text:
ImportError: No module named caffe

Hi,
I followed the instruction for building Digits on Ubuntu 14.04.2 LTS here:
https://github.com/NVIDIA/DIGITS/blob/master/docs/InstallCaffe.md
$ conda -version
ubuntu@ip-10-0-1-195:~/caffe$ conda -V
conda 3.16.0
ubuntu@ip-10-0-1-195:~/caffe$ anaconda -V
anaconda Command line client (version 1.0.1)
ubuntu@ip-10-0-1-195:~/caffe$ conda update anaconda
Fetching package metadata: ....
All requested packages already installed.
packages in environment at /home/ubuntu/anaconda:

anaconda                  2.3.0                np19py27_0
ubuntu@ip-10-0-1-195:~/caffe$ make py
make: Nothing to be done for `py'.
ubuntu@ip-10-0-1-195:~/caffe$ make distribute
add include
cp -r include distribute/
mkdir -p distribute/include/caffe/proto
cp .build_release/src/caffe/proto/caffe.pb.h distribute/include/caffe/proto
add tool and example binaries
cp .build_release/tools/upgrade_net_proto_binary.bin .build_release/tools/convert_imageset.bin .build_release/tools/train_net.bin .build_release/tools/finetune_net.bin .build_release/tools/upgrade_net_proto_text.bin .build_release/tools/net_speed_benchmark.bin .build_release/tools/extract_features.bin .build_release/tools/device_query.bin .build_release/tools/test_net.bin .build_release/tools/compute_image_mean.bin .build_release/tools/caffe.bin distribute/bin
cp .build_release/examples/cifar10/convert_cifar_data.bin .build_release/examples/siamese/convert_mnist_siamese_data.bin .build_release/examples/mnist/convert_mnist_data.bin distribute/bin
add libraries
cp .build_release/lib/libcaffe-nv.a distribute/lib
cp .build_release/lib/libcaffe-nv.so.0.12.2 distribute/lib
cd distribute/lib; rm -f libcaffe-nv.so.0; ln -s libcaffe-nv.so.0.12.2 libcaffe-nv.so.0
cd distribute/lib; rm -f libcaffe-nv.so;   ln -s libcaffe-nv.so.0 libcaffe-nv.so
add python - it's not the standard way, indeed...
cp -r python distribute/python
ubuntu@ip-10-0-1-195:~/caffe$ ipython
Python 2.7.10 |Anaconda 2.3.0 (64-bit)| (default, May 28 2015, 17:02:03)
Type "copyright", "credits" or "license" for more information.
IPython 3.2.0 -- An enhanced Interactive Python.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.
In [1]: import caffe
ImportError                               Traceback (most recent call last)
 in ()
----> 1 import caffe
ImportError: No module named caffe
Any ideas?

Caffe doesn't automatically add $CAFFE_HOME/python to your PYTHONPATH, which is a required step to be able to import caffe. See their instructions here:
http://caffe.berkeleyvision.org/installation.html#python
DIGITS sets PYTHONPATH for you automatically based on your DIGITS configuration. You should be able to use this build of Caffe with DIGITS without any errors.
https://github.com/NVIDIA/DIGITS/blob/v2.0.0-rc3/digits/config/caffe_option.py#L228-L229
Also, make distribute isn't necessary. It doesn't even work right, last time I checked.

ubuntu@ip-10-0-1-195:~/DIGITS$ ./digits-devserver
modprobe: ERROR: could not insert 'nvidia_346': No such device
cudaRuntimeGetVersion() failed with error #38
Did you forget to "make pycaffe"?
Traceback (most recent call last):
File "./digits-devserver", line 42, in
config.load_config('quiet')
File "/home/ubuntu/DIGITS/digits/config/load.py", line 147, in load_config
option.apply()
File "/home/ubuntu/DIGITS/digits/config/caffe_option.py", line 236, in apply
import caffe
File "/home/ubuntu/caffe/python/caffe/init.py", line 1, in
from .pycaffe import Net, SGDSolver
File "/home/ubuntu/caffe/python/caffe/pycaffe.py", line 13, in
from ._caffe import Net, SGDSolver
ImportError: libhdf5_hl.so.9: cannot open shared object file: No such file or directory
ubuntu@ip-10-0-1-195:~/DIGITS$

On Aug 13, 2015, at 11:00 AM, Luke Yeager notifications@github.com wrote:
Caffe doesn't automatically add $CAFFE_HOME/python to your PYTHONPATH, which is a required step to be able to import caffe. See their instructions here:
http://caffe.berkeleyvision.org/installation.html#python http://caffe.berkeleyvision.org/installation.html#python
DIGITS sets PYTHONPATH for you automatically based on your DIGITS configuration. You should be able to use this build of Caffe with DIGITS without any errors.
https://github.com/NVIDIA/DIGITS/blob/v2.0.0-rc3/digits/config/caffe_option.py#L228-L229 https://github.com/NVIDIA/DIGITS/blob/v2.0.0-rc3/digits/config/caffe_option.py#L228-L229
Also, make distribute isn't necessary. It doesn't even work right, last time I checked.
—
Reply to this email directly or view it on GitHub https://github.com/NVIDIA/DIGITS/issues/216#issuecomment-130781505.

It appears related to this issue:
https://github.com/BVLC/caffe/issues/2720 https://github.com/BVLC/caffe/issues/2720

On Aug 13, 2015, at 12:00 PM, David Laxer davidl@softintel.com wrote:
ubuntu@ip-10-0-1-195:~/DIGITS$ ./digits-devserver
modprobe: ERROR: could not insert 'nvidia_346': No such device
cudaRuntimeGetVersion() failed with error #38
Did you forget to "make pycaffe"?
Traceback (most recent call last):
File "./digits-devserver", line 42, in
config.load_config('quiet')
File "/home/ubuntu/DIGITS/digits/config/load.py", line 147, in load_config
option.apply()
File "/home/ubuntu/DIGITS/digits/config/caffe_option.py", line 236, in apply
import caffe
File "/home/ubuntu/caffe/python/caffe/init.py", line 1, in
from .pycaffe import Net, SGDSolver
File "/home/ubuntu/caffe/python/caffe/pycaffe.py", line 13, in
from ._caffe import Net, SGDSolver
ImportError: libhdf5_hl.so.9: cannot open shared object file: No such file or directory
ubuntu@ip-10-0-1-195:~/DIGITS$

On Aug 13, 2015, at 11:00 AM, Luke Yeager <notifications@github.com mailto:notifications@github.com> wrote:
Caffe doesn't automatically add $CAFFE_HOME/python to your PYTHONPATH, which is a required step to be able to import caffe. See their instructions here:
http://caffe.berkeleyvision.org/installation.html#python http://caffe.berkeleyvision.org/installation.html#python
DIGITS sets PYTHONPATH for you automatically based on your DIGITS configuration. You should be able to use this build of Caffe with DIGITS without any errors.
https://github.com/NVIDIA/DIGITS/blob/v2.0.0-rc3/digits/config/caffe_option.py#L228-L229 https://github.com/NVIDIA/DIGITS/blob/v2.0.0-rc3/digits/config/caffe_option.py#L228-L229
Also, make distribute isn't necessary. It doesn't even work right, last time I checked.
—
Reply to this email directly or view it on GitHub https://github.com/NVIDIA/DIGITS/issues/216#issuecomment-130781505.

No, I'm pretty sure it's not related.

ImportError: libhdf5_hl.so.9: cannot open shared object file: No such file or directory

Looks like the same issue as #205. I'm closing this issue as I think I've answered your original question.

can you please tell me how you solve your this problem
(venv)ibrhaim@ibrhaim-B85M-D3H:~/digits/digits$ ./digits-server
2015-08-25 11:09:59 [5679] [INFO] Starting gunicorn 17.5
cudaRuntimeGetVersion() failed with error #38
Did you forget to "make pycaffe"?
Traceback (most recent call last):
File "/home/ibrhaim/venv/bin/gunicorn", line 9, in
load_entry_point('gunicorn==17.5', 'console_scripts', 'gunicorn')()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/wsgiapp.py", line 42, in run
WSGIApplication("%(prog)s [OPTIONS] APP_MODULE").run()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/base.py", line 144, in run
Arbiter(self).run()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py", line 165, in run
self.start()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py", line 124, in start
self.cfg.on_starting(self)
File "gunicorn_config.py", line 9, in on_starting
config.load_config()
File "/home/ibrhaim/digits/digits/digits/config/load.py", line 147, in load_config
option.apply()
File "/home/ibrhaim/digits/digits/digits/config/caffe_option.py", line 243, in apply
import caffe
File "/home/ibrhaim/caffe/python/caffe/init.py", line 1, in
from .pycaffe import Net, SGDSolver
File "/home/ibrhaim/caffe/python/caffe/pycaffe.py", line 14, in
import caffe.io
File "/home/ibrhaim/caffe/python/caffe/io.py", line 2, in
import skimage.io
ImportError: No module named skimage.io

Hi,
The information below suggests a different issue.
I see you are using a python 2.7 distribution (I was using Anaconda).
Is the skiimage.io package installed?
What happens if you run:
$ pip install -U scikit-image

On Aug 24, 2015, at 7:29 PM, ibrahim sarker notifications@github.com wrote:
can you please tell me how you solve your this problem
(venv)ibrhaim@ibrhaim-B85M-D3H:~/digits/digits$ ./digits-server
2015-08-25 11:09:59 [5679] [INFO] Starting gunicorn 17.5
cudaRuntimeGetVersion() failed with error #38 https://github.com/NVIDIA/DIGITS/pull/38
Did you forget to "make pycaffe"?
Traceback (most recent call last):
File "/home/ibrhaim/venv/bin/gunicorn", line 9, in
load_entry_point('gunicorn==17.5', 'console_scripts', 'gunicorn')()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/wsgiapp.py", line 42, in run
WSGIApplication("%(prog)s [OPTIONS] APP_MODULE").run()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/base.py", line 144, in run
Arbiter(self).run()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py", line 165, in run
self.start()
File "/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py", line 124, in start
self.cfg.on_starting(self)
File "gunicorn_config.py", line 9, in on_starting
config.load_config()
File "/home/ibrhaim/digits/digits/digits/config/load.py", line 147, in load_config
option.apply()
File "/home/ibrhaim/digits/digits/digits/config/caffe_option.py", line 243, in apply
import caffe
File "/home/ibrhaim/caffe/python/caffe/init.py", line 1, in
from .pycaffe import Net, SGDSolver
File "/home/ibrhaim/caffe/python/caffe/pycaffe.py", line 14, in
import caffe.io
File "/home/ibrhaim/caffe/python/caffe/io.py", line 2, in
import skimage.io
ImportError: No module named skimage.io
—
Reply to this email directly or view it on GitHub https://github.com/NVIDIA/DIGITS/issues/216#issuecomment-134454115.

so you suggest me to install anaconda>
after run  $ pip install -U scikit-image
(venv)ibrhaim@ibrhaim-B85M-D3H:~/caffe$ pip install -U scikit-image
Requirement already up-to-date: scikit-image in /home/ibrhaim/venv/lib/python2.7/site-packages
Requirement already up-to-date: matplotlib>=1.1.0 in /home/ibrhaim/venv/lib/python2.7/site-packages (from scikit-image)
Requirement already up-to-date: six>=1.3 in /home/ibrhaim/venv/lib/python2.7/site-packages (from scikit-image)
Requirement already up-to-date: networkx>=1.8 in /home/ibrhaim/venv/lib/python2.7/site-packages (from scikit-image)
Requirement already up-to-date: pillow>=1.7.8 in /home/ibrhaim/venv/lib/python2.7/site-packages (from scikit-image)
Requirement already up-to-date: numpy>=1.6 in /home/ibrhaim/venv/lib/python2.7/site-packages (from matplotlib>=1.1.0->scikit-image)
Downloading/unpacking python-dateutil from https://pypi.python.org/packages/any/p/python-dateutil/python_dateutil-2.4.2-py2.py3-none-any.whl#md5=c817289a1d95a355cc088e2292791a77 (from matplotlib>=1.1.0->scikit-image)
Downloading python_dateutil-2.4.2-py2.py3-none-any.whl (188kB): 188kB downloaded
Requirement already up-to-date: pytz in /home/ibrhaim/venv/lib/python2.7/site-packages (from matplotlib>=1.1.0->scikit-image)
Requirement already up-to-date: pyparsing>=1.5.6 in /home/ibrhaim/venv/lib/python2.7/site-packages (from matplotlib>=1.1.0->scikit-image)
Requirement already up-to-date: nose>=0.11.1 in /home/ibrhaim/venv/lib/python2.7/site-packages (from matplotlib>=1.1.0->scikit-image)
Requirement already up-to-date: mock in /home/ibrhaim/venv/lib/python2.7/site-packages (from matplotlib>=1.1.0->scikit-image)
Requirement already up-to-date: decorator>=3.4.0 in /home/ibrhaim/venv/lib/python2.7/site-packages (from networkx>=1.8->scikit-image)
Installing collected packages: python-dateutil
Found existing installation: python-dateutil 1.5
Uninstalling python-dateutil:
Successfully uninstalled python-dateutil
Successfully installed python-dateutil
Cleaning up...

thanks for replay,
now digits run. but it seems some problems like
(venv)ibrhaim@ibrhaim-B85M-D3H:~/digits/digits$ ./digits-server
2015-08-25 14:02:02 [8051] [INFO] Starting gunicorn 17.5
cudaRuntimeGetVersion() failed with error #38
https://github.com/NVIDIA/DIGITS/pull/38
2015-08-25 14:02:06 [8051] [DEBUG] Arbiter booted
2015-08-25 14:02:06 [8051] [INFO] Listening at: http://0.0.0.0:8080 (8051)
2015-08-25 14:02:06 [8051] [INFO] Using worker:
socketio.sgunicorn.GeventSocketIOWorker
2015-08-25 14:02:06 [8087] [INFO] Booting worker with pid: 8087
2015-08-25 14:29:38 [20150825-142937-d69e] [INFO ] Parse Folder (train/val)
task started.
2015-08-25 14:29:39 [20150825-142937-d69e] [WARNING] Parse Folder
(train/val) unrecognized output: cudaRuntimeGetVersion() failed with error
#38 https://github.com/NVIDIA/DIGITS/pull/38
https://github.com/NVIDIA/DIGITS/pull/38
in is it need to configure http://0.0.0.0:8080 http://0.0.0.0:8080
(8051) tohttp://0.0.0.0:5000/ http://0.0.0.0:5000/ ????
and i see in tutorial,after running ./digits-server , it looks like
% ./digits-devserver

|   _ / | | / |
| |) | | ( || |  | | _
|/_|| || |__/
*but in my case it shows some error, *
and when i train a dataset then it shows like...
2015-08-25 14:29:42 [20150825-142937-d69e] [DEBUG] Total images added: 10
2015-08-25 14:29:42 [20150825-142937-d69e] [INFO ] Create DB (train) task
completed.
2015-08-25 14:29:42 [20150825-142937-d69e] [DEBUG] Total images added: 3
2015-08-25 14:29:42 [20150825-142937-d69e] [INFO ] Create DB (val) task
completed.
2015-08-25 14:29:43 [20150825-142937-d69e] [INFO ] Job complete.
2015-08-25 14:30:57 [20150825-143055-1f9f] [INFO ] Train Caffe Model task
started.
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
If you get time please replay...
On Tue, Aug 25, 2015 at 12:08 PM, dbl notifications@github.com wrote:

Hi,
The information below suggests a different issue.
I see you are using a python 2.7 distribution (I was using Anaconda).
Is the skiimage.io package installed?
What happens if you run:
$ pip install -U scikit-image

On Aug 24, 2015, at 7:29 PM, ibrahim sarker notifications@github.com
wrote:
can you please tell me how you solve your this problem
(venv)ibrhaim@ibrhaim-B85M-D3H:~/digits/digits$ ./digits-server
2015-08-25 11:09:59 [5679] [INFO] Starting gunicorn 17.5
cudaRuntimeGetVersion() failed with error #38 <
https://github.com/NVIDIA/DIGITS/pull/38>
Did you forget to "make pycaffe"?
Traceback (most recent call last):
File "/home/ibrhaim/venv/bin/gunicorn", line 9, in
load_entry_point('gunicorn==17.5', 'console_scripts', 'gunicorn')()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/wsgiapp.py",
line 42, in run
WSGIApplication("%(prog)s [OPTIONS] APP_MODULE").run()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/base.py",
line 144, in run
Arbiter(self).run()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py",
line 165, in run
self.start()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py",
line 124, in start
self.cfg.on_starting(self)
File "gunicorn_config.py", line 9, in on_starting
config.load_config()
File "/home/ibrhaim/digits/digits/digits/config/load.py", line 147, in
load_config
option.apply()
File "/home/ibrhaim/digits/digits/digits/config/caffe_option.py", line
243, in apply
import caffe
File "/home/ibrhaim/caffe/python/caffe/init.py", line 1, in
from .pycaffe import Net, SGDSolver
File "/home/ibrhaim/caffe/python/caffe/pycaffe.py", line 14, in
import caffe.io
File "/home/ibrhaim/caffe/python/caffe/io.py", line 2, in
import skimage.io
ImportError: No module named skimage.io
—
Reply to this email directly or view it on GitHub <
https://github.com/NVIDIA/DIGITS/issues/216#issuecomment-134454115>.

—
Reply to this email directly or view it on GitHub
https://github.com/NVIDIA/DIGITS/issues/216#issuecomment-134461305.

--
With  Regards,
싸커모하매드이브라힘
Mohammad Ibrahim Sarker
ROBOT VISION LAB, Room No. 407, Engineering Building No. 2-7
Division of Electronics Engineering
Chonbuk National University
664-14 1ga Duckjin-Dong Duckjin-Gu
Jeonju Jeonbuk 561-756 SOUTH KOREA

Not sure about the severity.
Something in ‘GUnicorn’:
https://github.com/benoitc/gunicorn/issues/449 https://github.com/benoitc/gunicorn/issues/449
https://github.com/defunkt/unicorn/blob/ea730f93b20ef582128f7c664f7b57953f855a09/lib/unicorn/http_server.rb#L303 https://github.com/defunkt/unicorn/blob/ea730f93b20ef582128f7c664f7b57953f855a09/lib/unicorn/http_server.rb#L303

On Aug 24, 2015, at 10:56 PM, ibrahim sarker notifications@github.com wrote:
thanks for replay,
now digits run. but it seems some problems like
(venv)ibrhaim@ibrhaim-B85M-D3H:~/digits/digits$ ./digits-server
2015-08-25 14:02:02 [8051] [INFO] Starting gunicorn 17.5
cudaRuntimeGetVersion() failed with error #38
https://github.com/NVIDIA/DIGITS/pull/38
2015-08-25 14:02:06 [8051] [DEBUG] Arbiter booted
2015-08-25 14:02:06 [8051] [INFO] Listening at: http://0.0.0.0:8080 (8051)
2015-08-25 14:02:06 [8051] [INFO] Using worker:
socketio.sgunicorn.GeventSocketIOWorker
2015-08-25 14:02:06 [8087] [INFO] Booting worker with pid: 8087
2015-08-25 14:29:38 [20150825-142937-d69e] [INFO ] Parse Folder (train/val)
task started.
2015-08-25 14:29:39 [20150825-142937-d69e] [WARNING] Parse Folder
(train/val) unrecognized output: cudaRuntimeGetVersion() failed with error
#38 https://github.com/NVIDIA/DIGITS/pull/38
https://github.com/NVIDIA/DIGITS/pull/38
in is it need to configure http://0.0.0.0:8080 http://0.0.0.0:8080
(8051) tohttp://0.0.0.0:5000/ http://0.0.0.0:5000/ ????
and i see in tutorial,after running ./digits-server , it looks like
% ./digits-devserver

| _ / | | / |
| |) | | ( || | | | _
|/_|| || |__/
*but in my case it shows some error, *
and when i train a dataset then it shows like...
2015-08-25 14:29:42 [20150825-142937-d69e] [DEBUG] Total images added: 10
2015-08-25 14:29:42 [20150825-142937-d69e] [INFO ] Create DB (train) task
completed.
2015-08-25 14:29:42 [20150825-142937-d69e] [DEBUG] Total images added: 3
2015-08-25 14:29:42 [20150825-142937-d69e] [INFO ] Create DB (val) task
completed.
2015-08-25 14:29:43 [20150825-142937-d69e] [INFO ] Job complete.
2015-08-25 14:30:57 [20150825-143055-1f9f] [INFO ] Train Caffe Model task
started.
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
2015-08-25 14:31:48 [8051] [INFO] Handling signal: winch
2015-08-25 14:31:48 [8051] [INFO] SIGWINCH ignored. Not daemonized
If you get time please replay...
On Tue, Aug 25, 2015 at 12:08 PM, dbl notifications@github.com wrote:

Hi,
The information below suggests a different issue.
I see you are using a python 2.7 distribution (I was using Anaconda).
Is the skiimage.io package installed?
What happens if you run:
$ pip install -U scikit-image

On Aug 24, 2015, at 7:29 PM, ibrahim sarker notifications@github.com
wrote:
can you please tell me how you solve your this problem
(venv)ibrhaim@ibrhaim-B85M-D3H:~/digits/digits$ ./digits-server
2015-08-25 11:09:59 [5679] [INFO] Starting gunicorn 17.5
cudaRuntimeGetVersion() failed with error #38 <
https://github.com/NVIDIA/DIGITS/pull/38>
Did you forget to "make pycaffe"?
Traceback (most recent call last):
File "/home/ibrhaim/venv/bin/gunicorn", line 9, in
load_entry_point('gunicorn==17.5', 'console_scripts', 'gunicorn')()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/wsgiapp.py",
line 42, in run
WSGIApplication("%(prog)s [OPTIONS] APP_MODULE").run()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/app/base.py",
line 144, in run
Arbiter(self).run()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py",
line 165, in run
self.start()
File
"/home/ibrhaim/venv/local/lib/python2.7/site-packages/gunicorn/arbiter.py",
line 124, in start
self.cfg.on_starting(self)
File "gunicorn_config.py", line 9, in on_starting
config.load_config()
File "/home/ibrhaim/digits/digits/digits/config/load.py", line 147, in
load_config
option.apply()
File "/home/ibrhaim/digits/digits/digits/config/caffe_option.py", line
243, in apply
import caffe
File "/home/ibrhaim/caffe/python/caffe/init.py", line 1, in
from .pycaffe import Net, SGDSolver
File "/home/ibrhaim/caffe/python/caffe/pycaffe.py", line 14, in
import caffe.io
File "/home/ibrhaim/caffe/python/caffe/io.py", line 2, in
import skimage.io
ImportError: No module named skimage.io
—
Reply to this email directly or view it on GitHub <
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Reply to this email directly or view it on GitHub
https://github.com/NVIDIA/DIGITS/issues/216#issuecomment-134461305.

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With Regards,
싸커모하매드이브라힘
Mohammad Ibrahim Sarker
ROBOT VISION LAB, Room No. 407, Engineering Building No. 2-7
Division of Electronics Engineering
Chonbuk National University
664-14 1ga Duckjin-Dong Duckjin-Gu
Jeonju Jeonbuk 561-756 SOUTH KOREA
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Reply to this email directly or view it on GitHub https://github.com/NVIDIA/DIGITS/issues/216#issuecomment-134489181.

Try installing the requirements in order:
https://github.com/NVIDIA/DIGITS/blob/a0dd82f679/docs/BuildCaffe.md#python-packages

I did:
sudo apt-get install python-skimage
to fix this since my firewall blocks pip. Seemed to work . . .

repo: NVIDIA/DIGITS


text:
test_cpp.py

run  cpp/tests/resources/scripts/test_cpp.py
subprocess.CalledProcessError: Command '['cmake', '--build', '/media/data/wwz/TensorRT-LLM/cpp/build', '--config', 'Release', '-j', '--target', 'modelSpec']' returned non-zero exit status 2.
      If there is an issue finding model_spec.so in engine building, manually build model_spec.so by

make -C cpp/build/ modelSpec
error:
make: Entering directory '/media/data/wwz/TensorRT-LLM/cpp/build'
make: *** No rule to make target 'modelSpec'.  Stop.
make: Leaving directory '/media/data/wwz/TensorRT-LLM/cpp/build'

Thanks for sharing the issue.
If you are trying to install from source code, please follow the build-from-source-linux instructions. If not, please offer more information by following issue template.

@Superjomn ,I build tensorrt llm completely according to the source code( build-from-source-linux ), but there is no target modelSpec' in the generated cpp/build/makefile

@Superjomn Thank you. In addition, my other functions can be used normally, but the modelSpec function does not work.

source: gharchive/issue

authors: Superjomn weizhi-wang

repo: NVIDIA/TensorRT-LLM

license_type: permissive

license_source: github-api


text:
undefined reference to `__libc_single_threaded'

System Info
System:

CPU Architecture: x86_64
GPU: NVIDIA H100 - 80GB - CUDA 12.4
TensorRT-LLM: main branch, commit 535c9cc6730f5ac999e4b1cb621402b58138f819
Operating System: Ubuntu 22.04

Dependencies (All these components were sourced from NVIDIA-provided tar files):

NCCL: 2.23.4-1
TensorRT: 10.6.0.26

Additional Environment Details:

ldd: 2.35
gcc: 11.4.0

Who can help?
No response
Information

[ ] The official example scripts
[x] My own modified scripts

Tasks

[ ] An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
[ ] My own task or dataset (give details below)

Reproduction
I built TensorRT-LLM within a CONDA environment using the following command:
python scripts/build_wheel.py --clean --trt_root /data0/tien/TensorRT-10.6.0.26 --nccl_root /data0/tien/nccl-2.23.4-1/build
Expected behavior
The build process should complete without errors.
actual behavior
But at the end, it raises the following error:
...
[100%] Building CXX object tensorrt_llm/plugins/CMakeFiles/nvinfer_plugin_tensorrt_llm.dir/ncclPlugin/recvPlugin.cpp.o
[100%] Building CXX object tensorrt_llm/plugins/CMakeFiles/nvinfer_plugin_tensorrt_llm.dir/common/gemmPluginProfiler.cpp.o
[100%] Building CXX object tensorrt_llm/plugins/CMakeFiles/nvinfer_plugin_tensorrt_llm.dir/common/plugin.cpp.o
[100%] Building CXX object tensorrt_llm/plugins/CMakeFiles/nvinfer_plugin_tensorrt_llm.dir/api/tllmPlugin.cpp.o
[100%] Linking CXX shared library libnvinfer_plugin_tensorrt_llm.so
/data0/tien/anaconda3/envs/trt-llm/bin/../lib/gcc/x86_64-conda-linux-gnu/11.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: ../kernels/cutlass_kernels/libfpA_intB_gemm_src.a(bf16_int4_gemm_fg_scalebias.cu.o): in function `std::string::_Rep::_M_dispose(std::allocator<char> const&) [clone .part.0]':
tmpxft_0022667f_00000000-6_bf16_int4_gemm_fg_scalebias.compute_90a.cudafe1.cpp:(.text+0x13): undefined reference to `__libc_single_threaded'
/data0/tien/anaconda3/envs/trt-llm/bin/../lib/gcc/x86_64-conda-linux-gnu/11.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: ../kernels/cutlass_kernels/libfpA_intB_gemm_src.a(bf16_int4_gemm_fg_scalebias.cu.o): in function `void tensorrt_llm::common::Logger::log<>(tensorrt_llm::common::Logger::Level, char const*) [clone .constprop.0]':
tmpxft_0022667f_00000000-6_bf16_int4_gemm_fg_scalebias.compute_90a.cudafe1.cpp:(.text+0x1766): undefined reference to `__libc_single_threaded'
/data0/tien/anaconda3/envs/trt-llm/bin/../lib/gcc/x86_64-conda-linux-gnu/11.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: tmpxft_0022667f_00000000-6_bf16_int4_gemm_fg_scalebias.compute_90a.cudafe1.cpp:(.text+0x17db): undefined reference to `__libc_single_threaded'
/data0/tien/anaconda3/envs/trt-llm/bin/../lib/gcc/x86_64-conda-linux-gnu/11.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: ../kernels/cutlass_kernels/libfpA_intB_gemm_src.a(bf16_int4_gemm_fg_scalebias.cu.o): in function `virtual thunk to tensorrt_llm::kernels::cutlass_kernels::CutlassFpAIntBGemmRunner<__nv_bfloat16, cutlass::integer_subbyte<4, false>, (cutlass::WeightOnlyQuantOp)3, __nv_bfloat16, __nv_bfloat16, __nv_bfloat16>::~CutlassFpAIntBGemmRunner()':
tmpxft_0022667f_00000000-6_bf16_int4_gemm_fg_scalebias.compute_90a.cudafe1.cpp:(.text._ZN12tensorrt_llm7kernels15cutlass_kernels24CutlassFpAIntBGemmRunnerI13__nv_bfloat16N7cutlass15integer_subbyteILi4ELb0EEELNS4_17WeightOnlyQuantOpE3ES3_S3_S3_ED1Ev[_ZN12tensorrt_llm7kernels15cutlass_kernels24CutlassFpAIntBGemmRunnerI13__nv_bfloat16N7cutlass15integer_subbyteILi4ELb0EEELNS4_17WeightOnlyQuantOpE3ES3_S3_S3_ED1Ev]+0x17d): undefined reference to `__libc_single_threaded'
/data0/tien/anaconda3/envs/trt-llm/bin/../lib/gcc/x86_64-conda-linux-gnu/11.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: tmpxft_0022667f_00000000-6_bf16_int4_gemm_fg_scalebias.compute_90a.cudafe1.cpp:(.text._ZN12tensorrt_llm7kernels15cutlass_kernels24CutlassFpAIntBGemmRunnerI13__nv_bfloat16N7cutlass15integer_subbyteILi4ELb0EEELNS4_17WeightOnlyQuantOpE3ES3_S3_S3_ED1Ev[_ZN12tensorrt_llm7kernels15cutlass_kernels24CutlassFpAIntBGemmRunnerI13__nv_bfloat16N7cutlass15integer_subbyteILi4ELb0EEELNS4_17WeightOnlyQuantOpE3ES3_S3_S3_ED1Ev]+0x1f3): undefined reference to `__libc_single_threaded'
/data0/tien/anaconda3/envs/trt-llm/bin/../lib/gcc/x86_64-conda-linux-gnu/11.4.0/../../../../x86_64-conda-linux-gnu/bin/ld: ../kernels/cutlass_kernels/libfpA_intB_gemm_src.a(bf16_int4_gemm_fg_scalebias.cu.o):tmpxft_0022667f_00000000-6_bf16_int4_gemm_fg_scalebias.compute_90a.cudafe1.cpp:(.text._ZN12tensorrt_llm7kernels15cutlass_kernels24CutlassFpAIntBGemmRunnerI13__nv_bfloat16N7cutlass15integer_subbyteILi4ELb0EEELNS4_17WeightOnlyQuantOpE3ES3_S3_S3_E16getWorkspaceSizeEiii[_ZN12tensorrt_llm7kernels15cutlass_kernels24CutlassFpAIntBGemmRunnerI13__nv_bfloat16N7cutlass15integer_subbyteILi4ELb0EEELNS4_17WeightOnlyQuantOpE3ES3_S3_S3_E16getWorkspaceSizeEiii]+0x208): more undefined references to `__libc_single_threaded' follow
collect2: error: ld returned 1 exit status
gmake[3]: *** [tensorrt_llm/plugins/CMakeFiles/nvinfer_plugin_tensorrt_llm.dir/build.make:730: tensorrt_llm/plugins/libnvinfer_plugin_tensorrt_llm.so] Error 1
gmake[2]: *** [CMakeFiles/Makefile2:2167: tensorrt_llm/plugins/CMakeFiles/nvinfer_plugin_tensorrt_llm.dir/all] Error 2
gmake[1]: *** [CMakeFiles/Makefile2:2174: tensorrt_llm/plugins/CMakeFiles/nvinfer_plugin_tensorrt_llm.dir/rule] Error 2
gmake: *** [Makefile:452: nvinfer_plugin_tensorrt_llm] Error 2
Traceback (most recent call last):
  File "/data0/tien/TensorRT-LLM/scripts/build_wheel.py", line 434, in <module>
    main(**vars(args))
  File "/data0/tien/TensorRT-LLM/scripts/build_wheel.py", line 208, in main
    build_run(
  File "/data0/tien/anaconda3/envs/trt-llm/lib/python3.10/subprocess.py", line 526, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command 'cmake --build . --config Release --parallel 384 --target tensorrt_llm nvinfer_plugin_tensorrt_llm th_common bindings   executorWorker  ' returned non-zero exit status 2.

additional notes
I attempted to modify the LD_LIBRARY_PATH environment to closely match the setup in the Docker container, but it did not resolve the issue.
(trt-llm) tien@h100:/data0/tien/TensorRT-LLM$ echo $LD_LIBRARY_PATH
/data0/tien/anaconda3/envs/trt-llm/lib/python3.10/site-packages/torch/lib:/data0/tien/anaconda3/envs/trt-llm/lib/python3.10/site-packages/torch_tensorrt/lib:/usr/local/cuda/compat/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/usr/local/cuda/lib64:/data0/tien/TensorRT-10.6.0.26/lib

@hoangvictor Could you use the docker image to install Trt-LLM?

@hello-11 Unfortunately, at the moment, I am unable to use the Docker for this setup due to permission restrictions in my team's environment. It is better if I can install TensorRT-LLM using Conda environment.


text:
Some doubts about the usage of DelayedScaling.interval.

Is the interval attribute of DelayedScaling not used in PyTorch within the current TransformerEngine? In other words, does the value of DelayedScaling.interval affect the computation frequency of the scaling factor in PyTorch? I have carefully reviewed the source code of TransformerEngine and didn't find any usage of DelayedScaling.interval in PyTorch to control the computation frequency of the scaling factor.

Hi @wzzju, thanks for reporting the bug and sorry for the delayed response! The interval argument in the recipe is indeed unused and has been removed in #892. During the initial days of developing an FP8 recipe, interval was something we experimented with but it never ended up using. It accidentally made it into the TE release and has incorrectly remained there since.

authors: ksivaman wzzju

repo: NVIDIA/TransformerEngine


text:
Prettify hook JSON

Run prettier on the hook to beautify the JSON.
By the way: Do you mind enabling issues for this repository? Do you think it would be possible to cut a first release? :innocent:

@saschagrunert Issues and pull requests are managed in Gitlab. This is just a mirror
https://gitlab.com/nvidia/container-toolkit/container-toolkit/-/issues

source: gharchive/pull-request

repo: NVIDIA/container-toolkit


text:
Implement tuning db merger

Tuning on multiple nodes or GPUs leads to multiple tuning databases. To facilitate analysis, we should implement a merger that would apply Mann–Whitney U test to base variants across databases and only then merge corresponding variant records into a single database.

What's the purpose of testing with a U-test before merging the database? So long as a given variant is built from the same commit hash with the same compiler and environment, it should be fine to merge, shouldn't it?

@jrhemstad, the idea was that the results achieved on different GPUs might vary. The base versions are run on each GPU while variants are distributed. This means the variant is relative to its base, not to all the bases. To be safe, I wanted to double-check that the base results have a high probability of belonging to a single distribution. For instance, if one of 8 GPUs is 3% faster, the united base distribution will be biased to 3% slower results. If a given variant was executed only on the fast GPU and afterwards compared to the united base distribution, it'll gain ~3% speedup, obscuring other variants.

Ah, of course, that makes sense.

authors: jrhemstad senior-zero

repo: NVIDIA/cub

license_source: bigquery


text:
[Pod GPU Metrics Exporter] Fix GPU metrics watcher stuck

When kubelet is unavailable temporally, watchAndWriteGPUmetrics() return empty and will be to do nothing.
The patch fixes this kind of issue.

Hi @takmatsu the patch looks good. Before I merge it, can you please share a test run. Thanks

With this patch, exporter retry to connect if kubelet is temporary unavailable:
$ kubectl logs dcgm-exporter-xxxxx  pod-nvidia-gpu-metrics-exporter
I1119 11:57:33.148434   30561 main.go:18] Starting OS watcher.
I1119 11:57:33.148913   30561 main.go:23] Starting FS watcher.
E1119 11:57:50.952539   30561 watchers.go:43] failed to get devices Pod information: failure connecting to /var/lib/kubelet/pod-resources/kubelet.sock: context deadline exceeded
E1119 11:58:00.952858   30561 watchers.go:43] failed to get devices Pod information: failure connecting to /var/lib/kubelet/pod-resources/kubelet.sock: context deadline exceeded
E1119 11:58:10.953107   30561 watchers.go:43] failed to get devices Pod information: failure connecting to /var/lib/kubelet/pod-resources/kubelet.sock: context deadline exceeded
E1119 11:58:20.953388   30561 watchers.go:43] failed to get devices Pod information: failure connecting to /var/lib/kubelet/pod-resources/kubelet.sock: context deadline exceeded

Okay, can you sign off https://github.com/NVIDIA/gpu-monitoring-tools/blob/master/CONTRIBUTING.md
This time I will merge the PR here but next time please open a PR on  https://gitlab.com/nvidia/container-toolkit/gpu-monitoring-tools :)

I think already signed off, thanks.
https://github.com/NVIDIA/gpu-monitoring-tools/pull/36/commits/9cc6a173f225cc476191d0a2e4d4e1637ca9fba6
Oh, Github's is mirror repo, right? I will take care next time.

Hello!
With V2, this issue is addressed!
Thank you for your contribution!

authors: RenaudWasTaken guptaNswati takmatsu

repo: NVIDIA/gpu-monitoring-tools


text:
[submodule-sync] bot-submodule-sync-branch-23.08 to branch-23.08 [skip ci] [bot]

submodule-sync to create a PR keeping thirdparty/cudf up-to-date.
HEAD commit SHA: d22dfcff3ec9589cb374161381911fbdb64edf81, cudf commit SHA: https://github.com/rapidsai/cudf/commit/515e2250ccd371d61128750e7c469a4a477577ce
This PR will be auto-merged if test passed. If failed, it will remain open until test pass or manually fix.

HEAD commit SHA: d22dfcff3ec9589cb374161381911fbdb64edf81, CUDF commit SHA: https://github.com/rapidsai/cudf/commit/515e2250ccd371d61128750e7c469a4a477577ce
Test passed: False

HEAD commit SHA: cdea2081141742356935465f37bbebda03569f3a, CUDF commit SHA: https://github.com/rapidsai/cudf/commit/84578d793e4bf5a0924766d39ff717f5d9d71820
Test passed: True

SUCCESS - auto-merge

repo: NVIDIA/spark-rapids-jni


text:
[submodule-sync] bot-submodule-sync-branch-23.12 to branch-23.12 [skip ci] [bot]

submodule-sync to create a PR keeping thirdparty/cudf up-to-date.
HEAD commit SHA: ebda4dfac649d3859ca95e6bd04817bb2891e6fc, cudf commit SHA: https://github.com/rapidsai/cudf/commit/68cb1d944b8b2f1c7e3564dc66eacc7f0b19ecee
This PR will be auto-merged if test passed. If failed, it will remain open until test pass or manually fix.

HEAD commit SHA: ebda4dfac649d3859ca95e6bd04817bb2891e6fc, CUDF commit SHA: https://github.com/rapidsai/cudf/commit/68cb1d944b8b2f1c7e3564dc66eacc7f0b19ecee
Test passed: True

SUCCESS - auto-merge


text:
[submodule-sync] bot-submodule-sync-branch-24.02 to branch-24.02 [skip ci] [bot]

submodule-sync to create a PR keeping thirdparty/cudf up-to-date.
HEAD commit SHA: b02969d10fae9c57f59ec1a13321565520302506, cudf commit SHA: https://github.com/rapidsai/cudf/commit/1c37c780ced37d6084c90b815b274b598665d60e
This PR will be auto-merged if test passed. If failed, it will remain open until test pass or manually fix.

HEAD commit SHA: b02969d10fae9c57f59ec1a13321565520302506, CUDF commit SHA: https://github.com/rapidsai/cudf/commit/1c37c780ced37d6084c90b815b274b598665d60e
Test passed: True

SUCCESS - auto-merge


text:
Databricks notebook readme fix

Fix command line and databricks setting instructions in README

build

repo: NVIDIA/spark-rapids-ml


text:
[AUDIT] [SPARK-46832] The new expressions Collate and Collation can change StringType

Is your feature request related to a problem? Please describe.
https://github.com/apache/spark/commit/861cca3da4c / https://github.com/apache/spark/pull/45064 modified StringType so now it can have a collation id associated with it.  The scare part is that this changes how strings can be compared.
See https://unicode-org.github.io/icu-docs/apidoc/dev/icu4j/com/ibm/icu/text/Collator.html for some more details on this, as this is the library that Spark is using.
This is used in a lot of places, including joins. I don't know if it is used everywhere, but I think in general we really should just fall back to the CPU for any StringType that we see that has a non-0 string type, the new method isDefaultCollation would need to return false. The other problem is that any place that we use a case statement to check the type of a string might need to be shimmed as StringType is not longer a case object, but is an actual case class.

Collation has been enabled by default in Spark 4 https://github.com/apache/spark/commit/43d881a94f2

Collation has been enabled by default in Spark 4 apache/spark@43d881a94f2

This PR has a very misleading description. They actually removed the COLLATION_ENABLED flag completely, which means that we probably need to support UTF8_BINARY (which is the original default) as the collation type. We should fall back to CPU if the default is set to anything else.

authors: NVnavkumar abellina revans2

repo: NVIDIA/spark-rapids


text:
[FEA] Scalar support of structs

This is very closely related to #1902 in fact we need lists of structs and structs with list, ... full nesting.

Related cuDF issue https://github.com/rapidsai/cudf/issues/7790

I think this was done a long time ago


text:
[FEA][Audit] [SPARK-37455][SQL] Replace hash with sort aggregate if child is already sorted

Is your feature request related to a problem? Please describe.
This PR https://github.com/apache/spark/commit/544865db77,  if the child of hash aggregate is already sorted on group-by columns, they replace hash aggregate with sort aggregate for better performance.
From the PR, it looks like its an optimization in AQE.
I don't think we need to implement this. Created the issue to get feedback from others.
There is a followon of the above PR - https://github.com/apache/spark/commit/41a940f071

We don't need to worry about the Spark change, as we replace all aggregates, sort or otherwise, with GpuHashAggregates and remove the corresponding pre-sort if it was a sort-based aggregate.  Therefore I don't see this as an audit issue, as we will still produce proper plans and do a hash aggregate.
However we could benefit from similar logic.  When we are going to put in a GpuHashAggregate but the reported child ordering matches the grouping keys then we could hint to libcudf that the data is already sorted.  There have been libcudf bugs in the past in sorted aggregates, so we need explicit testing to verify that the optimization is working as intended and producing correct output.

I don't think this is a high priority change.  This optimization is probably more relevant for CPU plans because it's common to have a join followed by an aggregate on the same join keys.  SortMergeJoin produces output sorted on the join keys, but we replace a SortMergeJoin with a GPU hash join and therefore don't have the same benefit of sorted output from the join.

Closing as won't fix for now

authors: jlowe mattahrens nartal1


text:
Fix setup-java step of blossom-ci [skip ci]

Signed-off-by: Peixin Li pxli@nyu.edu
to fix the java versions with in the V3 setup-java action

going to force merge this one to unblock premerge CI. I will update status later


text:
Which is worse: too long utterances or too short utterances?

I'm using automated subtitling with the Google Speech to Text API to prepare a dataset. By default, the sections it breaks subtitles down into are approximately 2 seconds long on average. I've found a tool to assemble the subtitle sections on sentence boundaries and it gives longer sections, but my speaker isn't scripted and so he sometimes corrects his train of thought midsentence and now I have some sentences upwards of 20 seconds long. So I'm wondering which is the better option. I think it might be better to eliminate problematic (too long) sentences and go with the 2nd option, but I'd like to hear from someone who has more expertise in this area.

If you train with 2 second phrases I think you will run into problems generating longer phrases. I've noticed the model will "rush" through phrases that are significantly longer than what it has been trained on.
The long phrases might cause memory issues and force you to use a small batch size - unless you have bountiful GPU memory :) Or the other option is to eliminate the longer phrases as you suggested.
There are pro's and con's to each method. I agree with you the second option will probably work the best and simplest. Another thought is that after training on the "medium" length phrases you can fine-tune the model on the long/short phrases with a small batch size to learn how a variety of phrase lengths should be handled. I've noticed that you need a variety of phrase lengths in the training pool to be robust at inference time, it appears to be challenging to generalize outside the phrase lengths in the training pool.

authors: johnjaniczek sajattack


text:
Why did I report this error and what should I do

FP16 Run: False
Dynamic Loss Scaling: True
Distributed Run: False
cuDNN Enabled: True
cuDNN Benchmark: False
Warm starting model from checkpoint 'tacotron2_statedict.pt'
Epoch: 0
Traceback (most recent call last):
File "train.py", line 291, in
args.warm_start, args.n_gpus, args.rank, args.group_name, hparams)
File "train.py", line 208, in train
for i, batch in enumerate(train_loader):
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 521, in next
data = self._next_data()
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
return self._process_data(data)
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
data.reraise()
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/torch/_utils.py", line 425, in reraise
raise self.exc_type(msg)
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
data = fetcher.fetch(index)
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/s1home/hyy523/tacotron2/tacotron2/data_utils.py", line 61, in getitem
return self.get_mel_text_pair(self.audiopaths_and_text[index])
File "/s1home/hyy523/tacotron2/tacotron2/data_utils.py", line 34, in get_mel_text_pair
mel = self.get_mel(audiopath)
File "/s1home/hyy523/tacotron2/tacotron2/data_utils.py", line 49, in get_mel
melspec = torch.from_numpy(np.load(filename))
File "/home/hyy523/anaconda3/envs/tacotron2/lib/python3.6/site-packages/numpy/lib/npyio.py", line 457, in load
raise ValueError("Cannot load file containing pickled data "
ValueError: Cannot load file containing pickled data when allow_pickle=False

Try the pre-trianed modle order directly after you have download the pre-trained modle：python train.py --output_directory=outdir --log_directory=logdir -c tacotron2_statedict.pt --warm_start

chinese?work together?weixin?


text:
max_workspace_size_bytes can't set in config.pbtxt

I use triton server docker 20.03.
I try to set precision_mode in config.pbtxt and can load model successful.
But if I set max_workspace_size_bytes or minimum_segment_size, it always loads failed like:
error: creating server: INTERNAL - failed to load all models
How to solve it ? Thanks
optimization { execution_accelerators {
gpu_execution_accelerator : [ {
name : "tensorrt"
parameters
{
key: "precision_mode" value: "FP16"
key: "minimum_segment_size" value: 3
key:"max_workspace_size_bytes" value:(11<32)
}
}]
}}

Run the server with --log-verbose=1 and share the console output, there may be more information in there to help determine what is happening.

The parameter value should be in string type, so "max_workspace_size_bytes" should be an integer string

authors: GuanLuo deadeyegoodwin loveppdog

repo: NVIDIA/triton-inference-server


text:
[BUG] wp.array Fails to Convert dtype to wp.vec3 without warning

Bug Description
Hello!
import warp as wp
x = wp.zeros((100, 3), dtype=wp.float32)
print(x.dtype)  # Expected: <class 'warp.types.float32'>

x = wp.array(x, dtype=wp.vec3)
print(x.dtype)  # Expected: <class 'warp.types.vec3'>, but you get: <class 'warp.types.float32'>

It may not be a bug inherently, but an error could be raised if the conversion is not possible.
System Information
No response

Thanks Mehdi, this seems like a limitation we should address.

authors: mehdiataei nvlukasz

repo: NVIDIA/warp


text:
Adds NucleusViewList class

Is this pull request associated with an issue(s)?
No
Description
Adds yet another backend to NucleiView. This one holds a list of NucleusView objects. This backend is needed for when you're cherry-picking NucleusView objects from among different sources.
TODOs
None. R2g.

🚀 [bumpr] Bumped! New version:v1.1.0 Changes:v1.0.6...v1.1.0

repo: NWChemEx/Chemist


text:
Typo in Table 1 from Paper

Hello,
Thank you for the awesome work. While going through the paper, I found that in Table 1, there seems to be a typo in the following statement "Entries in gray are trained trained for 400 epochs more than ViL."
I wanted to bring this to your attention. If this was intentional, would you mind clarifying the meaning of "trained trained?"
Thank you! I attached a screenshot of it as well.

Hi, thanks for reading it carefully and pointing that out.
It's not intentional, just a case of repetition blindness :)
Well fix that in the next version.

authors: BenediktAlkin willxxy

repo: NX-AI/vision-lstm


text:
Macos build instructions

This modifies the CMakeList.txt to make them compatible with macOS using brew to supply a current version of OpenSSL. The X-Code supplied system library cannot be linked against.

please fix travis ci build error, which for Mac OS

sorry, travic-ci is another problem.  Please forget previous comments.
MacOS will auto load MacOS HID driver when ROM is HID status, which cause libusb open failure.
Did you success run uuu at MacOS to download image?
I remember it needs a signature to remove such HID driver.

As far as I am currently aware this should work. I can verify it again next week with the help of some colleagues.
Concerning the Travis CI build failures running the instructions brew install libusb libzip pkg-config cmake openssl (or equivalent, see also https://docs.travis-ci.com/user/reference/osx#homebrew ) should fix the issue, i.e. the missing OpenSSL dependency.
I am aware that the pull request currently points at a very specific OpenSSL version supplied via brew. I can think about a more robust solution next week.

Sorry, I revert your patch in mainline, wait for new solution.

authors: Okeanos nxpfrankli

repo: NXPmicro/mfgtools


text:
Finalize/clean up ingest models, add additional preprocessing

I'd say the biggest thing for review here is still the models. See changes here https://github.com/NYCPlanning/data-engineering/pull/1166/files#diff-6fffbad770f67b00cdc414260a7922b1de4258230b2dab380fefbb7843401fd0
For model changes, easier to look at that file with overall changes than by commit. Other than that, easier to go by commit (see below the yml/json for commit summaries)
The new templates look like this
id: dcp_commercialoverlay
acl: public-read

attributes:
  name: DCP NYC Commercial Overlay Districts
  description: |
    Polygon features representing the within-tax-block limits for commercial overlay districts,
    as shown on the DCP zoning maps. Commercial overlay district designations are indicated in the OVERLAY attribute.
  url: https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-gis-zoning.page#metadata

ingestion:
  target_crs: EPSG:4326
  source:
    type: edm_publishing_gis_dataset
    name: dcp_commercial_overlays
  file_format:
    type: shapefile
    crs: EPSG:2263
  processing_steps:
  - name: rename_columns
    args:
      map: {"geom": "wkb_geometry"}

columns:
- id: overlay
  data_type: text
- id: shape_leng
  data_type: decimal
- id: shape_area
  data_type: decimal
- id: wkb_geometry
  data_type: geometry

And the computed config then looks like this
{
    "id": "dcp_commercialoverlay",
    "version": "20240831",
    "crs": "EPSG:4326",
    "attributes": {
        "name": "DCP NYC Commercial Overlay Districts",
        "description": "Polygon features representing the within-tax-block limits for commercial overlay districts,\nas shown on the DCP zoning maps. Commercial overlay district designations are indicated in the OVERLAY attribute.\n",
        "url": "https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-gis-zoning.page#metadata"
    },
    "archival": {
        "acl": "public-read",
        "raw_filename": "dcp_commercial_overlays.zip",
        "archival_timestamp": "2024-09-30T16:23:51.693603-04:00"
    },
    "ingestion": {
        "target_crs": "EPSG:4326",
        "file_format": {
            "crs": "EPSG:2263",
            "encoding": "utf-8",
            "type": "shapefile"
        },
        "processing_steps": [
            {
                "name": "reproject",
                "args": {
                    "target_crs": "EPSG:4326"
                }
            },
            {
                "name": "rename_columns",
                "args": {
                    "map": {
                        "geom": "wkb_geometry"
                    }
                }
            }
        ],
        "source": {
            "type": "edm_publishing_gis_dataset",
            "name": "dcp_commercial_overlays"
        }
    },
    "columns": [
        {
            "id": "overlay",
            "data_type": "text"
        },
        {
            "id": "shape_leng",
            "data_type": "decimal"
        },
        {
            "id": "shape_area",
            "data_type": "decimal"
        },
        {
            "id": "wkb_geometry",
            "data_type": "geometry"
        }
    ],
    "run_details": {
        "type": "manual",
        "runner": {
            "username": "Finn van Krieken"
        },
        "timestamp": "2024-09-30T16:23:51.693603-04:00",
        "logged": false
    }
}

Other than that, it's helpful to go by commit

Some cleanup from #1155 - getting test coverage up and fixing a random literal
New ingest template that's been my guinea pig
Move the current "ingest" templates to a "dev" folder - in next PR, want to add logic to start using any found ingest templates, then fall back to library for our cli calls, but don't want to throw these ones which are sort of "in-progress" away (and didn't want to update them all right now without validating the data)
add Attributes section to Template and Config models, falling in line a little bit with our product metadata
use SortedBase across the models that get dumped to config.json for nice serialization. Make a few more submodels of config to simplify what's at the top level
adjustments for tests from commit 5 (just seemed easier to have these separate). Also tweaked run.run to make it easier to test (and aim it somewhere other than the "production" template folder)
For the four datasets that I've actually archived with ingest, move them to the production folder (and adjust to new structure)
Add columns to ingest templates. Include sthe most basic validation step, that all defined columns exist in the dataset. This is also where eventually column-specific checks could be specified
Fix a bug - gdfs get unhappy if you try to naively rename the geometry column (as in, just treating it as any other pandas column)
Add a default step that any dataset with geometry converts its geom to multi. Effectively same as calling ST_MULTI in postgis. See test cases for the utility for expected behavior. This seems like maybe a necessary evil for now - I'd prefer to keep geometry as they are, but this keeps things more consistent with library outputs (and makes sure things are consistent when we import to postgis, which can be more finicky/specific with types)

Ok, finished my review! Love how the new dataset templates look like and the models representing them make so much more sense. Great work.
The first commit is a bit obscure to me since I missed the previous related PR - can't provide feedback
Questions:

ongoing convo above about Template vs Config
Could you please explain the need for the last commit creating a multi geometry? What is the benefit

@fvankrieken One immediate though: would it make sense to publish JSON schemas for these models? (similar to admin.ops.update_product_metadata_schema)

Notes from group chat

separate out default processing step logic into helper
clean up language "processing" vs "preprocessing" (make consistent)
type signature for reprojection func
raise not implemented for custom script
ability for steps to log information about themselves

I'll do these in a follow-up pr

authors: alexrichey fvankrieken sf-dcp

repo: NYCPlanning/data-engineering

license: MIT


text:
betterdiscordlibrary source code link 404

Didn't want to make an account on betterdiscordlibrary
Clicking the source code link on the betterdiscordlibrary page 404s

Links to an old path (https://github.com/NYRI4/Comfy/blob/master/comfy.theme.css) instead of the current path or to the repo itself.

Fixed

license: WTFPL


text:
Lights start flashing at Night

Me and my Admins experience some problems at night ( StormFox night )
at Night the map lights are constantly flashing, they are turning on and off repeatedly, but like with Transition.
I don't know why it happens or what causes it, so I am asking if that is a Bug, or a feature.
Also: awesome work. This is the best weather addon for GMod I have ever seen.
Thanks for reading,
~ApDev

I'm not able to replicate the old problem and the code have changed since then.
If the problem keeps happening, feel free to re-open.


text:
10.0.0 support / not working

I appear to have installed the extension but do not see it.  I looked at the previous issue and went through those steps but it's not in the experimental plugins section any more, so it doesn't seem to apply.

Any help would be appreciated!  Thank you.

for what it's worth I just tried with 9.2.4 and it didn't appear to work there either.  I used the cloning into Ghidra/Extensions method of installing.

Hi!
Seems it's better to build extension with gradle and add it with File → Install Extensions... menu option. Or you can download release build from here for ghidra 10-BETA.
And as you can see, experimental configure menu works ok:

Thank you for responding.  I was able to download the precompiled plugin and have it show up (not under experimental for me, it shows up normally)
If I understand your plugin correctly it should launch a python to run angr.  However, this does not happen for me (python is on my path).  The plugin simply immediately tells me that a solution cannot be found.  Is there an easy way to enable debugging for this?  I'm guessing I have to do the whole  Ghidra from source debugging thing.  I'll see if I can find some time to do that.

You should check that "python3" is in your enviroment like this:
C:\Users\Nalen>python3
Python 3.9.5 (tags/v3.9.5:0a7dcbd, May  3 2021, 17:27:52) [MSC v.1928 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import angr
>>>

If plugin was installed correctly this one should work.


text:
fix: 9004「1月にプレイ」の判定日を修正

close #139
何故か、間違った判定日になっていたので修正実施。

@ikazuchizero @yuuki1293
確認お願いします

やだ！！えいぷりるふーるだもん！！

そっかぁ...👀

クソでかいdiff

クソでかいdiff

インデント弄るとdiff扱いされるのどうにかできないもんだろうか

Files Changedで見る表示設定でHide Whitespaceを有効にすれば無視はできるが、微妙かしら
https://zenn.dev/rabee/articles/github-pull-request-hide-whitespace-changes

commitごとの変更見れば変更箇所わかるので大丈夫だった

ほへーーー

まーじしていいの？

まーじしていいの？

@ikazuchizero
まーじよろしゃす

repo: NamagomiNetwork/Namagomi-bot


text:
assignment 3

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)
What did you learn from the changes you have made?
Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?
Were there any challenges? If so, what issue(s) did you face? How did you overcome it?
How were these changes tested?
A reference to a related issue in your repository (if applicable)
Checklist

[ ] I can confirm that my changes are working as intended

A3 complete

repo: NamreenSyed/visualization


text:
Merge coreclr branch into master

:scream:
Prerequisites

[x] I have written a descriptive pull-request title
[x] I have verified that there are no overlapping pull-requests open
[x] I have verified that I am following the Nancy code style guidelines
[x] I have provided test coverage for my change (where applicable)

Description
Let's do this!

https://media.giphy.com/media/2Faz8bcnGfXVrV2ZG/giphy.gif

This Pull Request has too many commits and violates the Single Pull Request Responsibility Principal so we can't merge it in, sorry - better luck next time.

authors: Yantrio grumpydev jchannon khellang thecodejunkie

repo: NancyFx/Nancy


text:
Enable enr field in GetPeers and GetPeerById (#8641)

Cherry-picked from master

Decided to not get too far from master and keep it

repo: Nashatyrev/teku


text:
Add Early Voting instructions.

edits to the Add Event page around early voting

@fozzieblue Please review code for branch.

@kyletcook Reviewed and approved pull request.

authors: fozzieblue kyletcook

repo: Nashville-Public-Library/bedework-guide


text:
直接通过比赛列表识别比赛

不点开比赛详情直接识别比赛
这样操作少 纯白背景准确度应该也高 就是需要先定位选项位置

非固定位置的文字识别，只要位置有偏差，准确率就会降低，我之前识别技能就是这样，识别了好几天的技能，过几天又一直识别不了，这种感觉还是hash粗筛+直接比图细筛，是最好的

我实现了 没出现你说的问题 你说的问题应该是你没加测试的原因

authors: NateScarlet yourlies

repo: NateScarlet/auto-derby


text:
NPM_DEPENDENCIES vs APP_ASSETS, and how to include dependencies in general

Hey Guys,
This is a help/advice/question, and probably benefit being documented somewhere (maybe as inline comments on the project.config.ts).
I had a look at the project.config.ts, and found it a little vague and confusing. Well, it has 2 sections for APP_ASSETS and NPM_DEPENDENCIES, then each of those has options like inject: 'libs', inject: true, vendor: false. For starters, i dont really understand what all the options are and what each of the options mean. Secondly, what exactly is the difference between NPM_DEPENDENCIES array and the APP_ASSETS array.
So, lets define some use cases. How would i go about them:

I want to add a JS library to the frontend (web/view). Lets say ng2 bootstrap?
What if the library came with dependencies, how do i attach them (ie bootstrap's main js file is required by ng2 bootstrap)?
What if the library (or its dependencies) came with css files, how do i attach them (ie bootstrap's css file)?
What if the library was not pulled from npm. What if it was a JS file or couple of js files pulled off from another project, where would i save them and how would i attach/include them? (for frontend use)
How would i add a js library for backend/electron? (lets say socket.io as an example downloaded from npm install).
How would i add a JS library/files to backend/electron that were NOT pulled from npm. ie. some files/classes written for another project form example. Where would i save them and how would i include/use them?

PS. im sure i can figure them out on my own, i sort of have already anyway. But its good to know the official/proper way to do it incase im hacking it and not doing how things should be done. Also its good to have it documented.
Cheers,
TY in advance.

Hi @syrys a lot of questions like these can be answered in parent seed wiki here:
https://github.com/mgechev/angular2-seed/wiki
https://github.com/mgechev/angular2-seed/wiki/Add-external-dependency
I would recommend studying the wiki there as this seed is merely an expansion of it's parent.
As far as electron, the electron implementation here is very much experimental and intended to generate community discussion around best ways to integrate. As such, it is open to interpretation as well as improvement via Pull Request.
The parent seed has done a good job of answering the majority of your questions above so I will close this, however in regards to electron, I am planning to provide a single example of what all files need modification when integrating an example library and that will be addressed via this issue here: https://github.com/NathanWalker/angular2-seed-advanced/issues/176

@NathanWalker Alright, Thanks, that should give me enough info to figure things out. Could we probably add a section in the readme file or this repo's wiki that links people to corresponding repo's wiki's for specific sections (ie. base -> go here, electron -> go here, gulp -> go here, nativescript -> go here)?
Main problem for me is, i came here not knowing much about the angular2-seed (and im new to angular2 as well), and i see that this repo has not much documentation for noobs, but since this is an extension of a lot of repo's, its hard for me to tell where one repo starts and the other repo begins (specially as you said, with electron integration and all that). Therefore, makes it a bit hard to know where to look for info (if its unique to this repo or related to another repo).

Any news on how to get dependencies added into the electron version? Right now, I can only get it to work when npm installing to both root and src/client, but that's a lot of overhead with multiple libraries.

Found out the missing piece of information in another issue. Adding the node_modules to desktop.libs.js did the trick. Why is it necessary to specify this manually? It seems adding them to project.config.js should automagically include them into the desktop.libs step?

authors: NathanWalker kbrandwijk syrys

license: mit


text:
What is the "local_res" label?

Hi,
Just curious what the "local_res" label refers to. I understand it represents "local reserved", but I'm looking for some more clarity. It does appear to be a value relevant beyond the local scope, and it's clearly not an argument. Example below.

What is shadow space?

@ryanmkurtz This hasn't been answered!

Oops

authors: OmarDarwish Wall-AF as0ni ryanmkurtz

repo: NationalSecurityAgency/ghidra


text:
___chkstk_ms not recognized automatically

Describe the bug
The x86-64 processor cspec is missing a callfixup for ___chkstk_ms. It behaves the same way as __chkstk and friends.
To Reproduce
Steps to reproduce the behavior:

Get the sample binary: sample.zip
Go to the function at 0x004c5910.
Check the decompiler output.
See that the function's local variables are messed up. The decompiler even created a variable for the stack pointer.

Expected behavior
In the decompiler output, the stack assignments should be omitted.
Environment (please complete the following information):

OS: Arch Linux (up to date on 2020-05-21)
Java Version: jre11-openjdk 11.0.6.u10-1
Ghidra Version: ghidra 9.1.2-1 from the Arch Linux repos.

upvoted


text:
Unable to open project file (java.io.EOFException)

Describe the bug
For some reason, when I tried to open a file I had worked on reverse engineering before, I got greeted by a java.io.EOFException. This has happened to me before with different files, and back then I just decompiled the file again as I didn't have many changes done to it, but this reverse engineering project is something I've worked on for months and would really prefer to not lose all that work.
I've tried updating my JDK but that didn't help. I'll be happy to provide any extra info you need.
To Reproduce
I am not sure how to reproduce this. It has happened on seemingly random occasions to multiple files.
Expected behavior
What I expected is for the project to load normally.
Screenshots

I have ghidra-dark installed, but I believe that to not be the issue as I got this problem with a different file before.
Attachments
Log file
error.txt
Environment

OS: Windows 10 19042.867
Java Version: 16
Ghidra Version: 9.2.2
Ghidra Origin: Official ghidra-sre.org distro

Looking at the stack trace, Ghidra is having a problem reading the database files that used to store your work.
Ghidra is very forgiving when it comes to being shut down / terminated unexpectedly and can usually just recover or rollback to the last good state.  However, it seems the database files got truncated or corrupted in some manner.  Did you have any OS or hardware issues?
On a side note, you listed java16 as your java version.  From reports of other users this will cause issues running Ghidra.  (probably not your issue though)

I shouldn't have any OS or hardware issues, although now that you mention it, on bootup Windows often wants to "fix" my F: drive which is where my Ghidra projects are located. I don't know why Windows wants to try to fix it, I've never noticed anything wrong with it, but it might be that which is causing the issue. I'll try moving my projects away to a different drive and see if the issue appears again.
Can you think of a way I could recover data from the project that got corrupted? Doing all of the RE work again would be one pain

I don't have any suggestions other than using backup software to protect against this type of issue, especially if your OS is marking your file system as damaged.
You can also manually export a ghidra project or a single program to a file to make manual 'backups'.


text:
Low-level Error: Could not find op at target address: (ram,...)

Describe the bug
Attempting to implement a conditional skip instruction with dynamc pcode injection (THE ONLY POSSIBLE WAY), results in the above error.
To Reproduce
Steps to reproduce the behavior:

See the attachments for an example implementation.

Expected behavior
It should be possible to implement a skip instruction.
Screenshots
If applicable, add screenshots to help explain your problem.
Attachments
InjectSkipPayload.java.txt
RL78InjectLibrary.java.txt
<callotherfixup targetop="SKIP">
  <pcode dynamic="true">
    <input name="cond"/>
  </pcode>
</callotherfixup>

define pcodeop SKIP;

# SKC
:SKC is opcode=0x61; opcode=0xC8 {
    SKIP($(CY));
}

# SKNC
:SKNC is opcode=0x61; opcode=0xD8 {
    SKIP(!$(CY));
}

# SKZ
:SKZ is opcode=0x61; opcode=0xE8 {
    SKIP($(Z));
}

# SKNZ
:SKNZ is opcode=0x61; opcode=0xF8 {
    SKIP(!$(Z));
}

# SKH
:SKH is opcode=0x61; opcode=0xE3 {
    SKIP($(Z) | $(CY));
}

# SKNH
:SKNH is opcode=0x61; opcode=0xF3 {
    SKIP(!($(Z) | $(CY)));
}

Additional context
#4241

Please forgive my origin post, I was a tiny bit frustrated.
Anyway, I think this is due to a limitation of the sleigh compiler and how it handles possible branches. Since the sleigh compiler does not see it as a possible branch it doesn't make it possible to branch to the address I want or something like that.


text:
Throw exception on invalid address space element during decode

When decoding an address space, the translator will peek the next element from the decoder and select the appropriate address space based on the element id returned by the decoder. The translator currently assumes the address space must be a <space> element if it doesn't match any of the others. This change confirms that the address space is a <space> element and throws an exception if the element is not a valid address space.
This situation can also occur if the ElementId::initialize() function has not been called before decoding. In that case, the decoder will fail to recognize any address space element and will always return ELEM_UNKNOWN.
The exception includes the internal element id since that is all that's available in the translator. Additional information could be obtained if instead of peeking an arbitrary element, a separate decoding function peeking a set of valid elements was called instead. The decoder could then throw an exception naming the unexpected element.

@jobermayr Revisiting old PRs to see if anything needs to be followed up on.


text:
Android WebViewCoreThread

From @x4080 on January 15, 2016 4:10
Hi, is ns in android using also this WebViewCoreThread? If not, we can use it to get another JavaScript thread?
Copied from original issue: NativeScript/NativeScript#1376

From @x4080 on January 15, 2016 5:23
i wait for your testing

From @NathanaelA on January 15, 2016 4:26
Wow, I LIKE it.   That is a really great idea!    Going to go try this right now....  :grinning:

From @NathanaelA on January 15, 2016 6:18
Ok, who is up to help build a plugin for this.   I personally think we should use the WebWorker interface/model since their is lots of documentation and information on how it works for new developers and it best matches this model since the WebView thread will be a totally limited thread without any access to NativeScript other than via our communication channel.

From @NathanaelA on January 15, 2016 6:7
I had to go look up WW support; it looks like you have to have 4.4, webworkers weren't present in earlier versions.    But it does give us another thread.   This idea might also work for iOS, I bet there webview runs in its own thread also...

From @NathanaelA on January 15, 2016 5:58
Tested on an Android 4.4 emulator and it appears like this idea will work great!  Brilliant idea!   And guess what; at least with android 4.4, we also get WebWorkers inside the WebView.  So I was running three threads.  :grinning:    The fullscreen WebView was having some issues on display while the main thread was busy in NS (which was expected); but it still updated.  It was running the non-visual code at full steam.  Now I haven't coded anything up but a simple test.  So to actually realize this idea; is going to require a bit of work to be able to setup the communication layer...   And of course you don't have access to the NS runtimes from inside WebView; so it is a limited JS environment.

From @x4080 on January 15, 2016 6:26
@NathanaelA thanks for your testing. This will be good for ns fans like me. Hopefully ios can do the same thing and with wkwebview will run faster

From @NathanaelA on January 15, 2016 6:25
Hey NativeScript Android team ( @atanasovg @ErjanGavalji @Plamen5kov @slavchev @blagoev ), what is the current way to do annotations in JS code?   If you read the docs at
http://developer.android.com/reference/android/webkit/WebView.html#addJavascriptInterface(java.lang.Object, java.lang.String)
It states that for the AddJavaScriptInterface to be able to call functions from the JS bridge they need to be tagged with: @JavascriptInterface for API 17 and above...

@NathanaelA We discussed this approach internally with the team. While it is a valid one, it is definitely NOT recommended by the {N} team. There are several major concerns we have, the least being utilizing two virtual JS machines which would hurt the performance - probably significantly.
The annotations feature is with low priority in our backlog so far. Still, if you wish to do further research on this idea - @slavchev has made a POC implementation for annotations through JS. It currently resides in a separate branch -
https://github.com/NativeScript/android-runtime/tree/slavchev/generate-annotations - which you may use as a reference.

@atanasovg, Awesome thanks for the pointer to the annotations.   I'll check it out.
I actually had the same theory as you; that performance would be adversely affected.  So part of my test had timings to do both prime determination and sorting 50k worth of records.  The timings running them stand alone vs running then at the same time were almost identically; so I'm happy to report the good news; at least on an emulator (I haven't tried my test on an actual phone); using the Webview vm actually had little to no effect on the NS vm.     Since most phones now actually have two or more cpus; the odds are very likely it will work equally well on an actual device.  Obviously on a single core device it will effect everything...
I would be interested in any other pitfalls the team has come up with...

The annotation fork, will it be in the master branch? @NathanaelA , we still have hope then

@x4080 oh yeah; I might have a version done later today.  ;-)
I was just researching different ways to do it; I tend to research things to death before I implement, but actual implementation is fairly simple now that I have a plan of attack...

You're the man :)

Ok, I have a running version on Android, that is plugin ready!   And I have confirmed (at least on the emulator) that their is minimal to no slowdown in running both threads.   I embedded the plugin into my app that has a massive startup time and made the background thread generate primes.   Both threads clocked the same amount of time as running them separately.   (The time is based off of a starting timestamp; so if one thread was delaying the other; then that thread time would jump by the others cpu time).  I can confirm both started at the same time; and can see the console.log interleaving the log messages from both threads.  :grinning:
The nice thing at least on android is I do NOT have to attach it to a view.   So it can remain available for the entire app life cycle if you want.   It is wrapped exactly like you would use a WebWorker; which actually opens the app I'm writing to eliminate a large part of the startup time...  :grinning:
Just Like NS has a bit of time starting up; the webview actually has a tad of startup time; so I added a onready event for those who might need it.  However the postmesages code is designed to queue up the messages until it is ready so you can use it exactly as you would a normal webworker.

cool, is the annotation workaround works? or just using 4.4?

Not using annotations; using the JsConfirm system.  This way it will work on everything, and won't have any issues when NS starts working on API 16/17/18 devices.  I'm working on the repo right now;
https://github.com/NathanaelA/nativescript-webworkers

You can close this issue; I have the webworkers working on both iOS and Android.   Still doesn't solve my request: https://github.com/NativeScript/NativeScript/issues/85 but it allows us to work around it in some cases.

closing the issue by request

authors: NathanaelA atanasovg enchev slavchev x4080

repo: NativeScript/android-runtime


text:
ListView multiple template dont Work

From @domzinhuu on April 3, 2018 20:46
Please, provide the details below:
When use mutiple template with observable and async pipe the itemTemplateSelector dont call the method when array is empyt
Which platform(s) does your issue occur on?
Android
Please provide the following version numbers that your issue occurs with:
nativescript     │ 3.4.2
tns-core-modules │ 3.4.1
tns-android      │ 3.4.2
tns-ios          │ 3.4.1
Is there code involved? If so, please share the minimal amount of code needed to recreate the problem.
Components.ts
Observable<Objetct[]>
this.beneficiaries$ = this.beneficiaryService.getAll();

templateSelector(item:any, index: number, items: any) {
    return items.length > 0 ? 'base' : 'empty'; }

Html
<ListView [items]="beneficiaries$ | async" class="list-group" [itemTemplateSelector]="templateSelector">
      <ng-template nsTemplateKey="base" let-item="item" let-odd="odd" let-index="item.id" let-even="even">
        <StackLayout class="list-group-item" (tap)="onItemTap(item.id)">

        </StackLayout>
      </ng-template>

      <ng-template nsTemplateKey="empty">
        <StackLayout class="list-group-item">

        </StackLayout>
      </ng-template>
    </ListView>

Copied from original issue: NativeScript/NativeScript#5615

Cool , thanks!
Obter o Outlook para Android
On Thu, Apr 5, 2018 at 5:32 AM -0300, "Nikolay Tsonev" notifications@github.com wrote:
Hi @domzinhuu,
We checked your scenario, and this seems to be expected behaviour. The itemTemplateSelector will be fired for every ListView item, If there are no items, this will not templateSelector method will not be executed.
In your scenario, you could set up the No Items label outside the ListView and hide it when the ListView has items, which to be displayed. For example:
HTML

<ListView row="0" [items]="myItems | async" class="list-group" [itemTemplateSelector]="templateSelector" [visibility]="isItemVisible ? 'visible' : 'collapsed'">

        <ng-template nsTemplateKey="green" let-item="item" let-i="index" let-odd="odd" let-even="even">
            <GridLayout class="list-group-item" backgroundColor="green">

            </GridLayout>
        </ng-template>
    </ListView>
</GridLayout>

TypeScript
import { Component, ChangeDetectionStrategy } from "@angular/core";
import { Observable as RxObservable } from "rxjs/Observable";
export class DataItem {
constructor(public id: number, public name: string) { }
}
@Component({
selector: "ns-items",
moduleId: module.id,
templateUrl: "./items.component.html",
})
export class ItemsComponent {
public myItems: RxObservable<Array>;
public isItemVisible:boolean = false;
constructor() {
let items = [];
    let subscr;
    this.myItems = RxObservable.create(subscriber => {
        this.isItemVisible = false;
        subscr = subscriber;
        subscriber.next(items);
        return function () {
            console.log("Unsubscribe called!");
        };
    });

    let counter = 2;
}
public templateSelector = (item: DataItem, index: number, items: any) => {

    return index%2 === 0 ? "red" : "green";
}

}
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub, or mute the thread.

authors: domzinhuu tsonevn

repo: NativeScript/nativescript-angular


text:
page-router-outlet child of named page-router-outlet is not displaying content

Environment
Provide version numbers for the following components (information can be retrieved by running tns info in your project folder or by inspecting the package.json of the project):

CLI: 6.4.1
Cross-platform modules: 6.4.2
Android Runtime: 6.4.1
iOS Runtime: 6.4.2
NativeScript-Angular: 8.20.4
Angular: 8.2.14

Describe the bug
've encountered an issue in Nativescript Angular where content of a page-router-outlet is not rendered when it is a child of a named page-router-outlet.
I've created a playground to show the differences between the behavior of a PRO that is a child of the default PRO, and a PRO that is the child of a named (auxiliary) page router outlet.
Structure is as follows

Root PRO (app component)

PRO (Home Component)

PRO (Unnamed component) (yellow part in preview)

Content (named child component) (gray part in preview)

Named PRO (Home Component)

PRO (Named Component) (Red part in preview)

Content (Unnamed child component) (Orange part in preview)
With the above structure, the content that should be rendered in the page-router-outlet of the Named component, ie. that is a child of a named page router outlet, is not rendered, and no orange part is shown.

If the PRO of the Named Component is changed to an Angular  element, the orange part is rendered.
I am doing something wrong in attempting to render the orange content?
To Reproduce
Here's the url of the playground app https://play.nativescript.org/?template=play-ng&id=3GMdit
Expected behavior
Content of PRO that is a child of a named PRO should be rendered

@jeanpaulattard this seems to be a limitation as the root PRO is already unnamed and you can't have a nested PRO that s unnamed. Still, the workaround is pretty straight forward - just don't create unnamed outlet. This should be easy to follow as I see not actual benefit of having an unnamed outlet (compared to creating a named one).
https://play.nativescript.org/?template=play-ng&id=3GMdit&v=2

@NickIliev can you please provide a plunker to show me what you're trying to say please?
Because in https://play.nativescript.org/?template=play-ng&id=3GMdit&v=3 the gray and orange parts, that should be rendered (these are children of another page router outlet in UnnamedComponent and NamedComponent respectively are still not being rendered.
Would appreciate it if you could offer a working plunker with the solution, otherwise reopen the issue please.

authors: NickIliev jeanpaulattard


text:
Fixed: CSS is not automatically livesynced in angular apps

https://github.com/NativeScript/nativescript-cli/issues/1958
ping @vakrilov

👍

authors: tzraikov vakrilov


text:
multiple logistic regression with floating explanatory variables?

In your specs I noticed that all sample cases use binary input values to train the model.
I'm looking for a typical logistic regression where only the dependent variable is binary, either 1 or 0, but the explanatory variables can be any number.
Also, I am using multiple variables as explanatory variables.
Is this library right for me or am I looking for a different use case?

Also interested in knowing if the learning rate, amount of iterations etc. are supposed to be fixed as they currently seem to be?

repo: NaturalNode/apparatus


text:
Hi my account got hacked and i saw your tool i got it back but he changed pin

Hi my account got hacked and i saw your tool i got it back but he changed pin.
so i wanted to know how to use your tool

@Ne3tCode

Hi my account got hacked and i saw your tool i got it back but he changed pin.
so i wanted to know how to use your tool

I will advise you to contact the best hacker around he helped me recover my facebook,fast and reliable his telegram is
https://t.me/Blackhathacker110

authors: Harrystarafk ofekasor


text:
Make pretty JSON formatting optional in basic formatter

Some log processors can't process JSON logs if they are spread across multiple lines.
I saw the other formatters don't have pretty at all so I'm not sure if it's intentional.

@hkrutzer Good catch! Please instead of making it an option just remove it. This is just a leftover after a total rewrite.

Thanks, removed it!

Thank you! @hkrutzer

Thanks @AndrewDryga. Are you planning on publishing 6.x to hex.pm anytime soon?

@hkrutzer yes, I'm just trying to test it in our production to make sure it won't fail in yours. I think I will bump the version early next week.

authors: AndrewDryga hkrutzer


text:
gateway-mfr-rs: Update to latest release v0.1.7

Update gateway-mfr-rs to latest release v0.1.7
Ref https://github.com/helium/gateway-mfr-rs/releases/tag/v0.1.7

Deleting this PR to fix the checksum check

authors: mr-bump shawaj

repo: NebraLtd/hm-pyhelper


text:
[/boss battle] switching Pokemon after death makes the player stuck on "Waiting..."

When participating in the new boss battle, after one of your Pokemon is defeated, you are prompted to switch to another Pokemon as usual. Upon selecting another Pokemon, the battle will be stuck on "Waiting...".
Steps to reproduce the behavior:

type /boss battle
make your Pokemon die to the boss
Upon being prompted to select another Pokemon, do exactly that.
Get stuck on "Waiting..."

Expected behavior
The Pokemon gets switched out normally.
Username: Uschalabamba
Server name: Orion
Logs are have been added:
latest.log
Additional Infos: When the boss is defeated by other players, the battle ends and you still get rewards. This will take a hot minute though.

Does this always happen or is this a one off? If so, I need a full party log.

Closing, feature was overhauled. If issue persists, please open a new ticket.

authors: Rasgnarok Uschalabamba

repo: NebulaMC-GG/Bugs-and-Issues


text:
Host logging panic

This PR was ultimately inspired by a fix which could have been 5 lines, but while in the code I found some stuff which needed cleanup. And then I felt compelled to write a regression test.
Regression test is passing, shutdown for the host tester is cleaner, and some of the files are now sorted alphabetically.

The host was panicking sometimes because shutdown would trigger before the gothread managing port forwarding had properly initialized. The fix is to have the port management thread call h.tg.Add

repo: NebulousLabs/Sia


text:
attr -> prop

Issue #132

Coverage remained the same at 38.916% when pulling 42e53472888b24a14f8aa89b8417a63790934b26 on attrs_to_props into f4dd9f2efe00c4759b6c1bf6fd196c06947e6d55 on master.

authors: KrysKruk coveralls

repo: Neft-io/neft


text:
🛑 Becpl Angular UI is down

In 5c57a22, Becpl Angular UI ($BECPL_ANGULAR_URL) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Becpl Angular UI is back up in a78d510 after 8 minutes.

repo: NehalDamania/becpl-uptime


text:
🛑 Becpl Angular UI is down

In 763da95, Becpl Angular UI ($BECPL_ANGULAR_URL) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Becpl Angular UI is back up in 4739b78 after 5 minutes.


text:
🛑 Becpl Angular UI is down

In 19b0b97, Becpl Angular UI ($BECPL_ANGULAR_URL) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Becpl Angular UI is back up in 54c29ae after 7 minutes.


text:
🛑 Becpl Angular UI is down

In c9e1546, Becpl Angular UI ($BECPL_ANGULAR_URL) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Becpl Angular UI is back up in ab0a94a after 5 minutes.


text:
ファイルのリスト化

言語が増えるにつれてファイルの場所が分かりづらくなっているので、ディレクトリでまとめたい。
音夢が直々にやろうと思うので、意識だけお願いします。

やります


text:
Support Keep/Drop rolls

In many dice rollers this is supported with k and/or d short for keep and drop respectively. Usually these optionally contain h or l for highest and lowest. Sometimes k defaults to kh and d defaults to dl.
Some examples:
4d6d1 — roll 4d6 drop the lowest
2d20k1 — roll 2 d20 keep highest(advantage)
2d20kl1 — roll 2d20 keep lowest(disadvantage)
I'm happy to implement this but I wanted to make sure that you're on board with it first. Also curious if you think it's worth implementing both or just doing one of the two as you can represent the same thing with either. Advantage could be 2d20dl1 for example.

I didn't know this ! I'm very happy with your proposal !

d, k for drop and keep
l and h for lowest and highest
if no l or h specified, we consider l as the default value (for both k and d)

If you don't do it I will anyway ! Very cool idea :)
Thanks !

Sounds great. I'll take a stab at this today since I have time.
Just for some context. I'm rather new to Crystal but I've been following it for some time as I love Ruby. I'm looking to convert a simple discord dice bot I have trescenzi/dice-n-easy from Ruby to Crystal now that it's 1.0. Starting with the rolling part since that's simpler than the discord api part haha.

Nice ! I use it myself for a IRC bot. I didn't look at the discord api implementations crystal but I saw some on crystalshards, I bet they are pretty easy If you stick to connect/on message/reply :)

I'll try to merge this noon !

authors: Nephos trescenzi


text:
Add Tailwind to Entry Overview Component.

Co-authored-by: med4kat med4kat@users.noreply.github.com
We added tailwind style to the Overview
It's now kind of a table. It could be final for MVP, but we will want something better in the future.
PS: A small errore happened in this commit, but please ignore. It's minor and it will be ok as soon as the next pull request goes through. I know this is not good practice, but this is it:
The notFound.css should not be deleted in this commit but in the next one.

Is it correct that the "notebook" button to show the notes of the Entry does not work?

authors: steph-koopmanschap xintamosaik

repo: Nerds-Who-Code/Mental-Health-Tracker


text:
Cargo installation generates build erorr

I tried installing using cargo install taizen. Before that I did a fresh installation of rust and others with curl -sSf https://sh.rustup.rs | sh and selecting the default options.
The install failed with the status
   Compiling taizen v0.1.0
error: linking with `cc` failed: exit code: 1

Then a bunch of options following = note: "cc" and finally
  = note: /usr/bin/ld: cannot find -lncursesw
          collect2: error: ld returned 1 exit status

error: aborting due to previous error

error: failed to compile `taizen v0.1.0`, intermediate artifacts can be found at `/tmp/cargo-installu9ladT`

Caused by:
  could not compile `taizen`.

I'm running Ubuntu 16.04.

@rodrigolece
The error is telling you that it can't find ncursesw, try
$ apt install libncursesw5-dev

Thank you so much, that fixed it!
Can you give a piece of advice to a Linux beginner? How can I tell the name of the package that I should apt install, which is usually different from the name of the package that the error says is missing? Eg. how can I deduce that the ncursesw translates into libncursesw5-dev?

@rodrigolece
In this particular instance, it was a simple net search of the error.
If this wasn't the case, the Cursive library wiki lists what version you need for each distribution.

authors: kusinara rodrigolece

repo: NerdyPepper/taizen


text:
[Improvement]: Extract the deleting dangling files from the cleaning orphan files

Search before asking

[X] I have searched in the issues and found no similar issues.

What would you like to be improved?
Currently, the deletion of dangling files is performed during the process of cleaning orphan files where com.netease.arctic.server.optimizing.maintainer.IcebergTableMaintainer#cleanDanglingDeleteFiles.
We should separate these two functions so that they can be executed independently.
How should we improve?
Deleting dangling files should be a specialized funtion of com.netease.arctic.server.optimizing.maintainer.TableMaintainer.
Are you willing to submit PR?

[ ] Yes I am willing to submit a PR!

Subtasks
No response
Code of Conduct

[X] I agree to follow this project's Code of Conduct

Please assign it to me so that I can complete it. @wangtaohz

repo: NetEase/amoro


text:
haproxy: Support running a custom haproxy image

Many users may want to change their default haproxy to run custom
configuration of some sort. This patch allows them to by simply
calling a SetImage function.

meh
broken, closing

repo: NetSys/quilt


text:
Are there any future plans to migrate hystrix from java 6 to java 8 (sourceCompatibility) ?

I have seen that hystrix uses JDK 6 platform. Are there any plans to change the platform to JDK 8
and there by updating all other old dependencies like updating servlet-api from 2.5 to 3.0 and many others like these?

jdk 8 and the servlet api aren't related are they?

Sorry for my vague question.
I see that the jetty implementation of servlet api 3.1 are build for java 7 or higher and we use jetty 9. And so I thought that we could not use it for java 6.
But as the travis build uses jdk 8, I don't think there would be an issue. But I think as the gradle checks for source compatibility of java 6, we would not be able to use java 8 features like lambdas or stream. Or could we?
My actual intension of the question is:
I see we are using web.xml in web apps. If we could change the servlet specs to 3.1 or more than we can use annotations on top of servlet classes or filter classes instead of these configuration files. I wanted to implement these. Can I do that?
Thanks!

There was a decent amount of pushback from Hystrix users in updating from JDK 1.6 -> 1.8 within the Hystrix 1 series.
As far as Servlet 3.1 goes, what specific bits of Hystrix relate to servlets?

authors: mattrjacobs sajjusajuu spencergibb

repo: Netflix/Hystrix


text:
Dashboard 1.4.x: Missing count of timed out calls in case of semaphore isolated commands

"Timed-out Request Count" is returned/used by all commands since hystrix 1.4

Good catch.  I'll add this.
On Sat, Apr 11, 2015 at 12:28 PM, gbrehmer notifications@github.com wrote:

"Timed-out Request Count" is returned/used by all commands since hystrix
1.4
—
Reply to this email directly or view it on GitHub
https://github.com/Netflix/Hystrix/issues/751.

authors: gbrehmer mattrjacobs


text:
Cannot pass null for optional arguments of inner types

Issue copied over from discussion: https://github.com/Netflix/dgs-framework/discussions/818
I've a problem using the dgs-codegen client (v4.9.15) which seems like a bug.
I have a schema which looks like:
type Query {
    findFoo(fooCode: String): Foo
}

type Foo {
   bars(first: Int, after: String): BarConnection
}

# Standard relay spec types...

type Bar {
  stuff : String
}

Each Foo can have many Bars, so I want to paginate the results. I believe this is a standard pattern.
I now want to run a query that looks something like:
query barPage($fooCode: String, $first : Int, $after : String) {
  findFoo(fooCode: $fooCode) {
    bars(first: $first, after: $after) {
      edges {
        node {
          stuff
        }
      }
    }
  }
}

The dgs-codegen library generates a client which is used like so:
List<Foo> fetchPageOfBars(String fooCode, int first, String after) {
   var projection = new FooProjectionRoot()
                .bars(first, after)
                    .edges()
                        .node()
                            .stuff()

     FooGraphQLQuery.newRequest()
                        .fooCode(fooCode)
                        .build(),
   // client stuff
}

Note that the fooCode is passed to the query builder, but the first and after arguments (which control the paging) are passed to the projection. This feels a little odd but I understand why it is necessary given the way the client works.
This works, unless the after argument is null which is necessary for the first page fetched. It's a cursor and we do not have a previous cursor value to provide. We hit a NullPointerException in BaseProjectionNode$InputArgument.<init>, which requires that the arguments provided are non-null. This comes from the generated code for FooProjectionRoot, which looks something like:
  public Foo_BarProjection bars(Integer first, String after) {
    Foo_BarProjection projection = new Foo_BarProjection(this, this);
    getFields().put("bars", projection);
    getInputArguments().computeIfAbsent("bars", k -> new ArrayList<>());
    InputArgument firstArg = new InputArgument("first", first);
    getInputArguments().get("bars").add(firstArg);
    InputArgument afterArg = new InputArgument("after", after);
    getInputArguments().get("bars").add(afterArg);
    return projection;
  }

The problem is apparently that new InputArgument("after", after) does not permit a null value for after.
The codegen generates a no-arg version of bars, but not one with just first. It seems legit to me to pass a null in here to represent "unspecified".
Is this just a case of needing to relax a null check, or should there be some conditioning code in the projection to only add the input argument if it is non-null?

Thanks for reporting!

I'm also seeing this issue, tried to change configuration settings to remedy, but I haven't found a solution.

I did find a workaround for this by explicitly attaching InputArgument objects to projection.
   var projection = new FooProjectionRoot();

   projection.getInputArguments().computeIfAbsent("bars", k -> new ArrayList<>());
   projection.getInputArguments().get("bars").add(new BaseProjectionNode.InputArgument("first", first));
   if (after != null) {
      projection.getInputArguments().get("bars").add(new BaseProjectionNode.InputArgument("after", after));
   }
   projection.bars().edges().node().stuff();

   GraphQLQueryRequest req = new GraphQLQueryRequest(
         FooGraphQLQuery.newRequest().fooCode(fooCode).build(),
         projection);

   System.out.println(req.serialize());

repo: Netflix/dgs-codegen


text:
How to access API

I am looking at the user guide, how do I access API?

Nervermind, I found it: http://localhost:8000/api/v1/

Hello, where to get api token?

repo: Netflix/dispatch-docker