Can you give me some advises on  makefile?

I encounter some difficulties during make makefile, as follow:
make: Circular tf_nndistance.cu <- tf_nndistance.cu.o dependency dropped.
g++ tf_nndistance.cpp tf_nndistance.cu.o -o tf_nndistance_so.so \
-I /usr/local/cuda-10.0/include/ -I /home/machinelearning/.local/lib/python3.6/site-packages/tensorflow_core/include -L /usr/local/cuda-10.0/lib64/ -lcudart -L /home/machinelearning/.local/lib/python3.6/site-packages/tensorflow_core -ltensorflow_framework \
    -shared -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11 -fPIC -O2
/usr/bin/ld: cannot find -ltensorflow_framework
collect2: error: ld returned 1 exit status
makefile:13: recipe for target 'tf_nndistance_so.so' failed
make: *** [tf_nndistance_so.so] Error 1

The environment is tf.version  == 1.15 and cuda == 10.0, and the modified content as follow:
cuda_inc = /usr/local/cuda-10.0/include/
cuda_lib = /usr/local/cuda-10.0/lib64/
nvcc = /usr/local/cuda-10.0/bin/nvcc
tf_inc = /home/machinelearning/.local/lib/python3.6/site-packages/tensorflow_core/include
tf_lib =/home/machinelearning/.local/lib/python3.6/site-packages/tensorflow_core

Thanks a lot.

Hi. The same problem appears. Have you got the solution?

Hi. The same problem appears. Have you got the solution?

This can be solved by:
cd /home/machinelearning/.local/lib/python3.6/site-packages/tensorflow_core
ln -s libtensorflow_framework.so.1 libtensorflow_framework.so

@TerenceCYJ
Thank you.
I've executed same commands, but problem is not solved.
Can you please once look into the same.

@TerenceCYJ
Thank you.
I've executed same commands, but problem is not solved.
Can you please once look into the same.

Hi, @GopiRajuMatta. I just fix the bug by what I wrote. And my environment is tf.version == 1.15 and cuda == 10.0.

Hi, @TerenceCYJ
Thank you Terence, It was built without any error. But when I try to run demo.py, it is throwing this error.
"
Traceback (most recent call last):
File "demo.py", line 32, in
model_module = importlib.import_module('.%s' % args.model_type, 'models')
File "/home/gopi/anaconda3/envs/pcn_test2/lib/python3.6/importlib/init.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "", line 994, in _gcd_import
File "", line 971, in _find_and_load
File "", line 955, in _find_and_load_unlocked
File "", line 665, in _load_unlocked
File "", line 678, in exec_module
File "", line 219, in _call_with_frames_removed
File "/home/gopi/git/pcn/models/pcn_cd.py", line 4, in
from tf_util import *
File "/home/gopi/git/pcn/tf_util.py", line 4, in
from pc_distance import tf_nndistance, tf_approxmatch
File "/home/gopi/git/pcn/pc_distance/tf_nndistance.py", line 5, in
nn_distance_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_nndistance_so.so'))
File "/home/gopi/anaconda3/envs/pcn_test2/lib/python3.6/site-packages/tensorflow_core/python/framework/load_library.py", line 61, in load_op_library
lib_handle = py_tf.TF_LoadLibrary(library_filename)
tensorflow.python.framework.errors_impl.NotFoundError: /home/gopi/git/pcn/pc_distance/tf_nndistance_so.so: undefined symbol: _ZN10tensorflow12OpDefBuilder5InputESs
"
Any idea, where it went wrong?
Thank you
Gopi

Hi, Gopi. I think you should check your pc_distance.
After successful make, there should be a tf_nndistance_so.so
In my setting, after make, ls pc_distance
Get：__init__.py  __pycache__         tf_approxmatch.cu    tf_approxmatch.py     tf_nndistance.cpp  tf_nndistance.cu.o  tf_nndistance_so.so makefile     tf_approxmatch.cpp  tf_approxmatch.cu.o  tf_approxmatch_so.so  tf_nndistance.cu   tf_nndistance.py

Thank you Terence.
These are the files after make, it has all the files as you reported.
"init_.py  tf_approxmatch.cpp   tf_approxmatch.py     tf_nndistance.cu    tf_nndistance_so.so makefile     tf_approxmatch.cu    tf_approxmatch_so.so  tf_nndistance.cu.o pycache  tf_approxmatch.cu.o  tf_nndistance.cpp     tf_nndistance.py"
However, I am getting the reported error.
Thank you
Gopi

Hi Terence,
It seems, problem is with file tf_nndistance_so.so. It was compiled without errors. But I am not sure, why I am getting that particular error. I have used cuda=9.0, tf.version == 1.15. Is there any problem with this? If you don't mind, can you please share with me your compiled pc_distance folder.
Thank you
Gopi

I have encountered the same issues. After several days of bashing my head against the desk, I got the code up and running using the following steps:

Create a conda environment with cuda=9.2, tf=1.12, open3d=<IP_ADDRESS> and python=3.6:

conda create -n pcn tensorflow=1.12 python=3.6
conda activate pcn
conda install -c conda-forge nvcc_linux-64=9.2
conda install -c nvidia cudatoolkit=9.2
conda install -c open3d-admin open3d=0.8

Modify makefile as follows (replace user with your username):

cuda_inc = /home/user/.conda/envs/pcn/include/
cuda_lib = /home/user/.conda/envs/pcn/lib/
nvcc     = /home/user/.conda/envs/pcn/bin/nvcc
tf_inc   = /home/user/.conda/envs/pcn/lib/python3.6/site-packages/tensorflow/include
tf_lib   = /home/user/.conda/envs/pcn/lib/python3.6/site-packages/tensorflow

all: tf_nndistance_so.so tf_approxmatch_so.so

tf_nndistance.cu.o: tf_nndistance.cu
	$(nvcc) tf_nndistance.cu -o tf_nndistance.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC

tf_nndistance_so.so: tf_nndistance.cpp tf_nndistance.cu.o
	g++ tf_nndistance.cpp tf_nndistance.cu.o -o tf_nndistance_so.so -shared -std=c++11 -fPIC -O2 \
            -I $(tf_inc) -D_GLIBCXX_USE_CXX11_ABI=1 -L $(tf_lib) -ltensorflow_framework -L $(cuda_lib) -lcudart

tf_approxmatch.cu.o: tf_approxmatch.cu
	$(nvcc) tf_approxmatch.cu -o tf_approxmatch.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC

tf_approxmatch_so.so: tf_approxmatch.cpp tf_approxmatch.cu.o
	g++ -shared $(CPPFLAGS) tf_approxmatch.cpp tf_approxmatch.cu.o -o tf_approxmatch_so.so -shared -std=c++11 -fPIC -O2 \
            -I $(tf_inc) -D_GLIBCXX_USE_CXX11_ABI=1 -L $(tf_lib) -ltensorflow_framework -L $(cuda_lib) -lcudart

clean:
	rm -rf *.o *.so

Run make clean && make inside the conda environment.

Hope this helps!

@mihaimorariu, I really appreciate your help.
I am getting new errors. I will work around. I will try to resolve those.
I will update you.
Thank you so much
Gopi

Hi I am having exactly same error when running demo.py. Have you figured it out @GopiRajuMatta ?

Hi I am having exactly same error when running demo.py. Have you figured it out @GopiRajuMatta ?

Hello @francisduan
Presently, I am working on other problem.
I will try again and let you know, if it is resolved.
@mihaimorariu would be able to help you.
Thank you
Gopi

Has anyone of you resolved the ''make: Circular tf_nndistance.cu <- tf_nndistance.cu.o dependency dropped.'' problem mentioned above? I have tried pretty much anything i could find and no result.
I am working with tf .version==1.12, cuda== 9.0

Hello guys,
Has anyone of you resolved the ''make: Circular tf_nndistance.cu <- tf_nndistance.cu.o dependency dropped.'' problem mentioned above? I have tried pretty much anything i could find and no result.
I am working with tf .version==1.12, cuda== 9.0
Thanks in advance,
Yakamoko

hi @Yakamoko,
have you got the solution?
Thanks!

Hi guys, removing -D_GLIBCXX_USE_CXX11_ABI=1 solved my probelm

Hi guys, removing -D_GLIBCXX_USE_CXX11_ABI=1 solved my probelm

Hi gillbam,
Could you plz  define  the version of tf and cuda-gcc you are using in order to achieve this?
Furthermore, the code you are mentioning for removal is on the makefile right?
Thanks in advance,
Yakamoko

Hi gillbam,
Could you plz  define  the version of tf and cuda-gcc you are using in order to achieve this?
Furthermore, the code you are mentioning for removal is on the makefile right?
Thanks in advance,
Yakamoko

Hi gillbam,
Could you plz define the version of tf and cuda-gcc you are using in order to achieve this?
Furthermore, the code you are mentioning for removal is on the makefile right?
Thanks in advance,
Yakamoko

Hi Yakamoko,
I have tf 1.12, cuda 10.1 installed. Check my attached code.
`cuda_inc = /usr/local/cuda-10.1/include/
cuda_lib = /usr/local/cuda-10.1/lib64/
nvcc     = /usr/local/cuda-10.1/bin/nvcc
tf_inc   = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow/include
tf_lib   = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow
all: tf_nndistance_so.so tf_approxmatch_so.so
tf_nndistance.cu.o: tf_nndistance.cu
$(nvcc) tf_nndistance.cu -o tf_nndistance.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
tf_nndistance_so.so: tf_nndistance.cpp tf_nndistance.cu.o
g++ tf_nndistance.cpp tf_nndistance.cu.o -o tf_nndistance_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared -std=c++11 -fPIC -O2
tf_approxmatch.cu.o: tf_approxmatch.cu
$(nvcc) tf_approxmatch.cu -o tf_approxmatch.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
tf_approxmatch_so.so: tf_approxmatch.cpp tf_approxmatch.cu.o
g++ -shared $(CPPFLAGS) tf_approxmatch.cpp tf_approxmatch.cu.o -o tf_approxmatch_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared  -std=c++11 -fPIC -O2
clean:
rm -rf *.o *.so`

Hi gillbam,
Could you plz define the version of tf and cuda-gcc you are using in order to achieve this?
Furthermore, the code you are mentioning for removal is on the makefile right?
Thanks in advance,
Yakamoko

Hi Yakamoko,
I have tf 1.12, cuda 10.1 installed. Check my attached code.
`cuda_inc = /usr/local/cuda-10.1/include/
cuda_lib = /usr/local/cuda-10.1/lib64/
nvcc     = /usr/local/cuda-10.1/bin/nvcc
tf_inc   = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow/include
tf_lib   = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow
all: tf_nndistance_so.so tf_approxmatch_so.so
tf_nndistance.cu.o: tf_nndistance.cu
$(nvcc) tf_nndistance.cu -o tf_nndistance.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
tf_nndistance_so.so: tf_nndistance.cpp tf_nndistance.cu.o
g++ tf_nndistance.cpp tf_nndistance.cu.o -o tf_nndistance_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared -std=c++11 -fPIC -O2
tf_approxmatch.cu.o: tf_approxmatch.cu
$(nvcc) tf_approxmatch.cu -o tf_approxmatch.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC
tf_approxmatch_so.so: tf_approxmatch.cpp tf_approxmatch.cu.o
g++ -shared $(CPPFLAGS) tf_approxmatch.cpp tf_approxmatch.cu.o -o tf_approxmatch_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared  -std=c++11 -fPIC -O2
clean:
rm -rf *.o *.so`

Hi gillbam,
Could you plz define the version of tf and cuda-gcc you are using in order to achieve this?
Furthermore, the code you are mentioning for removal is on the makefile right?
Thanks in advance,
Yakamoko

Hi Yakamoko,
I have tf 1.12, cuda 10.1 installed. Check my attached code.
cuda_inc = /usr/local/cuda-10.1/include/
cuda_lib = /usr/local/cuda-10.1/lib64/
nvcc = /usr/local/cuda-10.1/bin/nvcc
tf_inc = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow/include
tf_lib = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow

all: tf_nndistance_so.so tf_approxmatch_so.so

tf_nndistance.cu.o: tf_nndistance.cu
$(nvcc) tf_nndistance.cu -o tf_nndistance.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC

tf_nndistance_so.so: tf_nndistance.cpp tf_nndistance.cu.o
g++ tf_nndistance.cpp tf_nndistance.cu.o -o tf_nndistance_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared -std=c++11 -fPIC -O2

tf_approxmatch.cu.o: tf_approxmatch.cu
$(nvcc) tf_approxmatch.cu -o tf_approxmatch.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC

tf_approxmatch_so.so: tf_approxmatch.cpp tf_approxmatch.cu.o
g++ -shared $(CPPFLAGS) tf_approxmatch.cpp tf_approxmatch.cu.o -o tf_approxmatch_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared -std=c++11 -fPIC -O2

clean:
rm -rf *.o *.so

Hi gillbam,
Could you plz define the version of tf and cuda-gcc you are using in order to achieve this?
Furthermore, the code you are mentioning for removal is on the makefile right?
Thanks in advance,
Yakamoko

Hi Yakamoko,
I have tf 1.12, cuda 10.1 installed. Check my attached code.
cuda_inc = /usr/local/cuda-10.1/include/
cuda_lib = /usr/local/cuda-10.1/lib64/
nvcc = /usr/local/cuda-10.1/bin/nvcc
tf_inc = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow/include
tf_lib = /root/anaconda3/envs/pcn/lib/python3.6/site-packages/tensorflow

all: tf_nndistance_so.so tf_approxmatch_so.so

tf_nndistance.cu.o: tf_nndistance.cu
$(nvcc) tf_nndistance.cu -o tf_nndistance.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC

tf_nndistance_so.so: tf_nndistance.cpp tf_nndistance.cu.o
g++ tf_nndistance.cpp tf_nndistance.cu.o -o tf_nndistance_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared -std=c++11 -fPIC -O2

tf_approxmatch.cu.o: tf_approxmatch.cu
$(nvcc) tf_approxmatch.cu -o tf_approxmatch.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC

tf_approxmatch_so.so: tf_approxmatch.cpp tf_approxmatch.cu.o
g++ -shared $(CPPFLAGS) tf_approxmatch.cpp tf_approxmatch.cu.o -o tf_approxmatch_so.so
-I $(cuda_inc) -I $(tf_inc) -L $(cuda_lib) -lcudart -L $(tf_lib) -ltensorflow_framework
-shared -std=c++11 -fPIC -O2

clean:
rm -rf *.o *.so

Hi, I am getting the same error:
make: Circular tf_nndistance.cu <- tf_nndistance.cu.o dependency dropped.
g++ tf_nndistance.cpp tf_nndistance.cu.o -o tf_nndistance_so.so
....
make (e=2): The system cannot find the file specified.
make: *** [makefile:13: tf_nndistance_so.so] Error 2
and I have intalled:
conda install -c nvidia/label/cuda-11.3.0 cuda-nvcc and
conda install -c nvidia cudatoolkit=9.2
using the same makefile above. Is it a version problem still?
Thanks in advance.

I am trying to run demo.py with the right versions and with the updated versions, and I keep getting an error that I cannot fix. I've been working around this for some days already and can't manage to get anything better.
Traceback (most recent call last):
  File "demo.py", line 121, in <module>
    main()
  File "demo.py", line 77, in main
    model_module = importlib.import_module('.%s' % args.model_type, 'models')
  File "/home/mk/anaconda3/envs/pcnconda/lib/python3.6/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 994, in _gcd_import
  File "<frozen importlib._bootstrap>", line 971, in _find_and_load
  File "<frozen importlib._bootstrap>", line 955, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 665, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 678, in exec_module
  File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
  File "/home/mk/Downloads/pcn-master/models/pcn_cd.py", line 26, in <module>
    from tf_util import *
  File "/home/mk/Downloads/pcn-master/tf_util.py", line 26, in <module>
    from pc_distance import tf_nndistance, tf_approxmatch
  File "/home/mk/Downloads/pcn-master/pc_distance/tf_approxmatch.py", line 21, in <module>
    @ops.RegisterShape('ApproxMatch')
AttributeError: module 'tensorflow.python.framework.ops' has no attribute 'RegisterShape'


Incorectly rendered sentence

Last sentence's two word appears on next line, this create huge gap between application's word and Main

Document Details
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ID: 2fa64390-1232-8dd3-f170-7d5c68e29ad9
Version Independent ID: 245bbba8-3102-4a66-15eb-650e05eb9e62
Content: Design-time DbContext Creation - EF Core
Content Source: entity-framework/core/miscellaneous/cli/dbcontext-creation.md
Product: entity-framework
GitHub Login: @bricelam
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@VPeruS which browser and OS?

Firefox 61 + linux.

And from IE

Should be because fixed-length lines.

There were actually extra spaces there. In markdown two spaces is a line separator.

The fix will make it to live later. Thanks @VPeruS.


Add support for continuous measurements

[x] This contribution adheres to CONTRIBUTING.md.

What does this Pull Request accomplish?
AB#2376465
This PR adds support for running continuous measurements.

Adds a new method to the MeasurementContext class called Update Results.vi. The name of this new method mirrors the name of the output cluster, which is called Measurement Results. This new method takes a variant for the output cluster to be updated.
The MeasurementContext class now contains both an IMeasurementService interface and a MeasurementPluginService class. Those two new data members are injected during creation. When a measurement author calls the  Update Results.vi on the MeasurementContext class, the call will be forwarded to an Update Results.vi method that is on the IMeasurementService interface. This call will be dispatched to either the V1 or V2 implementation, depending on what was passed in during creation.
As alluded to above, there is now an Update Results.vi method on the IMeasurementService interface. The V1 implementation is a no-op and the V2 implementation calls Pack for gRPC followed by Set Output. The Streaming? input is hardcoded to true.
I missed changing a V1 control in Get Measurement Details.vi so I changed it to be the version agnostic control. This should have been done in my previous refactoring PR.
The changes to MeasurementServiceBaseV1.lvclass and Read Pin Map Context.vi shouldn't be necessary, but LabVIEW insists on the dirty dot.

Why should this Pull Request be merged?
With this PR, we officially support continuous measurements in LabVIEW.
What testing has been done?
I wrote a new example that does continuous updates. It will be submitted in another PR.

With your example, have you gotten to the point where you've successfully seen continuous updates in InstrumentStudio with locally built packages?

Yep! It all works.

FYI @jasonmreding

These changes look good to me.


Update ColorCodedCans.netkan

Add supports section

Hey @Artemonim, I just wanted to say thanks for the flurry of updates!
This might be taking advantage, but there's an under-used "tagging" feature that needs a lot of metadata added to be useful. See:

KSP-CKAN/CKAN#1021
KSP-CKAN/CKAN#2034
KSP-CKAN/CKAN#2155
https://github.com/KSP-CKAN/CKAN/wiki/Suggested-Tags

If you were interested, we would also be grateful for any pull requests along those lines.

I just wanted to choose mods for my game and stuck here for a week :D
Maybe I'll try it, but I can't understand where CKAN users can see that tags and how they use it now. This could be the basis of an excellent mod recommendation system by the way.


opening a zarr dataset taking so much time

What is your issue?
I have an era5 dataset stored in GCS bucket as zarr. It contains 273 weather related variables and 4 dimensions. It's an hourly stored data from 1940 to 2023.
When I try to open with ds = xr.open_zarr("gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3") it takes 90 seconds to actually finish the open call.
The chunk scheme is { 'time': 1 }.

I've felt the pain on this particular store as well, it's a nice test case for the whole stack. 3PB total, >1,000,000 chunks in each of the pressure level variables!
Looks like this is a dask problem though. All the time is spent in single-threaded code creating the array chunks.

If we skip dask with xr.open_zarr(..., chunks=None) it takes 1.5s.
We currently have a drop_variables arg. When you have a dataset with 273 variables and you only want a couple, the inverse keep_variables would be a lot easier.  It looks like drop_variables gets applied before we create the dask chunks for the arrays, so reading the store once and on the second read adding drop_variables=[v for v in ds.data_vars if v != "geopotential"], I recover a ~1.5s read time.

Nice profile!
@jrbourbeau https://github.com/dask/dask/pull/10648 probably improves this by a lot. Can that be reviewed/merged please?

Interestingly things are almost a factor of 4x worse on both those PRs, but both are out of date so may be missing other recent improvements.

@slevang, do you mind sharing how you are generating these profiles and associated graphs? I've struggled in the past to do this effectively with a dask cluster. This looks great!

This is snakeviz: https://jiffyclub.github.io/snakeviz/

^yep. Probably not useful for distributed profiling but I haven't really tried.
In this case the creation of this monster task graph would be happening serially in the main process even if your goal was to eventually use a distributed client to run processing. The graph (only describing the array chunks) is close to a billion objects in this case, so would run into issues even trying to serialize that out to workers.

FYI something like:
ds = xr.open_zarr("gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3", chunks=dict(time=-1, level=-1, latitude='auto', longitude='auto'))

...may give a better balance between dask task graph size and chuck size.
(I do this "make the dask chunks bigger than the zarr chunks" a lot, because simple dask graphs can become huge with a 50TB dataset, since the default zarr encoding has a maximum chunk size of 2GB. Not sure it's necessarily the best way, very open to ideas...)

I'm losing track of which versions I've profiled on, but this is what I have now on dask=2024.3.1 xarray=2024.3.0 (looks fairly different):

On the xarray side it seems we are expending quite a bit of time on itertools.accumulate and set differences just to potentially throw up this warning. And on the dask side its basically cumsum and tokenize in building the array graph.
Maybe it's worth trying to optimize these further, but at the end of the day dask is building billion item python objects which is inherently going to be slow. Either combining chunks or filtering variables on the user side will be needed at some point.

How do I get my work done?

I opened the dataset with chunks=None.
Then filter that as required like select only a few data variables only for some fixed time ranges and for some fixed lat lon ranges.
Then chunk that small dataset only.

ds = xr.open_zarr("gs://gcp-public-data-arco-era5/ar/full_37-1h-0p25deg-chunk-1.zarr-v3", chunks=None)
data_vars = ['surface_pressure', 'temperature']
ds = ds[data_vars]
ds = ds.sel(time=slice('2013-01-01', '2013-12-31'))
... and a few more filters ...
ds = ds.chunk()

This will only generate chunks for a filtered dataset.
These steps worked for me so closing the issue.

This will only generate chunks for a filtered dataset.

This should absolutely be the way that xarray works! There is no need to create chunks for variables that never are accessed.

Yup saving 30s with #9808 . The cache is quite effective: CacheInfo(hits=826, misses=4, maxsize=None, currsize=4)

Belatedly realizing that Xarray's call to normalize_chunks is a major time waster here given that chunks contains a tuple with O(1 million) elements hehe.


HA - Vista 20P zones cycling from open to close and back again

Many of us using HA are seeing the above behavior with our 20P.  The behavior appears to happen when we have more than 4 or 5 sensors open.  Below that number, everything behaves as expected; and once exceeded, the log shows these sensors gyrating between open and closed incessantly even when the related item is not changing.
I am not sure why this is and if it is even an issue with pyenvisalink.  It is worth noting that the Envisalink web GUI does not show this behavior and so it feels like this is something specific to pyenvisalink/HA.
Any help anyone can provide would be appreciated.
I am currently running with HA 2022.9.1, but I have seen this with previous releases as well.

Hi @jl-678, apologies for the very slow response - I never got a notification when the issue was opened and just noticed it now.
If this is still an issue for you, I can try to help.  I don't have a Honeywell system myself but if you could provide debug info I may be able to help track down the issue.

Yes, it is definitely still an issue. It is not front and center for me as it is winter time so few zones are open.  However, once Spring returns and we open doors and windows, this problem will return.
The description above is consistent.  I think that the best place to get more information is to look at the thread linked below where Scott and I discuss the issue.  We also tried some troubleshooting related to zonedump Interval and that did not seem to help.
https://community.home-assistant.io/t/envisalink-evl-4-zones-randomly-opening-and-closing/336978/14?u=jl_678

If you're able to provide debug logging of the issue occurring then I can have a look at what's going on.  I'd need the pyenvisalink debug logging and not just the HA envisalink integration logging.  But keep in mind that the password and alarm code might be present so best to scrub them before uploading any logs (or use temporary values).
It would be best if you could attach the log file rather than cut-and-pasting it.  Thanks!

Thank you! I will work to get you some logs this weekend and have asked others to do the same.

While I was working on the envisalink_new refactor I had a look at the zone timer dump code as it seems to be a point of issue for many people and is likely related to your issue here.
I found a bug in it but there are also some comments in the code from the original author about how the EVL seems to report the data inaccurately.  However, my own EVL works as documented which has be wondering whether the bug I found was original cause or perhaps an older version of the EVL firmware had a problem that has subsequently been fixed.
Would you be able to send me some debug logs with the zone dump timer enabled so I can verify the behaviour on a Honeywell system?  I don't need you to reproduce the case when there are several zones open.  I just need to see what happens when a single zone opens and then closes.
A simple test as follows would be great:

Set your zone dump timer interval to 5 seconds.
Turn on pyenvislink debug logging as described above.
Start up HA.
Once things are started up, open a zone (e.g. open a door) and leave it open for 10 or 15 seconds and then close it and leave it closed for 10-15 seconds.
Grab the logs.

If your system behaves like mine is, there might be a really simple fix for this.  🤞

Once you have the logging enabled, it will be the main home-assistant.log file that I'd like to have a look at.  Probably the easiest way to get the file is to go into Settings->Logs and then at the bottom of the page there should be a "Download Full Log" button.  That will download and save the log file to your computer.  From there you should be able to just drag-and-drop it into a comment box on this page.
Thanks!

Please also confirm which version of the Envisalink you have (3 or 4).  Thanks!

EVL-4 with firmware version  01.04.182A
Honeywell Vista 20p
On Tue, Jan 24, 2023 at 5:24 PM ufodone @.***> wrote:

Please also confirm which version of the Envisalink you have (3 or 4).
Thanks!
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home-assistant_2023-01-25T02-17-21.711Z.zip

Hope this helps.  Thanks for all your help on this!!!

I see why your log file was filling up so fast now.  It looks like debug was on for all of Home Assistant.  But for future maybe setting the default to info would be good:
logger:
  logs:
    homeassistant.components.envisalink: debug
    pyenvisalink: debug

Just to confirm: are these logs reproducing the flapping issue that was initially reported or was it meant to be a test about the zone timer dumps?  If the latter, it looks like you didn't set the zonedump_interval to 5 in the configuration.  The high frequency here (vs the 30 second default) is needed so I can see how they are functioning in the envisalink itself.
If the repro was showing the flapping from the initial issue, I'll dig into the logs to see what I can find.

Weird, that is exactly how I set my config, here is a direct copy from my
config.yaml (I had to rem it out with the # on each line to stop it from
filling the drive but what I sent before was obviously without the #)  When
I remove those #'s my log fills about 20mb per minute, not sure why???
#logger:
logs:
homeassistant.components.envisalink: debug
pyenvisalink: debug
And yes, to confirm, the log I uploaded was showing the flapping in full
swing.  I am more than happy / willing to test in any way that can help you
to fix this issue, just let me know what it is I can do or provide to help.
On Tue, Jan 24, 2023 at 7:36 PM ufodone @.***> wrote:

I see why your log file was filling up so fast now. It looks like debug
was on for all of Home Assistant. But for future maybe setting the default
to info would be good:
logger:
logs:
homeassistant.components.envisalink: debug
pyenvisalink: debug
Just to confirm: are these logs reproducing the flapping issue that was
initially reported or was it meant to be a test about the zone timer dumps?
If the latter, it looks like you didn't set the zonedump_interval to 5 in
the configuration. The high frequency here (vs the 30 second default) is
needed so I can see how they are functioning in the envisalink itself.
If the repro was showing the flapping from the initial issue, I'll dig
into the logs to see what I can find.
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I cut-and-pasted the log config from above and realized I didn't add the extra "default: info" line and then went back and edited it so you maybe didn't see that addition.
I had a look at the logs and confirmed my suspicions as to what's going on.  Would be able to run the other test I posted above of setting the zonedump_interval to 5?  That would help determine how I could approach fixing the issue.

OK, I changed my config to add "default: info" and also changed zonedump_interval to 5.  I then restarted HA and opened 4 windows and 1 door.  I let that run for several minutes and should show the flapping of the windows and doors.  The motion entries are probably me walking around initially opening the above but after the motion stops you should see the flapping.  I let it run for a few minutes then saved the first log.  After that I manually closed the door and windows then saved the log again in case you wanted to see it including me actually closing the door and windows.
Thanks again for all your help with this.
Scott
[home-assistant_2023-01-25T18-20-20.1
home-assistant_2023-01-25T18-22-42.361Z.log
28Z.log](https://github.com/ufodone/pyenvisalink/files/10502422/home-assistant_2023-01-25T18-20-20.128Z.log)

Thanks for the additional logs.  That gives me enough to go on.
I'll see what I can put together for a fix.  I'll likely put and changes into the new version I'm working on first as it's faster to iterate on changes there.  I'll update here once I think I've got something worth testing.

Thank you!!!  If you need anything additional at all from me please let me know. Am happy to helpSent from my iPhoneOn Jan 25, 2023, at 6:20 PM, ufodone @.***> wrote:
Thanks for the additional logs.  That gives me enough to go on.
I'll see what I can put together for a fix.  I'll likely put and changes into the new version I'm working on first as it's faster to iterate on changes there.  I'll update here once I think I've got something worth testing.
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Envisalink_new seems to be working ok for me except that the open zone flapping is still happening just as before.  My config shows the following for logging to try to help you.  Attached is the log, I restarted then opened around 5 doors / windows.  Let me know what else I can do to help with this...
Thank you
logger:
default: info
logs:
homeassistant.components.envisalink: debug
pyenvisalink: debug
home-assistant_2023-01-30T21-40-10.693Z.log

Hey @sheyman1 Let's take the conversation over here since this isn't the HA core version.  Thanks!

Sorry, my bad, missed the log change.  I will run the test again now with
the _new logging info corrected and post the log to the link above.
Thanks
On Mon, Jan 30, 2023 at 2:15 PM ufodone @.***> wrote:

Hey @sheyman1 https://github.com/sheyman1 Let's take the conversation
over here https://github.com/ufodone/envisalink_new/issues/9 since this
isn't the HA core version. Thanks!
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Closing this issue as I believe this issue is fixed in envisalink_new.

What was the fix... I do not want to change my 32 zones from the old plugin to the new one, I have so many automations on them all... is there another way @ufodone

@AntoineDeveloper The algorithm used to track zone state changes was replaced with one that used a different set of updates from the EVL devices.  I have no plans to move these changes back into the old integration so currently the only option is to switch to envisalink_new.
FYI, envisalink_new can import all your config from the stock integration which will preserve the entity names and so I suspect that most of your automations wouldn't need updating.  Only places where the integration name is used should be affected.  E.g. service calls to Alarm Keypress and Invoke Custom Function.


Don't return redirect error codes

This might lead to redirect loops.
See
https://github.com/RSS-Bridge/rss-bridge/pull/1071#issuecomment-515632848
Cherry-picked from eb21d6f.

Very good, thank you for the fix :+1:


XS Max 12.4 some apps wont launch after successful jailbreak

Subject says it all.
To Reproduce
Steps to reproduce the behavior:

Go to Snapchat, app opens and immediately quits

Device (please complete the following information):

iOS Version: 12.4
iOS Device: XS Max 512gb
unc0ver Version: 3.5.4

Place an "x" between the brackets if true:

[ ] this is a bug others will be able to reproduce
[ ] this issue is present with all tweaks uninstalled(except for default packages) or disabled
[ ] this issue is present after a rootfs restore
[ ] this issue is present on the latest version of unc0ver

Snap seems to be working for me

iOS Version: 12.4
iOS Device: XS Max 256GB
unc0ver Version: 3.5.5

You might want to update to 3.5.5

3.5.5 fixed it


Fixing performance issue with deeply nested structures

Fixing performance issue with deeply nested structures (https://github.com/AngleSharp/AngleSharp.Xml/issues/18)
Types of Changes
Prerequisites
Please make sure you can check the following two boxes:

[ ] I have read the CONTRIBUTING document
[x] My code follows the code style of this project

Contribution Type
What types of changes does your code introduce? Put an x in all the boxes that apply:

[x] Bug fix (non-breaking change which fixes an issue, please reference the issue id)
[ ] New feature (non-breaking change which adds functionality, make sure to open an associated issue first)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] My change requires a change to the documentation
[ ] I have updated the documentation accordingly
[ ] I have added tests to cover my changes
[ ] All new and existing tests passed

Description
This fixes the parent hierarchy being traversed over and over again. Testing with the structure from https://github.com/AngleSharp/AngleSharp.Xml/issues/18 the time goes down from 35 seconds to ~500 ms.
This might not be the preferred approach to fixing this issue. It's something that works for my specific case and could be fix. Although at the price of introducing a new property to IElement. Which will require other implementors of IElement to be extended as well, although the functionality stays the same (is this a breaking change?).

 Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.Wiki Transformation Project - Heinrich Ulbricht seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.You have signed the CLA already but the status is still pending? Let us recheck it.

Thanks! This is now part of devel!


In ha k3s cluster (with reverse proxy as api-server), kubectl failed to get log from part of pods

Environmental Info:
K3s Version:
k3s version v1.25.7+k3s1 (f7c20e23)
go version go1.19.6
Node(s) CPU architecture, OS, and Version:
Linux k3sh2m1-vms01 5.15.0-69-generic #76-Ubuntu SMP Fri Mar 17 17:19:29 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
QUEM/KVM virtual hosts with 'MACVTAP' network adapter.
Cluster Configuration:
3 servers, 7 agents.
Details:
I got 4 physical hosts, with 10 vms resides in. Among these 10 vms, there are 3 k3s masters + 7 agents. Both physical & virtual hosts (and other virtual host which do not join k3s cluster) have network cidr <IP_ADDRESS>/24.
There is a standalone host who got ip address <IP_ADDRESS> worked as HA load-balancer, with 'haproxy':
front server:              <IP_ADDRESS>:6443
upstream servers:     <IP_ADDRESS>:6443 (k3sh2m1-vms01)
<IP_ADDRESS>:6443 (k3sh3m1-vms02)
<IP_ADDRESS>:6443 (k3sh4m1-vms03)
Describe the bug:
For some pods in cluster, kubectl logs -n <namespace> <pod-name> results error:

Error from server: Get "https://<IP_ADDRESS>:10250/containerLogs/calico-apiserver/calico-apiserver-6f47ffb766-p9mfq/calico-apiserver": proxy error from <IP_ADDRESS>:6443 while dialing <IP_ADDRESS>:10250, code 503: 503 Service Unavailable

Steps To Reproduce:
This is a fresh setup cluster, I just did following jobs:

Installed K3s:

curl -sfL https://get.k3s.io | INSTALL_K3S_MIRROR=cn INSTALL_K3S_EXEC="--bind-address=<IP_ADDRESS> --advertise-address=<IP_ADDRESS> --node-ip=<IP_ADDRESS> --tls-san="<IP_ADDRESS>"  --disable servicelb,traefik --flannel-backend=none --selinux --kube-proxy-arg proxy-mode=ipvs ipvs-scheduler=lc --disable-network-policy --disable-cloud-controller --cluster-cidr=<IP_ADDRESS>/16 --service-cidr=<IP_ADDRESS>/16" K3S_NODE_NAME="$(hostname)" sh -s - server --cluster-init

Install Calico:
(Straight forward with BGP mode, since I already got ToR router to be BGP enabled.)

Configure HAProxy
(Straight forward with previous settings.)

Key Changes:

flannel disabled (I'm using calico)
servicelb disabled (I've got metal lb)
traefix disabled (Going to use istio)
kube-proxy in IPVS mod (I think it's easier to go with Calico-BGP and Top of Rack routers.)

Expected behavior:
Using kubectl logs -n <namespace> <pod-name> towards any pod could work.
Actual behavior:
For some of the pods in my cluster, the kubectl logs command could not work.
Clues:

This behavior seems to be hosts related. If pod-x from host-x fails, other pods from same host fails too.
Not network issue. I 've verified using curl -vk https://x:y to any pod-ip, service-ip, host-ip from masters.

Additional context / logs:
root@k3sh2m1-vms01:~# kubectl get pods  --all-namespaces -o wide

NAMESPACE          NAME                                       READY   STATUS      RESTARTS   AGE     IP               NODE            NOMINATED NODE   READINESS GATES
calico-apiserver   calico-apiserver-6f47ffb766-2ggr6          1/1     Running     0          40h     <IP_ADDRESS>    k3sh3a2-vms06
calico-apiserver   calico-apiserver-6f47ffb766-p9mfq          1/1     Running     0          40h     <IP_ADDRESS>    k3sh3a3-vms09
calico-system      calico-kube-controllers-54d6cdbb68-rqlpb   1/1     Running     0          40h     <IP_ADDRESS>     k3sh4a2-vms07
calico-system      calico-node-5g7gm                          1/1     Running     0          5d1h    <IP_ADDRESS>   k3sh3a2-vms06
calico-system      calico-node-5l87b                          1/1     Running     0          40h     <IP_ADDRESS>   k3sh1a1-vms04
calico-system      calico-node-6r64b                          1/1     Running     0          5d1h    <IP_ADDRESS>   k3sh4a3-vms11
calico-system      calico-node-6xfwt                          1/1     Running     0          6d14h   <IP_ADDRESS>   k3sh4m1-vms03
calico-system      calico-node-bsfdx                          1/1     Running     0          5d1h    <IP_ADDRESS>   k3sh3a3-vms09
calico-system      calico-node-cj6pc                          1/1     Running     0          5d1h    <IP_ADDRESS>   k3sh2a2-vms05
calico-system      calico-node-dmdtm                          1/1     Running     0          6d15h   <IP_ADDRESS>   k3sh2m1-vms01
calico-system      calico-node-ht6s7                          1/1     Running     0          5d1h    <IP_ADDRESS>   k3sh4a2-vms07
calico-system      calico-node-kxgjv                          1/1     Running     0          5d1h    <IP_ADDRESS>   k3sh2a3-vms08
calico-system      calico-node-mtp6j                          1/1     Running     0          6d14h   <IP_ADDRESS>   k3sh3m1-vms02
calico-system      calico-typha-5f485f5769-j2vrq              1/1     Running     0          5d1h    <IP_ADDRESS>   k3sh2a3-vms08
calico-system      calico-typha-5f485f5769-jdq76              1/1     Running     0          6d15h   <IP_ADDRESS>   k3sh2m1-vms01
calico-system      calico-typha-5f485f5769-qqws2              1/1     Running     0          6d14h   <IP_ADDRESS>   k3sh4m1-vms03
calico-system      csi-node-driver-52whf                      2/2     Running     0          6d15h   <IP_ADDRESS>     k3sh2m1-vms01
calico-system      csi-node-driver-85tsr                      2/2     Running     0          5d1h    <IP_ADDRESS>    k3sh3a2-vms06
calico-system      csi-node-driver-92n4l                      2/2     Running     0          6d14h   <IP_ADDRESS>    k3sh3m1-vms02
calico-system      csi-node-driver-dqj8n                      2/2     Running     0          40h     <IP_ADDRESS>      k3sh1a1-vms04
calico-system      csi-node-driver-ghslb                      2/2     Running     0          5d1h    <IP_ADDRESS>    k3sh4a3-vms11
calico-system      csi-node-driver-gqxtj                      2/2     Running     0          5d1h    <IP_ADDRESS>      k3sh2a3-vms08
calico-system      csi-node-driver-h62gh                      2/2     Running     0          5d1h    <IP_ADDRESS>     k3sh4a2-vms07
calico-system      csi-node-driver-kjs2c                      2/2     Running     0          6d14h   <IP_ADDRESS>     k3sh4m1-vms03
calico-system      csi-node-driver-r6mdm                      2/2     Running     0          5d1h    <IP_ADDRESS>     k3sh2a2-vms05
calico-system      csi-node-driver-sf49d                      2/2     Running     0          5d1h    <IP_ADDRESS>    k3sh3a3-vms09
cert-manager       cert-manager-87f5555f-lmmdr                1/1     Running     0          35h     <IP_ADDRESS>    k3sh4a3-vms11
cert-manager       cert-manager-cainjector-8448ff8ddb-n5kcw   1/1     Running     0          35h     <IP_ADDRESS>    k3sh3a2-vms06
cert-manager       cert-manager-startupapicheck-b8lw8         0/1     Completed   0          35h     <IP_ADDRESS>     k3sh2a2-vms05
cert-manager       cert-manager-webhook-5468b675b-lpknv       1/1     Running     0          35h     <IP_ADDRESS>      k3sh2a3-vms08
istio-system       istio-ingressgateway-6974766b9c-j29s5      1/1     Running     0          37h     <IP_ADDRESS>     k3sh2a2-vms05
istio-system       istiod-5987b4bb4f-d4qv6                    1/1     Running     0          37h     <IP_ADDRESS>      k3sh1a1-vms04
kube-system        calicoctl                                  1/1     Running     0          6d15h   <IP_ADDRESS>   k3sh2m1-vms01
kube-system        coredns-597584b69b-n29v2                   1/1     Running     0          6d15h   <IP_ADDRESS>     k3sh2m1-vms01
kube-system        local-path-provisioner-79f67d76f8-h4pbj    1/1     Running     0          6d15h   <IP_ADDRESS>     k3sh2m1-vms01
kube-system        metrics-server-5f9f776df5-kz6vm            1/1     Running     0          6d15h   <IP_ADDRESS>     k3sh2m1-vms01
tigera-operator    tigera-operator-64db64cb98-krn8z           1/1     Running     0          40h     <IP_ADDRESS>   k3sh4a3-vms11

root@k3sh2m1-vms01:~# kubectl get ep --all-namespaces

NAMESPACE          NAME                              			ENDPOINTS                                                           AGE
calico-apiserver   calico-api                        			<IP_ADDRESS>:5443,<IP_ADDRESS>:5443                               6d15h
calico-system      calico-kube-controllers-metrics   			<IP_ADDRESS>:9094                                                   6d15h
calico-system      calico-typha                      			<IP_ADDRESS>:5473,<IP_ADDRESS>:5473,<IP_ADDRESS>:5473         6d15h
cert-manager       cert-manager                      			<IP_ADDRESS>:9402                                                  36h
cert-manager       cert-manager-webhook              			<IP_ADDRESS>:10250                                                   36h
default            kubernetes                        			<IP_ADDRESS>:6443                                                   6d15h
istio-system       istio-ingressgateway              			<IP_ADDRESS>:15021,<IP_ADDRESS>:8080,<IP_ADDRESS>:8443              38h
istio-system       istiod                            			<IP_ADDRESS>:15012,<IP_ADDRESS>:15010,<IP_ADDRESS>:15017 + 1 more...   38h
kube-system        kube-dns                          			<IP_ADDRESS>:53,<IP_ADDRESS>:53,<IP_ADDRESS>:9153                   6d15h
kube-system        metrics-server                    			<IP_ADDRESS>:10250                                                  6d15h

root@k3sh2m1-vms01:~# journalctl -xu k3s --since="2023-04-10 00:00:00"

Apr 11 03:54:51 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T03:54:51.844Z","caller":"etcdserver/server.go:1383","msg":"triggering snapshot","local-member-id":
"48030991e053f968","local-member-applied-index":3040318,"local-member-snapshot-index":3030317,"local-member-snapshot-count":10000}
Apr 11 03:54:51 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T03:54:51.850Z","caller":"etcdserver/server.go:2396","msg":"saved snapshot","snapshot-index":304031
8}
Apr 11 03:54:51 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T03:54:51.850Z","caller":"etcdserver/server.go:2426","msg":"compacted Raft logs","compact-index":30
35318}
Apr 11 03:54:55 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T03:54:55.319Z","caller":"fileutil/purge.go:77","msg":"purged","path":"/var/lib/rancher/k3s/server/
db/etcd/member/snap/0000000000000002-00000000002da0e9.snap"}
Apr 11 03:55:01 k3sh2m1-vms01 k3s[1074]: E0411 03:55:01.042364    1074 dns.go:157] "Nameserver limits exceeded" err="Nameserver limits were exceeded, some nameservers have
been omitted, the applied nameserver line is: <IP_ADDRESS> <IP_ADDRESS> <IP_ADDRESS>"
Apr 11 03:59:01 k3sh2m1-vms01 k3s[1074]: E0411 03:59:01.042039    1074 dns.go:157] "Nameserver limits exceeded" err="Nameserver limits were exceeded, some nameservers have
been omitted, the applied nameserver line is: <IP_ADDRESS> <IP_ADDRESS> <IP_ADDRESS>"
...
(repeating messages)
...
Apr 11 03:57:01 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T03:57:01.251Z","caller":"mvcc/index.go:214","msg":"compact tree index","revision":2665048}
Apr 11 03:57:01 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T03:57:01.282Z","caller":"mvcc/kvstore_compaction.go:57","msg":"finished scheduled compaction","com
pact-revision":2665048,"took":"30.401283ms"}
Apr 11 04:02:09 k3sh2m1-vms01 k3s[1074]: E0411 04:02:09.041495    1074 dns.go:157] "Nameserver limits exceeded" err="Nameserver limits were exceeded, some nameservers have
been omitted, the applied nameserver line is: <IP_ADDRESS> <IP_ADDRESS> <IP_ADDRESS>"
...
Apr 11 04:06:49 k3sh2m1-vms01 k3s[1074]: time="2023-04-11T04:06:49Z" level=info msg="Updating TLS secret for kube-system/k3s-serving (count: 16): map[listener.cattle.io/cn-
<IP_ADDRESS>:<IP_ADDRESS> listener.cattle.io/cn-<IP_ADDRESS>:<IP_ADDRESS> listener.cattle.io/cn-<IP_ADDRESS>:<IP_ADDRESS> listener.cattle.io/cn-<IP_ADDRESS>:<IP_ADDRESS> listener
.cattle.io/cn-<IP_ADDRESS>:<IP_ADDRESS> listener.cattle.io/cn-<IP_ADDRESS>:<IP_ADDRESS> listener.cattle.io/cn-__1-f16284:::1 listener.cattle.io/cn-api-server.cluste
r.cdyhamc.com:api-server.cluster.cdyhamc.com listener.cattle.io/cn-k3sh2m1-vms01:k3sh2m1-vms01 listener.cattle.io/cn-k3sh3m1-vms02:k3sh3m1-vms02 listener.cattle.io/cn-k3sh4
m1-vms03:k3sh4m1-vms03 listener.cattle.io/cn-kubernetes:kubernetes listener.cattle.io/cn-kubernetes.default:kubernetes.default listener.cattle.io/cn-kubernetes.default.svc:
kubernetes.default.svc listener.cattle.io/cn-kubernetes.default.svc.cluster.local:kubernetes.default.svc.cluster.local listener.cattle.io/cn-localhost:localhost listener.ca
ttle.io/fingerprint:SHA1=FFFD9F85B91033C360DEF6646DCC3D89D1AA713F]"
...
Apr 11 04:07:01 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T04:07:01.265Z","caller":"mvcc/index.go:214","msg":"compact tree index","revision":2668404}
Apr 11 04:07:01 k3sh2m1-vms01 k3s[1074]: {"level":"info","ts":"2023-04-11T04:07:01.298Z","caller":"mvcc/kvstore_compaction.go:57","msg":"finished scheduled compaction","com
pact-revision":2668404,"took":"32.038835ms"}
...

Many thanks @brandond, it worked for me by removing "--advertise-address" and "--bind-address" arguments from all nodes' install script.
However, forgive me for asking questions here, I really need to be clear that, is it suitable/best solution for production deployment, what' the difference?
Especially that I have metal 'top of rack router and load-balancer', and I'm going to distribute data-traffic to service/pod ip directly via lb. With the capability of calico-bgp mode,  service/pod ip in cluster will be routable from host natwork.

You didn't get into why you're trying to put the apiserver behind an external load balancer, so I can't say whether you'd consider your current configuration production ready. I will say that the 'fixed registration address' provided by your external load-balancer is only used during node join; after that everything is handled directly between cluster members so what you have is probably overkill. A DNS alias that points at active cluster members would work just as well and be less work to maintain.

With the capability of calico-bgp mode, service/pod ip in cluster will be routable from host natwork.

This is generally considered an anti-pattern; you should use ingress and/or loadbalancer services to get traffic in to pods, as opposed to just exposing everything directly to the rest of your network. If you are going to erode the security provided by having pods on a network overlay, you should definitely make sure that you are deploying network policy rules to lock down access to pods.

That make sense to me. Thank you so much.

For network reason, i didn't see this comment when closing the issue. so I'm putting a new comment as reply.

This is generally considered an anti-pattern; you should use ingress and/or loadbalancer services to get traffic in to pods, as opposed to just exposing everything directly to the rest of your network.

Got that. I'm doing this for some reason, here is my scenario:
All my physical servers are isolated, and accessing that network from outter zone is blocked, except pass through lb and  firewalls.
From the cluster side, I'm going to deploy 'istio ingress-gateway' service. So only this service is exposed via lb, the routine to upstream service/pods will be handled by istio. As for lb to api-servers, It's completely internal use only. The HAProxy is an intermediate solution, and I 'm going to create other rules on the lb but not expose them to outter zone.
Your kind advice is much appreciated.

For network reason, i didn't see this comment when closing the issue. so I'm putting a new comment as reply.

This is generally considered an anti-pattern; you should use ingress and/or loadbalancer services to get traffic in to pods, as opposed to just exposing everything directly to the rest of your network.

Got that. I'm doing this for some reason, here is my scenario:
All my physical servers are isolated, and accessing that network from outter zone is blocked, except pass through lb and  firewalls.
From the cluster side, I'm going to deploy 'istio ingress-gateway' service. So only this service is exposed via lb, the routine to upstream service/pods will be handled by istio. As for lb to api-servers, It's completely internal use only. The HAProxy is an intermediate solution, and I 'm going to create other rules on the lb but not expose them to outter zone.
Your kind advice is much appreciated.


How to dotnet pack a specific framework only

How to dotnet pack a specific framework only
My .csproj has multiple:
<TargetFrameworks>net45;netstandard2.0</TargetFrameworks>
Building on a linux machine, I would like to:
dotnet pack -f netstandard2.0 -c Release
The -f (framework) does not exist. A dotnet pack fails because of the lack of a targeting pack for net45.
I hope this is a simple oversight on my part.

@livarcocc @dsplaisted @KathleenDollard can you help with this question? Can he achieve that by using the MSBuild switches? Thanks.

@rohit21agrawal who owns pack.

Pass in /p:TargetFrameworks=net45 from command line .

Thanks @rohit21agrawal. I've created a new PR to add this as an example. Please take a look.


Fix Cargo.lock file that gets generated after build

Running cargo build on the main branch generates a diff in Cargo.lock file.
Signed-off-by: Harshal Patil<EMAIL_ADDRESS>

@saschagrunert Thanks for your review 🙏

Codecov Report

Merging #734 (8722bd9) into main (673d2e1) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main     #734   +/-   ##
=======================================
  Coverage   72.52%   72.52%
=======================================
  Files          86       86
  Lines       11740    11740
=======================================
  Hits         8515     8515
  Misses       3225     3225


chore(travis): only update npm in Node<5 and only to npm@3

Fixes #2677

The AppVeyor failures don't seem related to my PR.


Extend no_std support

Feedback very much welcome as I haven't studied this library in close enough detail to see the full consequences of this PR.
I've changed a few things:

Switched from std to core and alloc in macros, fixing #106.
Added atomic-polyfill as an optional feature to support targets which don't have full atomic CAS support.
As lazy_static does not support atomic-polyfill, I changed over to using once_cell::race::OnceBox instead. The downside to using a OnceBox is that multiple threads can start generating the same cell, but in the end only the first completed cell is used. The upside is that it requires simpler synchronization primitives.
Added a default-enabled feature atomic which enables creating worlds with the atomically increasing ID that is currently used in World::new() and World::default(). When disabled the function World::new_with_id(id: u64) must be used instead.

1 and 2 feel pretty natural, but 3 and 4 definitely requires some scrutiny to ensure I didn't mess something up. The tests run green and the Bundle derive macro seems to work as well.

Makes perfect sense, I've adjusted accordingly. I also got rid of the atomic feature and just used spin instead as it's more clear.

Makes perfect sense, I've adjusted accordingly. I also got rid of the atomic feature and just used spin instead as it's more clear.

But do we need to spin-based at all if we have either core::sync::atomic or atomic-polyfill? Does atomic-polyfill not support MIPS and PPC?
Or conversely, could we use only spin because it provides lazy-initialization as well? Does that require atomic-polyfill or something similar?
I think having both present is a significant increase in complexity that should be avoid if at all possible.

Thanks for working on this (and thanks @adamreichold for helping with the review)! If you like, you could split out the more straightforward parts of this into a separate PR that we can merge right away.

The downside to using a OnceBox is that multiple threads can start generating the same cell

This is completely fine. It will rarely occur in practice and do no harm when it does.

But do we need to spin-based at all if we have either core::sync::atomic or atomic-polyfill? Does atomic-polyfill not support MIPS and PPC?

I don't think so. Any architectures not listed in the README will just forward to the core::atomic implementation.

Or conversely, could we use only spin because it provides lazy-initialization as well? Does that require atomic-polyfill or something similar?

spin relies on compare_exchange_weak or another polyfill called portable-atomics, which does not currently support the platform I'm on (thumbv4t). Both of them do essentially the same thing, though I find atomic-polyfill easier to extend to your own platform without first having to get it merged upstream.

I think having both present is a significant increase in complexity that should be avoid if at all possible.

Another option would be to optionally outsource the ID generation using a macro (to set a global ID generator), but that doesn't sound much better to me.

Another option would be to optionally outsource the ID generation using a macro (to set a global ID generator), but that doesn't sound much better to me.

Maybe we should approach this differently: The world ID are used only to match up prepared queries. So maybe we should make the ID field and all types related to prepared queries dependent on the presence of atomics or (either atomic-polyfill or spin but not both)?
That would mean that platforms not supporting the atomics fallback of our choice do not have access to prepared queries, but that appears to be a rather graceful degradation of functionality to me since everything else should continue to work.

That would mean that platforms not supporting the atomics fallback of our choice do not have access to prepared queries, but that appears to be a rather graceful degradation of functionality to me since everything else should continue to work.

Seeing as MIPS/PPC support using spin is already merged, I suppose that'd be the fallback of choice. I guess it's an ok compromise, but it doesn't sit completely well with me seeing as it could work.
But ultimately it's up to you, if that's the way you want to go I could try implementing it.

Restricting prepared query availability makes sense to me. It prevents limitations on one platform from affecting everyone else, I'm not sure anyone's even using those APIs, and we can always restore them in the future without breaking anything.

As atomics are used elsewhere in hecs, keeping only spin was not an option for supporting both MIPS/PPC and non-CAS targets. I've instead kept only atomic-polyfill and put prepared queries behind a feature which is enabled by default.
If you're happy with the outline I can tidy up the query::prepared module, move it into its own folder and so on, but is this roughly what you intended?

As atomics are used elsewhere in hecs, keeping only spin was not an option for supporting both MIPS/PPC and non-CAS targets. I've instead kept only atomic-polyfill and put prepared queries behind a feature which is enabled by default.

If atomics are used elsewhere and hence atomic-polyfill is a mandatory dependency, we should be able to keep prepared queries without a feature gate as well as the world ID does not require a mutex but just as well be generated using an atomic counter. (We probably just need to reduce it to AtomicUsize and make sure to check for overflow to maximise portability?)
So for leading you down the wrong path here. I completely forgot that borrow checking is thread safe and uses atomics as well.

If atomics are used elsewhere and hence atomic-polyfill is a mandatory dependency, we should be able to keep prepared queries without a feature gate as well as the world ID does not require a mutex but just as well be generated using an atomic counter. (We probably just need to reduce it to AtomicUsize and make sure to check for overflow to maximise portability?)

Just to double check, do you then want me to rip out the spin feature? Because that would remove MIPS/PPC support, which seems unfortunate. Or should I just roll back the last commit?

Just to double check, do you then want me to rip out the spin feature? Because that would remove MIPS/PPC support, which seems unfortunate. Or should I just roll back the last commit?

I was suggesting to remove spin but to keep MIPS/PPC support by using AtomicUsize instead of AtomicU64 to generate the world ID. This should work as MIPS/PPC would be without AtomicBorrow otherwise.

Hi @Jinxit. I'm a maintainer of portable-atomics crate.

spin relies on compare_exchange_weak or another polyfill called portable-atomics, which does not currently support the platform I'm on (thumbv4t).

I would love to add thumbv4t support to portable-atomics.
I created a list of approaches I think will work and implemented one of them... Which approach do you prefer? (off-topic for this PR, so I'd appreciate if you could comment in https://github.com/taiki-e/portable-atomic/issues/26)

remove MIPS/PPC support

FWIW, portable-atomics supports 64-bit atomics on these targets using lock-based fallback; crates such as metrics are using portable-atomics to support these targets.

Gonna revisit this with the new changes to portable-atomics whenever I have time.

Any progress on this PR? I'd love to see the macro crate can actually support no_std.

I don't think this work is active right now. If you're interested in working on this yourself, the portion that fixes #106 could probably be split out and merged pretty quickly, independent of the more difficult stuff. Maybe now that OnceCell is in core the rest of this is simpler too.

Yeah I'd love to take over the job and at least fix #106

Please feel free! I'd recommend making a new PR.


Build fails

build.py fails at  generator_tflite = generator_converter.convert()
Error disappears adding:
generator_converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
    tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]

But that probably causes testlite.py to fail.
check #2 comments
Full output of build.py:
Building
2021-04-02 00:52:54.522699: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-02 00:52:54.522905: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-02 00:53:02.259372: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
2021-04-02 00:53:54.463387: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-04-02 00:53:54.463405: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-04-02 00:53:54.463412: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:325] Ignored change_concat_input_ranges.
2021-04-02 00:53:54.465174: I tensorflow/cc/saved_model/reader.cc:32] Reading SavedModel from: saved_models/vox/kp_detector
2021-04-02 00:53:54.490987: I tensorflow/cc/saved_model/reader.cc:55] Reading meta graph with tags { serve }
2021-04-02 00:53:54.491013: I tensorflow/cc/saved_model/reader.cc:93] Reading SavedModel debug info (if present) from: saved_models/vox/kp_detector
2021-04-02 00:53:54.586574: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2021-04-02 00:53:54.607108: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.
2021-04-02 00:53:54.838562: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: saved_models/vox/kp_detector
2021-04-02 00:53:54.937545: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 472375 microseconds.
2021-04-02 00:53:55.382793: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:194] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2021-04-02 00:54:04.149392: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-04-02 00:54:04.149406: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-04-02 00:54:04.149410: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:325] Ignored change_concat_input_ranges.
2021-04-02 00:54:04.149530: I tensorflow/cc/saved_model/reader.cc:32] Reading SavedModel from: saved_models/vox/generator
2021-04-02 00:54:04.201645: I tensorflow/cc/saved_model/reader.cc:55] Reading meta graph with tags { serve }
2021-04-02 00:54:04.201668: I tensorflow/cc/saved_model/reader.cc:93] Reading SavedModel debug info (if present) from: saved_models/vox/generator
2021-04-02 00:54:04.473078: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.
2021-04-02 00:54:05.033412: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: saved_models/vox/generator
2021-04-02 00:54:05.263320: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 1113790 microseconds.
loc(callsite(callsite("model_1/kp_to_gaussian/pow@__inference___call___19628" at "StatefulPartitionedCall@__inference_signature_wrapper_19975") at "StatefulPartitionedCall")): error: 'tf.Pow' op is neither a custom op nor a flex op
loc(callsite(callsite("model_1/kp_to_gaussian_1/pow@__inference___call___19628" at "StatefulPartitionedCall@__inference_signature_wrapper_19975") at "StatefulPartitionedCall")): error: 'tf.Pow' op is neither a custom op nor a flex op
error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Pow {device = ""}
Traceback (most recent call last):
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 210, in toco_convert_protos
    model_str = wrap_toco.wrapped_toco_convert(model_flags_str,
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/wrap_toco.py", line 32, in wrapped_toco_convert
    return _pywrap_toco_api.TocoConvert(
Exception: <unknown>:0: error: loc(callsite(callsite("model_1/kp_to_gaussian/pow@__inference___call___19628" at "StatefulPartitionedCall@__inference_signature_wrapper_19975") at "StatefulPartitionedCall")): 'tf.Pow' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: loc(callsite(callsite("model_1/kp_to_gaussian_1/pow@__inference___call___19628" at "StatefulPartitionedCall@__inference_signature_wrapper_19975") at "StatefulPartitionedCall")): 'tf.Pow' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Pow {device = ""}

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/SERG/development/first-order-model/first-order-model-tf-fork/build.py", line 60, in <module>
    build(checkpoint_path,config_path,output_name)
  File "/Users/SERG/development/first-order-model/first-order-model-tf-fork/build.py", line 40, in build
    generator_tflite = generator_converter.convert()
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/lite.py", line 739, in convert
    result = _convert_saved_model(**converter_kwargs)
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 632, in convert_saved_model
    data = toco_convert_protos(
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 216, in toco_convert_protos
    raise ConverterError(str(e))
tensorflow.lite.python.convert.ConverterError: <unknown>:0: error: loc(callsite(callsite("model_1/kp_to_gaussian/pow@__inference___call___19628" at "StatefulPartitionedCall@__inference_signature_wrapper_19975") at "StatefulPartitionedCall")): 'tf.Pow' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: loc(callsite(callsite("model_1/kp_to_gaussian_1/pow@__inference___call___19628" at "StatefulPartitionedCall@__inference_signature_wrapper_19975") at "StatefulPartitionedCall")): 'tf.Pow' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Pow {device = ""}

Looks like I went overboard in removing reshape hacks I didn't think were necessary. Fixed that stuff now. Can you try again and confirm it works?

Build output:
Building
2021-04-02 12:56:08.257659: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-02 12:56:08.257942: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-02 12:56:19.063495: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
2021-04-02 12:56:30.622428: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-04-02 12:56:30.622448: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-04-02 12:56:30.622453: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:325] Ignored change_concat_input_ranges.
2021-04-02 12:56:30.623858: I tensorflow/cc/saved_model/reader.cc:32] Reading SavedModel from: saved_models/vox/kp_detector
2021-04-02 12:56:30.658746: I tensorflow/cc/saved_model/reader.cc:55] Reading meta graph with tags { serve }
2021-04-02 12:56:30.658776: I tensorflow/cc/saved_model/reader.cc:93] Reading SavedModel debug info (if present) from: saved_models/vox/kp_detector
2021-04-02 12:56:30.779738: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2021-04-02 12:56:30.807684: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.
2021-04-02 12:56:31.103520: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: saved_models/vox/kp_detector
2021-04-02 12:56:31.241560: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 617703 microseconds.
2021-04-02 12:56:31.809943: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:194] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_1@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): error: 'tf.StridedSlice' op is neither a custom op nor a flex op
loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_3@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): error: 'tf.StridedSlice' op is neither a custom op nor a flex op
loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_5@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): error: 'tf.StridedSlice' op is neither a custom op nor a flex op
error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.StridedSlice {begin_mask = 0 : i64, device = "", ellipsis_mask = 1 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 2 : i64}
Traceback (most recent call last):
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 210, in toco_convert_protos
    model_str = wrap_toco.wrapped_toco_convert(model_flags_str,
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/wrap_toco.py", line 32, in wrapped_toco_convert
    return _pywrap_toco_api.TocoConvert(
Exception: <unknown>:0: error: loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_1@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): 'tf.StridedSlice' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_3@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): 'tf.StridedSlice' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_5@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): 'tf.StridedSlice' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.StridedSlice {begin_mask = 0 : i64, device = "", ellipsis_mask = 1 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 2 : i64}

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/SERG/development/first-order-model/first-order-model-tf-main-d/build.py", line 54, in <module>
    build(checkpoint_path,config_path,output_name)
  File "/Users/SERG/development/first-order-model/first-order-model-tf-main-d/build.py", line 27, in build
    kp_detector_tflite = kp_detector_converter.convert()
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/lite.py", line 739, in convert
    result = _convert_saved_model(**converter_kwargs)
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 632, in convert_saved_model
    data = toco_convert_protos(
  File "/Users/SERG/opt/anaconda3/envs/fom_tf/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 216, in toco_convert_protos
    raise ConverterError(str(e))
tensorflow.lite.python.convert.ConverterError: <unknown>:0: error: loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_1@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): 'tf.StridedSlice' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_3@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): 'tf.StridedSlice' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: loc(callsite(callsite("model/anti_alias_interpolation2d/strided_slice_5@__inference___call___6195" at "StatefulPartitionedCall@__inference_signature_wrapper_6330") at "StatefulPartitionedCall")): 'tf.StridedSlice' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.StridedSlice {begin_mask = 0 : i64, device = "", ellipsis_mask = 1 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 2 : i64}

Looks like it's seeing TF::StridedSliceOp in AntiAliasInterpolation2d because of the ellipsis in [...,i] indexing for k and im in the call., when it's supposed to see the expression as normal slicing, with keeping everything from all dims except the last one, from which it should only keep values in ith channel. This is still weird, as it's building without problems on my end. I added a workaround. can you try again?

kp_detector_converter is fixed
generator_converter still needs tf.lite.OpsSet.SELECT_TF_OPS

I've removed strided slicing from GridSample too. Let's hope no other tf::-only version-dependent op/dtype/shape combinations pop out. Can you try again?

Yeah, give me a moment

Nice! Everything works without tf.lite.OpsSet.SELECT_TF_OPS

Nice! If you encounter any other issues please ping me again.

@lshug @HashedViking  I am still getting the same error
`During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "build.py", line 85, in
build(checkpoint_path, config_path, output_name, parser.module, parser.predictiononly, parser.tfjs, parser.jsquantize)
File "build.py", line 51, in build
generator_tflite = generator_converter.convert()
File "/home/faraz/.local/lib/python3.6/site-packages/tensorflow/lite/python/lite.py", line 739, in convert
result = _convert_saved_model(**converter_kwargs)
File "/home/faraz/.local/lib/python3.6/site-packages/tensorflow/lite/python/convert.py", line 637, in convert_saved_model
enable_mlir_converter=True)
File "/home/faraz/.local/lib/python3.6/site-packages/tensorflow/lite/python/convert.py", line 216, in toco_convert_protos
raise ConverterError(str(e))
on  line         generator_tflite = generator_converter.convert()
`
Any thoughts?


Functions on arrays that only rely on read-only access

What is the difference and why is there a difference between a (non-mut) reference to a OwnedRepr (owned array) and a ViewRepr?
Both are read-only, so what's the difference? The functions available for a OwnedRepr is a strict subset of those available for ViewRepr's.
How to make function that works for both without copying?
Say you have a function some_fn that works read-only on (refs to) 1D arrays, i.e. vectors.
How can you construct that function so that you can, without copying,

Pass a (ref to a) regular vector, i.e. some_fn(&arr1(&[0, 1]))
Pass rows of a 2D vector, i.e.

for some_1d_arr in some_2d_arr.outer_iter() {
    some_fn(&some_1d_arr);
}

If you make a function that takes &Array, you can't pass things from outer_iter, which are ArrayViews.
If you make a function that takes ArrayView or &ArrayView you can't pass (refs to regular arrays).
Is the only solution to make the function take views only, and convert owned arrays to views each time you call it?
Thank you.

I got my answer on the rust gitter chat. It is possible to do e.g.
fn some_fn<S, E,, D>(arr: ArrayBase<S, D>)
where
    S: Data<Elem = E>,
    D: Dimension,
{
    ...
}


Regenerates the BSD3 License so GitHub recognises it

Review by @emilio? (Just pulling the top name from recent git history 😄)


[documentation] improve a bit registration/login.html

Hello,
In the example template here https://docs.djangoproject.com/en/1.8/topics/auth/default/#django.contrib.auth.views.login two extremely common patterns are missing:

inform the user that he has been redirected to this page because he doesn't have the rights to see it
link to password reset workflow

I can only see very few situations where those two features aren't wanted, thus, it seems a reasonable idea to include them in the reference example.
This pull request is split into two commits, this allow cherry-picking if only a part of it is accepted.
Kind regards,

merged in 1369da676f3bd83d694ca5f00c5012f16b007acb, thanks.


feat: auto accept proofs

This adds automatically accepting of proof requests. This functions the same as #336 to stay as consistent as possible, yet they work completely seperate to eachother.
It allows for an agent- and recordwide configuration where the record configuration has priority.
Three states are allowed to be set:

always: accept any proof request/proposal no matter what the content might be.
contentApproved: accept any proof request/proposal as long as none of the content changes and it has to be accepted manually at least once.
never (DEFAULT) Does not automate anything and will just behave the same as before these changes.

Note: I am not sure whether we want to mention this as a feature in the README, so if there is any problem with this I will remove it.

Codecov Report

Merging #367 (bc5aa3e) into main (ee81d01) will increase coverage by 0.16%.
The diff coverage is 90.52%.

@@            Coverage Diff             @@
##             main     #367      +/-   ##
==========================================
+ Coverage   88.21%   88.38%   +0.16%
==========================================
  Files         220      221       +1
  Lines        4039     4123      +84
  Branches      475      495      +20
==========================================
+ Hits         3563     3644      +81
- Misses        476      479       +3

Impacted Files
Coverage Δ

...ules/proofs/handlers/ProposePresentationHandler.ts
82.60% <77.77%> (-17.40%)
:arrow_down:

...ules/proofs/handlers/RequestPresentationHandler.ts
88.00% <85.00%> (-12.00%)
:arrow_down:

src/modules/proofs/handlers/PresentationHandler.ts
89.47% <85.71%> (-10.53%)
:arrow_down:

src/agent/AgentConfig.ts
100.00% <100.00%> (ø)

src/modules/proofs/ProofResponseCoordinator.ts
100.00% <100.00%> (ø)

src/modules/proofs/ProofsModule.ts
88.88% <100.00%> (+0.57%)
:arrow_up:

src/modules/proofs/repository/ProofRecord.ts
92.30% <100.00%> (+2.83%)
:arrow_up:

src/types.ts
100.00% <100.00%> (ø)

src/storage/IndyStorageService.ts
87.50% <0.00%> (+2.50%)
:arrow_up:

... and 3 more

Continue to review full report at Codecov.

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Tests on Github are failing while local tests work. Will resolve this asap.

If I recall correctly, we're waiting for additional followup to move the relevant validation changes into the service regardless of the automation configuration? Or am I remembering incorrectly here?

My understanding was first to merge this. And in a seprate PR abstract the functionality, add documentation stating the types of autoAccept.  I think for now this will suffice, but I would not mind to change that stuff and merge it later.

Merging. Additional improvements can be made in follow-up PRs


Search: regex doesn't support character classes (e.g. \s, \b, \W, etc.)

When entering a regex into the search page, I expected to be able to use character classes (e.g. \s, \b, \W, etc.). However, when I tried, the results were identical to searching a non-regex with the \ character stripped.
Example:

non-regex
regex

Search for sales
Search for regex sale\s
Results are, erroneously, identical

Search for saleb
Search for regex sale\b
Results are, erroneously, identical

Duplicate of #258


feat: watch for on-chain role changes

This PR adds a feature to automatically start and stop different protocols using the on-chain data based on the current restaker roles.
Closes #95

Opened a few tasks for the missing systems:
#116

That's exactly what these changes do. It will start a task to listen to finality notifications, on which we query the active roles for the current restaker, then we do a diff between the current and new roles to see what has changed (added, removed) then we start or stop services accordingly.
And there is no need for additional channels, for each task we already have an abort handle that will be used to abort the service if it has been removed from the roles.

We should also check there's no active jobs too just for extra safety.

That's exactly what these changes do. It will start a task to listen to finality notifications, on which we query the active roles for the current restaker, then we do a diff between the current and new roles to see what has changed (added, removed) then we start or stop services accordingly.
And there is no need for additional channels, for each task we already have an abort handle that will be used to abort the service if it has been removed from the roles.

In my experience, tokio's abort handle is not reliable. I have aborted tasks, yet they keep running. That's why I like using oneshot channels selected with the async function to make them cancellable.

We should also check there's no active jobs too for extra safety for each role in question.

Yes. On shutdown, we should also get the WorkManager and manually shutdown all the tasks.

What are the blocker for this PR?


fix(obstacle_stop_planner): skip objects when not using predicted objects

object_ptr is required only if using predicted objects. Otherwise there it gets stuck.
Description

Tests performed

Not applicable.
Effects on system behavior

Not applicable.
Pre-review checklist for the PR author
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[x] I've confirmed the contribution guidelines.
[x] The PR follows the pull request guidelines.

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The PR reviewers must check the checkboxes below before approval.

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The PR author must check the checkboxes below before merging.

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After all checkboxes are checked, anyone who has write access can merge the PR.

@satoshi-ota Hello, will this PR be automatically merged or is furthur work needed? The failed CI steps do not seem to be relevant to the changes here.

Hi @alireza-moayyedi

Hello, will this PR be automatically merged or is further work needed?

No. I'll merge this PR soon. Thanks :+1:


'package:flutter/src/widgets/framework.dart': Failed assertion: line 6207 pos 12: 'child == _child': is not true.

Either the assertion indicates an error in the framework itself, or we should provide substantially more information in this error message to help you determine and fix the underlying cause.
In either case, please report this assertion by filing a bug on GitHub:
  https://github.com/flutter/flutter/issues/new?template=2_bug.md

The relevant error-causing widget was:
 Scrollbar Scrollbar:file:///Users//lib/.../view.dart:95:32
When the exception was thrown, this was the stack:
#2      SingleChildRenderObjectElement.forgetChild (package:flutter/src/widgets/framework.dart:6207:12)
#3      Element._retakeInactiveElement (package:flutter/src/widgets/framework.dart:3752:14)
...     Normal element mounting (10 frames)
#13     Element.inflateWidget (package:flutter/src/widgets/framework.dart:3817:16)
#14     Element.updateChild (package:flutter/src/widgets/framework.dart:3545:20)
#15     ComponentElement.performRebuild (package:flutter/src/widgets/framework.dart:4832:16)
#16     StatefulElement.performRebuild (package:flutter/src/widgets/framework.dart:4977:11)
#17     Element.rebuild (package:flutter/src/widgets/framework.dart:4529:5)
#18     StatefulElement.update (package:flutter/src/widgets/framework.dart:5009:5)
#19     Element.updateChild (package:flutter/src/widgets/framework.dart:3530:15)
#20     SingleChildRenderObjectElement.update (package:flutter/src/widgets/framework.dart:6222:14)
#21     Element.updateChild (package:flutter/src/widgets/framework.dart:3530:15)
#22     ComponentElement.performRebuild (package:flutter/src/widgets/framework.dart:4832:16)
#23     Element.rebuild (package:flutter/src/widgets/framework.dart:4529:5)
#24     ProxyElement.update (package:flutter/src/widgets/framework.dart:5154:5)
#25     Element.updateChild (package:flutter/src/widgets/framework.dart:3530:15)
#26     ComponentElement.performRebuild (package:flutter/src/widgets/framework.dart:4832:16)
#27     Element.rebuild (package:flutter/src/widgets/framework.dart:4529:5)
#28     ProxyElement.update (package:flutter/src/widgets/framework.dart:5154:5)
#29     Element.updateChild (package:flutter/src/widgets/framework.dart:3530:15)
#30     ComponentElement.performRebuild (package:flutter/src/widgets/framework.dart:4832:16)
#31     StatefulElement.performRebuild (package:flutter/src/widgets/framework.dart:4977:11)
#32     Element.rebuild (package:flutter/src/widgets/framework.dart:4529:5)
#33     StatefulElement.update (package:flutter/src/widgets/framework.dart:5009:5)
#34     Element.updateChild (package:flutter/src/widgets/framework.dart:3530:15)
#35     ComponentElement.performRebuild (package:flutter/src/widgets/framework.dart:4832:16)
#36     Element.rebuild (package:flutter/src/widgets/framework.dart:4529:5)
#37     StatelessElement.update (package:flutter/src/widgets/framework.dart:4883:5)
#38     Element.updateChild (package:flutter/src/widgets/framework.dart:3530:15)
#39     ComponentElement.performRebuild (package:flutter/src/widgets/framework.dart:4832:16)
#40     StatefulElement.performRebuild (package:flutter/src/widgets/framework.dart:4977:11)
#41     Element.rebuild (package:flutter/src/widgets/framework.dart:4529:5)
#42     BuildOwner.buildScope (package:flutter/src/widgets/framework.dart:2659:19)
#43     WidgetsBinding.drawFrame (package:flutter/src/widgets/binding.dart:891:21)
#44     RendererBinding._handlePersistentFrameCallback (package:flutter/src/rendering/binding.dart:370:5)
#45     SchedulerBinding._invokeFrameCallback (package:flutter/src/scheduler/binding.dart:1146:15)
#46     SchedulerBinding.handleDrawFrame (package:flutter/src/scheduler/binding.dart:1083:9)
#47     SchedulerBinding._handleDrawFrame (package:flutter/src/scheduler/binding.dart:997:5)
#51     _invoke (dart:ui/hooks.dart:151:10)
#52     PlatformDispatcher._drawFrame (dart:ui/platform_dispatcher.dart:308:5)
#53     _drawFrame (dart:ui/hooks.dart:115:31)
(elided 5 frames from class _AssertionError and dart:async)

======== Exception caught by widgets library =======================================================
The following assertion was thrown while finalizing the widget tree:
Duplicate GlobalKey detected in widget tree.

The following GlobalKey was specified multiple times in the widget tree. This will lead to parts of the widget tree being truncated unexpectedly, because the second time a key is seen, the previous instance is moved to the new location. The key was:
- [GlobalKey#64ef3]
This was determined by noticing that after the widget with the above global key was moved out of its previous parent, that previous parent never updated during this frame, meaning that it either did not update at all or updated before the widget was moved, in either case implying that it still thinks that it should have a child with that global key.
The specific parent that did not update after having one or more children forcibly removed due to GlobalKey reparenting is:
- MouseRegion(listeners: [exit, hover], cursor: defer, renderObject: RenderMouseRegion#97edf NEEDS-LAYOUT NEEDS-PAINT NEEDS-COMPOSITING-BITS-UPDATE DETACHED)
A GlobalKey can only be specified on one widget at a time in the widget tree.
When the exception was thrown, this was the stack:
#0      BuildOwner.finalizeTree.<anonymous closure> (package:flutter/src/widgets/framework.dart:2991:15)
#1      BuildOwner.finalizeTree (package:flutter/src/widgets/framework.dart:3016:8)
#2      WidgetsBinding.drawFrame (package:flutter/src/widgets/binding.dart:893:19)
#3      RendererBinding._handlePersistentFrameCallback (package:flutter/src/rendering/binding.dart:370:5)
#4      SchedulerBinding._invokeFrameCallback (package:flutter/src/scheduler/binding.dart:1146:15)
#5      SchedulerBinding.handleDrawFrame (package:flutter/src/scheduler/binding.dart:1083:9)
#6      SchedulerBinding._handleDrawFrame (package:flutter/src/scheduler/binding.dart:997:5)
#10     _invoke (dart:ui/hooks.dart:151:10)
#11     PlatformDispatcher._drawFrame (dart:ui/platform_dispatcher.dart:308:5)
#12     _drawFrame (dart:ui/hooks.dart:115:31)
(elided 3 frames from dart:async)
====================================================================================================

@duncan2235
When opening an issue, please try to provide all or some of the information indicated in the template.

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1. ...
2. ...
3. ...

Please tell us which target platform(s) the problem occurs (Android / iOS / Web / macOS / Linux / Windows)
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Target OS version/browser:
Devices:

Run your application with `flutter run --verbose` and attach all the
log output below between the lines with the backticks. If there is an
exception, please see if the error message includes enough information
to explain how to solve the issue.

Run `flutter analyze` and attach any output of that command below.
If there are any analysis errors, try resolving them before filing this issue.

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Closing this issue, since no information has been provided.

Duplicate GlobalKey detected in widget tree.
The following GlobalKey was specified multiple times in the widget tree. This will lead to parts of the widget tree being truncated unexpectedly, because the second time a key is seen, the previous instance is moved to the new location. The key was:

[GlobalKey#64ef3]
This was determined by noticing that after the widget with the above global key was moved out of its previous parent, that previous parent never updated during this frame, meaning that it either did not update at all or updated before the widget was moved, in either case implying that it still thinks that it should have a child with that global key.
The specific parent that did not update after having one or more children forcibly removed due to GlobalKey reparenting is:
MouseRegion(listeners: [exit, hover], cursor: defer, renderObject: RenderMouseRegion#97edf NEEDS-LAYOUT NEEDS-PAINT NEEDS-COMPOSITING-BITS-UPDATE DETACHED)
A GlobalKey can only be specified on one widget at a time in the widget tree.
When the exception was thrown, this was the stack:

This error message should give indication what could be causing the exception. This may be happening due to the way code is implemented and usage of GlobalKey. So I suggest you to look on Stackoverflow for further resolution.


New package: GiulioSorrentino.BackgroundsOtto version 1.0

[ ] Have you signed the Contributor License Agreement?
[ ] Have you checked that there aren't other open pull requests for the same manifest update/change?
[ ] This PR only modifies one (1) manifest
[ ] Have you validated your manifest locally with winget validate --manifest <path>?
[ ] Have you tested your manifest locally with winget install --manifest <path>?
[ ] Does your manifest conform to the 1.4 schema?

Note: <path> is the name of the directory containing the manifest you're submitting.

Microsoft Reviewers: codeflow:open?pullrequest=https://github.com/microsoft/winget-pkgs/pull/110944&drop=dogfoodAlpha

http://numerone.altervista.org/Downloads/numeronesoft-backgrounds-otto.exe


Enhancement: Add "Feature:" title in Gherkin feature files as features in results

I would request the following enhancement:
When using cucumber preprocessor ("@badeball/cypress-cucumber-preprocessor") with cypress, I think it would be relevant for the feature title to be added as feature in allure results:
"Feature: A feature I want to test"
Workaround today:
In order to define a feature in the file contents as a feature, you have to use the "@feature("")" tag in the feature file.
If you're using badeball as preprocessor, it will work with the tag, but it will be ugly because whitespaces aren't allowed in tags:
"@feature("A_feature_I_want_to_test")"
There could be multiple "Features" in a feature file also, i.e. each time the "Feature:" title is used.

Hello, @Alvis-pixel
I would need to think about that whether we need to add it into plugin
but for now you can add this into your setup file (be aware it will not add features on before hooks failure :
beforeEach(function () {
  cy.readFile(Cypress.spec.absolute, { log: false }).then(text => {
    const match = text.match(/^Feature: ([^\n]*)$/m);

    if (match && match[1]) {
      cy.allure().feature(match[1]);
    }
  });
});

Update: Feature is a suite name, so it can be simplified:
Cypress.Allure.on('test:started', test => {
  if (test.parent?.title) {
    Cypress.Allure.feature(feature);
  }
});

with this it is better add this on you side which you can control better
Closing it for now.

Update: Feature is a suite name, so it can be simplified:
Cypress.Allure.on('test:started', test => {
  if (test.parent?.title) {
    Cypress.Allure.feature(feature);
  }
});

This one didn't work for me, or I didn't understand. Add in a setup spec, or cypress.config.ts? Also, "Cannot find name 'feature'"

Ops, sorry
this should go to setup file:
Cypress.Allure.on('test:started', test => {
  if (test.parent?.title) {
    Cypress.Allure.feature(test.parent?.title);
  }
});