GPU kernels get stopped after 2 seconds on Pixel 4

Describe the bug
I tried to run batch mode benchmarking on MobileNetV2. With tracy, I know that it actually takes ~60ms to finish one inference on Pixel 4 on GPU. If I benchmark the model with size=32, it reports ~60 ms. However, I found something weird when setting the size larger. I found that a launch of GPU kernel always gets stopped after 2 seconds on Pixel 4. The launch will always end at 2 seconds if I set the size >= 64.
This only happens on Pixel 4 (or andoird). For desktop, it can take more than 2 seconds to finish the work.

For CPU, it can also takes more than 2 seconds to finish the work.

To Reproduce
$ build-tracy/iree/tools/iree-translate --iree-mlir-to-vm-bytecode-module --iree-hal-target-backends=vulkan-spirv ~/mobilenet_v2.mlir -o a.vmfb --iree-hal-benchmark-dispatch-repeat-count=5
$ adb push a.vmfb /data/local/tmp
$ adb shell LD_LIBRARY_PATH=/data/local/tmp/ taskset 80 /data/local/tmp/iree-benchmark-module \
  --flagfile=/data/local/tmp/mobilenet_v2_flagfile \
  --module_file=/data/local/tmp/a.vmfb \
  --driver=vulkan \
  --batch_size=5

It can be reproduced by any models. I simplified the command to default config and verified that it can be repro with above instructions. By default it runs in ~1 sec. Making it 5 still make the program end in 2 seconds.

Interesting. Probably due to some emulated timeline semaphore weirdness. We don't have native timeline semaphore support on Pixel4 so enumerated timeline semaphore is chosen there.

You could use emulated timeline semaphores on non-Android to investigate that theory.
My first thought was some other Vulkan ICD timeout. If a particular dispatch takes too long, we may need to split it up into several dispatches.

+1 to what @ScottTodd said. You can force timeline semaphore on by --vulkan_force_timeline_semaphore_emulation.

I tried it on desktop. Looks like it is not the issue of emulated timeline semaphores. It takes 4 seconds to finish the work.

Are you getting a VK_ERROR_DEVICE_LOST return if trying to call vulkan after the 2s? OS usually has a watchdog that will reset the device if it thinks the GPU is hung.

Is the issue just that the work is finishing faster than you expect? you aren't inserting barriers between the dispatches (which is fine), which means all will run concurrently, and you may just be completing it fast :)
Try running on CPU with more than 1 thread enabled and you should see the same - 2 threads should run 2x faster, etc.

I get this error if I run with --benchmark_repetitions=2
F ../iree/hal/vulkan/emulated_semaphore.cc:140] Check failed: outstanding_semaphores_.empty() Destroying an emulated timeline semaphore without first waiting on outstanding signals
Aborted

Is the issue just that the work is finishing faster than you expect? you aren't inserting barriers between the dispatches (which is fine), which means all will run concurrently, and you may just be completing it fast :)
Try running on CPU with more than 1 thread enabled and you should see the same - 2 threads should run 2x faster, etc.

No, I think the issue is not the work is finishing faster than I expect. It should converge to a number when batch_size gets larger. But it always stops after 2 seconds.

No. So far I can only repro it on Pixel 4.

This looks like a Pixel 4 specific issue. Maybe it only happens on my Pixel 4...
Below is the result from S10, which looks good to me.

hi, I think it is the adreno driver that stops too long  tasks. There may be an internal watchdog inside the driver.
For practical uses, do not encode very large command, split into several small commands as a workaround.

Thanks @nihui for the insights! Now we know 2s is the kill threshold for Pixel 4 or Qualcomm SoC. :) Yes, this is just duplicating dispatches in a command buffer for benchmarking purposes, so that we can hide overhead. It is not meant for production usage.

Thanks, I will keep this in mind when setting benchmarking configurations!


[ETFE-3426] demo comments

Ready to be reviewed but please don't merge if approved - want to demo locally to Lexi tomorrow before merging in case anything has been missed.


pandas cleaning notebook

Below is a list of comments. If you want me to make any of the changes let me know:

When you start the section on string methods you include the pure python example of making "$123.45" a float two times. I think just once is enough.
I've often used string methods and pd.to_datetime to convert three numeric columns for year, month, day into a single column with a pandas date time type.
When you introduce selecting variables and observations I also hear the term indexing often (in addition to subsetting, filtering, and slicing)
There is a typo in the 4th bullet point when showing all the ways you can index into df. You wrote df[nlist]] instead of df[nlist]
When talking about the boolean selection we might want to introduce the query method. It is very concise and compiles the expressions and runs them in a more efficient way than we do when constructing these series/DataFrames of booleans by hand. I also like that it makes us not have to manage the boolean objects ourselves -- there's less room for bugs when we handle less temporary variables.
Formatting preferences: for the exercise at the bottom, I'd include the list of questions after the code snippet that loads the data instead of before it.

Question:  what do you make of the rsplit method changing from maxsplit=1
in pure Python to n=1 in Pandas.  Are these the same methods?
On Mon, Apr 4, 2016 at 7:51 AM, Spencer Lyon<EMAIL_ADDRESS>wrote:

Below is a list of comments. If you want me to make any of the changes let
me know:

When you start the section on string methods you include the pure
python example of making "$123.45" a float two times. I think just
once is enough.
I've often used string methods and pd.to_datetime to convert three
numeric columns for year, month, day into a single column with a pandas
date time type.
When you introduce selecting variables and observations I also hear
the term indexing often (in addition to subsetting, filtering, and
slicing)
There is a typo in the 4th bullet point when showing all the ways
you can index into df. You wrote df[nlist]] instead of df[nlist]
When talking about the boolean selection we might want to introduce
the query method. It is very concise and compiles the expressions and
runs them in a more efficient way than we do when constructing these
series/DataFrames of booleans by hand. I also like that it makes us not
have to manage the boolean objects ourselves -- there's less room for bugs
when we handle less temporary variables.
Formatting preferences: for the exercise at the bottom, I'd include
the list of questions after the code snippet that loads the data
instead of before it.

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I don't think they are the same. Look at the different pandas series str method names:
In [8]: s = pd.Series(pd.np.random.randn(10), dtype=object)

In [9]: s.str.
s.str.capitalize     s.str.find           s.str.islower        s.str.lstrip         s.str.rjust          s.str.swapcase
s.str.cat            s.str.findall        s.str.isnumeric      s.str.match          s.str.rpartition     s.str.title
s.str.center         s.str.get            s.str.isspace        s.str.normalize      s.str.rsplit         s.str.translate
s.str.contains       s.str.get_dummies    s.str.istitle        s.str.pad            s.str.rstrip         s.str.upper
s.str.count          s.str.index          s.str.isupper        s.str.partition      s.str.slice          s.str.wrap
s.str.decode         s.str.isalnum        s.str.join           s.str.repeat         s.str.slice_replace  s.str.zfill
s.str.encode         s.str.isalpha        s.str.len            s.str.replace        s.str.split
s.str.endswith       s.str.isdecimal      s.str.ljust          s.str.rfind          s.str.startswith
s.str.extract        s.str.isdigit        s.str.lower          s.str.rindex         s.str.strip

Compared to methods on a python string:
In [9]: s1 = "foo"

In [10]: s1.
s1.capitalize    s1.expandtabs    s1.isalpha       s1.isprintable   s1.lower         s1.rindex        s1.splitlines    s1.upper
s1.casefold      s1.find          s1.isdecimal     s1.isspace       s1.lstrip        s1.rjust         s1.startswith    s1.zfill
s1.center        s1.format        s1.isdigit       s1.istitle       s1.maketrans     s1.rpartition    s1.strip
s1.count         s1.format_map    s1.isidentifier  s1.isupper       s1.partition     s1.rsplit        s1.swapcase
s1.encode        s1.index         s1.islower       s1.join          s1.replace       s1.rstrip        s1.title
s1.endswith      s1.isalnum       s1.isnumeric     s1.ljust         s1.rfind         s1.split         s1.translate

It is unfortunate that they chose the same method name, but different argument names. I'm not sure there is much more to say about it

Ah, good to know.
What's curious here is that help generates docs for the second.  Eg,
df['var'].str.rsplit?
On Mon, Apr 4, 2016 at 11:58 AM, Spencer Lyon<EMAIL_ADDRESS>wrote:

I don't think they are the same. Look at the different pandas series str
method names:
In [8]: s = pd.Series(pd.np.random.randn(10), dtype=object)
In [9]: s.str.
s.str.capitalize     s.str.find           s.str.islower        s.str.lstrip         s.str.rjust          s.str.swapcases.str.cat            s.str.findall        s.str.isnumeric      s.str.match          s.str.rpartition     s.str.title
s.str.center         s.str.get            s.str.isspace        s.str.normalize      s.str.rsplit         s.str.translate
s.str.contains       s.str.get_dummies    s.str.istitle        s.str.pad            s.str.rstrip         s.str.upper
s.str.count          s.str.index          s.str.isupper        s.str.partition      s.str.slice          s.str.wrap
s.str.decode         s.str.isalnum        s.str.join           s.str.repeat         s.str.slice_replace  s.str.zfill
s.str.encode         s.str.isalpha        s.str.len            s.str.replace        s.str.split
s.str.endswith       s.str.isdecimal      s.str.ljust          s.str.rfind          s.str.startswith
s.str.extract        s.str.isdigit        s.str.lower          s.str.rindex         s.str.strip
Compared to methods on a python string:
In [9]: s1 = "foo"
In [10]: s1.
s1.capitalize    s1.expandtabs    s1.isalpha       s1.isprintable   s1.lower         s1.rindex        s1.splitlines    s1.upper
s1.casefold      s1.find          s1.isdecimal     s1.isspace       s1.lstrip        s1.rjust         s1.startswith    s1.zfill
s1.center        s1.format        s1.isdigit       s1.istitle       s1.maketrans     s1.rpartition    s1.strip
s1.count         s1.format_map    s1.isidentifier  s1.isupper       s1.partition     s1.rsplit        s1.swapcase
s1.encode        s1.index         s1.islower       s1.join          s1.replace       s1.rstrip        s1.title
s1.endswith      s1.isalnum       s1.isnumeric     s1.ljust         s1.rfind         s1.split         s1.translate
It is unfortunate that they chose the same method name, but different
argument names. I'm not sure there is much more to say about it
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Not sure I follow. Continuing from my same example:
In [10]: s.str.rsplit?
Signature: s.str.rsplit(pat=None, n=-1, expand=False)
Docstring:
Split each string in the Series/Index by the given delimiter
string, starting at the end of the string and working to the front.
Equivalent to :meth:`str.rsplit`.

.. versionadded:: 0.16.2

Parameters
----------
pat : string, default None
    Separator to split on. If None, splits on whitespace
n : int, default -1 (all)
    None, 0 and -1 will be interpreted as return all splits
expand : bool, default False
    * If True, return DataFrame/MultiIndex expanding dimensionality.
    * If False, return Series/Index.

Returns
-------
split : Series/Index or DataFrame/MultiIndex of objects
File:      ~/anaconda3/lib/python3.5/site-packages/pandas/core/strings.py
Type:      method

In [11]: s1.rsplit?
Docstring:
S.rsplit(sep=None, maxsplit=-1) -> list of strings

Return a list of the words in S, using sep as the
delimiter string, starting at the end of the string and
working to the front.  If maxsplit is given, at most maxsplit
splits are done. If sep is not specified, any whitespace string
is a separator.
Type:      builtin_function_or_method

We're getting different docs for the string methods.  If I try
docs['Country'].str.rsplit?
I get the maxsplit version.
On Mon, Apr 4, 2016 at 12:06 PM, Spencer Lyon<EMAIL_ADDRESS>wrote:

Not sure I follow. Continuing from my same example:
In [10]: s.str.rsplit?
Signature: s.str.rsplit(pat=None, n=-1, expand=False)
Docstring:
Split each string in the Series/Index by the given delimiter
string, starting at the end of the string and working to the front.
Equivalent to :meth:str.rsplit.
.. versionadded:: 0.16.2
Parameters
pat : string, default None
Separator to split on. If None, splits on whitespace
n : int, default -1 (all)
None, 0 and -1 will be interpreted as return all splits
expand : bool, default False
* If True, return DataFrame/MultiIndex expanding dimensionality.
* If False, return Series/Index.
Returns
split : Series/Index or DataFrame/MultiIndex of objects
File:      ~/anaconda3/lib/python3.5/site-packages/pandas/core/strings.py
Type:      method
In [11]: s1.rsplit?
Docstring:
S.rsplit(sep=None, maxsplit=-1) -> list of strings
Return a list of the words in S, using sep as the
delimiter string, starting at the end of the string and
working to the front.  If maxsplit is given, at most maxsplit
splits are done. If sep is not specified, any whitespace string
is a separator.
Type:      builtin_function_or_method
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That's a mystery to me. Not sure what's going on there

I now made all the changes, but have yet to synch w GH.  Except one.  I
didn't add the query.  After playing around, it seemed to me not to apply
to the same examples, so I skipped it.  Maybe next time?
Thanks again.
On Mon, Apr 4, 2016 at 12:24 PM, Spencer Lyon<EMAIL_ADDRESS>wrote:

That's a mystery to me. Not sure what's going on there
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Done.  Except query, which we can think about next time.


Need GPU (cuda) access while deploying the model

I need assistance with deploying a pre-trained model. I have created a custom score.py file for the deployment process. However, the docker created on the CPU instance does not provide access to the GPU, which poses a problem for predicting with PyTorch or TensorFlow models as they require input to be converted to tensors loaded on the GPU. Can you suggest a solution?
My score.py script -
import something

# original = torch.load

# def load(*args):
#     return torch.load(*args, map_location=torch.device("cpu"),pickle_module=None)

# def init():
#     global model
#     model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "use-case1-model")
#     # "model" is the path of the mlflow artifacts when the model was registered. For automl
#     # models, this is generally "mlflow-model".

#     with mock.patch("torch.load", load):
#         model = mlflow.pyfunc.load_model(model_path)

#     logging.info("Init complete")

def init():
    global model

    model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "use-case1-model")

    model = mlflow.pytorch.load_model(model_path, map_location=torch.device('cpu'))
    logging.info("Init complete")

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

def run(data):

    json_data = json.loads(data)

    title = json_data["input_data"]["title"]
    att = json_data["input_data"]["attributes"]

    result = {}

    for i in range(len(title)):

        my_dict = {}
        for j in range(len(att)):

            attr = att[i][j]

            t, a = nobert4token(tokenizer, title[i].lower(), attr)

            x = X_padding(t)
            y = tag_padding(a)

            tensor_a = torch.tensor(y, dtype=torch.int32)
            tensor_a = torch.unsqueeze(tensor_a, dim=0).to("cuda")

            tensor_t = torch.tensor(x, dtype=torch.int32)
            tensor_t = torch.unsqueeze(tensor_t, dim=0).to("cuda")

            output = model([tensor_t, tensor_a])

            predict_list = output.tolist()[0]

            my_dict[attr] = " ".join(words_p)

        result[title[i]] = my_dict

    return result

My invoke script-
ml_client.online_endpoints.invoke(
    endpoint_name=endpoint_result.name,
    deployment_name=green_deployment_uc1.name,
    request_file=os.path.join("./dependencies", "sample.json"),
)

My conda.yaml-
channels:
  - conda-forge
dependencies:
  - python=3.8
  - pip=22.1.2
  - numpy=1.21.2
  - scikit-learn=0.24.2
  - scipy=1.7.1
  - 'pandas>=1.1,<1.2'
  - pytorch=1.10.0
  - pip:
      - 'inference-schema[numpy-support]==1.5.0'
      - xlrd==2.0.1
      - mlflow== 1.26.1
      - azureml-mlflow==1.42.0
      - tqdm==4.63.0
      - pytorch-transformers==1.2.0
      - pytorch-lightning==2.0.2
      - seqeval==1.2.2
      - azureml-inference-server-http==0.8.0
name: model-env

Error that I am getting -
<IP_ADDRESS> - - [29/May/2023:10:03:32 +0000] "GET / HTTP/1.0" 200 7 "-" "kube-probe/1.18"
2023-05-29 10:03:34,291 E [70] azmlinfsrv - Encountered Exception: Traceback (most recent call last):
  File "/azureml-envs/azureml_d587e0800be72e17d773ddca63762cd1/lib/python3.8/site-packages/azureml_inference_server_http/server/user_script.py", line 130, in invoke_run
    run_output = self._wrapped_user_run(**run_parameters, request_headers=dict(request.headers))
  File "/azureml-envs/azureml_d587e0800be72e17d773ddca63762cd1/lib/python3.8/site-packages/azureml_inference_server_http/server/user_script.py", line 154, in <lambda>
    self._wrapped_user_run = lambda request_headers, **kwargs: self._user_run(**kwargs)
  File "/var/azureml-app/dependencies/score.py", line 129, in run
    tensor_a = torch.unsqueeze(tensor_a, dim=0).to("cuda")
  File "/azureml-envs/azureml_d587e0800be72e17d773ddca63762cd1/lib/python3.8/site-packages/torch/cuda/__init__.py", line 247, in _lazy_init
    torch._C._cuda_init()
RuntimeError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx

The above exception was the direct cause of the following exception:

If you think why I used "model = mlflow.pytorch.load_model(model_path, map_location=torch.device('cpu'))"
please refer to this forum- https://learn.microsoft.com/en-us/answers/questions/1291498/facing-problem-while-deploying-model-on-azure-ml-a
Documentation - https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&tabs=sdk

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

ID: de18260e-5e21-5055-eb2e-6360109596ae
Version Independent ID: 7793aabb-ad79-af81-6f56-c20baa2bc7ea
Content: Deploy MLflow models to online endpoint - Azure Machine Learning
Content Source: articles/machine-learning/how-to-deploy-mlflow-models-online-endpoints.md
Service: machine-learning
Sub-service: core
GitHub Login: @santiagxf
Microsoft Alias: fasantia

@rishavmandal771
Thanks for your feedback! We will investigate and update as appropriate.

@rishavmandal771
If you are deploying a pre-trained model that requires a GPU, you can use Azure Machine Learning's GPU-enabled compute instances to deploy your model. You can create a GPU-enabled compute instance and deploy your model on it. You can also use Azure Kubernetes Service (AKS) to deploy your model on a GPU-enabled cluster.
If you want to deploy your model on a CPU instance, you can try using the torch.device('cpu') argument when loading the model. This will load the model on the CPU instead of the GPU. You can also try using the torch.no_grad() context manager to disable gradient computation during inference. This can help reduce memory usage and speed up inference.
Another option is to convert your input to tensors on the CPU and then move them to the GPU using the to() method. For example, you can convert your input to a tensor on the CPU using torch.tensor() and then move it to the GPU using tensor.to('cuda').
You can also try using a CPU-only version of PyTorch or TensorFlow if available. This will allow you to run your model on a CPU instance without requiring a GPU.
Finally, you can consider using a cloud-based GPU service such as Azure's Batch AI or Amazon's EC2 instances to run your model. These services provide access to GPU-enabled instances that can be used for training and inference.

For now, I do not have access to some of the GPU compute instances that support the online endpoints deployment such as “Standard_NC4as_T4_v3”, “Standard_NC16as_T4_v3”, “Standard_NC8as_T4_v3”, “Standard_NC6s_v3”. Already raised ticket to get access to these GPU instances.
For now, I found an alternative, which worked for me-
device = torch.device("cuda"  if  torch.cuda.is_available() else  "cpu")

Replace below code in run () function:
 tensor_a = torch.tensor(y, dtype=torch.int32)
 tensor_a = torch.unsqueeze(tensor_a, dim=0).to("device")

 tensor_t = torch.tensor(x, dtype=torch.int32)
 tensor_t = torch.unsqueeze(tensor_t, dim=0).to("device")


SetUserProperty not work correctly in Xamarin iOS

Hi, the function Analytics.SetUserProperty(valor, key) in Xamarin iOS it does not work almost always. Why this happens?
I used Xamarin.Firebase.iOS.Analytics package in version 5.0.1.

@MaxiTalenti could you please provide a small sample project that demonstrates the issue that you are having.
Regards

`public static void UpdateInfo(Context context)
{
Task.Run(async () =>
{
try
{
var value = await Services();
PortfolioPreferences.setConfig(FirebaseProperties.Key1, value, context);
                SubmitInfo(context);
            }
            catch (Exception e)
            {
                Client.Services.AppCenter.AppCrashes.CreateCrash(e);
            }
        });
    }

    public static void SubmitInfo(Context context)
    {
        FirebaseCustom.FirebaseProperty.setProperty(PortfolioPreferences.GetValue(FirebaseProperties.Key1, context).ToString(), FirebaseProperties.Key1, context);

    }

public static void setProperty(string valor, FirebaseProperties name, Context context)
{
Firebase.Analytics.FirebaseAnalytics.GetInstance(context).SetUserProperty(valor, name.ToString());
}
}`

@MaxiTalenti  Thanks for the code.  Does it produce and error/exception or does it simply do nothing?
Regards

@MaxiTalenti Thanks for the code. Does it produce and error/exception or does it simply do nothing?
Regards

it simply does not do anything but does not record it.

@MaxiTalenti can you, please, try enabling the debug mode for events and see if they are reaching to Firebase console?
Thanks in advance!

No response for more than fourteen days, closing. If you still have this issue, please open a new issue.


[C-2295] Add member link in toast message after member creation

Add "View member" button to toast message after successfully creating a member.
After editing a member, the app should redirect user to updated member. Toast message does not have any action button in this scenario

C-2295

Can I work on this issue?

Can I work on this issue?

Of course 😄 Will assign you to the issue

picking this up


Crash in door rando in Metroid Prime 1

In door rando , the door between Main Plaza and Ruined Fountain acces cause crashes if it has a cover on it .
in addition to that going through an other door than the one you've entered the room will also cause a crash .
to avoid it i had to unload main plaza and enter it again once the cover is destroyed.

relevant bug report:
https://discord.com/channels/914291389293027329/1282788456904265768
v8.40
Hash: De-WRudt_Sq52VUvksCj9KzeTYn4WsW6ypMRnPEXYT5kroux-9j-SCtdfSj34pIcXJwazktWwRTBpsUAAA8s


Release Dialogflow version 1.2.0-beta01

New features since 1.1.0:

BrowseCarousel intent
MediaContent intent
TableCard intent
ListSelect subtitle

This fixes #2377 (at last!)


Unable to install Semaphore with curl + sh

Hi,
I am trying to run Semaphore on Ubuntu and executing below command.
$ curl -k https://raw.githubusercontent.com/jexia/semaphore/master/install.sh | sh -s -- -b ./bin v2.0.0-beta.19
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  9626  100  9626    0     0   4022      0  0:00:02  0:00:02 --:--:--  4020
jexia/semaphore info checking GitHub for tag 'v2.0.0-beta.19'
jexia/semaphore crit unable to find 'v2.0.0-beta.19' - use 'latest' or see https://github.com/jexia/semaphore/releases for details

But getting above mentioned error. Is there anything wrong in command. Could you please help me?
Originally posted by @Purushottamlpa in https://github.com/jexia/semaphore/issues/94#issuecomment-695212620

👋 @Purushottamlpa, thanks for the report. Would you be able to run the command again with debugging enabled?
$ curl https://raw.githubusercontent.com/jexia/semaphore/master/install.sh | sh -s -- -d -b ./bin v2.0.0-beta.19

@jeroenrinzema, thanks for your response. Tried with above command. Please find response:
curl -k https://raw.githubusercontent.com/jexia/semaphore/master/install.sh | sh -s -- -d -b  ./bin v2.0.0-beta.19
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
Dload  Upload   Total   Spent    Left  Speed
100  9626  100  9626    0     0   6210      0  0:00:01  0:00:01 --:--:--  6210
jexia/semaphore info checking GitHub for tag 'v2.0.0-beta.19'
jexia/semaphore debug http_download https://github.com/jexia/semaphore/releases/v2.0.0-beta.19
jexia/semaphore debug http_download_curl received HTTP status 000
jexia/semaphore crit unable to find 'v2.0.0-beta.19' - use 'latest' or see https://github.com/jexia/semaphore/releases for details

@Purushottamlpa, the received HTTP status code is 000 which often means that a network error occurred. Would you be able to check if you are able to resolve the DNS for github.com and githubusercontent.com?
$ dig github.com
$ dig githubusercontent.com

In the meantime would you be able to download the binaries manually from the latest release. Please select a linux binary for your CPU architecture.

@jeroenrinzema, thanks again. I made semaphore up with binaries.


Comments showing on home page

As an example, on this page there are comments:
https://dennisdel.com/blog/
However, when we look at the comment, it should be associated with a different page:
https://dennisdel.com/blog/building-docs-for-nuget-packages-with-vsts-and-github-pages/

You're using the "issue title contains page url" mode. This works well for blog post pages but not for a homepage. All your issue titles will match https://dennisdel.com/blog/:
https://github.com/dend/blog/issues?utf8=✓&q=is%3Aissue is%3Aopen "https%3A%2F%2Fdennisdel.com%2Fblog%2F"
For home pages it's better to use a hard coded search term or issue number.
For example, you could create an issue titled "homepage" and use this configuration:
<script src="https://utteranc.es/client.js"
        repo="dend/blog"
        branch="master"
        issue-term="homepage"
        async>
</script>

Or if the homepage issue already exists, reference it by number:
<script src="https://utteranc.es/client.js"
        repo="dend/blog"
        branch="master"
        issue-number="123"
        async>
</script>

The trick here will be preserving your existing configuration on "blog post" pages while using a hard-coded term on the home page. Here's how you could do that:
- <script src="https://utteranc.es/client.js"
-         repo="dend/blog"
-         branch="master"
-         issue-term="url"
-         async>
- </script>
+ <script id="utterances-bootstrapper">
+   var s = document.createElement('script');
+   s.src = 'https://utteranc.es/client.js';
+   s.async = true;
+   s.setAttribute('repo', 'dend/blog');
+   s.setAttribute('branch', 'master');
+   s.setAttribute('issue-term', /^\/blog\/?$/.test(location.pathname) ? 'homepage' : 'url');
+
+   var me = document.getElementById('utterances-bootstrapper');
+   me.insertAdjacentElement('afterend', s);
+ </script>

@dend are you all set- can this be closed?

@jdanyow thanks for the help! I fixed the template in this situation with a check on the title (there is only one page with a specific title in my set) to determine where not to render. This can be closed.

nice- thanks


Update test retrier to avoid long test

Description of changes:
This test was taking almost 3 minutes because it was using the default provider retrier.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

/lgtm

/approve


Update 2024-08-15-newcomers-meeting.md

Notes for Reviewers
This PR fixes #
Signed commits

[ ] Yes, I signed my commits.

🚀 Preview for commit 4e40af4e7e0e5a096f5ab7a1837ba2fc46b3aee4 at: https://66bdff5da001ab2e15801b39--meshery-docs-preview.netlify.app


TPI Euler equations as functions

In TPI, all the euler equations are not functions.  We might want to change this so we only have to change the euler equations in one place.

We could use the same euler equation functions as the SS does, but add inputs for b_s_t, b_s_tplus1, etc.  Then, it would work for TPI (assuming it has the right shapes), and in SS we would just put the same value for b_s_t and b_s_tplus1.

Resolved in PR #349


Remove git pinned version of Xpublish

Tests were failing to install xpublish from https://github.com/xarray-contrib/xpublish.git, so I updated it to pin to xpublish>=0.3.0,<0.4.0 from PyPI.

Thank you!


use conventional-recommended-bump instead of commit-analyzer

[Current coverage][cc-pull] is 100%

Merging [#19][cc-pull] into [master][cc-base-branch] will not change coverage

@@           master   #19   diff @@
===================================
  Files           1     1
  Lines          87    87
  Methods         0     0
  Messages        0     0
  Branches        0     0
===================================
  Hits           87    87
  Misses          0     0
  Partials        0     0

Powered by Codecov. Last updated by [4476389...4c9579f][cc-compare]
[cc-base-branch]: https://codecov.io/gh/safareli/free/branch/master?src=pr
[cc-compare]: https://codecov.io/gh/safareli/free/compare/4476389cb81101e10a14507c9d2c67e1a7f7b458...4c9579fc9480a38944174231179e03639ac44e3e?src=pr
[cc-pull]: https://codecov.io/gh/safareli/free/pull/19?src=pr


DCOS-37751: Add new JobOverview meditator which uses data layer

This is part 2 of the new overview page, to play around with this, you can navigate to http://localhost:4200/#/jobs/overview2
in order to test this, apply this patch to jobs.js:
@@ -3,7 +3,7 @@ import React from "react";
 /* eslint-enable no-unused-vars */
 import { IndexRoute, Redirect, Route } from "react-router";

-import JobsTabContainer from "#PLUGINS/jobs/src/js/JobsTabContainer";
+import JobsOverview from "#PLUGINS/jobs/src/js/JobsOverview";
 import TaskDetailsTab from "#PLUGINS/services/src/js/pages/task-details/TaskDetailsTab";
 import TaskFileBrowser from "#PLUGINS/services/src/js/pages/task-details/TaskFileBrowser";
 import TaskFileViewer from "#PLUGINS/services/src/js/pages/task-details/TaskFileViewer";
@@ -28,12 +28,12 @@ const jobsRoutes = [
     children: [
       {
         type: Route,
-        component: JobsTabContainer,
+        component: JobsOverview,
         path: "overview",
         children: [
           {
             type: Route,
-            path: ":id"
+            path: ":path"
           }
         ]
       },
There is a very basic UI to verify that the Mediator works as expected, also there are some `console.log` commands to help verifying/debugging it. All of this will be removed and replaced with the proper UI in DCOS-37752 👍

@DanielMSchmidt @nLight comments addressed, its ready for review again 👍

is it how it is supposed to look like?

@nLight exactly like this - UI components will be updated in next PR. The code isnt in use yet.
@DanielMSchmidt I'm on it.

@DanielMSchmidt @nLight pushed a fix for 'failureCount' issue, please revisit 🙇

Ping @nLight - regarding your UI question, #3046 is the follow-up PR I was talking about 👍


Adding report card and coverage badge.

Also change the badge for godoc with a more shiny one.

I'm trying to add this awesome repo to Awesome-go list. 😁

:+1: looks good to me!


[TensorFlow Android Camera Demo] NOTHING DETECTED with SSD FPN model

System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Window 10 64bit
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: Samsung Galaxy S7 Edge, RAM 4GB
TensorFlow installed from (source or binary): binary with pip: pip install tensorflow
TensorFlow version (use command below): 1.9.0
Python version: Python 3.6.4 |Anaconda custom (64-bit)
Bazel version (if compiling from source): None
GCC/Compiler version (if compiling from source): None
CUDA/cuDNN version: None
GPU model and memory: None
Exact command to reproduce: None

Describe the problem
I retrained the model using SSD FPN. The training process was ok, total loss was acceptable at 0.5 after 1.7K steps.
The problem: I imported to TensorFlow Android Camera Demo, at DetectorActivity, I modified:
private static final String TF_OD_API_MODEL_FILE = "file:///android_asset/frozen_inference_graph.pb"; private static final String TF_OD_API_LABELS_FILE = "file:///android_asset/my_list.txt";
NOTHING DETECTED.

Just to confirm that, the app was working with SSD Mobilenet V1 model (re-trained with my own data)
I also changed:
private static final int TF_OD_API_INPUT_SIZE = 640;  at DetectorActivity.java because SSD FPN uses input-size 640x640, but not working

This question is better asked on StackOverflow since it is not a bug or feature request. There is also a larger community that reads questions there.
If you think we've misinterpreted a bug, please comment again with a clear explanation, as well as all of the information requested in the issue template. Thanks!


WIP: allow translate log entry buffer to grow

Overview
In the case where a translate log entry contained
many key/id pairs, it was possible for the read
buffer (which was allocated at 65536 bytes) to
fail to handle it. This happened when the serialized
LogEntry was larger than 65536 bytes.
This PR adds logic which returns a custom error called
ErrTranslateReadTargetUndersized notifying the reader
to reallocate a larger read buffer and try the read
again.
TODO:

[x] Add a max buffer size check to prevent this from doubling the
buffer size with no limit.
[x] Add tests.

Fixes #1784
Pull request checklist

[x] I have read the contributing guide.
[x] I have agreed to the Contributor License Agreement.
[ ] I have updated the documentation.
[x] I have resolved any merge conflicts.
[ ] I have included tests that cover my changes.
[x] All new and existing tests pass.

Code review checklist
This is the checklist that the reviewer will follow while reviewing your pull request. You do not need to do anything with this checklist, but be aware of what the reviewer will be looking for.

[ ] Ensure that any changes to external docs have been included in this pull request.
[ ] If the changes require that minor/major versions need to be updated, tag the PR appropriately.
[ ] Ensure the new code is properly commented and follows Idiomatic Go.
[ ] Check that tests have been written and that they cover the new functionality.
[ ] Run tests and ensure they pass.
[ ] Build and run the code, performing any applicable integration testing.

ok, i'll address the TODOs and send it back to you

@yuce I completed the TODOs on this PR.

Tests run OK, testing it with a sample cluster.

The original problem remains resolved. Running the second node and immediately querying the second node is still problematic. Here are Pilosa logs of two outcomes in case it helps to diagnose the problem later:

{"error":"executing: index not found"} (automatically resolved in time)

$ ./pilosa-translate-read-buffer-increase server -c node2.config.toml
2018/12/18 18:57:41 Pilosa v1.1.0-483-g6b93dc03, build time 2018-12-18T18:52:04+0000
2018/12/18 18:57:41 load NodeID: node2_data/.id
2018/12/18 18:57:41 open server
2018/12/18 18:57:41 752969fc-9b72-433d-8cdd-cafe057ca303 wait for joining to complete
2018/12/18 18:57:41 merge cluster status: &{26248814-c5db-4fae-8916-21f4799539af NORMAL [Node:http://localhost:10101:READY:71c0a25e-80d6-450b-926b-52a90b6c4c18 Node:http://localhost:10102:DOWN:752969fc-9b72-433d-8cdd-cafe057ca303]}
2018/12/18 18:57:41 change cluster state from STARTING to NORMAL on 752969fc-9b72-433d-8cdd-cafe057ca303
2018/12/18 18:57:41 joining has completed
2018/12/18 18:57:41 open holder path: node2_data
2018/12/18 18:57:41 open holder: complete
2018/12/18 18:57:41 sending state READY (71c0a25e-80d6-450b-926b-52a90b6c4c18)
2018/12/18 18:57:41 merge cluster status: &{26248814-c5db-4fae-8916-21f4799539af NORMAL [Node:http://localhost:10101:READY:71c0a25e-80d6-450b-926b-52a90b6c4c18 Node:http://localhost:10102:READY:752969fc-9b72-433d-8cdd-cafe057ca303]}
2018/12/18 18:57:41 listening as http://localhost:10102
2018/12/18 18:57:41 diagnostics disabled
2018/12/18 18:57:41 holder sync monitor initializing (10m0s interval)

{"results":[0]} on both nodes after the import:

2018/12/18 18:59:05 Pilosa v1.1.0-483-g6b93dc03, build time 2018-12-18T18:52:04+0000
2018/12/18 18:59:05 load NodeID: node2_data/.id
2018/12/18 18:59:05 open server
2018/12/18 18:59:05 46403179-de65-4e9b-bf55-a2a0a972b471 wait for joining to complete
2018/12/18 18:59:05 follow resize instruction on 46403179-de65-4e9b-bf55-a2a0a972b471
2018/12/18 18:59:05 merge cluster status: &{1de68381-2ca8-4f08-b548-65c5af9fca66 RESIZING [Node:http://localhost:10101:READY:6846bdab-98e3-423a-aa84-026d891beaba]}
2018/12/18 18:59:05 change cluster state from STARTING to RESIZING on 46403179-de65-4e9b-bf55-a2a0a972b471
2018/12/18 18:59:05 done MergeClusterStatus, start goroutine
2018/12/18 18:59:05 joining has completed
2018/12/18 18:59:05 open holder path: node2_data
2018/12/18 18:59:05 open holder: complete
2018/12/18 18:59:05 sending state READY (6846bdab-98e3-423a-aa84-026d891beaba)
2018/12/18 18:59:05 listening as http://localhost:10102
2018/12/18 18:59:05 holder sync monitor initializing (10m0s interval)
2018/12/18 18:59:05 diagnostics disabled
2018/12/18 18:59:06 get shard 0 for index i1 from host http://localhost:10101
2018/12/18 18:59:06 retrieve shard 0 for index i1 from host http://localhost:10101
2018/12/18 18:59:06 merge cluster status: &{1de68381-2ca8-4f08-b548-65c5af9fca66 NORMAL [Node:http://localhost:10102:DOWN:46403179-de65-4e9b-bf55-a2a0a972b471 Node:http://localhost:10101:READY:6846bdab-98e3-423a-aa84-026d891beaba]}
2018/12/18 18:59:06 mismatched state in mergeClusterStatus got DOWN have READY
2018/12/18 18:59:06 change cluster state from RESIZING to NORMAL on 46403179-de65-4e9b-bf55-a2a0a972b471
2018/12/18 18:59:06 sending state READY (6846bdab-98e3-423a-aa84-026d891beaba)
2018/12/18 18:59:06 merge cluster status: &{1de68381-2ca8-4f08-b548-65c5af9fca66 NORMAL [Node:http://localhost:10102:READY:46403179-de65-4e9b-bf55-a2a0a972b471 Node:http://localhost:10101:READY:6846bdab-98e3-423a-aa84-026d891beaba]}


register

@JuliaRegistrator register

Error while trying to register: "Tag with name v0.4.0 already exists and points to a different commit"


Replace all instances of @vanderbilt.edu in the code

For the transition in Dec 2019, i noticed that there are several @vanderbilt.edu emails listed in the framework that should be changed to @vumc.org.

Yep, I've been holding off on changing them since we were looking into changing way emails work in general.  It sounds like that's not going to happen anytime soon, so I'll go ahead and update them.

Done!


After packing with electron, the running program reported an error that the module could not be found

SerialPort Version
12.0.0
Node Version
16.14.1
Electron Version
28.2.1
Platform
No response
Architecture
No response
Hardware or chipset of serialport
No response
What steps will reproduce the bug?
My project environment: pnpm + vite5 + electron-builder24.13.3
After packaging with electron-builder, running an error occurred:

I tried to install the missing module and found that all the serialport dependencies were missing
What happens?
The packaged exe cannot be opened
What should have happened?
Normal open
Additional information
No response

I had the same problem.Has the problem been solved now?

I have a problem with pnpm, which is going to have a problem in electron.  I can change yarn

我有同样的问题。现在问题解决了吗？


PHP should not have to manually decode postgres json columns

When the PHP code fetches from the orderedStations view the json columntypes are brought in as "string" types into the OrderedStation model, and must then be manually json_decode()'d.
This is bad, and causes a lot of overhead in the PHP code. This issue should be investigated to see if we can get PHP to bring in the JSON columns into properties with type stdClass rather than String.
Currently unknown if this is possible with PDO. Might need to research (or set up a clean project and test) if PHP is even capable of dealing with column type "json". If it is determined that it is, then we'll need to dig into Respect\Data and Respect\Relational to see if they're the thing causing us problems or not.

It appears that PHP is unable to accomplish this without manually calling the json_decode() method on the string.
I'm closing this in favor of #3, because we know we're going to have to call json_decode(), so the focus should now become how and where we call it.


Fix 2327: Detecting OLM existence before start to install

Signed-off-by: Ying Mo<EMAIL_ADDRESS>
Description of the change:
Adding logic in install.sh to check if OLM has already been installed in olm namespace before going forward.
Motivation for the change:
This PR is aimed to fix the issue documented here
Reviewer Checklist

[x] Implementation matches the proposed design, or proposal is updated to match implementation
[x] Sufficient unit test coverage
[x] Sufficient end-to-end test coverage
[x] Docs updated or added to /doc
[x] Commit messages sensible and descriptive

/lgtm

/approve


Have callback been completely replaced by promises?

I used swal2 prior to the newest release, and this particular section of my code was running properly using the same callback structure as the original swal:
swal(warning.alert,
    function(isConfirm) {
        console.log(isConfirm);
            if (isConfirm === true) {
                console.log('auth warning acknowledged.');
            }
    }
)

Now, the if block never executes (followed by an else, which I never see either), which leads me to believe the entire callback is dead, in favor of promises.  Is that correct, or am I missing something in the implementation?

You are correct, callback was completely replaced by promise in v1.0.0

Well, that pretty much ruins Swal2 for me.  I was using Swal2 without queueing for the situations that needed promises, and standard construction from swal to work with the alert queueing I asked you about before.  Since the queue won't accept promises, I don't really have an option to stay with swal2, which is unfortunate, as I prefer the other improvements you've made to the other repo.


feat: allow single tap selection on mobile devices

This is would be one possibility to solve #166
closes #166

:tada: This PR is included in version 0.9.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:

@scttcper Thank you so much for merging this PR so quickly! :-)
Unfortunately I found an issue with my addition that I have previously overlooked:
The event is also triggered when the user tries to swipe the emoji list. This can be easily bypassed by adding the following to the component using emoji-mart:
        @HostListener('body:touchmove', ['$event']) touchmove(event) {
		this.dragging = true;
	}

	@HostListener('body:touchstart', ['$event']) touchstart(event) {
		this.dragging = false;
        }

... and then setting the new property like this: [emitOnTouch]="!dragging". This will not be apparent to other users though, so this logic should maybe be added to the emoji-mart.
However, this might not be the best approach at all: The touchend event is always triggered with a delay. So I started researching why the click event's not happening in the first place, and found this rule disables it:
The target element, or any of its ancestors up to but not including the <body>, has an explicit event handler set for any of the mouse events. This event handler may be an empty function.

(s. https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html - Safari's oddities)
So in my fork I just made adding the mouse hover events depend on [showPreview]="true". This works fine in the demo app, but for some reason not in the app I'm using emoji-mart for. I haven't yet figured out why, so I haven't created another PR yet.
Sorry for this, I really should have done more testing.

@floAtEbcont 	can you make a pr if you get a chance?

@floAtEbcont forgot to respond. But all the styles are taken from the react project this is based on.


Missing flags in grunt build

The issue appears to be that the flag images are not present in the production site (post grunt build), but are available in the development site (grunt serve, for example). This might be related to missing bower statements, requiring manual css stylesheet and js inclusion in index.html:
<link rel="stylesheet" href="bower_components/intl-tel-input/build/css/intlTelInput.css" />

<script src="bower_components/intl-tel-input/build/js/intlTelInput.min.js"></script>
<script src="bower_components/intl-tel-input/lib/libphonenumber/build/utils.js"></script>
<script src="bower_components/ng-intl-tel-input/ng-intl-tel-input.module.js"></script>
<script src="bower_components/ng-intl-tel-input/ng-intl-tel-input.provider.js"></script>
<script src="bower_components/ng-intl-tel-input/ng-intl-tel-input.directive.js"></script>

Development:

Production:

@pattinsont check out step three of intl-tel-input Getting Started
In the samples you provided, is the production sample public?

@pattinsont any updates?

@bighappyface, great.
That helped ! Thanks


fix: Destroy stream to prevent memroy leak

If leave the voice channel or stream another music while streaming, a memory leak occurs.
Active handles and allocated memory size are increased over time.
Add _destroy() function to  fix it.
_destroy() is executed automatically when destroyed, so users don't have to change their code.

Could you try the latest version of this module and see if you can still reproduce the memory leaks? Thanks!

@amishshah I think v1.3.0 does not cause memory leaks on 'canDemux' case. Thank you!


Issue with distorted meshes

I'm having an issue with my model being distorted after turning it into a JSX component. I have one glb file with all the scene's meshes, but it seems like some of them are not being scaled/positioned correctly (At least I think that's the issue?). Not sure where exactly the problem is stemming from, just wondering if you had any insight on a possible cause?
When viewing the glb in a model viewer it appears correctly:

After creating the JSX:

PS: Thank you for your work :+1:

could you try without the compress option? like disabling it. it tries to avoid "empty" objects but maybe it's overlooking something. if your scene is correct with compression disabled (see the options on the main page) then that must be it. in that case i would be glad if i could have the gltf for bug fixing.

Appreciate the fast response. Just tried it with npx gltfjsx gltf/ForestScene2.glb --types --compress=false and I'm getting the same result. More than happy to send over the glb if you'd like to have a look.

could you append it here?

After debugging some more, it looks like its actually just an issue with the precision I was using. The source models were scaled down very small and I did not use the proper precision arg which resulted in some bad rounding.

Everything's as expected! Closing this issue.


Impossible to have other data than tweet

Here is my code and when I run it I can only obtain the tweets but no information like: #of likes, RT, username,...
Thanks in advance
from twitterscraper import query_tweets
import datetime as dt
import pandas as pd
from twitterscraper import query_tweets_from_user
import json

start_date=dt.date(2019,4,10)
end_date=dt.date(2019,4,15)
limit=100
lang='english'

with open('output.csv', 'w+') as file:
    for tweet in query_tweets('Tiger King',begindate=start_date,enddate=end_date,limit=limit,lang=lang):
        file.write(str(tweet.text.encode('utf-8'))+'\n')
file.close()

you are using tweet.text. If you observe the Tweet class in tweet.py, it has the attribute Tweet.likes, Tweet.retweets, etc. Please check out that file to see what attributes are available for your consumption.


ModuleDependencyResolutionError: 'SimpleTransaction' not exported from '@aptos-labs/ts-sdk' after dependencies update(@aptos-labs/wallet-adapter-react)

Issue Description
After updating to the latest versions of the Aptos wallet adapter and its dependencies, our Next application fails to compile due to a ModuleDependencyError. The error indicates that SimpleTransaction, which is expected to be exported from @aptos-labs/ts-sdk, is not found.
Environment
Node.js version: v20.13.1
React version: ^18
Next.js version: 14.1.4
Wallet Adapter packages:
@aptos-labs/ts-sdk: ^1.9.1
@aptos-l,abs/wallet-adapter-core: ^3.3.0
@aptos-labs/wallet-adapter-react: ^3.0.3
Steps to Reproduce
Update the following dependencies to their specified versions:
@aptos-labs/ts-sdk: ^1.9.1
@aptos-labs/wallet-adapter-core: ^3.3.0
@aptos-labs/wallet-adapter-react: ^3.0.3
Run the application.
Observe the compilation error in the console.
Expected Behavior
The application should compile without errors.
Actual Behavior
The application fails to compile with the following error:
index.js:618 Uncaught ModuleDependencyError: export 'SimpleTransaction' (imported as 'SimpleTransaction') was not found in '@aptos-labs/ts-sdk' (possible exports: APTOS_BIP44_REGEX, APTOS_COIN, ...<omitted for brevity>)

please update @aptos-labs/ts-sdk package https://github.com/aptos-labs/aptos-wallet-adapter/blob/main/apps/nextjs-example/package.json#L14

please update @aptos-labs/ts-sdk package https://github.com/aptos-labs/aptos-wallet-adapter/blob/main/apps/nextjs-example/package.json#L14

Working now thanks


feat: add completions, man pages and linux package builds

Description

[x] Add completion and man pages
[x] Generate builds for var linux distros
[x] Rename generated artifacts to fga* instead of openfga-cli*
[x] Run a sample release on PRs

References

Sources:

Shipping completions for Go CLIs using GoReleaser and Cobra
GoReleaser Docs

Review Checklist

[x] I have clicked on "allow edits by maintainers".
[ ] I have added documentation for new/changed functionality in this PR or in a PR to openfga.dev [Provide a link to any relevant PRs in the references section above]
[x] The correct base branch is being used, if not main
[ ] I have added tests to validate that the change in functionality is working as expected

Should we expect https://github.com/openfga/cli/actions/runs/5532329691/jobs/10094255157?pr=87 to fail?


Check API id removal doesn't break anything, and fix where needed.

[ ] /stackscript customer_id
[ ] /account/events user_id

The only places these are referenced are in docs and test objects.


Command /usr/bin/codesign failed with exit code 1

模拟器 也有证书签名问题?

thanks

http://blog.csdn.net/sico2sico/article/details/53068332


issue during local deployment (urlparse)

When comes the time to run ./manage.py runserver, I get an error. It looks like it could be related to python3/python differences, because of the requirement for python3-openid.
(oh_env) pdehaye@PaulOliviersMBP open-humans (master)*$ ./manage.py runserver
Unhandled exception in thread started by <function wrapper at 0x1068c5410>
Traceback (most recent call last):
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/django/utils/autoreload.py", line 226, in wrapper
    fn(*args, **kwargs)
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/django/core/management/commands/runserver.py", line 109, in inner_run
    autoreload.raise_last_exception()
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/django/utils/autoreload.py", line 249, in raise_last_exception
    six.reraise(*_exception)
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/django/utils/autoreload.py", line 226, in wrapper
    fn(*args, **kwargs)
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/django/__init__.py", line 18, in setup
    apps.populate(settings.INSTALLED_APPS)
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/django/apps/registry.py", line 108, in populate
    app_config.import_models(all_models)
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/django/apps/config.py", line 202, in import_models
    self.models_module = import_module(models_module_name)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/importlib/__init__.py", line 37, in import_module
    __import__(name)
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/social/apps/django_app/default/models.py", line 9, in <module>
    from social.storage.django_orm import DjangoUserMixin, \
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/social/storage/django_orm.py", line 8, in <module>
    from social.storage.base import UserMixin, AssociationMixin, NonceMixin, \
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/social/storage/base.py", line 12, in <module>
    from openid.association import Association as OpenIdAssociation
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/openid/association.py", line 30, in <module>
    from openid import cryptutil
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/openid/cryptutil.py", line 33, in <module>
    from openid.oidutil import toBase64, fromBase64
  File "/Users/pdehaye/tests/open-humans/oh_env/lib/python2.7/site-packages/openid/oidutil.py", line 17, in <module>
    from urllib.parse import urlencode
ImportError: No module named parse

@pdehaye Was @beaugunderson's comment helpful in resolving this?

I had missed the comment, and haven't tried since...

Seems that this has gone away.


quote!{} including other macros

A question by email:

I want to include macro definitions and invocations in a quote!ed block (e.g. quote! { macro_rules! ... }). Doing this naively results in horrible name conflicts, as rust expands the macros on the spot.
Is there a way to include macros inside a quote!{} that are NOT expanded within the block?

As you noticed, the input to quote! cannot contain dollar signs because of limitations of macro_rules. But dollar signs can come from interpolations! So this is one workaround:
#[macro_use]
extern crate quote;

fn main() {
    let dollar = quote::Ident::new("$");
    let tt = quote::Ident::new("$tt");

    println!("{}", quote! {
        macro_rules! id {
            (#dollar(#tt:tt)*) => {
                #dollar(#tt)*
            }
        }
    });
}

Very useful to know. Thanks!

This method no longer works, Ident provided by syn denies $ being a valid ident.
How to quote! a macro currently?


Add master branch of aspnet/aspnet-docker

Added aspnet/aspnet-docker master branch
Also, but all aspnet repos into the same section
@dagood @mmitche

Thanks


Popping up browser to renew SSO Login

https://stackoverflow.com/questions/62311866/how-to-use-the-aws-python-sdk-while-connecting-via-sso-credentials

Okay, so this is really no longer necessary, as the web dashboard goes on ECS/Load Balancer with the SageWorks Execution Role.


Move derivitive doc so it won't be treated as a manpage

Anything with the .md suffix in docs/ gets compiled into a manpage, so let's get things that aren't manpages out of there. This isn't precisely a tutorial, but it seems like the next best place.

LGTM

Might consider adding a link to the docs/tutorials/README.md to this page, otherwise LGTM

Do this in a separate PR.
/lgtm


Use CDI API for clone authorization

What this PR does / why we need it:
CDI API can now hide away the authorization pieces (DataSources, clone types) behind dv.Authorize() which is now useable,
plus, this will also take snapshot clone auth into account.
Which issue(s) this PR fixes (optional, in fixes #<issue number>(, fixes #<issue_number>, ...) format, will close the issue(s) when PR gets merged):
Fixes #
Special notes for your reviewer:
Release note:

Use auth API for DataVolumes, stop importing kubevirt.io/containerized-data-importer

/test all

/test all

/test pull-kubevirt-fossa

/test all

/test all

/cc @mhenriks
/cc @dhiller should resolve fossa failures

/retest

/cc @dhiller @mhenriks

/retest-required

/retest-required

Just rebased, no conflicts

/retest

/retest-required

/lgtm
/approve

/cherrypick release-1.0


Enable reading gzip files faster with the python main thread using python-isal.

This adds python-isal to the mix as an optional dependency. Optional, because it is only available on linux and requires extra libraries to be present.

Some benchmarks involving cutadapt in single-threaded mode:
Before:
$ hyperfine -w 3 -r  10 'cutadapt -a AGATCGGAAGAG 500000reads.fastq.qz -Z -o ramdisk/big2.cut.fastq.gz'
Benchmark #1: cutadapt -a AGATCGGAAGAG 500000reads.fastq.qz -Z -o ramdisk/big2.cut.fastq.gz
  Time (mean ± σ):      5.824 s ±  0.030 s    [User: 5.793 s, System: 0.029 s]
  Range (min … max):    5.785 s …  5.875 s    10 runs

$ du -h ramdisk/big2.cut.fastq.gz
66M	ramdisk/big2.cut.fastq.gz

$ zcat ramdisk/big2.cut.fastq.gz | md5sum
97fc2a9c78e6597912a02b168e4cd068  -

After:
$ hyperfine -w 3 -r  10 'cutadapt -a AGATCGGAAGAG 500000reads.fastq.qz -Z -o ramdisk/big2.cut.fastq.gz'
Benchmark #1: cutadapt -a AGATCGGAAGAG 500000reads.fastq.qz -Z -o ramdisk/big2.cut.fastq.gz
  Time (mean ± σ):      4.010 s ±  0.067 s    [User: 3.973 s, System: 0.036 s]
  Range (min … max):    3.946 s …  4.190 s    10 runs

$ du -h ramdisk/big2.cut.fastq.gz
62M	ramdisk/big2.cut.fastq.gz

$ zcat ramdisk/big2.cut.fastq.gz | md5sum
97fc2a9c78e6597912a02b168e4cd068  -

From 5.8 -> 4 seconds on average is quite a substantial improvement. Also the filesize is slightly smaller when using python-isal 66 -> 62m. So it is an approvement all around. Last but not least, the checksum of the content is the same, which is required.
This will be very very nice for people using cutadapt in single threaded mode. Galaxy users come to mind @bernt-matthias will probably like this.
If we get this into the conda release we can set the python-isal requirement for linux installs only. This will then be automatically available for biocontainers users as well on the next cutadapt release.

Wow, the results looks really good!
This should definitely go in, just let me find some time to review the code changes, which may take until after the holidays. I hope that’s fine.

I also was wondering about deployment. When this is merged, is it correct that the Conda package should be updated to depend on python-isal (on Linux)?

Yes, since that would propagate to the bioconda package for cutadapt as well. And as a result also the biocontainer. This will enable fast decompression/compression for a lot of users.

Also, for the pip install case, have you considered providing wheels for python-isal? Then one would no longer need to install libisal-dev.

I have looked into this because of your comment. Initially I did not want to do it because it is a lot of hassle, and conda exists. I came to the conclusion that providing wheels adds no extra value. The isa-l libraries are still needed on the user system. So it does not eliminate a step for the user. I did however make the installation instructions more clear on the python-isal readme so it should be easier for users to install.
I changed the readme text on xopen to refer to this updated installation text.

Or you could go even one step further by linking statically against libisal (thus including it in the wheel), and then the user doesn’t need to use their distribution package manager at all.

That would be a good solution. The dynamic linking works very well with conda and gives the benefit of automatically updating any bugs in isa-l away.
Static linking would however be more practical for more python users and would lead to less compatibility nightmares for me as the required version is simply included. The pace of change in isa-l is not that big and therefore it is very easy to keep up. There are however license issues that need to be discussed with the project maintainers before I am able to redistribute isa-l in that form. So I will do that.

I also was wondering about deployment. When this is merged, is it correct that the Conda package should be updated to depend on python-isal (on Linux)?

Yes, since that would propagate to the bioconda package for cutadapt as well. And as a result also the biocontainer. This will enable fast decompression/compression for a lot of users.

Also, for the pip install case, have you considered providing wheels for python-isal? Then one would no longer need to install libisal-dev.

I have looked into this because of your comment. Initially I did not want to do it because it is a lot of hassle, and conda exists. I came to the conclusion that providing wheels adds no extra value. The isa-l libraries are still needed on the user system. So it does not eliminate a step for the user. I did however make the installation instructions more clear on the python-isal readme so it should be easier for users to install.
I changed the readme text on xopen to refer to this updated installation text.

Or you could go even one step further by linking statically against libisal (thus including it in the wheel), and then the user doesn’t need to use their distribution package manager at all.

That would be a good solution. The dynamic linking works very well with conda and gives the benefit of automatically updating any bugs in isa-l away.
Static linking would however be more practical for more python users and would lead to less compatibility nightmares for me as the required version is simply included. The pace of change in isa-l is not that big and therefore it is very easy to keep up. There are however license issues that need to be discussed with the project maintainers before I am able to redistribute isa-l in that form. So I will do that.

I agree: If you provide wheels at all, they should include a statically linked isa-l.
Another reason: Many people still use pip to get Cutadapt (judging from the numbers for last month at https://pypistats.org/packages/cutadapt vs https://anaconda.org/bioconda/cutadapt/files), and almost all of these will not get the improvements automatically because python-isal is just an optional dependency. If there was a wheel, it could be made a hard dependency instead (using a suitable environment marker so you only require it on Linux).

I agree: If you provide wheels at all, they should include a statically linked isa-l.
Another reason: Many people still use pip to get Cutadapt (judging from the numbers for last month at https://pypistats.org/packages/cutadapt vs https://anaconda.org/bioconda/cutadapt/files), and almost all of these will not get the improvements automatically because python-isal is just an optional dependency. If there was a wheel, it could be made a hard dependency instead (using a suitable environment marker so you only require it on Linux).


How long is run_unbiased function in ravefuncs.py expected to take?

Hi,
I am running run_unbiased in ravefuncs.py via line rave.run_unbiased(on_gpu,plumedfile,dt,temp,freq,nstep,index) in the "Unbiased simulations" code block. This is taking a long time however.
I added some debugging statements and it seems to be stuck at
modeller.addSolvent(forcefield) in line 98 (https://github.com/tiwarylab/alphafold2rave/blob/31df0e05eeb8db7122c3cd3ddc0e7443a29a5084/ravefuncs.py#L98)
for a very long time, upwards of 25 minutes.
Is this normal? How long is this function expected to take?
Thanks

Hi ackbar03,
That's really strange-- that specific step should not take more than a couple of seconds, if that. One question- are you using GPUs?

Hi,
I'm using GPU. I can see some memory is also occupied in the GPU. However, the addSolvent step is done before the use GPU check in https://github.com/tiwarylab/alphafold2rave/blob/31df0e05eeb8db7122c3cd3ddc0e7443a29a5084/ravefuncs.py#L111
, I'm not sure if its related.
I've added some print statements for debugging to both ravefuncs.py and the relevant Code section in the heavydemo notebook
Notebook:
`os.system('export CUDA_VISIBLE_DEVICES=0')
#@title Unbiased simulations
t1 = time.perf_counter()
if os.path.isdir("Structures")==False:
print("You have not run prior AF2 predictions, copying them from github")
os.system("unzip alphafold2rave/CSP_data/structures.zip -d .")
num_samples=128
listindices=[1, 224, 627, 533]
#@markdown Check the below box to run on GPU
on_gpu=True #@param {type:"boolean"}
on_gpu=False #@param {type:"boolean"}
#@markdown MD parameters
#@markdown Integration Timestep (ps)
dt =0.004 #@param{type:"number"}
#@markdown Temperature (K)
temp=300 #@param{type:"number"}
freq=1 #param{type:"number"}
#@markdown Number of steps
nstep=250000 #@param{type:"number"}
#markdown Plumed file
plumedfile="plumed_unb.dat" #param{type:"string"}
if os.path.isdir("unbiased")==False:
os.mkdir("unbiased")
os.chdir("./unbiased")
cpath=os.getcwd()
print(cpath)
os.system('cp /content/alphafold2rave/CSP_data/plumed_unb.dat .')
os.system('cp /home/[redacted]/alphafold2rave_new/alphafold2rave/CSP_data/plumed_unb.dat .')
plumedfile=os.path.join(cpath,plumedfile)
print("check 1")
If you dont want a plumed file in unbiased
Then specify "None" as your plumedfile variable
for index in listindices:
print(cpath)
if not os.path.isdir(f'{index}'):
os.mkdir(f'{index}')
os.chdir(f'./{index}')
os.system(f"cp /content/Structures/pred_{index}.pdb .")
os.system(f"cp /home/[redacted]/alphafold2rave_new/alphafold2rave/content/Structures/pred_{index}.pdb .")
print("check2")
rave.run_unbiased(on_gpu,plumedfile,dt,temp,freq,nstep,index)
print(cpath)
print("check3")
os.chdir("..")
for index in listindices:
os.chdir(f'/content/unbiased/{index}')
os.chdir(f'/home/[redacted]/alphafold2rave_new/alphafold2rave/content/unbiased/{index}')
if os.path.isfile('bck.0.COLVAR_unb.dat'):
os.remove('COLVAR_unb.dat')
os.replace('bck.0.COLVAR_unb.dat','COLVAR_unb.dat')
os.chdir('/content')
os.chdir('/home/[redacted]/alphafold2rave_new/alphafold2rave/content')
t2 = time.perf_counter()
print(f'time taken to run:{(t2-t1)/60:.2f} mins')`
ravefuncs.py:
`def run_unbiased(on_gpu,plumedfile,dt,temp,freq,nstep,index):
"""
Runs an unbiased simulation on the cluster center using openMM.
The MD engine also uses plumed for on the fly calculations
input : raw pdb from colabfold
forcefields : amber03 and tip3p
output : fixed_{index}.pdb, unb_{index}.pdb, COLVAR_unb
"""
if plumedfile != "None":
use_plumed=True
outfreq = 0
chkpt_freq=0
save_chkpt_file=False
print(f'We are at {os.getcwd()}')
#fixing PDBs to avoid missing residue or terminal issues
fix_pdb(index);
pdb_fixed=f'fixed_{index}.pdb'
print(pdb_fixed)
#Get the structure and assign force field
print(f'Loading PDB file')
print(os.path.isfile(pdb_fixed))
pdb = PDBFile(pdb_fixed)
print(f'Loading forcefield')
forcefield = ForceField('amber03.xml', 'tip3p.xml')
Placing in a box and adding hydrogens, ions and water
print(f'Loading modeller')
modeller = Modeller(pdb.topology, pdb.positions)
print(f'Adding stuff')
modeller.addHydrogens(forcefield)
print(f'Adding solvent')
modeller.addSolvent(forcefield, padding=0.5*nanometers, model='tip3p', neutralize=True, positiveIon='Na+', negativeIon='Cl-')`
The output is:
/home/[redacted]/alphafold2rave_new/alphafold2rave/content/unbiased check 1 /home/[redacted]/alphafold2rave_new/alphafold2rave/content/unbiased check2 We are at /home/[redacted]/alphafold2rave_new/alphafold2rave/content/unbiased/1 fixed_1.pdb Loading PDB file True Loading forcefield Loading modeller Adding stuff Adding solvent

This is really weird! We have run this multiple times on colab without issues. To clarify, you are running this on your own system with openmm 7.5.1? The only other thing I can think of is that there are version compatibility issues. What is your openmm forcefield package version? I don't think it's a CUDA issue because I don't think the modeller actually uses GPU.
I think the next solution would be to run this on your local machine or server using whatever MD package you know works compatibly on it. I (or my co-author Akash) can help you with this-- any MD engine should be fine as long as it is Plumed compatible.
The other option is to run this on colab, but while it will work fine for CSP, it will not be enough for your actual system of interest.

Hi,
It seems that I am able to run it directly using a .py file instead of from a Jupiter notebook.
I have no idea why. It might be because I had to rerun the cells out of order since the initial run stopped due to erroneous directories and file paths and I wanted to skip the AF2 inference stage.
In any case I am able to successfully run the rest of the pipeline. I'm not completely sure what it is I am running yet at the moment though and  have yet to look in detail but thanks a lot for your prompt help!


feat: adding support for child lookups

Currently partial implementation of #1. Still missing is the fallback that looks up the file system for files.
Closes #1

While this is a useful feature, the implementation needs better testing and in most cases it is sufficient to have separate plugins instead of multiple plugins registered by package, for the time being.


Explicit Column Selection preview feature

The Explicit column selection feature is not available as an option when using the Teams PowerApps app.

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

ID: b8dc20a4-0732-c43c-ec0c-2f56b8149f83
Version Independent ID: 88bc2af8-df2f-8947-4408-017d2d2f1929
Content: Common canvas apps performance issues and resolutions - Power Apps
Content Source: powerapps-docs/maker/canvas-apps/common-performance-issue-resolutions.md
Service: powerapps
GitHub Login: @JinManAhn-MSFT
Microsoft Alias: jiahn

@csteelatgburg , thanks for the good point. ECS feature is on by default for the Teams PowerApps app.
Let me know if you have any concerns on ECS feature.

The Explicit column selection feature is not available as an option when using the Teams PowerApps app.
Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: b8dc20a4-0732-c43c-ec0c-2f56b8149f83
Version Independent ID: 88bc2af8-df2f-8947-4408-017d2d2f1929
Content: Common canvas apps performance issues and resolutions - Power Apps
Content Source: powerapps-docs/maker/canvas-apps/common-performance-issue-resolutions.md
Service: powerapps
GitHub Login: @JinManAhn-MSFT
Microsoft Alias: jiahn

@csteelatgburg , thanks for the good point. ECS feature is on by default for the Teams PowerApps app.
Let me know if you have any concerns on ECS feature.


tdrop: 2018-11-13 -> 2019-10-04

Motivation for this change
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nix-review --run "nix-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Ensured that relevant documentation is up to date
[x] Fits CONTRIBUTING.md.

Notify maintainers
cc @

Interesting... I'll try to reproduce the problem.

@redvers Got it. You can't run it without arguments.
You can run it like this from a terminal where   opens some gui: tdrop -a auto_hide && <command> && tdrop -a auto_show. it'll hide a terminal window, open an app with gui and when you close the app, it'll show the same terminal window.

Closed in favour of #94985


NTAG 424

Hi,
are there any plans to add support for ntag 424?
would be really nice!
Thanks!

Yes, when ? 😅🥳

@tekktrik
someone implemented it in arduino
https://github.com/bitcoin-ring/Adafruit-PN532-NTAG424
maybe this will help you integrate it for circuitpython

There aren't any current plans to add this functionality, but if you'd like to submit a PR to add it, we'd love to take a look!

If it is the same as the one used in Bolty its not stable yet

would this may help to implement it?
https://github.com/pieterjm/Adafruit-PN532-NTAG424/commits/master


To create a partition the size of 137 GB

Should people use 128000, 134217 or 137438? This has something to do with LBA28.

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

ID: a6603f33-5341-f4cb-2439-a845e637ebed
Version Independent ID: 838af916-d6d6-688f-5319-beef37a7c34b
Content: create partition primary
Content Source: WindowsServerDocs/administration/windows-commands/create-partition-primary.md
Product: windows-server-threshold
Technology: manage-windows-commands
GitHub Login: @coreyp-at-msft
Microsoft Alias: coreyp

Thanks for your dedication to our documentation. If we have been unable to review your issue in a timely manner, we sincerely apologize for the delayed response. We have captured the informtion you have provided for review. We are closing this issue for now, but if you feel that it's still a concern, please respond and let us know. If you determine another possible update to our documentation, please don't hesitate to reach out again. #please-close

I don't think there is an easy solution, however, floats snapping to a power of 2, or correct documentation remains important.
Any solution helps, but I'm not gonna re-open this, it's too complicated.


Error building with 2.3.1

Not sure what other information I need to provide here. Please let me know.
+------------------------------------------------------------------------------+

| (6) xcode-archive                                                            |
+------------------------------------------------------------------------------+
| id: xcode-archive                                                            |
| version: 2.3.1                                                               |
| collection: https://github.com/bitrise-io/bitrise-steplib.git                |
| toolkit: go                                                                  |
| time: 2017-09-25T10:37:32-07:00                                              |
+------------------------------------------------------------------------------+
|                                                                              |
INFO[10:37:32]  * [OK] Step dependency (xcode) installed, available.
INFO[10:37:32]  * [OK] Step dependency (go) installed, available.
ipa export configs:
- ExportMethod: auto-detect
- UploadBitcode: yes
- CompileBitcode: yes
- TeamID:
- UseDeprecatedExport: no
- CustomExportOptionsPlistContent:
xcodebuild configs:
- OutputTool: xcpretty
- Workdir: /Users/vagrant/git
- ProjectPath: /Users/vagrant/git/ios/Poe.xcodeproj
- Scheme: Poe
- Configuration:
- OutputDir: /Users/vagrant/deploy
- IsCleanBuild: no
- XcodebuildOptions: -configuration Release
- ForceTeamID:
- ForceProvisioningProfileSpecifier:
- ForceProvisioningProfile:
- ForceCodeSignIdentity:
step output configs:
- IsExportXcarchiveZip: no
- ExportAllDsyms: yes
- ArtifactName: Poe
step determined configs:
- xcodebuildVersion: 9.0.0 (9A235)
- xcprettyVersion: 0.2.8

...

xcode major version > 9, generating exportOptions with provisioningProfiles node
Failed to create target code sign properties mapping, error: failed to run ruby script, output: {"error":"scheme does not contain buildable target\n/var/folders/90/5stft2v13fb_m_gv3c8x9nwc0000gn/T/__ruby-script-runner__871297540/script.rb:219:in `read_code_sign_infos'\n/var/folders/90/5stft2v13fb_m_gv3c8x9nwc0000gn/T/__ruby-script-runner__871297540/script.rb:275:in `<main>'"}, error: exit status 1
|                                                                              |
+---+---------------------------------------------------------------+----------+
| x | xcode-archive (exit code: 1)                                  | 235 sec  |
+---+---------------------------------------------------------------+----------+
| Issue tracker: https://github.com/bitrise-io/steps-xcode-archive/issues      |
| Source: https://github.com/bitrise-io/steps-xcode-archive                    |
+---+---------------------------------------------------------------+----------+

Rolled back to 2.2.0 and there is no issue.

Having same issue.
Roll back didn't work, now im having other error "Failed to find matching provisioning profiles"

Hi @jasonfma and @sbehrends ,
thanks for the report, we are working on the fix. Until we release a new version please use this workaround: https://discuss.bitrise.io/t/xcode-9-gm-failed-to-create-target-code-sign-properties-mapping-error/2705/15?u=godreikrisztian

Hi @jasonfma!
Just started to fix the issue, and we'll need more info to check what could be the issue in your case. Could you please contact us on our on-site chat and drop us your *.xcodeproj?
Thanks!

@trapacska I could help as well. Having same issue.

Hi, we released a new version of the step: 2.3.2.
Please update the step in your workflows and do not forget to update Certificate and Profile Installer step to the latest version (1.9.0) as well.
Let us know how does it work four you!

Looks like it works for me. Thanks!

Working as well. I think we can close the issue.

Thanks for the report guys!

@trapacska I've got same issue on 2.3.2 - after rolling back to 2.3.1 builds are passing as usual


Ghost ignores sub-path in the url argument

I'm running ghost:2.1.2-alpine and I'm trying to host the /ghost panel under a separate domain.
I'm doing this because I have multiple ghost installations and I want one Google-based OAuth for all installations.
Now, I'm supplying an environment variable to the docker continer in the form of:
admin__url: "https://ghost.domain.mine/xyz/"

But despite that the configuration is ignored, and when the API call to /ghost/api/v0.1/configuration/ is made it is made for:
https://ghost.domain.mine/ghost/api/v0.1/configuration/

Which suggests to me that the xyz/ sub-path of the admin__url argument is ignored.
Why is this?
My proxy config:
   location /xyz {
       rewrite /xyz/(.*) /ghost/$1 break;
       proxy_pass http://<IP_ADDRESS>:8081/ghost;
       proxy_redirect https://ghost.domain.mine/ghost/ /xyz/;
       proxy_set_header Host $host;
       proxy_set_header X-Forwarded-Proto $scheme;
       proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
       proxy_set_header X-Real-IP $remote_addr;
   }

Proof:

All other reources load just fine, but the API call fails because it insists on using the /ghost sub-path instead of /xyz one.

I've managed to make it work with an ugly hack using the Referer header:
   location /ghost {
       if ($http_referer ~* "xyz") {
           rewrite /ghost/(.*) /xyz/$1 last;
       }
       if ($http_referer ~* "abc") {
           rewrite /ghost/(.*) /abc/$1 last;
       }
       return 502;
   }

But this really should be fixed.

https://docs.ghost.org/docs/config#section-admin-url

🤦♂ my bad, got confused by how the table is formatted

Also, Ghost seems to insists on redirecting when it's not necessary(since I'm using a proxy) on adding the service port to the redirect header:
http://ghost.domain.mine:8091/xyz/

And also, Ghost seems to ignore the x-forwarded-proto: https header, which causes an infinite loop:
curl -skv http://<IP_ADDRESS>:8081/ghost/ -H 'x-forwarded-proto: https' -H 'host: ghost.domain.mine' > /dev/null

*   Trying <IP_ADDRESS>...
* TCP_NODELAY set
* Connected to <IP_ADDRESS> (<IP_ADDRESS>) port 8081 (#0)
> GET /ghost/ HTTP/1.1
> host: ghost.domain.mine
> User-Agent: curl/7.58.0
> Accept: */*
> x-forwarded-proto: https
> x-forwarded-for: <IP_ADDRESS>
>
< HTTP/1.1 301 Moved Permanently
< X-Powered-By: Express
< Cache-Control: public, max-age=31536000
< Location: https://ghost.domain.mine/ghost/
< Vary: Accept, Accept-Encoding
< Content-Type: text/plain; charset=utf-8
< Content-Length: 64
< Date: Sun, 16 Sep 2018 14:08:11 GMT
< Connection: keep-alive
<
{ [64 bytes data]
* Connection #0 to host <IP_ADDRESS> left intact

What's interesting is that the x-forwarded-proto header works just fine for the main site, but not for the Admin interface:
% curl -sv http://<IP_ADDRESS>:8081/ -H 'x-forwarded-proto: https' > /dev/null
...
< HTTP/1.1 200 OK

And:
% curl -sv http://<IP_ADDRESS>:8081/ghost/ -H 'x-forwarded-proto: https' > /dev/null
...
< HTTP/1.1 301 Moved Permanently
< Location: https://ghost.status.im/ghost/

But when I don't set admin__url at all  Ghost respects the x-forwarded-proto and does not do the redirect:
% curl -sv http://<IP_ADDRESS>:8081/ghost/ -H 'x-forwarded-proto: https' > /dev/null
,,,
< HTTP/1.1 200 OK

@jakubgs from the docs on admin url: https://docs.ghost.org/docs/config#section-admin-url

As per the SSL section above, admin.url can be used to specify a different protocol for your admin panel. It can also be used to specify a different hostname (domain name). It cannot be used to affect the path at which the admin panel is served (this is always /ghost/).

There is no support for changing the location of the admin panel at the moment.  It is hardcoded as /ghost/ in many parts of the application and is not a bug, but a well known and documented limitation.
That being said, it is something we would be happy to accept a PR for 🙂

I see, that is a real pain...

That being said, it is something we would be happy to accept a PR for slightly_smiling_face

I wish I had the time to help you with this, but as it stands I am a single devops for a whole company. Maybe in the future. for now I'll probably implement a hack around this using domains. Thanks for explaining this.


bugfix for declaring new metatranscriptomic class

bug fix

@antgonza @RodolfoSalido bugfix for the bug Rodolfo found. hilariously bad.

Thanks @RodolfoSalido ! You should be able to update your local branch with git pull origin master (I believe we were testing in master branch.)


App freeze using air proxy

Describe the bug
A clear and concise description of what the bug is.
Video showing the issue.  When using air proxy, after I navigate between a few routes to and from, the browser tab freezes and I keep getting canceled requests in the network tab.  If I use the app normally without air proxy, I don't see this issue.  See attached video:
https://github.com/user-attachments/assets/bbe295e8-2e34-4fac-8502-2f4fa7497d35
Here are all the canceled requests after it gets into a bad state.

if I try to open new tabs to port 7331, they also sit there stuck until I close the original tab which caused the issue.
I checkout a new branch which demonstrates this bug here if interested https://github.com/webdevcody/go-mailing-list/tree/air-proxy-bug
run it with Make dev and go to http://localhost:7331/dashboard and click between the tabs.

Thanks. I tracked it down to the client side reload script not closing connections - fixed in https://github.com/a-h/templ/commit/889c8330cc4438492ed9c858a9aec40a871eb9c2
Didn't notice it because I'd been using it for partial page updates in HTMX, so wasn't triggering the same issue.


Cutie Outputs Mask of Incorrect Object in Subsequent Frames

Hello,
First, thank you for your amazing work on Cutie!
I am using SAM to generate a mask for an object, and then tracking it with a 640x360 camera with 10hz FPS. However, starting from the second frame, Cutie outputs the mask of another identical object instead of the intended one. Could you please advise on any parameters I can adjust to correct this behavior?
Additionally, how does Cutie's tracking performance compare to DEVA? Is Cutie significantly more capable?
Thank you!

Which script are you using?
One thing worth trying is to label all three objects (such that there is some "mutual exclusion") instead of just one. In general tracking similarly-looking objects is hard as we don't have strong positional constraints and don't really have these types of training data, but decaying like this so soon at the second frame is also quite rare.
Cutie should be better than DEVA in terms of just tracking.

On February 27th, I cloned the code and made a specific modification to it. The change I made was to replace the read mp4 function with read camera frame. However, upon revisiting the current directory, I could not find the exact version of the code that I had previously worked on.
I am wondering if there has been an update to the repository since then, which might have altered or replaced the code I was working with. Below is the code that I'm using. I change frames_to_propagate to 500.

from omegaconf import open_dict
from hydra import compose, initialize
import torch
import numpy as np
from PIL import Image
from cutie.model.cutie import CUTIE
from cutie.inference.inference_core import InferenceCore
from cutie.inference.utils.args_utils import get_dataset_cfg
import os

import cv2
from gui.interactive_utils import image_to_torch, torch_prob_to_numpy_mask, index_numpy_to_one_hot_torch, overlay_davis

initialize(version_base='1.3.2', config_path="cutie/config", job_name="eval_config")
cfg = compose(config_name="eval_config")

with open_dict(cfg):
  cfg['weights'] = './weights/cutie-base-mega.pth'

data_cfg = get_dataset_cfg(cfg)

# Load the network weights
cutie = CUTIE(cfg).cuda().eval()
model_weights = torch.load(cfg.weights)
cutie.load_weights(model_weights)

video_name = 'examples/example2.mp4'
mask_name = 'result/resized_mask.png'
results_dir = 'result/result2'
if not os.path.exists(results_dir):
  os.makedirs(results_dir)

img= Image.open(mask_name)
img.show()

mask = np.array(Image.open(mask_name))
print(np.unique(mask))
num_objects = len(np.unique(mask)) - 1

device = 'cuda'
torch.cuda.empty_cache()

processor = InferenceCore(cutie, cfg=cfg)
cap = cv2.VideoCapture(video_name)

# You can change these two numbers
frames_to_propagate = 200
visualize_every = 20

current_frame_index = 0

if __name__ == "__main__":

  with torch.inference_mode():
    with torch.cuda.amp.autocast(enabled=True):
      while (cap.isOpened()):
        # load frame-by-frame
        _, frame = cap.read()
        if frame is None or current_frame_index > frames_to_propagate:
          break

        # convert numpy array to pytorch tensor format
        frame_torch = image_to_torch(frame, device=device)
        if current_frame_index == 0:
          # initialize with the mask
          mask_torch = index_numpy_to_one_hot_torch(mask, num_objects+1).to(device)
          # the background mask is not fed into the model
          prediction = processor.step(frame_torch, mask_torch[1:], idx_mask=False)
        else:
          # propagate only
          prediction = processor.step(frame_torch)

        # argmax, convert to numpy
        prediction = torch_prob_to_numpy_mask(prediction)

        if current_frame_index % visualize_every == 0:
          visualization = overlay_davis(frame, prediction)
          # 构建保存图像的文件路径
          result_img_path = os.path.join(results_dir, f"frame_{current_frame_index:06d}.png")
          # 保存图像
          cv2.imwrite(result_img_path, cv2.cvtColor(visualization, cv2.COLOR_RGB2BGR))
          print("saved once")

        current_frame_index += 1

You can check the commit history. Your script might have come from the Colab notebook.
Have you tried labeling all three objects instead of just one?

I apologize for the confusion. I'm not quite clear on how the labeling process works. Is it about segmenting different objects before they enter 'Cutie' and then outputting them to it?
Also for my final project, I'm aiming to track just one object at a time. I was thinking about whether it's possible to adjust the parameters of the diffusion or detection algorithm to focus more on the tracked object, making the model less sensitive to other objects. Could you provide some guidance on this if there is any possibles?

It would help to label other objects as it provides some "mutual exclusion" information to the model. I am not sure how you are obtaining the input masks, but I am indeed suggesting tracking multiple objects at once.
There is no parameter that directly controls the sensitivity as far as I know. The model has been trained to minimize confusion, but like any other model, it can make mistakes. Providing masks for the other objects is one of the ways to help it avoid these mistakes.

Thank you for your prompt response. I have been manually utilizing the bounding box feature in the SAM model for my project, which aims to enable the robot to grasp the object that I select. As a result, the focus is on tracking only one object at a time.
I have been testing the situation as we previously discussed. Currently, I am unable to reproduce the issue. It seems that the problem may be occurring randomly.
I will continue to closely monitor. Thanks for your support!!!

I'm glad to see that it's a rare occurrence. Good luck with your project!

Sorry for the bothering again. The issue has been recurring frequently. My current question pertains to the functionality of the mask application during the initial step. Is it possible to load all object masks at the first step, but then selectively output the mask of only one specific object in the final output? The reason for this request is to avoid the aforementioned errors and to streamline the process by focusing on a single object of interest.

Hi. Sure, you can post-process the output mask to keep only one object.


Implement New toolkit version csharp samples

Sample List -
1. bot-archive-groupchat-messages/csharp
2. msgext-search-sso-config/csharp
3. graph-appcatalog-lifecycle/csharp
4. meetings-context-app/csharp
5. bot-sharepoint-list/csharp

Build Pipeline -
https://github.com/OfficeDev/Microsoft-Teams-Samples/actions/runs/11327628738/workflow


Bump Terraform AWS provider from version 4 to 5

https://github.com/hashicorp/terraform-provider-aws/releases/tag/v5.0.0
Ref:

https://trello.com/c/MIAiwlJ4/25-update-terraform-modules-to-support-aws-v5-providers

I noticed that this PR did not trigger a README update. Is this something we need to address?

I will merge this and we can address the potential README update separately 🙂.


Implementation is insecure

No offense but you should really make sure you have strong and good understanding of security before implementing such a project.
This can be great learning experience but if that's the case you should make it clear in the start of the readme that this is the case. People will still probably use your code not caring but at least you've been honest about it.
I can go into more details but since I'm not sure if this is used anywhere sensitive I'll leave the exact details out for now.
You should really validate user input. including cookies (which are also user input).
There are other major issues with the way you implemented the auth but validating input is coding 101 so you need to go back to the basics and see you understand them before moving to more advance issues.
Sorry if this is a bit harsh.

Hey @yonjah, I appreciate your feedback. The very reason I posted this project on reddit was to have people critique it, I apologize for not making it as clear. I don't believe my project has been used anywhere sensitive as I just released it, but we can talk on twitter if you would prefer; @yanglorenzana is my twitter handle. I would like to hear all about these issues so we can fix them. Thanks!

@ylorenzana your project has been posted on node weekly so you can expect substantial traffic reaching it.
I would put a note on the top of the project that this is both a learning and a WIP.
Once  it's more mature you might get a decent module out of it.
I can DM you the details, but I think it has more value if the information is publicly available so other can learn from.

@yonjah I'll do that. Please do DM me the details, don't worry, I'll make sure that every legitimate concern that is raised is addressed and fixed—publicly on this repo.

I attempted a few different example noSQL injection queries as the value of the token, and none of them seemed to work. I'm closing this issue because I was unable to recreate it. Feel free to reopen if you manage to do it.

What do you mean none of them seem to work ?
Did you properly inspect your code ?
Where did it fail ?
Did you manage to pass Object to the session token  ?
Did you make sure you have other active sessions so the injection will actually have a session to steal ?
Did you try setting the token cookie to j:{"$ne":"0"} as I told you ? did it not give you an active session ?
I don't think exploiting this issue is very hard but even if for some reason you don't succeed or even sure at 100% there is no way to exploit it leaving your inputs with no validation is extremely  bad practice.

Like I said, I tried exploiting the issue like you suggested. I used the exact string you gave me and set it as the token cookie, and tried quite a few different variations to it as well to see if they would work. None of them passed the authentication middleware.  I could very easily be missing something.
Thanks.

I had tried setting the token to what you sent me originally on twitter and to a variation of that along with other known noSQL injection queries, and none of them worked at first. The object you specified on this thread did work though, so thanks for pointing it out. I added checks where necessary to prevent these sorts of attacks.

Sorry for not being too direct with the injection.
I think it's important to you to understand exactly what your code does and find it yourself.
I'm sure there are more issues to find so I would go back to OWASP top 10 and make sure I know how to recognize and protect form each and every issue mention there.
I didn't have time to go over all your code and check that you added proper validations everywhere.
The places you did enter it in seem to be ok and will probably prevent injections. But one thing I like to do when I validate input is check not only it's type but that it follows the exact rules of what I expect.
For example if the token is 32 chars of hex  I would validate it to be 32 chars of hex and not just a plain string. A great module for validating user input is joi though for vary basic validations you can use vanilla JS as well.
I'll try to get some more time to go over the code and open new issues about the more advance issues I already noticed, but I hope you'll find most of them as you go through OWASP


feat: Add hostname to payload

What type of PR is this?
/kind feature
Any specific area of the project related to this PR?
/area config
/area outputs
What this PR does / why we need it:
Adds optional hostname field to the incoming falco payload. Conditionally adds the hostname to all outputs.
Which issue(s) this PR fixes:
Allows sidekick to retrieve hostname once  https://github.com/falcosecurity/falco/issues/1520 is resolved.
Special notes for your reviewer:

Thanks for your pull request. Before we can look at it, you'll need to add a 'DCO signoff' to your commits.
:memo: Please follow instructions in the contributing guide to update your commits with the DCO
Full details of the Developer Certificate of Origin can be found at developercertificate.org.
The list of commits missing DCO signoff:

4dc5254 make optional
45756a5 feat: add hostname to slack and pagerduty

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Hi,
Thank you. As hostname is a new field, we should add it in all outputs, not only Slack and PagerDuty.

Hi, Thank you. As hostname will be a new field, we should add it in all outputs, not only Slack and PagerDuty.

Agreed, let me added it to the remaining outputs.

You can take reference with the PR #310 I did it for source and tags

@Issif added to all outputs

/do-not-merge

@adnankobir: Adding label do-not-merge/contains-merge-commits because PR contains merge commits, which are not allowed in this repository.
Use git rebase to reapply your commits on top of the target branch. Detailed instructions for doing so can be found here.

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Hi,
Can you resolve the conflicts and apply the suggestion I made please, I'm  hoping to release the 2.27 in the next 2 weeks

#383 has been merged, this PR is not useful anymore finally.


Preparation for release 0.3.0

Issue raised by @rasolca fixed in DLA-Future by https://github.com/eth-cscs/DLA-Future/pull/1248 and in DLA-Future-Fortran by #28. This PR is now blocked by DLA-Future release 0.7.2.