Multimodal Large Model Joint Learning Algorithm: Reproduction Based on KubeEdge-Ianvs

What would you like to be added/modified:
A benchmark suite for multimodal large language models deployed at the edge using KubeEdge-Ianvs:

Modify and adapt the existing edge-cloud data collection interface to meet the requirements of multimodal data collection;
Implement a Multimodal Large Language Model (MLLM) benchmark suite based on Ianvs;
Reproduce mainstream multimodal joint learning (training and inference) algorithms and integrate them into Ianvs single-task learning;
(Advanced) Test the effectiveness of multimodal joint learning in at least one of Ianvs' advanced paradigms (lifelong learning, incremental learning, federated learning, etc.).

Why is this needed:
KubeEdge-Ianvs currently focuses on edge-cloud collaborative learning (training and inference) for a single modality of data. However, edge devices, such as those in autonomous vehicles, often capture multimodal data, including GPS, LIDAR, and Camera data. Single-modal learning can no longer meet the precise inference requirements of edge devices. Therefore, this project aims to integrate mainstream multimodal large model joint learning algorithms into KubeEdge-Ianvs edge-cloud collaborative learning, providing multimodal learning capabilities.
Recommended Skills:
TensorFlow/Pytorch, LLMs, KubeEdge-Ianvs
Useful links:
KubeEdge-Ianvs
KubeEdge-Ianvs Benchmark Test Cases
Building Edge-Cloud Synergy Simulation Environment with KubeEdge-Ianvs
Artificial Intelligence - Pretrained Models Part 2: Evaluation Metrics and Methods
Example LLMs Benchmark List
awesome-multimodal-ml
Awesome-Multimodal-Large-Language-Models

@CreativityH @MooreZheng, is there any preferred communication channel to connect with the community and discuss this project more?
Any pretest to get started with this?
cc: @Shelley-BaoYue

@CreativityH Any recommended community channel to connect and discuss more about project

Hi @CreativityH ,
I'm excited about the opportunity to contribute to the "Multimodal Large Model Joint Learning Algorithm" project. My background in edge computing and machine learning, particularly with TensorFlow/PyTorch, aligns well with the project's goals. Here’s my proposed approach:
Proposed Approach

Multimodal Data Collection Interface:

I will modify and adapt the existing edge-cloud data collection interface to handle multimodal data, including GPS, LIDAR, and Camera inputs. This will involve creating a unified data schema and preprocessing modules for each data type to ensure compatibility and consistency.

Multimodal Large Language Model (MLLM) Benchmark Suite:

I will develop a benchmark suite for multimodal LLMs based on Ianvs. This will involve identifying suitable multimodal LLMs and defining relevant performance metrics, such as accuracy, latency, and resource utilization, to evaluate their effectiveness when deployed at the edge.

Multimodal Joint Learning Algorithms:

I will reproduce mainstream multimodal joint learning algorithms (training and inference) and integrate them into Ianvs’ single-task learning framework. This step will ensure the system can effectively handle the complexities of multimodal data.

Advanced Testing and Optimization:

I will test the effectiveness of multimodal joint learning in at least one of Ianvs' advanced paradigms (lifelong learning, incremental learning, federated learning). I will benchmark the system to ensure performance improvements without compromising accuracy.
I will explore possible optimizations to enhance the efficiency of the edge-cloud collaborative learning setup, focusing on resource usage and latency reduction.
Looking forward to your feedback and the way forward to contributing!
Best Regards,
Sargam

Hello @CreativityH @MooreZheng
I am Aryan Nanda and I am currently pursuing my Bachelor in Computer Science from VJTI, Mumbai, India. I want to contribute to KubeEdge-Ianvs through this project. This project aligns with my interests and experience.
Why me?

Clarity of Basics:
- Linux - Link(These are installation Scripts that I wrote for setup of different repositories in Linux /MacOS.). Also, link to coursework certificate.
- My Github Stats showing proficiency in Git/Gihub and Python:

- Deep Learning - Coursework Certificates Links:
1) Neural Networks and Deep Learning
2) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
3) Convolutional Neural Networks
- TensorFlow - Link
- Pytorch - Link
Experience in Relevant Domains:

GSoC - I am also a contributor in the Google Summer of Code under a project that involves the detection of commercials and then replacing them with alternate content using deep learning. Project Link.
Mentor feedback after mid-term evaluation:

The project will be completed by the end of this month.
I have done a research internship in the CoE CNDS lab, VJTI where I developed LLM models for a network IDS for the detection of attacks.(repo is private)
I along with my team participated in Form and Function Challenge by Mass Robotics in which I implemented the YOLO model for the detection of objects in real-time and then implemented the SLAM algorithm from readings of lIDAR sensor and Camera. Code Link

Please share any suggestions you have on how to start this project.
I will share my findings and research here.

Hi @CreativityH ,
I'm excited about the opportunity to contribute to the "Multimodal Large Model Joint Learning Algorithm" project. My background in edge computing and machine learning, particularly with TensorFlow/PyTorch, aligns well with the project's goals. Here’s my proposed approach:
Proposed Approach

Multimodal Data Collection Interface:

I will modify and adapt the existing edge-cloud data collection interface to handle multimodal data, including GPS, LIDAR, and Camera inputs. This will involve creating a unified data schema and preprocessing modules for each data type to ensure compatibility and consistency.

Multimodal Large Language Model (MLLM) Benchmark Suite:

I will develop a benchmark suite for multimodal LLMs based on Ianvs. This will involve identifying suitable multimodal LLMs and defining relevant performance metrics, such as accuracy, latency, and resource utilization, to evaluate their effectiveness when deployed at the edge.

Multimodal Joint Learning Algorithms:

I will reproduce mainstream multimodal joint learning algorithms (training and inference) and integrate them into Ianvs’ single-task learning framework. This step will ensure the system can effectively handle the complexities of multimodal data.

Advanced Testing and Optimization:

I will test the effectiveness of multimodal joint learning in at least one of Ianvs' advanced paradigms (lifelong learning, incremental learning, federated learning). I will benchmark the system to ensure performance improvements without compromising accuracy. I will explore possible optimizations to enhance the efficiency of the edge-cloud collaborative learning setup, focusing on resource usage and latency reduction.
Looking forward to your feedback and the way forward to contributing!
Best Regards, Sargam

Hi @SargamPuram, What a great proposal you did!
Further more, I'm curious to know how you would alter the data collection interface to make it possible to add new data formats without changing the content of the original data collection. Flowcharts and other ways that you can show your thinking are welcome.
Please feel free to contact me if you have any questions. Looking forward to your amazing ideas!

Please share any suggestions you have on how to start this project. I will be sharing my findings and research here.

Hello @AryanNanda17,
From your introducing I see that you are an active code contributor and community member, and also that you have earned a lot of certifications, which is awesome.
I learned that you have experience in collecting radar and camera data in Evo-Borne. So, I think you could start by familiarizing yourself with Ianvs platform, find its interface for data collection, and then think about how to modify that interface in conjunction with multimodality.
Looking forward to your amazing ideas!

Hii
Are there any pre-tests for this project?

Hello @CreativityH ,I’m Aryan Yadav, and I’m excited to contribute to the Multimodal Large Model Joint Learning Algorithm project with KubeEdge-Ianvs. I have extensive experience in ML, PyTorch, LLMs, and multimodal AI, and have worked on some very good projects related to LLMs and won goodies for that. Looking forward to collaborating on this!
Here is a potential solution:
Upgrade of the data collection interface: The approach my system collects its information from edge devices needs to be changed to accommodate several streams at any particular time, such as GPS, Lidar, or camera images. This involves making flexible setup for the collection of data in such a way that it can seamlessly process varied data types.
Creation of a Multimodal Benchmark Suite:
Next, I will set up a number of tests to see how well my system manages and integrates various types of data. This will include testing the system for its handling and making sense of combined data types.
Integrate Joint Learning Algorithms:
I will be integrating algorithms that train on mixed data types. This shall be necessary for making accurate predictions where the data inputs may be complex. I will ensure that these algorithms work well with the system's current learning methods.
Advanced Testing :
I will further test this system in more advanced learning scenarios, such as continuous or federated learning. Learning across time—in this case—that the system learns to adapt, but it will learn from data across different devices without sharing the raw data in learning.
By designing a modular and pluggable data collection system, one can integrate new data formats without modifying the existing content flow. This approach allows for flexibility and scalability in handling diverse types of data. I have tried to explain it using a simple flowchart :)
               Upgrade Data Collection Interface
                                   |
                                   v
              Change Collection Approach --> Adapt to Multiple Streams (GPS, Lidar, Camera Images)
                                   |                            |
                                   v                            v
                    Flexible Setup for Varied Data Types   Create Multimodal Benchmark Suite
                                   |                                     |
                                   v                                     v
                     Set Up Tests for Data Integration --> Test Handling of Combined Data Types
                                   |
                                   v
                Integrate Joint Learning Algorithms
                                   |
                                   v
                 Train on Mixed Data Types --> Ensure Compatibility with Current Learning Methods
                                   |
                                   v
                             Advanced Testing
                                   |
                                   v
          Test in Advanced Scenarios --> Continuous Learning
                                   |              |
                                   v              v
                  Federated Learning   Adapt and Learn from Data Across Devices Without Sharing Raw Data
                                   |
                                   v
            Design Modular and Pluggable System
                                   |
                                   v
                  Integrate New Data Formats --> Maintain Existing Content Flow
                                   |
                                   v
                     Ensure Flexibility and Scalability

Also are there any pre tests for this project?

Hii Are there any pre-tests for this project?

Hello, @staru09
I think three pre-tests are required to verify the risks of the idea.

successfully run Ianvs on your device;
figure out what kinds of multimodal data you want to collect and use;
test your selected multimodal data and corresponding algorithm.

After the three steps, I guess you have the way to handle the issue out.

Hello @CreativityH ,I’m Aryan Yadav, and I’m excited to contribute to the Multimodal Large Model Joint Learning Algorithm project with KubeEdge-Ianvs. I have extensive experience in ML, PyTorch, LLMs, and multimodal AI, and have worked on some very good projects related to LLMs and won goodies for that. Looking forward to collaborating on this!
Here is a potential solution: Upgrade of the data collection interface: The approach my system collects its information from edge devices needs to be changed to accommodate several streams at any particular time, such as GPS, Lidar, or camera images. This involves making flexible setup for the collection of data in such a way that it can seamlessly process varied data types.
Creation of a Multimodal Benchmark Suite: Next, I will set up a number of tests to see how well my system manages and integrates various types of data. This will include testing the system for its handling and making sense of combined data types.
Integrate Joint Learning Algorithms: I will be integrating algorithms that train on mixed data types. This shall be necessary for making accurate predictions where the data inputs may be complex. I will ensure that these algorithms work well with the system's current learning methods.
Advanced Testing : I will further test this system in more advanced learning scenarios, such as continuous or federated learning. Learning across time—in this case—that the system learns to adapt, but it will learn from data across different devices without sharing the raw data in learning.
By designing a modular and pluggable data collection system, one can integrate new data formats without modifying the existing content flow. This approach allows for flexibility and scalability in handling diverse types of data. I have tried to explain it using a simple flowchart :)

Hello @aryan0931 , nice job!
You have made a great flowchart which clearly show your idea and design. What I was wondering now is that you might combine Ianvs into your flowchart. I think you can run ianvs first on your device to know ianvs better.
Looking forward to your enhanced design!

Hello @CreativityH ,I’m Aryan Yadav, and I’m excited to contribute to the Multimodal Large Model Joint Learning Algorithm project with KubeEdge-Ianvs. I have extensive experience in ML, PyTorch, LLMs, and multimodal AI, and have worked on some very good projects related to LLMs and won goodies for that. Looking forward to collaborating on this!
Here is a potential solution: Upgrade of the data collection interface: The approach my system collects its information from edge devices needs to be changed to accommodate several streams at any particular time, such as GPS, Lidar, or camera images. This involves making flexible setup for the collection of data in such a way that it can seamlessly process varied data types.
Creation of a Multimodal Benchmark Suite: Next, I will set up a number of tests to see how well my system manages and integrates various types of data. This will include testing the system for its handling and making sense of combined data types.
Integrate Joint Learning Algorithms: I will be integrating algorithms that train on mixed data types. This shall be necessary for making accurate predictions where the data inputs may be complex. I will ensure that these algorithms work well with the system's current learning methods.
Advanced Testing : I will further test this system in more advanced learning scenarios, such as continuous or federated learning. Learning across time—in this case—that the system learns to adapt, but it will learn from data across different devices without sharing the raw data in learning.
By designing a modular and pluggable data collection system, one can integrate new data formats without modifying the existing content flow. This approach allows for flexibility and scalability in handling diverse types of data. I have tried to explain it using a simple flowchart :)

Hello @aryan0931 , nice job!
You have made a great flowchart which clearly show your idea and design. What I was wondering now is that you might combine Ianvs into your flowchart. I think you can run ianvs first on your device to know ianvs better.
Looking forward to your enhanced design!

sure sir, @CreativityH I will update you with this in some time.

@CreativityH I am interested on working this under LFX this term, Can you locate me to relevant docs and pre tests?

Hello @MooreZheng @CreativityH
I'm interested in the project focused on developing a benchmark suite for Multimodal Large Language Models using KubeEdge-Ianvs. The integration of multimodal joint learning into edge-cloud collaborative systems is crucial, and I'd love to contribute. I have experience with TensorFlow/PyTorch, LLMs, and KubeEdge-Ianvs, and I'm eager to be part of this effort.
Looking forward to discussing this further!

@CreativityH I am interested on working this under LFX this term, Can you locate me to relevant docs and pre tests?

Hi @octonawish-akcodes , here are relevant docs:
KubeEdge-Ianvs
KubeEdge-Ianvs Benchmark Test Cases
Building Edge-Cloud Synergy Simulation Environment with KubeEdge-Ianvs
Artificial Intelligence - Pretrained Models Part 2: Evaluation Metrics and Methods
Example LLMs Benchmark List
awesome-multimodal-ml
Awesome-Multimodal-Large-Language-Models
Show your creative ideas is just fine!

During testing the quickstart of ianvs there were lots of dependency issues and yaml file paths irregularities, somehow I fixed them but not sure how to fix this error? Can you have a look? @CreativityH

Is this FPN_TensorFlow a custom module? because i couldn't find such module on pip :/

Hello @CreativityH @MooreZheng ,Thanks for your feedback! I applied your recommendation to the flowchart and have included Ianvs. I firstly gave Ianvs a try to figure out how it fuses in the end to the whole procedure before I made the modifications. This integration is now clearly visible in the new flowchart.
I really would like to know what you think about the redesign and if there are any other ideas you have for it to become even better.
Upgrade Data Collection Interface
|
|---> Change Collection Approach
|     |
|     |---> Adapt to Multiple Streams (GPS, Lidar, Camera Images)
|
|---> Flexible Setup for Varied Data Types
|
|---> Create Multimodal Benchmark Suite
|     |
|     |---> Set Up Tests for Data Integration
|               |
|               |---> Test Handling of Combined Data Types
|
|---> Integrate Joint Learning Algorithms with Ianvs
|     |
|     |---> Train on Mixed Data Types
|               |
|               |---> Ensure Compatibility with Current Learning Methods using Ianvs
|
|---> Advanced Testing in Ianvs Environment
|     |
|     |---> Test in Advanced Scenarios
|               |
|               |---> Continuous Learning with Ianvs
|
|---> Federated Learning with Ianvs Integration
|     |
|     |---> Adapt and Learn from Data Across Devices Without Sharing Raw Data
|
|---> Design Modular and Pluggable System Compatible with Ianvs
|
|---> Integrate New Data Formats with Ianvs
|     |
|     |---> Maintain Existing Content Flow
|
|---> Ensure Flexibility and Scalability using Ianvs
This integration leverages Ianvs for key components like joint learning algorithms, advanced testing, federated learning, and continuous learning. It also aligns with our focus on cloud-edge collaboration, ensuring that the system remains scalable, flexible, and ready for future challenges.

hey @CreativityH , Siddhant here, I would like to take part in the project under your guidance in the LFX Mentorship.
I am a beginner in the field of open source contribution but I have interest in LLMs and kubernetes.
I am very excited about working on this project, and also on working with the KuberEdge-ianvs framework.
Currently I am trying to learn more about kubernetes, LLMs and multimodal ml. If there are any resources you could provide that could help me with preparing for this project, it would be of much help.

Thanks again for considering this request.

Hello @CreativityH,
I followed the instructions mentioned in the Quick Start guide. I faced the same issues as @octonawish-akcodes (a bunch of compatibility issues, it says to use python3.6.9 but when this is used, other packages give an error saying that they need a higher version of Python) and so on. The quick-start guides need an update.
Also, should I write a proposal for this project and do a PR?

sudo apt-get install libgl1-mesa-glx -y
this command is creating an error in the quick start guide

Hello @CreativityH , I think there are some issues with quick start guide, sudo apt-get install libgl1-mesa-glx -y this command is causing some issues, also there are some issues related to python version can you provide a quick solution for this. I am working on the issues I will update you if I get any feasible solution for the same.

I raised a RTM PR for this yaml path inconsistencies for the pcb-aoi example in the quickstart https://ianvs.readthedocs.io/en/latest/guides/quick-start.html#step-3-ianvs-execution-and-presentation
cc @CreativityH
Here is the PR https://github.com/kubeedge/ianvs/pull/133

Hello @CreativityH @MooreZheng ,Thanks for your feedback! I applied your recommendation to the flowchart and have included Ianvs. This integration is now clearly visible in the approach below I really would like to know what you think about the redesign and if there are any other ideas you have for it to become even better.
Upgrade Data Collection Interface

Change Collection Approach
Adapt to Multiple Streams (GPS, Lidar, Camera Images)

Flexible Setup for Varied Data Types
Create Multimodal Benchmark Suite

Set Up Tests for Data Integration
Test Handling of Combined Data Types

Integrate Joint Learning Algorithms with Ianvs

Train on Mixed Data Types
Ensure Compatibility with Current Learning Methods using Ianvs

Advanced Testing in Ianvs Environment

Test in Advanced Scenarios
Continuous Learning with Ianvs

Federated Learning with Ianvs Integration

Adapt and Learn from Data Across Devices Without Sharing Raw Data

Design Modular and Pluggable System Compatible with Ianvs
Integrate New Data Formats with Ianvs

Maintain Existing Content Flow

Ensure Flexibility and Scalability using Ianvs

This integration leverages Ianvs for key components like joint learning algorithms, advanced testing, federated learning, and continuous learning. It also aligns with our focus on cloud-edge collaboration, ensuring that the system remains scalable, flexible, and ready for future challenges.

Nice done! Maybe you can list what specific multimodal learning (training/inference) algorithm you want to use. What the improvement the algorithm can achieve. Next, specify the function name of Ianvs (e.g., data collection interface) and display where your modified functions locate.

@CreativityH Thanks for the comment it worked, also I have shared my proposal on slack can you have a look and give feedback?

KubeEdge-Ianvs
KubeEdge-Ianvs Benchmark Test Cases
Building Edge-Cloud Synergy Simulation Environment with KubeEdge-Ianvs
Artificial Intelligence - Pretrained Models Part 2: Evaluation Metrics and Methods
Example LLMs Benchmark List
awesome-multimodal-ml
Awesome-Multimodal-Large-Language-Models

Hello @Sid260303 , maybe the following links are useful:
KubeEdge-Ianvs
KubeEdge-Ianvs Benchmark Test Cases
Building Edge-Cloud Synergy Simulation Environment with KubeEdge-Ianvs
Artificial Intelligence - Pretrained Models Part 2: Evaluation Metrics and Methods
Example LLMs Benchmark List
awesome-multimodal-ml
Awesome-Multimodal-Large-Language-Models
Maybe you can display your idea in a flowchart like @aryan0931 .

hey @CreativityH , Siddhant here, I would like to take part in the project under your guidance in the LFX Mentorship. I am a beginner in the field of open source contribution but I have interest in LLMs and kubernetes. I am very excited about working on this project, and also on working with the KuberEdge-ianvs framework. Currently I am trying to learn more about kubernetes, LLMs and multimodal ml. If there are any resources you could provide that could help me with preparing for this project, it would be of much help.  Thanks again for considering this request.

Maybe the following links are usefull:
KubeEdge-Ianvs
KubeEdge-Ianvs Benchmark Test Cases
Building Edge-Cloud Synergy Simulation Environment with KubeEdge-Ianvs
Artificial Intelligence - Pretrained Models Part 2: Evaluation Metrics and Methods
Example LLMs Benchmark List
awesome-multimodal-ml
Awesome-Multimodal-Large-Language-Models
Maybe you can display your idea via a flowchart like @aryan0931 .

this project and do a PR?

@MooreZheng There are some common installation issues encountered by @AryanNanda17 @staru09 .

@CreativityH Thanks for the comment it worked, also I have shared my proposal on CNCF slack can you have a look and give feedback?

Sure, it is my honor.

@AryanNanda17 Youre looking at wrong document, It clearly states the 2023 mentorship session, correct link is this https://github.com/cncf/mentoring/blob/main/programs/lfx-mentorship/2024/03-Sep-Nov/README.md Which has the 2024 timelines.

ugh, looks like I missed it. :(

Hello @CreativityH @MooreZheng ,Thanks for your feedback! I applied your recommendation to the flowchart and have included Ianvs. This integration is now clearly visible in the approach below I really would like to know what you think about the redesign and if there are any other ideas you have for it to become even better.
Upgrade Data Collection Interface
>

Change Collection Approach
Adapt to Multiple Streams (GPS, Lidar, Camera Images)

Flexible Setup for Varied Data Types
Create Multimodal Benchmark Suite
>

Set Up Tests for Data Integration
Test Handling of Combined Data Types

Integrate Joint Learning Algorithms with Ianvs
>

Train on Mixed Data Types
Ensure Compatibility with Current Learning Methods using Ianvs

Advanced Testing in Ianvs Environment
>

Test in Advanced Scenarios
Continuous Learning with Ianvs

Federated Learning with Ianvs Integration
>

Adapt and Learn from Data Across Devices Without Sharing Raw Data

Design Modular and Pluggable System Compatible with Ianvs
Integrate New Data Formats with Ianvs
>

Maintain Existing Content Flow

Ensure Flexibility and Scalability using Ianvs

This integration leverages Ianvs for key components like joint learning algorithms, advanced testing, federated learning, and continuous learning. It also aligns with our focus on cloud-edge collaboration, ensuring that the system remains scalable, flexible, and ready for future challenges.

Nice done! Maybe you can list what specific multimodal learning (training/inference) algorithm you want to use. What the improvement the algorithm can achieve. Next, specify the function name of Ianvs (e.g., data collection interface) and display where your modified functions locate.

sure @CreativityH , I am working on it will update you with this in some time.

Was anyone able to complete the setup and run the quick start?

Hello @CreativityH @MooreZheng , I have mentioned some algorithms which can be relevant to this project and also mentioned improvements,
Integrating Multimodal Learning with Ianvs
1. Algorithms of Multimodal Learning
Multimodal Transformer Networks (MTN): It improves accuracy and focuses on key features across GPS, Lidar, and Camera Images.
Deep Canonical Correlation Analysis (DCCA): The methods improve feature extraction and reduce dimensionality.
Multimodal Variational Autoencoder (MVAE): It ensures robust representation and better generalization.
There are some more algorithms which will be helpful in this project, I am going through them and learing about them.
2. Key Improvements
Learning Efficiency: Optimized learning from combined data types.
Flexibility: Adapts to new data formats, improving scalability.
Robust Inference: Handles noisy data for reliable results.
3. Ianvs Functions
Data Collection Interface
Data Integration & Testing
Joint Learning Algorithms
Federated Learning Integration
Continuous Learning
I am presently dealing with the detailed identification of places where all these functions need to be implemented. In itself, this exercise is enriching my understanding of the repository, which is an important requirement to have these modifications integrated seamlessly.I will update information about modified functions location in some time.
I’ve also specified some of these functions in relation to the overall project objectives in my proposal also.
Please do suggest or ask for more information if needed.

Hello @CreativityH , I think there are some issues with quick start guide, sudo apt-get install libgl1-mesa-glx -y this command is causing some issues, also there are some issues related to python version can you provide a quick solution for this. I am working on the issues I will update you if I get any feasible solution for the same.

If others are encountering issues while installing Ianvs, it may be due to outdated dependencies that haven’t been updated to match the latest versions. Some APIs from older versions, like Sedna, have been deprecated, which can lead to confusion and installation errors.
To resolve these issues, it’s recommended to update the outdated packages and upgrade the interface to the latest versions. Aligning the dependencies and interface with the new versions will not only help Ianvs overcome these complex dependency challenges but also enhance overall functionality.

Hi @CreativityH , @MooreZheng
Over the past few weeks, I have extensively researched and explored several critical areas related to enhancing KubeEdge-Ianvs with multimodal learning capabilities. My research activities included:

KubeEdge-Ianvs Documentation: Reviewed the current documentation and capabilities of KubeEdge-Ianvs, understanding its focus on edge-cloud collaborative learning and its current single-modal data handling.

Benchmark Test Cases: Analyzed existing benchmark test cases to identify the metrics and methods needed for evaluating multimodal models effectively. This involved studying various benchmarks to ensure that the new multimodal benchmarks will be comprehensive and relevant.

Pretrained Models Evaluation Metrics and Methods: Delved into evaluation metrics and methods for pretrained models, understanding how to assess the performance of large language models and other pretrained systems. This research is crucial for setting up effective benchmarks and evaluation strategies.

Awesome Multimodal Machine Learning Repositories: Explored cutting-edge multimodal learning algorithms and integration techniques through resources such as the “Awesome Multimodal Machine Learning” and “Awesome-Multimodal-Large-Language-Models” repositories. This research provided insights into state-of-the-art approaches and how they can be applied to enhance KubeEdge-Ianvs.

Summary:
The research has provided a deep understanding of the current limitations of KubeEdge-Ianvs in handling multimodal data and has highlighted the need for integrating advanced multimodal learning algorithms. The next steps will involve adapting the edge-cloud data collection interface and implementing a comprehensive benchmark suite to evaluate and improve multimodal learning capabilities within Ianvs.
I look forward to applying these insights to advance the project and contribute to its development. If anyone has additional resources or insights related to this area, I would greatly appreciate your input!

Hello @CreativityH , I think there are some issues with quick start guide, sudo apt-get install libgl1-mesa-glx -y this command is causing some issues, also there are some issues related to python version can you provide a quick solution for this. I am working on the issues I will update you if I get any feasible solution for the same.

If others are encountering issues while installing Ianvs, it may be due to outdated dependencies that haven’t been updated to match the latest versions. Some APIs from older versions, like Sedna, have been deprecated, which can lead to confusion and installation errors.
To resolve these issues, it’s recommended to update the outdated packages and upgrade the interface to the latest versions. Aligning the dependencies and interface with the new versions will not only help Ianvs overcome these complex dependency challenges but also enhance overall functionality.

@MooreZheng @hsj576 These suggestions sound great. Is there any mismatch between Ianvs version and Sedna version?

ed for integrating advanced multimodal learning algorithms. The next steps will involve adapting the edge-cloud data collection interface and implementing a comprehensive benchmark suite to evaluate and improve multimodal learning capabilities within Ianvs.

I think in the next step, you may pay attention to the location of the data collection function in the original source code. Tell me where these codes are and attach the figures of the code here. Also, for multidata collection, you may list the pseudo code first.

Hi @CreativityH , @MooreZheng Over the past few weeks, I have extensively researched and explored several critical areas related to enhancing KubeEdge-Ianvs with multimodal learning capabilities. My research activities included:

KubeEdge-Ianvs Documentation: Reviewed the current documentation and capabilities of KubeEdge-Ianvs, understanding its focus on edge-cloud collaborative learning and its current single-modal data handling.
Benchmark Test Cases: Analyzed existing benchmark test cases to identify the metrics and methods needed for evaluating multimodal models effectively. This involved studying various benchmarks to ensure that the new multimodal benchmarks will be comprehensive and relevant.
Pretrained Models Evaluation Metrics and Methods: Delved into evaluation metrics and methods for pretrained models, understanding how to assess the performance of large language models and other pretrained systems. This research is crucial for setting up effective benchmarks and evaluation strategies.
Awesome Multimodal Machine Learning Repositories: Explored cutting-edge multimodal learning algorithms and integration techniques through resources such as the “Awesome Multimodal Machine Learning” and “Awesome-Multimodal-Large-Language-Models” repositories. This research provided insights into state-of-the-art approaches and how they can be applied to enhance KubeEdge-Ianvs.

Summary:
The research has provided a deep understanding of the current limitations of KubeEdge-Ianvs in handling multimodal data and has highlighted the need for integrating advanced multimodal learning algorithms. The next steps will involve adapting the edge-cloud data collection interface and implementing a comprehensive benchmark suite to evaluate and improve multimodal learning capabilities within Ianvs.
I look forward to applying these insights to advance the project and contribute to its development. If anyone has additional resources or insights related to this area, I would greatly appreciate your input!

@aryan0931 I think in the next step, you may pay attention to the location of the data collection function in the original source code. Tell me where these codes are and attach the figures of the code here. Also, for multidata collection, you may list the pseudo code first.

@CreativityH I still didnt got any feedback on my proposal :/

Hi @CreativityH , @MooreZheng Over the past few weeks, I have extensively researched and explored several critical areas related to enhancing KubeEdge-Ianvs with multimodal learning capabilities. My research activities included:

KubeEdge-Ianvs Documentation: Reviewed the current documentation and capabilities of KubeEdge-Ianvs, understanding its focus on edge-cloud collaborative learning and its current single-modal data handling.
Benchmark Test Cases: Analyzed existing benchmark test cases to identify the metrics and methods needed for evaluating multimodal models effectively. This involved studying various benchmarks to ensure that the new multimodal benchmarks will be comprehensive and relevant.
Pretrained Models Evaluation Metrics and Methods: Delved into evaluation metrics and methods for pretrained models, understanding how to assess the performance of large language models and other pretrained systems. This research is crucial for setting up effective benchmarks and evaluation strategies.
Awesome Multimodal Machine Learning Repositories: Explored cutting-edge multimodal learning algorithms and integration techniques through resources such as the “Awesome Multimodal Machine Learning” and “Awesome-Multimodal-Large-Language-Models” repositories. This research provided insights into state-of-the-art approaches and how they can be applied to enhance KubeEdge-Ianvs.

Summary:
The research has provided a deep understanding of the current limitations of KubeEdge-Ianvs in handling multimodal data and has highlighted the need for integrating advanced multimodal learning algorithms. The next steps will involve adapting the edge-cloud data collection interface and implementing a comprehensive benchmark suite to evaluate and improve multimodal learning capabilities within Ianvs.
I look forward to applying these insights to advance the project and contribute to its development. If anyone has additional resources or insights related to this area, I would greatly appreciate your input!

@aryan0931 I think in the next step, you may pay attention to the location of the data collection function in the original source code. Tell me where these codes are and attach the figures of the code here. Also, for multidata collection, you may list the pseudo code first.

@CreativityH , sure I will do it and update you in some time.

Hello @CreativityH , I think there are some issues with quick start guide, sudo apt-get install libgl1-mesa-glx -y this command is causing some issues, also there are some issues related to python version can you provide a quick solution for this. I am working on the issues I will update you if I get any feasible solution for the same.

If others are encountering issues while installing Ianvs, it may be due to outdated dependencies that haven’t been updated to match the latest versions. Some APIs from older versions, like Sedna, have been deprecated, which can lead to confusion and installation errors.
To resolve these issues, it’s recommended to update the outdated packages and upgrade the interface to the latest versions. Aligning the dependencies and interface with the new versions will not only help Ianvs overcome these complex dependency challenges but also enhance overall functionality.

@MooreZheng @hsj576 These suggestions sound great. Is there any mismatch between Ianvs version and Sedna version?

@CreativityH , yes sedna has released 0.6.0, while the built-in package in Ianvs is 0.4.1 I think.

@CreativityH I still didnt got any feedback on my proposal :/

Just give me more time 💪🏻

@CreativityH I still didnt got any feedback on my proposal :/

Hey @octonawish-akcodes , I cannot find your proposal. Please tell me where you send or send the proposal again to my email<EMAIL_ADDRESS>

@CreativityH Just sent you on mail, its also available on LFX platform of mentorship application I submitted.

Hi @CreativityH , here is the Pseudo-Code for Multimodal Data Collection Interface,
`# Pseudo-Code: Multimodal Data Collection Interface for KubeEdge-Ianvs
class MultimodalDataCollector:
def init(self):
# Initialize data streams for different modalities
self.gps_stream = GPSStream()
self.lidar_stream = LidarStream()
self.camera_stream = CameraStream()
    # Define a dictionary to store synchronized data
    self.synchronized_data = {
        'timestamp': None,
        'gps_data': None,
        'lidar_data': None,
        'camera_data': None
    }

def collect_data(self):
    # Collect data from each modality
    gps_data = self.gps_stream.read_data()
    lidar_data = self.lidar_stream.read_data()
    camera_data = self.camera_stream.capture_image()

    # Synchronize data based on timestamps
    timestamp = self._synchronize_data(gps_data, lidar_data, camera_data)

    # Store synchronized data
    self.synchronized_data['timestamp'] = timestamp
    self.synchronized_data['gps_data'] = gps_data
    self.synchronized_data['lidar_data'] = lidar_data
    self.synchronized_data['camera_data'] = camera_data

    # Return synchronized multimodal data
    return self.synchronized_data

def _synchronize_data(self, gps_data, lidar_data, camera_data):
    # Assume each data stream has a timestamp attribute
    # Find the closest matching timestamp across all modalities
    timestamps = [gps_data.timestamp, lidar_data.timestamp, camera_data.timestamp]
    synchronized_timestamp = min(timestamps, key=lambda t: abs(t - mean(timestamps)))

    return synchronized_timestamp

def send_data_to_edge(self, data):
    # Send collected data to the edge node for processing
    EdgeNode.send(data)

def run(self):
    while True:
        # Collect and send synchronized multimodal data continuously
        synchronized_data = self.collect_data()
        self.send_data_to_edge(synchronized_data)
        sleep(1)  # Delay for next collection cycle

Usage
if name == "main":
collector = MultimodalDataCollector()
collector.run()
`
Initialization: The MultimodalDataCollector class sets up data streams for GPS, LiDAR, and Camera inputs, representing different modalities.
Data Collection: The collect_data() method retrieves and synchronizes data from each stream based on timestamps.
Synchronization: The _synchronize_data() method aligns data from different modalities by matching timestamps.
Edge Node Transmission: The send_data_to_edge() method transmits synchronized multimodal data to the edge node for processing.
Continuous Collection: The run() method continuously collects and transmits data in real-time.
Next Steps:
Implementation: Translate the pseudo-code into a working implementation within the KubeEdge-Ianvs framework.
Benchmark Development:  Develop the multimodal benchmark suite, ensuring it includes evaluation metrics and methods aligned with your research on pretrained models.
Integration: Apply advanced multimodal learning algorithms to optimize KubeEdge-Ianvs performance.
Regarding the location of data collection function in original source code , I am working on it and will update you with it in some time.

Hello @CreativityH , I have worked on Pseudo-Code for Multimodal Data Collection Interface,
here is the pseudo-code,
`# Pseudo-Code: Multimodal Data Collection Interface for KubeEdge-Ianvs
class MultimodalDataCollector:
def init(self):
# Initialize data streams for different modalities
self.gps_stream = GPSStream()
self.lidar_stream = LidarStream()
self.camera_stream = CameraStream()
    # Define a dictionary to store synchronized data
    self.synchronized_data = {
        'timestamp': None,
        'gps_data': None,
        'lidar_data': None,
        'camera_data': None
    }

def collect_data(self):
    # Collect data from each modality
    gps_data = self.gps_stream.read_data()
    lidar_data = self.lidar_stream.read_data()
    camera_data = self.camera_stream.capture_image()

    # Synchronize data based on timestamps
    timestamp = self._synchronize_data(gps_data, lidar_data, camera_data)

    # Store synchronized data
    self.synchronized_data['timestamp'] = timestamp
    self.synchronized_data['gps_data'] = gps_data
    self.synchronized_data['lidar_data'] = lidar_data
    self.synchronized_data['camera_data'] = camera_data

    # Return synchronized multimodal data
    return self.synchronized_data

def _synchronize_data(self, gps_data, lidar_data, camera_data):
    # Assume each data stream has a timestamp attribute
    # Find the closest matching timestamp across all modalities
    timestamps = [gps_data.timestamp, lidar_data.timestamp, camera_data.timestamp]
    synchronized_timestamp = min(timestamps, key=lambda t: abs(t - mean(timestamps)))

    return synchronized_timestamp

def send_data_to_edge(self, data):
    # Send collected data to the edge node for processing
    EdgeNode.send(data)

def run(self):
    while True:
        # Collect and send synchronized multimodal data continuously
        synchronized_data = self.collect_data()
        self.send_data_to_edge(synchronized_data)
        sleep(1)  # Delay for next collection cycle

Usage
if name == "main":
collector = MultimodalDataCollector()
collector.run()
`
Key Points
Initialization: The MultimodalDataCollector class sets up data streams for GPS, LiDAR, and Camera inputs, representing different modalities.
Data Collection: The collect_data() method retrieves and synchronizes data from each stream based on timestamps.
Synchronization: The _synchronize_data() method aligns data from different modalities by matching timestamps.
Edge Node Transmission: The send_data_to_edge() method transmits synchronized multimodal data to the edge node for processing.
Continuous Collection: The run() method continuously collects and transmits data in real-time.
Next Steps:
Implementation: Translate the pseudo-code into a working implementation within the KubeEdge-Ianvs framework.
Benchmark Development: Create a multimodal benchmark suite with evaluation metrics.
Integration: Apply advanced multimodal learning algorithms to optimize KubeEdge-Ianvs performance.
For the location of the data collection function in the original source code part, I am working on it and will update you in some time.

Hello @CreativityH , I have worked on Pseudo-Code for Multimodal Data Collection Interface,
here is the pseudo-code,
 # Pseudo-Code: Multimodal Data Collection Interface for KubeEdge-Ianvs

 class MultimodalDataCollector:
    def __init__(self):
        # Initialize data streams for different modalities
        self.gps_stream = GPSStream()
        self.lidar_stream = LidarStream()
        self.camera_stream = CameraStream()

        # Define a dictionary to store synchronized data
        self.synchronized_data = {
            'timestamp': None,
            'gps_data': None,
            'lidar_data': None,
            'camera_data': None
        }

    def collect_data(self):
        # Collect data from each modality
        gps_data = self.gps_stream.read_data()
        lidar_data = self.lidar_stream.read_data()
        camera_data = self.camera_stream.capture_image()

        # Synchronize data based on timestamps
        timestamp = self._synchronize_data(gps_data, lidar_data, camera_data)

        # Store synchronized data
        self.synchronized_data['timestamp'] = timestamp
        self.synchronized_data['gps_data'] = gps_data
        self.synchronized_data['lidar_data'] = lidar_data
        self.synchronized_data['camera_data'] = camera_data

        # Return synchronized multimodal data
        return self.synchronized_data

    def _synchronize_data(self, gps_data, lidar_data, camera_data):
        # Assume each data stream has a timestamp attribute
        # Find the closest matching timestamp across all modalities
        timestamps = [gps_data.timestamp, lidar_data.timestamp, camera_data.timestamp]
        synchronized_timestamp = min(timestamps, key=lambda t: abs(t - mean(timestamps)))

        return synchronized_timestamp

    def send_data_to_edge(self, data):
        # Send collected data to the edge node for processing
        EdgeNode.send(data)

    def run(self):
        while True:
            # Collect and send synchronized multimodal data continuously
            synchronized_data = self.collect_data()
            self.send_data_to_edge(synchronized_data)
            sleep(1)  # Delay for next collection cycle

 if __name__ == "__main__":
     collector = MultimodalDataCollector()
     collector.run()

Key Points
Initialization: The MultimodalDataCollector class sets up data streams for GPS, LiDAR, and Camera inputs, representing different modalities.
Data Collection: The collect_data() method retrieves and synchronizes data from each stream based on timestamps.
Synchronization: The _synchronize_data() method aligns data from different modalities by matching timestamps.
Edge Node Transmission: The send_data_to_edge() method transmits synchronized multimodal data to the edge node for processing.
Continuous Collection: The run() method continuously collects and transmits data in real-time.
Next Steps:
Implementation: Translate the pseudo-code into a working implementation within the KubeEdge-Ianvs framework.
Benchmark Development: Create a multimodal benchmark suite with evaluation metrics.
Integration: Apply advanced multimodal learning algorithms to optimize KubeEdge-Ianvs performance.
For the location of the data collection function in the original source code part, I am working on it and will update you in some time.

@CreativityH Just sent you on mail, its also available on LFX platform of mentorship application I submitted.

@octonawish-akcodes I read your Proposal. it is a great job. However, I think you need to elaborate your proposed approach in conjunction with the diagram. Add or modify your design in conjunction with the Ianvs architecture diagram. And, I think you should list the names of the Ianvs functions that will be modified, and clarify where you expect the added modules to be located. And, you need to state whether your changes will cause changes to other existing methods. Anyway, please add the architecture diagram first.

Thanks @CreativityH For selecting me as the LFX mentee for this term, really excited to tackle this task!!!
Just wanted to know where should I reach out to you for further plans to work on this project idea, I couldn't find you out on the kubeedge slack channel. Also which community meeting do I need to join, Although I added the calender but not sure which one to attend. Can you guide me with further instructions?

Congratulations @octonawish-akcodes !!

Thanks @CreativityH For selecting me as the LFX mentee for this term, really excited to tackle this task!!!
Just wanted to know where should I reach out to you for further plans to work on this project idea, I couldn't find you out on the kubeedge slack channel. Also which community meeting do I need to join, Although I added the calender but not sure which one to attend. Can you guide me with further instructions?

Hi @octonawish-akcodes , congratulations! The KubeEdge SIG AI community meeting link is here. For further plans to work, please contact my graduate student<EMAIL_ADDRESS>He would assist you to plan.

Thanks @CreativityH For selecting me as the LFX mentee for this term, really excited to tackle this task!!!
Just wanted to know where should I reach out to you for further plans to work on this project idea, I couldn't find you out on the kubeedge slack channel. Also which community meeting do I need to join, Although I added the calender but not sure which one to attend. Can you guide me with further instructions?

Hi @octonawish-akcodes , congratulations! The KubeEdge SIG AI community meeting link is here. For further plans to work, please contact my graduate student<EMAIL_ADDRESS>He would assist you to plan.

I sent the mail :))

Thanks, @CreativityH, for selecting me as an LFX mentee for this term! I'm really excited to tackle this task and contribute to the project.
Just wanted to check where I should reach out to you to discuss the next steps and further plans for working on this project idea.
Looking forward to collaborating! 🙌

Dear Aryan Yadav，
My student Tianyu Tu will contact you for follow-up arrangements. He has a
lot of experience in this research direction, and you can communicate
more. We can discuss at any time.
Bests,
Chuang.
Aryan Yadav @.***> 于2024年9月6日周五 00:57写道：

Thanks, @CreativityH https://github.com/CreativityH, for selecting me
as an LFX mentee for this term! I'm really excited to tackle this task and
contribute to the project.
Just wanted to check where I should reach out to you to discuss the next
steps and further plans for working on this project idea.
Looking forward to collaborating! 🙌
—
Reply to this email directly, view it on GitHub
https://github.com/kubeedge/ianvs/issues/123#issuecomment-2332220712,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ADURL3UKZMX2XJMOEBC2GCTZVCEPHAVCNFSM6AAAAABLF4MFJGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMZSGIZDANZRGI
.
You are receiving this because you were mentioned.Message ID:
@.***>

Dear Aryan Yadav， My student Tianyu Tu will contact you for follow-up arrangements. He has a lot of experience in this research direction, and you can communicate more. We can discuss at any time. Bests, Chuang. Aryan Yadav @.> 于2024年9月6日周五 00:57写道：
…
Thanks, @CreativityH https://github.com/CreativityH, for selecting me as an LFX mentee for this term! I'm really excited to tackle this task and contribute to the project. Just wanted to check where I should reach out to you to discuss the next steps and further plans for working on this project idea. Looking forward to collaborating! 🙌 — Reply to this email directly, view it on GitHub <#123 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/ADURL3UKZMX2XJMOEBC2GCTZVCEPHAVCNFSM6AAAAABLF4MFJGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDGMZSGIZDANZRGI . You are receiving this because you were mentioned.Message ID: @.>

Okay, sir, I received the mail from him.


appAvailableMemory exceeding mobile_context limit on newer iPad Pros

Describe the bug
The appAvailableMemory on the updated mobile_context schema added as part of this issue has a maximum value of<PHONE_NUMBER>, just under 4.3GB. This is causing events sent from newer devices, specifically iPad Pros with 16GB of Ram, to fail, as these devices have greater than 4.3GB of memory available to the app.
These results in a schema error
{
  "schemaKey": "iglu:com.snowplowanalytics.snowplow/mobile_context/jsonschema/1-0-2",
  "error": {
    "error": "ValidationError",
    "dataReports": [
      {
        "message": "$.appAvailableMemory: must have a maximum value of<PHONE_NUMBER>",
        "path": "$.appAvailableMemory",
        "keyword": "maximum",
        "targets": [
          "4294967295"
        ]
      }
    ]
  }
}

To Reproduce
Instantiate the tracker on an app running on an iOS device with greater than 4.3GB available memory, and send any events that include the mobile context attached.
Expected behavior
Events sent from devices with the context do not fail validation due to having more than 4.3GB of memory.
Device information (please complete the following information):

Device: iPad Pro 3, iPad Pro 4, iPad Pro 11-inch (3rd generation), iPad Pro 12.9-inch (5th generation) (These are only the ones we've witnessed, may have affected others)
OS: iOS 15.3
v_tracker version: ios-3.0.2

Thank you for letting us know about this.
We'll prepare an updated schema on Iglu Central to resolve this issue. We will look to see if we can do an in-place patch of this schema to raise the maximum value. This should resolve the issue quickly and prevent you needing to roll out updates to your application with an updated tracker. However, it will be next week before we can roll out an Iglu Central release, we tread quite carefully with Iglu Central releases.
I'll keep you up to date via this issue.

Hi @austensd
We've patched the schema in question and released an Iglu Central update R132. This means the schema is the same version but has an updated maximum value.
You might need to reboot your enrich servers for this to take effect quickly.


Specify highp sampler2D for clipping plane textures to improve reliability on some devices

Fixes https://github.com/CesiumGS/cesium/issues/9023.
Some devices with Mali GPUs (mostly Android, but also seen on some Chromebooks) were having trouble with clipping plane reliability, apparently due to precision problems. As observed in https://github.com/CesiumGS/cesium/issues/8311, WebGL implementations are free to use lower precision sampling if the precision isn't specified, so this PR explicitly specifies highp on texture sampling for clipping planes. This seems to help on the devices in the issue above.
I also tested Model, pnts, b3dm, i3dm, and Globe clipping planes with these changes on Chrome and Firefox in Linux and on Internet Explorer and Chrome in Windows, both using float textures where supported and using Cesium.ClippingPlaneCollection.useFloatTexture = function() { return false; }; to force the uint8 RGBA codepath, and it looks like this doesn't cause problems there.
@lilleyse can you test in Safari on macos?

Thanks for the pull request @likangning93!

:heavy_check_mark: Signed CLA found.
:grey_question: Unit tests were not updated.

Make sure you've updated tests to reflect your changes, added tests for any new code, and ran the code coverage tool.

Reviewers, don't forget to make sure that:

[ ] Cesium Viewer works.
[ ] Works in 2D/CV.
[ ] Works (or fails gracefully) in IE11.

Works in Safari on my 2015 Macbook with AMD card.
I tested Cesium.ClippingPlaneCollection.useFloatTexture = function() { return false; }; as well.

Thanks @likangning93!


Support .vsi (Olympus) images

Thank for this great repo.
Are you planning to work on supporting .vsi files (Olympus)? If not, I would be willing to contribute to it with some guidance from your side.

Thank you very much for your message!
I have been considering adding support for Olympus VSI slides. However, I am currently lacking testing images. If you could share some images with me or direct me to a public source of such images, I would be able to create the VSI driver as my next task. You can share links to images here or send to my private email<EMAIL_ADDRESS>I am currently finishing work on a new version, which should be ready in the next couple of weeks. After that, I can shift my focus to this new task. I would be grateful for any contribution you can provide, with no obligation on your part.
Best regards,
Stanislav

Support for Olympus vsi image format is added in version 2.5.0


FreeBSD: pre-built tarball is not available

I'm attempting to use this per these directions on a 10.2-RELEASE system.
After checking out the repository ( git clone --depth 1 https://github.com/4commerce-technologies-AG/meteor ), I try to run ./meteor, and I get the following message:
Unable to download: https://dl.bintray.com/4commerce-technologies-ag/meteor-universal/bsd_dev_bundles/dev_bundle_FreeBSD_amd64_0.6.15.tar.gz
It appears that https://dl.bintray.com/4commerce-technologies-ag/meteor-universal/bsd_dev_bundles/ contains dev_bundle_FreeBSD_amd64_0.5.16.tar.gz (note 0.5.16 vs. 0.6.15).
I'm not sure whether the code that generated the tarball is creating an incorrect name, or if the code that's looking for that tarball is using an incorrect name. Either way, they're not in agreement!
I tried running the build scripts and, well, let's just say I'd rather have the binaries :)

Hi Allie @vauxia
Nice to see you are using our fork :-) and sorry for late answer, I am on tour currently.
You are running into that problem, because we currently have not build the latest branch 1.3.4.x
You have to options to proceed:
a. Use branch 1.2.1 with already build binaries
git clone --depth 1 -b "release-1.2.1-universal" https://github.com/4commerce-technologies-AG/meteor.git

b. Use latest branch and follow the section

Building manually [optional]

Please let me know if one helps you. I guess we will do the binaries within next weeks.
Cheers
Tom

P.S.: Sorry Allie - have over-seen that you already had build the binaries.
Yeah, currently it is necessary - Sry for any inconvenience.
Cheers
Tom

Even that I close the issue please feel free to add more comments if you like.


croc is actually good

croc is actually good
why?

almost everyone loves the croc. so this is a big big ratio.
also just look at the croc its soooo lovely 😻
staff members (including senior) love the croc. and are superior to you.
there are many types of croc. twitter croc, racing car, sam croc and many more, its impossible to hate all of them.

what now?

delete this repo or change it to croc-best
donate the domain to the croc foundation
stop hating on croc

TL;DR
don't hate on croc or attack it with a fricking hammer because of your invalid opinions.

blasphemy

when gweeloh learns of Labels 😔


[bug] Rabby wallet disconnects on page refresh

Is there an existing issue for this?

[X] I have searched the existing issues

RainbowKit Version
2.1.3
wagmi Version
2.9.2
Current Behavior
When using Rabby as the connected wallet, if the refresh button is pressed on the browser, the wallet will not always automatically reconnect. This seems to only happen in production- currently my app is deployed to vercel. In the dev environment, the autoreconnect does seem to function as expected. This behavior does not occur with metamask.
Expected Behavior
The wallet will remember the connection and automatically reconnect each time the page is refreshed.
Steps To Reproduce

Create a new repo using rainbowkit with npm init @rainbow-me/rainbowkit@latest
Deploy to vercel
Load the production environment in chrome browser and connect rabby wallet
Press the refresh button.

For me, the autoreconnect fails around ~50% of the time. Here's a fresh repo inited with rainbowkit@latest on vercel to demonstrate: https://rainbowkit-connect-bug.vercel.app/
Link to Minimal Reproducible Example (CodeSandbox, StackBlitz, etc.)
https://github.com/Sayuki0x/rainbowkit-connect-bug
Anything else?
Thank you guys for all your work on rainbowkit! It is very nice to use

It only happens when you install Phantom, just disable or uninstall Phantom then issue will be fixed.
Looks like Phantom do something wired with window.ethereum


Reason for change from scalikejdbc to slick?

Could you please explain the reasoning for switch from  scalikejdbc to slick earlier this year (searched, but could not find an explanation).

Hi, very simple really. Slick is backed by Lightbend, formerly Typesafe. Slick allows for a pure functional way to access the database. Writing queries is a breeze with Slick as the DSL integrates with Scala, allowing you to write database access code much like working with Scala collections we know and love. Also, Slick is a more popular database access library than ScalikeJDBC and more importantly, when working with the Lightbend Reactive Platform, Slick is supported by Lightbend. As of earlier this year the enterprise database drivers became Open Source which is great! I hope this answers you question.

Thanks


Improve Coordinate System and Map Projection

[ ] Replace 2D coordinate system with one primarily driven by latitude/longitude

[ ] Move all flat x/y operations used in canvas to be generated from getters that take into consideration the below
[ ] Ensure proper projection when zoomed out to view a very large area, so great circle arcs between two points are always straight. Expected maximum typical zoom level is ~1000 miles.

What we want (see Google Earth / Google Maps Satellite): :+1:

Note that the US/Canada border (same latitude) is shown as a curve, and that the great circle arc from point A to point B is straight.

What we have (Mercator Projection, see Google Maps): :-1:

Note that the US/Canada border (same latitude) is depicted straight, and that the great circle arc from point A to point B is curved.

Hello erikquinn
For that one, you might find helpful the following answer in aviation stack exchange about map projections. Gnomonic projection depicts all great circles as straight lines, so you might want to give it a try.

The ATC repository is being migrated to it's new home at https://github.com/openscope/openscope,
and thus, all issues are being closed. If this is still an issue with the latest version of the sim
(accessible at http://www.openscope.co), or is a feature you still think we are lacking,
please reopen the issue at the new repo.
Please note that the vast majority of these issues have been copied to the new repository, or else are covered by other issues created there. See the below screenshot for what it looks like when your issue is known in the new repo:

Thank you!
Closing this issue.


[WIP] Redesign settings pages

Details

Fixed Issues
$ https://github.com/Expensify/App/issues/12261
Tests

[ ] Verify that no errors appear in the JS console

Offline tests

QA Steps

[ ] Verify that no errors appear in the JS console

PR Author Checklist

[ ] I linked the correct issue in the ### Fixed Issues section above
[ ] I wrote clear testing steps that cover the changes made in this PR

[ ] I added steps for local testing in the Tests section
[ ] I added steps for the expected offline behavior in the Offline steps section
[ ] I added steps for Staging and/or Production testing in the QA steps section
[ ] I added steps to cover failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[ ] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)
[ ] I tested this PR with a High Traffic account against the staging or production API to ensure there are no regressions (e.g. long loading states that impact usability).

[ ] I included screenshots or videos for tests on all platforms
[ ] I ran the tests on all platforms & verified they passed on:

[ ] Android / native
[ ] Android / Chrome
[ ] iOS / native
[ ] iOS / Safari
[ ] MacOS / Chrome / Safari
[ ] MacOS / Desktop

[ ] I verified there are no console errors (if there's a console error not related to the PR, report it or open an issue for it to be fixed)
[ ] I followed proper code patterns (see Reviewing the code)

[ ] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick)
[ ] I verified that comments were added to code that is not self explanatory
[ ] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[ ] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method

[ ] If any non-english text was added/modified, I verified the translation was requested/reviewed in #expensify-open-source and it was approved by an internal Expensify engineer. Link to Slack message:

[ ] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[ ] I verified any copy / text that was added to the app is correct English and approved by marketing by adding the Waiting for Copy label for a copy review on the original GH to get the correct copy.
[ ] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[ ] I verified the JSDocs style guidelines (in STYLE.md) were followed

[ ] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[ ] I followed the guidelines as stated in the Review Guidelines
[ ] I tested other components that can be impacted by my changes (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar are working as expected)
[ ] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[ ] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[ ] I verified that if a function's arguments changed that all usages have also been updated correctly
[ ] If a new component is created I verified that:

[ ] A similar component doesn't exist in the codebase
[ ] All props are defined accurately and each prop has a /** comment above it */
[ ] The file is named correctly
[ ] The component has a clear name that is non-ambiguous and the purpose of the component can be inferred from the name alone
[ ] The only data being stored in the state is data necessary for rendering and nothing else
[ ] For Class Components, any internal methods passed to components event handlers are bound to this properly so there are no scoping issues (i.e. for onClick={this.submit} the method this.submit should be bound to this in the constructor)
[ ] Any internal methods bound to this are necessary to be bound (i.e. avoid this.submit = this.submit.bind(this); if this.submit is never passed to a component event handler like onClick)
[ ] All JSX used for rendering exists in the render method
[ ] The component has the minimum amount of code necessary for its purpose, and it is broken down into smaller components in order to separate concerns and functions

[ ] If any new file was added I verified that:

[ ] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[ ] If a new CSS style is added I verified that:

[ ] A similar style doesn't already exist
[ ] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(themeColors.componentBG)

[ ] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[ ] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[ ] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[ ] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.
[ ] I have checked off every checkbox in the PR author checklist, including those that don't apply to this PR.

Screenshots/Videos

Web

Mobile Web - Chrome

Mobile Web - Safari

Desktop

iOS

Android

Woo! Any screenies available to preview?

Woo! Any screenies available to preview?

Not yet, but I did manage to resolve this issue with this PR. Like I said, that was the biggest issue I was having. So hopefully we can get that merged soon and get this unblocked. It will also substantially simplify this PR.

Sounds great! Keep me posted when there are some early screenshots to review please, thank you!

@shawnborton ready for review

Comparison:

PR
Mockup

Maybe it's mostly mobile web chrome that looks the most off?

Also @shawnborton I think that the lottie animation for the security page is kind of off-center, idk if you noticed the same.

I agree with you there!
I was hoping to get this as close to its final form as possible, and then we can take videos of the various pages and share with Augenblick to get some adjustments. If you have a good video of the vault for instance, I can share with them so they can move some negative space around.

Just noting here that I've observed some frame-drops on iOS safari for the Security and About animations. For some reason, the Workspaces and Preference animations are running much smoother.

There's also this minor bug on iOS safari where the safe is layered wrong or something:

@shawnborton I've made changed according to your feedback. It looks better + more consistent now, and the styles have been substantially simplified. I updated all the screenshots and included videos for some platforms as well so you can see the animations in action.

Cool, started some conversation in the TCW room about this, we'll see what Augenblick says.

I figured out an easy way to squeeze better performance out of these animations on web + to fix the weird display bug:
Before (svg renderer)
https://github.com/Expensify/App/assets/47436092/b6422bd4-687b-4b09-9017-0b39f7c7a14f
After (canvas renderer)
https://github.com/Expensify/App/assets/47436092/a814a867-2e34-4857-adc7-75b9ac408a41

How is this screen happening here?

Everything is working great. Animations are working well. Few Questions:

"You appear to be offline" has background color same as the status bar. Is it expected?

Workspace General Settings page has purple background around the animation in Android/Native (tested in emulator).

Transition of status bar is quite slow. Is it expected?

https://github.com/Expensify/App/assets/25876548/06dee933-e56e-420a-a6b6-61a9ed63f3b5

Ok, so unfortunately this PR was completely blown up by the react-navigation refactor. So I'm going to try and take it more piece-by-piece and break it up into a few smaller PRs.

Marking this a WIP as I break it up into more managable pieces and ultimately resolve all the conflicts from the react-navigation refactor.

Sounds good. Do you have a rough ETA of when you might be able to finish this one by?

any reason to keep this PR open? @roryabraham

I have been referencing it but we can close it. @grgia feel free to review the latest
here: https://github.com/Expensify/App/pull/21852


Documentation for Vector-based Searches (Explore, ...)

In #883 we realized that the docs will be out of date with the new vector-based searches. However, designs are still being finalized as we are already implementing parts of it. Therefore we aim to have this issue to group all documentation tasks into once the designs have been agreed upon
Todos

[ ] Document Explore { Concepts }

[ ] usage
[ ] explain normalized distance

[ ] ...

@laura-ham If you have some time in the near future, your help would be appreciated.


Add Gizmos plugin

[x] #512
[x] #517
[ ] #513
[x] #514
[x] #515
[x] #516
[x] #518
[x] #842
[ ] #717
[x] #668
[ ] #751

Drawing Gizmos
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start = {0,0,0};
 glm::vec3 end = {10,0,0};
 gizmos.drawLine("sample line", start, end, Gizmos::Space::WorldSpace);
}

Drawing Wireframes
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start1 = {0,0,0};
 glm::vec3 end1 = {2,2,2};
 gizmos.drawBox("filled box", start1, end1);

 glm::vec3 start2 = {3,0,0};
 glm::vec3 end2 = {5,2,2};
 gizmos.drawWireBox("wire only box", start2, end2);
}

Changing Colour
void sampleSystem(Write<Gizmos> gizmos) {
 gizmos.colour = {1,0,0,1};
 glm::vec3 start1 = {0,0,0};
 glm::vec3 end1 ={10,0,0};
 gizmos.drawLine("red line", start1, end1);

 gizmos.colour = {0,1,0,1};
 glm::vec3 start2 = {0,1,0};
 glm::vec3 end2 = {10,1,0};
 gizmos.drawLine("green line", start2, end2);
}

Changing Lifetimes
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start1 = {0,0,0};
 glm::vec3 end1 = {10,0,0};
 // will stay on screen for exactly one frame, and DrawLine() will have to be called again the next frame to draw it.
 gizmos.drawLine("instant line", start1, end1);
 gizmos.drawLine("instant line", start1, end1, 0); // this is equivalent

 glm::vec3 start2 = {0,1,0};
 glm::vec3 end2 ={10,1,0};
 // will stay on screen for ten seconds, resetting each time DrawLine() is called
 gizmos.drawLine("green line", start2, end2, 10);
}

Changing Space
It's likely someone will want to draw their gizmos on the world. For example drawing a raycast to check a bullet's direction.
It's also likely that someone will want to draw on screen. For example, a crosshair.
We'll need something to differentiate those calls, to avoid requiring the user from having to do the conversation maths.
World Space
The gizmo is drawn in world coordinates.
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start = {0,0,0};
 glm::vec3 end = {10,0,0};
 gizmos.drawLine("sample line", start, end, 0, Gizmos::Space::WorldSpace);
}

Viewport Space
The gizmo is drawn in viewport coordinates. This means it will be drawn in relation to its position from the camera.
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start = {0,0,0};
 glm::vec3 end = {0.7,0.7,0};
 gizmos.drawLine("sample line", start, end, 0, Gizmos::Space::ViewportSpace);
}

Screen Space
The gizmo is drawn in screen coordinates. The may difference from the Viewport Space is that the viewport space is attached to a camera, and thus is dependant on its output, the Screen Space is drawn on top of the entire application, so, for example, in a split screen game, the gizmo would cross screens.
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start = {0,0,0};
 glm::vec3 end = {0.7,0.7,0};
 gizmos.drawLine("sample line", start, end, 0, Gizmos::Space::ScreenSpace);
}

Gizmo interaction
We'll want to interact with gizmos, from clicking on them to dragging.
glm::vec4 colour = {1,0,0,1};
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start = {0,0,0};
 glm::vec3 end = {8,8,0};
 gizmos.colour = colour;
 gizmos.drawBox("colour changing box", start, end);
 if(gizmos.pressed("colour changing box")) {
  colour = {0,1,0,1};
 }
 else {
  colour = {1,0,0,1};
 }
}

In this case the box drawn is in a different colour depending on whether or not it is being held down.
IDs
For the interaction purposes, we'll need a way of identifying which gizmo is which. That is the purpose of the IDs, the first argument of every draw function I've written here. I wrote in every call to better identify what the drawn gizmo should be, but it make no sense to me requiring every single gizmo to have an explicit identifier, so we can default them to "". It also makes sense to me to allow multiple gizmos sharing an id, and having the interaction behaviour trigger on any of them being pressed. For example:
glm::vec4 colour = {1,0,0,1};
void sampleSystem(Write<Gizmos> gizmos) {
 glm::vec3 start = {0,0,0};
 glm::vec3 end = {2,2,2};
 gizmos.drawBox("pair of boxes", start, end);

 glm::vec3 start2 = {3,0,0};
 glm::vec3 end2 = {5,2,2};
 gizmos.drawBox("pair of boxes", start2, end2);

 if(gizmos.pressed("pair of boxes")) {
  // Some behaviour
 }
}

(If we don't make a specific check for it, this system will allow us to ask if(gizmos.pressed("")) to get if any gizmo without an ID has been pressed. Seems chaotic)

Closing this as the gizmos plugin is pretty much done. The remaining sub issues will remain open


WHY THIS EXIST ON GITHUB ?

WHY THIS EXIST ON GITHUB?

Why not?

Yea, why not? Plus if you code in an obscure language it can be argued that there are more cybersecurity benefits.


sdk: update local execution limitations

/assign @chensun

@hbelmiro should we close this PR?

@varodrig This PR should target https://www.kubeflow.org/docs/components/pipelines/user-guides/core-functions/execute-kfp-pipelines-locally/ now. But, AFAIK, Connor is no longer contributing to Kubeflow.
Would you like to send a new PR with this change?

/close
content changed to another page. new PR submitted #3944
requested by @hbelmiro


Hbui Modal component not pop up even the showOpen props is true

I recently tried out the Modal component with the passive trigger props showOpen as a true value and the model is not popping up.

it resolved by updating the hbui version


Update to bevy 0.11

Just needed a few quick changes

Np, thanks for the quick merge. Would you mind bumping the version on the crate as well? I forgot


[BUG] - Updating existing cluster name does not work

Also mentioned in https://github.com/rancher/rancher/issues/34390 , but I can confirm that updating existing cluster name does not work with rancher v2.7.2-rc7 .

same with rancher 2.7.3
i get this error
"Validation failed in API: cannot upgrade cluster version from [v1.25.8+rke2r1] to [v1.24.10+rke2r1]. New version must be higher."
i think it should be possible to rename the cluster without doing an upgrade.

Same issue with v2.7.5

When will this problem be fixed?

Same issue in 2.7.9

Can confirm issue in my install of 2.7.9

It's weird if you kubectl edit clusters.management.cattle.io c-m-..... the cluster directly. It changes in the UI then a few seconds later it switches back.

I actually tried this and did the cluster upgrade (for an imported cluster) at the same time. But it didn't work anyway.
Rancher version: 2.7.6
Cluster: RKE2 1.25.15
Imported cluster: RKE2 1.25.15

+1 here, rancher 2.7.9 and imported cluster

+1 I also have this issue on 2.7.x

The issue continues to exist even in version 2.8.1, but since deleting the cluster from rancher and adding it back will leave everything except the agent stuff on cluster withouting corrupting any resources, the issue can be ignored safely.

@usrlibzy  - in case you delete cluster from rancher, then you will also lose namespace relations with projects etc. So I guess it is not "safe" workaround.

I have found the reason, if you comment code blow in "pkg/controllers/management/cluster/clustercontroller.go" capsSync function:
	//if cluster.Spec.ImportedConfig != nil {
	//	return nil, nil
	//}

looks like for imported cluster, any modification will be ignored, I don't know why and I wonder this modification will break something.

I tried to remove code on "pkg/controllers/management/cluster/clustercontroller.go" capsSync function:
	if cluster.Spec.ImportedConfig != nil {
		return nil, nil
	}

And it works, but only once. Seems once you edit the display name, there will be a handler modify it back.

The handler is provisioningv2/cluster/controller.go  the "cluster-create" hook:
	// Register a generating handler in order to generate clusters.management.cattle.io/v3 objects based on
	// clusters.provisioning.cattle.io/v1 objects.
	rocontrollers.RegisterClusterGeneratingHandler(ctx,
		clients.Provisioning.Cluster(),
		clusterCreateApply,
		"Created",
		"cluster-create",
		h.generateLegacyClusterFromProvisioningCluster,
		&generic.GeneratingHandlerOptions{
			AllowClusterScoped: true,
		},
	)

Seems it is hard to rename the cluster since the clusters.provisioning.cattle.io/v1 using the displayName as the clusters.management.cattle.io/v3 metadata.name and also the clusters.fleet.cattle.io.  Only local cluster can rename because the local cluster use a different logical


runtime: fix gdb pretty print for slices

gdb version sha:e86ca25fd6cc06488c9dda936cf83f0fcdf6b58b (17-Apr-2018)
removed TYPE_TAG_NAME in favour of TYPE_NAME to 'simplify' usage. the
fallout due to the new implementation in 'gdb/python/py-type.c'
resulting in premature removal of the 'struct' identifier that the go
'SliceTypePrinter' was using to match on
fix adds a trivial 'TypePrinter' base class to allow for a specialised
'match()' in the 'SliceTypePrinter' class which considers the type's
structure in addition to its (now simplified) type name

Bisecting gdb resulted in this being the pivot:
commit e86ca25fd6cc06488c9dda936cf83f0fcdf6b58b (refs/bisect/bad)
Author: Tom Tromey<EMAIL_ADDRESS>Date:   Tue Apr 17 13:51:24 2018 -0600

    Remove TYPE_TAG_NAME

I believe the relevant part is as follows:
iff --git a/gdb/python/py-type.c b/gdb/python/py-type.c
index 80055b05e0..dd8c0aa9dc 100644
--- a/gdb/python/py-type.c
+++ b/gdb/python/py-type.c
@@ -416,10 +416,16 @@ static PyObject *
 typy_get_tag (PyObject *self, void *closure)
 {
   struct type *type = ((type_object *) self)->type;
+  const char *tagname = nullptr;

-  if (!TYPE_TAG_NAME (type))
+  if (TYPE_CODE (type) == TYPE_CODE_STRUCT
+      || TYPE_CODE (type) == TYPE_CODE_UNION
+      || TYPE_CODE (type) == TYPE_CODE_ENUM)
+    tagname = TYPE_NAME (type);
+
+  if (tagname == nullptr)
     Py_RETURN_NONE;
-  return PyString_FromString (TYPE_TAG_NAME (type));
+  return PyString_FromString (tagname);
 }
 /* Return the type, stripped of typedefs. */

gdb debug sessions
good (pre-breaking change)
(gdb) ptype
type = struct []int32 {
    int32 *array;
    int len;
    int cap;
}
(gdb) p arr
$3 = []int32 = {1, 2, 4, 5, 7, 8, 10}

(gdb) python import pdb
(gdb) python pdb.run('gdb.execute("print arr")')
> <string>(1)<module>()
..
(Pdb) n
> /usr/lib/go/src/runtime/runtime-gdb.py(225)matcher()
-> if klass.pattern.match(str(val.type)):
(Pdb) p str(val.type)
'struct []int32'
(Pdb)

bad (e86ca25fd6cc06488c9dda936cf83f0fcdf6b58b)
(gdb) ptype arr
type = struct []int32 {
    int32 *array;
    int len;
    int cap;
}
(gdb) p arr
$1 = {array = 0xc0000142a0, len = 7, cap = 8}

(gdb) python import pdb
(gdb) python pdb.run('gdb.execute("print arr")')
...
(Pdb) n
-> def matcher(val):
(Pdb) p val
<gdb.Value object at 0x7f7e8b9ba070>
(Pdb) print(val)
{array = 0xc00001a2a0, len = 7, cap = 8}
...
(Pdb) n
> /usr/lib/go/src/runtime/runtime-gdb.py(225)matcher()
-> if klass.pattern.match(str(val.type)):
(Pdb) p klass
<class '__main__.SliceTypePrinter'>
(Pdb) p klass.pattern
re.compile('^struct \\[\\]')
(Pdb) p str(val.type)
'[]int32'
(Pdb) gdb.execute('ptype arr')
type = struct []int32 {
    int32 *array;
    int len;
    int cap;
}

The difference is in the p str(val.type) result, the "struct " prefix has gone. Following this patch the original behaviour is restored.
Testing environment used go v1.12.6, and dozens of gdb versions up to HEAD.

I signed it!


Classic crafting turns on when opening crafting using square

if you turn off classic crafting, it turns on on its own if you open the 2x2 crafting using square. you cant turn it off at all in servers

Fixed in version 1.6.4, thanks for reporting.


Using artifact-name breaks uploading artefact

Hi,
Using the artifact-name option to upload the sbom with a custom name makes the upload-artfact action fail with a ENOENT: no such file or directory error.

Hi @ajmalab could you provide the YAML configuration you're using?

Hi, apologies, I figured out the issue. I was using ${{ github.repository }} in the name, and that outputs a forward slash. The upload part interprets that as a folder path rather than the string. Replacing the forward slash with another character solved this.

Thanks for following up @ajmalab and thanks for using our tools!


koordlet: support blkio-cost user cost model configuration

Ⅰ. Describe what this PR does
support blkio-cost user model configuration, see https://www.alibabacloud.com/help/en/alinux/user-guide/configure-the-weight-based-throttling-feature-of-blk-iocost
Ⅱ. Does this pull request fix one issue?
NONE
Ⅲ. Describe how to verify it

configure enableUserModel: true and other user cost model parameters in resource-qos-config
Verify the content of /sys/fs/cgroup/blkio/blkio.cost.model file on the k8s node

Ⅳ. Special notes for reviews
V. Checklist

[ ] I have written necessary docs and comments
[ ] I have added necessary unit tests and integration tests
[ ] All checks passed in make test

/lgtm

/approve


First draft of config builder

Why?
I think having a UX to build configs will help us get folks excited by OT, teach users how to write configs, and make it fun to create new configs.
My other recent PRs that have been building towards this:

#69
#70
#75

How?

Refactor RenderConfig out of RenderFrom{Endpoint,File}.

This lets the config builder render its in-memory config that it has built.

Add zod to demo-app
First draft of config builder UX

Currently it only knows how to handle primitive types, but these start to show the possibilities.
I updated ClientPageTitle to have some silly props to showcase what's possible.
The UI is partying like it's 1989.

https://github.com/open-truss/open-truss/assets/3272924/bca0b8ce-abb6-4d51-9186-488df397ea1e
Next up is handling children so we can get sub-frames going 🚀 And adding more types, including a recursive handling for arrays and objects.

This isn't working for me locally :thinking: I see this:

This isn't working for me locally 🤔 I see this:

Fixed in https://github.com/open-truss/open-truss/pull/76/commits/a4db092b493252c14bca52d5cd1c3073503dbc07! The zod types had a default, but the component didn't.

Ready to merge I think 👍 Thanks @kmcq!

Out of curiosity, what do you see as next steps for this? Thinking about when / how to get this added to the app at our day job.

I'd love to hear what would be useful to you! For me, I'm interested in making it so you can edit the config instead of just building it up, being able to import a config and work on it, and making its UI look better.


Fix test cases failing with "ValueError: q must be exactly 160, 224, or 256 bits long"

We have 5 failing test cases all with the same error message:
ValueError: q must be exactly 160, 224, or 256 bits long

We cannot track down anymore how long this error exists, as the logs of workflow executions are not archived by Github. It seems like and outdated encyrption algorithm may be the cause: https://stackoverflow.com/questions/75452354/paramiko-rsakey-private-key-file-is-encrypted
PR #439 might help to solve this.

The issue was that usernames that would not exist has been used: https://github.com/fabric/fabric/issues/2182#issuecomment-1362940149


Error installing "@napi-rs/blake-hash-android-arm64" via npm and yarn

Hello LongYinan,
I found a problem.
when I type the command "npm i @napi-rs/blake-hash-android-arm64" I get this error:
npm ERR! 404 Not Found - GET https://registry.npmjs.org/@napi-rs%2Fblake-hash-android-arm64 - Not found
npm ERR! 404
npm ERR! 404 '@napi-rs/blake-hash-android-arm64@*' is not in this registry.
When I try to install it with yarn I get this error:
error<EMAIL_ADDRESS>Request failed "404 Not Found"
can you help with the error
How can I install it via package.json?

error<EMAIL_ADDRESS>Request failed "404 Not Found"

I can download it without any problem. Can you please retry?


React-Virtualized support?

would it be possible to add support for react-virtualized? Would help greatly in rendering large stack grids efficiently.

Hi @meds Sorry for the late reply...
You're absolutely right., virtual scrolling is very attractive in terms of performance :+1:
But, I have considered support once, but I have abandoned it because I can not get much work time...
If you are interested in improving react-stack-grid, we are always waiting for PR :smile:

No worries I might give it a look :)

Thanks :+1:


Json files

Hi,
Thanks for sharing the amazing configurator, I wonder how you setup json files? Is there any tools to export them?
Thanks

No I don't have any tools. I've made it manually


[dev pick] Add coq-lsp to dev pick.

cc #319

@ejgallego : I currently have CI issues and don't want to merge if CI doesn't run through. I will merge as soon as CI is working.
You should remove the fixes line above, because the inclusion is not done by adding it to dev - the reference is more the previous pick, and it is included if it is in the next official pick. So the issue should not be closed before this.

Thanks @MSoegtropIMC for the help! I will wait, no problem.
I'm sorry I didn't fully understand the pick process, should I open another pull request doing the pick, or it is something you folks do when preparing the release?

Hi @MSoegtropIMC , I've updated this PR, let's see how the CI works. A couple of questions:

I put coq-lsp into the extended section, but IMHO document managers should maybe go in the base section, WDYT?
I see coq-serapi is not documented into the readme.dev, should that be fixed?
I guess it would make sense to add the package to the 8.17~2023.08 pick too?

There is a board which decides what goes where. In the full section only things should go which are production ready which means people can rely on that it will be available in the next release as well and that they don't have to put a high effort into adopting. For a tool the adoption effort should be negligible (a few settings) and I expect that you port it in due time to the next Coq releases, so in my opinion "full" should be fine.

The dev pick is broken, so I didn't regenerate the docs in a while.
For 8.17~2023.08 the release is likely today, so it is a bit late, but we can do a .1 if required. But since the next release will be there in a few weeks, I am not sure it is worthwhile.

Thanks for the feedback @MSoegtropIMC , a few comments:

regarding the section, maybe a section "tools" could work? But anyways that is not so important now, we can keep extended and see later on.
sorry to hear about the broken dev pick, my goal with this PR is to indeed be sure that everything works fine so the platform maintainers have no work to do. What do you think the best way is?

The only contraint I have for the latter item is that I'd like the windows installer to be generated, so users can test.
IMHO it is fine if we don't do a platform release for coq-lsp, I'm happy to distribute the .exe installer on the coq-lsp website unsigned for now, so poeple can test, etc...


support replying to server pushes

if a Phoenix.Channel can reply with a reply signature ({:reply, reply, socket}) or Phoenix.Channel.reply/2, surely we the client writers should be able to reply to server pushes?

While I don't have a quote from the official docs to back me up, I'm relatively sure that clients are not allowed to reply to messages. A quick survey of other client implementations only brings up re

While I don't have a quote from the official docs to back me up, I'm relatively sure that clients are not allowed to reply to messages. A quick survey of other client implementations only brings up re


surface: linux 6.4.16 -> 6.5.5, 6.1.53 -> 6.1.55, linux-surface: 32c55fe0 -> b82e8acd

Description of changes

Update surface kernels

6.4.16 -> 6.5.5
6.1.53 -> 6.1.55

Update linux-surface patches:

32c55fe0 -> b82e8acd

Tested on

[X] Surface Laptop 4 AMD

Things done

[X] Tested the changes in your own NixOS Configuration
[X] Tested the changes end-to-end by using your fork of nixos-hardware and
importing it via <nixos-hardware> or Flake input

@mergify queue


OmniSharp unhandled exception after server starts listening: "Value does not fall within the expected range"

OmniSharp crashes shortly after running Sublime, and running the server straight from the plugin folder yields this error:

I really don't have any idea what's going on here, hopefully this is useful to someone who knows what' what. Is it my fault or a problem within OmniSharp?

I'm pretty sure it's my fault. The mono install didn't work and I didn't notice.
A better error message for that might be useful though.

(I thought I'd made a grave error that was causing this issue -- I didn't have mono installed -- but even with it installed, the crash persists.)

Closing due to inactivity.


Failing to read ttbin file - run after a predefined route

Hi,
I don't own a TomTom 3, but some of our users using our software (Runalyze/Runalyze) do.
A user complained that a ttbin file couldn't be read.

It's a running activity
There is a new feature in TomTom3, where you can run after a defined route. You upload a gpx file to the watch and can just see that track (I think during the run). The user who wrote us did this.

Would be great if anyone could give us a hint. I'm not that deep in that ttbin /TomTom topic
00910000_20161018_203136.ttbin.zip

Hi Michael,
That file seems to be corrupt. It's pretty much perfect except there are two extra bytes on the end of the file. If you remove them, the file should load perfectly. I don't know whether this will always happen, but I guess if you ignore a truncated record at the very end of the file, then it should be ok.
The file is describing an 8.1km run with laps every 1000m. There are a few records at the beginning of the file that I don't know the structure of, but they are described adequately in the file header so they can be skipped without issue. I presume these records hold information about the new feature you mentioned.
The ttbincnv program reads and converts this file without problem (after I fixed a minor bug) after removing the last two bytes.

Hi Ryan,
I really appreciate your support and this tools!
With your last commit everything works fine (and without removing those two bytes at the end!)
So from my side I just can say - It works!


Some times the carousel isn't initialized

Hey,
I've got the problem, that some times the carousel isn't initialized after route change. Some times it is.
Then, the carousel isn't working and the carousel-items just shown among each other

@nachtwandler how can i simulate this :) . can you provide example.

@emalikterzi I'va a REST api, which provided image URLs I use in the slider. If it loads fast, it works but if the api takes a second it seems it doesn't initialize. Can i trigger the reinitialize? So I can do it after the promise is resolved?

@nachtwandler one solution might be to use ng-if to prevent the directive from rendering initially like so:
controller:
$scope.owlSlidesReady = false;
view:
<div ng-if="owlSlidesReady">
when data finish loading:
$scope.owlSlidesReady = true
i think this should delay the binding of the directive until the slides are loaded, solving your problem.

@nachtwandler just using owl-items attribute should be fine. directive watching changes on owl-items
<ng-owl-carouse> owl-items="items"


Bittrex is Down Dec 4, 2023 Another SBF/FTX Casualty!!!!!!  NXS needs a 2nd Exchange listing from Argentina!

https://market.southxchange.com/
The Listing has been paid for Over a Year Now, from Bitcointalk.org comment, but NO DEV has worked with SouthXchange to set up the NXS Listing!
A Dev must set up an Account there and Open a Ticket to contact SouthXchange for Listing NXS as it's already been Paid .165 Bitcoin

I signed up to nonkyc.io it's simple and easy enough and allows longer passwords than Tradeogre..........however it requires phone app 2-factor which ties phone GPS to Physical Withdraw Addresses which poses risks to Physical Safety if the data is ever leaked or hacked with the GPS from the Phone!  But it's maybe ok to most people, still way better than most other choices available, the Exchange seems well done almost like using Binance Clone!
The Monero Exchange is coming, it's still being worked on apparently after the main developer of it left last year and it's still getting a lot of Code Done to make it happen!
https://github.com/haveno-dex
https://haveno.exchange/
This would be another good option coming up, but yet I have no idea how it will work out from this perspective until it's actually released as Official Launch of the Haveno Exchange!

https://github.com/Nexusoft/LLL-TAO/issues/133#issuecomment-1581959108
I think it would be better to Integrate with Haveno and use a Different Application for the Exchange would be fastest way to integrate with NXS!  And it wouldn't require a NXS Module installed to operate the Exchange!
Videlicet

That's what I was thinking yeah, and our legacy code is based on Bitcoin so we could modify it to support connections to Bitcoin network too. This is an awesome feature

Thanks mate, we will take a look at these!