Blank Study Page

Study page is blank when I search for the following study:
https://genetics-app.netlify.com/study/GCST005837
One possible reason is that it is flagged as a 'targeted or exome array study' in GWAS catalog.

It does work as expected for other 'targeted' studies, though. For example:
https://genetics-app.netlify.com/study/GCST005550

Two solutions spring to mind about this:

show a message saying we have no data
filter studies with this property out of the search results (then there's no way get to the page)

I think option 2 makes more sense. @mkarmona @edm1 @emschmidt, thoughts?

I think we should prepare a nice 404 page saying no data but with sensible
alternative resources as results from our search endpoint suggesting
silimsr studies or so. not having doesn't mean we can't keep engaging our
(future) users.
On Thu, 27 Sep 2018, 14:25 iandunham<EMAIL_ADDRESS>wrote:

GWAS catalog reports 0 associations for this study
https://www.ebi.ac.uk/gwas/search?query=Huang H
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/opentargets/genetics/issues/138#issuecomment-425090783,
or mute the thread
https://github.com/notifications/unsubscribe-auth/AFJPOIGJOsUp0Msx6m8odmBMOh1wpcY0ks5ufNHKgaJpZM4W8Ywh
.

Here is an example of a blank 'no association' study page from Neale UK Biobank: https://genetics-app.netlify.com/study/NEALEUKB_20002_1454

Recently added handling means that users now see at least a (no data) message instead of an empty screen.

There is currently no LD or finemapping data for GCST004132. This will change with the most recent run of the pipeline.
However, navigating to the comparison page gives a blank screen: https://genetics-app.netlify.com/study-comparison/GCST004132
Is it possible to have a (no data) message here too?

Added handling for cases like GCST004132 (https://github.com/opentargets/genetics-app/commit/b73cba76a43956670807ed8eb4b8ced29d969823).


binary vs library issue

Hi men, I have a stupid question, how do you build a static library ?
the standalone binary works great, but  when I try to build a static library I got
### "fatal error: tensorflow/core/platform/env.h: No such file or directory"
How do you specify the relationship between a static library and the package?
If I manually include_directories("/usr/local/include/tensorflow/") I got another error:
### In file included from /usr/local/include/tensorflow/tensorflow/core/lib/core/errors.h:19:0,
from /usr/local/include/tensorflow/tensorflow/core/platform/env.h:24,
from /home/payas/source/trunk/iadl_model/iadlmodel.cc:1:
/usr/local/include/tensorflow/tensorflow/core/lib/core/status.h:22:53: fatal error: tensorflow/core/lib/core/error_codes.pb.h: No such file or directory
compilation terminated.

Hi @payas,
It seems that tensorflow is not compiled properly. Has the make step finished successfully? Can you remove the build folder and try to rebuild the project from scratch?

I just rm and rebuild all with no error,  standalone binary build and works fine...
tensorflow/core/lib/core/error_codes.pb.h does even exists ?
it's not on my system, now I'm wondering why standalone binary works
make install never copy that file to tensorflow/core/lib/core/
-- Up-to-date: /usr/local/include/tensorflow/tensorflow/contrib/makefile/gen/host_obj/tensorflow/core/lib/core/error_codes.pb.h
-- Up-to-date: /usr/local/include/tensorflow/tensorflow/contrib/makefile/gen/proto/tensorflow/core/lib/core/error_codes.pb.h
Im pretty lost

I just figure out
find_package(TensorflowCC REQUIRED )
ADD_LIBRARY(model STATIC
main.cc)
target_link_libraries(model TensorflowCC::Static) <<<<<----- THIS WAS MISSING
sorryyyyyyy!

Yes, that is necessary, glad you solved it. :-)


Update manageiq en po and pot for August 9th

Result from running bundle exec rake locale:update_all locally

Skipping backport to oparin, because it is already in the branch.


g1.wen.lu is blocked

Please open a new subdomain...
  :(

It seem that (*.)wen.lu is DNS poisoned.


YAMNet error

I'm getting into the following error when trying to run a prediction using a converted yamnet tflite model.
[E:onnxruntime:, sequential_executor.cc:494 ExecuteKernel] Non-zero status code returned while running Slice node. Name:'yamnet_frames/tf_op_layer_StridedSlice_2/StridedSlice_2;StatefulPartitionedCall/yamnet_frames/tf_op_layer_StridedSlice_2/StridedSlice_2' Status Message: slice.cc:197 FillVectorsFromInput Starts and axes shape mismatch
I started by downloading the tflite model from the TensorFlow Hub.  I then used tf2onnx to convert the model to onnx from Terminal.
python -m tf2onnx.convert --tflite lite-model_yamnet_tflite_1.tflite --output yamnet.onnx

Here's the Ruby code that I used to invoke the model.  I've used this exact same sample array to run predictions in Node.js using the YAMNet TensorFlow.js model, so I'm expecting the same results here in Ruby/ONNX.
model = OnnxRuntime::Model.new("yamnet.onnx")
samples = [-0.000396728515625, 0, 0.000457763671875, 0.000457763671875, ...]
model.predict({ "waveform" => samples })

This is the shape of the inputs
[{:name=>"waveform", :type=>"tensor(float)", :shape=>[-1]}]

Do you have any thoughts as to why I'm getting this error returned?  Do you have any suggestions on how to resolve?  Thanks!

Hi @ajporterfield, can you try running it in Python?
import onnxruntime as ort

ort_sess = ort.InferenceSession('yamnet.onnx')
output = ort_sess.run(None, {'waveform': samples})

It may be something with the exported model (and would be good to rule that out).

Thanks for the reply @ankane.  I really appreciate your help.  I'm getting the same/similar error when running it in Python.
[E:onnxruntime:, sequential_executor.cc:494 ExecuteKernel] Non-zero status code returned while running Slice node. Name:'yamnet_frames/tf_op_layer_StridedSlice_2/StridedSlice_2;StatefulPartitionedCall/yamnet_frames/tf_op_layer_StridedSlice_2/StridedSlice_2' Status Message: slice.cc:197 FillVectorsFromInput Starts and axes shape mismatch

I'd try exporting the TF model instead of the TFLite one (it sounds like it's worked for others - https://github.com/onnx/tensorflow-onnx/issues/1743).

Yes, I saw that too.  I am unable to get tf2onnx.convert to work for the TF model.
python -m tf2onnx.convert --saved-model ./yamnet_1 --output yamnet.onnx

Outputs quite a few warnings/errors
runpy.py:126: RuntimeWarning: 'tf2onnx.convert' found in sys.modules after import of package 'tf2onnx', but prior to execution of 'tf2onnx.convert'; this may result in unpredictable behaviour
  warn(RuntimeWarning(msg))
2023-04-18 14:52:55,693 - WARNING - ***IMPORTANT*** Installed protobuf is not cpp accelerated. Conversion will be extremely slow. See https://github.com/onnx/tensorflow-onnx/issues/1557
2023-04-18 14:52:55,694 - WARNING - '--tag' not specified for saved_model. Using --tag serve
2023-04-18 14:53:00,666 - ERROR - Unable to load fingerprint when loading saved model.
Traceback (most recent call last):
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/site-packages/tensorflow/python/saved_model/load.py", line 1015, in load_partial
    fingerprint = fingerprinting.read_fingerprint(export_dir)
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/site-packages/tensorflow/python/saved_model/fingerprinting.py", line 172, in read_fingerprint
    raise FileNotFoundError(f"SavedModel Fingerprint Error: {e}") from None  # pylint: disable=raise-missing-from
FileNotFoundError: SavedModel Fingerprint Error: Could not read fingerprint from directory: ./yamnet_1
2023-04-18 14:53:00,686 - INFO - Signatures found in model: [serving_default].
2023-04-18 14:53:00,686 - WARNING - '--signature_def' not specified, using first signature: serving_default
Traceback (most recent call last):
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/site-packages/tf2onnx/convert.py", line 710, in <module>
    main()
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/site-packages/tf2onnx/convert.py", line 242, in main
    graph_def, inputs, outputs, initialized_tables, tensors_to_rename = tf_loader.from_saved_model(
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/site-packages/tf2onnx/tf_loader.py", line 611, in from_saved_model
    _from_saved_model_v2(model_path, input_names, output_names,
  File "/Users/andrewporterfield/.pyenv/versions/3.10.11/lib/python3.10/site-packages/tf2onnx/tf_loader.py", line 573, in _from_saved_model_v2
    graph_captures = concrete_func.graph._captures  # pylint: disable=protected-access
AttributeError: 'FuncGraph' object has no attribute '_captures'. Did you mean: 'captures'?

I'm not sure about that error. Probably worth reporting to tf2onnx (could also see if they know about the original error - I think it may have something to do with the dynamic dimension).


[E2E] Adds test harness

Description

This PR adds the E2E test harness with an example of how it can drive around a KubeClient to test  commands in a Pod against the built binary. It also adds a button to the Tilt UI that manually triggers the E2E test suite to run for development purposes.
Issue
FIxes #580 as part of a set of changes addressed by #466
Type of change
Please mark the relevant option(s):

[x] New fexature, functionality or library
[ ] Bug fix
[ ] Code health or cleanup
[ ] Major breaking change
[ ] Documentation
[ ] Other

List of changes

Adds the e2e/tests directory.
Adds the godog test suite runner in the e2e/tests subdirectory.
Adds a simple root feature that tests the help output of the CLI as an example.
Updates the Tiltfile for LocalNet to include a button for manually triggering e2e tests.

Testing

[x] make develop_test
[ ] LocalNet w/ all of the steps outlined in the README

Required Checklist

[ ] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have tested my changes using the available tooling
[ ] I have updated the corresponding CHANGELOG

If Applicable Checklist

[ ] I have updated the corresponding README(s); local and/or global
[ ] I have added tests that prove my fix is effective or that my feature works
[ ] I have added, or updated, mermaid.js diagrams in the corresponding README(s)
[ ] I have added, or updated, documentation and mermaid.js diagrams in shared/docs/* if I updated shared/*README(s)

@dylanlott I know you've added a lot of new code, so please resolve the comments that you've tended to and re-request a review once ready. Maybe @okdas should review as well?

Closing, addressed by #644.


Make README.md as the default package documentation

This PR includes the following changes:

Rename Module.md to README.md in the default module and submodules
Remove Package.md from the default module

Related to https://github.com/ballerina-platform/ballerina-library/issues/7404

Closing with https://github.com/ballerina-platform/module-ballerinax-salesforce/pull/378


Yoga Posture Detection

Title and Issue number
Yoga Posture Detection #3328
Close #3328
Checklist:

[X] I have mentioned the issue number in my Pull Request.
[X] I have commented my code, particularly in hard-to-understand areas
[X] I have created a helpful and easy to understand README.md

Hi @MohitGupta121!
It would be great if you could review my pr. Thank you!


Some problem about the calibration?

How to properly project the LiDAR point cloud onto the camera, Is the extrinsic calibration accurate?

Do you also have issues with the calibration? Have you tried projecting LiDAR points on top of an image?

@thaotrongtran I have the same question: have you succeeded in projecting lidar to the image?


remove python-* in base.yaml, which already defined in python.yaml

rewrited version of https://github.com/ros/rosdistro/pull/15257
some of rosdep keys is multiply defined, and that sometime confuse users.

This just to remove duplicates entry from base.yaml, no regressions.

To avoid further confusion, what about moving all python-* keys into python.yaml by policy?


Typed API Call?

Hi
I read the documentation http://nest.azurewebsites.net/elasticsearch-net/handling-responses.html and stumbled upon your suggestion:
var result = client.Search<MyType>()

(By the way the Search method needs at least the body parameter!!!)
But that will never do the job! Except my MyType looks like this:
    public class MyType
    {
        public int took { get; set; }
        public MyHits hits { get; set; }
    }

    public class MyHits
    {
        public int total { get; set; }
        public double max_score { get; set; }
        public MyHit[] hits { get; set; }
    }

    public class MyHit
    {
        public string _index { get; set; }
        public string _type { get; set; }
        public string _id { get; set; }
        public double _score { get; set; }
        public dynamic _source { get; set; }
        public Dictionary<string, string[]> highlight { get; set; }
    }

Ok, let's assume I use this workaround. The next obstacle is I will never get my concrete Type hidden in MyHit._source except with a hack like this:
var realResult = JsonConvert.DeserializeObject<MyRealType>(
    JsonConvert.SerializeObject(
        result.Response.hits.hits[0]._source
    )
);

But that's ridiculous! So I did some refactoring to allow my POCO MyRealType to be passed as a generic argument:
    public class MyType<T> where T : new()
    {
        public int took { get; set; }
        public MyHits<T> hits { get; set; }
    }

    public class MyHits<T> where T : new()
    {
        public int total { get; set; }
        public double max_score { get; set; }
        public MyHit<T>[] hits { get; set; }
    }

    public class MyHit<T> where T : new()
    {
        public string _index { get; set; }
        public string _type { get; set; }
        public string _id { get; set; }
        public double _score { get; set; }
        public T _source { get; set; }
        public Dictionary<string, string[]> highlight { get; set; }
    }

But calling
var result = client.Search<MyType<MyRealType>>("myindex",  @"{query:{match:{""Title"":""Elasticsearch""}}}");

leads to a System.InvalidCastException.
So currently there's only one decent way:
var result = client.Search<string>("myindex");
var results = JsonConvert.DeserializeObject<MyType<MyRealType>>(result.Response);

But sending strings as an HTTP request and handling strings from an HTTP response is too low level even for Elasticsearch.net!!!
Please provide a decent result type with a generic argument for the peoples' POCOs!

There are two .NET clients for Elasticsearch - Elasticsearch.NET which is a very low level, dependency free, client that has no opinions about how you build and represent your requests and responses, and NEST, a high level client that has the advantage of having mapped all the request and response objects and is probably the first starting point for interacting with Elasticsearch.
Based on the links that you've supplied, you're using the Elasticsearch.NET client but I think what you're really after is the NEST client. With NEST, all you would need to do is define the type that maps to the document mapping that you have in Elasticsearch, and the serializer used by NEST will take care of deserializing the search response and your types. For example,
void Main()
{
	var settings = new ConnectionSettings();
	var connection = new InMemoryConnection(settings);
	var client = new ElasticClient(connection: connection);

	var response = client.Search<MyType>(s => s
		.Query(q => q
			.Term(p => p.Property, "value")
		)
	);

	// do something with results, which will be an IEnumerable<MyType>
	var results = response.Documents;
}

public class MyType
{
	public string Property { get; set; }
}

Thanks for the code snippet! There wasn't one in the NEST documentation.
But nevertheless: Don't provide client.Search<MyType>() anymore. It's simply confusing and won't help anyone who wants get type-safe documents from the Elasticsearch response.

_search doesn't require a body.
client.Search<MyType>()

will result in a GET /myType/_search with no body, which is the equivalent to performing a match_all query on myType.

https://raw.githubusercontent.com/elastic/elasticsearch-net/master/src/Elasticsearch.Net/ElasticsearchClient.Generated.cs:
ElasticsearchResponse<T> Search<T>(object body, Func<SearchRequestParameters, SearchRequestParameters> requestParameters = null)
ElasticsearchResponse<T> Search<T>(string index, object body, Func<SearchRequestParameters, SearchRequestParameters> requestParameters = null)
ElasticsearchResponse<T> Search<T>(string index, string type, object body, Func<SearchRequestParameters, SearchRequestParameters> requestParameters = null)

@aureole82 You're looking at the wrong client there - take a look at the search methods in the master branch
In the current 1.x NEST client, a match_all query can be performed with
client.Search<MyType>(s => s)

In the client in the 2.0 branch (that we're currently working on), the Func delegate is nullable as @gmarz mentioned, so it's simply
client.Search<MyType>()


Problems with Parsl Monitoring

There are some issues around using Parsl monitoring with TheadPoolExecutors. It currently prevents using monitoring on with our runs Theta.
The issue on Parsl is (parsl/parsl#1837)

@yadudoc

Fixed in the Parsl master branch


Switch ACR Helm chart to GHCR for rad init

Description

Switching ACR to GHCR
Updating Helm pull config to support OCI registry

Type of change

This pull request adds or changes features of Radius and has an approved issue (issue link required).

Fixes: #6502
Auto-generated summary

🤖 Generated by Copilot at 7590304
Summary
🚚🐳⛵

Support OCI registries for Helm charts and migrate Radius chart to GitHub Container Registry. This change allows the CLI to pull Helm charts from any OCI-compatible registry and updates the default Radius chart URL to use the new hosting service.

Sing, O Muse, of the valiant code review
That changed the source of radiusHelmRepo
From azure's cloud to github's shining vault
Where OCI charts are stored with skill and fault

Walkthrough

Support pulling Helm charts from OCI registries (link,link,link)
Update Radius Helm chart repository URL to GitHub Container Registry in radiusclient.go (link)

/ok-to-test

How will we test this in production?

This code path will be called on every rad init and rad install kubernetes :) we will know if it's not working. I'm worried that we don't have any way to test this before merge yet though.

How will we test this in production?

This code path will be called on every rad init and rad install kubernetes :) we will know if it's not working. I'm worried that we don't have any way to test this before merge yet though.

We should make sure that rad install kubernetes is covered by our functional tests. That will verify that our dependencies (Helm + GHCR + Publishing to GHCR) are covered. This is also covered by the release verification tests.
The unique logic of rad init is pretty well tested by unit tests.

How will we test this in production?

This code path will be called on every rad init and rad install kubernetes :) we will know if it's not working. I'm worried that we don't have any way to test this before merge yet though.

We should make sure that rad install kubernetes is covered by our functional tests. That will verify that our dependencies (Helm + GHCR + Publishing to GHCR) are covered. This is also covered by the release verification tests.
The unique logic of rad init is pretty well tested by unit tests.

Looks like today we test using rad install kubernetes but we set the chart path manually (--chart ./Deploy/chart) instead of using it from GHCR.

How will we test this in production?

This code path will be called on every rad init and rad install kubernetes :) we will know if it's not working. I'm worried that we don't have any way to test this before merge yet though.

We should make sure that rad install kubernetes is covered by our functional tests. That will verify that our dependencies (Helm + GHCR + Publishing to GHCR) are covered. This is also covered by the release verification tests.
The unique logic of rad init is pretty well tested by unit tests.

Looks like today we test using rad install kubernetes but we set the chart path manually (--chart ./Deploy/chart) instead of using it from GHCR.

Then we should add a GH issue that will add a functional test for the rad install kubernetes.


Exception running on one's own dataset

By imitating Amos_ CT wrote the "data_builder. py" and "config" files of its own 2D dataset, and ran the algorithm using 2D methods without modifying the network structure. However, during the training process, the loss was always negative.
The dataset consists of 5 categories of 2D images labeled 0-4, with each image containing only one or two types of data. The labels for each image are not all 0, and loss also indicates this (different categories randomly appear with loss values, not always nan, while background class loss always exists). At first, it was thought that the loss did not pay attention to nan, but it was found that it had been changed to 0 for nan. At the same time, the jax architecture was the first time I came into contact and it was difficult to debug during jit compilation. Therefore, I have been researching for a long time and have no clue. I hereby seek advice.
Here is my "data_builder. py" and "config" file
my_dataset.zip
Perhaps some configuration information in these two files was written incorrectly, because my dataset is not a medical image, it is a 2D semantic segmentation that does not contain z-axis information. However, I have done my best to modify any areas that I believe are different. I also adapted some of the medical image processing libraries you called, mainly due to the lack of a dimension for addition.
Mainly modifiedimgx/datasets/preprocess.py，such as
    image_volume = sitk.ReadImage(str(image_path),sitk.sitkInt8)
    label_volume = sitk.ReadImage(str(label_path),sitk.sitkInt8)

    image_array = sitk.GetArrayFromImage(image_volume)
    if len(image_array.shape) == 2:
        image_array = np.expand_dims(image_array, axis=0)
        image_volume = sitk.GetImageFromArray(image_array)

The program can run normally, but as shown in the figure, there is a large amount of nan in the loss. After running, only the background class loss drops normally, while other classes cannot, which is meaningless.

This is an example of annotated images, which only includes four categories and is missing one category.

If you could spare some time from your busy schedule to take a look, I would greatly appreciate it. The quality and speed of the code you wrote also impressed me, as compared to Pytorch's diffusion model, it can train very quickly.

Hi @theneao, thanks for your message. Please see my responsed below, hope it could be helpful for you.
nan dice
As in dice_score, the nan may happen when the class does not exist in the image. Have you looked at the metrics f"class_{i}_proportion_{mask_name}"  to see when you have nan loss, if the proportion equals zero?
The original codebase / network was assuming the existence of all classes, however, as we used jnp.nanmean for dice loss it should not be a problem.
Slow training / convergence
In this version of code, the network followed code in MONAI and therefore it is different from the unet here. Note that hojonathanho's unet contains attention mechanism, which can be easily added using multi head attention.
Otherwise, maybe you can try to use a different set of hyper parameters. Given it's not medical images, you may consider following SAM model from Meta for hyper parameters settings.
Moreover, have you compared with different batch sizes? This is often another important hyper parameters to tune.

Hi, I am closing this issue. Please feel free to reopen it if there are more questions. Thanks.


react-empty error for local browser open

1、change environments.config set  compiler_public_path = "./"

2、 npm run deploy:prod

3、open index.html by chrome browser ，console has no error. but the page is blank.

of course your router doesn't find match in file:///Users/gordanyang/blah/blah/index.html
use nginx or apache httpd

I am running in httpd still getting the same issue.
Please advice.


Prohibit HashMap(capacity), HashMap(capacity, loadFactor), HashSet, LinkedHashMap constructors

They are pretty much always misused. See this SO answer for the explanation. They should be prohibited using forbidden-apis with suggested alternatives: Guava's Maps.new(Linked)HashMapWithExpectedSize(), Sets.newHashSetWithExpectedSize().

Also LinkedHashSet

++++1


Suggest packages

We can use the suggest key to show all of the notiifyme packages that are available.

Yeh, forgot to do that. Thanks.

Done :) https://github.com/notifymehq/notifyme/commit/03f114da3aa48997ad7e0f82f52657612022dd8f


runFileInteractive command to suggest Jupyter extension

For https://github.com/microsoft/vscode-python/pull/20218
Before removing the Jupyter extensions dependency, can we ensure that when users run runFileInteractive command (i.e., right click on Python code -> select Run in Interactive Window), we recommend and/or install the Jupyter extension (i.e., workbench.extensions.installExtension)?
According to the IW usage data that @amunger gathered, one of the biggest entry points for interactive window is from Python extension's command, runFileInteractive.

Verification:
Make sure Jupyter is not installed


Optimize qdigest histogram, allow for custom long-to-float conversion for middle

Here are some benchmarks.
New:
Benchmark                                                                     Mode  Cnt      Score     Error   Units
BenchmarkQuantileDigest.benchmarkHistogram                                   thrpt   30  40014.014 ± 468.709   ops/s
BenchmarkQuantileDigest.benchmarkHistogram:·gc.alloc.rate                    thrpt   30     25.622 ±   0.296  MB/sec
BenchmarkQuantileDigest.benchmarkHistogram:·gc.alloc.rate.norm               thrpt   30   1008.044 ±   0.068    B/op
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Eden_Space           thrpt   30     26.961 ±  12.018  MB/sec
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Eden_Space.norm      thrpt   30   1059.251 ± 472.383    B/op
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Survivor_Space       thrpt   30      0.172 ±   0.326  MB/sec
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Survivor_Space.norm  thrpt   30      6.737 ±  12.821    B/op
BenchmarkQuantileDigest.benchmarkHistogram:·gc.count                         thrpt   30     21.000            counts
BenchmarkQuantileDigest.benchmarkHistogram:·gc.time                          thrpt   30     21.000                ms

Old:
Benchmark                                                                     Mode  Cnt      Score      Error   Units
BenchmarkQuantileDigest.benchmarkHistogram                                   thrpt   30  21228.284 ±  629.031   ops/s
BenchmarkQuantileDigest.benchmarkHistogram:·gc.alloc.rate                    thrpt   30     14.348 ±    0.427  MB/sec
BenchmarkQuantileDigest.benchmarkHistogram:·gc.alloc.rate.norm               thrpt   30   1064.082 ±    0.126    B/op
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Eden_Space           thrpt   30     16.697 ±   13.900  MB/sec
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Eden_Space.norm      thrpt   30   1232.183 ± 1027.360    B/op
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Survivor_Space       thrpt   30      0.165 ±    0.331  MB/sec
BenchmarkQuantileDigest.benchmarkHistogram:·gc.churn.PS_Survivor_Space.norm  thrpt   30     12.065 ±   24.129    B/op
BenchmarkQuantileDigest.benchmarkHistogram:·gc.count                         thrpt   30     12.000             counts
BenchmarkQuantileDigest.benchmarkHistogram:·gc.time                          thrpt   30     25.000                 ms

The increase in throughput is likely due to the removal of the unnecessary Atomic variables, instead a private struct, HistogramBuilderStateHolder, holds the intermediate state as plain member variables.
This also introduces a simple lambda passed to the histogram function to allow one to properly convert the middle for the upper and lower bound of a bucket.  This allows the results to be correct when the long bits actually represent something else, like doubles or floats whose bits are represented as sortable longs.
The real intent for this PR was to allow this overrideable interpretation of the long bits for calculating the mean, and the performance refactoring made that easier and cleaner.
CC: @martint

Thanks for the comments @martint.  I addressed your feedback, can you please give it another pass?


Discuss Spera & Mapelli MMax(z)

[ ] Motivate tapered power law
[ ] Cluster formation could give larger MMax
[x] How much does it evolve in redshift.
[x] Note that H(z) uncertainty gets better with a sharp cutoff.

http://adsabs.harvard.edu/abs/2017MNRAS.472.2422M

Decided to leave out the cluster discussion for space considerations; can put it back of the referee or you folks think it's important.


LiveShare: Error message popup when client joined live share session

VS Version: 16.2.0 Preview 3.0 [29003.219.d16.2stg]
Python Tool Version: 16.2.19142.2
Repro step:

Create a python application
Start Live Share Session
Join live share from client
Check client

Actual Result
Error message popup when client joined live share

Expect Result:
No error message popup
Below attachment is ActivityLog.xml:
ActivityLog.zip
Note:
IntelliSense is not working after this error occurs.

There is a known issue on Liveshare side and we are following up with LiveShare team.

The liveshare issue is fixed in preview 2 RC build. please regress and close it as the part of your test pass.


Replace @bind with bind-...

This change introduces a 'tag helper' that replaces @bind with custom
code generation that accomplishes roughly the same thing.
This feature lights up by dynamically generating tag helpers that are
visible to tooling and affect the code generation based on:

pattern recognition of component properties
attributes that create definitions for elements
a 'fallback' case for elements

'bind' also supports format strings (currently only for DateTime) via
a separate attribute.
This change introduces the basic framework for bind and tooling support.
We know that we'll have to do more work to define the set of default
'bind' cases for the DOM and to flesh out the conversion/formatting
infrastructure.
This change gets us far enough to replace all of the cases we currently
have tests for :) with the new features. The old @bind technique still
works for now.
Examples:
@* bind an input element to an expression *@
<input bind="@SelectedDate" format="mm/dd/yyyy" />
@functions {
    public DateTime SelectedDate { get; set; }
}

@* bind an arbitrary expression to an arbitrary set of attributes *@
<div<EMAIL_ADDRESS>
@* write a component that supports bind *@

@* in Counter.cshtml *@
<div>...html omitted for brevity...</div>
@functions {
    public int Value { get; set; } = 1;
    public Action<int> ValueChanged { get; set; }
}

@* in another file *@
<Counter bind-Value="@CurrentValue" />
@functions {
    public int CurrentValue { get; set; }
}

@rynowak

The old @Bind technique still works for now.

I take this to mean you will be removing the old @bind support fairly soon which will mean https://github.com/aspnet/Blazor/issues/386 will then no longer be an issue?

I take this to mean you will be removing the old @Bind support fairly soon which will mean #386 will then no longer be an issue?

That's the plan 👍

Appveyor succeeded on rerun. The first failure was due to network problems.


fix: Fix links to configuration in source tree.

These were broken by a70454c7b2c9.

Thanks @varungandhi-src 👍🏼


-m option is included in the pytest command which is conflicted with only  perform mypy checks option

-m option is not available.

My own fault. Sorry for my stupidness


Reinstatement of Revoke+Assign events in v1?

I'm targeting 1.0.1 but have scenarios where I'm trying to self-monitor progress using algorithms from Burrow. As part of this, I need to react to rebalancing of partition assignments involving the Consumer under observation; ideally I'd not have to infer that by caching and looking for deltas in assignments.
In the 0.x API, assignment/revocation events were exposed on the consumer, whereas when flipping (I have a shim that provides a v0/v1 interop) between v0 and v1, it seems like I'll need to wrap and expose such events myself (by wiring in callbacks to a buddy class when Building the Consumer).
Thus the question: Do you envisage ever reinstating a facility where the v1 APIs expose such notification events, or is the freezing of subscriptions/config after the initial Build of the Consumer a Final Decision?

there's no plan to re-instate notification of consumer group rebalance events via the c# event language feature - specifying these in the builder class is the only way going forward.

Thanks for the clarity; closing - I agree this makes sense for the lib. I went all the way down the road of shimming them as events, but asking this question helped me find a more sensible answer (inferring based on Assignment) so I've since removed that (WIP PR https://github.com/jet/Jet.ConfluentKafka.FSharp/pull/41)


Add board version to BoardOptions and add it to web-config requests

Board Version should be in the boardoptions save EEPROM.
Subsequent calls to BoardOptions will use the board flash data to determine the version rather than the build environment.

This is added to latest, and a new web-ui issue was opened.


Support for Trusted Advisor Organization dashboard in a different region other than Quicksight identity region

Cudos and Trends dashboard supports deployment to regions that is different from QuickSight Identify region. But Trusted Advisor Organization dashboard does not have such a configuration.
Following error is displayed when the target region is selected for dashboard deployment:
An error occurred (AccessDeniedException) when calling the ListUsers operation: Operation is being called from endpoint <target_region>, but your identity region is <identity_region>

Please try new automation (pip package)


HEVC / H.265 Video Recording

Proposal
When video recording is implemented, please support H.265 or HEVC video recording. This would be a great improvement over the H.264 in Flutter's camera plugin.

Thx for the suggestion. 👍
Could you elaborate on the advantages of these?

What H.265 allows is for substantially lower bandwidth costs for those of us building video-intensive flutter apps due to lower file sizes. Hardware decoding support for H.265 is improving. I might be able to help out with this endeavor.

Thank you for clarifications! This seems good reasons. I add these in notes


Add a lock file

Add a Cargo.lock now that wasm-tools has a cli.

Oh! Actually would you be up for adding --locked to CI jobs as well? That'll ensure that it's always up-to-date relative to what's in the repo

Oh! Actually would you be up for adding --locked to CI jobs as well? That'll ensure that it's always up-to-date relative to what's in the repo

Absolutely! I'll make another PR.


Vulkano shouldn't link to MoltenVK by default on macOS

OK, so this is going to be a long block of text, and some code (there is a TL;DR at the end). With the open source release of MoltenVK and the 1.0.69 release of the macOS LunarG Vulkan SDK I believe that the linking by default to the MoltenVK framework should no longer happen.
On Windows, Linux and Android, a shared object is loaded and the vkGetInstanceProcAddr symbol is loaded from it. They rely on vulkan.{dll,so} being available to load. However, through the Loader trait it allows crate users to load vkGetInstanceProcAddr however they see fit. That means that if I want to add println!("cargo:rustc-link-lib=vulkan"); to my build.rs and provide vkGetInstanceProcAddr I can (I think... didn't try it to be honest).
As of the  LunarG Vulkan SDK 1.0.69 for macOS there is a libvulkan.1.dylib loader that acts more like its Windows, Linux and Android counterparts (more on that later). That means that if libvulkan.1.dylib is in an expected place, then the DynamicLibraryLoader implementation of Loader will work. If we choose to go down that path we are still linking to MoltenVK (and other frameworks) even if it is no longer needed.
But why not just use the MoltenVK framework? The LunarG Vulkan SDK loader has a bunch of cool features that you might want (eg. support for VK_LAYER_PATH and VK_INSTANCE_LAYERS environment variables). You can take a look at this gist: https://gist.github.com/nlordell/184d363cffe0d7895155ee5fee89aa9a
The important bit is the output of running with dynamic loading of libvulkan.1.dylib vs linking against MoltenVK:
$ export VK_LAYER_PATH="/path/to/layer/configs"
$ cargo run # with dynamic loading of libvulkan.1.dylib
   Compiling mvk v0.1.0 (file:///Users/nlordell/Developer/rust/mvk)
    Finished dev [unoptimized + debuginfo] target(s) in 1.51 secs
     Running `target/debug/mvk`
VK_LAYER_GOOGLE_unique_objects
VK_LAYER_GOOGLE_threading
VK_LAYER_LUNARG_standard_validation
VK_LAYER_LUNARG_core_validation
VK_LAYER_LUNARG_parameter_validation
VK_LAYER_LUNARG_object_tracker
$ cargo run --features "framework-linking" # with linking against MoltenVK
   Compiling mvk v0.1.0 (file:///Users/nlordell/Developer/rust/mvk)
    Finished dev [unoptimized + debuginfo] target(s) in 0.90 secs
     Running `target/debug/mvk`
MoltenVK

In terms of code breakage, using the dynamic loading of libvulkan.1.dylib can be gated behind a feature to prevent breakage. Additionally, if the method of linking to MoltenVK gets completely removed, crate users that prefer to use MoltenVK framework can always add the cargo:rustc-link-lib=... lines to their build.rs and implement a StaticallyLinkedVulkanLoader implementation to the Loader trait.
TL;DR: LunarG Vulkan SDK provides a vulkan loader analogous to its Windows, Linux and Android counterpart that is "better" than MoltenVK framework - Vulkano shouldn't force crate users to link against a framework that is no longer needed and provide the ability to use the vulkan loader instead. It will also be more consistent with what Vulkano does in Windows, Linux and Android.
PS: I have no idea about iOS, but according to the docs there is currently no vulkan loader library, so maybe it should still link to MoltenVK framework.

The triangle example works with using the DynamicLibraryLoader with "libvulkan.1.dylib" from the LunarG Vulkan SDK and not linking to the MoltenVK framework:

I totally agree with that. The reason why it's automatically linked today is purely for convenience.

Would you be willing to accept a PR for defaulting to using DynamicLibraryLoader with the appropriate documentation on how to use MoltenVK and StaticallyLinkedVulkanLoader? I understand if the answer is "no" as this could pentially break how a lot of people currently build.
The other option is to create a no-moltenvk-linking feature that if enabled will use DynamicLibraryLoader and libvulkan.1.dylib. Either way, I don't mind submitting a PR, just let me know how you prefer it. Just want to be able to use libvulkan.1.dylib somehow :D.

@tomaka I'm curious if you've thought more about this. I'd also be happy to try helping.
Using the LunarG SDK would be very helpful.
Additionally, @nlordell's gist shows the proper build.rs to work for static linking, which I didn't find documented anywhere obvious. Would you like a PR to document that somewhere?

I think this was fixed by https://github.com/vulkano-rs/vulkano/pull/948

Yep, looks like it


evergreen build variation

We should definitely have an evergreen easy preset version of this plugin. My apologies to all the users who've suggested this already.

This is supported fine by setting:
System.config({
  babelOptions: {
    es2015: false
  }
});


Simplify core logic and support BSD

The improvements:

simplified the core logic (newpwd)
replaced the sed used by -si with perl, to make it work in BSD too (dropped the special note about BSD)
renamed OLDPWD to oldpwd, to avoid messing with the real OLDPWD automatically set by the shell when you change directories

I added a test case for /tmp/a/b/c/d/a/b -> /tmp/a/b/c/d/a to reveal the bug you found, and then fixed it.

I guess I can just sit back, you'll merge this sometime around early 2017 ;-)

Ping. Is there an issue with this pull request?

I accepted your pull request. Thanks!
There was a minor issue. You had fixed only the 3rd case (without arguments -s or -si). I put the .* back into the other two cases which solved the issue.
Also the test cases currently test only the 3rd case. Need to support running tests for the other two cases as well. Will do it when I get time.
Have a nice day!


Fix message of NotFoundError in scipy/linalg/setup.py

'lapack/blas' should be 'lapack'

@@            master   #6039   diff @@
======================================
  Files          238     238
  Stmts        43831   43831
  Branches      8215    8215
  Methods          0       0
======================================
  Hit          34262   34262
  Partial       2602    2602
  Missed        6967    6967

Review entire Coverage Diff as of 944aa0e

Powered by Codecov. Updated on successful CI builds.

It may also fail if blas is not available, hence the slash.


run npm 5.4.0 in CI

in response to https://github.com/facebookincubator/create-react-app/pull/2792#issuecomment-325726021

I restarted it, might be a Travis flake.

Nope, it's dead. 🤷♀
I'd be interested in hearing what this AppVeyor failure is about, though.

retrying with npm cache clean --force

@Timer
ENOENT: no such file or directory, open 'C:\c\projects\create-react-app-a3khu\packages\babel-preset-react-app'
the path seems weird...

I glazed right over that; npm 5 must not like file:/c/projects/create-react-app-a3khu/packages/babel-preset-react-app; is there a newer format?

the previous code is using npm link, i'll revert that changes.

https://github.com/npm/npm/pull/15900
seems like the new format is link: i'll try that when this fails.
Also, https://github.com/facebook/flow/issues/4733

We can also just cp things if npm link is broken.

Sorry i've fallen asleep... Seems like npm link works correctly, any caveat relying on it for e2e?

soo (maybe) the remaining issue to get green npm 5++ in CI is for flow-bin to get a proper permission.

5.4.1 has resolved executable permissions.

updated the code for accuracy :)

Yaaaay!


Ready() provide a synchronous alternative

Hi,
I just upgraded the nuget package from the alpha2 to 5.0.0 and I found out that there is a new method called Ready() to call before you can actually use elasticsearch.
Anyway, the method is async and my test classes/methods are all synchronous.
I tried all different combinations suggested here to make the method call synchronous:
http://stackoverflow.com/questions/22628087/calling-async-method-synchronously
With no luck.
https://ci.appveyor.com/project/gigi81/elastico/history
Can you please provide a synchronous alternative for the Ready() method?
Many Thanks
Luigi

Hello, in 5.0.1 I have added a ReadySync() method including a test that returns void:
.ReadySync()
Does that work for you?
Thanks,
Poul

Hi @poulfoged
Fantastic! I will try it out and let you know how it goes. But I see you had a proper unit test so I don't expect any problem now.
Many Thanks
Luigi

Hi @poulfoged
I finally managed to try the ReadySync method but with not much luck.
On my local machine it works. Anyway on appveyor the build still stalls forever and I have to kill it.
For example:
https://ci.appveyor.com/project/gigi81/elastico/build/<IP_ADDRESS>
I came to the conclusion that the problem is not specifically in your implementation but anyway it has something to do with xunit or appveyor or the combination of the two together.
The underlying problem I think that is still the fact that the method is not really a sync method, but an async method that is called and waited for.
Any idea or suggestion at this point are welcome.
Thanks

Hm.. ok wierd that it works here:
https://ci.appveyor.com/project/poulfoged/elasticsearch-inside/build/1.0.70
Maybe we need a fully sync path - only problem is HttpClient ..
I'll reopen and try some things.

Yeah. I noticed that! That's why I feel like it could be xunit and/or the fact that I'm creating the server in the constructor of the unit test class.

I found the root cause of the issue.
The problem is with XUnit and how it handles async calls that can lead to deadlocks if using .Result and friends. This is why the ReadSync() was dead locking for me.
XUnit provides an interface called IAsyncLifetime to implement in a test class. When calling the Ready() async from the InitializeAsync method in the test class, it finally worked for me.
My life would have probably been easier if I just had the sync call as the previous versions of the library, anyway I guess I learned some new things.


[bug] LlamaHandler format

I think there is a bug in the way formatting is handled for Llama models here in this function:
def _format_prompt(prompt, function, test_category):
    conversations = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>{SYSTEM_PROMPT_FOR_CHAT_MODEL}<|eot_id|><|start_header_id|>user<|end_header_id|>{USER_PROMPT_FOR_CHAT_MODEL.format(user_prompt=prompt, functions=str(function))}<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
    return conversations

Given placeholder system and user messages, if I use the Llama-3 tokenizer as follows:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
messages = [
    {"role": "system", "content": "SYSTEM"},
    {"role": "user", "content": "USER"}
]
conversations = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(conversations)

then I get the following output:
'<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nSYSTEM<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nUSER<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'

The current format misses the "\n\n" which appears after the end of each header id.

Thank you for catching this! This is indeed an oversight on our end.
Right now, we observe minor improvements to both llama3-8b and llama3-70b models, not affecting their position on the leaderboard. We will verify the results and address this issue in the next release by updating the handler alongside leaderboard scores on llama3 results, making llama handler's prompt template consistent with the official one. Thanks again!

No problem! Awesome -- thanks for your great work on this benchmark.
I have another (mostly unrelated) question as well -- are the benchmark numbers reported when running with temperature=0? I was unable to reproduce the scores for Llama-3-8B-Instruct and noticed the default temperature setting was 0.7.

Also -- I want to do local inference with gorilla-llm/gorilla-openfunctions-v2, and it seems that there is a discrepancy between the prompt formatting in the inference_local.py file and how formatting is handled with the model's tokenizer.
Using the setup in the example file:
import json

def get_prompt(user_query: str, functions: list = []) -> str:
    """
    Generates a conversation prompt based on the user's query and a list of functions.

    Parameters:
    - user_query (str): The user's query.
    - functions (list): A list of functions to include in the prompt.

    Returns:
    - str: The formatted conversation prompt.
    """
    system = "You are an AI programming assistant, utilizing the Gorilla LLM model, developed by Gorilla LLM, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer."
    if len(functions) == 0:
        return f"{system}\n### Instruction: <<question>> {user_query}\n### Response: "
    functions_string = json.dumps(functions)
    return f"{system}\n### Instruction: <<function>>{functions_string}\n<<question>>{user_query}\n### Response: "

query_1: str = "What's the weather like in the two cities of Boston and San Francisco?"
functions_1 = [
    {
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA",
                },
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
            },
            "required": ["location"],
        },
    }
]

prompt_1 = get_prompt(query_1, functions=functions_1)

the value of prompt_1 is:
You are an AI programming assistant, utilizing the Gorilla LLM model, developed by Gorilla LLM, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
### Instruction: <<function>>[{"name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}, "required": ["location"]}}]
<<question>>What's the weather like in the two cities of Boston and San Francisco?
### Response:

However, if I run the following in an attempt to match the above formatting:
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("gorilla-llm/gorilla-openfunctions-v2")
TEMPLATE = """"<<function>>{function}\n<<question>>{prompt}"""
messages = [
    {"role": "user", "content": TEMPLATE.format(function=str(functions_1), prompt=query_1)}
]
print(tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, add_special_tokens=False))

then the result is:
### Instruction:
<<function>>[{'name': 'get_current_weather', 'description': 'Get the current weather in a given location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit']}}, 'required': ['location']}}]
<<question>>What's the weather like in the two cities of Boston and San Francisco?
### Response:

Specifically, there is a new line character after ### Instruction: when using the tokenizer's chat template.
I'm curious how the model was trained and which prompt format is correct.

No problem! Awesome -- thanks for your great work on this benchmark.
I have another (mostly unrelated) question as well -- are the benchmark numbers reported when running with temperature=0? I was unable to reproduce the scores for Llama-3-8B-Instruct and noticed the default temperature setting was 0.7.

We are using temperature = 0.7 for reported llama3 numbers. If you would like to provide more information on what is different, we can investigate why you are not able to reproduce.

If we are sampling but not seeding, then results will naturally not be deterministic. There is some unavoidable non-determinism that vLLM introduces, but I imagine most of the discrepancy stems from sampling with non-zero temperature.

temperature is changed to 0.001 in #574


MP-8556 - Factory for converting Primitive types into "Formula"

formula is a computed field, so no need a mapper to an entity
thanks


superfluous response.WriteHeader

I am running 0.15.0-alpha and seeing this error in the logs every couple of hours.
2020/04/08 17:00:14 http: superfluous response.WriteHeader call from github.com/prometheus/client_golang/prometheus/promhttp.httpError (http.go:306)

Will update the client go lib for prometheus as soon as possible. - Maybe this fixes it.
We are not effected by this but thanks for reporting!

I was having this issue. and it turns out that Prometheus was timing out.
so just increase scrape_timeout from Prometheus to maybe 20 sec(default scrape timeout is 10)

Closing this. Please tag me if there is still a problem after increasing the scrape_timeout

What seems to have solved the issue was the upgrade of the prometheus golang client to 1.6.0.


LeetRooms does not recognize submission result on leetcode's new Dynamic Layout

When I use the new Dynamic Layout on leetcode, LeetRooms won't update with the submission results.
I.e. I solve a problem in the current leetroom, hit submit and '<user> submitted' will appear in chat. However, on AC the text '<user> solved <problem>' won't appear and on theoretically solving the whole room, the final message (<user> solved room in <x> minutes or something) is also not shown.
I can switch back to Split View and the extension will work properly again. I have to resubmit all problems on Split View if I already submitted on the Dynamic Layout though.

LeetCode is always going to be updating some part of their UI and the extension will be a little behind. Most recently, this was fixed by https://github.com/marwanhawari/LeetRooms/pull/61 and as of 4/11/24, the accepted submission detection is working again.


Add tile index prefixes in tile map editor plugin

Closes #5420.

Looks good to me :)

Should it be made optional? (genuine question, I don't have a strong opinion myself)

@akien-mga unless you have very long tile names, it shouldn't bother anybody IMO

Most of the time I generate my tilesets by code, so the tiles would have names like "1: Tile 1" .
Making it optional would be neat in this case, but I can also live without it ;)

@Hinsbart ahh -- just change the scripts :P


error when using XLA with multiple GPUs

The TF version is 2.4.1. When I enable XLA with mulit-gpu training by using MirroredStrategy, I got the following errors.
...
while/NcclAllReduce_271: unsupported op: No registered 'NcclAllReduce' OpKernel for XLA_GPU_JIT devices compatible with node {{node while/NcclAllReduce_271}}
        Stacktrace:
                Node: __inference_train_multiple_steps_260276, function:
                Node: while, function: __inference_train_multiple_steps_260276
                Node: while/NcclAllReduce_271, function: while_body_135159

while/NcclAllReduce_272: unsupported op: No registered 'NcclAllReduce' OpKernel for XLA_GPU_JIT devices compatible with node {{node while/NcclAllReduce_272}}
        Stacktrace:
                Node: __inference_train_multiple_steps_260276, function:
                Node: while, function: __inference_train_multiple_steps_260276
                Node: while/NcclAllReduce_272, function: while_body_135159

while/NcclAllReduce_273: unsupported op: No registered 'NcclAllReduce' OpKernel for XLA_GPU_JIT devices compatible with node {{node while/NcclAllReduce_273}}
        Stacktrace:
                Node: __inference_train_multiple_steps_260276, function:
                Node: while, function: __inference_train_multiple_steps_260276
                Node: while/NcclAllReduce_273, function: while_body_135159

while/NcclAllReduce_274: unsupported op: No registered 'NcclAllReduce' OpKernel for XLA_GPU_JIT devices compatible with node {{node while/NcclAllReduce_274}}
        Stacktrace:
                Node: __inference_train_multiple_steps_260276, function:
                Node: while, function: __inference_train_multiple_steps_260276
                Node: while/NcclAllReduce_274, function: while_body_135159
....

How to fix it?

Looks like nccl drivers are missing from your system config. By default TF uses NcclAllReduce() you may switch to Hierarchical Copy and try.
tf.distribute.mirrorstrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())

https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy?version=nightly#args

Looks like nccl drivers are missing from your system config. By default TF uses NcclAllReduce() you may switch to Hierarchical Copy and try.
tf.distribute.mirrorstrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())

https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy?version=nightly#args

The task can run without XLA enabled. It seems that nccl drivers are installed correctly.

Hi @cswhjiang, #45940 has some more info on using XLA with a distribution strategy. Please take a look at that thread. If that does not answer your question, please provide more information (are you using a custom training loop?) or reproducible code. Thanks!


Cherry pick 6f03a10e4fdb4f22651a9dcc5d6ab318724235e8

To satisfy dependencies also cherry-pick

08a1b07e7c19d14896f8be501c763ba8aff5b427
70e99f387a482bfe0ad2b7accc88e9e8b2a4a59d
e99fae899710b041994cef4beb6764f8dfbe8ef0
ea460b7ddb8adc9c9c48a968f1155d8757849d76

Sorry @antiagainst for the multiple PRs on this. Turns out I had to cherry pick a lot more things for this to apply cleanly.


Configure LED for boot and playbook runs

I would like to add a separate python script that accepts a few simple arguments and is run on startup (maybe via init script).

On boot: Turn on the LED to 'green' (e.g. 'ready/waiting')
On playbook run start: Switch LED to 'red' (e.g. 'something's being deployed')
On playbook run end: Switch LED to 'green'

Also, maybe have 'drupal' for drupal blue as a demo for doing serial vs. clustered deployment.

I'm adding a new leds role that will add scripts like rgb, colors, and fade to the user's path (in /usr/bin, so they can be run as normal commands.
I'm also adding a cron job at system boot to turn on the green LED so the rack looks like it's 'ready to rock and roll!'.
Examples:
# Switch all Pi LEDs to 'green', then 'blue'.
ansible -i inventory all -a "rgb green" -s
ansible -i inventory all -a "rgb blue" -s

# Fade all Pi LEDs.
ansible -i inventory all -a "fade" -s

It's even more fun if you play around with the forks parameter in /etc/ansible/ansible.cfg, for example:
[defaults]
forks = 2

You now have a nice visual indication of which Pis are getting hit by Ansible when if you run the above color-changing commands. Change forks to 1, 2, 5, 10, etc. and watch how quickly the command is run on all the Pis. Very helpful in illustrating how quickly Ansible can run things on all your servers, if need be, or how Ansible can operate on batches using serial and max_fail_percentage, etc.

See complete writeup in the Wiki:
https://github.com/geerlingguy/raspberry-pi-dramble/wiki/RGB-LEDs-controlled-via-GPIO


Custom @end prefix tag could have arguments, and if they start witn end stops highlight

I have custom blade directives, coincidentally they start with end, and the highlighting of the arguments does not work

  @modal(['id'=>'ModalFirmaElectCheck'])

  @endmodal(['cancel' => true])

  @endorse(['save' => true])

  @endpoint('custom_value')

I've added a quick fix for the php highlight and a logic to detect custom directive blocks.
But I think the ticket can be left opened.
https://github.com/haidubogdan/netbeans-php-blade-plugin/releases/tag/nb180-v.2.0.6

I will close the ticket.
Starting from v.2.1.21.
You can also disable the hints from Editor -> Hints -> Language Blade


Use more chars instead of strings

And it improves things like gsub(' ', "").

where you able to measure any performance increase or is this just to make intent clearer?

@wontruefree See benchmark in #5882

Can we maybe merge this now?

@straight-shoota, @RX14 or @sdogruyol
it seems to passed @bcardiff and seems overall like a good addition, can we have this merged?

This is ready for a review I think. @jhass @mverzilli

@ysbaddaden can we merge this please?

@RX14 This might be a good one to add in to 0.27.

What's blocking for the merge?

@r00ster91 Could you rebase on master to make sure it's all good?

Ouch, CI is going rogue again :/
Multiple:

GC Warning: Finalization cycle involving 0xf9ff990
GC Warning: Failed to expand heap by 16777216 bytes

Then:
Unhandled exception in spawn: Error writing to socket: Broken pipe (Errno)
  from src/socket.cr:77:13 in 'unbuffered_write'
  from src/io/buffered.cr:123:14 in 'write'
  from src/io.cr:853:5 in 'write_byte'
  from src/io/buffered.cr:156:14 in 'write_byte'
  from src/char.cr:767:9 in 'to_s'
  from src/io.cr:184:5 in '<<'
  from src/http/client/response.cr:63:5 in 'to_io'
  from spec/std/http/client/client_spec.cr:13:7 in '->'
  from src/fiber.cr:255:3 in 'run'
  from src/fiber.cr:72:34 in '->'
  from ???

Aaand a timeout, triple strike!

@Sija Yes, I did. I did exactly this on the branch:
git fetch origin
git rebase origin/master

Suspicious... I'm not sure it's rebased against master. Did you git pull in master first?

It's isn't rebased. Don't forget to git push -f

No, this branch is based on c163a85b which is 4 days old and doesn't include #7397.

Oh, no I didn't do git pull. I just fetched and rebased and then force-pushed.


Drop support for EOL Python 2.6 and 3.3, add support for 3.6

Fixes #426. Includes #471 and last commit also drops EOL Python 3.3.
Here's the pip installs for arrow from PyPI for the last month:
$ pypinfo --percent --pip arrow pyversion
python_version percent download_count
-------------- ------- --------------
2.7              59.8%        880,090
3.6              35.4%        520,727
3.4               2.5%         36,115
3.5               2.2%         32,627
3.3               0.0%            616
2.6               0.0%            534
3.7               0.0%            386
3.2               0.0%              7
None              0.0%              1

Codecov Report

Merging #472 into master will not change coverage.
The diff coverage is 100%.

@@          Coverage Diff          @@
##           master   #472   +/-   ##
=====================================
  Coverage     100%   100%
=====================================
  Files          14     13    -1
  Lines        3189   3175   -14
  Branches      229    229
=====================================
- Hits         3189   3175   -14

Impacted Files
Coverage Δ

arrow/util.py
100% <ø> (ø)
:arrow_up:

arrow/parser.py
100% <100%> (ø)
:arrow_up:

arrow/arrow.py
100% <100%> (ø)
:arrow_up:

arrow/factory.py
100% <100%> (ø)
:arrow_up:

tests/arrow_tests.py
100% <100%> (ø)
:arrow_up:

Continue to review full report at Codecov.

Legend - Click here to learn more
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@crsmithdev Hello, any thoughts on this PR (and my other ones)? Thank you!

My personal thought is to wait until a formal 1.0 before dropping 2.6/3.3. There's no technical reason why they need to be dropped now and it will just create problems for them to install.

@rhan-mentad Good point, but good news! That impediment has been removed:
I see 0.10 (2016-11-29) is the last one listed at https://github.com/crsmithdev/arrow/releases, but https://pypi.python.org/pypi/arrow has a recent 0.12 (2017-11-25).
I've also added python_requires to setup.py, so pip can install the correct release for a user's Python version (see https://stackoverflow.com/a/44660547/724176).

Rebased on master to resolve conflicts.

Any updates @Syeberman @crsmithdev ? Would love to see this merged.

Added a commit to upgrade Python syntax using https://github.com/asottile/pyupgrade.
And stats for June 2018:

python_version
percent
download_count

3.6
85.29%
1,126,421

2.7
10.96%
144,792

3.4
1.87%
24,728

3.5
1.82%
23,975

3.7
0.04%
513

2.6
0.01%
159

3.3
0.01%
132

3.8
0.00%
1

Total

1,320,721

I'm looking this one over now, just reran the builds and everything passed ok.

At the moment I'm waiting on input from @crsmithdev regarding whether we do this since it involves dropping support for old versions. Technically all seems ok. The other question is whether we release a new version now or wait until this is merged in.


refactor: Fix dark mode execution time

Description
For some reason the useDarkMode composable has a significantly higher execution time than everything else. My first attempt to fix that was to move the composable into a global provider so it's only instantiated once. This doesn't seem to have fixed the problem.

Type of change

[x] Bug fix (non-breaking change which fixes an issue)

How should this be tested?

[ ] Run lighthouse on the preview and check the execution time stats shown in the image above.

Checklist:

[ ] I have performed a self-review of my own code
[ ] I have requested at least 1 review (If the PR is significant enough, use best judgement here)
[ ] I have commented my code where relevant, particularly in hard-to-understand areas
[ ] If package-lock.json has changes, it was intentional.
[ ] The base of this PR is master if hotfix, develop if not

@agualis I broke the tests here. Do I need to add a provider somewhere to the test setup?

Seems this is more to do with SDK dependency than dark mode specifically.


g-bind with class

The behavior of g-bind with the class attribute is very confusing, since it adds new classes, but does not allow to remove existing classes. Now I don't have an example code at hand, but in my modest opinion this behavior should be reconsidered for a more coherent one.

Any update on this topic?

Hi Peter, we are working on this design to extend the current g-bind:class functionality. We plan to maintain backward compatibility while introducing new features for more flexible class bindings.
Cases

g-bind:class="string" (Current behavior)
Assigning a simple string adds that specific class to the element.
Example:
g-bind:class="myClass"

This will always add "myClass" to the element.

g-bind:class="array" (Current behavior)
Providing an array of strings adds all listed classes.
Example:
g-bind:class="['class1', 'class2']"

This adds both "class1" and "class2".

g-bind:class="object" (New feature)
Using an object with class names as keys and boolean values as conditions allows adding or removing classes dynamically.

true means the class is added.
false means the class is not added (and if previously present, it is effectively removed).
Example:

g-bind:class="{ 'classA': conditionA, 'classB': conditionB }"

If conditionA is true, "classA" is added; if conditionB is true, "classB" is added. If either condition is false, that respective class is not included.

g-bind:class="array[string, object]" (New feature)
Combining arrays and objects provides a hybrid approach:

Strings in the array are always added as classes.
Objects in the array are evaluated to add or remove classes conditionally.
Example:

g-bind:class="['classAlways', { 'classConditional': condition }]"

This always adds "classAlways". If condition is true, it also adds "classConditional". If condition is false, "classConditional" is not included.

Next Steps:

We will implement this logic while ensuring the existing functionality (string and array bindings) continues to work as before.
Additional tests and documentation will clarify the usage patterns, especially for the new object and mixed array-object approaches.
We aim to keep the syntax simple, intuitive, and flexible, allowing users to easily handle dynamic class assignment within the Graphane environment.

Please check the new version 1.0.0-beta.8 for these features.

It's ok. Thks


Use KadTools instead of reimplementing Kademlia?

KadTools is very mature, and used by at least one production system. It includes several useful additions like basic Pub/sub and NAT traversal strategies.
https://github.com/kadtools

Interesting, thank you for pointing to this, will investigate :)

fyi, this project has migrated to https://gitlab.com/deadcanaries/kadence

This looks like a closed project now and we have https://github.com/libp2p/js-libp2p-kad-dht so there's nothing left to do here.


can you please add useful models

Please remove all current models in "models/"
please add to models:

Alexnet owt: https://github.com/e-lab/imagenet-multiGPU.torch/blob/master/models/alexnetowt.lua
resnet: https://raw.githubusercontent.com/facebook/fb.resnet.torch/master/models/resnet.lua
VGG: https://github.com/e-lab/imagenet-multiGPU.torch/blob/master/models/vggbn.lua
at least

Added all three of them


make random enemies to shoot a bullet

Make random enemies to shoot a bullet. Make a bullet twice slower than the player's bullet.
When player's bullet and enemies' bullet hits together, both will be gone.

see the changes here e9097f2

and here for javaScript code f94165e


chore(deps): update common-dev-assets digest to 347487f

This PR contains the following updates:

Package
Update
Change

common-dev-assets
digest
a3aa2cf -> 347487f

Configuration
📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.

[ ] If you want to rebase/retry this PR, check this box

This PR has been generated by Renovate Bot.

/run pipeline

/run pipeline

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:tada: This PR is included in version 1.41.2 :tada:
The release is available on GitHub release
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Show warning that a file was skipped for placeholder replacement, because it is not valid utf8

Implemented by commit: https://github.com/BoolPurist/crust_boot_you/commit/d498d449536eb1ba6bfae176bbfc2ad260aae914


Save explored areas state

Is there a way to save the state of explored areas in the fog so when loading a game we could load the explored areas?

Yes, if you take a look at the example scenes there is an implementation of saving/loading the buffers.
In MangoFogInstance there is two methods:
SaveChunkBufferToSlot
LoadSlotBufferToChunk

Got it thank you so much!


dashboard: kibana dashboard with support for > 8.0

What
Support Kibana version > 8.0 in addition to 7.17.
Changes

Use index pattern "traces-apm*,apm-*,logs-apm*,apm-*,metrics-apm*,apm-*"
Remove hardcoded graph for Agent Allocation Duration
Average Job Durations doesn't work in Kibana 7.17 but it works in Kibana 8.0

Somehow the metrics use numeric_ in Kibana 8.0

Potentially I could create two dashboards the one for 7 and the new one for 8. But i'd rather prefer to keep one with support to the latest major version.
Issue
Closes https://github.com/jenkinsci/opentelemetry-plugin/issues/378

Thanks for working on a fix.
I was wondering why we had this attribute ci.pipeline.run.durationMillis which overlaps a lot with the transaction duration (transaction being the root span of the job).
Could we replace our usage of ci.pipeline.run.durationMillis by the transaction.duration.us of the pipeline root spans (e.g. spans matching labels.ci_pipeline_run_result:*). It would mostly needs a conversion  from microseconds (us to millis).
With this, could we have a dashboard compatible with 7.x and 8.x?

With this, could we have a dashboard compatible with 7.x and 8.x?

I just updated the new dashboard that's compatible with both versions, thanks for the tip about using the transaction time.

It would mostly needs a conversion from microseconds (us to millis).

IIUC, Kibana does not support aggregations with bucket script -> https://github.com/elastic/kibana/issues/4707
Therefore, it will require to use Kibana Lens to allow this transformation, for now, I didn't make that change but in a follow up to use Kibana Lens for all the graphs instead the traditional Kibana graphs


AttributeError: 'SetFitHead' object has no attribute 'multitarget'

# Load a SetFit model from Hub
model = SetFitModel.from_pretrained(
    "/content/drive/MyDrive/custom_model",
    use_differentiable_head=True,
    head_params={"out_features": 2},
)
model.sequence_length = 128

trainer = SetFitTrainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss_class=losses.MultipleNegativesRankingLoss(model),
    metric="accuracy",
    batch_size=8,
    num_iterations=20, # The number of text pairs to generate for contrastive learning
    num_epochs=10, # The number of epochs to use for contrastive learning
    # column_mapping={"text": "text", "labels": "label"} # Map dataset columns to text/label expected by trainer
)

trainer.unfreeze(keep_body_frozen=False)

trainer.train(
    num_epochs=5, # The number of epochs to train the head or the whole model (body and head)
    batch_size=64,
    max_length = 128,
    body_learning_rate=1e-5, # The body's learning rate
    learning_rate=1e-4, # The head's learning rate
    l2_weight=0.0, # Weight decay on **both** the body and head. If `None`, will use 0.01.
)

Outputs: "AttributeError: 'SetFitHead' object has no attribute 'multitarget'"

Above, "custom_model" is the "sentence-transformers/all-mpnet-base-v2" trained on the same hyperparameters and SetFit release version v0.5.0.
The problem was resolved once I installed SetFit v0.5.0, but it persists in SetFit v0.6.0.

Did you resolve this issue? No need to close this if not. I assume others may encounter this issue, too.

Did you resolve this issue? No need to close this if not. I assume others may encounter this issue, too.
Reopening it.

The new v0.6.0 does introduce this new value. It's a bit unfortunate that this may mean that SetFit v0.5.0 can't easily be loaded on v0.6.0 onwards, especially as we introduce further changes from v0.5.0. I'm not quite sure how best to tackle this.


Auto replace default arguments

Can you replace default argument on call some function? Like this:
build.gradle.kts
plugins {
    // other plugins
    id("com.jakewharton.cite")
}
// configuring auto replacer of default argument
autoApply {
    // select class and method
    to("com.foo.bar.Logger.info") {
        // mapping what pass to arguments
        mapLineToArgument(argumentName = "line")
        mapFileToArgument(argumentName = "file")
    }
}
// other code

com/foo/bar/Logger.kt
package com.foo.bar

object Logger {
    fun info(msg:String, file:String = "", line:Int = 0) {
        println("$file:$line: $msg")
    }
}

and then try to use logger:
fun main(){
    Logger.info("Hello world") // default values of "file" and "line" will be replaced by your plugin according ruls in build.gradle.kts file
}

This is #20 and almost certainly will never be supported by this plugin because of the problems described in that issue.

@JakeWharton you are not right. My version is easier to implement

Well you are free to implement it in a fork or separate plugin then. I will not be pursuing it.


[Request] Great app but it could use a way to add blacklists directly from query.logs

I love the app , i've used the cli version of dnscrypt for years on all platforms today i installed SimpleDnsCrypt on Win7 and it works very well, spotted all my nics gave me option to chose resolvers with varying options ..great stuff , Thanks
An easy way to either be able to access the logs directly - the link fails to open the directory or to select them from the running query.logs -it should be able to scroll down the query.logs, it doesn't and pointing to the scroll down arrow selects window adjustment.The only way is to latch on the scroll tab on the side and pull it up/down.
I could see tons of ads links flashing by but too difficult to add one by one to blacklist. Also there isn't a minimizing to tray option which would free up desktop and still have it running .
There's a ddwrt/packages here on github with adguard lists and tons of other features that you might be able to integrate . just a thought
Thanks again .

@marxenegls thank you for the feedback!
Tray: https://github.com/bitbeans/SimpleDnsCrypt/issues/125
Multiselect: https://github.com/bitbeans/SimpleDnsCrypt/issues/271


firebase_ml_vision example sensor orientation

On a Pixel 3XL, the firebase vision example doesn't work very well.  With no errors, it doesn't decode barcodes out of the box, and the 'camera preview' example doesn't find anything.
To reproduce this, you just have to build this example and run it on a Google Pixel 3XL.
There is a lot of advice about this problem on stackoverflow and other places.  One thing that seems to improve the barcode scanning example is to do this:
    defaultTargetPlatform == TargetPlatform.android
        ? ResolutionPreset.high // was medium
        : ResolutionPreset.low;

I can verify that does indeed improve performance, but I don't think it's doing so the right way.  My first clue to this is that if you run "Material Barcode Scanner" and hold the phone in portrait mode, it scans with MUCH better reliability if you hold the bar code sideways.  You can watch it perform by looking at the bottom where it says "Hold the bar code closer".
As a second example, if you run the Camera Preview Scanner and put it into barcode mode, it draws boxes around where it thinks it sees an area to decode.  Again holding the phone vertically (portrait), it doesn't see much.  Turn the barcode sideways, and it detects it with a long, skinny box that's "tall" rather than "wide".
I think something has the orientation wrong.  There are a number of references like This that explain why orientation is so confusing.  Orientation is really talking about the sensor's orientation, not the image.  If the sensor is 90 degrees off, then in landscape you might have to rotate 90 + 90 (or possibly 90 - 90) to get the image upright.
Here's an example of that with the barcode detector selected. If I turn the bottle sideways, it doesn't detect ANYTHING.  If I turn it the way shown, you can see that it does.

Now here's where it gets even stranger.  It doesn't detect any text at all in ResolutionPreset.medium (the default for android).  But if I change that to Resolution.high it does.  But only in the correct (horizontal, normal, whatever you want to call it) orientation.  In other words, in 'high' resolution, text recognition works fine.  On this device, at least, there doesn't seem to be any performance penalty for selecting 'high' resolution.

I can fix all these problems with various hard-coding but that's a terrible way to make something that should, in theory, work everywhere.  And like I said, these kinds of problems haunted me back in Kotlin as well.  But in the context of this project, you have a great example that mostly works out-of-the-box, so i'm wondering if, rather than wait for firebase to fix these issues, we can just make this example more reliable.
Thoughts?
dependencies:
  flutter:
    sdk: flutter
  firebase_core: ^0.4.0+9
  firebase_ml_vision: ^0.9.4
  firebase_analytics: ^5.0.2
  firebase_auth: ^0.14.0+5
  cloud_firestore: ^0.12.9+5
  camera: ^0.5.8+2      /* I tried several different versions with same results */
  image_picker: '0.6.7+2'
  path_provider: '1.6.10'
  cupertino_icons: ^0.1.3

[√] Flutter (Channel stable, v1.17.1, on Microsoft Windows [Version 10.0.19041.329], locale en-US)

[√] Android toolchain - develop for Android devices (Android SDK version 28.0.3)
[√] Android Studio (version 3.6)
[√] IntelliJ IDEA Ultimate Edition (version 2019.3)
[√] VS Code (version 1.45.1)
[√] Connected device (1 available)

Hi @wz2b
you are making a good point,
honestly I'm not sure that this is caused by flutterfire plugin or the native plugins that builds on,
(also I believe flutter doesn't use camera2 to keep compatibility with sdk16)
Nonetheless I cannot be sure of the opposite.
For now I'd keep the issue open.
Thank you

Yeah, agreed - I don't think it's caused by flutterfire, either.  I think,
though, that if I can find a workaround in flutterfire that might be faster
than getting it fixed in Firebase itself or, even worse, Android.
I found some sample code that actually tweaks the settings based on a
static list of quirky phones they know about.  Blech.
On Fri, Jun 19, 2020 at 9:57 AM Francesco Iapicca<EMAIL_ADDRESS>wrote:

Hi @wz2b https://github.com/wz2b
you are making a good point,
honestly I'm not sure that this is caused by flutterfire plugin or the
native plugins that builds on,
(also I believe flutter doesn't use camera2 to keep compatibility with
sdk16)
Nonetheless I cannot be sure of the opposite.
For now I'd keep the issue open.
Thank you
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/FirebaseExtended/flutterfire/issues/2793#issuecomment-646651067,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/AALTMOJL33Q5VJZTE7SDU3DRXNVEVANCNFSM4OCBPHKQ
.

Firebase Vision documentation strongly suggests you use YUV_420_888 as the image format.  I implemented this once using the GPU and android Render scripts.  What I did was took the camera image (RGBA format) and converted it to YUV in a renderscript.  It worked well, performance was good, and it improved recognition quite a bit.
The image created in this example is converted to a FirebaseVisionImage using
      FirebaseVisionImage.fromBytes(
        _concatenatePlanes(image.planes),
        _buildMetaData(image, _rotationIntToImageRotation(imageRotation)),
      )

I stopped to inspect there to see what that format is and it is returned as:

So it definitely is YUV420.  The detected sensor orientation is 90, and its notion of resolution is 720h x 1280w.  I'm holding the phone vertically, so when you apply a 90 degree orientation, you get a resolution that seems right.  That image is eventually passed to BarcodeVisionDetector.detectInImage().
One thing I wonder is if detectInImage() genuinely pays attention to the rotation flag in FirebaseVisionImageMetadata.  I think when I was using Vision in kotlin I tried rotating it myself but I don't remember the results.
It looks like this code here sends the entire image to the decoder.  That's a little misleading - there's a guide box that makes you think that's where it's scanning, but I don't think that's true, is it?  If I'm right, then this might be worth looking at, as part of this flutter example.  Cropping isn't that expensive an operation compared to decoding.  That's more true for character/text recognition (barcode scanning is pretty fast).

I also found that barcode scanning works in High resolution (1280x720) but this results in frequent GCs on old android phones.  I have a fork  of firebase_ml_vision which uses the newer google ml-kit on device lib and the problem exists there as well.
The ml-kit Vision-quickstart samples written in Java/Kotlin (but use older camera api) work perfectly fine with 720x480 (medium) resolution. I am not able to figure out what's wrong in Flutter land with medium resolution.
Here there is a talk of incompatibility with some devices and certain Camera2 versions- https://developers.google.com/ml-kit/known-issues

I also found that barcode scanning works in High resolution (1280x720) but this results in frequent GCs on old android phones.  I have a fork  of firebase_ml_vision which uses the newer google ml-kit on device lib and the problem exists there as well.
The ml-kit Vision-quickstart samples written in Java/Kotlin (but use older camera api) work perfectly fine with 720x480 (medium) resolution. I am not able to figure out what's wrong in Flutter land with medium resolution.
Here there is a talk of incompatibility with some devices and certain Camera2 versions- https://developers.google.com/ml-kit/known-issues


API discussion

I looked through the API (various points apply as well for mirage-kv, which imho mirage-fs should extend, rather than having similar functions defined multiple times (such as read / mem / size / error), and have various thoughts (I can see some room for improvement):

path is currently a string, which basically means each implementation needs to do with normalisation on their own.  we should consider to use Fpath.t from https://github.com/dbuenzli/fpath
the page aligned bits are imho not necessary here
read requires both offset and length, which should be optional (and int64, as done in mirage-kv)
read also returns a Cstruct.t list, which should be a Cstruct.t
provide readv and writev for Cstruct.t list
write requires an offset atm, should be optional
semantics of write: add padding bytes if writing at some offset?  maybe some options such as append and truncate will be useful?
error semantics (and specification): e.g. fail hard in read if specified offset and length are inappropriate (-1 or bigger than file), specify which errors are to be returned in which cases
remove create, it's the same as write with Cstruct.empty

maybe it would be worth to specify a nice interface for the user, and then another one which implementations needs to satisfy.  the bounds checks and other common code could then be implemented within this library, rather than in each implementation.

What do you think?  And who is maintaining any implementations of this interface?  mirage-fs-unix and fat-filesystem come to mind, git-mirage as well.

write_error should include Directory_not_empty, mirage-fs-unix and mirage-fs-mem extend their write_error already with this variant.

I think these are all sensible changes that would've made sense to include in Mirage 3, and I regret that they didn't make it.

All of this make sense. Please burn the current mirage-fs interface, it was only made as a proof-of-concept for the FAT backend.

first step is a revision of mirage-kv interface at https://github.com/mirage/mirage-kv/pull/12, please let me know what you think (we'll try to find the consumers and update them as well next week).

I propose that key becomes an abstract type in KV / KV_RO. It could become a string for KV and an Fpath.t for FS and something else for Irmin.
But I am just not totally sure that we want the full complexity of Fpath.t for this (e.g. we don't care about volumes and windows). Irmin uses this (and usually have reverse lists to represent paths to speed-up basename/dirname operations).

the page aligned bits are imho not necessary here

It might be worth considering a general interface for page-aligned streaming, that could be used by all the I/O backends.  It's extremely pervasive to have this requirement for high performance I/O, and is particularly painful to ensure (without a data copy) ahead of time.  And for the common usecase usecase of proxies, it is also very easy to implement as pages can be transferred via DMA.
However, I do agree that exposing it directly from mirage-fs makes it a hard to use interface as-is...

largely superseeded by https://github.com/mirage/mirage-kv/pull/14 and https://github.com/mirage/mirage-kv/issues/16 -- we're phasing out the mirage-fs interface, and instead providing the Mirage_kv.RW interface. let's move the discussion to mirage-kv.


Emoji "pile up" in Conversations snippets

Any emoji I type in Yakyak seem to be permanently stuck at the end of the "Conversations" snippet.
See the attached screenshot, where it is stuck at the end of the other participant's message, and contains emoji I used before my last message.

This seems to reset every time I open the program

which OS and version of YakYak are you using? (can you try 1.3.3 and see if the "pile up" is still going on)
I don't see it in 1.3.3 (an in none of past versions)

( Excuse me @averissimo , I apologize for the offtopic; but did you use any special tool to generate such gif easily? )

(Sorry, also, how could you use hangout's icons in Mac OS? did you replace the font?)

Offtopic 1: Easily enough in any Linux system with two scripts working together and ffcast + avconv installed
I do them with one command (adding -s -f to select window)
$ my_ffcast

Scripts here (just add them to your path):
https://gist.github.com/averissimo/3f40deac1ca22cc6658e2b2b56131ccc
For a windows and Mac I've read http://www.cockos.com/licecap/ is pretty good
Offtopic 2: Linux baby!! but seriously it comes pre-packaged in Yakyak, it was up to a certain version, removed by accident and then back in (I think).
If you install the Noto font system-wide then you also get it in YakYak (and by consequence in chrome)
Again in Linux there is a tweak necessary ::angry emoji here::, see my comments in #132

@Arithmomaniac sorry for the offtopic responses, did updating YakYak solve the problem? or is still persists in latest version?

Haven't had a chance to test yet. Will update when do

Sent from my phone. Please excuse brevity and typos.
On Sep 29, 2016 7:04 AM, "André Veríssimo"<EMAIL_ADDRESS>wrote:

@Arithmomaniac https://github.com/Arithmomaniac sorry for the offtopic
responses, did updating YakYak solve the problem? or is still persists in
latest version?
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Got a chance.
It seems that every time I send an emoji, it and the subsequent text in the body, get stuck to the end of the preview snippet.

Super strange!!
Which opeating system are you using?
Is there any warning/error on the console (press Ctrl + Shift + I) and open the console tab.
If you can paste it here. Thanks

Windows 10.

I also have that error lol. Any other?
Does your recipient see the original message? or the "piled up emojis" version you see

The original message.
On Fri, Sep 30, 2016 at 3:18 PM, André Veríssimo<EMAIL_ADDRESS>wrote:

I also have that error lol. Any other?
Does your recipient see the original message? or the "piled up emojis"
version you see
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@Arithmomaniac that error is corrected in latest 1.4.0-beta release, can you test it and see if problem still appears
see #451 to download beta (sorry in advance for more repeated comments)

I still see this issue in 1.4.0.

@Arithmomaniac is this only for the snippet? or this also happens in the message pane

Hi, @averissimo,
in my case this only happens for conversations sidebar. Here's the screenshot:

Windows 7 32, YakYak v1.4.1

If someone is interested in trying to figure this one out
The snipped is being drawn in src/ui/views/convlist.coffee
I would suggest starting by adding a breakpoint (or a console.log('e',e)) in the first line of drawMessage function to see what is the contents of e (the chat event) and namely e.chat_message.message_content
After that either the contents are correct and is the formatting that breaks this, or the problem is upstream (how we choose the last message or even how that is kept in the conversation)
Anyway, I can't reproduce this at all so someone has to pick this up and investigate :)

@kli0rf @Arithmomaniac can you confirm that this is still happening?
@davibe made a correction in windows that should have solved this problem.

@averissimo i've just unpacked version 1.4.2 (released by You 4 days ago). Looks ok so far. Gonna let You know if that happens again. Thank You!

@averissimo btw, now all emoticons are colourless. I'm not sure if that's intented though... Just sayin'. :)

Works for me now! And mine have color. Thanks!

My bad, i thought yakyak's using it's own integrated emoticons... Just noticed it's an emoji font we're talking about, so colourless emojis are completely windows 7 problem. Please ignore my previous post.

Yeah, that is something we probably have to tackle before releasing 1.4.2
The way it was being done injected snippets in weird places and we removed and defaulted to windows emoji font (that is colorless)
anyway @kli0rf that is being tracked in #568
I'm closing this issue as it has been corrected

btw, major props to @davibe that corrected this bug! :)

What commit fixed it?

I think this one: ec1c6b41583e712d3c2316f9812f7c832feeca34


Adjust blend priority to make new face mask more prominent?

Hi, I'm trying to figure out if its somehow possible to adjust the blending between the source and destination face. The detection/replacement itself works fine, but I would like to preserve the color of the new face and give it also a bit more priority. Depending on the source image/video I feel that some results still have too much features/similarities to the original source and I would like to figure out if its somehow possible to preserve more infos from the new face.
Would be great if someone could point me towards the corresponding modules that have the most influence on the visual outcome of the swap.

Hi, @DennisBora!
There are a few options you can try to keep more information from the new face:

You can try to train the model with a higher identity loss --weight_id - this makes the generated face look more like the original face than the target/destination face.
Edit masks.py. You get the final result by superimposing the generated face on the original frame using a mask. This way, varying the shape of mask makes the final face closer to the new face or the destination face.


How to train? thx!

I can only get 79.13% acc@1 on ImageNet under off-the-shelf, and I find the average FLOPs is 3.4G  instead of 2.9G as reported in the paper (I sum up the calculations on each batch and draw a tie with profile_macs().)


⚠ Bofrost.be has degraded performance

In 877ef96, Bofrost.be (https://www.bofrost.be) experienced degraded performance:

HTTP code: 200
Response time: 1236 ms

Resolved: Bofrost.be performance has improved in 3cc8ef0 after 6 minutes.


shift click doesn't select as expected

shift click is working in the editor debug app and in the egui client; re-labeling & re-assigning