How to use huggingface concepts Or is there a guide doc of original IMG->pt->SD？

All your work is amazing!
I feel the guide in this rep is Incoherence. especially those "path/to" is Confusing.
If a female colleague asks to use her photo to generate a style for her after watching the pictures output by SD.What should I do?
So,If someone has a esay doc-guide with an example in ubuntu18/nvidiaA5000-24G?
Thanks!

If you want to use the huggingface concepts, you'll have to follow their colab / guide. This repo has no support for the huggingface concepts.
If you do use this repo, just put your images in a folder, for example: /home/inspire-boy/my_images/
And then point do them when you run the script by using --data_root /home/inspire-boy/my_images/

如果你想使用拥抱脸的概念，你必须遵循他们的 colab / guide。这个 repo 不支持拥抱脸的概念。
如果您确实使用此 repo，只需将您的图像放在一个文件夹中，例如：/home/inspire-boy/my_images/ 然后在运行脚本时使用--data_root /home/inspire-boy/my_images/

Thanks a lot! I will try it immediately.

如果你想使用拥抱脸的概念，你必须遵循他们的 colab / guide。这个 repo 不支持拥抱脸的概念。
如果您确实使用此 repo，只需将您的图像放在一个文件夹中，例如：/home/inspire-boy/my_images/ 然后在运行脚本时使用--data_root /home/inspire-boy/my_images/

Do you now this why this error appears?
python main.py --base configs/stable-diffusion/v1-finetune.yaml -t --actual_resume "models/ldm/text2img-large/model.ckpt" -n "liangbox" --data_root ../liangbox --init_word "box" --placeholder_tokens "liangbox"
output:
Global seed set to 23
Loading model from models/ldm/text2img-large/model.ckpt
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 4, 64, 64) = 16384 dimensions.
making attention of type 'vanilla' with 512 in_channels
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.23.self_attn.out_proj.bias', 'vision_model.encoder.layers.0.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.mlp.fc2.weight', 'visual_projection.weight', 'vision_model.encoder.layers.1.mlp.fc1.bias', 'vision_model.encoder.layers.9.mlp.fc1.bias', 'vision_model.encoder.layers.4.self_attn.k_proj.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.bias',......................
...............
............

This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Traceback (most recent call last):
File "main.py", line 614, in
model = load_model_from_config(config, opt.actual_resume)
File "main.py", line 29, in load_model_from_config
model = instantiate_from_config(config.model)
File "/root/textual_inversion/ldm/util.py", line 85, in instantiate_from_config
return get_obj_from_str(config["target"])(**config.get("params", dict()), **kwargs)
File "/root/textual_inversion/ldm/models/diffusion/ddpm.py", line 481, in init
self.init_from_ckpt(ckpt_path, ignore_keys)
File "/root/textual_inversion/ldm/models/diffusion/ddpm.py", line 205, in init_from_ckpt
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
File "/root/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1482, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for LatentDiffusion:
size mismatch for model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([320, 768])...............
..............
..........
) from checkpoint, the shape in current model is torch.Size([320, 768]).
size mismatch for model.diffusion_model.output_blocks.11.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([320, 768]).

During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "main.py", line 826, in
if trainer.global_rank == 0:
NameError: name 'trainer' is not defined

如果你想使用拥抱脸的概念，你必须遵循他们的 colab / guide。这个 repo 不支持拥抱脸的概念。
如果您确实使用此 repo，只需将您的图像放在一个文件夹中，例如：/home/inspire-boy/my_images/ 然后在运行脚本时使用--data_root /home/inspire-boy/my_images/

Do you now this why this error appears?
python main.py --base configs/stable-diffusion/v1-finetune.yaml -t --actual_resume "models/ldm/text2img-large/model.ckpt" -n "liangbox" --data_root ../liangbox --init_word "box" --placeholder_tokens "liangbox"
output:
Global seed set to 23 Loading model from models/ldm/text2img-large/model.ckpt LatentDiffusion: Running in eps-prediction mode DiffusionWrapper has 859.52 M params. making attention of type 'vanilla' with 512 in_channels Working with z of shape (1, 4, 64, 64) = 16384 dimensions. making attention of type 'vanilla' with 512 in_channels Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.23.self_attn.out_proj.bias', 'vision_model.encoder.layers.0.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.mlp.fc2.weight', 'visual_projection.weight', 'vision_model.encoder.layers.1.mlp.fc1.bias', 'vision_model.encoder.layers.9.mlp.fc1.bias', 'vision_model.encoder.layers.4.self_attn.k_proj.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.bias',...................... ............... ............

This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Traceback (most recent call last):
File "main.py", line 614, in
model = load_model_from_config(config, opt.actual_resume)
File "main.py", line 29, in load_model_from_config
model = instantiate_from_config(config.model)
File "/root/textual_inversion/ldm/util.py", line 85, in instantiate_from_config
return get_obj_from_str(config["target"])(**config.get("params", dict()), **kwargs)
File "/root/textual_inversion/ldm/models/diffusion/ddpm.py", line 481, in init
self.init_from_ckpt(ckpt_path, ignore_keys)
File "/root/textual_inversion/ldm/models/diffusion/ddpm.py", line 205, in init_from_ckpt
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
File "/root/miniconda3/envs/ldm/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1482, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for LatentDiffusion:
size mismatch for model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([320, 768])...............
..............
..........
) from checkpoint, the shape in current model is torch.Size([320, 768]).
size mismatch for model.diffusion_model.output_blocks.11.1.transformer_blocks.0.attn2.to_v.weight: copying a param with shape torch.Size([320, 1280]) from checkpoint, the shape in current model is torch.Size([320, 768]).

During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "main.py", line 826, in if trainer.global_rank == 0: NameError: name 'trainer' is not defined

Thanks!This is solved:
1、I leave --gpus item(shhold be --gpus 0)
2、 font error, solved by: apt install fonts-dejavu-core

Sounds good! Is everything resolved then? If so, please close the issue!

Okay!


Add LSB header

Latest Debian's init script requires LSB header, at least Jessie requires this.

Thank you so much for your contribution to enrich the SoftEther VPN source code.
Your patch has been merged on the main source-tree of SoftEther VPN.
As a token of our gratitude, your GitHub username has been added on the AUTHORS.TXT file and on the header of the related source file.
Please see: https://github.com/SoftEtherVPN/SoftEtherVPN/blob/master/AUTHORS.TXT
Thanks again for your contribution.


Can not download a model with current version

Hi guys,
when I run the client locally and select a model to download (e.g. T5-small, but I have tried different LLama2 models as well)
I am always running into this error:
Traceback (most recent call last):   File "C:\Users\adria\h2ogpt\generate.py", line 16, in <module>     entrypoint_main()   File "C:\Users\adria\h2ogpt\generate.py", line 12, in entrypoint_main     H2O_Fire(main)   File "C:\Users\adria\h2ogpt\src\utils.py", line 64, in H2O_Fire     fire.Fire(component=component, command=args)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\fire\core.py", line 141, in Fire     component_trace = _Fire(component, args, parsed_flag_args, context, name)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\fire\core.py", line 475, in _Fire     component, remaining_args = _CallAndUpdateTrace(   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\fire\core.py", line 691, in _CallAndUpdateTrace     component = fn(*varargs, **kwargs)   File "C:\Users\adria\h2ogpt\src\gen.py", line 1344, in main     max_seq_len = model_state0['tokenizer'].model_max_length AttributeError: 'NoneType' object has no attribute 'model_max_length'

Sorry was just fixed.  A test fount it too, but didn't complete testing by time the other fix led to this bug.

Hi @pseudotensor, just tried it with latest master branch and the h20ai/h20gpt-4096-llama2-7b-chat and the error is still the same

It's impossible to get the same error since the code changed, and it checks for that attribute.
If you give the full command I can run, but I don't think it can hit same issue.
E.g. if I run:
python generate.py --base_model=h2oai/h2ogpt-4096-llama2-7b-chat

there is no issue.  Your model name is not quite right with 0, but that won't mean it hits that error.

Correct, that works. I have not been starting the application from the command line but from inside my PyCharm editor. But now when I try to upload a document this error is happening:
to include: ['All'] or set allow_custom_value=True.   warnings.warn( Traceback (most recent call last):   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\queueing.py", line 388, in call_prediction     output = await route_utils.call_process_api(   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\route_utils.py", line 219, in call_process_api     output = await app.get_blocks().process_api(   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\blocks.py", line 1440, in process_api     data = self.postprocess_data(fn_index, result["prediction"], state)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\blocks.py", line 1341, in postprocess_data     prediction_value = block.postprocess(prediction_value)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\components\dropdown.py", line 248, in postprocess     [self._warn_if_invalid_choice(_y) for _y in y] TypeError: 'Dropdown' object is not iterable Traceback (most recent call last):   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\queueing.py", line 388, in call_prediction     output = await route_utils.call_process_api(   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\route_utils.py", line 219, in call_process_api     output = await app.get_blocks().process_api(   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\blocks.py", line 1440, in process_api     data = self.postprocess_data(fn_index, result["prediction"], state)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\blocks.py", line 1341, in postprocess_data     prediction_value = block.postprocess(prediction_value)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\components\dropdown.py", line 248, in postprocess     [self._warn_if_invalid_choice(_y) for _y in y] TypeError: 'Dropdown' object is not iterable Traceback (most recent call last):   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\queueing.py", line 388, in call_prediction     output = await route_utils.call_process_api(   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\route_utils.py", line 219, in call_process_api     output = await app.get_blocks().process_api(   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\blocks.py", line 1440, in process_api     data = self.postprocess_data(fn_index, result["prediction"], state)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\blocks.py", line 1341, in postprocess_data     prediction_value = block.postprocess(prediction_value)   File "C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\components\dropdown.py", line 248, in postprocess     [self._warn_if_invalid_choice(_y) for _y in y] TypeError: 'Dropdown' object is not iterable C:\Users\adria\.conda\envs\h2ogpt\lib\site-packages\gradio\components\dropdown.py:240: UserWarning: The value passed into gr.Dropdown() is not in the list of choices. Please update the list of choices to include: dropdown or set allow_custom_value=True.   warnings.warn(

Probably your dependencies are off.  Reinstall env or ensure your dependencies like gradio are right version.  Or you are doing something non-standard.


Missing target_include_directories to third_party/upb

What version of gRPC and what language are you using?
Latest release (v1.20.1)
C++
What operating system (Linux, Windows,...) and version?
CentOS 7
What runtime / compiler are you using (e.g. python version or version of gcc)
gcc 8.2.1 (devtoolset-8)
What did you do?
Build with CMake/Ninja with tests enabled.
What did you see instead?
Compile error about "cannot find upb/msg.h".
It is referenced by third_party/upb/upb/def.c but third_party/upb is not listed in target_include_directories():
https://github.com/grpc/grpc/blob/7741e806a213cba63c96234f16d712a8aa101a49/CMakeLists.txt#L5556-L5567
This issue can be worked around with a global -I to env var $CFLAGS/$CXXFLAGS or cmake var CMAKE_C{,XX}_FLAGS.

What is the status of this, @nicolasnoble ?

I think this was fixed


OSError Exception, [WinError 123] when training through Rasa NLU server

Rasa version:
0.14.6
Python version:
3.6.8
Operating system (windows, osx, ...):
Windows 10
Issue:
Training through the server fails.
I tried debugging using Web-PDB and here's what I get:
data = '{"rasa_nlu_data":{"common_examples":[{"intent":"greet","text":"hey","entities":[]},{"intent":"greet","text":"hello there","entities":[]},{"intent":"greet","text":"hi","entities":[]},{"intent":"greet","text":"hello there","entities":[]},{"intent":"greet","text":"good morning","entities":[]},{"intent":"greet","text":"good evening","entities":[]},{"intent":"greet","text":"mornin","entities":[]},{"intent":"greet","text":"hey there","entities":[]},{"intent":"greet","text":"let\'s go","entities":[]},{"intent":"greet","text":"hey dude","entities":[]},{"intent":"greet","text":"goodmorning","entities":[]},{"intent":"greet","text":"goodevening","entities":[]},{"intent":"greet","text":"good afternoon","entities":[]},{"intent":"goodbye","text":"cu","entities":[]},{"intent":"goodbye","text":"good by","entities":[]},{"intent":"goodbye","text":"cee you later","entities":[]},{"intent":"goodbye","text":"good night","entities":[]},{"intent":"goodbye","text":"good afternoon","entities":[]},{"intent":"goodbye","text":"bye","entities":[]},{"intent":"goodbye","text":"goodbye","entities":[]},{"intent":"goodbye","text":"have a nice day","entities":[]},{"intent":"goodbye","text":"see you around","entities":[]},{"intent":"goodbye","text":"bye bye","entities":[]},{"intent":"goodbye","text":"see you later","entities":[]},{"intent":"affirm","text":"yes","entities":[]},{"intent":"affirm","text":"indeed","entities":[]},{"intent":"affirm","text":"of course","entities":[]},{"intent":"affirm","text":"that sounds good","entities":[]},{"intent":"affirm","text":"correct","entities":[]},{"intent":"affirm","text":"that\'s right","entities":[]},{"intent":"affirm","text":"true that","entities":[]},{"intent":"deny","text":"no","entities":[]},{"intent":"deny","text":"never","entities":[]},{"intent":"deny","text":"I don\'t think so","entities":[]},{"intent":"deny","text":"don\'t like that","entities":[]},{"intent":"deny","text":"no way","entities":[]},{"intent":"deny","text":"not really","entities":[]},{"intent":"happy","text":"perfect","entities":[]},{"intent":"happy","text":"very good","entities":[]},{"intent":"happy","text":"great","entities":[]},{"intent":"happy","text":"amazing","entities":[]},{"intent":"happy","text":"feeling like a king","entities":[]},{"intent":"happy","text":"wonderful","entities":[]},{"intent":"happy","text":"I am feeling very good","entities":[]},{"intent":"happy","text":"I am great","entities":[]},{"intent":"happy","text":"I am amazing","entities":[]},{"intent":"happy","text":"I am going to save the world","entities":[]},{"intent":"happy","text":"super","entities":[]},{"intent":"happy","text":"extremely good","entities":[]},{"intent":"happy","text":"so so perfect","entities":[]},{"intent":"happy","text":"so good","entities":[]},{"intent":"happy","text":"so perfect","entities":[]},{"intent":"unhappy","text":"my day was horrible","entities":[]},{"intent":"unhappy","text":"I am sad","entities":[]},{"intent":"unhappy","text":"I don\'t feel very well","entities":[]},{"intent":"unhappy","text":"I am disappointed","entities":[]},{"intent":"unhappy","text":"super sad","entities":[]},{"intent":"unhappy","text":"I\'m so sad","entities":[]},{"intent":"unhappy","text":"sad","entities":[]},{"intent":"unhappy","text":"very sad","entities":[]},{"intent":"unhappy","text":"unhappy","entities":[]},{"intent":"unhappy","text":"bad","entities":[]},{"intent":"unhappy","text":"very bad","entities":[]},{"intent":"unhappy","text":"awful","entities":[]},{"intent":"unhappy","text":"terrible","entities":[]},{"intent":"unhappy","text":"not so good","entities":[]},{"intent":"unhappy","text":"not very good","entities":[]},{"intent":"unhappy","text":"extremly sad","entities":[]},{"intent":"unhappy","text":"so saad","entities":[]}],"regex_features":[]}}'
data_file = 'C:\\Users\\Asus\\AppData\\Local\\Temp\\tmplj1rufx2_training_data'
e = TrainingException('ag-first', OSError(22, 'La syntaxe du nom de fichier, de répertoire ou de volume est incorrecte'))
model_config = {'language': 'en', 'pipeline': [{'name': 'nlp_spacy'}, {'name': 'tokenizer_spacy'}, {'name': 'intent_featurizer_spacy'}, {'name': 'intent_entity_featurizer_regex'}, {'name': 'ner_crf'}, {'name': 'ner_synonyms'}, {'name': 'intent_classifier_sklearn'}], 'data': None}
model_name = '2019-04-22T09:52:38.723Z'
project = 'ag-first'
request = <Request at 0x18395e6c780 method=POST uri=/train?project=ag-first&model=2019-04-22T09:52:38.723Z clientproto=HTTP/1.1>
self = <__main__.RasaNLU object at 0x000001838B457400>

The exception says OSError(22, 'The filename, directory name, or volume label syntax is incorrect')
OSError: [WinError 123] LOSError: [WinError 123] The filename, directory name, or volume label syntax is incorrect 'C:\Users\Asus\sandbox\serve\projects\ag-first\2019-04-22T15:10:09.283Z'
So i'm guessing the problem is the ':'

Here's what shows up in the console:

The training file only contains the data, and I launch the server with a YML configuration file:
Content of configuration file (config.yml):
language: "en"
pipeline: "spacy_sklearn"

Thanks for raising this issue, @MetcalfeTom  will get back to you about it soon.

Hi @nadabou,
Yes, using ":" in a file name does cause that error to be thrown up.  Try re-naming the model or training a new one (did you use the --fixed_model_name option?  Perhaps try training without)
Let us know if that does or doesn't solve the problem!


Drone using outdated docker images

It looks like Drone isn't pulling in new versions of docker images when one is available.
I haven't inspected the code yet, but I suspect that Drone simply runs docker run, which does download the image if missing, but doesn't download a new image if one is already available.
Instead, it should first docker pull before running the container.

Yep, agreed. Let's continue tracking this in #697


all_gather for TPU doesn't support backward gradients.

Currently, we rely on AllGatherGrad to compute gather for GPUs.
TODO:

[] Extend this class to support TPU
[] Add tests

Although all_gather will now work, it still doesn't support grad. so re-opening :)

Is anyone looking into this? I would like to take it up but I would need some help fixing this.

sorry for the late reply, @vatch123 would be great if you can tackle this! @kaushikb11 can prob help.

Ya sorry for the delayed response. Would love to start on this if someone can describe the issue a little more. Like what is the current state and what do we want to achieve? Thanks


py3k: urlparse -> urllib.parse

The change is documented here.

It seems like this pr can be abandoned and closed.  Py3k is supported in feedfinder2 and the urlparse isn't necessary anymore.

Good call. Thanks!


Added a high_unbound? method, used it in shift_dates, and added tests

When an Interval has DateTimes, values that are "unbound" (e.g., the interval doesn't end, such as an ongoing diagnosis) are represented by a high DateTime in year 9999. This caused issues when we shifted dates into the future (or the past, for that matter) because the shift_dates function shifts those 9999-valued high dates into years after 9999. This PR adds a method to recognize those unbound high values, and keeps them from being date-shifted.
Pull requests into cqm-models require the following. Submitter and reviewer should :white_check_mark: when done. For items that are not-applicable, note it's not-applicable ("N/A") and :white_check_mark:.
Submitter:

[x] This pull request describes why these changes were made.
[ ] Internal ticket for this PR:
[ ] Internal ticket links to this PR
[x] Code diff has been done and been reviewed
[x] Tests are included and test edge cases
[x] Tests have been run locally and pass
[ ] If there were any JavaScript changes, this PR has updated the dist directory using npm run dist
[ ] If applicable, the library version number in package.json and cqm-models.gemspec has been updated
[ ] All changes can be reproduced by running the generator script
[ ] Cqm-execution fixtures have been updated with the update_cqm_execution_fixtures.sh script inside server-scripts using this branch in the cqm-converter

Bonnie Reviewer:
Name:

[ ] Code is maintainable and reusable, reuses existing code and infrastructure where appropriate, and accomplishes the task’s purpose
[ ] The tests appropriately test the new code, including edge cases
[ ] You have tried to break the code
[ ] You have executed the generator script, and made sure no changes were made that the generator did not reproduce itself

Cypress Reviewer:
Name:

[ ] Code is maintainable and reusable, reuses existing code and infrastructure where appropriate, and accomplishes the task’s purpose
[ ] The tests appropriately test the new code, including edge cases
[ ] You have tried to break the code
[ ] You have executed the generator script, and made sure no changes were made that the generator did not reproduce itself

Looks like the test that Mike added is no longer relevant since high_unbound? has been removed.


Bluteooth: False comment: all cmd_complete have status field

Describe the bug
The following code is documenting a false assumption that all command complete events have a status field. This is not true for at least one command, HCI_Disconnect. Core 5.4, 4.E.7.1.6 says there are no return parameters.
https://github.com/zephyrproject-rtos/zephyr/blob/4e946262cbcf712e01c9d9d631d3f843789e6c91/subsys/bluetooth/host/hci_core.c#L2335-L2338

You are right. I will close this ticket.
Still, it seems like this comment is relying on happenstance. Do you know if there has been any effort to promote the 'status' field into the 'Command complete' event itself, if there really is consensus that every 'Command complete' has a status.

You are right. I will close this ticket.
Still, it seems like this comment is relying on happenstance. Do you know if there has been any effort to promote the 'status' field into the 'Command complete' event itself, if there really is consensus that every 'Command complete' has a status.

I'm not aware of such efforts in the CSWG, i.e. this seems like an unwritten rule. Maybe @holtmann has some info if this has ever been discussed in the SIG?
Btw, also Linux makes the same assumption, so if a command ever deviates from this it's likely to cause breakage in other systems as well:
https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/tree/net/bluetooth/hci_event.c#n4258


Define some error code to prepare for retry logic

Proposed Changes

define three kinds of error, internalError/responseStatusConflictError/responseExecError
for internalErro, such as json decode error, it should retry to call the function.
for responseStatusConflictError, this happen when the container is in paused state when call pause(which means the daemon part should check the state when really pause/unpause the container).
for responseExecError, this is happen when the container exec command failed.

There's a PR to add this to upstream, so closing this one


Add failing test fixture for RemoveUnusedRequestParamRector with abstract parent method

Failing Test for RemoveUnusedRequestParamRector
Based on https://getrector.com/demo/381c0067-b8fa-4c7b-8bb1-80f0737a4799
See #366

Thank you @JohJohan


⚠ gorzdrav.org has degraded performance

In b1ba260, gorzdrav.org (https://gorzdrav.org) experienced degraded performance:

HTTP code: 200
Response time: 5719 ms

Resolved: gorzdrav.org performance has improved in 5b69e30.


It should bypass if the author of the PR is in the list

I believe If the author was one of the maintainers (listed in required-approvers-list) it should not require approval as the author cannot approve his own PR
I suggest to add an optional flag to enable/disable this behaviour

@shqear93 Good scenario, this sounds like the default behavior should be changed in this way - want to make the change to support this?


[Component] Remove state colors on icons, message and label for TextFields

The ask changes are not applied on Figma, should I wait for them to be applied to be sure I'm will modify the right values? @adevinta/spark-design

@elisa-hery You can find the before after in the PR description

Cool !
Who wants to check it on design side @Bibisirius @JeremiasUX ? (available in latest version)

@elisa-hery @soulcramer @thomas-leguellec @kazaky

Alert textfield: ✅

Success textfield : ✅

Error textfield : ✅

Can you check that the icon colour is Neutral #353741 by default?

If so, we're good ✅
If not, you'll need to update it


recipe jq-1.5-r0 : failed to compile

Hi all,
I tried to make an image for raspberry pi 3 target under master branch (by using ./resin-yocto-scripts/build/barys -r --shared-downloads $(pwd)/shared-downloads/ --shared-sstate $(pwd)/shared-sstate/ -m raspberrypi3 command)  and I experienced the following problem:
...
NOTE: Running task 1755 of 3227 (/home/khang/Workspace/GIT/resinos/resin-raspberrypi/build/../layers/meta-openembedded/meta-oe/recipes-devtools/jq/jq_1.5.bb:do_compile)
NOTE: recipe jq-1.5-r0: task do_compile: Started
ERROR: jq-1.5-r0 do_compile: oe_runmake failed
ERROR: jq-1.5-r0 do_compile: Function failed: do_compile (log file is located at /home/khang/Workspace/GIT/resinos/resin-raspberrypi/build/tmp/work/cortexa7hf-neon-vfpv4-poky-linux-gnueabi/jq/1.5-r0/temp/log.do_compile.31306)
ERROR: Logfile of failure stored in: /home/khang/Workspace/GIT/resinos/resin-raspberrypi/build/tmp/work/cortexa7hf-neon-vfpv4-poky-linux-gnueabi/jq/1.5-r0/temp/log.do_compile.31306
NOTE: recipe jq-1.5-r0: task do_compile: Failed
ERROR: Task (/home/khang/Workspace/GIT/resinos/resin-raspberrypi/build/../layers/meta-openembedded/meta-oe/recipes-devtools/jq/jq_1.5.bb:do_compile) failed with exit code '1'
...
Could anyone have a solution, please?
Best regards,
Pascal
20171002045420.log
console-latest.log

Hi @FXRer ,
Can you attach the compile log as it is referenced in the log?
Thanks,
Andrei

Hi Andrei,
I already the log files at the end of my previous comment.
Best regards,
Pascal

I downloaded both of your file but I can't see anything that would explain the error. Did I miss something?

Hi Pascal, can you paste here the command you used to clone the resin-raspberrypi repository?
Also, can you try and build using resin-yocto-scripts/automation/jenkins_build.sh script? Run this script with -h and see the required usage. This script builds in a docker container so you should be able to build it this way if there's a problem building it directly on your development machine

HI Florion,
The command I used to clone the resin-raspberrypi repository is :
$ git clone https://github.com/resin-os/resin-raspberrypi
For your suggestion of using resin-yocto-scripts/automation/jenkins_build.sh script, I will give it a try and update the result ASAP.
Best regards,
Pascal

Hi Pascal,
the correct clone command should be:
git clone --recursive https://github.com/resin-os/resin-raspberrypi
Can you try to re-clone using the above command and build it again using barys?

Hi Florion,
Thanks fr your help. your solution works well.


Bug: print of IAM policy on gitlab CI not working

Hi guys. I ran rony's CI on gitlab but the policy does not get printed as it should by iamlive. Below is a screenshot of the final logs of the pipeline:
aws_s3_bucket_object.job_spark: Creation complete after 1s [id=emr-code/pyspark/spark_censo_escolar_to_parquet.py]
aws_lambda_function.decompresss3: Still creating... [10s elapsed]
aws_lambda_function.decompresss3: Still creating... [20s elapsed]
aws_lambda_function.decompresss3: Creation complete after 22s [id=prod-prod_censo_ingestion_igti-ney]
Apply complete! Resources: 11 added, 0 changed, 0 destroyed.
PERMISSIONS USED:
cat: ./policy.json: No such file or directory
Cleaning up file based variables
00:00
ERROR: Job failed: exit code 1

Any help? Many thanks!!!

UPDATE
I have modified apply.sh to this
#!/bin/bash
set -e

#### Terraform Apply #####

./CI/scripts/pre_terraform.sh

export AWS_CSM_ENABLED=true
export AWS_CSM_PORT=31000
export AWS_CSM_HOST=<IP_ADDRESS>

cd infrastructure/aws/

(iamlive --output-file './policy.json' > /dev/null &)

terraform init
terraform workspace select $1 || terraform workspace new $1
terraform apply $2

pkill iamlive

sleep 1
echo ""
GREEN='\033[0;32m'
NC='\033[0m' # No Color

echo -e "${GREEN}Files found:${NC}"
ls

echo -e "${GREEN}PERMISSIONS USED:${NC}"
#cat ./policy.json

cd ../..

###########################

in order just to print the found files and not cat the policy.json file.  Resources: 1 added, 0 changed, 1 destroyed.
Files found:
backend.tf
ecr.tf
function.tf
iam.tf
permissions
policy.json
provider.tf
s3_files.tf
storage.tf
variables.tf
PERMISSIONS USED:
Cleaning up file based variables
00:01
Job succeeded

Update 2
This is very interesting... it seems that sometimes, policy.json file is created, sometimes it is not. I am not sure why this happens...

I was not able to reproduce this issue, but I think it might be related to the fact that IAM live takes some time to write the policy file after its process is killed.
What the current code does to address this, is to sleep for 1 second after killing IAMLive. I guess it might not be enough in some cases (maybe on more complex projects). For a simple project running locally, it usually takes 0.4 seconds to create the file.
Could you try running the pipeline with this new apply.sh? If you share your code I can make some tests as well.
What this new code does is to wait for the file to be created (up to 60 seconds).
This code prints the timeout, for debugging purposes. Later we should remove it.
If it works, we should also update ./CI/scripts/destroy.sh
#!/bin/bash
set -e

#### Terraform Apply #####

./CI/scripts/pre_terraform.sh

export AWS_CSM_ENABLED=true
export AWS_CSM_PORT=31000
export AWS_CSM_HOST=<IP_ADDRESS>

cd infrastructure/aws/

[ -e "./policy.json" ] && rm "./policy.json"

(iamlive --output-file './policy.json' > /dev/null &)

terraform init
terraform workspace select $1 || terraform workspace new $1
terraform apply $2

pkill iamlive

# Wait 60 seconds for the file policy.json to be generated
export timeout=600
while [ ! -f './policy.json' ]
do
  if [ "$timeout" == 0 ]; then
    echo "ERROR: Timeout while waiting for the file permissions file to be generated"
    break
  fi
  timeout=$((timeout-1))
  echo $timeout
  sleep 0.1
done
##########################

if [ -f './policy.json' ]; then
  echo ""
  GREEN='\033[0;32m'
  NC='\033[0m' # No Color
  echo -e "${GREEN}PERMISSIONS USED:${NC}"
  cat ./policy.json
  cd ../..
fi

###########################


BMI Unit Test + Updates to BMI Functions

BMI Unit Testing

script added
loops through all bmi components (38) and tests functionality
provides pass/fail console output

Results Summary
Three (variable information) bmi functions showing error

get_var_type() AttributeError: 'float' object has no attribute 'dtype'
get_var_itemize() AttributeError: 'float' object has no attribute 'dtype' (based on get_var_type())
get_var_nbytes() AttributeError: 'float' object has no attribute 'nbytes'

Note these functions PASS when executing after update() call, suggesting attributes may need to be initialized prior?  Or the defs themselves are faulty; possibly numpy object (or just version) related?
// TODO: set_value_at_indices() PASSES yet get_value*()  do not match so something is off here (def or the manner in which it is called)
Sample Console Out
BEGIN BMI UNIT TEST
*******************

 configuration found: bmi_config_files\01022500_A.yml
 initializing...

MODEL INFORMATION
*****************
 component name: LSTM for Next Generation NWM
 input item count: 8
 output item count: 1
 input var names:
  land_surface_radiation~incoming~longwave__energy_flux
  land_surface_air__pressure
  atmosphere_air_water~vapor__relative_saturation
  atmosphere_water__liquid_equivalent_precipitation_rate
  land_surface_radiation~incoming~shortwave__energy_flux
  land_surface_air__temperature
  land_surface_wind__x_component_of_velocity
  land_surface_wind__y_component_of_velocity
 output var names:
  land_surface_water__runoff_volume_flux

VARIABLE INFORMATION
********************
 land_surface_water__runoff_volume_flux:
  units: mm
**BMI ERROR** in get_var_itemsize()
**BMI ERROR** in get_var_type()
**BMI ERROR** in get_var_nbytes()
  grid id: 0
  location: node
 atmosphere_water__liquid_equivalent_precipitation_rate:
  units: kg m-2
**BMI ERROR** in get_var_itemsize()
**BMI ERROR** in get_var_type()
**BMI ERROR** in get_var_nbytes()
  grid id: 0
  location: node
 land_surface_air__temperature:
  units: K
**BMI ERROR** in get_var_itemsize()
**BMI ERROR** in get_var_type()
**BMI ERROR** in get_var_nbytes()
  grid id: 0
  location: node

TIME INFORMATION
****************
 start time: 0.0
 end time: 1.7976931348623157e+308
 current time: 0
 time step: 1
 time units: 1 hour

GRID INFORMATION
****************
 grid id: 0
  rank: 1
  size: 1
  type: scalar

GET AND SET VALUES
******************
 atmosphere_water__liquid_equivalent_precipitation_rate:
  set value: 0.0
  set value at indices: -9.0
  get value ptr: 0.0
  get value: 0.0
  get value at indices: [0.]
 land_surface_air__temperature:
  set value: -9.496578
  set value at indices: -9.0
  get value ptr: -9.496578
  get value: -9.496578
  get value at indices: [-9.49657822]

CONTROL FUNCTIONS
*****************
 updating...        time 1
 updating until...  time 100
 finalizing...

 Total BMI function PASS: 25
 Total BMI function FAIL: 3
  get_var_itemsize()
  get_var_type()
  get_var_nbytes()

Fixes & Additions to LSTM-BMI bmi_lstm

Fully implemented all BMI functions; e.g. grid, time & model info mostly
Revised definitions and support components based on test results; getters & setters were/are a little tricky
No changes to model control functions
All model code adjustments are commented and prefixed with # JG Edit so its easily searchable

Testing & Env

Python 3.7.11
conda 4.10.3 (bmi_lstm)
Windows 10 🤢
primary run script run-lstm-with-bmi unaffected by model code changes

Oh, I guess I cannot merge it. @SnowHydrology needs to do it. Just keep in mind that I am going to submit a pull request right after this that fixes several of the issues.


Personal Life

Letters for women are more likely to discuss personal life.
Goal: Develop code that can read text for terms related to personal life like family, children, etc. If the text includes personal life details; return a summary that directs the author to review the personal life details for relevance and consider removing them if they are not relevant to the recommendation or evaluation.

Claiming this!

Yay!


Basics of Bitcoins and Blockchains

An Introduction to Cryptocurrencies and the Technology that Powers Them.

Thanks for the PR. It has been merged.
I'll be glad if you can leave a star on this repository.


debug doesnt work with alternative jestcommand

It seems that running in debug doesnt use the alternative jestCommand option.

@hoeghen thx for reporting. that needs to be fixed!

In order to be able to debug tests VSCode has to know which executable to run - we link to the jest executable in the node_modules by default.
You can define a custom jestPath which will be used instead.
Can you check if this works for you?

https://github.com/firsttris/vscode-jest-runner/blob/master/src/jestRunner.ts#L105
Debug uses alternativ jestPath instead of alternativ JestCommand.
JestCommand (like npm run test) is not possible for RunDebug


[ci][distributed] disable hanging tests

need to investigate, but i have seen several hang in the ci , e.g. https://buildkite.com/vllm/ci-aws/builds/11196#0193290b-cea7-43cf-b2d4-c4b3f10ff571

sorry for the back and forth, will not enable it in the ci before figuring it out.


virtiolib: Remember interrupt message numbers

This is a workaround for a WDF bug, last seen in version 1.9 and
confirmed fixed in 1.11. On resource rebalance WDF does not
preserve the MessageNumber field of its internal data structures
describing interrupts. As a result, we fail to report the right
MSI message number to the virtio device when re-initializing it
and it may stop working.
This manifests as a hang while trying to write to a virtual serial
port after a CPU is hotplugged to Windows 2008 R2, for example.
The fix is to keep the message number in a separate context
structure, attached to the interrupt object the first time it is
passed to us. Getting the message number can be subsequently
satisfied from this context, rather than from the buggy
WdfInterruptGetInfo call.

The first commit is a fix for https://bugzilla.redhat.com/show_bug.cgi?id=1435778
The second commit fixes a minor issue found when working on the bug.
It is not related to BZ #1435778 other than by also touching interrupt context.

@hammerg, can you please take a look?

Other than the Hungarian notation naming I'm okay with these changes.
@ladipro Do you have a reference to the WDF bug? Thanks.

Hungarian is used in all of VirtIO/WDF. Is there a good reason for not using it? For what it's worth, VS2015 project templates still use the Hungarian notation also:
int APIENTRY wWinMain(_In_ HINSTANCE hInstance,
                     _In_opt_ HINSTANCE hPrevInstance,
                     _In_ LPWSTR    lpCmdLine,
                     _In_ int       nCmdShow)
{
  ...

The WDF bug has not been officially documented, as far as I know. The best we have is the binary diff between version 1.9 and 1.11 of FxInterrupt::RevokeResources where the code to preserve m_InterruptInfo.MessageNumber was added. Thanks!


ZFPlayerControlView showTitle:nil coverURLString:nil fullScreenMode:ZFFullScreenModePortrait doesn't support device rotation

When set
[self.controlView showTitle:nill
coverURLString:nill
fullScreenMode:layout.ZFFullScreenModePortrait:];
The player no longer goes full screen when the user rotates the device to landscape orientation.
Can you update so that when user rotates to landscape, the video is played in Portrait orientation but full screen?  Video should continue in full screen mode until the user clicks the full screen button.  v2.0 of the zfplayer had this behaviour.

You can set allowOrentitaionRotation to NO and use ZFPlayerControlView showTitle:nil coverURLString:nil fullScreenMode:ZFFullScreenModeLandscape

I still want to set allowOrentitaionRotation to YES.  When user rotates screen it should go full screen, but I believe ZF player should allow this for ZFFullScreenModePortrait.  Right now it does not.

If you set:
    [self->playerView setAllowOrentitaionRotation:YES];
    [self->controllerView showTitle:self.item[@"title"]
                     coverURLString:nil
                     fullScreenMode:ZFFullScreenModePortrait];

The device does not go full screen when you rotate it.  I believe it should.


no test.txt in VOC dataset

Hi
Traceback (most recent call last):
File "C:/PY_SOURCE2/RCNN/Keras-FasterRCNN-master/train_frcnn.py", line 99, in
all_imgs, classes_count, class_mapping = get_data(options.train_path)
File "C:\PY_SOURCE2\RCNN\Keras-FasterRCNN-master\keras_frcnn\pascal_voc_parser.py", line 46, in get_data
with open(imgsets_path_test) as f:
FileNotFoundError: [Errno 2] No such file or directory: 'F:\VOCdevkit\VOC2012\ImageSets\Main\test.txt'

Hi @nnZidan
this repo is simply modified version of keras-frcnn cloned from https://github.com/yhenon/keras-frcnn/ for my own dataset(including testset).
so, i added the code for testset
now, i fixed pascal_voc_parser.py for pascal VOC 2012 dataset(not including testset)
thank to comment!

Thank you for the reform
Do you have an idea of how to handle data like
icdar2015

I roughly search icdar2015. this competition has many sub competition.
I think that this challenge mainly deal with a problem like OCR.
However, i'm not yet studied about recognize text in image...
I'm sorry i can not help you

You can use the val.txt as test.txt. :)
Because the val.txt do not use during training.

Thank you for your attention


Fix broken connect links

/cc @mairaw

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Line 20: [Warning] The content part of NOTE/WARNING/CAUTION/IMPORTANT syntax is suggested to start in a new line.
Line Unspecified: [Warning] Invalid cross reference "Char.IsUpperCase".

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Line 28: [Warning] Invalid file link:(~/docs/csharp/getting-started/library-with-visual-studio_2017.md).

docs/csharp/getting-started/library-with-visual-studio-2017.md

Line 20: [Warning] The content part of NOTE/WARNING/CAUTION/IMPORTANT syntax is suggested to start in a new line.
Line Unspecified: [Warning] Invalid cross reference "Char.IsUpperCase".

docs/toc.md

Line Unspecified: [Warning] Invalid file link:(~/docs/library-with-visual-studio-2017.md).
Line Unspecified: [Warning] Invalid file link:(~/docs/testing-library-with-visual-studio-2017.md).
Line Unspecified: [Warning] Invalid file link:(~/docs/consuming-library-with-visual-studio-2017.md).

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rebased and did a force push. Need to close and reopen the PR to start a build again.

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Is Premium still in preview?

Does this page need an update to remove the 'preview' word?
If it is still in preview, these places should tell us so, and they do not

public pricing page
Azure Portal 'Create Managed Instance' experience

Can you please provide premium memory optimized in AustraliaSoutheast, or give us a rough timeline at least? 🙏 Thankyou

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

ID: fb1a8634-bd22-4a25-5f18-2a547a42c316
Version Independent ID: fb1a8634-bd22-4a25-5f18-2a547a42c316
Content: Resource limits - Azure SQL Managed Instance
Content Source: azure-sql/managed-instance/resource-limits.md
Service: sql-managed-instance
Sub-service: service-overview
GitHub Login: @vladai78
Microsoft Alias: vladiv

@ishepherd Thank you for the detailed feedback. This is being assigned to the content author to evaluate and update as appropriate.

Hi @ishepherd , thanks for reaching out! Premium-series hardware is now GA and preview has been removed from the documentation, the pricing page, and the Azure portal. However, the memory optimized premium-series is still in preview and the documentation reflects that.
Unfortunately, we do not have access to timelines of upcoming offerings.
I'll go ahead and close out this Git Issue but please feel free to comment should you need anything further.
Thanks again for reaching out and I hope you have a wonderful rest of your day!
Masha from the SQL Docs team
#please-close


💄update default tile mode for backdrop filter

While "decal" is the expected default when blurring (an image for instance), "clamp" is always used for backdrop filters.
This came up in https://github.com/Shopify/react-native-skia/discussions/226 which yield "strange" results.

Decal  Clamp


chore(main): release grades-view-solid 1.1.1

:robot: I have created a release beep boop
1.1.1 (2024-01-22)
Bug Fixes

Grades can now only be multiple of 0.5 (3922321), closes #13

This PR was generated with Release Please. See documentation.

:robot: Release is at https://github.com/jobtrek/grades-view-solid/releases/tag/grades-view-solid-v1.1.1 :sunflower:


Add automatically generated change log file.

Hi, as I can see, you are carefully fill tags and labels for issues in your repo.
I created a github_changelog_generator, that generate change log file based on tags, issues and merged pull requests (and split them to separate lists according labels) from GitHub issue tracker.
This Change Log was automatically generated by this script.
It helps to easily found what and when was fixed and implemented.
You can easily update this file in future by running script: github_changelog_generator in your repo folder.
Hope you find this commit as useful.

This is neat, thanks for letting us know about it. I'm not sure how we're handling changelogs for Argo yet, but I might look into this for automating some of the stuff I do for other projects.

So sad. It looks very nice in your well-maintained project. Let me know, if you have any wishes or feature request, that you miss in this generator and I will try to accomplish it in next releases.
And thanks for awesome library!
Cheers! :beers:


Error: spawn ps ENOENT

Thank you for taking time to open a new issue. Please answer a few questions to help us fix it faster. You can delete text that is irrelevant to the issue.
Is this a bug report or a feature request?
definitely a bug report ;)
If this is a bug report, please provide as much info as possible

version

1.13 / 1.14 running on cypress/included:9.5.2 and cypress/included:9.5.3

platform

Linux

expected behavior

start-server-and-tests quits webserver after tests

actual behavior

node:events:498       throw er; // Unhandled 'error' event       ^ Error: spawn ps ENOENT     at Process.ChildProcess._handle.onexit (node:internal/child_process:283:19)     at onErrorNT (node:internal/child_process:478:16)     at processTicksAndRejections (node:internal/process/task_queues:83:21) Emitted 'error' event on ChildProcess instance at:     at Process.ChildProcess._handle.onexit (node:internal/child_process:289:12)     at onErrorNT (node:internal/child_process:478:16)     at processTicksAndRejections (node:internal/process/task_queues:83:21) {   errno: -2,   code: 'ENOENT',   syscall: 'spawn ps',   path: 'ps',   spawnargs: [ '-A', '-o', 'ppid,pid,stat,comm' ] }
I have setup a demo repo here where you can see the setup:
https://gitlab.com/joelgrimberg/cypress-failing-with-953
If I run it within a cypress/included:9.5.1 container, all is fine:
https://gitlab.com/joelgrimberg/cypress-failing-with-953/-/jobs/2281876715
if I un it witin a cypress/included:9.5.2 container, the start-server-and-test fails:
https://gitlab.com/joelgrimberg/cypress-failing-with-953/-/jobs/2281897953
if I un it witin a cypress/included:9.5.2 container, the start-server-and-test fails:
https://gitlab.com/joelgrimberg/cypress-failing-with-953/-/jobs/2281921034
trying to debug the issue, already tried installing ps-tree and execa globally but that did not work out.

Got exactly same problem. From my (limited) tests on CircleCI:

✅ everything was working fine on: cypress/base:16.13.0 docker image and cypress v9.5.1 npm dependency
🚫 error started to appear after bumping dependencies to: cypress/base:16.14.2 and cypress v9.5.3
🚫 error also appeared on: cypress/base:16.13.0 and cypress v9.5.3*

I just tested with Cypress v9.6.0 and all is well now. 🥳 so I will close this issue.

Hm, interesting. I am still seeing the same issue with cypress/base:16.14.2 and v9.6.0.


drivers: spi: Adapt to new SPI dt-spec macros

The SPI dt-spec macros has been modified to make it compatible with C++.
But there are still a macros that haven't been modified completely.
And the macros are not available.
So I modified them to Adapt the new SPI dt-spec macros.

Hi @vacabun,
The problem with your compliance failure is that your Git email author is vacabun<EMAIL_ADDRESS>wheras your signed-off-by line is guotong ma<EMAIL_ADDRESS>What you need to do is:
git commit --amend --author="guotong ma<EMAIL_ADDRESS>git push -f


File Upload to Webserver

Dear Neil,
currently I am working on a project using the mongoose webserver. Unfortunately it turns out that it is quite cumbersome to write the html and javascript code due to the fact that I need to flip the SD card from the esp32 to my laptop and vice versa very frequently. So, I was asking myself if there is an easier way to do that. I tried to use the TFTP.cpp code but unfortunately this is only a simple ftp server which is not supported by e.g. filezilla.
Do you know a better solution for that?
Best regards

Very interesting that you ask about this ... Over the last few weeks a new component (prototype) has been added to the C++ utils (this repository).  Like many folks before me and like many folks after me, I decided to have a bash at writing my own HttpServer set of libraries.  This time, they were written to leverage C++ as opposed to C.  I am personally pleased with the results.   Having implemented an HttpServer, I then tackled WebSockets built on that .... and again I was pleased with the results.
From there, I wrote a class that uses WebSockets to receive incoming files over a web socket and write them to the file system.  The class is called "WebSocketFileTransfer" and is available in the current code base.  However, I have not yet documented it well nor have I had the opportunity to pilot it with interested consumers.  It might be that this is of use to you.  If interested, what I suggest we do is have a live chat via Skype (neil.kolban) or IRC (channel #ESP32) and see if we can't get a test running for you and see if it meets your needs and, if not, lets see what it is missing that would delight you.

Curretnly I am using mongoose.c with REST calls for reading data from the ESP32. It has the advantage that (maybe later) I could use MQTT also. If I should use your webserver library I need to rewrite that part of the code. Since this is a hobby project it would need some time to do it. So, thank you for offering your help via Skype but I think it will take some time until I will be ready for that.
Currently I am checking the solution of having the html and javascript files on a local drive on my PC and I modify the url of all REST calls to point to the ESP32. Unfortunately here I have the problem that my browser filters the REST calls and complains "Cross-Origin Request Blocked. (Reason: CORS header 'Access-Control-Allow-Origin' missing)". Now I am searching a solution for that. I don't want to modify the security settings of my browser. Maybe there is another solution available. I will check.

Add this header to response
("access-control-allow-origin", "*")

Where should I add it? Here:
mg_printf(nc, "%s", "HTTP/1.1 200 OK\r\nTransfer-Encoding: chunked\r\n\r\n"); ?
Before or ater this?

As I remember, Mongoose is a low-level implementation of an HTTP server.  Are you programming to the lowest level Mongoose APIs or using a higher level wrapper such as
https://github.com/nkolban/esp32-snippets/blob/master/cpp_utils/WebServer.h
If the latter, then I believe there is a class instance called HTTPResponse that encapsulates the response sent from your ESP32 application which has a method called "addHeader()" which could be used to add response headers to the response that will eventually be sent back to the browser.

yes, I am using the mongoose API directly. So, rewriting my webserver would be a bit more effort. First I would like to try if I am able at all to achieve my targets with this first step. Then I will decide if I proceed with this project and if I proceed I might change to your webserver code.

https://forum.mongoose-os.com/discussion/138/add-header-to-response

@squonk11 You might want to post the question to the Mongoose forum or to the Mongoose github.  This github issue forum is primarily for issues, enhancements and questions to the code found here.  If you have generic ESP32 questions, then the ESP32 forum here is a good place:
https://esp32.com/index.php
Or if you have queries about 3rd party software, maybe try the forums and communities related to those 3rd party packages?

Currently I am using successfully several of your cpp classes (WiFi, GPIO, Socket, System, Task, FreeRTOS, ...) and I appreciate the work your are doing.  I think I will also switch to one of your webserver classes (WebServer or HttpServer) if I continue my project. For the moment I am just checking the feasibility of the ESP32 plattform for my purposes.

No problems at all, and I'll be delighted to help in any way I can.  My previous post wasn't to try and run you off, on the contrary, I'm trying to get you the speediest and best response possible.  For code that I work upon, I hope to be "johnny on the spot" with fixes and responses.  But for code outside of my control, then forums and 3rd party web sites are the better alternatives.
If you decide to use one of the WebServer classes in this repository, my bias right now is the HttpServer.  However, I recommend that because it interests me more than WebServer class not because it is any more efficient or better in any obvious way ... only that I'm a big fan of C++ and a native C++ implementation appeals to my sense of purity more than a wrapped hybrid.

Closing for now.  Don't hesitate to re-open as needed.

hi,
I am using esp idf environment
I want to decode the string in url. mbedtls_base64_decode() function does not gives an error after compiling.   but when i see  decoded result in decode_pass variable, the processor gets restart again and again.
char *getpass="cabin336%40efc";
unsigned char *decode_pass=NULL;
size_t len;
mbedtls_base64_decode(decode_pass, sizeof(decode_pass), &len, (unsigned char *) get_pass, strlen(get_pass));

Did you try this way:
unsigned char *decode_pass[1000]={0};
if this works then you need to initialize decode_pass with malloc.

I tried this. processor is not restarting now, but decode_pass variable does not contain decoded value after calling of this function
mbedtls_base64_decode(decode_pass, sizeof(decode_pass), &len, (unsigned char *) get_pass, strlen(get_pass));

Try to ask on forum or esp-idf repo.

ok. thank you.


[Bug] Exported pdf only contains header

Is there an existing issue for this?

[X] Yes, I have searched the existing issues

Product Variant
Cloud (http://rxresu.me)
Current Behavior
I have exported my resume from reactive json, I was able to see all information in the website template. But resume body went missing when I export that resume.

Expected Behavior
Should include all information from json in body
Steps To Reproduce
Imported reactive Json
Click Export (1st time all went well)
Export Json with some modified data
Click Export (only include data in header)
What browsers are you seeing the problem on?
Chrome
Anything else?
Expected

Result

Same, +1.

@Sameerna. I cheated it by taking the resume link, open it in some website which can make pdf from the website link and cropped that pdf in some pdf cropper online. 😆
What I think the source case is when your resume is really long (more than you selected A4, A3, legal or whatever). Just follow my steps when you want your resume in pdf.

Hey, Thank you
On Sun, Jul 23, 2023 at 6:21 PM Nyi Zaw Aung @.***>
wrote:

@Sameerna https://github.com/Sameerna. I cheated it by taking the
resume link, open it in some website which can make pdf from the website
link and cropped that pdf in some pdf cropper online. 😆
What I think the source case is when your resume is really long (more than
you selected A4, A3, legal or whatever). Just follow my steps when you want
your resume in pdf.
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https://github.com/AmruthPillai/Reactive-Resume/issues/1386#issuecomment-1646833025,
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You are receiving this because you were mentioned.Message ID:
@.***>

I have been having this same issue and posted about it a few months ago. From what I can tell, the issue is localized only to this template.


Variable model_1/loss/ExponentialMovingAverage/ does not exist

HI, I'm running the dev branch code on Tensorflow 1.2.
And I got this error:
Variable model_1/loss/ExponentialMovingAverage/ does not exist or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope.
From the stack trace, it was from basic/model.py, in _build_ema, ema_op=ema.apply(tensors).
I tried to add  "with tf.variable_scope(tf.get_variable_scope(), reuse=False):" before eam.apply but that still doesn't work.
Any ideas how can I fix this?
Thanks!
I'm using CUDA8.0 and Cudnn5.1. Tensorflow v1.2, python 3.5.

I found this problem only occurs in multi-GPU training. It's fine to use the same code without --num_gpus>1.

@demiguo I have same issue, had you solved the problem?

@demiguo I have the same issue. Because of this I can't train on a multiple GPU setup. Has anyone solved the problem?

I have the same problem

Anyone found anything?

Same issue occurs on version patched to run on TF r.17
Note the following version works on 1 gpu but not on >1 gpu
https://github.com/klintan/bi-att-flow/tree/dev
Traceback (most recent call last):
File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main
"main", mod_spec)
File "/usr/lib/python3.5/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/levinth/bi-att-flow-zt1/basic/cli.py", line 128, in
tf.app.run()
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "/home/levinth/bi-att-flow-zt1/basic/cli.py", line 125, in main
m(config)
File "/home/levinth/bi-att-flow-zt1/basic/main.py", line 26, in main
_train(config)
File "/home/levinth/bi-att-flow-zt1/basic/main.py", line 85, in _train
models = get_multi_gpu_models(config)
File "/home/levinth/bi-att-flow-zt1/basic/model.py", line 21, in get_multi_gpu_models
model = Model(config, scope, rep=gpu_idx == 0)
File "/home/levinth/bi-att-flow-zt1/basic/model.py", line 68, in init
self._build_ema()
File "/home/levinth/bi-att-flow-zt1/basic/model.py", line 298, in _build_ema
ema_op = ema.apply(tensors)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/training/moving_averages.py", line 405, in apply
"VarHandleOp"]))
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/training/slot_creator.py", line 179, in create_zeros_slot
colocate_with_primary=colocate_with_primary)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/training/slot_creator.py", line 156, in create_slot_with_initializer
dtype)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/training/slot_creator.py", line 65, in _create_slot_var
validate_shape=validate_shape)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/ops/variable_scope.py", line 1297, in get_variable
constraint=constraint)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/ops/variable_scope.py", line 1093, in get_variable
constraint=constraint)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/ops/variable_scope.py", line 439, in get_variable
constraint=constraint)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/ops/variable_scope.py", line 408, in _true_getter
use_resource=use_resource, constraint=constraint)
File "/home/levinth/tf_r1.7_c91_712_py3/tensorflow/python/ops/variable_scope.py", line 765, in _get_single_variable
"reuse=tf.AUTO_REUSE in VarScope?" % name)
ValueError: Variable model_1/loss/ExponentialMovingAverage/ does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=tf.AUTO_REUSE in VarScope?

Hi David,
My name is Tian. I'm moving our discussion from email to this issue ticket so that other developers who have issues with this error could see it.
The issue with the original bidaf implementation is that it only creates one loss variable for the model. On a single GPU this is fine, because you only have one model. In a multi-GPU setting, the way bidaf implements multi-GPU training is that it replicates the model for every GPU device, and assigns one model on one device. This means that each model would require its own loss variable. If the developer only specifies one loss variable, tensorflow would try to reuse the loss variable for every model, which would create a conflict.
For example, in your error, if you only have one device, the name of the loss variable would be model_0/loss/ExponentialMovingAverage. If you have two devices, another loss variable called model_1/loss/ExponentialMovingAverage would be referenced by tensorflow. Since this variable is not created before you generate the whole model, tensorflow would try to reuse the variable you previously generated for model_0. Does that make sense?
The solution to resolve this conflict is by creating a loss variable for every model that's replicated:
https://github.com/stanford-futuredata/dawn-bench-models/blob/master/tensorflow/SQuAD/basic/model.py#L25:L36.
Unfortunately I don't have a mult-GPU node available. Would you mind try this patch on your node and see if it works?

I will modify the Andreas Klintberg fork of this..as that is the code base
that works on top of tree TF..nothing else does due to the change in the
handling of flags
https://www.linkedin.com/in/andreas-klintberg-b7655710/
but I have to wait until the 4 GPU machine I set this up on gets freed up...
I don't want to build everything again  LOL
d
On Tue, May 1, 2018 at 2:58 PM, kelayamatoz<EMAIL_ADDRESS>wrote:

Hi David,
My name is Tian. I'm moving our discussion from email to this issue ticket
so that other developers who have issues with this error could see it.
The issue with the original bidaf implementation is that it only creates
one loss variable for the model. On a single GPU this is fine, because you
only have one model. In a multi-GPU setting, the way bidaf implements
multi-GPU training is that it replicates the model for every GPU device,
and assigns one model on one device. This means that each model would
require its own loss variable. If the developer only specifies one loss
variable, tensorflow would try to reuse the loss variable for every model,
which would create a conflict.
For example, in your error, if you only have one device, the name of the
loss variable would be model_0/loss/ExponentialMovingAverage. If you have
two devices, another loss variable called model_1/loss/ExponentialMovingAverage
would be referenced by tensorflow. Since this variable is not created
before you generate the whole model, tensorflow would try to reuse the
variable you previously generated for model_0. Does that make sense?
The solution to resolve this conflict is by creating a loss variable for
every model that's replicated:
https://github.com/stanford-futuredata/dawn-bench-models/
blob/master/tensorflow/SQuAD/basic/model.py#L25:L36.
Unfortunately I don't have a mult-GPU node available. Would you mind try
this patch on your node and see if it works?
—
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new distro is https://github.com/klintan/bi-att-flow/tree/dev
On Tue, May 1, 2018 at 3:45 PM, David Levinthal<EMAIL_ADDRESS>wrote:

I will modify the Andreas Klintberg fork of this..as that is the code base
that works on top of tree TF..nothing else does due to the change in the
handling of flags
https://www.linkedin.com/in/andreas-klintberg-b7655710/
but I have to wait until the 4 GPU machine I set this up on gets freed
up...
I don't want to build everything again  LOL
d
On Tue, May 1, 2018 at 2:58 PM, kelayamatoz<EMAIL_ADDRESS>wrote:

Hi David,
My name is Tian. I'm moving our discussion from email to this issue
ticket so that other developers who have issues with this error could see
it.
The issue with the original bidaf implementation is that it only creates
one loss variable for the model. On a single GPU this is fine, because you
only have one model. In a multi-GPU setting, the way bidaf implements
multi-GPU training is that it replicates the model for every GPU device,
and assigns one model on one device. This means that each model would
require its own loss variable. If the developer only specifies one loss
variable, tensorflow would try to reuse the loss variable for every model,
which would create a conflict.
For example, in your error, if you only have one device, the name of the
loss variable would be model_0/loss/ExponentialMovingAverage. If you
have two devices, another loss variable called
model_1/loss/ExponentialMovingAverage would be referenced by tensorflow.
Since this variable is not created before you generate the whole model,
tensorflow would try to reuse the variable you previously generated for
model_0. Does that make sense?
The solution to resolve this conflict is by creating a loss variable for
every model that's replicated:
https://github.com/stanford-futuredata/dawn-bench-models/blo
b/master/tensorflow/SQuAD/basic/model.py#L25:L36.
Unfortunately I don't have a mult-GPU node available. Would you mind try
this patch on your node and see if it works?
—
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https://github.com/allenai/bi-att-flow/issues/54#issuecomment-385803301,
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took model.py from the dawn distribution and added it to my modified (for
printout and speed logging) version of the klintberg distro
It appears to not actually pay attention to the num_gpus flag and started 4
processes on the 4 V100s
used CUDA_VISIBLE_DEVICES=0 and num_gpus=1
default batch size
global_step: 100, avg_loss = 8.443472, time = 336.919500
global_step: 200, avg_loss = 7.691555, time = 333.118028
global_step: 300, avg_loss = 7.293336, time = 332.794547
global_step: 400, avg_loss = 6.585279, time = 330.941432
nvidia-smi showed 1 process
unsetting CUDA_VISIBLE_DEVICES and rerunning
one see 4 processes in nvidia-smi..but only 1 GPU being active  :-)
global_step: 100, avg_loss = 8.414022, time = 353.267652
global_step: 200, avg_loss = 7.680358, time = 343.982275
global_step: 300, avg_loss = 7.316520, time = 346.749630
global_step: 400, avg_loss = 6.531937, time = 344.386899
set CUDA_VISIBLE_DEVICES=0,1,2,3 and num_gpus=4
global_step: 100, avg_loss = 8.122040, time = 669.353025
global_step: 200, avg_loss = 6.920084, time = 651.585218
global_step: 300, avg_loss = 5.956660, time = 648.792006
global_step: 400, avg_loss = 5.126814, time = 650.647822
global_step: 500, avg_loss = 4.219713, time = 648.755034
so I am bit unsure exactly whether things are going faster when fanned out
lowering the batch size to 15 while running on 4 GPUs does not change the
output much
global_step: 100, avg_loss = 8.417717, time = 518.246054
global_step: 200, avg_loss = 7.639581, time = 492.312914
global_step: 300, avg_loss = 7.274171, time = 493.591461
global_step: 400, avg_loss = 6.699906, time = 506.484376
On Wed, May 2, 2018 at 1:29 PM, David Levinthal<EMAIL_ADDRESS>wrote:

new distro is https://github.com/klintan/bi-att-flow/tree/dev
On Tue, May 1, 2018 at 3:45 PM, David Levinthal <
<EMAIL_ADDRESS>wrote:

I will modify the Andreas Klintberg fork of this..as that is the code
base that works on top of tree TF..nothing else does due to the change in
the handling of flags
https://www.linkedin.com/in/andreas-klintberg-b7655710/
but I have to wait until the 4 GPU machine I set this up on gets freed
up...
I don't want to build everything again  LOL
d
On Tue, May 1, 2018 at 2:58 PM, kelayamatoz<EMAIL_ADDRESS>wrote:

Hi David,
My name is Tian. I'm moving our discussion from email to this issue
ticket so that other developers who have issues with this error could see
it.
The issue with the original bidaf implementation is that it only creates
one loss variable for the model. On a single GPU this is fine, because you
only have one model. In a multi-GPU setting, the way bidaf implements
multi-GPU training is that it replicates the model for every GPU device,
and assigns one model on one device. This means that each model would
require its own loss variable. If the developer only specifies one loss
variable, tensorflow would try to reuse the loss variable for every model,
which would create a conflict.
For example, in your error, if you only have one device, the name of the
loss variable would be model_0/loss/ExponentialMovingAverage. If you
have two devices, another loss variable called
model_1/loss/ExponentialMovingAverage would be referenced by
tensorflow. Since this variable is not created before you generate the
whole model, tensorflow would try to reuse the variable you previously
generated for model_0. Does that make sense?
The solution to resolve this conflict is by creating a loss variable for
every model that's replicated:
https://github.com/stanford-futuredata/dawn-bench-models/blo
b/master/tensorflow/SQuAD/basic/model.py#L25:L36.
Unfortunately I don't have a mult-GPU node available. Would you mind try
this patch on your node and see if it works?
—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub
https://github.com/allenai/bi-att-flow/issues/54#issuecomment-385803301,
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Hi @demiguo. On tensorflow 1.12.0, I had the same problem and fixed it by adding the line:
        with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):

before ema.apply

First I would like to give some contexto to this issue. It applies only when a different WorkerX/GPUX/etcX tf.name_scope() was created over different instantations of any model that uses tf.train.ExponentialMovingAverage (commonly used by Batch Normalization). If instead there had been used a "WorkerX" tf.variable_scope(), there would be no possibility of reuse, because variables created with tf.get_variable() only ignore tf.name_scopes(). Thus using tf.get_variable() inside different tf.variable_scope() can only be different variables.
On the other hand, if there were no tf.name_scope() nor tf.variable_scope() over different gpu workers, variables being created with either tf.Variables or tf.get_variable would have exactly the same scope, giving both the possibility of being properly reused, but probably not creating a very pretty underlying graph, because operations would not be agregated over workers (esthetical/design/maintenance issue).
But as I understand, using with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE) before ema.apply, as @shimafoolad suggests, will drop reuse of loss/ExponentialMovingAverage/ across GPUs, and any shadow variable created with tf.Variable by ema.apply. That would be bad for the learning of Batch Normalization layers in distributed learning, which does not seem to be a good solution.
Maybe there is a way in which main variables of BN layers would be reused anyway, but I have found no explanation of such mechanism and maybe this issue would be solved with such an explanation.

Thougth it again and realized that trainable parámeters of Batch Normalization can be defined with tf.get_variable() and use "with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE)" as @shimafoolad says, as follows:
def batch_norm_template(inputs, is_training, scope,
        moments_dims, bn_decay):
  with tf.variable_scope(scope) as sc:
    num_channels = inputs.get_shape()[-1].value
    beta = tf.get_variable('beta', None, None,
        tf.constant(0.0, tf.float32, [num_channels]), None, True)
    gamma = tf.get_variable('gamma', None, None,
        tf.constant(1.0, tf.float32, [num_channels]), None, True)
    batch_mean, batch_var = tf.nn.moments(inputs,
            moments_dims, name='moments')
    decay = bn_decay if bn_decay is not None else 0.9
    ema = tf.train.ExponentialMovingAverage(decay=decay)

    # Operator that maintains moving averages of variables.
    with tf.variable_scope(tf.get_variable_scope(),
        reuse=tf.AUTO_REUSE):
      ema_apply_op = tf.cond(is_training,
                lambda: ema.apply([batch_mean, batch_var]),
                lambda: tf.no_op())

    # Update moving average, return current batch's avg and var.
    def mean_var_with_update():
      with tf.control_dependencies([ema_apply_op]):
        return tf.identity(batch_mean), tf.identity(batch_var)

    # ema.average returns the Variable holding the average of var.
    mean, var = tf.cond(is_training,
        mean_var_with_update,
        lambda: (ema.average(batch_mean), ema.average(batch_var)))
    normed = tf.nn.batch_normalization(inputs, mean, var,
            beta, gamma, 1e-3)

  return normed
```

Hope it helps, especially those who update legacy tensorflow


windows support needed to select subsystem for compilation

Currently rustc seems to default to the console subsystem on windows (both for gnu and msvc builds). There should be some way to set this in Cargo.toml so that the correct subsystem is used.
Here are the known ways to deal with this currently, all of which suck:

After cargo builds a console subsystem app, if you want a windows subsystem one, run editbin.exe manually or as part of some higher level build script like mach.
Override the linker with fake one that runs editbin.exe on the executable and swaps the subsystem. This solution won't work if your link command is not a real compiled executable because the link command line might be too long due to windows command line argument limitations.
Set RUSTFLAGS="-C link-args=-Wl,--subsystem,windows" or whatever subsystem you want. Unfortunately this causes Cargo to link every executable with the windows subsystem. In particular this means all build scripts get compiled this way which means they pop up console windows constantly during the build process.

cc @vvuk @alexcrichton @larsbergstrom

See these for more context:
https://www.reddit.com/r/rust/comments/40l6t8/windowed_applications_with_rust/
http://stackoverflow.com/questions/29763647/how-to-make-a-program-that-does-not-display-the-console-window

I think this is just https://github.com/rust-lang/rfcs/pull/1665?

Or rather, a concern that should be addressed as part of that RFC.

OK guys. I'm a Windows developer who's looking into picking up Rust because of its safety advantages over C/C++.
The very first thing I need is to be able to target the Windows subsystem. I need to be able to target either the console subsystem or the graphical subsystem individually for each binary. That's the very first thing I need in order to use Rust.
Are you saying this is still not implemented at this time...?

@denisbider this is implemented, it's just not stabilized yet. You can track support at https://github.com/rust-lang/rust/issues/37499

This was stabilized in rust-lang/rust, so closing.


UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb6 in position 4: invalid start byte

Crash Report
This crash report was reported through the automatic crash reporting system 🤖
Traceback
  File "/Users/neil/src/electron-cash/gui/qt/history_list.py", line 170, in <lambda>
    menu.addAction(_("Details"), lambda: self.parent.show_transaction(tx))
  File "/Users/neil/src/electron-cash/gui/qt/main_window.py", line 796, in show_transaction
    show_transaction(tx, self, tx_desc)
  File "/Users/neil/src/electron-cash/gui/qt/transaction_dialog.py", line 46, in show_transaction
    d = TxDialog(tx, parent, desc, prompt_if_unsaved)
  File "/Users/neil/src/electron-cash/gui/qt/transaction_dialog.py", line 94, in __init__
    self.add_io(vbox)
  File "/Users/neil/src/electron-cash/gui/qt/transaction_dialog.py", line 243, in add_io
    self.update_io(i_text, o_text)
  File "/Users/neil/src/electron-cash/gui/qt/transaction_dialog.py", line 287, in update_io
    cursor.insertText(addr.to_ui_string(), text_format(addr))
  File "/Users/neil/src/electron-cash/lib/address.py", line 258, in to_ui_string
    ret += lookup(op[0]) + " " + op[1].decode('utf-8')

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb6 in position 4: invalid start byte

Reporter
This issue was reported by 1 user(s):

Electrum Version
Operating System
Wallet Type
Locale

3.3
Darwin-17.7.0-x86_64-i386-64bit
standard
ja_JP

Additional Information
The reporting user(s) did not provide additional information.

I don't immediately understand this code, so can't know what's in the tuple, but it's the OP_RETURN changes, right?

Something to do with them.  It happened when I tried to display a SatoshiDice tx

#801 PR added -- @kyuupichan please verify it works now if you have time.  If not, I'll just merge.  Worked in my testing with satoshidice tx's... 👍


luci-app-sqm

Buenas noches Estimados,
Tengo un inconveniente con un router Tplink Archer C60 V3, tiene instalada la versión, OpenWrt 22.03.0 r19685-512e76967f, pero no me permite instalar la función de QSM, si me pueden orientar como se hace, cuando entro en sistemas>software>Update list y sale el siguiente error
Downloading https://downloads.openwrt.org/releases/22.03.0/targets/ath79/generic/packages/Packages.gz
*** Failed to download the package list from https://downloads.openwrt.org/releases/22.03.0/targets/ath79/generic/packages/Packages.gz
Downloading https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/base/Packages.gz
*** Failed to download the package list from https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/base/Packages.gz
Downloading https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/luci/Packages.gz
*** Failed to download the package list from https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/luci/Packages.gz
Downloading https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/packages/Packages.gz
*** Failed to download the package list from https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/packages/Packages.gz
Downloading https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/routing/Packages.gz
*** Failed to download the package list from https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/routing/Packages.gz
Downloading https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/telephony/Packages.gz
*** Failed to download the package list from https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/telephony/Packages.gz
Errors
Failed to send request: Operation not permitted
Failed to send request: Operation not permitted
Failed to send request: Operation not permitted
Failed to send request: Operation not permitted
Failed to send request: Operation not permitted
Failed to send request: Operation not permitted
Collected errors:

opkg_download: Failed to download https://downloads.openwrt.org/releases/22.03.0/targets/ath79/generic/packages/Packages.gz, wget returned 4.

opkg_download: Check your network settings and connectivity.

opkg_download: Failed to download https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/base/Packages.gz, wget returned 4.

opkg_download: Check your network settings and connectivity.

opkg_download: Failed to download https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/luci/Packages.gz, wget returned 4.

opkg_download: Check your network settings and connectivity.

opkg_download: Failed to download https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/packages/Packages.gz, wget returned 4.

opkg_download: Check your network settings and connectivity.

opkg_download: Failed to download https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/routing/Packages.gz, wget returned 4.

opkg_download: Check your network settings and connectivity.

opkg_download: Failed to download https://downloads.openwrt.org/releases/22.03.0/packages/mips_24kc/telephony/Packages.gz, wget returned 4.

opkg_download: Check your network settings and connectivity.
The opkg update command failed with code 6.

Luego, No information available, si hay alguna manera de instalar este paquete por favor orientarme, ya que lo necesito de manera urgente

wget returned 4.
opkg_download: Check your network settings and connectivity.

You have somehow invalid basic network settings.
Not a LuCI problem.

Hay alguna forma de bajar ese paquete adicional y instalarlo manual en el router?

Valitan, enpä ymmärrä espanjaa.
English, please.

Is there a way to download that additional package and install it manually on the router?
Since I tried several ways and it does not allow me to add the sqm.

The error message  "wget returned 4., opkg_download: Check your network settings and connectivity." means that your network settings in the router are wrong. It can't use wget/curl/whatever to download the packages.
You need to fix the network settings.
Alternatively, use e.g. USB stick to transfer the downloaded packages from your PC.


Removing an item from a list of checkboxes unchecks the next item

Description:
Weird title, I know, but it is a bit of a weird issue. So we have a list of checkboxes. The list is dynamic and populated by an async API. When a checkbox is unchecked, it is removed from the list and then we update the view model with the new list. When we do that update, the item gets removed correctly, but the next checkbox in the list becomes unchecked, even though it should still be checked. The DOM element has the "checked" attribute, the view model has the data that says it should be checked, but then if you inspect the DOM and look at the "checked" property of the Node, it says checked === false.
Now, I've found a way around it but I am not fully happy with the work around. I found that if you clear out the list first and then populate the list with the data, then it will render correctly. I've reproduced the bug on a basic level in the JSFiddle link below. Try clicking on a checkbox in the middle of the list. The workaround is putting another line in updateSources that does this.set('inputSources', []); first.
Thanks in advance for your help.
Versions affected:
0.8.14
Platforms affected:
Latest Chrome
Reproduction:
https://jsfiddle.net/03mu07e5/3/
Code:
var sources = [{
    key: 1,
    checked: true
},{
    key: 2,
    checked: true
},{
    key: 3,
    checked: true
},{
    key: 4,
    checked: true
}];

function getSources () {
  return sources;
}

new Ractive({
    template:`

            {{#each inputSources:i}}

{{>checkbox checked as checked, i as index}}Source {{key}}

            {{/each}}

        {{#partial checkbox}}
            <input type="checkbox" on-change="@this.removeSource(index)" {{#checked}}checked{{/checked}} />
        {{/partial}}
    `,
    el: '#app',
    data: () => {
        inputSources: []
    },

    oninit: function () {
        this.updateSources();
    },

    updateSources: function () {
        this.set('inputSources', getSources());
    },

    removeSource: function (index) {
        sources.splice(index, 1);
        this.updateSources();
    }
});

So...Ractive treats the checked attribute in a special way. Instead of being a presence attribute which is how it works in regular HTML, Ractive actually checks its value.
https://ractive.js.org/concepts/data-binding/#checkboxes
checked="{{ checked }}"

With that, your code works fine
https://jsfiddle.net/Lc6v10n7/

Awesome, thanks for the help. I recall seeing Rich talk about that issue but I missed the solution to it. Appreciate it.


Readme is missing system dependency on imagemagick

The issue is mostly in the title. convert is the program used to make logos and such. This program is a part of the ImageMagick suite. For most system package managers this is referenced with the package name imagemagick.
I encountered this when making my Docker in Docker container to test the makefile problem over on issue #48.
The fix is to add imagemagick to the list of packages for apt install over on the readme.

Ah, that's a fairly recent additon to the build system so I have forgotten it. I'll just add it to the readme, thanks.

I added ImageMagick to the dependency list in the readme and I also implemented a failsafe into the build system to make sure a compatible version is installed. (64542b5161624dca4b8bfe830b480ca9a79943b6)


"GeometryUtils" - part of the CSSOM. Provides much better position information than existi...

"GeometryUtils" - part of the CSSOM. Provides much better position information than existing methods.
Spec: http://dev.w3.org/csswg/cssom-view/#the-geometryutils-interface
Tutorial: https://hacks.mozilla.org/2014/03/introducing-the-getboxquads-api/

This issue was imported from Google Moderator
Moderator votes: +3
Issue added by jmo7 on 2014-08-15
To vote this issue up or down, simply include +1 or -1 in your comment.

+1

+1

+1

https://bugs.chromium.org/p/chromium/issues/detail?id=468662


Mongock.execute needs to be public

In the "Usage without Spring" section, the README shows manually calling runner.execute();.  However, the execute method is package-protected.  This needs to be public to work as documented.  As a workaround, it looks like I can manually call afterPropertiesSet().

Hello @mmindenhall, thank you for your comment.
I believe you are right. The workaround you mention feels totally right to me.
However, I will provide a fix soon, if not today, tomorrow. I will keep you updated


✨ JSON serialization standardization

Is there an existing issue for this?

[X] I have searched the existing issues

Suggestion
We are using both System.Text and Newtonsoft.JSON now. Newtonsoft was added to resolve some serializer issues we were seeing hydrating the guardian private data.
Determine best path to use only one JSON library.
Possible Implementation
No response
Anything else?
No response

The only place using the System.Text Json classes are int he DBService.cs class and the interop generator.


Convert project to external ESPHome component

This change makes it possible to load the code within ESPHome as an external component, and solves #55.

Thanks for figuring this out and pushing it! i think we still need to make an example yaml file which does the pulling in the right order, putting the git stuff in the large_framesize and legacy_framesize yaml's feels like the wrong location, as a user now still has to pull all files or merge the stuff himself as the lr_mhi_ac_ac_ctrl.yaml is the "main" file. i am trying this out now and see how far i can get :)

Ah, sorry about that, you are right, it should be in the lr_mhi_ac_ac_ctrl.yaml file. I didn't investigate the reason for the large and legacy framesize yaml files, and just put combined some things within the ESPHome configuration and it worked for me. I assumed it was only a different initial_value between them too, but I see now that there are more changes between the files. I would move lr_mhi_ac_ac_ctrl.yaml to the conf directory as well, and explain things in the readme.

If you want to keep the " !include conf/large_framesize.yaml" within the yaml, the directory should probably be moved insides the "components/MHI-AC-Ctrl" directory to work properly. lr_mhi_ac_ac_ctrl.yaml should be in the root and renamed to example_MHI-AC-Ctrl-ESPHhome.yaml according to the docs.

yes, i am trying that but it seems either i am doing it wrong, or i just don't understand it, i am getting some compile errors :)
as the includes are still in the lr_mhi_ac_ac_ctrl.yaml i think the custom component was not loaded yet (it gives me some errors about the naming of the vars with the dashes for example) i am checking it out

Does it work if you paste the contents of the yaml file within ESPHome like I did?

no that doesn't work for me, but to be honest, i think it worked because the includes were still in the main yaml, and so it was not using the external components part of the new files in the sub-yaml's. i am debugging the stuff in my own fork to see if i can get it to work.

I am very sorry, but I think you are right. I tried with a fresh ESPHome docker install and am getting errors now as well

Sorry, this isn't working as I thought it would

no worries, it gave me the needed motivation to pick this up further. i finally got it to compile and install on the ESP, but nothing works, so that's a start :)