text:
Training process crashes suddenly

Prerequisites

[X] I have read the documentation.
[X] I have checked other issues for similar problems.

Backend
Local
Interface Used
CLI
CLI Command
autotrain app --port 8080 --host 127.0.0.1
UI Screenshots & Parameters

Error Logs
Loading checkpoint shards:  75%|███████▌  | 3/4 [00:09<00:03,  3.21s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [00:10<00:00,  2.31s/it]
Loading checkpoint shards: 100%|██████████| 4/4 [00:10<00:00,  2.63s/it]
INFO     | 2024-05-15 23:14:00 | autotrain.trainers.clm.train_clm_sft:train:66 - model dtype: torch.float16
INFO     | 2024-05-15 23:14:00 | autotrain.trainers.clm.train_clm_sft:train:79 - creating trainer
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 0 examples [00:00, ? examples/s]
ERROR    | 2024-05-15 23:14:02 | autotrain.trainers.common:wrapper:120 - train has failed due to an exception: Traceback (most recent call last):
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/builder.py", line 1748, in _prepare_split_single
for key, record in generator:
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/packaged_modules/generator/generator.py", line 30, in _generate_examples
for idx, ex in enumerate(self.config.generator(**gen_kwargs)):
File "/home/dejan/python39venv/lib/python3.9/site-packages/trl/trainer/sft_trainer.py", line 536, in data_generator
yield from constant_length_iterator
File "/home/dejan/python39venv/lib/python3.9/site-packages/trl/trainer/utils.py", line 458, in iter
buffer_len += len(buffer[-1])
TypeError: object of type 'NoneType' has no len()
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/dejan/python39venv/lib/python3.9/site-packages/trl/trainer/sft_trainer.py", line 539, in _prepare_packed_dataloader
packed_dataset = Dataset.from_generator(
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 1117, in from_generator
return GeneratorDatasetInputStream(
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/io/generator.py", line 47, in read
self.builder.download_and_prepare(
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/builder.py", line 1789, in _download_and_prepare
super()._download_and_prepare(
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/builder.py", line 1627, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/dejan/python39venv/lib/python3.9/site-packages/datasets/builder.py", line 1784, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/dejan/python39venv/lib/python3.9/site-packages/autotrain/trainers/common.py", line 117, in wrapper
return func(*args, **kwargs)
File "/home/dejan/python39venv/lib/python3.9/site-packages/autotrain/trainers/clm/main.py", line 28, in train
train_sft(config)
File "/home/dejan/python39venv/lib/python3.9/site-packages/autotrain/trainers/clm/train_clm_sft.py", line 86, in train
trainer = SFTTrainer(
File "/home/dejan/python39venv/lib/python3.9/site-packages/trl/trainer/sft_trainer.py", line 283, in init
train_dataset = self._prepare_dataset(
File "/home/dejan/python39venv/lib/python3.9/site-packages/trl/trainer/sft_trainer.py", line 435, in _prepare_dataset
return self._prepare_packed_dataloader(
File "/home/dejan/python39venv/lib/python3.9/site-packages/trl/trainer/sft_trainer.py", line 543, in _prepare_packed_dataloader
raise ValueError(
ValueError: Error occurred while packing the dataset. Make sure that your dataset has enough samples to at least yield one packed sequence.
ERROR    | 2024-05-15 23:14:02 | autotrain.trainers.common:wrapper:121 - Error occurred while packing the dataset. Make sure that your dataset has enough samples to at least yield one packed sequence.
INFO     | 2024-05-15 23:14:03 | autotrain.utils:get_running_jobs:57 - Killing PID: 165343
Additional Information
The txt file I am using for testing has about 300 lines and not sure if this the reason or something else.

it should be a csv file not a text file, as for the dataset columns try to respect the suggested format regading your use case

repo: huggingface/autotrain-advanced


text:
error while loading shared libraries: libnvrtc.so.12

Hi,
This is my Cargo.toml:
[package]
name = "myapp"
version = "0.1.0"
edition = "2021"

[dependencies]
candle-core = { git = "https://github.com/huggingface/candle.git", version = "0.5.0", features = ["cuda"] }

And this is my code:
use candle_core::{Device, Tensor};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let device = Device::new_cuda(0)?;

    let a = Tensor::randn(0f32, 1., (2, 3), &device)?;
    let b = Tensor::randn(0f32, 1., (3, 4), &device)?;

    let c = a.matmul(&b)?;
    println!("{c}");
    Ok(())
}

When compiling the code, an error occurred:
(py311) [wangjw@localhost myapp]$ cargo run
   Compiling cudarc v0.10.0
   Compiling candle-core v0.5.0 (https://github.com/huggingface/candle.git#b2e81675)
   Compiling myapp v0.1.0 (/home/wangjw/data/work/projects/practice/myapp)
    Finished `dev` profile [unoptimized + debuginfo] target(s) in 7.96s
     Running `target/debug/myapp`
target/debug/myapp: error while loading shared libraries: libnvrtc.so.12: cannot open shared object file: No such file or directory

The libnvrtc.so.12 is in "/home/wangjw/programs/miniconda3/envs/py311/lib", how to specify the path ?

You can probably use the LD_LIBRARY_PATH environment variable for this.

Yes, now it can be compiled. but I failed to run the example because of this error:
(py311) [wangjw@localhost myapp]$ cargo run
    Finished `dev` profile [unoptimized + debuginfo] target(s) in 0.07s
     Running `target/debug/myapp`
Error: Cuda(Cublas(CublasError(CUBLAS_STATUS_NOT_INITIALIZED)))

Never seen this error, it seems to come from the cublas library that candle uses for matmuls on cuda, my guess is that there is an issue with the way cuda is installed on the box where you're trying to run this.

source: gharchive/issue

repo: huggingface/candle

license_type: permissive

license_source: github-api


text:
Beit fix: Add the gen_relative_position_index() function

Hi,
Following the PS comment in #2305 , I fixed the Beit code and added the magic function 'gen_relative_position_index()'.
I also updated the weights on Hugging Face accordingly, by removing the unnecessary tensor parameter (relative_position_index).
Beit code is fine for now.
Thank you for your time & sorry for the inconvenience.

Thanks!

Thank you Laurent for the validation,
Regards,

source: gharchive/pull-request

authors: LaurentMazare v-espitalier


text:
map() function removes columns when input_columns is not None

Describe the bug
The map function, removes features from the dataset that are not present in the input_columns list of columns, despite the columns being removed not mentioned in the remove_columns argument.
Steps to reproduce the bug
from datasets import Dataset
ds = Dataset.from_dict({"a" : [1,2,3],"b" : [0,1,0], "c" : [2,4,5]})

def double(x,y):
  x = x*2
  y = y*2
  return {"d" : x, "e" : y}

ds.map(double, input_columns=["a","c"])

Expected results
Dataset({
    features: ['a', 'b', 'c', 'd', 'e'],
    num_rows: 3
})

Actual results
Dataset({
    features: ['a', 'c', 'd', 'e'],
    num_rows: 3
})

In this specific example feature b should not be removed.
Environment info

datasets version: 2.4.0
Platform: linux (colab)
Python version: 3.7.13
PyArrow version: 6.0.1

Hi! Thanks for reporting! This looks like a bug. I've just opened a PR with the fix.

Awesome! Thank you. I'll close the issue once the PR gets merged. :-)

authors: mariosasko pramodith

repo: huggingface/datasets


text:
Update format, fingerprint and indices after add_item

Added fingerprint and format update wrappers + update the indices by adding the index of the newly added item in the table.

I renamed the variable, added a test for dataset._indices and fixed an issue with class_encode_column


text:
Unit 1: Error while pushing code to hub ('DummyVecEnv' object has no attribute 'render_mode')

While running the following block of code, I am getting the error 'DummyVecEnv' object has no attribute 'render_mode' as shown in the screenshot and the model is not pushed to the hub.  I checked in Discord and there are more users facing this issue
import gym
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.env_util import make_vec_env

from huggingface_sb3 import package_to_hub
repo_id = "Neomedallion/ppo-LunarLander-v2"
env_id = "LunarLander-v2"
eval_env = make_vec_env("LunarLander-v2", n_envs=1)
model_architecture = "PPO"
commit_message = "Push LunarLander-v2 model as part of HF Unit 1"

package_to_hub(model=model, # Our trained model
               model_name=model_name, # The name of our trained model
               model_architecture=model_architecture, # The model architecture we used: in our case PPO
               env_id=env_id, # Name of the environment
               eval_env=eval_env, # Evaluation Environment
               repo_id=repo_id,
               commit_message=commit_message)

Hey there 👋 sorry for this bug, we are updating the notebook with gymnasium (new version of gym) hopefully with it you'll not have these kind of bugs, you can find the new version here: https://github.com/huggingface/deep-rl-class/blob/GymnasiumUpdate/Unit1/notebooks/unit1/unit1.ipynb
I'm going to merge tonight but you can access it already.

Edit: We merged it I'm closing this issue 🤗

authors: krvicky simoninithomas

repo: huggingface/deep-rl-class


text:
FIX pip install evaluate[evaluator]

Fixes pip install evaluate[evaluator]
error currently is
ERROR: No matching distribution found for transformersscipy>=1.7.1; extra == "evaluator"

due to missing ,

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

authors: HuggingFaceDocBuilderDev philschmid

repo: huggingface/evaluate


text:
Is it possible to add support for Kosmos-2

Hello HuggingFace and Its Wonderful Employees!!,
I was just checking if It is possible for me to convert "https://huggingface.co/microsoft/kosmos-2-patch14-224" model to support coreml so that I can use it on my mac?
I have tried it now but it says this model is not supported, Is there any way I or we could add support for this?
Thanks!!!!

It is a Image to text (Image Captioning Model).

repo: huggingface/exporters


text:
Adds split_by_episodes to LeRobotDataset

What this does
For instance, the ACT datasets have only a single split ("train") and it would be great to retain some portion of the data for calculating metrics during training.
But in general, this functionality can also be useful in many other ways (for instance, one thing I have been curious about is how performance increases with the size of the dataset -- ACT trains on just 50 episodes, but what if we had only 10? 20? what about act scripted vs recorded from participant).
In general, I find this functionality to be very useful for experimentation and so I went ahead and added it.
I am not entire sure if there is interest in this functionality -- if yes, happy to make any changes that might be required in order to merge this.
One thing that I feel would be very valuable adding here is specifying a seed for the split. Can add this to this PR no problem (or a follow up one), just again -- not sure if there might be interest in this functionality from the maintainers, this is just to feel the waters.
I drew inspiration from how the train_test_split is implemented in datasets (some of the logic is verbatim from there) but the curve ball here was dealing with mapping of examples to episodes and the episode_data_index so had to add some functionality around this.
This is an example of how this functionality might be used:

Very new to the lib, not sure how this fits into the greater whole etc, etc, but excited to contribute if I can :slightly_smiling_face:
How it was tested
I ran a couple of scenarios, caught and ironed out some obvious issues.
How to checkout & try? (for the reviewer)
Call split_by_episodes on an instance of a LeRobotDataset, try various parameter combinations (ints, floats), verify that the properties set on the returned datasets look okay.
Try other datasets.
sim_transfer_cube_scripted_ds = LeRobotDataset('lerobot/aloha_sim_transfer_cube_scripted')
train, test = sim_transfer_cube_scripted_ds.split_by_episodes()  # attempt passing various combinations of arguments
test.episode_data_index

@radekosmulski Super useful! We wanted to support evaluation set. We didnt include it in the alpha release because the loss computed on evaluation set usually doesnt correlate well to success rate in the real-world. However we want to support it on the longer term because it's an interesting metric.
There might be an easier way to achieve this tho. And we might already support it. See: https://huggingface.co/docs/datasets/v2.19.0/loading#slice-splits

hey @Cadene! thank you very much for looking into this, appreciate it! :slightly_smiling_face:
I didn't know of the slice functionality existing in datasets, this is great to know! Also, I am 100% onboard with simplification/not duplicating functionality/there being less code/things not being coupled together, etc! Great there might be a simpler way of achieving the above.
I started looking at the slice functionality and here are a couple of thoughts (I might not be seeing the whole picture here though so some of this might be off the mark, apologies)
It is absolutely fantastic that we can do dataset surgery as follows, gives a lot of flexibility to the user:
ds1 = datasets.load_dataset('lerobot/aloha_sim_transfer_cube_scripted', split="train[:10%]")
ds2 = datasets.load_dataset('lerobot/aloha_sim_transfer_cube_scripted', split="train[:15%]")
ds3 = datasets.load_dataset('lerobot/aloha_sim_transfer_cube_scripted', split="train[-25%:]")

When we load partial dataset the episode_data_index goes out of sync:

It gets loaded from metadata and one could make an argument that other things (like stats or fps and the video flag) are properties valid across the whole dataset or arbitrary subsets of it, but not so much for the episode_data_index. (yes, argument could be made that using whole dataset stats when splitting into train - val set introduces leakage, but that is rarely relevant in practice and probably not relevant to how people will want to use this functionality).
So the only piece of metadata that is dependent on what subset gets loaded is episode_data_index.
My reasoning:

storing episode_data_index is an extra step that needs to be taken when writing / loading the dataset
adds conceptual ("huh, where does this come from?") and code complexity (needs to be saved and then loaded)

Would it not be better to calculate this when creating a LeRobotDataset? It would be ideal if the LeRobotDataset could work straight off a vanilla hf_dataset, but I understand that given that we want to store info like fps, the video flag, etc, this might not be possible. But at least we can limit the amount of new stuff that gets introduced, to streamline this.
If we move to calculating the episode_data_index when the data is loaded, we would maintain backwards compatibility (new version would just not use the metadata/episode_data_index.safetensors and the LeRobotDataset would work with a slice passed to it in the split!
How the episode_data_index gets created would be explicit (we can make it a cached calculated property or set it at loading, whichever would be simpler).
I think it might also make creating and saving a LeRobotDataset simpler if the functionality for creating the episode_data_index would be centralized to a single spot in the codebase and dynamic.
Anyhow, let me know your thoughts, please :slightly_smiling_face: Happy to start chipping away on the above or otherwise implement conditional recalculation of the episode_data_index when a slice is loaded (or some other alternative).

@radekosmulski thanks for all your work!
My take is that when split != "train"  is provided we should recalculate episode_data_index and maybe cache the result somewhere if it takes too much time to calculate it.
To update episode_data_index you would need to get all episode_index and index from hf_dataset. For all our dataset we can do this:
hf_dataset["episode_index"]
hf_dataset["index"]

Note:
hf_dataset["image"][0] # loads all your images in RAM and access the first one
hf_dataset[0]["image"] # loads a single item (with all its columns) in RAM
hf_dataset.select_columns("image")[0]["image"] # loads a single image in RAM

I think it would be great to merge this features into main!
I am not sure we should merge at the same time the possibility to evaluate on a validation set in train.py yet.
However, we could add it as an example inside the examples directory.
What do you think?

Hey @Cadene! Thank you for looking into this and for your guidance! :slightly_smiling_face: Your proposal sounds great!
I attempted mapping from the loaded split subset to the episode_data_index from the full dataset but there seems to be not enough information provided in the downloaded chunk as the frame_index gets recalculated on the returned subset and is not the frame_index from the original dataset

As such, I implemented this as a recalculation if split != 'train'.
The example sounds great -- I will work on it but might have it ready later in the week if that would be okay. It seems like it might require a little bit more work as I need to familiarize myself with other aspects of the library to do it right.
Happy to make any other changes that might be necessary, let me know, please! Also, we can merge this and I can open another PR for the example, or wait for the example to be ready, either one is okay on my end.
Sreenshot on how it works now:

No worries! Thanks for this great contribution. Dont hesitate to message me. I can jump on a call if you have any trouble.

Hey @Cadene -- I think it all should be done and ready for your review, including the example 🙂

Thank you very much for the review, @Cadene! :slightly_smiling_face: do appreciate it
Along the way, I slightly refactored test_examples.py and test_datasets.py. I like the changes as they remove a good bit of code repetition DRYing things up. On the other hand, an argument could be made that the tests were easier to follow when data creation was done inline. I feel we gained some code clarity with those changes, but happy to revert the changes if that would be preferred!
Also, added unit tests for calculate_episode_data_index and reset_episode_index as requested.
Please let me know if any additional changes might be needed

@radekosmulski
pre-commit install
pre-commit run --all-files

@Cadene pre-commit ran and formatting fixed :slightly_smiling_face:
I also squashed the commits but not sure if that is necessary?

authors: Cadene radekosmulski

repo: huggingface/lerobot


text:
a git clone is large because history has the "add model" commit

Since git works on cloning entire history, and since you did not "squash and merge" the PR https://github.com/huggingface/pytorch-openai-transformer-lm/pull/1
what is happening is that a fresh git clone is 400MB now, and in the git history, the model is included.
I suggest the following:

rebase so that the 3 commits a7ed, 368b and 341b are squashed, and then force-push to master.

Here's how you can do this:

git rebase -i aa800ef759275b575db324db5e6194c50488de57
Then replace "pick" with "s" in the commit entries for 341b and 368b
Exit the editor.

Then do: git push origin HEAD:master --force

Done! thanks a lot @soumith for the detailed steps!

authors: soumith thomwolf

license: MIT


text:
Running inference on AMD/ROCm and TGI 2.0.0

System Info
AMD 250, Rocm 6, TGI 2.0.0
Information

[X] Docker
[ ] The CLI directly

Tasks

[ ] An officially supported command
[ ] My own modifications

Reproduction
docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80  -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0.0-rocm --model-id  NousResearch/Llama-2-7b-hf
Expected behavior
2024-04-15T14:03:19.035250Z ERROR shard-manager: text_generation_launcher: Shard complete standard error output:

Traceback (most recent call last):

  File "/opt/conda/bin/text-generation-server", line 8, in <module>
    sys.exit(app())

  File "/opt/conda/lib/python3.10/site-packages/text_generation_server/cli.py", line 71, in serve
    from text_generation_server import server

  File "/opt/conda/lib/python3.10/site-packages/text_generation_server/server.py", line 16, in <module>
    from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch

  File "/opt/conda/lib/python3.10/site-packages/text_generation_server/models/vlm_causal_lm.py", line 14, in <module>
    from text_generation_server.models.flash_mistral import (

  File "/opt/conda/lib/python3.10/site-packages/text_generation_server/models/flash_mistral.py", line 18, in <module>
    from text_generation_server.models.custom_modeling.flash_mistral_modeling import (

  File "/opt/conda/lib/python3.10/site-packages/text_generation_server/models/custom_modeling/flash_mistral_modeling.py", line 29, in <module>
    from text_generation_server.utils import paged_attention, flash_attn

  File "/opt/conda/lib/python3.10/site-packages/text_generation_server/utils/paged_attention.py", line 4, in <module>
    from vllm._C import cache_ops, ops

@aminnsabet Please try again on ghcr.io/huggingface/text-generation-inference:2.0.4-rocm, or better yet the upcoming release.

authors: aminnsabet fxmarty

repo: huggingface/text-generation-inference


text:
Fix gated docs

When merging suggestions from https://github.com/huggingface/text-generation-inference/pull/797, a big part of it was deleted

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint.

authors: HuggingFaceDocBuilderDev osanseviero


text:
开启 fingerprint 时，PWA 缓存文件 失效

References:
https://github.com/reuixiy/hugo-theme-meme/blob/master/assets/js/sw.js

https://developers.google.com/web/fundamentals/primers/service-workers
https://developers.google.com/web/fundamentals/instant-and-offline/offline-cookbook
https://googlechrome.github.io/samples/service-worker/
https://developer.mozilla.org/en-US/docs/Web/API/Service_Worker_API/Using_Service_Workers
https://serviceworke.rs/
https://www.youtube.com/watch?v=baSiSIyTGSk

另外：未开启 footer 时，fixit.min.svg 打包时不会保留，缓存文件应当去除缓存 fixit.min.svg

该问题尚未解决，建议 fingerprint 和 PWA 功能不要同时开启！

repo: hugo-fixit/FixIt


text:
Faltan objetivos

Para esta segunda práctica tienes que tener el 80% de los ejercicios del tema 2 y el 80% de los objetivos del tema 2 y las sesiones 3 y 4. En caso contrario, tendras un cero.

Acabo de actualizar el fichero de objetivos. Los ejercicios los subí a principios de la semana pasada.

authors: FranciscoNaveros hugobarzano

repo: hugobarzano/osl-computer-management

license_source: bigquery


text:
Add the SSL proxy enable for Enabling Voice/Cam over foundry

At this moment due to the way that the configuration works on foundry even if enable SSL on the load balancer endpoint the application consider that there's no SSL and the audio features are disbled.
Create a way to use self-certificates internally for end-to-end HTTPS to achieve above configuration.

Added the SSL Proxy on.

repo: hugoprudente/charts


text:
SteeringMethod Straight for very close configurations

When the initial and final configurations are very close to one another (about 3e-17) but not exactly the same, steering method Straight (hpp-core/src/steering-method/straight.cc) may compute distance to be 0, and create an assertion error in StraightPath constructor (hpp-core/src/straight-path.cc.
Ignoring the assertion error still causes some problems for the StatesPathPlanner in hpp-manipulation: when merging the successful roadmap from inner state planner to the global roadmap, the global roadmap does not see the two configurations as connected.

The difference implementation for SO3 in pinocchio for quaternions quat0 and quat1 is log3((quat0.matrix().transpose() * quat1.matrix()).eval()). The result of the matrix product is an identity matrix. The matrix product is not precise enough to see the 1e-17 difference. Indeed, when running on python, numpy.float128 needs to be used to get the precise result:
In [1]: import pinocchio as pin
In [2]: import numpy as np
In [3]: x = pin.XYZQUATToSE3(np.array([ 0.0,
   ...:  0.0,
   ...:  -0.19537114500111052,
   ...:  0.9807293794421349,
   ...: ]))
In [4]: y = pin.XYZQUATToSE3(np.array([ 0,
   ...:  0,
   ...:  -0.1953711450011105,
   ...:  0.9807293794421349,
   ...: ]))
In [5]: np.matmul(y.rotation.T, x.rotation)
Out[5]:
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])
In [6]: np.matmul(np.array(y.rotation.T, np.float64), np.array(x.rotation, np.float64))
Out[6]:
array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]])
In [7]: np.matmul(np.array(y.rotation.T, np.float128), np.array(x.rotation, np.float128))
Out[7]:
array([[ 1.00000000e+00,  5.12827628e-17,  0.00000000e+00],
       [-5.12827628e-17,  1.00000000e+00,  0.00000000e+00],
       [ 0.00000000e+00,  0.00000000e+00,  1.00000000e+00]],
      dtype=float128)

The new implementation in pinocchio to calculate difference between SO3 groups is highly accurate. It generates a non-zero distance even for configurations differing only 3e-17 in some coordinates. I will close this issue now.

repo: humanoid-path-planner/hpp-core


text:
[SDK][Agreement] Add agreement package to sdk.

Description
This PR adds a submodule to the the python sdk for calculating Interrater Agreement.
Summary of changes

Copied source code from CVAT example to python sdk.
Copied tests from CVAT example to python sdk tests.
Added dependencies hypothesis (dev) and pyerf and numpy.

How test the changes
make unit-test
Related issues
Closes #777.
Operational checklist

[x] All new functionality is covered by tests
[ ] Any related documentation has been changed or added

I wonder if you have access to gitbook, but we'll need to update SDK docs.
In addition to, is this agreement module only limited to Python SDK? We don't want Typescript version of it?

I wonder if you have access to gitbook, but we'll need to update SDK docs.
I can access the gitbook and make change requests. I suppose this is sufficient. Will make a change request and let you know once that is ready for review.

In addition to, is this agreement module only limited to Python SDK? We don't want Typescript version of it?
Currently it is only planned to be part of the python SDK. It is currently also only an optional extra module for the python SDK, to keep its size small.
Maybe I should add checks and appropriate warnings, if people try to import functions from the agreement package and they have not installed the required extras? At the moment, it will throw an import error, saying that required libraries are not installed.

repo: humanprotocol/human-protocol


text:
Add config to use custom phpunit.humbug.xml (e.g for symfony project)

{
    "source": {
        "directories": [
            "src"
        ],
        "excludes": [
            "vendor"
        ]
    },
    "chdir": "app",
    "configure": "app/humbug/phpunit.humbug.xml",
    "timeout": 30,
    "logs": {
        "text": "humbuglog.txt",
        "json": "humbuglog.json"
    }
}

Also needs to be documented. It's relatively simple, so I might look at it later over the weekend.

@tabbabi could you add the missing doc? After that I think it will be ready to be merged :)

authors: padraic tabbabi theofidry

repo: humbug/humbug


text:
A misaligned icon on the Themes Page of the website on the button(dropdown) Security Level

Describe the bug
Just a little misaligned icon, that is supossed to be in the right side of the button, I think.

Reproduction

Access the path /themes from the website https://www.shadcn-svelte.com/
scroll down until you find the card entitled "Report an issue"
Look for the button label "Security Level" where the text is "Severity 2"

Logs
No response
System Info
Browsers:
- MS Edge - v. 123.0.2420.81
- G. Chrome v. 123.0.6312.107

Severity
annoyance

#910 addresses this, but has not been updated for some time now.

#910 addresses this, but has not been updated for some time now.

Thanks for the reply! Maybe I've searched for the wrong words, so I didn't find that old issue, before I post.

repo: huntabyte/shadcn-svelte


text:
tailwind.config.ts deleted after init

Current Behavior
Hello,
I'm using sveltekit with tailwindcss with a tailwind.config.ts config file and want to init shadcn-svelte via bunx shadcn-svelte@latest init. With that done my tailwind.config.ts gets deleted and not modified. But it works fine with a tailwind.config.js.
Expected Behavior
tailwind.config.ts should be modified and not deleted.
Steps To Reproduce

Using existing sveltekit with tailwindcss project
run bunx shadcn-svelte@latest init
go through the questions, doesn't matter what you chose but please use the correct path and file names
watch your tailwind.config.ts disappearing magically
don't hand clap...do facepalm
:)

Link to Reproduction / Stackblitz
No response
More Information
No response

Right now we only have a .js template defined, so we replace whatever you have at the start with that template. We need another template for TS and to detect if the path ends with .ts or .js/.cjs and respond accordingly. Not going to add a .cjs template, that will always be replaced by .js

Yeah this isn't something that I think is worth the squeeze tbh, considering it overwrites the file anyways. If you want to use .ts you're more than welcome to modify it after initializing.
Reference: https://github.com/shadcn-ui/ui/pull/1743

authors: LifeGlitch huntabyte


text:
Valid PATH manipulation on macOS

This PR makes travis-cargo setup valid on macOS.
Resolves #70

Thanks for the pull request, and welcome! You should hear from @huonw (or someone else) soon.

@huonw Wonder if there's any reason for this not to get merged?

authors: aldanor autohuonw opilar

repo: huonw/travis-cargo


text:
ROBOT_NETWORK parameter not setting ROS_IP

I have a Husky that I recently updated to Ubuntu 16.04 and Kinetic using the method outlined in the ROS - Husky tutorials.
I was just now trying to configure the robot to network with my desktop PC, also running Kinetic. I added export ROBOT_NETWORK=wlan0 to /etc/ros/setup.bash and I can see that this sets the (ROBOT_NETWORK) environment variable successfully, but nothing else seems to happen. Based on the documentation, I was expecting this to configure the ROS_IP variable, however this does not appear to be the case.
Things I've checked:

The name of my wireless interface is definitely called wlan0

Any assistance would be much appreciated.

The ROBOT_NETWORK variable is passed to robot_upstart as one of the parameters for the Job class (see http://docs.ros.org/en/melodic/api/robot_upstart/html/jobs.html#robot_upstart.job.Job).  This in turn is used in the /usr/sbin/ros-start script that robot_upstart generates:
@[if interface]@
export ROS_IP=`rosrun robot_upstart getifip @(interface)`
if [ "$ROS_IP" = "" ]; then
  log err "@(name): No IP address on @(interface), cannot roslaunch."
  exit 1
fi
@[else]@
export ROS_HOSTNAME=$(hostname)
@[end if]@

(The above is taken from robot_upstart's job-start.em template, see https://github.com/clearpathrobotics/robot_upstart)
Could you please double-check the output of the ROS bringup service by running sudo systemctl restart ros, waiting a moment, and then running sudo journalctl -u ros --since "1min ago"?  Also, open up /usr/sbin/ros-start to make sure that the interface was correctly set when the file was created.

Thanks for the clarification!

authors: Gregory-Baker civerachb-cpr

repo: husky/husky


text:
Request for turning off/on battery status

Hey,
for off, I gotta say, I really like timeboxed.
If I could have one wish, it would be to be able to disable the battery status display
(or maybe even hide it until < 15% or so)
If you are interested for the feature but don't want to add it, I could take a look too.

Hi! I have plans to make the battery indicator a regular module like the other ones, so this should be done soon :)

Also like the watchface a lot.  If you are adding to battery, having time since last charge would be useful.

Battery is now a module on v4.0, so you can hide it or replace it with something else :)

authors: TobiasBales hussin pcause

repo: hussin/timeboxed-watchface

license: mit


text:
failed while expanding pvc

zyliang-worker2 kubelet[9465]: E0317 13:55:28.144305    9465 nestedpendingoperations.go:348] Operation for "{volumeName:kubernetes.io/csi/lvm.hwameistor.io^pvc-6fae3dae-386e-42e0-bf41-b28d329626c8 podName: nodeName:}" failed. No retries permitted until 2023-03-17 13:57:30.144276591 +0800 CST m=+2750108.522417022 (durationBeforeRetry 2m2s). Error: MountVolume.Setup failed while expanding volume for volume "pvc-6fae3dae-386e-42e0-bf41-b28d329626c8" (UniqueName: "kubernetes.io/csi/lvm.hwameistor.io^pvc-6fae3dae-386e-42e0-bf41-b28d329626c8") pod "mcamel-common-es-cluster-masters-es-masters-1" (UID: "195cc26b-8e25-454f-b5df-adf8bacadff8") : Expander.NodeExpand failed to expand the volume : rpc error: code = Unknown desc = exit status 1

Need to install xfsprogs

authors: FloatXD amazingpnc

repo: hwameistor/hwameistor


text:
Add LaTeX splitter

This PR is adding a LatexTextSplitter, closing #1731
It's based on the MarkdownTextSplitter, but the separators are adjusted.
I also added an entry for it to the according test_examples.ipynb
One issue I have, is that I can't run the complete notebook without an error in a later part, I'll open up a new issue for that.

P.S. My twitter is also @Janldeboer  :)


text:
weight.values,bias.values error

Hi @hx173149 @gudongfeng @chuckcho @cc786537662
File "/home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py", line 171, in run_training
varlist1 = list(set(weights.values() + biases.values()) - set(varlist2))
TypeError: unsupported operand type(s) for +: 'dict_values' and 'dict_values'

Hi ,change to:
varlist1 = list( set(list(weights.values()) + list(biases.values())) - set(varlist2) )
and see if it work

Hello
Thanks the issue is resolved now.But
ValueError: Cannot feed value of shape (10, 0) for Tensor u'Placeholder:0', which has shape '(?, 16, 128, 128, 3)'
Can you please suggest what could be the problem?

@abhisheksurya578 Can you post the whole terminal output?
And I think this issue is something about py3 and py2 .Authers's code was base on py2 , however you did this on py3 . (Right? You can run it on py3 after changed some code also)

thanks for responding @highway007 , @hx173149
/home/ltroot/PycharmProjects/C3D/venv/bin/python /home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py
2019-03-18 05:26:10.050805: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2019-03-18 05:26:10.050833: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2019-03-18 05:26:10.050838: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2019-03-18 05:26:10.050843: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2019-03-18 05:26:10.050848: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Traceback (most recent call last):
File "/home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py", line 262, in
tf.app.run()
File "/home/ltroot/PycharmProjects/C3D/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "/home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py", line 259, in main
run_training()
File "/home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py", line 224, in run_training
labels_placeholder: train_labels
File "/home/ltroot/PycharmProjects/C3D/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/home/ltroot/PycharmProjects/C3D/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 975, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (20, 0) for Tensor 'Placeholder:0', which has shape '(20, 16, 112, 112, 3)'
help me to resolve this

Traceback (most recent call last):
File "/home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py", line 262, in
tf.app.run()
File "/home/ltroot/PycharmProjects/C3D/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "/home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py", line 259, in main
run_training()
File "/home/ltroot/data1/C3D-tensorflow-master/train_c3d_ucf101.py", line 224, in run_training
labels_placeholder: train_labels
File "/home/ltroot/PycharmProjects/C3D/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/home/ltroot/PycharmProjects/C3D/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 975, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (20, 0) for Tensor 'Placeholder:0', which has shape '(20, 16, 112, 112, 3)'
help me to resolve this

@abhisheksurya578 So did you run the demo with your own dataset? Can you show me lines of your train.list?

No I haven't tried on my own dataset.I have used ucf-101 and your
train.list and test.list from git repository.
On Thu, Mar 21, 2019 at 3:56 PM highway007 notifications@github.com wrote:

@abhisheksurya578 https://github.com/abhisheksurya578 So did you run
the demo with your own dataset? Can you show me lines of your train.list?
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@abhisheksurya578 My list isn't for ucf101,you should run ./list/**** like the readme to get it.(you should download the ucf101 dataset also.

Our Train.list
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c01 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c02 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c03 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c04 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c05 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c01 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c02 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c03 0
our Test.list
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c02 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c03 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c04 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c05 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c06 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g02_c01 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g02_c02 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g02_c03 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g02_c04 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c01 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c02 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c03 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c04 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c05 0
/ds_data/ucfimgs/ApplyEyeMakeup/v_ApplyEyeMakeup_g03_c06 0
We tried running convert_videos to imgs.sh but we  end up with
following error./list/convert_video_to_images.sh: line 25: ffmpeg:
command not foundrm: cannot remove 'ds_data/UCF-101//.avi': No such
file or directory
Inside the UCF-101 dataset every video file got erased and only
folder structure exists.
If we tried with existing train.list and test.list by modifying path
inside train.list and test.list paths.
2019-03-21 13:37:33.719790: I
tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device
(0): ,
WARNING:tensorflow:From
/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py:1266:
checkpoint_exists (from tensorflow.python.training.checkpoint_management)
is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
Traceback (most recent call last):
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 261, in
tf.app.run()
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py",
line 125, in run
_sys.exit(main(argv))
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 258, in main
run_training()
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 223, in run_training
labels_placeholder: train_labels
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 929, in run
run_metadata_ptr)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1128, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (20, 0) for Tensor 'Placeholder:0',
which has shape '(20, 16, 112, 112, 3)'
On Thu, 21 Mar 2019 at 5:39 PM, highway007 notifications@github.com wrote:

@abhisheksurya578 https://github.com/abhisheksurya578 My list isn't for
ucf101,you should run ./list/**** like the readme to get it.(you should
download the ucf101 dataset also.
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@abhisheksurya578 hi, you should make sure that ./list/convert_video_to_images.sh *** 5 is the same name with your ucf101 file.It is a issue that path in your list is different from path in error.suggest you check file name carefully.

Hello I’m done with running video to img and img to list.sh files.But while
running train.py I’m facing this issue
tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device
(0): ,
WARNING:tensorflow:From /root/PycharmProjects/c3d/venv/lib/python3.5/site-
packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from
tensorflow.python.training.checkpoint_management) is deprecated and will be
removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
Traceback (most recent call last):
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 261, in
tf.app.run()
File "/root/PycharmProjects/c3d/venv/lib/python3.5/site-
packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 258, in main
run_training()
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 223, in run_training
labels_placeholder: train_labels
File "/root/PycharmProjects/c3d/venv/lib/python3.5/site-
packages/tensorflow/python/client/session.py", line 929, in run
run_metadata_ptr)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1128, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (20, 0) for Tensor 'Placeholder:0',
which has shape '(20, 16, 112, 112, 3)
On Fri, 22 Mar 2019 at 7:23 AM, highway007 notifications@github.com wrote:

@abhisheksurya578 https://github.com/abhisheksurya578 hi, you should
make sure that ./list/convert_video_to_images.sh *** 5 is the same name
with your ucf101 file.It is a issue that path in your list is different
from path in error.suggest you check file name carefully.
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@abhisheksurya578 Can you see the images from the path in your train.list?

No I can just see the image paths not images
On Fri, 22 Mar 2019 at 5:31 PM, highway007 notifications@github.com wrote:

@abhisheksurya578 https://github.com/abhisheksurya578 Can you see the
images from the path in your train.list?
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@abhisheksurya578 if you run video to img and img to list.sh successfully,you can see images with the path in train.list.

Yeah I successfully ran video to img and img to list.sh but I’m facing the
issue
On Fri, 22 Mar 2019 at 5:37 PM, highway007 notifications@github.com wrote:

@abhisheksurya578 https://github.com/abhisheksurya578 if you run video
to img and img to list.sh successfully,you can see images with the path in
train.list.
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@abhisheksurya578 There must be some error when you run .sh :(

Hello ,
i have successfully trained UCF for 101 classes.But I just wanna train for
16 classes.I have changed the parameter no of classes to 16.It worked when
i make use_pretrain=FALSE.
If i use sports_1m  fine tune model weights I got the following error.
How to use sports1m for 16 classes and Is there any parameter we need to
change for 16 classes.
Use standard file APIs to check for files with this prefix.
Traceback (most recent call last):
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1334, in _do_call
return fn(*args)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1319, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign
requires shapes of both tensors to match. lhs shape= [4096,16] rhs shape=
[4096,101]
[[{{node save/Assign_21}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 1276, in restore
{self.saver_def.filename_tensor_name: save_path})
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 929, in run
run_metadata_ptr)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1152, in _run
feed_dict_tensor, options, run_metadata)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1328, in _do_run
run_metadata)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/client/session.py",
line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign
requires shapes of both tensors to match. lhs shape= [4096,16] rhs shape=
[4096,101]
[[node save/Assign_21 (defined at
/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py:197) ]]
Caused by op 'save/Assign_21', defined at:
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 261, in
tf.app.run()
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py",
line 125, in run
_sys.exit(main(argv))
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 258, in main
run_training()
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 197, in run_training
saver = tf.train.Saver(list(weights.values()) + list(biases.values()))
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 832, in init
self.build()
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 844, in build
self._build(self._filename, build_save=True, build_restore=True)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 881, in _build
build_save=build_save, build_restore=build_restore)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 513, in _build_internal
restore_sequentially, reshape)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 354, in _AddRestoreOps
assign_ops.append(saveable.restore(saveable_tensors, shapes))
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saving/saveable_object_util.py",
line 73, in restore
self.op.get_shape().is_fully_defined())
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py",
line 223, in assign
validate_shape=validate_shape)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/ops/gen_state_ops.py",
line 64, in assign
use_locking=use_locking, name=name)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py",
line 788, in _apply_op_helper
op_def=op_def)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py",
line 507, in new_func
return func(*args, **kwargs)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py",
line 3300, in create_op
op_def=op_def)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py",
line 1801, in init
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Assign requires shapes of
both tensors to match. lhs shape= [4096,16] rhs shape= [4096,101]
[[node save/Assign_21 (defined at
/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py:197) ]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 261, in
tf.app.run()
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py",
line 125, in run
_sys.exit(main(argv))
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 258, in main
run_training()
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 206, in run_training
saver.restore(sess, model_filename)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 1312, in restore
err, "a mismatch between the current graph and the graph")
tensorflow.python.framework.errors_impl.InvalidArgumentError: Restoring
from checkpoint failed. This is most likely due to a mismatch between the
current graph and the graph from the checkpoint. Please ensure that you
have not altered the graph expected based on the checkpoint. Original error:
Assign requires shapes of both tensors to match. lhs shape= [4096,16] rhs
shape= [4096,101]
[[node save/Assign_21 (defined at
/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py:197) ]]
Caused by op 'save/Assign_21', defined at:
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 261, in
tf.app.run()
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/platform/app.py",
line 125, in run
_sys.exit(main(argv))
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 258, in main
run_training()
File "/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py",
line 197, in run_training
saver = tf.train.Saver(list(weights.values()) + list(biases.values()))
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 832, in init
self.build()
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 844, in build
self._build(self._filename, build_save=True, build_restore=True)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 881, in _build
build_save=build_save, build_restore=build_restore)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 513, in _build_internal
restore_sequentially, reshape)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saver.py",
line 354, in _AddRestoreOps
assign_ops.append(saveable.restore(saveable_tensors, shapes))
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/training/saving/saveable_object_util.py",
line 73, in restore
self.op.get_shape().is_fully_defined())
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/ops/state_ops.py",
line 223, in assign
validate_shape=validate_shape)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/ops/gen_state_ops.py",
line 64, in assign
use_locking=use_locking, name=name)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py",
line 788, in _apply_op_helper
op_def=op_def)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py",
line 507, in new_func
return func(*args, **kwargs)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py",
line 3300, in create_op
op_def=op_def)
File
"/root/PycharmProjects/c3d/venv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py",
line 1801, in init
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Restoring from checkpoint
failed. This is most likely due to a mismatch between the current graph and
the graph from the checkpoint. Please ensure that you have not altered the
graph expected based on the checkpoint. Original error:
Assign requires shapes of both tensors to match. lhs shape= [4096,16] rhs
shape= [4096,101]
[[node save/Assign_21 (defined at
/home/ltroot/Downloads/C3D-tensorflow-master/train_c3d_ucf101.py:197) ]]
Please help
On Fri, Mar 22, 2019 at 12:42 PM highway007 notifications@github.com
wrote:

@abhisheksurya578 https://github.com/abhisheksurya578 There must be
some error when you run .sh :(
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/hx173149/C3D-tensorflow/issues/98#issuecomment-475607093,
or mute the thread
https://github.com/notifications/unsubscribe-auth/As_iWkH3EYXAVrSUFqOMad8JCxHJLNEfks5vZM_NgaJpZM4boaTy
.

Cannot feed value of shape (11, 16, 160, 160, 3) for Tensor 'Placeholder:0', which has shape '(12, 16, 160, 160, 3)'
What should I do about it?help me,thanks


text:
Take Picture and Send to server?

Is there anyway that we can use takePicture and send it back to the server. We are controlling the drone, but want to be able to take a picture from the drone and send it to a visual recognition API so if we can get it back to the server where we are controlling the drone we can do the rest.

Hi @clokam you could certainly use that API call, and then subsequently use the FTP protocol to download the image file to the computer from the drone. Of course, you would need to either use a bridge or change the drone to connect in infrastructure mode to be able to access both the drone and the Internet connection from the computer.
Hope that helps!

I am kind of new to this library and hence was slightly confused. Once we take the picture, how do we identify from internal memory that it was the picture taken just now. What sort of data structure is used to store the image?
Secondly, is there any documentation around the FTP protocol for the drone and how we can enable infrastructure mode so that we are connected to the drone and the Internet?
Your help is much appreciated!

The images are just normal jpg files. There is no special info about the FTP server on the drone, it is also just normal FTP.
Hope that helps!

authors: clokam deadprogram

repo: hybridgroup/node-bebop


text:
[2.x] Breaking: Change configuration option docs.sidebar_order to docs.sidebar.order

Abstract
This changes the configuration option docs.sidebar_order to docs.sidebar.order in order to normalize the configuration API. It targets HydePHP v2.x via https://github.com/hydephp/develop/pull/1568.
Upgrade guide
Move the sidebar_order option's array in the config/docs.php file into the sidebar array in the same file.
Before
return [
  'sidebar' => [
      // ...
  ],

  'sidebar_order' => [
      'readme',
      'installation',
      'getting-started',
  ],
];

After
return [
  'sidebar' => [
      // ...

    'order' => [
        'readme',
        'installation',
        'getting-started',
    ],
  ],
];

Just for everyone reading this, just know that this one hurt. I don't like making breaking changes, especially ones with as high impact as this one. I am really sorry, but I do think it's necessary to improve the codebase.

repo: hydephp/develop


text:
(indev branch) entrypoint.js crashes on older node versions

In entrypoint.js it has a message saying that it is not supported by versions < 0.12.7. But even on version 0.12.7, it crashes on this file as it uses the new ES6 template literal for the multiline string. Requiring 'lib/index.js' manually still works as intended as it is all compiled into ES5 past that point.
(audio playback doesn't work properly for me on node >=v4, so I have to stick to 0.12.x)

@nicholastay It's not a bug, it's intentional. 0.12.7 isn't supported.
https://github.com/hydrabolt/discord.js/blob/master/entrypoint.js#L20
Audio playback works fine on 4.x and higher. Your problem must originate from somewhere else.

I am on 0.12.9 now and it still doesn't work. If it is intentional then maybe you should raise the version required even higher? (indev branch I was saying, as template literals don't work on 0.12.x afaik)

Oh, you mean LN22-26 of the indev entrypoint.js is crashing node higher than 0.12.7 and lower than 4.x... I get it now! Yeah nice catch.

Whoever decided to add a node >= 4 feature into the node < 4 checker (facepalm)

Yea sorry about that one, I forgot that entrypoint is not babel'd.

Fixed in 3614b7897312f803f35c15144843a13f7e0c964b

authors: SimonSchick TehSeph abalabahaha hydrabolt nicholastay


text:
File upload and delete work differently in REST API and UI

@dtarb @pkdash @hyi The functions for file upload send different signals depending upon whether one is using the REST API or UI. The procedures should be the same. Notably, an important post-upload signal is skipped when using the REST API, which causes default logical files to fail to be created for composite resources.

@alvacouch

Notably, an important post-upload signal is skipped when using the REST API, which causes default logical files to fail to be created for composite resources

Which specific REST endpoint is missing the post file upload signal? Both resource create and file add rest endpoints seem to have this signal.

@alvacouch Is this still an issue? Please comment or close.

@alvacouch Is this still an issue? Please comment or close.

I am unaware of any fix of this problem. I have reviewed the REST API and its relationship with the UI in the more recent past, but this needs to be checked upon again.

@sblack-usu can you please look at this and comment or close if not needed or addressed.

adding @sblack-usu as this pertains to HS Core Services. Please review and comment or close if not needed or addressed.

Both the UI and API endpoints use resource_file_add_process which includes the noted post-process signal with a file add.  For file delete, both the UI and API endpoints use delete_resource_file which includes the post-process signal as well.

authors: Castronova alvacouch dtarb eclark-cuahsi pkdash sblack-usu

repo: hydroshare/hydroshare


text:
1515 Update database constraints for access control to match UI

@pkdash @hyi @dtarb @mauriel133 @horsburgh
Over a year ago, we changed the basic semantics of access control so that there could be at most one privilege record for

each user over a resource or a group, and
each group over a resource.

The reason for this rule was to ensure that there is always a unique grantor for a privilege. This was a UI-driven decision.
We implemented this mostly via modifications of the user interface. The capability remained -- in the access control system -- to violate this decision. So, enforcement was left to the UI layer. This is bad. Now, with the REST interface being developed, it is more important to disallow undesirable things at the lowest possible level.
This mini pull-request moves enforcement of these restrictions to the database layer. It modifies the database constraints so that the database will not accept non-conforming records, and modifies the access control code so that it will also refuse to construct invalid records.
The purpose of making this change now is that it makes it impossible for me -- when writing access control REST interfaces -- to mess up and violate the rules. This is really important because violating these particular rules would mess up the UI for the resource in question and might even crash it!
In theory, this PR doesn't need a data migration, because the UI is dependent upon not violating these constraints. However, I do not know whether any older records lurk in the system that violate the convention. If so, they were created before the UI was complete. But -- more importantly -- their existence will break the UI for the affected resource. So it is rather important to get rid of them if they exist.
@mjstealey I need some help with this PR, because I do not know if there are any errant access control records in the production database. If not, this PR will deploy properly; if so, I will need to write a simple data migration. Could you please do a test deploy and determine whether the constraint is violated by the production instance?

Note that this also fixes one bug in access control logic. In the original code, when the access control information was changed, the grantor remained the original grantor. This is a mistake! Now the grantor changes to the new user who changed the privilege. This should not affect anything in the UI.

This PR relates to issue #1515 .

@alvacouch If we are removing the 'grantor' from the unique constraints in some of these model classes, then we should be also be removing the use of 'grantor' in filtering objects in many place in the access control code.

@mjstealey to test against production database to determine if there is migration needs

@pkdash @dtarb See issue #1591 for what needs to be done to apply this change consistently throughout the whole access control system. Meanwhile, the system works, and there is no need to act on this quickly. I would prefer to put my attention to the REST API first and work on this after AGU.

I will wait for @mjstealey to deploy this work to see there are no migration issues. Then I can +1.

I will wait for @mjstealey to deploy this work to see there are no migration issues

Looks like we're hitting duplicates in the existing production database...
Snippet from the console output when building branch using copy of www database.
Operations to perform:
  Synchronize unmigrated apps: inplaceeditform, accounts, rest_framework, grappelli_safe, corsheaders, boot, jquery_ui, haystack, crispy_forms, postgres, hs_metrics, staticfiles, filebrowser_safe, sitemaps, gis, hs_sitemap, mobile, autocomplete_light, debug_toolbar, widget_tweaks, irods_browser_app, django_nose, heartbeat
  Apply all migrations: redirects, theme, ga_resources, galleries, hs_geo_raster_resource, hs_geographic_feature_resource, conf, ga_ows, hs_labels, forms, generic, hs_model_program, sites, blog, contenttypes, hs_tools_resource, hs_access_control, hs_app_netCDF, hs_app_timeseries, hs_modelinstance, core, ref_ts, django_comments, sessions, hs_swat_modelinstance, auth, hs_script_resource, django_docker_processes, pages, hs_collection_resource, oauth2_provider, hs_modflow_modelinstance, hs_tracking, admin, hs_core, django_irods
Synchronizing apps without migrations:
  Creating tables...
    Running deferred SQL...
  Installing custom SQL...
Running migrations:
  Rendering model states... DONE
  Applying hs_access_control.0015_auto_20161110_1812...Traceback (most recent call last):
  File "manage.py", line 10, in <module>
    execute_from_command_line(sys.argv)
  File "/usr/local/lib/python2.7/site-packages/django/core/management/__init__.py", line 354, in execute_from_command_line
    utility.execute()
  File "/usr/local/lib/python2.7/site-packages/django/core/management/__init__.py", line 346, in execute
    self.fetch_command(subcommand).run_from_argv(self.argv)
  File "/usr/local/lib/python2.7/site-packages/django/core/management/base.py", line 394, in run_from_argv
    self.execute(*args, **cmd_options)
  File "/usr/local/lib/python2.7/site-packages/django/core/management/base.py", line 445, in execute
    output = self.handle(*args, **options)
  File "/usr/local/lib/python2.7/site-packages/django/core/management/commands/migrate.py", line 222, in handle
    executor.migrate(targets, plan, fake=fake, fake_initial=fake_initial)
  File "/usr/local/lib/python2.7/site-packages/django/db/migrations/executor.py", line 110, in migrate
    self.apply_migration(states[migration], migration, fake=fake, fake_initial=fake_initial)
  File "/usr/local/lib/python2.7/site-packages/django/db/migrations/executor.py", line 148, in apply_migration
    state = migration.apply(state, schema_editor)
  File "/usr/local/lib/python2.7/site-packages/django/db/migrations/migration.py", line 115, in apply
    operation.database_forwards(self.app_label, schema_editor, old_state, project_state)
  File "/usr/local/lib/python2.7/site-packages/django/db/migrations/operations/models.py", line 359, in database_forwards
    getattr(new_model._meta, self.option_name, set()),
  File "/usr/local/lib/python2.7/site-packages/django/db/backends/base/schema.py", line 324, in alter_unique_together
    self.execute(self._create_unique_sql(model, columns))
  File "/usr/local/lib/python2.7/site-packages/django/db/backends/base/schema.py", line 111, in execute
    cursor.execute(sql, params)
  File "/usr/local/lib/python2.7/site-packages/django/db/backends/utils.py", line 79, in execute
    return super(CursorDebugWrapper, self).execute(sql, params)
  File "/usr/local/lib/python2.7/site-packages/django/db/backends/utils.py", line 64, in execute
    return self.cursor.execute(sql, params)
  File "/usr/local/lib/python2.7/site-packages/django/db/utils.py", line 98, in __exit__
    six.reraise(dj_exc_type, dj_exc_value, traceback)
  File "/usr/local/lib/python2.7/site-packages/django/db/backends/utils.py", line 64, in execute
    return self.cursor.execute(sql, params)
django.db.utils.IntegrityError: could not create unique index "hs_access_control_userresourcepri_user_id_424814b34310c9d3_uniq"
DETAIL:  Key (user_id, resource_id)=(846, 2728) is duplicated.

@alvacouch It seems there are migration issues with this PR.

@mjstealey @pkdash @dtarb This is truly bad. Will create simple data migration to deal with it and post to this PR. (This data will break the UI on resources with the migration problem.)

@pkdash @mjstealey Data migration code has been uploaded to this PR. Also, redundant aggregation has been removed as specified in issue #1591 . Please review while I test.

The only other thing to do with access control in issue #1591 is to remove undo_share_* routines that depend upon multiple grantor semantics. These conditions should be handled in unshare_*. I note that undo_share is called in one place: hs_core/views/init.py. This is probably erroneous and is likely the cause of issue #1147.

@mauriel133 I am looking at issue #1147. Exactly what does the UI think a user can do in changing a privilege for something that the user doesn't own? I will match the access control system to your thinking.

@pkdash @mjstealey Some tests aren't passing for the new code. Will fix and repost.

@pkdash @mjstealey All better now. Stupid typo in get_effective_privilege.....

@mjstealey @pkdash I am still verifying the migration against the production database. I have gotten it to complete but am in the process of printing what it does so that I can interpret that.

@pkdash @mjstealey Current status:

the data migration seems to work well.
I have addressed most issues in #1591 except for the last one: deprecating (can_)?undo_share_* and removing it. See the end of that issue for a detailed discussion.
I am writing a small number of tests of the revised semantics.
I am working on addressing other access control issues now while I am up to speed on the code.

Debugging the data migration was really difficult. See issue #1613. I have more data migrations to do before this is over. I need a better way to debug them.

@pkdash @mjstealey @mauriel133 I am now making an attempt -- in this PR -- to delete all undo methods and patch them out of the UI code. This only affects hs_core/views/__init__.py. However, you will have to be the judge of whether this works in the UI or not. I don't think I will be breaking anything.

Summary of changes to address #1591, #1616, #1147 :

data migration eliminates duplicates in sharing records ( #1616  )
eliminated can_undo_share_*, undo_share_* routines that exhibit damaging semantics now (#1591). No remaining routines use the grantor field as anything more than information.
deleted reference to undo_share_resource_with_user in hs_core/views/__init__.py.
modified share_resource_with_user, share_group_with_user to eliminate grantor semantics and enable self-downgrading of privilege (issues #1147, #1591 ).  This also replaces what the reference to undo_share_resource_with_user should have done in hs_core/views/__init__.py. Revised logic for higher clarity.
modified can_share_resource, can_share_group to add optional "user" parameter for more specific checking. This is a simple enhancement that does not affect existing use.
Simple new routines can_share_resource_with_user, can_share_group_with_user are friendly interfaces to can_share_resource, can_share_group with specific target user checking. These are parallel to can_share_resource_with_group in existing API.
added __str__ methods to UserResourcePrivilege, UserGroupPrivilege, and GroupResourcePrivilege to aid in debugging.

I have also created new tests. Some of these are failing. I will post a final version when they work.

@pkdash OK, tests are working and confidence is high that code is in final form. Could you start reviewing code while I write a few more sanity check tests?

Additionally, I might add that I do have the code written for a working "undo" function whenever people might want that. This code has no resemblance to current *\_undo\_* routines and depends upon access control provenance tables UserResourceProvenance, UserGroupProvenance, and GroupResourceProvenance.
Hence it is better to delete the erronious routines and then replace them completely later.
Too much work to do this before AGU.

@pkdash I think this is everything. Pending passing Jenkins, it's ready for review and merge.

@alvacouch I am now done with 1st round of review. Unit tests look pretty good.

@pkdash Ready for second round of review:

All comments addressed except idempotence of owner actions. That might break the UI and we can't afford that at this point. We don't have time to test it before AGU.
__check_share_X_with_Y contains all exception raising code and is used in both can_share_X_with_Y and share_X_with_Y where X/Y = resource/user, resource/group, group/user.
User sharing of resources with user limited to user privilege (ignoring group privilege) as discussed in Issue #1622 in preparation for future group-to-group sharing functions. see new routines ResourceAccess.get_effective_user_privilege and ResourceAccess.get_effective_group_privilege for details, and then check out the code in UserAccess.__check_share_resource_with_user for how they're used.  I do not think this will break the UI. It can be easily undone but now's the time to fix this if possible.
All code in hs_access_control passes flake8; flake8 exemptions turned off.

@dtarb @pkdash @mauriel133
One strangeness remains in this version.

Users can only share with other users when they hold user privilege, but
Users with group privilege can use that to share with other groups.

This is a backdoor that allows someone to share any resource with a group, by simply creating a group of one, sharing the resource with that group, and then sharing that with another group, e.g.,
For example, if

Morris creates group Cats
Morris puts Sylvester into group Cats
Morris shares CatFood with Cats.
Sylvester gets put into group Dogs by someone.

Then Sylvester can share CatFood with the group Dogs because of Sylvester's membership in Cats, even though Sylvester -- without that group membership -- cannot do that.
Should users only be able to share resources with groups based upon user privilege?

If so, this weirdness is prevented but the system is more limited.
If not, this weirdness persists and there's an end-run around the limitation of not sharing a resource with a group based upon group privilege.

This is an easy fix.

I have undone the limitation of not using group privilege to grant individual privilege. See Issue #1622 for details and resolution to be implemented after AGU.

@alvacouch Just have a small comment. See if that needs to be fixed. Otherwise, +1 from me.

@mjstealey @pkdash Deleted irrelevant comment. Ready to merge. Michael, you might want to merge this one.

Michael, you might want to merge this one

@alvacouch - Will do, once the Jenkins check comes back happy I'll merge it.

authors: alvacouch mjstealey pkdash


text:
Basic Configuration

Basic Configuration
Description
Project URL

checked

checked


text:
Can't set full path to audio file on iOS

react-native-audio-recorder-player 3.0.2
In 2.x version it was possible to specify full path to playing/recording audio file with file:// prefix. In version 3.x only cache directory is supported.
2.x: https://github.com/hyochan/react-native-audio-recorder-player/blob/d780d7fc4bdafbb2d5612a8bdb3fb0efe20592db/ios/RNAudioRecorderPlayer.m#L144-L149
3.x: https://github.com/hyochan/react-native-audio-recorder-player/blob/7d6e3bd6d11430ae04544cb5f0ed1e864d8f26d7/ios/RNAudioRecorderPlayer.swift#L41-L46

Try to use the rn fetch blob as provided in the readme

I'm not sure it can help to record audio to specific file. Of course as workaround I can record audio to cache directory and then move the file to directory I want but it is ugly solution.

@farwayer I understand your point. How do you like the PR in #301? I'll release this to 3.0.3.

@hyochan #301 has issue and raises an error https://github.com/hyochan/react-native-audio-recorder-player/issues/304
Correct change will be
-        } else if (path.contains("http")){
+        } else if (path.hasPrefix("http://") || path.hasPrefix("https://") || path.hasPrefix("file://")){

Or event better:
} else if (path.range(of: #"\w*://"#, options: .regularExpression)) {

Thanks for the update. I am not sure why in my env, error did not occur.
Also, I'd prefer the first one since the regular expression is not readable to everyone.
Next time please create a PR.

authors: farwayer hyochan


text:
Remove broken state first feature

Since hyperapp/router relies on History API especially "popstate" event, developers must add an event listener to window. In general, calling location.subscribe() acts for the purpose, but the function needs location.actions as an argument. This means that developers must wire location slice to actions, and to make matters worse location.actions updates state.location not window.location so hyperapp/router does not use the window.location as source of truth in this situation.
Consequently, there is no way to skip wiring the location slice to state and actions.
Another solusion is to provide location.subscribeWindowWithoutActions() but, in my opinion, it should be implemented as another npm package.

I think we introduced a bug here by using state.location instead of props.location.

Consequently, there is no way to skip wiring the location slice to state and actions.

Yes, to use the router we need to wire the router's state and actions slice as usual — what I want to avoid is passing window.location as a prop to every component.

Available as 0.6.0. 👌

Thank, you @yuku-t! 🎉 🎉

I added this functionality only for "compatibility" purpose, I mean to let the use of the previous behavior.

authors: Swizz jorgebucaran yuku-t

repo: hyperapp/router


text:
fix: check whether artifacts are listed more than once in repo

This will check both if addresses exist in the same file more than once, as well as if addresses are in multiple project files

@ccerv1 check out these logs to see which addresses are currently duplicated
https://github.com/hypercerts-org/oss-directory/actions/runs/6087494688/job/16516155965?pr=42
Feel free to just commit fixes directly to this branch until the CI passes

After rebasing on top of this migration
https://github.com/hypercerts-org/oss-directory/pull/44
We have a much more tractable list of duplicates that need resolving.
@ccerv1 could I get your help with this?

repo: hypercerts-org/oss-directory


text:
[QUESTION] MQ生产者初始化的时候如何绑定队列

Before you submit this issue, you has been search all existed issues and search the documentation
我们的系统是多语言实现的, 其中一项业务是php作为生产者, 向队列写入数据. java作为生产者消费队里.  所以hyperf里只有生产者代码, 并没有Consumer的代码. 那么hyperf在启动的时候, 生产者只有指定exchange和routeKey以及type. 那么队列怎么初始化呢?

生产者声明了 exchange type 和 routingKey 不就可以了。。
消费者启动的时候，再去绑定他的 queue 就好了啊

这就有一个先后顺序问题了, 在消费者尚未启动的时候,  那我生产者就没法往队列丢数据了.

所以我的想法是, 生产者初始化的时候 , 也同时能够绑定队列, 这样我就不依赖消费者什么时候消费数据了. 我只管保证我生产者的数据能够丢进队列即可

你这个想法不对啊。。如果你绑定的队列和实际消费的队列不是一个，那么这个队列的数据会越来越多。我还是认为投递是投递，如果没有队列消费，rabbitmq把消息扔掉就好了，等什么时候需要消费的，再来绑定。

那例如, 我有一个 订单的发货通知要推送到三方系统中, 由于这时候消费者队列没有绑定到routekey, 生产者在投递消息的时候,  也许并不知道此情况. 那么就导致此条消息扔掉了. 对于这种情况, 那我是不是还要自己额外做补偿机制和三方系统来互相确认消息的消费结果呢, 这样做虽然可以解决消息最终消费正确的结果, 但是也增加了很多额外的业务逻辑.

但如果生产者可以帮routekey和queue初始化一次生成, 那么生产者方和消费者方只需要约定好队列名称和routekey, 就可以解决了. 少了很多补偿机制的逻辑.  我是这么认为的. 望指教 -_-!

你消费者都没有上啊。。肯定是不会发到第三方系统里的。如果你实在需要类似功能，可以写一个脚本，系统上线后，主动执行一次就行了

嗯. 我现在是自己写一个listener, 服务器起来的时候自动绑定上.

如果后续你们方案修改了，可以来更新一下issue
发自我的iPhone
------------------ 原始邮件 ------------------
发件人: 250121244 notifications@github.com
发送时间: 2020年1月1日 17:14
收件人: hyperf/hyperf hyperf@noreply.github.com
抄送: 李铭昕 l@hyperf.io, Comment comment@noreply.github.com
主题: 回复：[hyperf/hyperf] [QUESTION] MQ生产者初始化的时候如何绑定队列 (#1212)
嗯. 我现在是自己写一个listener, 服务器起来的时候自动绑定上.
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You are receiving this because you commented.
Reply to this email directly, view it on GitHub, or unsubscribe.

repo: hyperf/hyperf


text:
[BUG] metric 关闭启动服务报错

Hyperf Version: #.#.#
PHP Version: (php -v) #.#.#
Swoole Version: (php --ri swoole) #.#.#

Description:

Steps To Reproduce:

Hyperf Version: #.#.#
PHP Version: (php -v) #.#.#
Swoole Version: (php --ri swoole) #.#.#

Description:

Steps To Reproduce:

需要提供一个总开关


text:
try catch中如何捕获异常??

控制器push了一个异步任务,在job中执行:
public function handle()
    {
        //下载图片-临时保存本地-上传至oss
        try {
            $this->clientFactory = ApplicationContext::getContainer()->get(ClientFactory::class);
            $client = $this->clientFactory->create();
            co(function () use ($client) {
                $channel = new Channel();
                $result = $client->get('https://oscimg.oschina.net/oscnet/up-3f535bd67c4744f7e08e08283146bf10.jpg');
                $imageRaw = $result->getBody()->getContents();
                file_put_contents('/tmp/a.jpg', $imageRaw);

                $channel->push(['origImage' => '/tmp/a.jpg']);
                co(function () use ($channel) {
                    $data = $channel->pop();
                    //if (!$origPic = $data['origImage']) {
                    if (!$origPic = $data['origImage'] || 1) { //手动抛出异常
                        throw new \Exception('图片读取失败[1],请重试');
                    }
                    //todo 同步到oss
                    echo '本地临时图片的路径是:' . $origPic;
                });
            });
        }catch (\Exception $exception){
            $message = $exception->getMessage();
            echo  $message;
            //捕获异常,记录日志到数据库 TODO
        }

    }

结果打印到了控制台里面,并没有在内部捕获,我需要捕获到这些异常来做数据库操作.....

协程里面捕获异常，协程外无法捕获协程内的异常。


text:
[BUG] 定时任务不执行

Linux 9da637e69dcc 3.10.0-1160.el7.x86_64 #1 SMP Mon Oct 19 16:18:59 UTC 2020 x86_64 Linux
PHP 8.1.1 (cli) (built: Jun 29 2022 21:16:09) (NTS)
Copyright (c) The PHP Group
Zend Engine v4.1.1, Copyright (c) Zend Technologies
    with Zend OPcache v8.1.1, Copyright (c), by Zend Technologies
96qbhy/hyperf-auth                v3.0.1  hyperf 的 auth 组件
alapi/hyperf-meilisearch          3.0.0   meilisearch for hyperf php
friendsofhyperf/lock              v3.0.40 The lock component for Hyperf.
hyperf/amqp                       v3.0.16 A amqplib for hyperf.
hyperf/async-queue                v3.0.16 A async queue component for hyperf.
hyperf/cache                      v3.0.17 A cache component for hyperf.
hyperf/collection                 v3.0.0  Hyperf Collection package which come from illuminate/collections
hyperf/command                    v3.0.17 Command for hyperf
hyperf/conditionable              v3.0.0  Hyperf Macroable package which come from illuminate/conditionable
hyperf/config                     v3.0.17 An independent component that provides configuration container.
hyperf/config-aliyun-acm          v3.0.8  An aliyun acm adapter for Hyperf config component.
hyperf/config-center              v3.0.16 The abstraction component of config center
hyperf/config-nacos               v3.0.16 A nacos adapter for config center component.
hyperf/constants                  v3.0.17 A constants component for hyperf.
hyperf/context                    v3.0.16 A coroutine/application context library.
hyperf/contract                   v3.0.0  The contracts of Hyperf.
hyperf/coordinator                v3.0.16 Hyperf Coordinator
hyperf/coroutine                  v3.0.0  Hyperf Coroutine
hyperf/crontab                    v3.0.17 A crontab component for Hyperf.
hyperf/database                   v3.0.17 A flexible database library.
hyperf/db-connection              v3.0.17 A hyperf db connection handler for hyperf/database.
hyperf/di                         v3.0.17 A DI for Hyperf.
hyperf/dispatcher                 v3.0.16 A HTTP Server for Hyperf.
hyperf/elasticsearch              v3.0.16 Elasticsearch client for hyperf
hyperf/engine                     v2.8.1  Coroutine engine provided by swoole.
hyperf/engine-contract            v1.7.0  Contract for Coroutine Engine
hyperf/event                      v3.0.0  an event manager that implements PSR-14.
hyperf/exception-handler          v3.0.17 Exception handler for hyperf
hyperf/filesystem                 v3.0.16 flysystem integration for hyperf
hyperf/framework                  v3.0.16 A coroutine framework that focuses on hyperspeed and flexible, specifically use for build microservices and middlewares.
hyperf/guzzle                     v3.0.17 Swoole coroutine handler for guzzle
hyperf/http-message               v3.0.16 microservice framework base on swoole
hyperf/http-server                v3.0.17 A HTTP Server for Hyperf.
hyperf/json-rpc                   v3.0.17 A JSON RPC component for Hyperf RPC Server or Client.
hyperf/load-balancer              v3.0.16 A load balancer library for Hyperf.
hyperf/logger                     v3.0.16 A logger component for hyperf.
hyperf/macroable                  v3.0.0  Hyperf Macroable package which come from illuminate/macroable
hyperf/memory                     v3.0.0  An independent component that use to operate and manage memory.
hyperf/model-cache                v3.0.17 A model cache component for hyperf.
hyperf/model-listener             v3.0.0  A model listener for Hyperf.
hyperf/nacos                      v3.0.15 Nacos SDK
hyperf/nsq                        v3.0.16 A nsq library for Hyperf.
hyperf/paginator                  v3.0.17 A paginator component for hyperf.
hyperf/pipeline                   v3.0.16 Hyperf Macroable package which come from illuminate/pipeline
hyperf/pool                       v3.0.16 An independent universal connection pool component.
hyperf/process                    v3.0.9  A process component for hyperf.
hyperf/redis                      v3.0.16 A redis component for hyperf.
hyperf/resource                   v3.0.17 A api resource component for hyperf.
hyperf/rpc                        v3.0.17 A rpc basic library for Hyperf.
hyperf/rpc-client                 v3.0.16 An abstract rpc server component for Hyperf.
hyperf/scout                      v3.0.16 Hyperf Scout provides a driver based solution to searching your Eloquent models. Inspired By Laravel Scout.
hyperf/server                     v3.0.17 A base server library for Hyperf.
hyperf/service-governance         v3.0.14 A service governance component for Hyperf.
hyperf/service-governance-nacos   v3.0.16 A nacos adapter for service governance.
hyperf/signal                     v3.0.16 A signal library for Hyperf.
hyperf/snowflake                  v3.0.0  A snowflake library
hyperf/stringable                 v3.0.17 Hyperf Stringable package which come from illuminate/support
hyperf/tappable                   v3.0.0  Hyperf Macroable package which come from illuminate/tappable
hyperf/translation                v3.0.16 An independent translation component, forked by illuminate/translation.
hyperf/utils                      v3.0.17 A tools package that could help developer solved the problem quickly.
hyperf/validation                 v3.0.17 hyperf validation
hyperf/websocket-server           v3.0.16 A websocket server library for Hyperf.
yansongda/hyperf-pay              v1.2.1  可能是基于 hyperf 的最优雅的支付宝、微信、银联支付 SDK 了

swoole

Swoole => enabled
Author => Swoole Team <team@swoole.com>
Version => 5.0.2
Built => Apr  6 2023 12:22:01
coroutine => enabled with boost asm context
epoll => enabled
eventfd => enabled
signalfd => enabled
spinlock => enabled
rwlock => enabled
openssl => OpenSSL 1.1.1o  3 May 2022
dtls => enabled
http2 => enabled
json => enabled
curl-native => enabled
pcre => enabled
zlib => 1.2.12
mutex_timedlock => enabled
pthread_barrier => enabled
async_redis => enabled

Directive => Local Value => Master Value
swoole.enable_coroutine => On => On
swoole.enable_library => On => On
swoole.enable_preemptive_scheduler => Off => Off
swoole.display_errors => On => On
swoole.use_shortname => Off => Off
swoole.unixsock_buffer_size => 8388608 => 8388608

定时任务不执行，只有在程序上线后执行过一次，然后后面都没有执行，也没有报错异常,
正常是要每半个小时执行一次，在 hyperf2.2 是正常的，然后升级了hyperf3, php8.1就不正常了
Steps To Reproduce:

肯定是执行的，我一直在用

不知道什么原因我这边是执行不正常的，只有启动后执行了一次

authors: anhao limingxinleo


text:
travis.ci: improve build options, add osx builds

1. Tell us something about your changes.
OSX won´t run currently. I need help to get a actual list of dep names.
the build still fails with qt5 not found. looks we need to tweak more, cause qt5 is installed. maybe not linked.

There is still a error not sure of this is fixed with the pr of @penfold42
We walking about arguments, can´t be that hard :)

of course penfolds pr is needed for that ...

authors: brindosch redPanther


text:
How to use hyper server with custom TLS/TCP/service handling

I apologize in advance, as I'm not sure the best way to describe this issue.
I'm using hyper's server, along with rustls. My service supports client certificates, and I need information about them in the server, so I had to write some pretty gnarly integration code: https://github.com/alex/ct-tools/blob/master/src/main.rs#L345-L438
This relied on hyper::server::Http::bind_connection, which has been removed in the latest version of hyper.
Is there a better way to connect hyper to my custom rustls server? As far as I can tell, there's no way to get the client certificate from a hyper request, so I still need the custom connector code.

You can get similar functionality by using Http::serve_connection.
See #1650 for the general issue of accessing the socket when constructing a Service.

Thanks!

authors: alex seanmonstar

repo: hyperium/hyper


text:
docs(service): add example of impl Service

Add examples/service_struct_impl.rs, which provides an up-to-date
example of implementing MakeService and Service on custom types.
The Svc struct has a Counter, which demonstrates how to instantiate
shared resources in the MakeSvc and pass the resource to the
services. Updates the examples/README.md and the doc in
src/service/mod.rs.
Closes #1691

@LucioFranco @seanmonstar apologies if I've missed parts of the process, but was hoping to get this reviewed. Let me know if I'm missing anything.

Sorry for the slow review, I've been on vacation :), I left some comments let me know if you have any questions.

Thanks for the feedback @LucioFranco ! Hope you had a good vacation. :)

Hmmm, tried to merge master and now ubuntu-latest is failing because of
##[error]failed to parse manifest at `/home/runner/.cargo/registry/src/github.com-1ecc6299db9ec823/pretty_env_logger-0.4.0/Cargo.toml`
Caused by:
  readme file with name 'README.md' was not found

Seems suspicious that this is a real issue - is there any way to kick off another run?

huh, I have not seen that error before, maybe retry the operation? We will have to look into why CI is failing.

It was a bug in cargo, it's been fixed and I gave CI a kick. All good, thanks so much!

authors: LucioFranco seanmonstar tkbrigham


text:
HealthService isn't public

Feature Request
As discussed on discord I'm attempting to route between two grpc services (one being the health service) and a http service. However, I've found I can't use the HealthService directly because it's not public https://github.com/hyperium/tonic/blob/master/tonic-health/src/server.rs#L108
Crates
tonic-health

Just making the struct pub isn't enough you'd also have to rework a bit of the API. I've done this locally and if it ends up being desired as a feature I can start a PR - I've not pushed anything yet because it's not currently working

This may be unnecessary, if #410 is solved this will be solved via that or this is unnecessary

Resolved via https://github.com/hyperium/tonic/pull/419.

repo: hyperium/tonic


text:
[701] add docker tag when pushing image to dockerhub

Monika Pull Request (PR)
What feature/issue does this PR add
Currently, we only have the latest tag in Dockerhub. When creating new release in the Dockerhub, we also need to create the version tag.
How did you implement / how did you fix it
By replacing the tag from latest to GITHUB_REF#refs/tags/, taken from the tag name the docker image is built.
How to test
Can only run when the DockerPush workflow is running on github

Can we push both latest and the version tags?

I think it won't work. You should follow this approach. Ideally, You should push 4 tags: latest, {major}, {major}.{minor}, and {major}.{minor}.{patch}. So that, all the users who use those tags will get the update.
For instance, if Monika releases version 1.2.3. You should push latest, 1, 1.2, and 1.2.3 tags.

I think it won't work. You should follow this approach. Ideally, You should push 4 tags: latest, {major}, {major}.{minor}, and {major}.{minor}.{patch}. So that, all the users who use those tags will get the update.
For instance, if Monika releases version 1.2.3. You should push latest, 1, 1.2, and 1.2.3 tags.

that's what I meant 😁

I think it won't work. You should follow this approach. Ideally, You should push 4 tags: latest, {major}, {major}.{minor}, and {major}.{minor}.{patch}. So that, all the users who use those tags will get the update.
For instance, if Monika releases version 1.2.3. You should push latest, 1, 1.2, and 1.2.3 tags.

I have applied this, please help check again.

authors: haricnugraha ilmiawan nicnocquee

repo: hyperjumptech/monika


text:
Add new org to the existing consortium

Description
As a developer, I would be able to add the new organization to the existing consortium.
Currently the new organization is only added to the existing channel.
Reference links

https://rangesh.medium.com/how-to-add-an-organization-dynamically-to-existing-consortium-hyperledger-fabric-36b4c923b937
https://medium.com/thinkdecentralized/updating-the-consortium-definition-in-hyperledger-fabric-d1b6a9d079b0

While fixing the issue, I realized that the functionality of adding a new organization to the network was not working. The error that was showing up was:  BAD_REQUEST -- error applying config update to existing channel 'allchannel': error authorizing update: error validating ReadSet: proposed update requires that key [Group]  /Channel/Application be at version 1, but it is currently at version 2.
This could be happening because the syschannel is not being updated / fetched when creating a new org, so new code will be developed for this to work correctly.

authors: alvaropicazo jagpreetsinghsasan


text:
Update invocation and execution diagrams

What this PR does / why we need it:

Revist based on HLGF meeting notes
Cleanup and printer friendly edits
Remove cc2cc calls from execution diagram
Resolve open questions regarding response encryption

Which issue(s) this PR fixes:
This PR addresses  #355
Special notes for your reviewer:
Does this PR introduce a user-facing changes and/or breaks backward compatability?:

Updated based on feedback from @g2flyer and recent discussion regarding component naming convention as discussed in https://github.com/hyperledger-labs/fabric-private-chaincode/pull/375_.
@bvavala Please consider for merging.

Updated based on feedback from @g2flyer and recent discussion regarding component naming convention as discussed in #375_.
@bvavala Please consider for merging.

You have a fixup commit! But Bruno obviously can also squash-merge (just needs some manual commit info cleanup but here it should be straight-forward).


text:
fix datarace

see https://github.com/hyperledger-labs/fabric-token-sdk/issues/142 for more details

LGTM

authors: adecaro mffrench

repo: hyperledger-labs/fabric-token-sdk


text:
Validation of anoncreds objects version

I was curious if there is any validation we have to do, could not find it in the spec, with the anoncreds objects version. Do we allow any string? If we have no intention of validation of versions this can be marked as completed.

What do you mean with validation of the anoncreds version? Where is this present? Do you have an example?

What do you mean with validation of the anoncreds version? Where is this present? Do you have an example?

Yeah should have elobaroted on this further, but I am talking about the version inside an anoncreds object like schema: (I thought it was present in more objects, but only schema it seems).
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
pub struct Schema {
    pub name: String,
    pub version: String, <----
    pub attr_names: AttributeNames,
    pub issuer_id: IssuerId,
}

Discussed on 2023.01.09 AnonCreds Spec Working Group Meeting -- add note in spec. about Indy using semver, but otherwise, it is just a string, and not validated in AnonCreds.

PR created

authors: TimoGlastra blu3beri rodolfomiranda swcurran

repo: hyperledger/anoncreds-spec


text:
Auto connect from author to endoposer on startup

Signed-off-by: Ian Costanzo ian@anon-solutions.ca
If the appropriate startup arguments are provided, auto-initiate the connection between the author and endorser on agent startup

Codecov Report

Merging #1461 (7000f8e) into main (ed9ab88) will decrease coverage by 0.08%.
The diff coverage is 18.91%.

@@            Coverage Diff             @@
##             main    #1461      +/-   ##
==========================================
- Coverage   95.81%   95.72%   -0.09%
==========================================
  Files         489      489
  Lines       30027    30061      +34
==========================================
+ Hits        28770    28777       +7
- Misses       1257     1284      +27

authors: codecov-commenter ianco

repo: hyperledger/aries-cloudagent-python