pods "kube-scheduler-ip-172-20-0-9" not found
E0603 16:20:00.175944   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:00.181663   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:00.367371   14108 mirror_client.go:68] Failed deleting a mirror pod "kube-apiserver-ip-172-20-0-9_default": pods "kube-apiserver-ip-172-20-0-9" not found
E0603 16:20:00.370820   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:00.375589   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
W0603 16:20:00.567404   14108 status_manager.go:60] Failed to updated pod status: error updating status for pod "kube-apiserver-ip-172-20-0-9": pods "kube-apiserver-ip-172-20-0-9" not found
E0603 16:20:00.770309   14108 kubelet.go:1161] Failed creating a mirror pod "kube-controller-manager-ip-172-20-0-9_default": pods "kube-controller-manager-ip-172-20-0-9" already exists
E0603 16:20:00.776245   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:00.777185   14108 kubelet.go:1153] Deleting mirror pod "kube-controller-manager-ip-172-20-0-9_default" because it is outdated
E0603 16:20:00.970012   14108 kubelet.go:1161] Failed creating a mirror pod "etcd-server-ip-172-20-0-9_default": pods "etcd-server-ip-172-20-0-9" already exists
E0603 16:20:00.975685   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:00.976571   14108 kubelet.go:1153] Deleting mirror pod "etcd-server-ip-172-20-0-9_default" because it is outdated
E0603 16:20:01.179328   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:20:01.185862   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:01.186739   14108 kubelet.go:1153] Deleting mirror pod "kube-scheduler-ip-172-20-0-9_default" because it is outdated
E0603 16:20:01.378477   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:01.380476   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:20:01.777982   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:01.778362   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:20:01.784859   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:01.785816   14108 kubelet.go:1153] Deleting mirror pod "kube-controller-manager-ip-172-20-0-9_default" because it is outdated
E0603 16:20:01.976221   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:01.977807   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:20:01.982537   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:01.983401   14108 kubelet.go:1153] Deleting mirror pod "etcd-server-ip-172-20-0-9_default" because it is outdated
E0603 16:20:02.175913   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:02.177500   14108 kubelet.go:1537] error getting node: node ip-172-20-0-9 not found
E0603 16:20:02.181969   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:02.182815   14108 kubelet.go:1153] Deleting mirror pod "kube-scheduler-ip-172-20-0-9_default" because it is outdated
E0603 16:20:02.370684   14108 kubelet.go:1161] Failed creating a mirror pod "kube-apiserver-ip-172-20-0-9_default": pods "kube-apiserver-ip-172-20-0-9" already exists
E0603 16:20:02.375463   14108 kubelet.go:2111] Cannot get host IP: cannot get node: node ip-172-20-0-9 not found
E0603 16:20:02.376429   14108 kubelet.go:1153] Deleting mirror pod "kube-apiserver-ip-172-20-0-9_default" because it is outdated

@justinsb You were super helpful troubleshooting last time if you have some spare time.

I can try to reproduce.  Is this with head or a release?

It was head as of yesterday, but I don't think anything impactful has changed since then. It was on us-east in case that matters. Also, I manually patched my IAM master policy with #9157.

Confirmed that the pods are indeed stuck in pending, although it doesn't seem to impact my ability to do anything (though I haven't tried creating a secret yet).
I suspect it is related to the name being ip-172-20-0-9 and not ip-172-20-0-9.<region>.compute.internal, and the errors around that.  I think there's also another bug open for that, which hopefully I will come across in my trawl through them all that I'm doing now.

Yeah, that was the strange thing for me as well, everything else seemed to be work. It wasn't until I tried to create a secret that I got an error.
Let me know if there are any code changes or investigation for which I can lend a hand?

FYI: the error I see when trying to create a secret:
Starting cluster using os distro: ubuntu
I0603 17:07:59.260415   80904 helpers.go:82] server response object: [{
  "metadata": {},
  "status": "Failure",
  "message": "the server could not find the requested resource",
  "reason": "NotFound",
  "details": {},
  "code": 404
}]
F0603 17:07:59.260931   80904 create.go:51] Error from server: the server could not find the requested resource

I just tried this again, with bc0816f5e5de9f2b13bf83f8df5f1bf464661f15 which reverts some of #9155.  I had seen some similar errors to the ones which were prompting the rollback.
My pods are now all "running".  Do you want to try pulling something that includes the rollback of #9155 and trying again?
(The issue I was thinking it was, with "Cannot get host IP", actually seems to be a different issue #8571)

Thanks! I created a cluster with bc0816f and all of my pods are now "running" and I'm now able to create my secret.
I'm going to close this issue now since it seems like it's resolved.


Crumbs defined in Route Config Do Not Show If App Refresh / Direct Access of URL

I am really liking your library but one quirk I've noticed is that if I define crumbs in routing config they work great when the app has already loaded and navigate around but if I directly access one of the URLs or hit Refresh on that browser page the breadcrumbs don't seem to render at all.
If I use component based crumbs they seem to load in both scenarios.  So I've been using that pattern for now.  Just wanted to ask if you've seen this behavior or maybe I am missing some configuration.
Thanks.

Hi @stevehaneytrailblazer
I was not able to reproduce the bug.
Please provide a link to a repo or Stackblitz that can reproduce the issue you described.


Switch to --use-lxd

Use lxd to build base18 snaps in travis

Travis doesn't like it...

Woohoo! Looks like it works now :)


Replicating results on CoNLl03

Hi,
Thank you very much for your model. I was wondering how can I go about replicating your results on the CoNLl dataset? Thanks.

Hi,
For CONLL'03 English, you can download the BERT Large WWM Cased from https://storage.googleapis.com/bert_models/2019_05_30/wwm_cased_L-24_H-1024_A-16.zip
I do not have rights to publish the dataset, so that you need to find on your own. I have used IOB2 encoded (every entity starts with B-TAG version of the data) and used the -DOCSTART- tokens to prepare documentwise input samples for CMV method.
I got the best results with maximum sequence length of 512, LR 2e-5, BS 2, Epochs 3 and using concatenated train & dev set for training the final model.

HI @jouniluoma,
I appreciate your help, however I might need a little more detail if possible? Where should I put the model when I extract it from the zip? In the models folder?
Also, where can I acquire the specific dataset you used to get the best results? Can you provide a link where I can download it? If not can you provide specific instructions how to transform the original CoNLl train.txt, testa.txt, testb.txt into what you used to get the best results?
Also, what is the command to run the model for evaluation? is it just python ner.py? Can I pass the hyper parameters you've specified to this? If yes, how?
Thanks for your help.

Please check the files in scripts folder e.g. run-turku-ner.sh on how to
use the code.  I do not have any specific download link for data, but you
can use linux tools to convert the data to utf-8 and then you may have to
convert also the mentions to IOB2 format. You should note that this is
research code provided as is, not a product with customer support. I hope
this helps you to run the code.
to 8. heinäk. 2021 klo 14.32 Dimitar Mihaylov @.***>
kirjoitti:

HI @jouniluoma https://github.com/jouniluoma,
I appreciate your help, however I might need a little more detail if
possible? Where should I put the model when I extract it from the zip? In
the models folder?
Also, where can I acquire the specific dataset you used to get the best
results? Can you provide a link where I can download it? If not can you
provide specific instructions how to transform the original CoNLl train.txt,
testa.txt, testb.txt into what you used to get the best results?
Also, what is the command to run the model for evaluation? is it just python
ner.py? Can I pass the hyper parameters you've specified to this? If yes,
how?
Thanks for your help.
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That's okay, I can easily choose another model for baseline. Appreciate it the help. Closing this now.