ValueError: operands could not be broadcast together with shapes (256,256,4) (3,) (256,256,4)

I am trying to use this package, and it is throwing as below. I am using the same pipeline from cassava lead detection problem but on different set where image size  is (256, 256)
Could you please help here.

Downloading: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth" to /root/.cache/torch/hub/checkpoints/efficientnet-b4-6ed6700e.pth
100%
74.4M/74.4M [00:00<00:00, 107MB/s]
Loaded pretrained weights for efficientnet-b4
0%|          | 0/51 [00:00<?, ?it/s]
ValueError                                Traceback (most recent call last)
 in ()
11     epochs=10,
12     callbacks=[es],
---> 13     fp16=True,
14 )
15 model.save("model.bin")
6 frames
/usr/local/lib/python3.6/dist-packages/tez/model/model.py in fit(self, train_dataset, valid_dataset, train_sampler, valid_sampler, device, epochs, train_bs, valid_bs, n_jobs, callbacks, fp16)
295             self.train_state = enums.TrainingState.EPOCH_START
296             self.train_state = enums.TrainingState.TRAIN_EPOCH_START
--> 297             train_loss = self.train_one_epoch(self.train_loader, device)
298             self.train_state = enums.TrainingState.TRAIN_EPOCH_END
299             if self.valid_loader:
/usr/local/lib/python3.6/dist-packages/tez/model/model.py in train_one_epoch(self, data_loader, device)
176         losses = AverageMeter()
177         tk0 = tqdm(data_loader, total=len(data_loader))
--> 178         for b_idx, data in enumerate(tk0):
179             self.train_state = enums.TrainingState.TRAIN_STEP_START
180             loss, metrics = self.train_one_step(data, device)
/usr/local/lib/python3.6/dist-packages/tqdm/std.py in iter(self)
1102                 fp_write=getattr(self.fp, 'write', sys.stderr.write))
1103
-> 1104         for obj in iterable:
1105             yield obj
1106             # Update and possibly print the progressbar.
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in next(self)
433         if self._sampler_iter is None:
434             self._reset()
--> 435         data = self._next_data()
436         self._num_yielded += 1
437         if self._dataset_kind == _DatasetKind.Iterable and \
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
1083             else:
1084                 del self._task_info[idx]
-> 1085                 return self._process_data(data)
1086
1087     def _try_put_index(self):
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _process_data(self, data)
1109         self._try_put_index()
1110         if isinstance(data, ExceptionWrapper):
-> 1111             data.reraise()
1112         return data
1113
/usr/local/lib/python3.6/dist-packages/torch/_utils.py in reraise(self)
426             # have message field
427             raise self.exc_type(message=msg)
--> 428         raise self.exc_type(msg)
429
430
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 198, in _worker_loop
data = fetcher.fetch(index)
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/usr/local/lib/python3.6/dist-packages/tez/datasets/image_classification.py", line 48, in getitem
augmented = self.augmentations(image=image)
File "/usr/local/lib/python3.6/dist-packages/albumentations/core/composition.py", line 171, in call
data = t(**data)
File "/usr/local/lib/python3.6/dist-packages/albumentations/core/transforms_interface.py", line 38, in call
res[key] = target_function(arg, **dict(params, **target_dependencies))
File "/usr/local/lib/python3.6/dist-packages/albumentations/augmentations/transforms.py", line 808, in apply
return F.normalize(image, self.mean, self.std, self.max_pixel_value)
File "/usr/local/lib/python3.6/dist-packages/albumentations/augmentations/functional.py", line 93, in normalize
img -= mean
ValueError: operands could not be broadcast together with shapes (256,256,4) (3,) (256,256,4)

It seems like your image is RGBA. Can you convert it to RGB? or do you have to use RGBA?
Can you provide data and code to reproduce the error?

Ok I converted everything to RGB. I have label from [0-9] with image size 256x256. But I come across this CUDA error.
Regarding the code, it is exactly taken from here ;)
Loaded pretrained weights for efficientnet-b4

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-29-de0853739e51> in <module>()
     11     epochs=10,
     12     callbacks=[es],
---> 13     fp16=True
     14 )
     15 model.save("model.bin")

6 frames
/usr/local/lib/python3.6/dist-packages/tez/model/model.py in fit(self, train_dataset, valid_dataset, train_sampler, valid_sampler, device, epochs, train_bs, valid_bs, n_jobs, callbacks, fp16)
    289             n_jobs=n_jobs,
    290             callbacks=callbacks,
--> 291             fp16=fp16,
    292         )
    293

/usr/local/lib/python3.6/dist-packages/tez/model/model.py in _init_model(self, device, train_dataset, valid_dataset, train_sampler, valid_sampler, train_bs, valid_bs, n_jobs, callbacks, fp16)
     81
     82         if next(self.parameters()).device != device:
---> 83             self.to(device)
     84
     85         if self.train_loader is None:

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in to(self, *args, **kwargs)
    610             return t.to(device, dtype if t.is_floating_point() else None, non_blocking)
    611
--> 612         return self._apply(convert)
    613
    614     def register_backward_hook(

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _apply(self, fn)
    357     def _apply(self, fn):
    358         for module in self.children():
--> 359             module._apply(fn)
    360
    361         def compute_should_use_set_data(tensor, tensor_applied):

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _apply(self, fn)
    357     def _apply(self, fn):
    358         for module in self.children():
--> 359             module._apply(fn)
    360
    361         def compute_should_use_set_data(tensor, tensor_applied):

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _apply(self, fn)
    379                 # `with torch.no_grad():`
    380                 with torch.no_grad():
--> 381                     param_applied = fn(param)
    382                 should_use_set_data = compute_should_use_set_data(param, param_applied)
    383                 if should_use_set_data:

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in convert(t)
    608             if convert_to_format is not None and t.dim() == 4:
    609                 return t.to(device, dtype if t.is_floating_point() else None, non_blocking, memory_format=convert_to_format)
--> 610             return t.to(device, dtype if t.is_floating_point() else None, non_blocking)
    611
    612         return self._apply(convert)

RuntimeError: CUDA error: device-side assert triggered

Code provided in examples works quite well. This seems like some problem with the model. I cant say without having data and full code to reproduce the error :)

@nvnvashisth I just added a multi-class classification example (flower classification with 104 classes). It might be useful for you: https://github.com/abhishekkrthakur/tez/blob/main/examples/image_classification/flower_classification.py
Let me know if it still doesnt work.

Code provided in examples works quite well. This seems like some problem with the model. I cant say without having data and full code to reproduce the error :)

I have the code privately on your twitter (DM). That's the only way I could figure to reach you privately.

@nvnvashisth I just added a multi-class classification example (flower classification with 104 classes). It might be useful for you: https://github.com/abhishekkrthakur/tez/blob/main/examples/image_classification/flower_classification.py
Let me know if it still doesnt work.

I'll give it a try.

@abhishekkrthakur  it is so weird. I didn't really change anything and it started working. No more cuda error. Thanks for quick support.

wow. maybe you updated torch?

Not really, I was running in colab, was using the default one.

It seems like your image is RGBA. Can you convert it to RGB? or do you have to use RGBA? If latter, then try writing your own dataloader.
Thank you! This helped me to solve the above error.


Meetecho Janus integration

Hi,
I am looking for a solution for serving MeetEcho Janus.

Someone mentioned that they successfully integrated STUNner with Janus but I don't know any specifics. Currently there's no prefab integration available that I know of. Happy to guide you making your own one on Discord though.

HI @raviprakash007 - we currently run Janus inside Kubernetes to great success (via CoTURN currently, but are planning on a move to STUNner). We don't configure Janus with any STUN/TURN information, so theoretically it'll "just work" ™ with STUNNer 😄

@chriswiggins Once you make the transition to STUNner, could you please share your experience? We'd be happy to add a new Janus tutorial for a starter, care to draft one? We're taking PRs...:-D It doesn't have to be perfect, just a bunch of YAMLs and some minimal text, we can always package that up into a nice tutorial.

@chriswiggins , Can you please share the sample yaml for kubernetes coturn and config for Janus?. The problem I am facing issues in ice connectivity.

@chriswiggins , Can you please share the sample yaml for kubernetes coturn and config for Janus?. The problem I am facing issues in ice connectivity.

Hi @raviprakash007 - what issues are you facing exactly? Configure Janus without any STUN/TURN, and then point your clients at a TURN server (STUNner or CoTURN) running inside kubernetes, via a TCP service.
Unfortunately I can't share any of our config, but if you're using STUNner then the only thing you have to set up is Janus

@chriswiggins I am using following topology in k8, for Janus.
Browser Clients <------> Nginx Load Balancer <---via nginx proxy----> Janus.
I am able to connect Janus from browser client, But when I initiate a video call, the ICE connectivity fails after sending offer. However, the signalling is stable.

@chriswiggins , I also used coturn inside k8 and no stun/turn related config in Janus and it worked.


Add "indexed" visitors

This patch adds a new set of visitor functions that include the index of the field within the struct, in addition to its name.  The indices are 0-based, and the field ordering is the same as the ordering of the parameters passed to the VISITABLE_STRUCT macro.
We also provide two new helpers in the visit_struct namespace.  One returns the number of fields in the struct, and the other gives you access to the field index enum.

Hi, I am currently on vacation, I will write in a few days after I looked at this. Thanks.

No problem, thanks!

Ok, here are my initial thoughts:
I'm not totally sold on the "indexed visitors" thing. A very simple way that you can get the indices when you visit is to make a function object that contains a counter, and increments the counter each time it is called. It might look like this:
template <typename V>
struct indexing_visitor {
   V v;
   int idx;

    template <typename T>
   void operator()(const char * name, T && t) {
       std::forward<V>(v)(idx++, name, std::forward<T>(t));
   }
};

template <typename V, typename S>
void apply_indexed_visitor(V && v , S && s) {
   indexing_visitor<V> v2{V, 0};
   visit_struct::apply_visitor(v2, std::forward<S>(s));
}

Now, it's true that this doesn't have the enum thing, but at the same time, is the enum actually useful? Given a structure, I can't actually get the field corresponding to one of the enum values. Maybe there are some people who use one of these "better enum" libraries, and they'd like that better than having either the index or the const char * name I guess? Maybe visit_struct should ultimately use one of those as a dependency.
For the moment I'm just trying to keep the interface very small and avoid little details that you need to remember. For instance like, why does the int come before the const char * when doing an indexed visitor? Idk, that's the kind of thing I would always have to look up, and in different projects it could easily work differently.

I think having the field count be easily accessible at compile time is a good thing. In c++14 you can still get it at compile time, by applying a function object that just increments a counter. But in c++11 I think there is no way. And that might surely be useful I guess. So I would merge that for sure I think.
I thought also about, having an interface so that you can do like visit_struct::at<2>(s), and it would get the second member of struct s. Or maybe visit_struct::get<2>(s)... That would let you use the structs similar to std::tuple and might be useful in some projects. I don't know, I didn't need this yet myself in anything I worked on.
Jarod42 commented something related yesterday though here: https://github.com/cbeck88/visit_struct/pull/3#issuecomment-306336489
I guess that both hana and fusion allow you to find the 1st, 2nd, etc. members at compile time, i.e. passing the index as a template parameter. So maybe we should support that too, and also let them get the number of members at compile-time, within C++11.
It occurred to me that you could try to simulate that within the existing visit_struct::apply_visitor framework; if you wanted to visit only the 3rd element, you could keep a counter and call the visitor only when you get to the third guy. But I don't think you can actually implement std::get this way, also, if your visitor is only defined for int or std::string etc. you would probably get a compiler error because of the elements you didn't intend to visit...
I don't know, is there a big advantage of the enums that you think I missed?

Thanks for the comments!  I think I agree with your overall suggestion — just keep the field_count part, take out the indexed visitor part.  I'll push up a modified patch with that.
For background, our overall goal is to compare field names inside of a visitor, but without having to use a full string compare each time.  We have a helper class that lets us intern the strings, which would cause the field comparisons to be simple integer comparisons.  But we can't intern the field name inside of the visitor call, since that involves a hashing operation, and would be at least as expensive as the naive string comparison.
So, we wanted to use this indexed visitor to build up an array of interned field names for each struct, and another indexed visitor to compare field names by indexing into that array.  For both of those, we can use the "increment a field in the visitor" trick.
The other thing that the enum gives us is the ability to refer to fields at compile time by their name, instead of having to remember which integer index each field has.  But we're not actually using that feature right now, so I have no problems culling this patch to only include the field_count part.
As an aside, in an earlier version of this patch, I had a much more thorough descriptor struct for each field, instead of just an enum.  The descriptor contained the index, field name, and member pointer.  Ideally I would've modified the apply_visitor methods to take in this descriptor instead of the field name, since then you'd be able to pull out whichever parts of the descriptor that you needed.  But that was going to be a much more invasive, and backwards-incompatible, patch, and so I held off on it.

Opened #7 containing just the field_count function.

Hi again,
You may be amused to learn, I have resurrected the "field_enum" in the develop branch.
I'm trying to extend the library so that users can use std::tuple-like methods with visitable structs. For instance, have visit_struct::get<3>(s) get the 4th element of the struct.
To my knowledge, there's no way to implement this using apply_visitor, so it ends up being a weakness of visit_struct if you need to do this.
I sort of held off on this for a long time because I didn't know a really good way to implement it without adding a lot of complexity. I know how to do things like, get the i'th element of a type-list using templates, but in the "basic" visit_struct library, there are no type-lists or variadic templates, it's all about the map macro. If the map macro were modified to pass indexes as well as the values, then it would be easy, but that would require complicating everything else.
Eventually I realized that your enum trick makes it much much easier, it means that a macro can get the int index corresponding to a given member very easily. And I guess this is why you added it? It's not really for utility for the user, it was just to help you implement the indexed visitors easily. (I think?)
Anyways, thanks for that. There's still some more work -- I need to implement visit_struct::get for the intrusive syntax, and also make the hana and fusion wrappers, and if that works on all the compilers then I'll probably merge it. If you have thoughts or comments they are welcome. I'm considering also trying to make a std::tie analogue.

You may be amused to learn, I have resurrected the "field_enum" in the develop branch.

Nice!

And I guess this is why you added it? It's not really for utility for the user, it was just to help you implement the indexed visitors easily. (I think?)

Yes that's right!  It was most useful because the indexed visitor that used the enum was easier to understand (at least to me).  That said, I liked your suggestion of separating non-indexed and indexed visitation, instead of clumping them together in a single interface, and I don't think you can get that separation with the enum implementation.  (If you implement the indexed visitor via the non-indexed visitor, that is; if you do it the other way around I think you can use either approach.)
Anyway, all of it is hidden away behind the public interface so it's more of a philosophical discussion than anything!


Fix bug where expanding depth setting does not work for File Overview

Purpose
$title
Fixes #18872
Check List

[x] Read the Contributing Guide
[ ] Required Balo version update
[ ] Updated Change Log
[ ] Checked Tooling Support (#)
[ ] Added necessary tests

[ ] Unit Tests
[ ] Spec Conformance Tests
[ ] Integration Tests
[ ] Ballerina By Example Tests

[ ] Increased Test Coverage
[ ] Added necessary documentation

[ ] API documentation
[ ] Module documentation in Module.md files
[ ] Ballerina By Examples

Codecov Report

Merging #18867 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master   #18867   +/-   ##
=======================================
  Coverage   20.53%   20.53%
=======================================
  Files          52       52
  Lines        1383     1383
  Branches      216      216
=======================================
  Hits          284      284
  Misses       1086     1086
  Partials       13       13

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 1e8dd3e...eda8290. Read the comment docs.


Data attribute module

Description
Module interface for the new data attribute.
Testing
Unit tested with mocks
Checklist:

[X] I have read and followed the CONTRIBUTING guidelines for this project.
[X] I have added or updated tests and they pass.
[X] I have added or updated documentation and it is accurate.
[X] I have noted any breaking changes in this module or downstream modules.

Codecov Report

Merging #196 (7bcb138) into main (647b729) will increase coverage by 0.13%.
The diff coverage is 76.86%.

@@            Coverage Diff             @@
##             main     #196      +/-   ##
==========================================
+ Coverage   68.57%   68.71%   +0.13%
==========================================
  Files          68       73       +5
  Lines        7854     7988     +134
==========================================
+ Hits         5386     5489     +103
- Misses       2468     2499      +31

Impacted Files
Coverage Δ

src/many-modules/src/lib.rs
81.81% <ø> (ø)

src/many-modules/src/_5_data/types.rs
9.67% <9.67%> (ø)

src/many-modules/src/_5_data.rs
97.00% <97.00%> (ø)

src/many-modules/src/_5_data/get_info.rs
100.00% <100.00%> (ø)

src/many-modules/src/_5_data/info.rs
100.00% <100.00%> (ø)

src/many-modules/src/_5_data/query.rs
100.00% <100.00%> (ø)

src/many-identity-webauthn/src/lib.rs
96.71% <0.00%> (-0.55%)
:arrow_down:

src/many-identity/src/address.rs
80.20% <0.00%> (+0.26%)
:arrow_up:

src/many-types/src/lib.rs
53.00% <0.00%> (+0.27%)
:arrow_up:

:mega: We’re building smart automated test selection to slash your CI/CD build times. Learn more


Unicode Support

For now fuzzy finder shows non-unicode results. Is unicode support in plans?

I have just rolled out a fix. Can you let me know if it works for you? @cpkio
Thanks :)

@shikaan Only when a Windows binary will be available, I'm not building from sources.

@shikaan Unicode seems to be working, thanks. Managed to crash immediately on key input)
panic: runtime error: slice bounds out of range [150:148]

goroutine 11 [running]:
golang.org/x/text/transform.String({0xceda68, 0xf5af90}, {0xc001e56000, 0x94})
        /go/pkg/mod/golang.org/x/text@v0.7.0/transform/transform.go:650 +0xbe5
github.com/lithammer/fuzzysearch/fuzzy.stringTransform({0xc001e56000, 0x94}, {0xceda68?, 0xf5af90?})
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:243 +0x5a
github.com/lithammer/fuzzysearch/fuzzy.match({0xc0021b1277?, 0x0?}, {0xc001e56000, 0x94}, {0xceda68, 0xf5af90})
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:57 +0x71
github.com/lithammer/fuzzysearch/fuzzy.find(...)
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:107
github.com/lithammer/fuzzysearch/fuzzy.FindFold({0xc0021b1277, 0x1}, {0xc001e7b000?, 0x364, 0xc0000e5d58?})
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:90 +0xc9
github.com/shikaan/keydex/tui/components.NewAutoComplete.func1({0xc001faa000?, 0xc0021b1277?})
        /source/tui/components/autocomplete.go:51 +0x4d
github.com/shikaan/keydex/tui/components.(*Input).handleContentUpdate(0xc001faa000, {0xcecc40, 0xc002082700}, 0xc0000e5dd0)
        /source/tui/components/input.go:198 +0xce
github.com/shikaan/keydex/tui/components.(*Input).HandleEvent(0xc0021b1276?, {0xcecc40?, 0xc002082700?})
        /source/tui/components/input.go:148 +0x11e
github.com/gdamore/tcell/v2/views.(*BoxLayout).HandleEvent(0xc8b7d2?, {0xcecc40?, 0xc002082700})
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/boxlayout.go:240 +0xbe
github.com/gdamore/tcell/v2/views.(*BoxLayout).HandleEvent(0xc0000e5ee0?, {0xcecc40?, 0xc002082700})
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/boxlayout.go:240 +0xbe
github.com/gdamore/tcell/v2/views.(*BoxLayout).HandleEvent(0xc002082700?, {0xcecc40?, 0xc002082700})
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/boxlayout.go:240 +0xbe
github.com/shikaan/keydex/tui.(*Layout).HandleEvent(0xc000091100?, {0xcecc40?, 0xc002082700?})
        /source/tui/layout.go:52 +0x1e9
github.com/gdamore/tcell/v2/views.(*Application).run(0xf079b0)
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/app.go:165 +0x2a9
created by github.com/gdamore/tcell/v2/views.(*Application).Start
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/app.go:175 +0x6a                                              ```

:facepalm: I did not add support for it in the fuzzy search. I'm reopening it and will take a look tonight. Thanks for the report

Hey @cpkio (: Thanks for your patience!
Can you try this build and see if it works for you?

@shikaan This build fails with panic on any input in fuzzy search:
panic: runtime error: slice bounds out of range [150:148]

goroutine 8 [running]:
golang.org/x/text/transform.String({0xbeec88, 0xe5bf50}, {0xc001ea2000, 0x94})
        /go/pkg/mod/golang.org/x/text@v0.7.0/transform/transform.go:650 +0xbe5
github.com/lithammer/fuzzysearch/fuzzy.stringTransform({0xc001ea2000, 0x94}, {0xbeec88?, 0xe5bf50?})
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:243 +0x5a
github.com/lithammer/fuzzysearch/fuzzy.match({0xc001ddbd58?, 0xc001dc6480?}, {0xc001ea2000, 0x94}, {0xbeec88, 0xe5bf50})
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:57 +0x71
github.com/lithammer/fuzzysearch/fuzzy.find(...)
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:107
github.com/lithammer/fuzzysearch/fuzzy.FindFold({0xc001ddbd58, 0x1}, {0xc001ec7000?, 0x365, 0x1?})
        /go/pkg/mod/github.com/lithammer/fuzzysearch@v1.1.5/fuzzy/fuzzy.go:90 +0xc9
github.com/shikaan/keydex/tui/components.NewAutoComplete.func1({0xc001ef6000?, 0xc001ddbd58?})
        /source/tui/components/autocomplete.go:50 +0x4d
github.com/shikaan/keydex/tui/components.(*Input).handleContentUpdate(0xc001ef6000, {0xbede60, 0xc001e90bc0}, 0xc000505860?)
        /source/tui/components/input.go:243 +0xba
github.com/shikaan/keydex/tui/components.(*Input).HandleEvent(0xc001ef6000, {0xbede60?, 0xc001e90bc0?})
        /source/tui/components/input.go:203 +0x19f
github.com/gdamore/tcell/v2/views.(*BoxLayout).HandleEvent(0xb8c82d?, {0xbede60?, 0xc001e90bc0})
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/boxlayout.go:240 +0xbe
github.com/gdamore/tcell/v2/views.(*BoxLayout).HandleEvent(0xc000473ee0?, {0xbede60?, 0xc001e90bc0})
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/boxlayout.go:240 +0xbe
github.com/gdamore/tcell/v2/views.(*BoxLayout).HandleEvent(0xc001e90bc0?, {0xbede60?, 0xc001e90bc0})
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/boxlayout.go:240 +0xbe
github.com/shikaan/keydex/tui.(*Layout).HandleEvent(0xc00048b100?, {0xbede60?, 0xc001e90bc0?})
        /source/tui/layout.go:52 +0x1e9
github.com/gdamore/tcell/v2/views.(*Application).run(0xe08970)
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/app.go:165 +0x2a9
created by github.com/gdamore/tcell/v2/views.(*Application).Start
        /go/pkg/mod/github.com/gdamore/tcell/v2@v2.5.4/views/app.go:175 +0x6a

🤔 Can you provide a string that leads to this error? I am unable to reproduce. Not sure how relevant, but maybe also your OS

@shikaan I do most of my daily work in terminal: Far, neovim, gitui, termscp, fd, fzf, and many others. None of console programs I use crashes like this, so OS is not a problem.
I've recorded a video for you: https://workupload.com/file/px52pJYtLYJ

Thanks once again for you help here :) I deeply appreciate you taking the time for this shot :bow:
I have tried reproducing the issue (same script, different scripts, emoji...) on linux-amd64 and darwin-arm64 with no luck: everything seems to be going smooth. I'll try virtualise a windows environment and see if I get to reproduce it.

None of console programs I use crashes like this, so OS is not a problem.

Yeah, I was not implying the OS is a problem, but rather one of the libraries (specifically the one that does fuzzy search) does not play well with a specific OS. Any information on the environment you are running would be extremely helpful: I'm kinda fishing in the dark, so anything could be relevant.
Specifically, it would help knowing:

OS
terminal
terminal multiplexer

Thanks once again for the help!

Good news: I have managed to reproduce the issue on a virtualised windows! I'll try to get a fix out tonight, as I'll have a bit of free time.
Thanks once again for your patience :raised_hands:

A new version is out (: Do you mind giving another shot? This solved it in my reproduction

@shikaan This seems to be working, congrats!
I think fuzzy search needs some tweaking in the future so it would push more relevant searched to top.

Amazing! Thanks yet another time for the help in finding the problem :)
If you have preferences on the way the fuzzy search works, feel free to open another issue


Fix kes period info command

Example output:
$ CARDANO_NODE_SOCKET_PATH=example/node-spo1/node.sock cardano-cli query kes-period-info --testnet-magic 42 --op-cert-file example/node-spo1/opcert.cert
✓ Operational certificate's KES period is within the correct KES period interval
✓ The operational certificate counter agrees with the node protocol state counter
{
    "qKesCurrentKesPeriod": 0,
    "qKesEndKesInterval": 60,
    "qKesKesKeyExpiry": null,
    "qKesMaxKESEvolutions": 60,
    "qKesNodeStateOperationalCertificateNumber": 0,
    "qKesOnDiskOperationalCertificateNumber": 0,
    "qKesRemainingSlotsInKesPeriod": 7775071,
    "qKesSlotsPerKesPeriod": 129600,
    "qKesStartKesInterval": 0
}

bors r+

Example output babbage:
$ CARDANO_NODE_SOCKET_PATH=example/node-spo1/node.sock cardano-cli query kes-period-info --testnet-magic 42 --op-cert-file example/node-spo1/opcert.cert
✓ Operational certificate's KES period is within the correct KES period interval
✓ The operational certificate counter agrees with the node protocol state counter
{
    "qKesCurrentKesPeriod": 0,
    "qKesEndKesInterval": 60,
    "qKesKesKeyExpiry": null,
    "qKesMaxKESEvolutions": 60,
    "qKesNodeStateOperationalCertificateNumber": 0,
    "qKesOnDiskOperationalCertificateNumber": 0,
    "qKesRemainingSlotsInKesPeriod": 7775071,
    "qKesSlotsPerKesPeriod": 129600,
    "qKesStartKesInterval": 0
}

is the plaintext output at the top written to /dev/stderr? otherwise it is a strang mixed format output of plaintext with json, thats not good.

question, will the qKesNodeStateOperationalCertificateNumber be set again to null  if there was no block produced in the current KesPeriod? or will this value show the latest opcert number used on chain in the past?

this should not greenlit with two checkmarks right? because the new opcert counter 3 is too high when the last one on chain is only 1. but the current output looks like that:
✓ Operational certificate's KES period is within the correct KES period interval
✓ The operational certificate counter agrees with the node protocol state counter
{
    "qKesCurrentKesPeriod": 16,
    "qKesEndKesInterval": 136,
    "qKesKesKeyExpiry": null,
    "qKesMaxKESEvolutions": 120,
    "qKesNodeStateOperationalCertificateNumber": 1,
    "qKesOnDiskOperationalCertificateNumber": 3,
    "qKesRemainingSlotsInKesPeriod": 10342913,
    "qKesSlotsPerKesPeriod": 86400,
    "qKesStartKesInterval": 16
}


Operator handleError && Error

That doesn't seem right, error_reporting is a bitmask, so error_reporting() & $level checks if the error of type $level should be reported.

@Aistina ah ok perfect


Test PCI device Slot location codes.

Signed-off-by: Pridhiviraj Paidipeddi<EMAIL_ADDRESS>

Merged to master as of d19b404df32076158b3b8c3b2d5cb5ef5fb9e39b


Remove margin, padding

This 2 css rules are creating troubles.
Please inspect a discussion and disactivate them in the inspector to see implied effect.
@Seb35 (If in your scope).

Since they got removed, i'm closing this issue. It's worth noting, however, that their removal also induced a bunch of other issues, which we'll be fixing down the line.

Yes. The top bar and footer should keep the * { margin:0; padding:0; } to restore the initial elegance. I havent fixed this yet.


Surpress error on update

In our use case chart.update() causes an error when tabbing out of a chart.
TypeError: node is null
This surpresses that error as it otherwise works okay. This is similar to https://github.com/julianna-langston/chartjs2music/blob/027c573a9d07385918aa893d439a1d14d5d2be32/src/c2m-plugin.ts#L194 I suspect this was added there for the same reason. The actual error should be further debugged.

This change is live in v0.2.0, which was just published.


CSS not displaying properly on IE11

I unfortunately need to support Grav in a corporate environment using IE11 as the default browser. Sites using the rtfm skeleton do not display properly on IE, no matter what the compatibility settings are. It displays just fine on FireFox and Chrome.
The admin panel displays just fine in IE11. Is it difficult to get this team to display in IE?

Update: The rtfm demo site works just fine in IE11, so something is different between my site and the demo site. Will things break if I do not have a logo?

a logo change shouldn't break the theme.  I really can't say what modifications you have made, and what those changes have broken.  This doesn't sound like a a problem with the RTFM skeleton itself, but more in your modifications.
maybe try making the modifications again to a fresh copy one at a time while testing in IE11 and seeing what you are doing that breaks it?

I made no modifications at all. None. Unzipped the skeleton in my Apache directory and pointed a browser at it. Not just me, got three other people to test it and it fails to display properly on all of them. I only mentioned the logo because there is a logo on the demo site but does not appear to be one in the skeleton.
I'll try looking at compatibility settings tomorrow, maybe I can narrow it down to something that we do at work. I never touch Windows at the house, so I can't test things here.
On Mar 21, 2017, 11:33 AM -0500, Andy Miller<EMAIL_ADDRESS>wrote:
a logo change shouldn't break the theme. I really can't say what modifications you have made, and what those changes have broken. This doesn't sound like a a problem with the RTFM skeleton itself, but more in your modifications.
maybe try making the modifications again to a fresh copy one at a time while testing in IE11 and seeing what you are doing that breaks it?
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHubhttps://github.com/getgrav/grav-skeleton-rtfm-site/issues/7#issuecomment-288137284, or mute the threadhttps://github.com/notifications/unsubscribe-auth/ALdv_y5Ab9GP_xXV7g_X3GSP0UKAi0xjks5rn_vKgaJpZM4Mj1rh.

I am also having a problem with IE 11. The menu CSS is delayed for each page load. This problem does not occur with Edge, Chrome or Firefox


Add possibility to search to all team and environment dropdowns

I have access to a lot of environments and teams and these team and environment dropdowns are very long. There are multiple places where they are not searchable and are not sorted either. On the team invite modal the team search is sorted and searchable. I propose we use those components everywhere where we need to list teams or environments.

Hi @zsaltys, thanks for the issue. This feature is implemented in some of the select dropdowns, so we could make it the standards approach for all field selectors. We will pick it up based on bandwidth

There are multiple places where we list Environments, Teams and Consumption roles. Some can be implemented using reusable frontend components, while others are more peculiar and will be implemented as standalone frontend code snippets.
Environment list:

[ ] RequestAccessModal.js  (PR)
[ ] DashboardImportForm.js
[ ] MLStudioCreateForm.js
[ ] NotebookCreateForm.js
[ ] OmicsRunCreateForm.js
[ ] PipelineEnvironmentCreateForm.js
[ ] PipelineEnvironmentEditForm.js
[ ] PipelineCreateForm.js
[ ] PipelineEditForm.js
[ ] DatasetCreateForm.js
[ ] DatasetImportForm.js
[ ] WorksheetView.js

....

Closing this issue as it is implemented in a series of PRs:

#1367
#1368
#1356
#1347

@dlpzx - if there still remain any follow up tasks or enhancements that are "nice to have", let's open a separate issue for them


the error.layout is not work

i want customer the default error page

please... read the doc :

You can customize the error page by adding a layouts/error.vue file.
https://nuxtjs.org/guide/views#error-page


Simplify timestamps in ICache tests

This patch updates the imported OpenTitan code to get support and then modifies the ICache testbench to dump waves which are aligned to 10ns boundaries.
@imphil: I had to squash two commits to get the vendoring right here (add a mapping item, then run vendor.py -U). Is there something cleverer I could have done?

I had to squash two commits to get the vendoring right here (add a mapping item, then run vendor.py -U). Is there something cleverer I could have done?

No, that's fine.


Is there a way to get round header and info?

At the top of each round, is there a way to insert a title and other info such as dates?
I am looking for something like:
"Round 1"
"Matches happen between Mon. and Wed."
Similar to this photo:
https://res.cloudinary.com/cmgverticals/image/upload/f_auto,q_80/v1491278076/ncaa_bracket_final_piwaax.jpg

Did anyone ever figure this out?

I was able to do this
`function infoWhereWhen() {
const matches = document.getElementsByClassName("teamContainer");
const tijdenknockout = !{JSON.stringify(tijdenknockout)};
const veldnamen = !{JSON.stringify(veldnamen)};
  for (let i = 0; i < matches.length; i++) {

    const div = document.createElement("div");
    div.style.position = "relative"
    div.style.width = "0"
    div.style.height = "0"

    const label = document.createElement("label");
    label.style.position = "absolute"
    label.style.width = "250px"
    label.style.fontSize = "85%"
    label.style.left = "10px"
    label.style.top = "-20px"
    label.style.padding = "0"
    label.style.color = 'rgba(0,0,0,0.6)'
    label.innerHTML = veldnamen[i] + "&nbsp&nbsp|&nbsp&nbsp" + tijdenknockout[i];

    div.append(label)
    matches[i].append(div)
  }

}`

I had to mess around for a bit before i was happy with the result tho...


Timestamp DataType are not getting deserialized in XML

if we define TIMESTAMP data type. Then while querying in HIVE table it returns NULL.
NOTE: if i give STRING data type for intended TIMESTAMP data then it returns value. But it is always wise to define TIMESTAMP data type for the kind of data.
Please help

Similar issues were raised in Hive-JSON-Serde. This is just for your reference.
https://github.com/rcongiu/Hive-JSON-Serde/issues/91

Thanks for raising this issue. At this point we do not have plans to fix that but we could consider this for the next release.

Thank you for reply. I am waiting for the next release.

@dvasilen  any time line when we will get next release including this fix. sorry to rush you.

I am afraid we do not have a timeline for the next release at this point. But please feel free to fork the repository and make the changes as needed. Please use HIVE_1.2_HADOOP_2.6_COMP-<IP_ADDRESS> branch as a starting point as it is the latest. If you need that urgently you can consider getting a string value and then using Hive Date UDFs to convert to timestamp.

Closed as no plans to fix.


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[2024-06-23 18:41:15,159]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51812 - "GET /api/v1/images/i/a5bf9d3b-a2f9-444e-9a5f-7af470a1000d.png HTTP/1.1" 200
[2024-06-23 18:41:16,034]::[InvokeAI]::ERROR --> Error while invoking session a88e6329-e086-41e3-b250-b2b36ba16792, invocation 7e28c47e-7280-488b-8ac3-07cc48eb18cf (esrgan): Cannot copy out of meta tensor; no data!
[2024-06-23 18:41:16,035]::[InvokeAI]::ERROR --> Traceback (most recent call last):
File "/opt/invokeai/invokeai/app/services/session_processor/session_processor_default.py", line 135, in run_node
  output = invocation.invoke_internal(context=context, services=self._services)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/baseinvocation.py", line 289, in invoke_internal
  output = self.invoke(context)
           ^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/upscale.py", line 99, in invoke
  upscaler = RealESRGAN(
             ^^^^^^^^^^^
File "/opt/invokeai/invokeai/backend/image_util/realesrgan/realesrgan.py", line 78, in __init__
  self.model = model.to(self.device)
               ^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1152, in to
  return self._apply(convert)
         ^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 802, in _apply
  module._apply(fn)
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 802, in _apply
  module._apply(fn)
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 802, in _apply
  module._apply(fn)
[Previous line repeated 1 more time]
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 825, in _apply
  param_applied = fn(param)
                  ^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1150, in convert
  return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
NotImplementedError: Cannot copy out of meta tensor; no data!

[2024-06-23 18:41:16,232]::[InvokeAI]::INFO --> Graph stats: a88e6329-e086-41e3-b250-b2b36ba16792
                        Node   Calls   Seconds  VRAM Used
           main_model_loader       1    0.001s     4.900G
                   clip_skip       1    0.000s     4.900G
                      compel       2    0.002s     4.901G
                     collect       2    0.001s     4.901G
                       noise       1    0.002s     4.940G
             denoise_latents       1   32.224s     1.852G
                         l2i       1    1.109s     1.855G
                      esrgan       1    0.930s     1.311G
TOTAL GRAPH EXECUTION TIME:  34.270s
TOTAL GRAPH WALL TIME:  34.281s
RAM used by InvokeAI process: 22.02G (+1.454G)
RAM used to load models: 1.76G
VRAM in use: 1.311G
RAM cache statistics:
 Model cache hits: 3
 Model cache misses: 0
 Models cached: 33
 Models cleared from cache: 0
 Cache high water mark: 16.19/64.00G

[2024-06-23 18:41:16,232]::[ModelManagerService]::INFO --> Released torch device cuda:1
[2024-06-23 18:41:16,233]::[ModelManagerService]::INFO --> Reserved torch device cuda:1 for execution thread<PHONE_NUMBER>57856
[2024-06-23 18:41:19,802]::[InvokeAI]::ERROR --> Error while invoking session b82f5a87-584c-4a52-bac9-f0e5e6c89eaa, invocation 6cf4ee6a-40e4-443e-bf0d-bd87e633b729 (denoise_latents): Cannot copy out of meta tensor; no data!
[2024-06-23 18:41:19,802]::[InvokeAI]::ERROR --> Traceback (most recent call last):
File "/opt/invokeai/invokeai/app/services/session_processor/session_processor_default.py", line 135, in run_node
  output = invocation.invoke_internal(context=context, services=self._services)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/baseinvocation.py", line 289, in invoke_internal
  output = self.invoke(context)
           ^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
  return func(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/contextlib.py", line 81, in inner
  return func(*args, **kwds)
         ^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/denoise_latents.py", line 725, in invoke
  with (
File "/usr/lib/python3.11/contextlib.py", line 137, in __enter__
  return next(self.gen)
         ^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/backend/model_manager/load/load_base.py", line 77, in model_on_device
  locked_model = self._locker.lock()
                 ^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/backend/model_manager/load/model_cache/model_locker.py", line 43, in lock
  self._cache.offload_unlocked_models(self._cache_entry.size)
File "/opt/invokeai/invokeai/backend/model_manager/load/model_cache/model_cache_default.py", line 301, in offload_unlocked_models
  self.move_model_to_device(cache_entry, self.storage_device)
File "/opt/invokeai/invokeai/backend/model_manager/load/model_cache/model_cache_default.py", line 356, in move_model_to_device
  raise e
File "/opt/invokeai/invokeai/backend/model_manager/load/model_cache/model_cache_default.py", line 352, in move_model_to_device
  cache_entry.model.to(target_device, non_blocking=True)
File "/opt/venv/invokeai/lib/python3.11/site-packages/transformers/modeling_utils.py", line 2724, in to
  return super().to(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1152, in to
  return self._apply(convert)
         ^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 802, in _apply
  module._apply(fn)
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 802, in _apply
  module._apply(fn)
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 802, in _apply
  module._apply(fn)
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 825, in _apply
  param_applied = fn(param)
                  ^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1150, in convert
  return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
NotImplementedError: Cannot copy out of meta tensor; no data!

[2024-06-23 18:41:19,915]::[InvokeAI]::INFO --> Graph stats: b82f5a87-584c-4a52-bac9-f0e5e6c89eaa
                        Node   Calls   Seconds  VRAM Used
           sdxl_model_loader       1    0.003s     0.312G
          sdxl_compel_prompt       2   10.533s     1.860G
                     collect       2    0.001s     1.852G
                       noise       1    0.005s     1.311G
             denoise_latents       1    4.572s     1.311G
TOTAL GRAPH EXECUTION TIME:  15.114s
TOTAL GRAPH WALL TIME:  15.711s
RAM used by InvokeAI process: 23.90G (+3.166G)
RAM used to load models: 3.05G
VRAM in use: 0.017G
RAM cache statistics:
 Model cache hits: 9
 Model cache misses: 5
 Models cached: 33
 Models cleared from cache: 0
 Cache high water mark: 16.19/64.00G

[2024-06-23 18:41:19,915]::[ModelManagerService]::INFO --> Released torch device cuda:0
[2024-06-23 18:41:19,975]::[ModelManagerService]::INFO --> Reserved torch device cuda:0 for execution thread<PHONE_NUMBER>43616

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[2024-06-23 18:41:55,125]::[InvokeAI]::ERROR --> Error while invoking session c4e56901-b8b5-493e-85ca-f98784b43df8, invocation 42ab349d-3029-48dc-aee4-cb0f1a63e453 (esrgan): Input type (float) and bias type (c10::Half) should be the same
[2024-06-23 18:41:55,125]::[InvokeAI]::ERROR --> Traceback (most recent call last):
File "/opt/invokeai/invokeai/app/services/session_processor/session_processor_default.py", line 135, in run_node
  output = invocation.invoke_internal(context=context, services=self._services)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/baseinvocation.py", line 289, in invoke_internal
  output = self.invoke(context)
           ^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/upscale.py", line 110, in invoke
  upscaled_image = upscaler.upscale(cv2_image)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
  return func(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/backend/image_util/realesrgan/realesrgan.py", line 231, in upscale
  self.tile_process()
File "/opt/invokeai/invokeai/backend/image_util/realesrgan/realesrgan.py", line 159, in tile_process
  output_tile = self.model(input_tile)
                ^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
  return self._call_impl(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
  return forward_call(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/backend/image_util/basicsr/rrdbnet_arch.py", line 118, in forward
  feat = self.conv_first(feat)
         ^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
  return self._call_impl(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
  return forward_call(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/conv.py", line 460, in forward
  return self._conv_forward(input, self.weight, self.bias)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/conv.py", line 456, in _conv_forward
  return F.conv2d(input, weight, bias, self.stride,
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Input type (float) and bias type (c10::Half) should be the same

[2024-06-23 18:41:55,297]::[InvokeAI]::INFO --> Graph stats: c4e56901-b8b5-493e-85ca-f98784b43df8
                        Node   Calls   Seconds  VRAM Used
           main_model_loader       1    0.001s     0.017G
                   clip_skip       1    0.001s     0.017G
                      compel       2    0.002s     0.017G
                     collect       2    0.002s     0.017G
                       noise       1    0.004s     0.017G
             denoise_latents       1   31.934s     2.087G
                         l2i       1    1.279s     2.676G
                      esrgan       1    1.842s     1.974G
TOTAL GRAPH EXECUTION TIME:  35.065s
TOTAL GRAPH WALL TIME:  35.078s
RAM used by InvokeAI process: 29.97G (+5.957G)
RAM used to load models: 6.54G
VRAM in use: 1.926G
RAM cache statistics:
 Model cache hits: 6
 Model cache misses: 2
 Models cached: 35
 Models cleared from cache: 0
 Cache high water mark: 19.83/64.00G

[2024-06-23 18:41:55,297]::[ModelManagerService]::INFO --> Released torch device cuda:0
[2024-06-23 18:41:55,870]::[ModelManagerService]::INFO --> Reserved torch device cuda:0 for execution thread<PHONE_NUMBER>43616

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[2024-06-23 18:42:27,790]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51820 - "GET /api/v1/images/i/0f4638c2-1f63-4312-add6-fb37da7d3aaa.png HTTP/1.1" 200

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[2024-06-23 18:42:29,741]::[InvokeAI]::ERROR --> Error while invoking session 1f847b22-3d11-41df-b522-d84f593f31d3, invocation b8162278-48cf-4f64-a600-969d79ff5f6a (esrgan): Input type (float) and bias type (c10::Half) should be the same
[2024-06-23 18:42:29,741]::[InvokeAI]::ERROR --> Traceback (most recent call last):
File "/opt/invokeai/invokeai/app/services/session_processor/session_processor_default.py", line 135, in run_node
  output = invocation.invoke_internal(context=context, services=self._services)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/baseinvocation.py", line 289, in invoke_internal
  output = self.invoke(context)
           ^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/app/invocations/upscale.py", line 110, in invoke
  upscaled_image = upscaler.upscale(cv2_image)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
  return func(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/backend/image_util/realesrgan/realesrgan.py", line 231, in upscale
  self.tile_process()
File "/opt/invokeai/invokeai/backend/image_util/realesrgan/realesrgan.py", line 159, in tile_process
  output_tile = self.model(input_tile)
                ^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
  return self._call_impl(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
  return forward_call(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/invokeai/invokeai/backend/image_util/basicsr/rrdbnet_arch.py", line 118, in forward
  feat = self.conv_first(feat)
         ^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
  return self._call_impl(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
  return forward_call(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/conv.py", line 460, in forward
  return self._conv_forward(input, self.weight, self.bias)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/venv/invokeai/lib/python3.11/site-packages/torch/nn/modules/conv.py", line 456, in _conv_forward
  return F.conv2d(input, weight, bias, self.stride,
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Input type (float) and bias type (c10::Half) should be the same

20% 20/100 [00:38<02:35,  1.95s/it][2024-06-23 18:42:29,939]::[InvokeAI]::INFO --> Graph stats: 1f847b22-3d11-41df-b522-d84f593f31d3
                        Node   Calls   Seconds  VRAM Used
           main_model_loader       1    0.001s     1.926G
                   clip_skip       1    0.000s     1.926G
                      compel       2    0.001s     1.926G
                     collect       2    0.001s     1.926G
                       noise       1    0.003s     1.926G
             denoise_latents       1   30.159s     2.087G
                         l2i       1    1.649s     2.676G
                      esrgan       1    2.000s     1.974G
TOTAL GRAPH EXECUTION TIME:  33.814s
TOTAL GRAPH WALL TIME:  33.825s
RAM used by InvokeAI process: 29.86G (-0.111G)
RAM used to load models: 1.76G
VRAM in use: 1.926G
RAM cache statistics:
 Model cache hits: 4
 Model cache misses: 0
 Models cached: 35
 Models cleared from cache: 0
 Cache high water mark: 19.83/64.00G

[2024-06-23 18:42:29,939]::[ModelManagerService]::INFO --> Released torch device cuda:0
[2024-06-23 18:42:30,119]::[ModelManagerService]::INFO --> Reserved torch device cuda:0 for execution thread<PHONE_NUMBER>43616

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89% 89/100 [02:52<00:21,  1.94s/it]
67% 67/100 [02:12<01:06,  2.00s/it]

90% 90/100 [02:54<00:19,  1.94s/it]
68% 68/100 [02:14<01:03,  2.00s/it]

91% 91/100 [02:56<00:17,  1.94s/it]

92% 92/100 [02:58<00:15,  1.94s/it]
69% 69/100 [02:16<01:01,  2.00s/it]

93% 93/100 [03:00<00:13,  1.94s/it]
70% 70/100 [02:18<00:59,  2.00s/it]

94% 94/100 [03:02<00:11,  1.94s/it]
71% 71/100 [02:20<00:57,  2.00s/it]

95% 95/100 [03:04<00:09,  1.94s/it]
72% 72/100 [02:22<00:55,  2.00s/it]

96% 96/100 [03:06<00:07,  1.94s/it]
73% 73/100 [02:24<00:53,  1.99s/it]

97% 97/100 [03:08<00:05,  1.94s/it]
74% 74/100 [02:26<00:51,  1.99s/it]

98% 98/100 [03:09<00:03,  1.94s/it]
75% 75/100 [02:28<00:49,  2.00s/it]

99% 99/100 [03:11<00:01,  1.94s/it]
76% 76/100 [02:30<00:47,  1.99s/it]

100% 100/100 [03:13<00:00,  1.94s/it]
100% 100/100 [03:13<00:00,  1.94s/it]

77% 77/100 [02:32<00:46,  2.01s/it]
78% 78/100 [02:35<00:46,  2.12s/it]
79% 79/100 [02:37<00:43,  2.08s/it]
80% 80/100 [02:39<00:41,  2.09s/it][2024-06-23 18:45:12,411]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51853 - "GET /api/v1/images/i/bccab588-dd62-45a4-8560-94bdc83d5079.png HTTP/1.1" 200
[2024-06-23 18:45:12,430]::[InvokeAI]::INFO --> Graph stats: 50844b2c-5f3e-48cc-883e-1d407b442448
                        Node   Calls   Seconds  VRAM Used
           sdxl_model_loader       1    0.001s     1.311G
          sdxl_compel_prompt       2    0.005s     1.311G
                     collect       2    0.001s     1.311G
                       noise       1    0.005s     1.311G
             denoise_latents       1  229.194s     5.471G
               core_metadata       1    0.001s     4.900G
                         l2i       1    6.763s     5.471G
TOTAL GRAPH EXECUTION TIME: 235.971s
TOTAL GRAPH WALL TIME: 235.981s
RAM used by InvokeAI process: 28.92G (+6.737G)
RAM used to load models: 4.94G
VRAM in use: 5.031G
RAM cache statistics:
 Model cache hits: 2
 Model cache misses: 2
 Models cached: 36
 Models cleared from cache: 0
 Cache high water mark: 20.97/64.00G

[2024-06-23 18:45:12,430]::[ModelManagerService]::INFO --> Released torch device cuda:1
[2024-06-23 18:45:13,113]::[ModelManagerService]::INFO --> Reserved torch device cuda:1 for execution thread<PHONE_NUMBER>57856
[2024-06-23 18:45:13,169]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51854 - "GET /api/v1/boards/?all=true HTTP/1.1" 200
[2024-06-23 18:45:13,174]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51857 - "GET /api/v1/images/i/bccab588-dd62-45a4-8560-94bdc83d5079.png/metadata HTTP/1.1" 200
[2024-06-23 18:45:13,189]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51855 - "GET /api/v1/images/i/bccab588-dd62-45a4-8560-94bdc83d5079.png/thumbnail HTTP/1.1" 200
[2024-06-23 18:45:13,228]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51856 - "GET /api/v1/images/i/bccab588-dd62-45a4-8560-94bdc83d5079.png/full HTTP/1.1" 200

81% 81/100 [02:41<00:39,  2.10s/it]
82% 82/100 [02:43<00:37,  2.07s/it]

0% 0/100 [00:00<?, ?it/s]
83% 83/100 [02:47<00:42,  2.49s/it]

1% 1/100 [00:04<07:52,  4.78s/it]
84% 84/100 [02:50<00:45,  2.84s/it]

2% 2/100 [00:08<07:12,  4.42s/it]
85% 85/100 [02:54<00:46,  3.13s/it]

3% 3/100 [00:13<06:56,  4.29s/it]
86% 86/100 [02:58<00:47,  3.39s/it]

4% 4/100 [00:17<06:40,  4.18s/it]
87% 87/100 [03:02<00:46,  3.61s/it]

5% 5/100 [00:21<06:30,  4.11s/it]
88% 88/100 [03:06<00:44,  3.69s/it]

6% 6/100 [00:25<06:23,  4.08s/it]
89% 89/100 [03:10<00:41,  3.78s/it]

7% 7/100 [00:29<06:18,  4.07s/it]
90% 90/100 [03:14<00:38,  3.81s/it]

8% 8/100 [00:33<06:22,  4.15s/it]
91% 91/100 [03:18<00:34,  3.82s/it]

9% 9/100 [00:37<06:12,  4.09s/it]
92% 92/100 [03:22<00:31,  3.95s/it]

10% 10/100 [00:41<06:00,  4.00s/it]
93% 93/100 [03:26<00:27,  3.96s/it]

11% 11/100 [00:45<05:53,  3.97s/it]
94% 94/100 [03:30<00:24,  4.13s/it]

12% 12/100 [00:48<05:35,  3.81s/it]
95% 95/100 [03:35<00:20,  4.15s/it]

13% 13/100 [00:52<05:32,  3.82s/it]
96% 96/100 [03:39<00:16,  4.20s/it]

14% 14/100 [00:56<05:28,  3.81s/it]

15% 15/100 [01:00<05:24,  3.81s/it]
97% 97/100 [03:43<00:12,  4.18s/it]

16% 16/100 [01:03<05:15,  3.75s/it]
98% 98/100 [03:48<00:08,  4.26s/it]

17% 17/100 [01:07<05:16,  3.82s/it]
99% 99/100 [03:51<00:04,  4.17s/it]

18% 18/100 [01:11<05:21,  3.92s/it]
100% 100/100 [03:55<00:00,  4.07s/it]
100% 100/100 [03:55<00:00,  2.36s/it]
[2024-06-23 18:46:32,475]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51862 - "GET /api/v1/images/i/8eb3b1cf-5d2b-43d0-bb5c-412df9cdba86.png HTTP/1.1" 200
[2024-06-23 18:46:32,487]::[InvokeAI]::INFO --> Graph stats: e35307e2-b63b-4508-aeb7-799ecd8de460
                        Node   Calls   Seconds  VRAM Used
           sdxl_model_loader       1    0.001s     1.926G
          sdxl_compel_prompt       2    0.001s     1.926G
                     collect       2    0.001s     1.926G
                       noise       1    0.004s     1.926G
             denoise_latents       1  238.227s     5.820G
               core_metadata       1    0.001s     5.021G
                         l2i       1    4.023s     8.332G
TOTAL GRAPH EXECUTION TIME: 242.258s
TOTAL GRAPH WALL TIME: 242.265s
RAM used by InvokeAI process: 28.92G (-0.943G)
RAM used to load models: 4.94G
VRAM in use: 5.332G
RAM cache statistics:
 Model cache hits: 3
 Model cache misses: 0
 Models cached: 36
 Models cleared from cache: 0
 Cache high water mark: 19.99/64.00G

[2024-06-23 18:46:32,487]::[ModelManagerService]::INFO --> Released torch device cuda:0
[2024-06-23 18:46:33,189]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51863 - "GET /api/v1/boards/?all=true HTTP/1.1" 200
[2024-06-23 18:46:33,192]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51866 - "GET /api/v1/images/i/8eb3b1cf-5d2b-43d0-bb5c-412df9cdba86.png/metadata HTTP/1.1" 200
[2024-06-23 18:46:33,207]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51865 - "GET /api/v1/images/i/8eb3b1cf-5d2b-43d0-bb5c-412df9cdba86.png/full HTTP/1.1" 200
[2024-06-23 18:46:33,209]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51864 - "GET /api/v1/images/i/8eb3b1cf-5d2b-43d0-bb5c-412df9cdba86.png/thumbnail HTTP/1.1" 200

19% 19/100 [01:17<06:05,  4.52s/it]

20% 20/100 [01:19<05:00,  3.76s/it]

21% 21/100 [01:21<04:15,  3.23s/it]

22% 22/100 [01:23<03:43,  2.86s/it]

23% 23/100 [01:25<03:20,  2.60s/it]

24% 24/100 [01:27<03:03,  2.42s/it]

25% 25/100 [01:29<02:52,  2.29s/it]

26% 26/100 [01:31<02:43,  2.20s/it]

27% 27/100 [01:33<02:36,  2.14s/it]

28% 28/100 [01:35<02:30,  2.09s/it]

29% 29/100 [01:37<02:26,  2.06s/it]

30% 30/100 [01:39<02:22,  2.04s/it]

31% 31/100 [01:41<02:19,  2.02s/it]

32% 32/100 [01:43<02:17,  2.02s/it]

33% 33/100 [01:45<02:14,  2.01s/it]

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35% 35/100 [01:49<02:09,  2.00s/it]

36% 36/100 [01:51<02:07,  1.99s/it]

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50% 50/100 [02:19<01:39,  2.00s/it]

51% 51/100 [02:21<01:37,  2.00s/it]

52% 52/100 [02:23<01:35,  2.00s/it]

53% 53/100 [02:25<01:33,  2.00s/it]

54% 54/100 [02:27<01:31,  2.00s/it]

55% 55/100 [02:29<01:29,  1.99s/it]

56% 56/100 [02:31<01:27,  2.00s/it]

57% 57/100 [02:33<01:25,  2.00s/it]

58% 58/100 [02:35<01:23,  1.99s/it]

59% 59/100 [02:37<01:21,  2.00s/it]

60% 60/100 [02:39<01:19,  2.00s/it]

61% 61/100 [02:41<01:17,  2.00s/it]

62% 62/100 [02:43<01:15,  1.99s/it]

63% 63/100 [02:45<01:13,  1.99s/it]

64% 64/100 [02:47<01:11,  1.99s/it]

65% 65/100 [02:49<01:09,  1.99s/it]

66% 66/100 [02:51<01:07,  1.99s/it]

67% 67/100 [02:53<01:05,  1.99s/it]

68% 68/100 [02:55<01:03,  1.99s/it]

69% 69/100 [02:57<01:01,  1.99s/it]

70% 70/100 [02:59<00:59,  1.99s/it]

71% 71/100 [03:01<00:57,  1.99s/it]

72% 72/100 [03:03<00:55,  1.99s/it]

73% 73/100 [03:05<00:53,  1.99s/it]

74% 74/100 [03:07<00:51,  1.99s/it]

75% 75/100 [03:09<00:49,  2.00s/it]

76% 76/100 [03:11<00:47,  2.00s/it]

77% 77/100 [03:13<00:45,  2.00s/it]

78% 78/100 [03:15<00:43,  2.00s/it]

79% 79/100 [03:17<00:41,  2.00s/it]

80% 80/100 [03:19<00:40,  2.00s/it]

81% 81/100 [03:21<00:37,  2.00s/it]

82% 82/100 [03:23<00:35,  2.00s/it]

83% 83/100 [03:25<00:34,  2.00s/it]

84% 84/100 [03:27<00:32,  2.00s/it]

85% 85/100 [03:29<00:30,  2.00s/it]

86% 86/100 [03:31<00:28,  2.00s/it]

87% 87/100 [03:33<00:25,  2.00s/it]

88% 88/100 [03:35<00:23,  2.00s/it]

89% 89/100 [03:37<00:21,  2.00s/it]

90% 90/100 [03:39<00:19,  2.00s/it]

91% 91/100 [03:41<00:17,  1.99s/it]

92% 92/100 [03:43<00:15,  2.00s/it]

93% 93/100 [03:45<00:13,  1.99s/it]

94% 94/100 [03:47<00:11,  1.99s/it]

95% 95/100 [03:49<00:09,  1.99s/it]

96% 96/100 [03:51<00:07,  1.99s/it]

97% 97/100 [03:53<00:05,  1.99s/it]

98% 98/100 [03:55<00:03,  1.99s/it]

99% 99/100 [03:57<00:01,  1.99s/it]

100% 100/100 [03:59<00:00,  1.99s/it]
100% 100/100 [03:59<00:00,  2.39s/it]
[2024-06-23 18:49:19,286]::[InvokeAI]::INFO --> Graph stats: 9953723f-cef2-4f25-8597-d2c2a79dbf16
                        Node   Calls   Seconds  VRAM Used
           sdxl_model_loader       1    0.001s     5.036G
          sdxl_compel_prompt       2    0.002s     5.084G
                     collect       2    0.001s     5.103G
                       noise       1    0.005s     5.042G
             denoise_latents       1  242.191s     8.332G
               core_metadata       1    0.001s     5.200G
                         l2i       1    3.857s     8.200G
TOTAL GRAPH EXECUTION TIME: 246.059s
TOTAL GRAPH WALL TIME: 246.071s
RAM used by InvokeAI process: 28.92G (+0.001G)
RAM used to load models: 4.94G
VRAM in use: 5.200G
RAM cache statistics:
 Model cache hits: 3
 Model cache misses: 0
 Models cached: 36
 Models cleared from cache: 0
 Cache high water mark: 19.99/64.00G

[2024-06-23 18:49:19,287]::[ModelManagerService]::INFO --> Released torch device cuda:1
[2024-06-23 18:49:19,302]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51897 - "GET /api/v1/images/i/8763c44b-18ea-4ac0-9940-445e31fe9b8b.png HTTP/1.1" 200
[2024-06-23 18:49:19,345]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51897 - "GET /api/v1/images/i/8763c44b-18ea-4ac0-9940-445e31fe9b8b.png/full HTTP/1.1" 200
[2024-06-23 18:49:19,371]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51898 - "GET /api/v1/boards/?all=true HTTP/1.1" 200
[2024-06-23 18:49:19,381]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51899 - "GET /api/v1/images/i/8763c44b-18ea-4ac0-9940-445e31fe9b8b.png/thumbnail HTTP/1.1" 200
[2024-06-23 18:49:19,386]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51900 - "GET /api/v1/images/i/8763c44b-18ea-4ac0-9940-445e31fe9b8b.png/full HTTP/1.1" 200
[2024-06-23 18:49:19,670]::[uvicorn.access]::INFO --> <IP_ADDRESS>:51900 - "GET /api/v1/images/i/8763c44b-18ea-4ac0-9940-445e31fe9b8b.png/metadata HTTP/1.1" 200

I tested your branch again. Model switching still errors out but only sometimes, with Cannot copy out of meta tensor; no data! but invoke can recover without having to restart the server.
Also new errors appeared:

RuntimeError: Input type (float) and bias type (c10::Half) should be the same
Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:1! (when checking argument for argument index in method wrapper_CUDA__index_select)

Full Logs

Thanks very much. I've got access to a multi-GPU system now and will be able to test more thoroughly. I'll let you know when there's a new version to look at.

@raldone01 I just committed a series of changes that should make the multi-GPU support more stable. If you have a chance to check it out, let me know how it works in your hands.

Awesome I just built the image. I will try to get some proper testing in, next week.

@lstein Do you know what the default unload timer is?
After queuing up a few images the GPUs are not fully released which causes them to consume 50W instead of 10W per GPU.
I will try to just add one GPU to the container to see if it still occurs.
No python errors so far. 👍

This PR is no longer being maintained. For InvokeAI Multi-GPU support, please see https://github.com/lstein/InvokeAI-MGPU


Edit API Survey intro to target public sector employees

@RobNicholsGDS can we modify the text slightly so that we target gov and public sector employees?

No problem - the change should appear shortly.
On Wed, 14 Aug 2024 at 17:25, Alex Smith @.***> wrote:

@RobNicholsGDS https://github.com/RobNicholsGDS can we modify the text
slightly so that we target gov and public sector employees?
You can view, comment on, or merge this pull request online at:
https://github.com/co-cddo/api-catalogue/pull/913
Commit Summary

caef194
https://github.com/co-cddo/api-catalogue/pull/913/commits/caef19421ffae5563d23b704b1c66d653a1f7c69
Edit API Survey intro to target public sector employees

File Changes
(1 file https://github.com/co-cddo/api-catalogue/pull/913/files)

M source/index.html.md
https://github.com/co-cddo/api-catalogue/pull/913/files#diff-953ec4c8cae7c7113d7ae3d7c5ec008a0d8a750e363b5473bfb3db7debe0cf20
(6)

Patch Links:

https://github.com/co-cddo/api-catalogue/pull/913.patch
https://github.com/co-cddo/api-catalogue/pull/913.diff

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Request: Input parameter for disabling default health rule upload

SUMMARY
As a DevOps engineer, I would like to be able to use ConfigMyApp to upload custom health rules without uploading the default rules included with ConfigMyApp. One of the current default rules, Agent Availability, is generating a lot of warnings because our nodes are short lived (see screenshot below). There doesn't seem to be a way currently to not deploy the default health rules which would solve this problem for us.
ADDITIONAL CONTEXT

Hi Steven,
Can you try to remove a rule from the health_rules/ folder, here: https://github.com/Appdynamics/ConfigMyApp/tree/master/health_rules and try running the healthRulesOnly module again. The idea behind this is that in dashboards, health rules, and BTs we provide examples that can be adjusted when you fork a project, mount a directory containing json files, or customize it in some alternative way.
Please do let me know if this provides the desired solution for your use case.


chore(queue): refactor queue package

Signed-off-by: Bo-Yi.Wu<EMAIL_ADDRESS>

Codecov Report

Merging #18 (aa856ba) into main (57842b7) will decrease coverage by 5.07%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main      #18      +/-   ##
==========================================
- Coverage   69.63%   64.56%   -5.08%
==========================================
  Files           3        3
  Lines         247      206      -41
==========================================
- Hits          172      133      -39
+ Misses         66       64       -2
  Partials        9        9

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ng2-bootstrap is looking for @angular/core, but I'm referencing this as angular2/core

How can I change my package alias to reference it as @angular instead of angular2? Or how can I change which lib ng2-bootstrap will load?
This is crashing my app when I use ng2-bootstrap!

I believe the namespaces were changed in angular 2 after RC2 was released. I'm guessing you need to upgrade your project to use the latest Angular 2 RC(4).

yeap, angular2/core was deprecated couple of months ago
so your only option is updating your app


'senior' coder test suite

The junior-v2 interview is showing it's age, I created it back when llama was all we had and at the time every single open source model failed the test.
The clustering we now see at the top of the leaderboard is a result of the massive improvements in open source coding models these past 6 months, anything above .95 is a binary pass and junior-v2 has no comparing ability up here.
A more difficult test suite is needed.

A senior interview suite mvp is now available, gpt4 can just barely pass it.
If you have any good ideas for interview questions please open PRs!


rkt fetch   stopped working

$ sudo ./bin/rkt fetch ./etcd-v2.0.0-rc.1-linux-amd64.aci
rkt: searching for app image ./etcd-v2.0.0-rc.1-linux-amd64.aci
Get http://.?ac-discovery=1: dial tcp: lookup .: No address associated with hostname

This also appears to be out of wack:
coreos_production_qemu-575-0-0-2 Downloads # rkt run etcd-v2.0.0-linux-amd64.aci
2015/01/30 17:52:32 no data-dir provided, using default data-dir ./default.etcd
2015/01/30 17:52:32 etcd: listening for peers on http://localhost:2380
2015/01/30 17:52:32 etcd: listening for peers on http://localhost:7001
2015/01/30 17:52:32 etcd: listening for client requests on http://localhost:2379
2015/01/30 17:52:32 etcd: listening for client requests on http://localhost:4001
2015/01/30 17:52:32 etcdserver: name = default
2015/01/30 17:52:32 etcdserver: data dir = default.etcd
2015/01/30 17:52:32 etcdserver: heartbeat = 100ms
2015/01/30 17:52:32 etcdserver: election = 1000ms
2015/01/30 17:52:32 etcdserver: snapshot count = 10000
2015/01/30 17:52:32 etcdserver: advertise client URLs = http://localhost:2379,http://localhost:4001
2015/01/30 17:52:32 etcdserver: initial advertise peer URLs = http://localhost:2380,http://localhost:7001
2015/01/30 17:52:32 etcdserver: initial cluster = default=http://localhost:2380,default=http://localhost:7001
2015/01/30 17:52:32 etcdserver: start member ce2a822cea30bfca in cluster 7e27652122e8b2ae
2015/01/30 17:52:32 raft: ce2a822cea30bfca became follower at term 0
2015/01/30 17:52:32 raft: newRaft ce2a822cea30bfca [peers: [], term: 0, commit: 0, applied: 0, lastindex: 0, lastterm: 0]
2015/01/30 17:52:32 raft: ce2a822cea30bfca became follower at term 1
2015/01/30 17:52:32 etcdserver: added local member ce2a822cea30bfca [http://localhost:2380 http://localhost:7001] to cluster 7e27652122e8b2ae
2015/01/30 17:52:33 raft: ce2a822cea30bfca is starting a new election at term 1
2015/01/30 17:52:33 raft: ce2a822cea30bfca became candidate at term 2
2015/01/30 17:52:33 raft: ce2a822cea30bfca received vote from ce2a822cea30bfca at term 2
2015/01/30 17:52:33 raft: ce2a822cea30bfca became leader at term 2
2015/01/30 17:52:33 raft.node: ce2a822cea30bfca elected leader ce2a822cea30bfca at term 2
2015/01/30 17:52:33 etcdserver: published {Name:default ClientURLs:[http://localhost:2379 http://localhost:4001]} to cluster 7e27652122e8b2ae
^]^]Container stage1 terminated by signal KILL.
coreos_production_qemu-575-0-0-2 Downloads # rkt run coreos.com/etcd:v2.0.0
rkt: searching for app image coreos.com/etcd:v2.0.0
^Ccoreos_production_qemu-575-0-0-2 Downloads # rkt run coreos.com/etcd:v2.0.0
rkt: searching for app image coreos.com/etcd:v2.0.0
rkt: fetching image from https://github.com/coreos/etcd/releases/download/v2.0.0/etcd-v2.0.0-linux-amd64.aci

The second rkt run should be a cache hit, right?

Yes but could you please file a separate issue for that?
On Jan 30, 2015 6:53 PM, "polvi"<EMAIL_ADDRESS>wrote:

This also appears to be out of wack:
coreos_production_qemu-575-0-0-2 Downloads # rkt run etcd-v2.0.0-linux-amd64.aci
2015/01/30 17:52:32 no data-dir provided, using default data-dir ./default.etcd
2015/01/30 17:52:32 etcd: listening for peers on http://localhost:2380
2015/01/30 17:52:32 etcd: listening for peers on http://localhost:7001
2015/01/30 17:52:32 etcd: listening for client requests on http://localhost:2379
2015/01/30 17:52:32 etcd: listening for client requests on http://localhost:4001
2015/01/30 17:52:32 etcdserver: name = default
2015/01/30 17:52:32 etcdserver: data dir = default.etcd
2015/01/30 17:52:32 etcdserver: heartbeat = 100ms
2015/01/30 17:52:32 etcdserver: election = 1000ms
2015/01/30 17:52:32 etcdserver: snapshot count = 10000
2015/01/30 17:52:32 etcdserver: advertise client URLs = http://localhost:2379,http://localhost:4001
2015/01/30 17:52:32 etcdserver: initial advertise peer URLs = http://localhost:2380,http://localhost:7001
2015/01/30 17:52:32 etcdserver: initial cluster = default=http://localhost:2380,default=http://localhost:7001
2015/01/30 17:52:32 etcdserver: start member ce2a822cea30bfca in cluster 7e27652122e8b2ae
2015/01/30 17:52:32 raft: ce2a822cea30bfca became follower at term 0
2015/01/30 17:52:32 raft: newRaft ce2a822cea30bfca [peers: [], term: 0, commit: 0, applied: 0, lastindex: 0, lastterm: 0]
2015/01/30 17:52:32 raft: ce2a822cea30bfca became follower at term 1
2015/01/30 17:52:32 etcdserver: added local member ce2a822cea30bfca [http://localhost:2380 http://localhost:7001] to cluster 7e27652122e8b2ae
2015/01/30 17:52:33 raft: ce2a822cea30bfca is starting a new election at term 1
2015/01/30 17:52:33 raft: ce2a822cea30bfca became candidate at term 2
2015/01/30 17:52:33 raft: ce2a822cea30bfca received vote from ce2a822cea30bfca at term 2
2015/01/30 17:52:33 raft: ce2a822cea30bfca became leader at term 2
2015/01/30 17:52:33 raft.node: ce2a822cea30bfca elected leader ce2a822cea30bfca at term 2
2015/01/30 17:52:33 etcdserver: published {Name:default ClientURLs:[http://localhost:2379 http://localhost:4001]} to cluster 7e27652122e8b2ae
^]^]Container stage1 terminated by signal KILL.
coreos_production_qemu-575-0-0-2 Downloads # rkt run coreos.com/etcd:v2.0.0
rkt: searching for app image coreos.com/etcd:v2.0.0
^Ccoreos_production_qemu-575-0-0-2 Downloads # rkt run coreos.com/etcd:v2.0.0
rkt: searching for app image coreos.com/etcd:v2.0.0
rkt: fetching image from https://github.com/coreos/etcd/releases/download/v2.0.0/etcd-v2.0.0-linux-amd64.aci
The second rkt run should be a cache hit, right?
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@jonboulle you got it, moved it to #451

@philips as far as I can tell, this behaviour never worked. Do you have reason to believe otherwise?
rkt run obviously does, but that's a much longer path: https://github.com/coreos/rocket/blob/master/rkt/run.go#L79

Regardless of previous behavior, I think that runFetch should also use findImage simply because there isn't (and correct me if I'm wrong) a way to just import a local ACI without also running it.

Yes, probably, it is basically a rkt import

Fixed by #684, to be followed up by #715


Recognize nil? as an is_a? ::Nil type check?

Hi everyone,
I don't know if this is possible - I was just wondering if for type checking purposes it was possible to have .nil? have the same effect as .is_a?(::Nil).
Point in case:
Given this Option class:
class Option
  def find(n : Int32) : String?
    if n % 2 == 0
      "strike"
    else
      nil
    end
  end
end

I want both
result = Option.new.find(2)
if result.nil?
  puts result
else
  puts result.upcase
end

and
result = Option.new.find(2)
if result.is_a?(::Nil)
  puts result
else
  puts result.upcase
end

to compile. Currently only the latter compiles while the first one gives a Nil trace :)

It's worth noting that the following program is usually equivalent:
result = Option.new.find(2)
if result
  puts result.upcase
else
  puts result
end

As well as
puts Option.new.find(2).try &.upcase

@PragTob Since each class may define #nil?, is seems not feasible right now to make the type inference narrow the type of the receiver object to not be nil.
The logic is wired to #is_a? and the if @jhass shows. For now that is the way it's supported.

ah sure how could I forget about if result - I'm just so used to if @result but that doesn't work in crystal that I completely let it go :)
It's a super minor thing then - do we want to keep the issue open for further thoughts of maybe doing it in the future far ahead or rather close it?

This keeps coming from time to time. Rubyist are used to #nil? unless either that method is fixed on the compiler it seems unlikely to be supported for type restriction.
I think we can close this issue for now.
Thanks!

I think we can close it. If you see @jhass alternative solutions, they are all shorter than using .nil?: less to type, less to read. Not having nil? forces you to write a shorter solution, and I think that's something good (and makes code consistent between programmers).


Land Library Resource display thing

With a LL resource that was imported from the legacy portal, there is a field on the right that says "replaces" and then the URL at the legacy LP, see for an example: https://landportal.info/library/resources/forest-management-and-use-kyrgyz-republic-development-potential
Can we remove this view? Doesn't seem very necessary to know for the user.

hidden for now, will likely disappear during the field cleaning.
(once we merge existing information somewhere > at least in a revision note)


Canonicalizing VarBin array shouldn't cast to Ut8View

https://github.com/spiraldb/vortex/blob/develop/vortex-array/src/array/varbin/flatten.rs#L14 should cast depending on dtype

Gah, good catch

https://github.com/spiraldb/vortex/pull/1145 should fix it


fixes needed by new versions of toolchain packages

this update provides the following:

a new location from where to get the msp430 headers (you need the msp430-gcc-support-files-*.zip file)
disable the -Werror CFLAG for binutils. otherwise a few warnings detected by gcc will be considered fatal
a note about not using gcc 7.x, 8.x due to internal compiler error

I haven't used this material in over three years, cannot test any changes to it, and won't be making any new releases.
I'm closing this so I can archive the project without leaving what your PR permanently open.  There are unmerged patches in the next branch that might be helpful if you plan to continue using this.
Thanks for contributing.


Avoid scroll when few item: How should be computed numberOfRows in slick.grid.updateRowCount?

Hi all!
I used to display the grid in a container with a fixed height. The scroll manages well the view when there is too much items... but the problem is that the scroll is always here, even if there is no items!
To fix that, I went to slick.grid.js, in updateRowCount() method, and I found a strange way to compute the number of rows to be displayed, line 1629:
var numberOfRows = dataLengthIncludingAddNew + (options.leaveSpaceForNewRows ? numVisibleRows - 1 : 0);
Replacing the previous line by:
var numberOfRows = dataLengthIncludingAddNew + (options.leaveSpaceForNewRows ? 0 : -1);
seems to be OK.
Do you have any feedback or idea about that?
Thank you for your help!
Adrien

Not sure about this. The non-editable grid scroll bar disappears as soon as the rows fit in the window.
It looks like the code you found forces a scroll bar in this situation where the grid is editable.
So I'd say if you don't want that, the change you make is reasonable. I'd have to think about whether that should be changed in the main trunk though, as it's a preference thing.


Save Image to android/media/app_folder

Is it possible to save media under this folder.
After Android 11, this is important based on these links link1 , link2

@lecicdjuro  Can you close this issue with a comment otherwise it becomes unclear.


Timeline Template Not Found

The timeline template is based on bulma version 0.1.1. I have not yet updated it the latest version.


319 Addeed property chains to onto-viewer graph

Signed-off-by: Patrycja Miazek<EMAIL_ADDRESS>Added property chains to onto-viewer graph.
Fixed: #319

Also, could you prepare the config files for https://github.com/edmcouncil/esg and https://github.com/edmcouncil/iofoundry?


Bug: No longer works with symbol overrides in Sketch 44

I upgraded Sketch today and found that symbol overrides no longer work it in version 44. I've attached an example file and video for debugging purposes.

This plugin is a lifesaver and I'd love to see it up and running again. Thank you!
bug-symbol-data populator-sketch 44.zip

+1

@lukasondrej will have a look once he's back from his vacation (this week).

@nitrada Thanks for the update. 😄

@seandellis overrides now work in 44.

@lukasondrej Symbol overrides still aren't working for me with:

Sketch 44.1
Data Populator 2.1.3
macOS 10.12.5

In addition to using a JSON file I normally use, I tried the demo.sketch file included in this repo using the demo presets without any luck. Is there anything more specific I can contribute that might help?

Yes, still not working for me either.
Sketch 44.1
Data Populator 2.1.3
macOS 10.11.13

It's not working for me either (building from master).
I managed to make it work by removing the symbols inside my main symbol.

We'll do more testing, thanks for the comments!

Thanks for looking into this! Do you might have an ETA on this?

Here's an extra piece of information. It DOES work if you select a symbol with nested symbols which you have previously tried to have populated with the plugin, and choose to replace the symbol with itself, either through the contextual menu or through the inspector panel. The data gets added that way. But if you then use the shortcut to repopulate the data, it goes back to the placeholders.

Doesn't work for me as well.
Sketch 44.1, Data Populator 2.3.1, macOS 10.12.5
@eaugustine Thanks for the workaround. It's really weird that when replaced, symbol has data from JSON, but subsequent repopulation resets symbol to placholder data

i can confirm that it's not working on sketch 44.1, data populator 2.1.3, macOs 10.12.4
as I am very happy that data populator allows the use of conditional actions, i would like to try the plugin with the newest version of sketch. might you give an ETA on this fix?

Really sorry for the inconvenience, the fix will be ready very soon (as part of the next release).

it should work with this release: https://github.com/preciousforever/sketch-data-populator/releases/tag/v2.2.0
please let us know if you're still running into troubles!

@nitrada @lukasondrej  Yep, the latest release now works on my example file. Thanks so much for fixing this!

sorry for bringing bad news but not working for me, the content gets replaced by empty spaces

not working for me :/ I'm getting empty spaces
Sketch: 46.2
Mac OS: 10.12.6
plugin: 2.2.0