迁移训练收敛很慢问题

你好，我使用labelme标注工具标注一千张图片后，只预测一个类别。然后将图片放到JPEGImages中，标注放到Annotations中，然后将1000个文件拆分为test.txt，train.txt，trainval.txt，val.txt中，其中每个文件都是如下只包含图片的名称。
0 1 10 100 1000 ...... ...... ......
然后运行python tools/voc2012.py切分为训练和验证的tfrecord数据集。
我通过阅读代码，并且debug发现，除了本人上述描述的，VOCdevkit不需要做其它数据处理，而且voc2012.py运行不报错，成功转换为tfrecord文件，其它没有改动任何代码，都是按照文档来的。
但是迁移训练时候非常慢，跑了100多轮后还是没有收敛，损失8.0左右。比着使用完整的VOC2012原始数据还要慢，请问是我数据制作有问题吗，因为我数据只是做了test.txt，train.txt，trainval.txt，val.txt切分，相比VOC2012没有做其他的处理？谢谢！

I do want to check whether the way i am passing bbox labels are correct. My labels are originally in xml format xmin, ymin, xmax, ymax wrt original image size, then i am converting xml file to csv and then converting to tfrecords by following the below link https://medium.com/@omcar17/how-to-convert-xml-files-into-tfrecords-in-tensorflow2-0-86120b553f0b. As i understand, the labels needs to be converted to center_x, center_y, and w, h before we pass to YOLO architecture, but i do not understand where this centralization gets calculated in the code. Any comments on the approach that i am following ?

I do want to check whether the way i am passing bbox labels are correct. My labels are originally in xml format xmin, ymin, xmax, ymax wrt original image size, then i am converting xml file to csv and then converting to tfrecords by following the below link https://medium.com/@omcar17/how-to-convert-xml-files-into-tfrecords-in-tensorflow2-0-86120b553f0b. As i understand, the labels needs to be converted to center_x, center_y, and w, h before we pass to YOLO architecture, but i do not understand where this centralization gets calculated in the code. Any comments on the approach that i am following ?

I do want to check whether the way i am passing bbox labels are correct. My labels are originally in xml format xmin, ymin, xmax, ymax wrt original image size, then i am converting xml file to csv and then converting to tfrecords by following the below link https://medium.com/@omcar17/how-to-convert-xml-files-into-tfrecords-in-tensorflow2-0-86120b553f0b. As i understand, the labels needs to be converted to center_x, center_y, and w, h before we pass to YOLO architecture, but i do not understand where this centralization gets calculated in the code. Any comments on the approach that i am following ?

My XML format is <annotation> 	<folder>slider</folder> 	<filename>0.jpg</filename> 	<path>D:\data\python\slider\0.jpg</path> 	<source> 		<database>Unknown</database> 	</source> 	<size> 		<width>320</width> 		<height>160</height> 		<depth>3</depth> 	</size> 	<segmented>0</segmented> 	<object> 		<name>slider</name> 		<pose>Unspecified</pose> 		<truncated>0</truncated> 		<difficult>0</difficult> 		<bndbox> 			<xmin>169</xmin> 			<ymin>61</ymin> 			<xmax>223</xmax> 			<ymax>116</ymax> 		</bndbox> 	</object> 	<object> 		<name>slider</name> 		<pose>Unspecified</pose> 		<truncated>0</truncated> 		<difficult>0</difficult> 		<bndbox> 			<xmin>235</xmin> 			<ymin>25</ymin> 			<xmax>289</xmax> 			<ymax>80</ymax> 		</bndbox> 	</object> </annotation>
---------------------------------------------------------------------------------------------------------------------
Did I pass the BBox tag in the right way，Here are the results of my training after several interruptions。
`Epoch 00127: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 128/500000
14/14 [==============================] - 50s 3s/step - loss: 510.0937 - yolo_output_0_loss: 0.1397 - yolo_output_1_loss: 16.9426 - yolo_output_2_loss: 484.3006 - val_loss:<PHONE_NUMBER>48.0000 - val_yolo_output_0_loss: 0.0000e+00 - val_yolo_output_1_loss:<PHONE_NUMBER>48.0000 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00128: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 129/500000
14/14 [==============================] - 53s 4s/step - loss: 20.2879 - yolo_output_0_loss: 0.0126 - yolo_output_1_loss: 6.1573 - yolo_output_2_loss: 0.9669 - val_loss: 134044728.0000 - val_yolo_output_0_loss: 7.3212e-30 - val_yolo_output_1_loss: 134044712.0000 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00129: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 130/500000
14/14 [==============================] - 50s 3s/step - loss: 17.6409 - yolo_output_0_loss: 0.0049 - yolo_output_1_loss: 2.6769 - yolo_output_2_loss: 0.2408 - val_loss: 1120306.8750 - val_yolo_output_0_loss: 1.8937e-08 - val_yolo_output_1_loss: 1120292.0000 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00130: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 131/500000
14/14 [==============================] - 51s 3s/step - loss: 16.5327 - yolo_output_0_loss: 0.0036 - yolo_output_1_loss: 1.4005 - yolo_output_2_loss: 0.1721 - val_loss: 23186.2012 - val_yolo_output_0_loss: 3.8120e-04 - val_yolo_output_1_loss: 23171.3066 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00131: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 132/500000
14/14 [==============================] - 52s 3s/step - loss: 16.0620 - yolo_output_0_loss: 0.0032 - yolo_output_1_loss: 1.0760 - yolo_output_2_loss: 0.1279 - val_loss: 2299.1086 - val_yolo_output_0_loss: 0.0026 - val_yolo_output_1_loss: 2284.3809 - val_yolo_output_2_loss: 5.4786e-04
Epoch 00132: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 133/500000
14/14 [==============================] - 51s 3s/step - loss: 15.6690 - yolo_output_0_loss: 0.0025 - yolo_output_1_loss: 0.8811 - yolo_output_2_loss: 0.1057 - val_loss: 314.5774 - val_yolo_output_0_loss: 0.0087 - val_yolo_output_1_loss: 299.7287 - val_yolo_output_2_loss: 0.3005
Epoch 00133: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 134/500000
14/14 [==============================] - 50s 3s/step - loss: 15.2416 - yolo_output_0_loss: 0.0023 - yolo_output_1_loss: 0.6571 - yolo_output_2_loss: 0.0878 - val_loss: 105.2177 - val_yolo_output_0_loss: 0.0096 - val_yolo_output_1_loss: 88.6406 - val_yolo_output_2_loss: 2.2104`

I do want to check whether the way i am passing bbox labels are correct. My labels are originally in xml format xmin, ymin, xmax, ymax wrt original image size, then i am converting xml file to csv and then converting to tfrecords by following the below link https://medium.com/@omcar17/how-to-convert-xml-files-into-tfrecords-in-tensorflow2-0-86120b553f0b. As i understand, the labels needs to be converted to center_x, center_y, and w, h before we pass to YOLO architecture, but i do not understand where this centralization gets calculated in the code. Any comments on the approach that i am following ?

My XML format is <annotation> 	<folder>slider</folder> 	<filename>0.jpg</filename> 	<path>D:\data\python\slider\0.jpg</path> 	<source> 		<database>Unknown</database> 	</source> 	<size> 		<width>320</width> 		<height>160</height> 		<depth>3</depth> 	</size> 	<segmented>0</segmented> 	<object> 		<name>slider</name> 		<pose>Unspecified</pose> 		<truncated>0</truncated> 		<difficult>0</difficult> 		<bndbox> 			<xmin>169</xmin> 			<ymin>61</ymin> 			<xmax>223</xmax> 			<ymax>116</ymax> 		</bndbox> 	</object> 	<object> 		<name>slider</name> 		<pose>Unspecified</pose> 		<truncated>0</truncated> 		<difficult>0</difficult> 		<bndbox> 			<xmin>235</xmin> 			<ymin>25</ymin> 			<xmax>289</xmax> 			<ymax>80</ymax> 		</bndbox> 	</object> </annotation>
---------------------------------------------------------------------------------------------------------------------
Did I pass the BBox tag in the right way，Here are the results of my training after several interruptions。
`Epoch 00127: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 128/500000
14/14 [==============================] - 50s 3s/step - loss: 510.0937 - yolo_output_0_loss: 0.1397 - yolo_output_1_loss: 16.9426 - yolo_output_2_loss: 484.3006 - val_loss:<PHONE_NUMBER>48.0000 - val_yolo_output_0_loss: 0.0000e+00 - val_yolo_output_1_loss:<PHONE_NUMBER>48.0000 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00128: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 129/500000
14/14 [==============================] - 53s 4s/step - loss: 20.2879 - yolo_output_0_loss: 0.0126 - yolo_output_1_loss: 6.1573 - yolo_output_2_loss: 0.9669 - val_loss: 134044728.0000 - val_yolo_output_0_loss: 7.3212e-30 - val_yolo_output_1_loss: 134044712.0000 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00129: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 130/500000
14/14 [==============================] - 50s 3s/step - loss: 17.6409 - yolo_output_0_loss: 0.0049 - yolo_output_1_loss: 2.6769 - yolo_output_2_loss: 0.2408 - val_loss: 1120306.8750 - val_yolo_output_0_loss: 1.8937e-08 - val_yolo_output_1_loss: 1120292.0000 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00130: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 131/500000
14/14 [==============================] - 51s 3s/step - loss: 16.5327 - yolo_output_0_loss: 0.0036 - yolo_output_1_loss: 1.4005 - yolo_output_2_loss: 0.1721 - val_loss: 23186.2012 - val_yolo_output_0_loss: 3.8120e-04 - val_yolo_output_1_loss: 23171.3066 - val_yolo_output_2_loss: 0.0000e+00
Epoch 00131: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 132/500000
14/14 [==============================] - 52s 3s/step - loss: 16.0620 - yolo_output_0_loss: 0.0032 - yolo_output_1_loss: 1.0760 - yolo_output_2_loss: 0.1279 - val_loss: 2299.1086 - val_yolo_output_0_loss: 0.0026 - val_yolo_output_1_loss: 2284.3809 - val_yolo_output_2_loss: 5.4786e-04
Epoch 00132: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 133/500000
14/14 [==============================] - 51s 3s/step - loss: 15.6690 - yolo_output_0_loss: 0.0025 - yolo_output_1_loss: 0.8811 - yolo_output_2_loss: 0.1057 - val_loss: 314.5774 - val_yolo_output_0_loss: 0.0087 - val_yolo_output_1_loss: 299.7287 - val_yolo_output_2_loss: 0.3005
Epoch 00133: saving model to /content/drive/notebook/slider/slider/checkpoint/slider_train.tf
Epoch 134/500000
14/14 [==============================] - 50s 3s/step - loss: 15.2416 - yolo_output_0_loss: 0.0023 - yolo_output_1_loss: 0.6571 - yolo_output_2_loss: 0.0878 - val_loss: 105.2177 - val_yolo_output_0_loss: 0.0096 - val_yolo_output_1_loss: 88.6406 - val_yolo_output_2_loss: 2.2104`

I do want to check whether the way i am passing bbox labels are correct. My labels are originally in xml format xmin, ymin, xmax, ymax wrt original image size, then i am converting xml file to csv and then converting to tfrecords by following the below link https://medium.com/@omcar17/how-to-convert-xml-files-into-tfrecords-in-tensorflow2-0-86120b553f0b. As i understand, the labels needs to be converted to center_x, center_y, and w, h before we pass to YOLO architecture, but i do not understand where this centralization gets calculated in the code. Any comments on the approach that i am following ?

I do want to check whether the way i am passing bbox labels are correct. My labels are originally in xml format xmin, ymin, xmax, ymax wrt original image size, then i am converting xml file to csv and then converting to tfrecords by following the below link https://medium.com/@omcar17/how-to-convert-xml-files-into-tfrecords-in-tensorflow2-0-86120b553f0b. As i understand, the labels needs to be converted to center_x, center_y, and w, h before we pass to YOLO architecture, but i do not understand where this centralization gets calculated in the code. Any comments on the approach that i am following ?

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?
I have 1000 pictures and annotated files. Can't I just run python tools/voc2012.py directly?Is there anything else I need to change in the code?
Does the BBox coordinate scale refer to the below?Or do I need to scale it extra?If I need to scale, is there any reference for your project?
If yes, could you please give me your contact information?
xmin.append(float(obj['bndbox']['xmin']) / width)             ymin.append(float(obj['bndbox']['ymin']) / height)             xmax.append(float(obj['bndbox']['xmax']) / width)             ymax.append(float(obj['bndbox']['ymax']) / height)

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?
I have 1000 pictures and annotated files. Can't I just run python tools/voc2012.py directly?Is there anything else I need to change in the code?
Does the BBox coordinate scale refer to the below?Or do I need to scale it extra?If I need to scale, is there any reference for your project?
If yes, could you please give me your contact information?
xmin.append(float(obj['bndbox']['xmin']) / width)             ymin.append(float(obj['bndbox']['ymin']) / height)             xmax.append(float(obj['bndbox']['xmax']) / width)             ymax.append(float(obj['bndbox']['ymax']) / height)

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

wondering where bbox coordinates scaled wrt to network input size which is 416?
Do you mean this place? [size, size, channels]? Then in train.py, size defaults to flags.DEFINE_integer('size', 416, 'image size')?The size of my picture is 320160 and 600300, I need to modify the default value of size, right?
def YoloV3Tiny(size=None, channels=3, anchors=yolo_tiny_anchors,                masks=yolo_tiny_anchor_masks, classes=80, training=False):     x = inputs = Input([size, size, channels], name='input')

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

wondering where bbox coordinates scaled wrt to network input size which is 416?
Do you mean this place? [size, size, channels]? Then in train.py, size defaults to flags.DEFINE_integer('size', 416, 'image size')?The size of my picture is 320160 and 600300, I need to modify the default value of size, right?
def YoloV3Tiny(size=None, channels=3, anchors=yolo_tiny_anchors,                masks=yolo_tiny_anchor_masks, classes=80, training=False):     x = inputs = Input([size, size, channels], name='input')

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

wondering where bbox coordinates scaled wrt to network input size which is 416?
Do you mean this place? [size, size, channels]? Then in train.py, size defaults to flags.DEFINE_integer('size', 416, 'image size')?The size of my picture is 320160 and 600300, I need to modify the default value of size, right?
def YoloV3Tiny(size=None, channels=3, anchors=yolo_tiny_anchors, masks=yolo_tiny_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input')

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

wondering where bbox coordinates scaled wrt to network input size which is 416?
Do you mean this place? [size, size, channels]? Then in train.py, size defaults to flags.DEFINE_integer('size', 416, 'image size')?The size of my picture is 320160 and 600300, I need to modify the default value of size, right?
def YoloV3Tiny(size=None, channels=3, anchors=yolo_tiny_anchors, masks=yolo_tiny_anchor_masks, classes=80, training=False): x = inputs = Input([size, size, channels], name='input')

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

wondering where bbox coordinates scaled wrt to network input size which is 416?
Do you mean this place? [size, size, channels]? Then in train.py, size defaults to flags.DEFINE_integer('size', 416, 'image size')?The size of my picture is 320*160 and 600*300, I need to modify the default value of size, and then I don't have to change anything else, and then I can do migration training, right?
def YoloV3(size=None, channels=3, anchors=yolo_anchors,            masks=yolo_anchor_masks, classes=80, training=False):     x = inputs = Input([size, size, channels], name='input')

Looks like correct as loss is keep decreasing, i have couple of questions. 1. you might be converting these xml files to tfrecords files, right? and then passing those tfrecords files to train.py? wondering where bbox coordinates scaled wrt to network input size which is 416? 2. what is your data size?

wondering where bbox coordinates scaled wrt to network input size which is 416?
Do you mean this place? [size, size, channels]? Then in train.py, size defaults to flags.DEFINE_integer('size', 416, 'image size')?The size of my picture is 320*160 and 600*300, I need to modify the default value of size, and then I don't have to change anything else, and then I can do migration training, right?
def YoloV3(size=None, channels=3, anchors=yolo_anchors,            masks=yolo_anchor_masks, classes=80, training=False):     x = inputs = Input([size, size, channels], name='input')


Add warning for insufficient measurement

This work is being postponed for other more important numbers-validity changes. Closing for now and can reopen later if needed in future.


Dockerfile for example usage?

I was asked to put this into a dockerfile as part of a coding challenge. I've completed it and wondered if the application Dockerfile would be of use to anyone?
I think further enhancements could be using environment variables for DB connection information because #160 mentions CloudFoundry. It's more native for Docker to use ENV vars for easily changable information.
I also have edits for the docker-compose.yml, the pom.XML (to generate WAR archive / fat JAR). which alongside the multi-stage build gives me a 122MB deploy (36.9MB is the .Jar archive)
Happy to contrib docs on using, uses standard openJDK docker images.

Should this use #349 JDK/JRE 10 or stick with JDK/JRE 8?
JDK/JRE 10 is 334MB with 39MB .Jar archive

We've already discussed the use of docker here and the proposal was declined, see #339 - please consider searching the issue tracker before opening a new issue.

please consider deleting the docker-compose.yml then as it's essentially useless and uses docker.


MySQL createDatabase with charset/collation from config

When first using createDatabase(), I had charset: utf8mb4 in phinx.yml and made the assumption charset was passed along beyond the connection dns #134. So when setting the collation $this->createDatabase('newDb', array('collation' => 'utf8mb4_unicode_ci'));, it naturally produced an syntax error without being accompanied by 'charset' => 'utf8mb4'.
[PDOException]
SQLSTATE[42000]: Syntax error or access violation: 1253 COLLATION 'utf8mb4_unicode_ci'
is not valid for CHARACTER SET 'utf8'

Changes that I would propose...
Under MySQL, createDatabase() would look to both the options array passed as well as the phinx.yml configuration file for charset & collation. If neither exist, it defaults to utf8 and the default collation of the character set.

Looks to phinx.yml configuration to set charset & collation if supplied
during createDatabase().
Passed attributes in array will override configuration parameters.
Leaving both off will default
Added collation: utf8_general_ci to default phinx.yml and Docs

This can be closed as the MysqlAdapter now supports a collation option.


SB-1112: problem liquibase changeset fixed

@steve-todorov you probably will need to drop our production DB again because I have fixed changeset which is already applied. This changes needed as an example to create valid changesets in the future.

Thanks for letting me know @sbespalov. Ping me again when this is merged so I can fix the instance :)

@carlspring can you please review?


UI improvements for group and stream settings.

First commit to add new "Group permissions" section in "Organization permissions".
Second commit is to rename "Who can manage this group" setting.
Third commit is to show the create button in disabled state when user is not allowed to create groups.
Fourth commit is to show banner at the top instead of showing text below the button.
Fifth commit is to update the text for stream creation permission.

Screenshots and screen captures:
Stream settings

Group settings

Self-review checklist

[ ] Self-reviewed the changes for clarity and maintainability
(variable names, code reuse, readability, etc.).

Communicate decisions, questions, and potential concerns.

[ ] Explains differences from previous plans (e.g., issue description).
[ ] Highlights technical choices and bugs encountered.
[ ] Calls out remaining decisions and concerns.
[ ] Automated tests verify logic where appropriate.

Individual commits are ready for review (see commit discipline).

[ ] Each commit is a coherent idea.
[ ] Commit message(s) explain reasoning and motivation for changes.

Completed manual review and testing of the following:

[ ] Visual appearance of the changes.
[ ] Responsiveness and internationalization.
[ ] Strings and tooltips.
[ ] End-to-end functionality of buttons, interactions and flows.
[ ] Corner cases, error conditions, and easily imagined bugs.

The font color still need work, but I see you didn't close #32212, so merging this as it's a great checkpoint with no visible regressions. Thanks @sahil839!


Cannot see IAM roles tab

I have tried both standard and premium license, when go to Admin Console, I cannot see IAM role tab, would you please advice?

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: d30ca6a2-5155-0a33-17d5-e3caad46f0e2
Version Independent ID: cb775058-f2d3-b53b-3343-095733ca4d69
Content: Enable workspace object access control - Azure Databricks
Content Source: databricks/administration-guide/access-control/workspace-acl.md
Service: azure-databricks
GitHub Login: @mamccrea
Microsoft Alias: mamccrea

@CharmanderJieniJieni Thanks for bringing this to our attention. Your feedback  has been shared with the content owner for further review.
This is old UI and the UI has been changed.

@mamccrea Could you please update the screenshot with the new changes?

I am updating the screenshot. Please allow a few days for the changes to take place.
#please-close


Internal error when 1.function calls enabled 2. multilingual prompt

Description of the bug:
const googleSearchDeclaration = {
  name: "google",
  parameters: {
    type: "OBJECT",
    description: "Set the query and how many docs to return.",
    properties: {
      query: {
        type: "STRING",
        description: "The query to search google for.",
      },
      topN: {
        type: "NUMBER",
        description: "Return top N related documents.",
      },
    },
    required: ["query"],
  },
};

import { GoogleGenerativeAI } from "@google/generative-ai";

// Access your API key as an environment variable (see "Set up your API key" above)
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const generativeModel = genAI.getGenerativeModel({
  // Use a model that supports function calling, like a Gemini 1.5 model
  model: "gemini-1.5-flash",
  // Specify the function declaration.
  tools: [{
    functionDeclarations: [googleSearchDeclaration],
  }],
});

const chat = generativeModel.startChat();
const prompt = "谁是xxx\nxxx";

// Send the message to the model.
const result = await chat.sendMessage([prompt]);

if (result.response.functionCalls()) {
  const call = result.response.functionCalls()[0];
  // Send the API response back to the model so it can generate
  // a text response that can be displayed to the user.
  const result2 = await chat.sendMessage([{functionResponse: {
    name: 'google',
    response: {
      docs: "answer is 42"
    }
  }}]);

  // Log the text response.
  console.log(result2.response.text());
}
console.log(result.response.text());

This script can trigger internal error with large probability.
    throw new GoogleGenerativeAIFetchError(`Error fetching from ${url.toString()}: [${response.status} ${response.statusText}] ${message}`, response.status, response.statusText, errorDetails);
          ^

GoogleGenerativeAIFetchError: [GoogleGenerativeAI Error]: Error fetching from https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent: [500 Internal Server Error] An internal error has occurred. Please retry or report in https://developers.generativeai.google/guide/troubleshooting

Actual vs expected behavior:
Should be no internal error.
Any other information you'd like to share?
Here's what I have tested:

disable function call fixes the issue
language unification also fixes this
REST api has no such problem

Hi @blindFS
Thank you reporting this issue. This looks like an intermittent error and should work now.
This repository is for issues related to Swift SDK client bugs or improvements and the client doesn't control the service's responses. For issues related to Gemini API, we would suggest you to use "Send Feedback" option in Gemini docs. Ref: Screenshot below. You can also post this issue on Google AI forum.

Hi @blindFS
Thank you reporting this issue. This looks like an intermittent error and should work now. This repository is for issues related to JS SDK client bugs or improvements and the client doesn't control the service's responses. For issues related to Gemini API, we would suggest you to use "Send Feedback" option in Gemini docs. Ref: Screenshot below. You can also post this issue on Google AI forum.

Thanks for your response, but I don't think it is an intermittent error. Still getting internal error with the script above. And it seems to me a bug only exists in JS SDK, since when I tested on REST api, it works fine.


Large recipe sets can cause hang

E.G. with all Bob's and Angel's recipes enabled, the app hangs. Unclear whether this is due to long running time in simplex or actual hang due to cycling or other problems.

Hangs are due to long run time of simplex, but found various other minor issues while investigating. Resolving those issues, and improving performance as far as I can, but enabling huge numbers of recipes will still cause the simplex algorithm to run very long. This is a major issue in linear programming in general that has no clear solution, I do not plan to advance the field of linear programming just for Factorio Lab 😄


Implement GetAll for TUF data

GetAll TUF data between a specific date range
Signed-off-by: Josh Chorlton<EMAIL_ADDRESS>

Thanks for adding this!  Can we add the two following test cases for QueryTUFFilesHandler?

Call QueryTUFFilesHandler with a valid createdAfter that gets parsed (maybe in TestQueryTUFFilesHandlerNoStorage?)
Call QueryTUFFilesHandler with a store that errors on GetAll

Closing for now as use case isn't materializing and further changes will likely be required by the time it does.


Rework pyobjectrc.rs and add PyObjectRcB

Not sure if we would want to do a mass find/replace in this PR or in a separate one, but I think it could be done here.

Either way sounds good. Separated commits will be enough to make easy to track change.

I am not understanding this PR very well. What's the benefit to have PyObjectRcB?


chisel: 1.7.6 -> 1.7.7

Motivation for this change
https://github.com/jpillora/chisel/releases/tag/v1.7.7
Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[x] Tested basic functionality of all binary files (usually in ./result/bin/)
22.05 Release Notes (or backporting 21.11 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module
[ ] (Release notes changes) Ran nixos/doc/manual/md-to-db.sh to update generated release notes

[x] Fits CONTRIBUTING.md.

Result of nixpkgs-review pr 157492 run on x86_64-linux 1

1 package built:

chisel


MediaIoBaseDownload Cannot Handle Resuming Partial Download

Title summarizes the issue. As is, you cannot resume a partial download by using MediaIoBaseDownload()
Steps to reproduce

Start a download with MediaIoBaseDownload() and interrupt it
Create a new request and pass the relevant Range header to resume the download
Starting a new download with the above request will always start the download from byte 0 due to the constructor for MediaIoBaseDownload() setting progress to 0, and the range header being overwritten in next_chunk()

Code example
try:
        headers = {'Range': f'bytes={partial_size}-'} if partial_size > 0 and partial_size < total_size else {}
        request = thread_service.files().get_media(fileId=file_id)
        request.headers.update(headers)

        with open(local_file_path, 'ab' if partial_size > 0 else 'wb') as f:
            downloader = MediaIoBaseDownload(f, request, chunksize=1024*1024*15)
            done = False
            while not done:
                _, done = downloader.next_chunk()
    except HttpError as error:
        print(f"An error occurred while downloading {file_name}: {error}")
        return False

This appears to be a feature request (not a bug) which we don't support in this library, see the ReadMe.
To request a new feature, you can follow this link: Submit feature request

Hi @ohmayr, JP on the issue tracker routed me back here... can I get some feedback on the right way to submit this feature request?
Also, is this truly a feature request if intended functionality (resuming downloads) is missing and there is no API available, as far as I can tell, in google-cloud-python to download files from drive?


Fix bug: --qe do not save compression scheduler

Bug: quantize a model which have thining_recipe  => loading checkpoint fail
The compression scheduler did not pass and save into checkpoint when --qe -e is set
It cause loading the checkpoint file failed since the thinning_recipe should combine with compression_ scheduler

Merged, thanks @chenys1995


Check api key doesn't do anything

It seems is_api_key_valid returns True whenever anything (truthy) is passed in. Is this the intended behaviour? I would have guessed this function queries the server to check that the key is valid.
https://github.com/cohere-ai/cohere-python/blob/29e118e390dbe116b7d0c6ff734de80f742c1dd1/cohere/utils.py#L44-L51

Hi @seabo, this function check the API key:
https://github.com/cohere-ai/cohere-python/blob/16454c56d646f7eebfc927257c7cf3a25678dd3a/cohere/client.py#L640

Hi @jairojair - thanks. So are you saying that the purpose of Client.check_api_key is simply to locally test for the presence of any string? Because this function returns True for any arbitrary string you pass as the API key.
Is there a way to test that the key being used is actually valid (i.e. authorised) on the server?

If you run:
import cohere

co = cohere.Client()
prediction = co.generate(prompt='co:here')

You will be receive this exception:
line 47, in is_api_key_valid
    raise CohereError(
cohere.error.CohereError: No API key provided. Provide the API key in the client initialization or the CO_API_KEY environment variable.

if you run:
import cohere

co = cohere.Client('YOUR_API_KEY')
prediction = co.generate(prompt='co:here')

You will be receive this exception:
line 640, in _check_response
    raise CohereAPIError(
cohere.error.CohereAPIError: invalid api token

Indeed, you are correct. It's crucial to validate keys exclusively on the server side for enhanced security and data integrity. Maybe in the future we could use  is_api_key_valid function to validate format and key size, is a good approach.


Apply taggable PMA to loads as well as stores

We currently have a PMA for stores:
https://github.com/riscv/riscv-cheri/blob/main/src/riscv-integration.adoc#physical-memory-attributes-pma
but nothing for loads. I think that we should reuse the same PMA and do the same for loads as stores i.e. give implementations the choice of

return an access fault
load the tag as zero

the point of this is to allow the memory system to avoid the tag lookup which may give performance advantages.

tag clearing already specified

(and required, otherwise you can’t write a tag-preserving memcpy that works on non-taggable memory)


Arctic admin causes JS errors in browser logs

I have error in browser console since I installed arctic admin:

Uncaught TypeError: Cannot read property 'getBoundingClientRect' of undefined

And not only me I guess:
https://stackoverflow.com/questions/52857201/uncaught-typeerror-cannot-read-property-getboundingclientrect-of-undefined-on

Hi, it seems to occur in this js file at two points.

In both those cases, the issue seems to be that the tabs array is empty, and there is a null value related exception when the first element of the empty array, is referenced as a DOM object and the getBoundingClientRect() function is called on that.

From what I understand, it shouldn't break compatibility with the existing code for the width to be initially set to zero and for the getBoundingClientRect() function to be called only where there are elements in the tabs array.

hello @wachub , thank you for your response
My dirty workaround for now was (in application.html.haml - head section):
- if request.url.include? '/admin'
      = javascript_include_tag 'active_admin', 'data-turbolinks-track': 'reload'

As indeed, it broke some of my JS files, I do not remember what it was exactly (old times) but I think related to owl-carousel plugin or iziModal.

#75 Could you check out this poll request.

@wachub in my example, the files were not only included on ActiveAdmin module - maybe that was mistake of my configurations somewhere, but above haml is simple HTML that says the JS files from ActiveAdmin should only be included under /admin path.

Hi @michalzynek . AFAIK, this bug is a fairly low level one (syntax level), which is independent of the logic/algorithm on top. The only assumption made is that the width can be taken to be zero when the array is empty, which seems consistent for whatever is written on top. I don't think it is likely to blow up anywhere else.


Typescript Property '$primary' does not exist on type error occurred

Reproduce
reproduce package : https://github.com/ChoSeoHwan/prisma-extension-read-replicas-bug-0

git clone https://github.com/ChoSeoHwan/prisma-extension-read-replicas-bug-0.git
yarn install
yarn prisma:generate
yarn build
see tsc build error

Error
Property '$primary' does not exist on type 'DynamicClientExtensionThis<TypeMap<InternalArgs & { result: {}; model: {}; query: {}; client: {}; }>, TypeMapCb, { result: {}; model: {}; query: {}; client: {}; }>'.

Solutions
This occurs when moduleResolution in tsconfig is NodeNext.
The project is an esm module (type:module in package.json),
Accordingly, the .js extension must be added when importing.
When checking dist/index.d.ts built from the current @prisma/extension-read-replicas library,
When importing, it is confirmed that there is no .js extension.
For compatibility with multiple projects, you must add the .js extension.

Hello, we're using this library within Typescript, and we found that when using prisma.$primary() as specified in this blog post, we encounter the following error mesage:
Property '$primary' does not exist on type 'PrismaClient<PrismaClientOptions, never, DefaultArgs> | DynamicClientExtensionThis<TypeMap<InternalArgs & { result: {}; model: {}; query: {}; client: { ...; }; }>, TypeMapCb, { ...; }>'.
  Property '$primary' does not exist on type 'PrismaClient<PrismaClientOptions, never, DefaultArgs>'.ts(2339)

By silencing the Typescript error with a // @ts-ignore comment, the client works as expected. Is our problem related? Is there anything we can do to solve this type issue?

Versions:
"@prisma/client": "^5.7.0",
"@prisma/extension-read-replicas": "^0.3.0",

+1 for this, I had something like this:
ctx.prisma
              .$replica()
              .$queryRawTyped(queryEventIdByIncident(wildSearchWord))

This won't work, but this works:
ctx.prisma
              // @ts-ignore
              .$replica()
              .$queryRawTyped(queryEventIdByIncident(wildSearchWord)),

I am having the same issue.
Cannot use $prismary() when necessary because typescript tells me that the function is undefined. Any news on this?


Execute IRNET on other datasets like  wikisql,ATIS,Advising

HI, I would like to convert other dataset to similiar SemQL format and test IRNET on other datasets as well. However since the file types are different,  Can you please suggest how i should go about it?
Thanks
Anshu

HI, I would like to convert other dataset to similiar SemQL format and test IRNET on other datasets as well. However since the file types are different, Can you please suggest how i should go about it?
Thanks
Anshu

can you solve the question? thanks


Completion of synthesis fails

Following test fails
unitTestCase "autosynthesis successfully completed" def $ do
                  setNetwork $
                      Bus.defineNetwork "net1" ASync $ do
                          Bus.addCustom "fram1" (framWithSize 32) FramIO
                          Bus.add "accum1" AccumIO
                  setBusType pInt
                  assignLua
                      [__i|
                          function sum(x1, x2)
                              x1 = x1 + x2
                              sum(x1, x2)
                          end
                          sum(1,2)
                          |]
                  synthesizeAndCoSim

Outcome:
autosynthesis successfully completed: FAIL (0.20s)
          test/NITTA/Model/ProcessorUnits/Tests/DSL/Tests.hs:361:
          synthesis is not complete: fromList ["x2^0#1","x2^0#1@buf"]

Here the problem is that we cant send x2 from LoopBegin to LoopEnd using one FRAM.
Here's DFG at the moment when synthesis stuck:

The point is that the variable is already in FRAM, so I think we can avoid resending. @ryukzak is it right?


Bug - Using Cancel Invite, Rejion and Dismiss window pops up in teams client

There should be a Possibility to disable “removed from call / rejoin” window in teams client after doing a cancel Invite. Cancel Invite should be in Graph Sdk, right now it's missing and only works with Graph Api.

I'm not sure if I understand that correctly, but what I understand is, you want to cancel invatations of participants, like invite a user and suddenly the user is not needed as participant anymore and you want to cancel the invite. That is possible, as far as I remember, you can achieve this with a cancellation token on the InviteAsync Method, as far as I know, The method starts a task that is not completed until either the user joined as participant or the user can not be reached, if you want to stop that task you would need to use a CancellationToken that should stop the Task earlier, that should also do a "cancel invite" request to the graph api
I hope I've got you're problem right

Do you have the same problem if you do the Graph API requests "manually", with a HttpClient etc.?

Right now cancel invite is only available with graph api and its not available in graph sdk. I am using Graph Api

I'm not working at Microsoft, I am also currently not in contact with the team or any team member for the sdk at microsoft. So I don't know when they will read our converation or you're error, and work on this. But my experience is that changes in this are processing slow, especially when a work arround is ready to use, as you could use the GraphClient, when I got this right(which has a getter in every Ressource interface) or a HttpClient.
A update with this could happen tonight, never, or in between.

@saadali We are having the same issue and there is no workaround at this point.
The problem, as I see it, with the current cancel invite implementation in Graph is that it uses the deleteParticipant API. The reason that this is a problem is that as a developer, I may want the cancel behavior to be different than the delete behavior. For example, I may want to know if an invite was accepted or not. So I send a cancel, but if the cancel does not succeed because the user already answered, then return an error.

@JapNolt but then the InviteAsync shouldn't be the correct method to use as it would complete successful if a user joins or throw an error if a user declines the call, I am not sure what happens with cancellation of this, with a cancellation token, but then you should be able to just check if the user is in the participant list or not.


[Enhancement] (nereids)implement showTableIdCommand in nereids

What problem does this PR solve?

implement show table id command in nereids

Issue Number: close #42728

Related PR: #xxx
Problem Summary:
Check List (For Committer)

Test

[ ] Regression test
[ ] Unit Test
[ ] Manual test (add detailed scripts or steps below)
[ ] No need to test or manual test. Explain why:

[ ] This is a refactor/code format and no logic has been changed.
[ ] Previous test can cover this change.
[ ] No colde files have been changed.
[ ] Other reason

mysql> show table 15053;
+----------------------------------------+-----------------------------------+-------+
| DbName                                 | TableName                         | DbId  |
+----------------------------------------+-----------------------------------+-------+
| regression_test_catalog_recycle_bin_p0 | test_truncate_no_partition_backup | 13004 |
+----------------------------------------+-----------------------------------+-------+
1 row in set (0.00 sec)

Behavior changed:

[ ] No.
[ ] Yes.

Does this need documentation?

[ ] No.
[ ] Yes.

Release note

None

Check List (For Reviewer who merge this PR)

[ ] Confirm the release note
[ ] Confirm test cases
[ ] Confirm document
[ ] Add branch pick label

run buildall

run build all

please add a test case

run buildall

run buildall

run buildall

run p0


Benchmarks

Pachyderm can provide both batch and stream processing, is it better than hadoop/storm? May be we need some benchmarks to compare the performance between pachyderm and hadoop/storm/spark, and publish a testing report.

Hi @cxxly,
Benchmarks that allow people to easily compare Pachyderm's performance to hadoop/spark are definitely something we would like to make readily available. However doing benchmarks correctly such that they actually help users compare the products in a meaningful way is quite an undertaking so we can't quite commit to a timeline right now. This thread would be a good place to collect our thoughts about what these benchmarks should look like.
It seems like the obvious starting point is terasort. I'd be interested to see how Pachyderm did both in the total throughput section and the cost/gb section.
One thing I think terasort misses is latency which I think should be a part of our benchmarks. The fixed cost of jobs can be just as detrimental to user experience, even more so, because it often slows down development.

Ok, I will make some tests about this step by step, and talk about here.

Some preliminary benchmarks are reported here.  Keeping this issue open as we plan to add more comprehensive benchmarks.

We've added some benchmarks to master now. We will open new issues w specifics on new things we want to benchmarks.

Is there a possibility to get some insights on the results of these benchmarks for users? Mainly interested in number crunching benchmarks


Add support for global endpoint names in public system

Refactors the existing name building to more easily support all the different
variants.
Last two commits are the interesting ones.

sdf


AroundConstructTest interceptor priority results in undefined ordering which the test relies on

Tracking issue for a TCK challenge - the test in question asserts interceptor ordering but both interceptors have the same priority

Resolved via https://github.com/jakartaee/cdi-tck/pull/423


Custom Metadata Pilot: manual Calculate succeeds, Lookup Rollup Calculate Job record remains

Fresh install of DLRS 2.7.  Created a handful of Metadata-based rollups, set for Automatic calculation; no issues.   I decided to run a couple of them manually (via "Calculate" button on the "Manage Lookup Rollup Summaries - Custom Metadata Pilot" as I (mistakenly) thought something was out of sync.  I can see in the Apex Jobs screen in setup that the jobs ran and finished (status="Completed") almost immediately (not much data to rollup).  However, the records remain in the Lookup Rollup Calculate Job table.

I believe this same thing is happening to me. Every Job I scheudle gets stuck in the Lookup Rollup Calculate Job table after it completes.  Which I then must go and delete.  Also a fresh install  DLRS 2.7.

Yes, I am experiencing this as well. The job runs once manually, successfully, but stays stuck in the LRCJ tab indefinitely. Real Time and Scheduled jobs just are not working...

I think this is the same issue: https://github.com/afawcett/declarative-lookup-rollup-summaries/issues/460

Fresh install of 2.7 into Unlimited Edition, and I am also seeing this behavior.
-Greg

Ok guys, i'll get on it. I'll close this as duplicate of https://github.com/afawcett/declarative-lookup-rollup-summaries/issues/460


Moving image to a different folder

As a user, I want to move an image to a different location, keep my collection in order.
I would want to be notified if there is already an item with the same name.
Overarching issue #19
This action would be primarily used within the folder view #129
We need a new API endpoint for moving an image/images.
This action should only be allowed by an admin user, as it exposes the file system.
The API should return if the action was successful, if there are elements with the same name or if it failed.
After selecting an image and choosing the "move photo" action, a modal pops up where you can view all the folders within the /data folder. This would be the same component as the selection of the scan directory. We can navigate deep within the folder structure. For that, we have to also implement #136
To confirm the new path, we click on a button, "Update path". The modal closes. If it was successful, a toaster should pop up with the message "Updating path successful".  If there are elements with the same name, a new modal should pop up, asking the user what action should be taken Rename / Overwrite / Do nothing. If the user cancels the modal, nothing should happen.
If the action failed, a toaster should pop up with the message "Updating path failed" with an explanation why.

Some progress?

...top up

👋

I'm quite curious as well! Especially in regard to #129


[Bug] Error message not shown when test fails

Bug summary
During the review of #195 we noticed that the error message is not being displayed when a test fails. This seems to happen on main as well.

Recorder Version
alpha-3
Steps to reproduce

Create a script
Make the script fail, whether this is done via an assertion or some invalid selector
Observe that the error message does not display after the test fails

Expected behavior
An error message should be displayed.
Additional information
No response

Resolved by https://github.com/elastic/synthetics-recorder/pull/233.


Fix typo in ConvertPixel64RGBX16ToPixel48RGB16()

The variable target_pixel16_buffer was being initialized to itself (while also being uninitialized), which is probably not what we want (and undefined behaviour). The function only makes sense if it's initialized to the address of the target_pixel_buffer argument instead.

OK, I've just noticed this is a duplicate of #7. Closing and fingers crossed for merging that one in. ;)


fix(side menu): collapsed by default

Describe pull-request

Added a connectedCallback check in child components to check the default collapsed state of the Side Menu.
Added a collapsed control to storybook.
Conditionally rendered the "Collapse" text based on it being collapsed or not.

Solving issue
Fixes: CDEP-2714
How to test

Go to Side Menu
Set collapsible and collapsed to true
Make sure design is looking fiiine.

Nice, also the former jumping of the icon when hovering is gone?


Add hooks in packetio.Wrapper for custom messages (jacdac)

@pelikhan you can add some routing between sim and wrapper if you want

@pelikhan this is now working in Maker; I'm not sure if I need any origin checks.
Also, it would be now good to stop Maker spinning two simulators on jacdac.

Also, it would be now good to stop Maker spinning two simulators on jacdac.

I've disabled it.

I've hookup the events to the message simulator info in pxtarget.


Colton build error

Mac info.
MacOS Ventura 13.3.19(a)
MacBook Pro 16-inch, 2019
2.3GHz 8core Intel Core i9
ROS2 Humble
bellow, command & error message
$cd ~/ros2_humble/
colcon build --symlink-install --packages-skip-by-dep python_qt_binding
<< error message >>

Starting >>> ament_cmake_pycodestyle
Starting >>> ament_cmake_pyflakes
--- stderr: mcap_vendor
In file included from /Users/hyeongabshin/ros2_humble/src/ros2/rosbag2/mcap_vendor/src/main.cpp:16:
In file included from /Users/hyeongabshin/ros2_humble/build/mcap_vendor/_deps/mcap-src/cpp/mcap/include/mcap/mcap.hpp:3:
In file included from /Users/hyeongabshin/ros2_humble/build/mcap_vendor/_deps/mcap-src/cpp/mcap/include/mcap/reader.hpp:710:
/Users/hyeongabshin/ros2_humble/build/mcap_vendor/_deps/mcap-src/cpp/mcap/include/mcap/reader.inl:6:10: fatal error: 'zstd_errors.h' file not found
#include
         ^~~~~~~~~~~~~~~
1 error generated.
make[2]: *** [CMakeFiles/mcap.dir/src/main.cpp.o] Error 1
make[1]: *** [CMakeFiles/mcap.dir/all] Error 2
make: *** [all] Error 2
---
Failed   <<< mcap_vendor [2.25s, exited with code 2]
Aborted  <<< ament_cmake_xmllint [0.43s]
Aborted  <<< ament_cmake_cppcheck [0.42s]

missing issue


Issue with RouteListCommand.php

As per the documentation you can now have a so-called 'Single Action Controller', works perfectly during normal operations, however, while using Artisans route:list command I'm greeted with the error.
[ErrorException]
Undefined offset: 1

This is due to the following method on RouteListCommand:
    /**
     * Get the middleware for the given Controller@action name.
     *
     * @param  string  $actionName
     * @return array
     */
    protected function getControllerMiddleware($actionName)
    {
        Controller::setRouter($this->laravel['router']);
        $segments = explode('@', $actionName);
        return $this->getControllerMiddlewareFromInstance(
            $this->laravel->make($segments[0]), $segments[1]
        );
    }

Seems fairly simple to fix at first instance, but because I'm not familiar with bug-fixing other libraries and such (e.g. pull requests/testing), I thought I'd at least raise the issue, and couldn't find it previously referenced elsewhere.
=]
[hr]
Proposed fix
Replace
return $this->getControllerMiddlewareFromInstance(
    $this->laravel->make($segments[0]), $segments[1]
);

With
return $this->getControllerMiddlewareFromInstance(
    $this->laravel->make($segments[0]),
    isset($segments[1]) ? $segments[1] : ''
);

Seems to sort it for me.

Thanks for reporting. :)


Nested property using (attribute: true) returns "NoMethodError: undefined method"

Hey there,
i have some trouble with the property/attribute option. For example:
<foo>
  <bar />
</foo>

instead of the expected one:
<foo>
  <bar title="this is my title" />
</foo>

and a mapping like this:
nested 'foo'
  nested 'bar' do
    property 'title', attribute: true,
      setter: ->(fragment:, represented:, **) { ... }
    end
  end
end

With this i get an Error NoMethodError: undefined method `title=' for #<Hash:0x00000006df7590>. I can solve this by adding a reader and skip_parse:
nested 'foo'
  nested 'bar' do
    property 'title', attribute: true,
      reader: ->(doc:, **){ doc[:title] || ''},
      skip_parse: fragment_empty,
      setter: ->(fragment:, represented:, **) { ... }
    end
  end
end

Isn't there a way to skip this by default? If the attribute does not exists just skip instead of the exception?
Any ideas?

Can you check if that problem persists with Representable::Hash instead of XML? Looks to me like an XML problem.


Update 2019-19-12.md

Description of the change:
Reason for the change:
Link to original source:

Closes #

Hello @Whatthefoxsays,Thanks again for the PR! We appreciate your contribution and look forward to                                              continued collaboration. Thanks!                                             Team SendGrid DX


Rbenv or RVM bundler issue when start to use XMLENC

I will re-open once other issues merged into master.


Support for Ubuntu 22.04

Hello,
Since Ubuntu 18.04 LTS will be end of life soon we had to upgrade. We decided to move to 22.04 LTS but I see now it is not in the list of supported Operating Systems and we have some issues with hipSYCL after the upgrade. The available prebuild images are quite old and I was wondering if there are any plans to upgrade to the newer Ubuntu versions now that it is also supported by AMD.
Best Alex

I use hipSYCL with Ubuntu 22.04 all the time. Where do we say that we do not support 22.04? If we do say that somewhere, we should remove that statement :)
If you are referring to the binary packages, those are indeed outdated because we are trying to migrate to a new approach how to package things (and got stuck on some issues :( ). For now my recommendation is to build from source until binary packages are updated.

Thanks, for the help. I have tried on a different server with a fresh install. I feel the upgraded server is broken in some way. Yes I was talking about the packages and since building from source segfaulted on that machine, I assumed it is not supported. I would consider this solved.


Tick version to 0.2.1, rerender

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.


fix: even more forceful clean

:tada: This PR is included in version 2.0.4 :tada:
The release is available on:

GitHub release
npm package (@latest dist-tag)

Your semantic-release bot :package::rocket:


[DEMO] IL.Register Employee method

Implement the subj according to the spec and service logic

cancelled


NEXT: Remove mention of Aegislash on NEXT banlist

Aegislash is already banned from OU, so there is no need to mention its
banning as one of the "differences from OU".

This seems like a good a place as any. Aegi and Cube and normal Kyu should all be unbanned. There's no way they'd be OP in the NEXT meta especially not compared to some questionable stuff we allow (just because protect doesn't give mblaziken a speed boost doesn't mean it isn't mental; and mlucario sacrificing weaker stabs for a stronger espeed is also iffy).
@Zarel @Temporaryanonymous @MonoHearted @NickMP

I could go on and argue further and quite vehemently but I'll see if there's any opposition first heh.

Was Mega-Blaziken's being allowed in NEXT intended?
It would seem that it's only Blaziken that should be allowed from the README...

It was intentional as it came up alongside megaluke way back iirc. The readme is just "behind" so to speak; aka it doesn't address every small change especially if they're old.

While Mega Blaziken's BST does pass 600, so does basically every Mega Evolution. It's safe to assume that Mega BST's aren't affected by NEXT's blanket-ban and would probably be banned individually if they needed to be.
(Actually, it's worth mentioning that technically, Aegislash doesn't have over 600 BST, but Stance Change does give it an effective 60/150/150/150/150/60, or 720 BST. So I'm not actually sure where it falls on the NEXT banlist.)

Yeah, NEXT doesn't ban megas, we just nerf them.
Incidentally... Megamence? How were we going to nerf that again?

Hmm, nerfing -ate abilities might make the other Pokemon too sad, but that's really the only thing allowed in the NEXT manifesto without banning the item

I looked at the -ate abilities; I don't mind nerfing them a bit. Question is, what makes megamence overpowered in singles, exactly? Can someone give an overview?

http://www.smogon.com/forums/threads/salamencite-is-now-banned-to-ubers.3523082/
The MegaMence ban according to haunter's announcement boiled down to two factors:

"Incredible physical bulk" - MegaMence has 95/13/90 backed up by an initial Intimidate, making it really easy to set up a Dragon Dance on many Pokemon, and also make it harder to revenge kill.

In fact, Mega Salamence's physical defense is comparable to that of Skarmory. - haunter

"Stupidly powerful offensive presence" - Aerialate-backed 145/120 offenses with a base 120 speed is very good for both sweeping and wallbreaking, and its wide movepool makes it very hard to predict the set that it's running.

No Pokemon outside of Eviolite Porygon-2 can safely switch into Mega Salamence. - haunter

Because of these two reasons, teams had to overprepare for Mega Salamence, and it could sweep a significant number of teams with little effort.

Nerfing the -ate abilities only needs to be done for MegaMence. All this does is put another nail into the coffin of MegaPinsir and screws over Sylveon.
Mence is too ridiculous, keep it banned. To give you a more "real" perspective of how insanely easy it is to sweep:
Player A has mega mence and lets say ttar, player B has scarf lando T with a breloom that's already swept something.
Ttar comes out. Player B switches in breloom on stealth rock. Kills ttar. Mence comes in, dances because nothing from breloom scares it.(let's say something is already asleep, or better yet the Incredibly common substitute set). As it dances you go to lando-t to lower its attack. Turns out at even just +1 speed double edge kills after rocks.
Without a focus sash mon, prankster twave, or CHOICE BAND weavile/mamo u can't do anything. Just a single speed boost let it clean 90% of OU mons with hazards. It also never has to go max speed or max attack, some investment into bulk and it survives much much more thanks to it's typing.
It also gets intimidate pre-mega making it even easier to get that first dance. If you need more proof - check out the usage he's getting in Ubers as both a wall and a cleaner (just today a DD/EQ/DE set was used by aim to help beat malefic in SPL).

slept something* (not swept)

Also this really wasn't as good a place as any, @CrestfallPS, you made me not realize that this is a pull request that needs to be accepted. :|

And you can't think of a way to nerf it?

Specifically, the core rules of NEXT are:

no base stat changes
no removing from movepools
no removing from ability distribution
no typing changes (except on formes)
no buffing OU mons, except maybe tiny buffs to mons at the bottom of OU
no doing things that make zero sense flavor-wise

We can't nerf it stat-wise or remove moves from its movepool, so unless we decide that Mega Evolution is a "forme" and remove its Flying type we'll have to nerf the Ability. If we nerf -ate, then we simply have to make sure it still isn't broken, and be careful of how heavily the other -ate users are hit by it.

We can remove its Flying type from its Mega.

What new typing would fit for MegaMence anyways? I don't know what would fit flavor-wise
Also as an aside, Hoopa-Unbound has a BST of 680, so it should probably be banned.

Also @MonoHearted says that Mega Mence isn't broken in NEXT because there are more things that can deal with it? No mention of what those Pokemon were, though.
(Mono also accused me of trying to muck around with NEXT mechanics while not knowing anything about the metagame, but that's what I get for bringing it up)

I'd love to hear what can "deal with" MegaMence at +1 (it always gets plus 1, you cannot stop this without priority taunt or mence coming into a strong ice type).
The only things we could pull off for Mence flavour wise would be:
Mono Dragon (I guess we ignore it's wings and design)
Dragon Normal (seems reasonable)
Dragon Fighting (due to Moxie, it's an offensive mon I guess, grasping at straws).
I'd suggest the first or second. Either way removing the stab on top of Aerilate is key.

To be fair, NEXT does have more walls.
Rhyperior?

Mega Aggron? Basically has zero weaknesses in NEXT.

Also: Dragon/Normal on an Aerilate mon? That's really mean. :P

Rhyperior should be going SpD anyways most of the time. Aggron I'll concede on. But what does Aggron do back? Let it boost up more?  Even then, both of these mons are dealt with the other 5 members of your team who exist solely to allow amega mence sweep.
And even with those 2 choices you're essentially requiring teams to run one of those 2 just for Mence. That's the definition of an over centralizing mon. Because if the team doesn't have mence/pinsir/zard/blaziken/luke/lop, they're dead weight and will just be taken out by whichever side they didn't invest into (phys def aggron even with filter still gets blasted by lets say, specs keldeo).

Every pokemon in this game has a counter. That doesn't mean it should be legal. Other than grass knot ekiller won't beat quagsire. Other than overheat, it won't beat skarm. Should we allow normal arceus into NEXT?

Hey, I never said not to nerf it. I was just trying to think of what mons Mono might be talking about.

We could make -ate abilities unaffected by boosting moves? I guess Pinsir would suffer.

Let's compare Megamence with Megazard X. What makes Megamence better?

It could still run (the sub par set of) DD/EQ/Dclaw/roost. That's still one boosted stab which you only need to clear out fairies for (and most teams I've played in NEXT have 0 or 1 of these).

It's bulkier. It's faster. It gets intimidate before mega. It can set up for free, zard needs to wait. It doesn't die to recoil (unless it CHOOSES double edge). Fire dragon is inherently shittier than dragon flying. It's stronger than zard.

Oh and zard can't afford sub AND roost. Mence can because flying stab has no immunities. Mence also gets defog.

I agree with the rest, but Fire/Dragon is better than Dragon/Flying offensively, isn't it?

We could make it so -ate abilities cap base power at 120? Affects Return but not Hyper Voice.

And critically doesn't affect Quick Attack so Mega Pinsir should still be usable.

Pinsir still loves Return (and sometimes runs Thrash, which has 120 BP). DD M-Altaria loves Return too.

We can't really find a nerf that has no side effects. We can buff Pinsir and M-Altaria in other ways.


Added Direct Bookmark Buttons

I'm done adding direct bookmark buttons.
The button is a green + (or red - if already bookmarked) that appears right next to the Level ID.
When clicked the course will automaticly be bookmarked/unbookmarked, without visiting the bookmarks site.
I tested this only in firefox. You should probably check if it works in chrome before accepting the Pull request.

Works beautifully in Chrome. Thank you so, so much. I should basically take my name off the extension at this point :)


Adding support for generating Kibana Discover app link

This PR adds support generating Kibana links to the Discover application.  This is low overhead alternative to the existing dashboard mechanism.  At D2L we immediately that the creation and maintaining of dashboards to match the rules to be a deterrent.
Kibana uses the rison encoding (rison-node) to represent its global and application state.  By using the python encoder prison (MIT License), we can more easily build more complex links to Kibana.  I based the state on the same state produced by creating filters in the Kibana application.  This state has remained mostly the same between 5.x, 6.x, and 7.x.

@JeffAshton
It may be possible to implement Kibana Discover. I am supposed to specify the index pattern ID, but couldn't this be handled by the index pattern name?

@JeffAshton
It may be possible to implement Kibana Discover. I am supposed to specify the index pattern ID, but couldn't this be handled by the index pattern name?

If you want to use the pattern name, you have use the advanced custom id option:

@JeffAshton
thank you for your answer.


Bookings Address Field Issue

When using a bookings public page, the address field doesn't display properly on the Outlook Calendar event page. If you manually create the booking it works fine, but if the booking is done from the booking page the address doesn't get added properly and shows up as "Other"

@jhambach Thank you for your feedback. Are you referring to this document - https://docs.microsoft.com/en-us/microsoft-365/bookings/create-a-manual-booking?view=o365-worldwide?
Thanks
Sri

@msbemba Thank you very much for the contribution and sharing this explanation. @jhambach Hope this comment is helpful for you. If you see a documentation update is required, please feel free to open an issue for the same. We proceed here to close it. Thanks for taking out some time to open the issue. Appreciate and encourage you to do the same in future also.


Still being developed? Abandoned? Still working (in WP 4.8.x)?

Hi folks,
I get the impression this plug-in has been abandoned. Is that correct?
Does it still work in newer versions of Wordpress? Like 4.8.x ?
Thanks...
Jonathan

it works. Just tested. Will update it on wordpress soon


Cleanup use statements

Removed unnecessary use statements
Updated doc-blocks with full class names (few places, not all)
removed unnecessary white space

Codecov Report

Merging #633 into master will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##             master     #633   +/-   ##
=========================================
  Coverage     31.43%   31.43%
  Complexity     1162     1162
=========================================
  Files           104      104
  Lines          3983     3983
=========================================
  Hits           1252     1252
  Misses         2731     2731

Impacted Files
Coverage Δ
Complexity Δ

src/Event/Plugin/Menu/View/SearchViewListener.php
0% <ø> (ø)
4 <0> (ø)
:arrow_down:

src/Shell/FakerShell.php
0% <ø> (ø)
30 <0> (ø)
:arrow_down:

src/Shell/PluginShell.php
0% <ø> (ø)
2 <0> (ø)
:arrow_down:

src/Shell/Task/UpgradeFileTask.php
0% <ø> (ø)
2 <0> (ø)
:arrow_down:

src/View/Helper/SystemInfoHelper.php
100% <ø> (ø)
8 <0> (ø)
:arrow_down:

src/Model/Table/ScheduledJobLogsTable.php
23.52% <ø> (ø)
4 <0> (ø)
:arrow_down:

src/Controller/SystemController.php
8.33% <ø> (ø)
16 <0> (ø)
:arrow_down:

src/Shell/AvatarsSyncShell.php
0% <ø> (ø)
6 <0> (ø)
:arrow_down:

src/Controller/Api/V1/V0/AppController.php
51.2% <ø> (ø)
33 <0> (ø)
:arrow_down:

src/Feature/Config.php
100% <ø> (ø)
7 <0> (ø)
:arrow_down:

... and 24 more

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Window positioning on Linux, Microsoft and OSX?

Recently I saw:
https://jakebruemmer.github.io/adamantite-info/

You can use the gem by running the adamantite command in your terminal after downloading the gem. The
window will appear on your screen (potentially not in the center of it, as that's a current limitation of the UI library
it uses Glimmer.

Is it possible to specify the position?
In ruby-gtk2 and ruby-gtk3 we could use .move(0,0) on a Gtk::Window instance to position it top-left, for instance.
This changed somewhat in ruby-gtk4, but if it is possible, perhaps it may be also possible on windows and OSX?
In that case, perhaps libui-ng could support it.
Anyway, just mentioning it here. If people have more information about this, it would be nice to share your
knowledge of this here. Perhaps libui-ng may support cross-platform widget position setting one day.

uiWindow requires uiControlShow and uiMainSteps before using uiWindowSetPosition, because it will wait until GTK finishes the queue.
(Maybe it's the same issue as #252?)
I'm using it in my apps like this and it's working as I expected for now.
#ifdef __linux__
    uiMainSteps();
#endif
uiControlShow(uiControl(winow));
uiWindowSetPosition(window, 0, 0);

uiMain();

Is it possible to specify the position?

Not in the way you are describing. There is uiWindowSetPosition() but that expects an x and y coordinate. We essentially need a new API call to query the screen dimensions which would enable users to place their windows manually.
While this will most likely fail on many Unix systems it might work on others. Centering a window seems surprisingly complex... reminds me of CSS.
We need a new API to get the screen dimensions and even then, things may still fail.

It's possible but uiWindow requires uiControlShow and uiMainSteps before using uiWindowSetPosition, because it will wait until GTK finishes the queue. (Maybe it's the same issue as #252?)

I'll have a fix ready soon. You might need to do a Show() though nonetheless on multi monitor setups, depending on how uiScreenDimensions() or similar is implemented.

Why not just have an function specifically to center a window? In GTK you can do something like:
gtk_window_set_position(GTK_WINDOW(window), GTK_WIN_POS_CENTER_ALWAYS);

Why not just have an function specifically to center a window? In GTK you can do something like:
gtk_window_set_position(GTK_WINDOW(window), GTK_WIN_POS_CENTER_ALWAYS);

I don't know if Win/Cocoa have a similar API.

win32 does not, so we would need to do this via calculations, in which case we might as well expose the screen dimensions API via libui-ng as well IMO.
Cocoa has [NSWindow center] which centers the window.
GTK_WIN_POS_CENTER_ALWAYS actually does something very different and is discouraged by the docs.

GTK_WIN_POS_CENTER_ALWAYS Keep window centered as it changes size, etc.

Note that using #GTK_WIN_POS_CENTER_ALWAYS is almost always a bad idea. It won’t necessarily work well with all window managers or on all windowing systems.

So most likely we would want to use GTK_WIN_POS_CENTER. I haven't tested this though.

So most likely we would want to use GTK_WIN_POS_CENTER. I haven't tested this though.

Upon further investigation I have come to the conclusion that the entire gtk_window_set_position API is broken.

gtk_window_set_position(GTK_WIN_POS_CENTER)  does center the window on my system: i3wm (xorg) as well as weston (wayland)
gtk_window_set_position(GTK_WIN_POS_CENTER_ALWAYS) will result in the window being placed center on the first call, but then breaks everything else like gtk_window_move(). Unsetting the ALWAYS and then trying to set it again will result in  gtk_get_position() returning -1 -1.

I fiddled around with various things. Weston ignores all hints completely. Not sure what other Xorg WMs do, but i3wm definitely only works on the first call and seemingly breaks the system entirely after. Since gtk3 is EOL no patches will be accepted, gtk4 removes all positioning APIs completely .
I also looked into the various screen/display APIs on the various platforms. It is a rather complex matter. Multi screen, multi DPI, etc... and then we still need somebody who can actually test all of this with multiple monitors on all systems (I personally don't).
I understand the frustration of "Why is it so difficult to place a window in the middle of the screen?"
I guess maybe we found the third hard problem in computer science? Naming things, cache invalidation, centering things on screen and off by one errors.


Refactoing in Client and Epoll

In this PR have made refactoring to improve API and code structure:

I've removed several magic numbers: size value that is passed to epoll_create is a hint and used for backward compatibility (see here).
I've changed timeout default value in Epoll::poll to wait infinity: this behavior is closer to how it's used in Pistache.
Pass Const::MaxEvents value to maxevents of epoll_wait function that is equal to events array size otherwise we should have additional check that the real size of array is greater or equal to maxevents (to prevent out of bound array access).
I've improved Transport::onReady, extract several methods because a lot of nested if's look cumbersome + added assert's.

Codecov Report

Merging #504 into master will decrease coverage by 0.22%.
The diff coverage is 72.88%.

@@            Coverage Diff             @@
##           master     #504      +/-   ##
==========================================
- Coverage   69.51%   69.29%   -0.23%
==========================================
  Files          49       49
  Lines        6872     6891      +19
==========================================
- Hits         4777     4775       -2
- Misses       2095     2116      +21

Impacted Files
Coverage Δ

include/pistache/os.h
100% <ø> (ø)
:arrow_up:

include/pistache/client.h
100% <ø> (ø)
:arrow_up:

src/common/os.cc
55.84% <100%> (ø)
:arrow_up:

src/common/reactor.cc
80.4% <100%> (+0.32%)
:arrow_up:

src/server/listener.cc
67.65% <100%> (ø)
:arrow_up:

src/client/client.cc
79.15% <69.23%> (-3.15%)
:arrow_down:

include/pistache/reactor.h
93.33% <0%> (-4.45%)
:arrow_down:

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bedpostx stick component concatenation

Contemplating how best to demonstrate bedpostx stick orientations during #92.
Something I realised early on is the contrast in precedent between having 3-vector images with multiple orientations stored as triplets, versus no such precedent for orientations stored in spherical coordinates and instead splitting them across files. Indeed bedpostx goes even further in the other direction, splitting each spherical coordinate between two files.
However the expectation currently being set by the BEP, being not only concatenation of polar angles to form spherical coordinates but also concatenation across multiple orientations, could possibly be disliked. I do think it should be supported by the specification, but perhaps not what is used for the demonstrative example. The multiple stick components should maybe instead be split across images. I don't think that it should be permissible to keep the two polar angles split between two images---since they can't be interpreted in isolation and need to be grouped to encapsulate the appropriate encoding---but having one spherical coordinate image and one volume fraction image per stick would both be consistent with the rest of the specification in terms of separate representation of separate compartments in a multi-compartment model, but also require less manipulation to get from a bedpostx output to a BIDS-compliant dataset.

The other factor to consider here is the relevant filenames if the multiple stick components are split across files. The simple option is to just encode it within the _param- entity; so eg.:
sub-01_model-ballsticks_param-polar1_dwimap.nii.gz
sub-01_model-ballsticks_param-vf1_dwimap.nii.gz
sub-01_model-ballsticks_param-polar2_dwimap.nii.gz
sub-01_model-ballsticks_param-vf2_dwimap.nii.gz
sub-01_model-ballsticks_param-polar3_dwimap.nii.gz
sub-01_model-ballsticks_param-vf3_dwimap.nii.gz

The only alternative I see currently is introducing a new entity, something like index:
sub-01_model-ballsticks_param-polar_index-1_dwimap.nii.gz
sub-01_model-ballsticks_param-vf_index-1_dwimap.nii.gz
sub-01_model-ballsticks_param-polar_index-2_dwimap.nii.gz
sub-01_model-ballsticks_param-vf_index-2_dwimap.nii.gz
sub-01_model-ballsticks_param-polar_index-3_dwimap.nii.gz
sub-01_model-ballsticks_param-vf_index-3_dwimap.nii.gz

Just noting that the question of "index" is also being discussed here, where the suggestion is to use "item" instead, because the word "index" is already being used elsewhere.


Sensu silently fails on non-processing queue

I'm stressing Sensu to try and find and remediate failures we may see in production.  In an attempt to simulate a failure mode I[m concerned about I set my RabbitMQ to not process messages (By setting the high watermark to 0: https://www.rabbitmq.com/memory.html) attempting to simulate a locked-up rabbit and observed the results.  In this case Rabbit is up, the server/API is connected, there are no messages on the queue, but no check data is coming through.
I have my checks configured with keepalives, I had expected all my checks to go into keepalive alarm.  However, no keepalive events occurred.  Listing the sensu queue in Rabbit it looks like keepalives process through Rabbit, so a non-processing queue will break keepalives.
I also polled the health API endpoint and it was returning healthy.  As the consumers were attached and the queues were empty the healthcheck passed.
My concern is that, while this is admittedly an edge case, it is also a silent failure.  The health API passing isn't surprising, but the keepalives failing is.  Is there any way to monitor Sensu to know if the queues are processing beyond /health?  Is it possible for the check keepalives to not use the queue, which would cause the checks to correctly go into alarm as they're not being updated?

This has impacted me in real-world. Please fix

@CVTJNII does a locked up RabbitMQ broker as a result of exceeding the watermark "block" connections? https://www.rabbitmq.com/connection-blocked.html

@portertech I'm not sure, that seems to be at the RabbitMQ connection level which would be deep in Sensu.  I'm assuming that message would come through if Rabbit isn't processing, but I'm not sure how to verify that without digging into the Sensu to Rabbit binding.
However, as a solution for this issue I don't think that's a good option.  I wouldn't be surprised if there are Rabbit failure modes where that isn't sent, and relying on Rabbit to say "Hey, I'm broken" seems like a poor design choice.  I think it would be better to have some timer external to Rabbit to ensure data is being received off the queue.  As I mentioned above I think doing this with the existing keepalives would be as graceful solution, or perhaps using message data fed by the servers themselves to heartbeat through Rabbit to drive the healthcheck.  In any case the block message mentioned seems like it will be too unreliable to catch such a serious failure mode, I'd like to see something more active for something as important as monitoring health.


Update NEWS for 2.3.0

I think we also want to update the default version for bst init in src/buildstream/_frontend/cli.py.

It looks like updating it before tagging makes CI unhappy. So I guess we'll just have to merge this with a red pipeline and tag afterwards.


Ignore markdown

For markdown file, it would be really useful to have a sort of  tag to tell the markdown parser to ignore this part which need to be processed by dokuwiki parser or plugins.
I see the --- front-matter but it seems it can’t be used several times and only on top of the document.
So a spécial tag for this, like a  tag for markdown files would be really useful.

I have found a way which seems to almost work but is problématic.
I surround my dokuwiki or plugin syntax with my not markdown synthax.
It works as it ends the markdown process and start a new.
But the problem is that footnotes are placed at the end of the first markdown statement and not at the end of the page. So this is not the solution.
Is there a way to deal with this. As the frontmatter but anywhere we wish in the page to revert only for a moment to dokuwuki syntax.


Feature/2.0/pytest options

adds 3 options when calling pytest:

--memory-skip: by default, all tests that use the storage fixture will ONLY use MemoryProvider.
-- usage: pytest --memory-skip will not use MemoryProvider.
--local: by default, the LocalProvider is NOT used. you have to explicitly use pytest --local.
--s3: by default, the S3Provider is NOT used. you have to explicitly use pytest --s3.

all providers are created when running the first test (in session scope), defined in conftest.py here.
you can call any combination of these parameters (like pytest --memory-skip --s3 to run tests only using s3).

EXAMPLES:
you can also set a test function to always use only a single storage type:
def test_something(s3_storage):
    # will always only use the S3Provider
    pass

or you can use all providers:
@parametrize_all_storage_providers
def test_something(storage):
    # will be parametrized with all providers [memory, local, s3]
    pass

or you can use a subset of all providers:
@pytest.mark.parametrize("storage", ["local", "s3"], indirect=True)
def test_something(storage):
    # will be parametrized with only 2 providers [local, s3]
    pass

NOTES FOR @AbhinavTuli
you should rewrite your cache tests, there are a lot of weird functions going on & i didn't want to tamper since i don't have context. please come up with a solution that we can generalize this, so i can simply use one prebuilt parametrize decorator in my tests and everything will work.
something like --cache-chain memory,local,s3 should mean that when i use the storage fixture, cache is an ordered chain [memory, local, s3].
this means you will have to rewrite your tests that use "skip if no s3 creds". this function should be entirely removed. when you call pytest without --s3 all these tests will be skipped. if you call pytest --s3 without credentials, the tests will fail.
you should also lump all tests that are like this into just 1 test:
def test_s3_something():
    pass

def test_local_something():
    pass

into
@parametrize_all_storage_providers
def test_something(storage):
    pass

here is the ideal use case:

i run pytest . --memory-skip --cache-chain memory,local,s3
all tests that use the storage fixture will only be ran once with storage being a cache chain defined as [memory, local, s3].

@AbhinavTuli TODOs:

pull release/2.0 into this branch & solve merge issues for caching.
for the chunk_engine tests, make it so they can be ran with the same exact fixture as the caching tests.

it should require extremely little effort for us to write tests that use storage providers / cache chains (it should look the exact same)

these changes also make our benchmarks more clear. instead of storage0, storage1 it looks like this:

@AbhinavTuli does cache support assignment? cache[something] = value

@AbhinavTuli does cache support assignment? cache[something] = value

Yes it does, I haven't put a fixture for it yet. So it's doing something weird now I guess. A fixture needs to be created in conftest.py using the code snippets that are commented out at the end.

ok ill do that

@benchislett @AbhinavTuli @imshashank tests no longer have to track their own keys. a storage provider is generated for every test & the entire root is cleared the next time you start a testing session. session IDs are assigned only for s3 tests & were moved to the prefix of the key. these are also not deleted (because the s3 bucket we are using now i have set up to clear all records after 3 days of being alive)


Is the Auth0 Fine Grained Authorization open-source?

Hello, how are you guys?
I have a question based on this text part:

Auth0 Fine Grained Authorization (FGA) is an open source Fine-Grained Authorization solution inspired by Google's Zanzibar paper. It was created by the FGA team at Auth0 based on Auth0 Fine-Grained Authorization (FGA), available under a permissive license (Apache-2) and welcomes community contributions.

Where is the source code of Auth0 Fine Grained Authorization?
I'm evaluating open-source projects like Zanzibar to use here in my company.

Hello, how are you guys?
I have a question based on this text part:

Auth0 Fine Grained Authorization (FGA) is an open source Fine-Grained Authorization solution inspired by Google's Zanzibar paper. It was created by the FGA team at Auth0 based on Auth0 Fine-Grained Authorization (FGA), available under a permissive license (Apache-2) and welcomes community contributions.

Where is the source code of Auth0 Fine Grained Authorization? I'm evaluating open-source projects like Zanzibar to use here in my company.

Hello @erickhgm ,
Please refer to OpenFGA (https://openfga.dev/) for the open source projects like Zanzibar.

Thank you @adriantam, yesterday I searched a little more and I found it.


Pulling in latest aviary for concurrency rename

See PR title

It's time for a no approval merge

Well, it turns out we are getting a uvicorn import error. I am symbolically getting a poop sticker and putting it on my hand


Update vulnerable dependencies

Signed-off-by: Jorge Padilla<EMAIL_ADDRESS>Related Issue:  stolostron/backlog#22734
Description of changes

Remove unnecessary module replacements.

/cherrypick release-2.6


[IgniteNetwork] Fix issues with Network Watcher flog-log configure

Merge to IgniteNetwork branch
Fixes #7065. Fixes #7066.
Adds a "get_arm_resource_by_id" method to core that allows us to get basic info on any resource (including classic resources) with an ARM ID. This unblocks the scenario to allow flow-log configuration on a classic NSG.
cc/ @zwswim

This checklist is used to make sure that common guidelines for a pull request are followed.

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@williexu please take a second look. If the core of this logic looks sound to you we will likely also need to update the az resource show command since it used the old logic.


Parse input, to pass arguments to Ex functions

Closed by 7f667d90e0282a73b88f5f2738cf272071c1a0df, arguments passed as splats since 55bb9f631907b3d49e90f830adbe9d33a6049348.


chore: bdd-spring-1573212011 to 0.0.1

chore: Promote bdd-spring-1573212011 to version 0.0.1

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Fix target translation bug in orbital camera

Hello, while using the orbital camera I noticed a small bug.
When translating the target there is a very small zoom out effect. It is barely noticeable but over time it will build up. The video below shows the radius increasing when i pan the camera.
https://github.com/bonsairobo/smooth-bevy-cameras/assets/17007091/11fbbb82-3eae-4028-8996-3a74f08e3873
The issue is that the new_radius calculation in the control_system is done using the new transform after it has been panned meaning that the radius will be slightly larger than previously so when the eye is transformed back to the same angle it will keep the longer radius.
The result of the PR is shown in the video below (where the radius is no longer increased when panning).
https://github.com/bonsairobo/smooth-bevy-cameras/assets/17007091/a8c349f9-ebfe-4302-90af-f841bdd658c9

Awesome, thank you!


[WIP] bazel: use experimental cmake_external for cares/nghttp2.

This demonstrates the use of the new cmake_external() rule provided by rules_foreign_cc.
There are a number of issues to address and discuss in this PR before merging.
Risk level: Low
Testing: bazel test //test/... on Linux.
Signed-off-by: Harvey Tuch<EMAIL_ADDRESS>

@htuch as is, it doesn't look like this works on Windows, bazel threw an exception while trying to build (see attached java.log)

@htuch as is, it doesn't look like this works on Windows, bazel threw an exception while trying to build (see attached java.log)

Hi, I was able to reproduce it and recorded an issue: https://github.com/bazelbuild/bazel/issues/6292.
The problem is not relevant to the cmake_external...
On Thursday I will ping the guys, tomorrow we are having a public holiday in Germany.

@irengrig thanks for reporting the issue -- we saw the same thing with bazel 0.17.2 and without these cmake_external changes.
As a side note, the master branch of envoyproxy/envoy doesn't currently build on Windows -- we are currently working on this. If you need a branch that does build on Windows, we maintain one here: https://github.com/greenhouse-org/envoy/tree/windows-linux-build

@htuch @irengrig We've taken another look at this and were able to get it working on Windows, so I think we can remove Windows from the blockers here

FWIW, I'm resuming this work now, hope to have a PR out in next day @sesmith177 @mattklein123


NameError: name '_C' is not defined

Hi, I was able to run the new version of hloc and lightglue
But once I install pixsfm, torch seems unable to work now:
When I import torch, the following error appears.
NameError: name '_C' is not defined

I found that I have a test program org.py which interfere with torch. after renaming, problem resolved


lnd-lnd force-closing due to invalid_commit_sig

two lnd trying to send 1 sat invoices to eachother. one froze in mutex-land. restarted both. channel got closed after restarts.
lnd version 0.4.2-beta commit=4bde4c1c262ddae5171240d47fcbd0b5ea1f1eae
might be closely related to #1340
2018-06-08 14:44:22.767 [ERR] HSWC: ChannelLink(522968:666:1) Failing link: ChannelPoint(9d3f4868ec2d1c4c8c7da14d4e60adb08a57ee9f73743066d9f9e0a31ecf9acd:1): unable to accept new commitment: rejected commitment: commit_height=594, invalid_commit_sig=3044022026ed0d5ac10ca73cd7358aeb30abfdaadc8cbd8f173f8b84b20e61d55ccc6d04022045b7363a5e1295d20a39b008be240c39a4f0a232982b3a75beed28eb8037d235, commit_tx=0200000001cd9acf1ea3e0f9d9663074739fee578ab0ad604e4da17d8c4c1c2dec68483f9d0100000000a1b71280023d0a020000000000220020a4d327d89eee074a20bbe25f5d0c5955a7ab50d1566a45ea1621222b71696a3577340d000000000016001409a2344fd318ff0478b2f5663cdcbf4ff70b6028fc55d720, sig_hash=2389fc4abd70c5c97696513bfd73f0393ee9b70f515403ee117f387d65f903ed
Background
Describe your issue here.
Your environment

version of lnd
which operating system (uname -a on *Nix)
version of btcd, bitcoind, or other backend
any other relevant environment details

Steps to reproduce
Tell us how to reproduce this issue. Please provide stacktraces and links to code in question.
Expected behaviour
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Actual behaviour
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Don't think it's related to #1340, instead it's related to #1345. Also are you able to reproduce with two fresh instances?


refactor tests, try to fix timeouts on slow runtimes

I might need to adjust some more code and make a new pr, because setting high timeouts by default is not a good option. I could move the horrendeous wait timeouts to the test


Docker image of Gravity

Is there a Docker Image of Gravity, so I can run it without needing to install locally?

Moving this to our community site: https://community.gravitational.com/t/docker-image-of-gravity/88


Disable static boost linkage by default

There are some problems with compiling python package and static boost according to this issue #515.
This pull request contains follow changes:

Boost static linkage is off by default.
pyproject.toml instead setup.cfg. setup.cfg was deprecated.
Some notes about complication with boost.
Run workflow for pull requests to develop branch.

Thanks and good job @KernelA ! Can you please check that the Allow edits from maintainers (see https://i.stack.imgur.com/wu4Wd.png) checkbox is checked? I added a couple of changes to the Python module installation guide that I'd like to add to this PR.

@boxanm, yes it is checked

@KernelA @boxanm I've added all four Compile python package github action to the develop branch protection rule until I have time to add the python binding test logic to the libpointmatcher-build-system.


Enable Redis Logging

SE.Redis has ILogger support now, update to use it

The issue that was blocking this PR is now fixed with #886.