I find the mxnet0.9.3 need a lot of memory. and mxnet0.7.0 need some memory.

in example(neural-style), mxnet0.9.3 is out of memory.
But in mxnet0.7.3 has a normal result.
in GTX650M 2G memory.

@eric-haibin-lin

Let me take a look

This result is in the mxnet0.7.0:
C:\Users\anjos\Downloads\Image_Style\Resourse>python -c "import mxnet; print mxnet. __version__"
0.7.0
C:\Users\anjos\Downloads\Image_Style\Resourse>python nstyle.py --style-image=input/Original_Image.jpg --content-image=style/Sytle_Image_1.jpg --output=output/out.jpg --stop-eps=0.005 --max-long-edge=800 --content-weight=10 --style-weight=1 --lr=0.001 --remove-noise=0.2 --tv-weight=0.01 --lr-sched-delay=75 --lr-sched-factor=0.9
INFO:root:load the content image, size = (547L, 1240L)
INFO:root:resize the content image to (352, 800)
[10:00:44] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
Skip argument target_gram_0
Skip argument target_gram_1
Skip argument target_gram_2
Skip argument target_gram_3
Skip argument target_gram_4
Skip argument target_content
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator\./reshape-inl.h:311: Using target_shape will be deprecated.
[8.6697666e-10, 8.6697666e-10, 8.6697666e-10, 8.6697666e-10, 3.4679066e-09, 10.0]
INFO:root:start training arguments Namespace(content_image='style/Sytle_Image_1.jpg', content_weight=10.0, gpu=0, lr=0.001, lr_sched_delay=75, lr_sched_factor=0.9, max_long_edge=800, max_num_epochs=1000, model='vgg19', output_dir='output/out.jpg', remove_noise=0.2, save_epochs=50, stop_eps=0.005, style_image='input/Original_Image.jpg', style_weight=1.0, tv_weight=0.01)
INFO:root:epoch 0, relative change 0.998924
INFO:root:epoch 1, relative change 0.604807
INFO:root:epoch 2, relative change 0.445041
forrtl: error (200): program aborting due to control-C event
Image              PC                Routine            Line        Source
KERNELBASE.dll     00007FFD939B7E23  Unknown               Unknown  Unknown
KERNEL32.DLL       00007FFD96598364  Unknown               Unknown  Unknown
ntdll.dll          00007FFD966A70D1  Unknown               Unknown  Unknown

C:\Users\anjos\Downloads\Image_Style\Resourse>

The second result is in the mxnet0.9.3:

C:\Users\anjos\Downloads\Image_Style\Resourse>python -c "import mxnet; print mxnet. __version__"
0.9.3

C:\Users\anjos\Downloads\Image_Style\Resourse>python nstyle.py --style-image=input/Original_Image.jpg --content-image=style/Sytle_Image_1.jpg --output=output/out.jpg --stop-eps=0.005 --max-long-edge=800 --content-weight=10 --style-weight=1 --lr=0.001 --remove-noise=0.2 --tv-weight=0.01 --lr-sched-delay=75 --lr-sched-factor=0.9
INFO:root:load the content image, size = (547L, 1240L)
INFO:root:resize the content image to (352, 800)
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:27] c:\deeplearning\mxnet0.9.3\src\operator\./cudnn_convolution-inl.h:55: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
Skip argument target_gram_0
Skip argument target_gram_1
Skip argument target_gram_2
Skip argument target_gram_3
Skip argument target_gram_4
Skip argument target_content
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor\./matrix_op-inl.h:156: Using target_shape will be deprecated.
[10:06:57] C:\deeplearning\mxnet0.9.3\nnvm\include\dmlc/logging.h:300: [10:06:57] c:\deeplearning\mxnet0.9.3\src\storage\./pooled_storage_manager.h:80: cudaMalloc failed: out of memory
Traceback (most recent call last):
  File "nstyle.py", line 259, in <module>
[    10train_nstyle(args)
:0  File "nstyle.py", line 183, in train_nstyle
6:57    ] style_loss, content_loss, size, dev)
C:  File "C:\Users\anjos\Downloads\Image_Style\Resourse\model_vgg19.py", line 65, in get_executor
\deeplearning\mxnet0.9.3\nnvm\include\dmlc/logging.h:300: [10:06:57] c:\deeplearning\mxnet0.9.3\src\storage\./pooled_storage_manager.h:80: cudaM    alexecutor = out.bind(ctx=ctx, args=arg_dict, args_grad=grad_dict, grad_req="write")
loc  File "C:\Python27\lib\site-packages\mxnet-0.9.3-py2.7.egg\mxnet\symbol.py", line 926, in bind
 failed: out of mem    orctypes.byref(handle)))
y
  File "C:\Python27\lib\site-packages\mxnet-0.9.3-py2.7.egg\mxnet\base.py", line 75, in check_call
[    10:06:57raise MXNetError(py_str(_LIB.MXGetLastError()))
] mxnet.baseC:\deeplear.niMXNetErrorng\mxnet0.9.3\nnvm\include\dmlc/logging.h: :[[30010:06:57] c:\deeplearning\mxnet0.9.3\src\storage\./pooled_storage_manager.h:80: cudaMalloc failed: out of memory:
[10:06:57] c:\deeplearning\mxnet0.9.3\src\engine\./threaded_engine.h:336: [10:06:57] c:\deeplearning\mxnet0.9.3\src\storage\./pooled_storage_manager.h:80: cudaMalloc failed: out of memory
An fatal error occurred in asynchronous engine operation. If you do not know what caused this error, you can try set environment variable MXNET_ENGINE_TYPE to NaiveEngine and run with debugger (i.e. gdb). This will force all operations to be synchronous and backtrace will give you the series of calls that lead to this error. Remember to set MXNET_ENGINE_TYPE back to empty after debugging.
1
C:\Users\anjos\Downloads\Image_Style\Resourse>

Thanks for the message. I have a fix for this, will put a PR after have it
is fully tested.
On 2017年2月23日 周四 at 18:08 HaoZhulin<EMAIL_ADDRESS>wrote:
This result is in the mxnet0.7.0:
C:\Users\anjos\Downloads\Image_Style\Resourse>python -c "import mxnet;
print mxnet. version"0.7.0
C:\Users\anjos\Downloads\Image_Style\Resourse>python nstyle.py
--style-image=input/Original_Image.jpg
--content-image=style/Sytle_Image_1.jpg --output=output/out.jpg
--stop-eps=0.005 --max-long-edge=800 --content-weight=10
--style-weight=1 --lr=0.001 --remove-noise=0.2 --tv-weight=0.01
--lr-sched-delay=75 --lr-sched-factor=0.9INFO:root:load the content
image, size = (547L, 1240L)INFO:root:resize the content image to (352,
800)
[10:00:44] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:44] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:45] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
Skip argument target_gram_0
Skip argument target_gram_1
Skip argument target_gram_2
Skip argument target_gram_3
Skip argument target_gram_4
Skip argument target_content
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[10:00:49] c:\deeplearning\mxnet\src\operator./reshape-inl.h:311:
Using target_shape will be deprecated.
[8.6697666e-10, 8.6697666e-10, 8.6697666e-10, 8.6697666e-10,
3.4679066e-09, 10.0]INFO:root:start training arguments
Namespace(content_image='style/Sytle_Image_1.jpg',
content_weight=10.0, gpu=0, lr=0.001, lr_sched_delay=75,
lr_sched_factor=0.9, max_long_edge=800, max_num_epochs=1000,
model='vgg19', output_dir='output/out.jpg', remove_noise=0.2,
save_epochs=50, stop_eps=0.005,
style_image='input/Original_Image.jpg', style_weight=1.0,
tv_weight=0.01)INFO:root:epoch 0, relative change
0.998924INFO:root:epoch 1, relative change 0.604807INFO:root:epoch 2,
relative change 0.445041
forrtl: error (200): program aborting due to control-C event
Image              PC                Routine            Line
SourceKERNELBASE.dll     00007FFD939B7E23  Unknown
Unknown  UnknownKERNEL32.DLL       00007FFD96598364  Unknown
Unknown  Unknown
ntdll.dll          00007FFD966A70D1  Unknown               Unknown  Unknown
C:\Users\anjos\Downloads\Image_Style\Resourse>
The second result is in the mxnet0.9.3:
C:\Users\anjos\Downloads\Image_Style\Resourse>python -c "import mxnet;
print mxnet. version"
0.9.3
C:\Users\anjos\Downloads\Image_Style\Resourse>python nstyle.py
--style-image=input/Original_Image.jpg
--content-image=style/Sytle_Image_1.jpg --output=output/out.jpg
--stop-eps=0.005 --max-long-edge=800 --content-weight=10
--style-weight=1 --lr=0.001 --remove-noise=0.2 --tv-weight=0.01
--lr-sched-delay=75 --lr-sched-factor=0.9
INFO:root:load the content image, size = (547L, 1240L)
INFO:root:resize the content image to (352, 800)
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:24] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:27] c:\deeplearning\mxnet0.9.3\src\operator./cudnn_convolution-inl.h:55:
Running performance tests to find the best convolution algorithm, this
can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT
to 0 to disable)
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:56] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
Skip argument target_gram_0
Skip argument target_gram_1
Skip argument target_gram_2
Skip argument target_gram_3
Skip argument target_gram_4
Skip argument target_content
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:57] c:\deeplearning\mxnet0.9.3\src\operator\tensor./matrix_op-inl.h:156:
Using target_shape will be deprecated.
[10:06:57] C:\deeplearning\mxnet0.9.3\nnvm\include\dmlc/logging.h:300:
[10:06:57] c:\deeplearning\mxnet0.9.3\src\storage./pooled_storage_manager.h:80:
cudaMalloc failed: out of memory
Traceback (most recent call last):
File "nstyle.py", line 259, in
[    10train_nstyle(args)
:0  File "nstyle.py", line 183, in train_nstyle
6:57    ] style_loss, content_loss, size, dev)
C:  File "C:\Users\anjos\Downloads\Image_Style\Resourse\model_vgg19.py",
line 65, in get_executor
\deeplearning\mxnet0.9.3\nnvm\include\dmlc/logging.h:300: [10:06:57]
c:\deeplearning\mxnet0.9.3\src\storage./pooled_storage_manager.h:80:
cudaM    alexecutor = out.bind(ctx=ctx, args=arg_dict,
args_grad=grad_dict, grad_req="write")
loc  File "C:\Python27\lib\site-packages\mxnet-0.9.3-py2.7.egg\mxnet\symbol.py",
line 926, in bind
failed: out of mem    orctypes.byref(handle)))
y
File "C:\Python27\lib\site-packages\mxnet-0.9.3-py2.7.egg\mxnet\base.py",
line 75, in check_call
[    10:06:57raise MXNetError(py_str(_LIB.MXGetLastError()))
] mxnet.baseC:\deeplear.niMXNetErrorng\mxnet0.9.3\nnvm\include\dmlc/logging.h:
:[[30010:06:57]
c:\deeplearning\mxnet0.9.3\src\storage./pooled_storage_manager.h:80:
cudaMalloc failed: out of memory:
[10:06:57] c:\deeplearning\mxnet0.9.3\src\engine./threaded_engine.h:336:
[10:06:57] c:\deeplearning\mxnet0.9.3\src\storage./pooled_storage_manager.h:80:
cudaMalloc failed: out of memory
An fatal error occurred in asynchronous engine operation. If you do
not know what caused this error, you can try set environment variable
MXNET_ENGINE_TYPE to NaiveEngine and run with debugger (i.e. gdb).
This will force all operations to be synchronous and backtrace will
give you the series of calls that lead to this error. Remember to set
MXNET_ENGINE_TYPE back to empty after debugging.
1
C:\Users\anjos\Downloads\Image_Style\Resourse>
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/dmlc/mxnet/issues/5123#issuecomment-282182869, or mute
the thread
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.
--
Best Regards,
Haibin Lin
Master of Science in Computer Science
School of Computer Science
Carnegie Mellon University
+1<PHONE_NUMBER>
www.linhaibin.com

You are welcome, It is very good If the mxnet API has a detailed use case especially mxnet.symbol for example choose_element_0index, Pad.
and, the api Provides the Pad function of the image(mxnet.symbol.Pad), Can you provide all the convolution units for extracting an image? I want to use all the convolution units of this image, and I don't know how to use the mxnet.symbol.Pad.


Infinite api calls being made

What happened?
[A bug happened!]
(https://nft.kodadot.xyz/rmrk/gallery/10249720-466e8b6338599eaf1e-GAVCUBE-GAV_CUBE_101-0000000000000101?page=1)
infinite calls being made to nftByIdMinimal query
https://www.loom.com/share/9a217118fa4b426aaac5ef4a7cd55cf9
Please reproduce in steps
Go to ... Click on .... See error
Expected Behavior
I've expected ...
maybe implement caching for this?
What browsers are you seeing the problem on?
No response
Are you logged in?
No response
At which address did you encounter bug?
No response
Screenshots
Paste from your clipboard or upload screenshots/video
Relevant log output
No response
Payment link for reward
https://kodadot.xyz/transfer/?target=<My_Kusama_Address_check_https://github.com/kodadot/nft-gallery/blob/main/CONTRIBUTING.md#creating-your-ksm-address>
Code of Conduct

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

Heh, I noticed that too a while ago. But @vikiival explained to me that we need this in order to prevent double spending on an NFT. That's why the call also only fetches very few properties.

Yup, new fire-squid will have subscription :)
for now we need to poll minimal data every couple of secs :)


Excuse me!!!

How to use on TWITCH?
It's not working!!
Can u fix it?
Thank you very much!!

Update version 0.0.5:
Thanks for your feedback.


Fix error when deploy with KS3

What problem does this PR solve?
Issue Number: close https://github.com/pingcap/tiflash/issues/7484
Problem Summary:
When deploy disagg arch with KS3, some behavior is different from AWS S3.

Query like "GET ?list-type=2..." to AWS S3 will cause an HTTP 302 and redirect to "GET /?list-type=2...", but KS3 returns HTTP 400 directly
Deleting a non-exist key on AWS S3 returns 200 while KS3 returns error with "NoSuchKey"
Create a bucket with existing name on AWS S3 returns "BucketAlreadyOwnedByYou" while KS3 returns "BucketAlreadyExists"
Creating a lifecycle rule returns invalid key on KS3 (non resolved yet)

What is changed and how it works?

Prepend "/" to the URI when it is empty, it could also help us reduce a redirection on AWS S3
Adjust deleting non-exist key result check
Adjust create bucket result check
Reduce error logging cause by HEAD request with 404 response code

Check List
Tests

[ ] Unit test
[ ] Integration test
[x] Manual test (add detailed scripts or steps below)
[ ] No code

Side effects

[ ] Performance regression: Consumes more CPU
[ ] Performance regression: Consumes more Memory
[ ] Breaking backward compatibility

Documentation

[ ] Affects user behaviors
[ ] Contains syntax changes
[ ] Contains variable changes
[ ] Contains experimental features
[ ] Changes MySQL compatibility

Release note

None

/run-all-tests

/merge


5531 add support for form-suggestions (#5581)

Pull request opened by the merge tool on behalf of #5581

Merged in fb9a959adcb4c46de84b229960d09c53b5f7c588


Several installing issue(I have fixed)

Today, when i tried to use the goose package developed by grangier, finally i found it doesnt support python 3.x. So i found this package.

i have to say the man who rename the package and main directory to goose3 is careless.
At line 30 in file setup.py,
version = load_source("version", os.path.join("goose", "version.py"))
should be
version = load_source("version", os.path.join("goose3", "version.py"))
The similar error happens in MANIFEST.in then the first 2 lines
recursive-include goose/resources/images * recursive-include goose/resources/text *
should be
recursive-include goose3/resources/images * recursive-include goose3/resources/text *`
When i tried to import this package, the console turned out no module named goose3.utils. So i supposed to add
recursive-include goose3/extractors * recursive-include goose3/utils *
into MANIFEST.in sequencely at the end

After i changed the code above, it works!

Thank you! would you like to submit a Pull Request and I will merge it in?
On Wed, Mar 22, 2017 at 7:35 AM, XNY<EMAIL_ADDRESS>wrote:

Today, when i tried to use the goose package developed by grangier,
finally i found it doesnt support python 3.x. So i found this package.

i have to say the man who rename the package and main directory to
goose3 is careless.
At line 30 in file setup.py,
version = load_source("version", os.path.join("goose", "version.py"))
should be
version = load_source("version", os.path.join("goose3", "version.py"))
The similar error happens in MANIFEST.in then the first 2 lines
recursive-include goose/resources/images * recursive-include
goose/resources/text *
should be
recursive-include goose3/resources/images * recursive-include
goose3/resources/text *`
When i tried to import this package, the console turned out no
module named goose3.utils. So i supposed to add
recursive-include goose3/extractors * recursive-include goose3/utils *
into MANIFEST.in sequencely at the end

After i changed the code above, it works!
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You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
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@IdiotYcw Thanks for your issue. I've updated the package.


A way to remove context menus

It would be wonderful to have commandline to remove context menus

There already is an option to remove context menus from the library itself, but can you elaborate what you mean by having a "commandline to remove context menus?"

like
context_menu remove 'Foo Menu' --type=files

while it would be nice to have, its not that important


Haytifx

Il m'arrive une chose dans mon projet. une fois que je veux lancer ca sur l emulateur Android voicice qu'on dit:
Launching lib\main.dart on Android SDK built for x86 in debug mode...
Running Gradle task 'assembleDebug'...
FAILURE: Build failed with an exception.

What went wrong:
Could not determine the dependencies of task ':app:compileDebugJavaWithJavac'.

Failed to find Build Tools revision 29.0.2

Try:
Run with --stacktrace option to get the stack trace. Run with --info or --debug option to get more log output. Run with --scan to get full insights.

Get more help at https://help.gradle.org

BUILD FAILED in 29s
Exception: Gradle task assembleDebug failed with exit code 1

@scorpion-4128
Please see these StackOverflow answers for more details:

https://stackoverflow.com/questions/62778318/failed-to-find-build-tools-revision-29-0-2-android-studio
https://stackoverflow.com/questions/69167522/error-could-not-determine-the-dependencies-of-task-appcompiledebugjavawithja

Closing as this is neither a Flutter bug nor a feature request. If you disagree, please write in the comments and I will reopen it.


Move sqlite db into own crate

This will allow us to support a second db such as postgres easier as we can duplicate the code and don't have to make it work for various implementations at the same time.
This is based on proposal by Thomas in https://github.com/itchysats/itchysats/issues/1545 and https://github.com/itchysats/itchysats/commit/1b2f851849697f30cd409460c57a468752b726ae

@bonomat: Ping me after the hefty rebase for a review!

@bonomat: Ping me after the hefty rebase for a review!

I'll only do a rebase if we are willing to get this in, otherwise it's too much work for the fish 🐟
Can I get a concept ACK?

This will allow us to support a second db such as postgres easier as we can duplicate the code and don't have to make it work for various implementations at the same time.

This makes sense. I think we should roll with this if it helps.
Would you push this through before or after we work on #2084 ?

This will allow us to support a second db such as postgres easier as we can duplicate the code and don't have to make it work for various implementations at the same time.

This makes sense. I think we should roll with this if it helps. Would you push this through before or after we work on #2084 ?

I'd get it in before #2084.

bors r+


Surround Keywords with doublequotes

When a fieldname is equal to a keyword, it leads to an error:

identifier expected; 'grid' is a keyword

Since doublequotes are getting removed, if the name does not contain special chars or spaces, you cannot circumvent this.
The basic keywords should be respected in encloseInQuotesIfNecessary:

codeunit
grid
table
page
profile
etc.

Example:
field(114; Grid; Boolean)
{
    Caption = 'Grid';
    DataClassification = CustomerContent;
}

Solution:
field(114; "Grid"; Boolean)
{
    Caption = 'Grid';
    DataClassification = CustomerContent;
}

Off course it is not the best naming convention, to call a field "grid" or "profile"... But I have to support legacy code, which converts a table into json.
Thanks a lot :)

Thanks for creating the issue.
Although I have a principal issue with calling this a "bug" .. I'll still do so ;-).  Indeed, in case of legacy it should be dealt with.
I'll do my best...

Hi, I'm working on this - but I am confused.  I'm not able to replicate this behaviour.
Can you share all your CRS.-settings with me - just to simulate the behaviour?
In my case, if you just put quotes around them yourself - it would accept the value.. .

I got a usecase:
A setting for prefix in combination with a table with these field names...

Hi @HerrRiebmann ,
I have a test-version ready.  You can download it here:
https://www.dropbox.com/s/3qafb1eucgxdo47/crs-al-language-extension-1.1.12.vsix?dl=1
just install it manually in VSCode by:

Hi waldo,
thank you very much for working on this!
I could not answer fast enough until you already fixed this issue (since it is not a bug ;) ).
Tested you version and it works fine 👍
PS:
I´ll celebratory promise to you to rename the table field as fast as I could.

No prob - another happy user, I hope ;-).
It will be in the next release (today).  You should get an update automatically .. although, I'm not sure since you now manually installed the vsix.


No warning on read only volume

Hello,
python3.3 dataserv-client --address XXXXXXXXXXXXXXXXXX --max_size<PHONE_NUMBER>00 --store_path /path/dataserv_client/bin/data/ poll
No warning when the file is on read only for the current user (rw-r--r-- root, current user: driveshare)
http://<IP_ADDRESS>/api/online -> show Height: 0
Thanks to fix it.

Did you use build?

No the version 1.1

Seems like an OS level error. We don't check permissions for folders we are going to write. You have to properly build first, or your height is going to be 0.
http://driveshare.org/dataserv.html

Yes it's OS level error, unable to open in read/write the file. But not show by the software. The build was done by root, then the build is successful. (properly build)


Enable OOM killing for etcd-events

Enable OOM killing like docker run etcd

ci check this

ci  passed.


Collaborator add/edit modal drawer

Pre-Flight checklist

[x] Screenshots and design review for any changes that affect layout or styling

[x] Desktop screenshots
[ ] Mobile width screenshots
[x] Tag @ferdi or @pdpinch for review

[ ] Testing

[ ] Code is tested
[ ] Changes have been manually tested

What are the relevant tickets?
Closes #166
What's this PR do?
Moves the add/edit contributor forms to a modal drawer
How should this be manually tested?
Go to the collaborators page.  Add, edit, and delete collaborators.  All 3 should work as expected without errors.
Screenshots (if appropriate)

Codecov Report

Merging #173 (a9a6cd2) into master (73477bc) will increase coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #173      +/-   ##
==========================================
+ Coverage   81.95%   81.97%   +0.01%
==========================================
  Files          44       44
  Lines        1308     1309       +1
  Branches      148      148
==========================================
+ Hits         1072     1073       +1
  Misses        219      219
  Partials       17       17

Impacted Files
Coverage Δ

websites/views.py
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 73477bc...a9a6cd2. Read the comment docs.

@ferdi @abdulkdawson screenshots are attached to the description.

Is the drop down same component as what George introduced in the other pr (react-select?)

@Ferdi the dropdown has not been modified, it is not using react-select.

@mbertrand I think we should switch using react select for consistency .  This or other PR.

@Ferdi there is at least one other form that doesn't use react-select (the site creation form) so I think it would be best to tackle them all as a separate issue/PR.  I can start on that one next.

@gsidebo I made the suggested changes if you want to take a look at them.


Removes PHP 7.1 tokens after method definition

There are two reasons, why it's not applicable patch:

You can lose some errors occurred when returned value type doesn't match to declared.
It's really bad idea to remove tokens from generated code which are present in original because we try to rewrite as little code as it possible.


kube-prometheus: Remove unnecessary namespace selectors

As ServiceMonitors by default select services from it's own namespace, these namespace selectors are unnecessary and in fact would prevent one from getting a functional cluster right now as they have hardcoded monitoring namespace in them.
This changes it to use whatever the ServiceMonitor's namespace is.
@mxinden @metalmatze

as CI is green I'll move forward with this, as it's a dependency elsewhere


bosh-lite - up to date and user friendly

Currently bosh-lite vagrant up will download year old boxes for vmware (fusion/workstation) without giving any indication to the user that these boxes are not maintained.
So we have made sure that the current Vagrantfile returns an error for providers we don't support
The provision_cf used to download an outdated stemcell. Now it downloads the correct stemcell. Curl follows the redirect to the latest.

The packer scripts for VMware are still up to date and can be used to create new bosh-lite images when necessary. I build them for myself regularly. I'd very much like to see the boxes published instead of having them dead-headed.

@sykesm merging this in for now since i've definitely seen people get very confused by this.


Add DL4J to README.md

The DeepLearning4J framework also uses MKL-DNN by default now if you use the nd4j-native-platform as the backend dependency.
See https://github.com/deeplearning4j/deeplearning4j/pull/5622 for the commit.

Thanks for the PR!


Integration Test - Release - 2019/04/26 16:01:14

Auto create for integration test. AnotherAuthorUpdatedArticleFlow

@PRMergerTest2 : Thanks for your contribution! The author, @v-caxian, has been notified to review your proposed change.


integrate csscomb in webpack

hi, would like some help
im a little lost on how to integrate csscomb with webpack. I had it working with Grunt but have no idea how to use it with webpack. should I:
npm install csscomb --save-dev
on webpack
a- const Comb = require('csscomb');
b- const comb = new Comb('zen');
c- under ... /\ .(css|scss)$/...
                            {
				loader: 'csscomb'
			}, {
				loader: 'other loader',
				options: {},
			},

something like this? also, what if you have a .csscomb.json? i think you can copy/paste that config and place it like you did under ./tools/postcss.config.js? please help and thanks!

Hi @csantiago132!
You'll have to create a loader for csscomb. You can see how other loaders are created in webpack-contrib.


throwing bad_alloc after calling model_fn

Awesome research! This is a huge breakthrough for NLP.
I'm running BERT-large on a Cloud TPU doing fine-tuning for squad, but I keep getting

I have nothing else running so I'm not sure why the machine is running out of memory, and followed the steps exactly for setup (ie putting the pre-trained model on a google bucket, set up for TPU, etc).

Hmm, I'm guessing the problem is that I do:
    d = tf.data.Dataset.from_tensor_slices(feature_map)
To avoid having to write out data files, and this creates very large allocations that may or may not fail depending on exactly how tensorflow/python was compiled (e.g., what version of the C++ standard library its using).
I don't think that Dataset.from_generator() will work on the TPU because it implemented with a pyfunc, which weren't supported on TPU last time I checked (even for CPU data-processing). So I may have to change this to write to intermediate files.

To check whether this is the issue, can you add a quick change where you truncate the SQuAD training data and if the bad alloc goes away.
def read_squad_examples(input_file, is_training):
...
# Add this right before return statement to only train on 10k examples
if is_training:
  examples = examples[0:10000]

return examples

It works when I truncate the training data!

Getting another error for memory during training after enqueuing and dequeuing batches of data from infeed and outfeed.

I'll work on a fix for the first issue. Actually I just realized that I can just write the TF record files to output_dir so I don't need to change the interface.
For the second issue, can you try reducing the batch size to 32? On our internal version of TF using a batch size of 48 only uses 7.48 GB of memory but things might be different between versions and that's cutting it close anyways. I may need to find a better learning rate and num_epochs for batch size 32, but it should work as well as 48 in terms of final accuracy.

A batch size of 32 still results in being 200 mb over the memory capacity. Using 24 works for now, not sure how this will impact performance yet.

That's a pretty huge difference, I'll coordinate with the TPU team here to figure out what's causing the mismatch.

Jason,
I just checked in a (hopeful) fix for the first issue. It might be a little slower to start because it has to write out a TFRecord file to output_dir, then read it back in. But at most a few minutes to start. Can you try it without truncation (but batch size 24) to see if it fixes the bad_alloc and can train all the way through for you?

For the memory issue, I just confirmed that the TPU memory usage different is in fact due to improvements that have been made to the TPU compiler since TF 1.11.0 was released. So with TF 1.11.0 it seems like 24 is the max batch size, and in the next upcoming version it will be 48. (I'm assuming you're using 1.11.0, since that's what the README said to use).

I confirmed that fine-tuning BERT-Large using batch size of 24 with a learning rate of 3e-5 and a 2.0 epochs consistently gets 90.7% F1, the same as the paper. I updated the README to reflect this.
Thanks for bringing up this issue!
Please let me know if your bad_alloc issue goes away and if you're able to obtain 90.5%+ on SQuAD dev with a Cloud TPU.

Thanks so much Jacob! I'm no longer getting the bad_alloc issue, and I'm able to run BERT-Large with those parameters and I was able to get that F1 score.
I also tried using a TPU v3.0 with the parameters you originally gave (batch size of 48) and ran it with no issues, and got a F1 score of 90.9!
Amazing work!

@JasonJPu Can you please comment on inference performance? Is it comparable to QANet?


This pull request is for self-review my Trial's Project Tribute Page

I have copied all the project's files for the review:

Copy the index.html file.
Copy the  license.txt file.
Copy the README.md file.
Copy the js folder and its content.
Copy the  css folder and its content.
Copy the babel folder and its content.

@jerryowusu, I appreciate your comments... thanks. 🙌.


function.Invoke in LiteCollection.Find

Version
5.0.2
Describe the bug
While trying to upgrade from v4 to v5, i found out that function can no longer be invoked in Find Expression.
Code to Reproduce
var logs = GetLogs(x => x.ConnectorId == connector.Id && x.Enabled == true);

        public IEnumerable<Log> GetLogs(Func<Log, bool> function)
        {
            var connectors = database.GetCollection<Log>(Database.Types.Log.TableName);
            return connectors.Find(x=> function.Invoke(x)).OrderByDescending(x => x.LogDate);
        }

Expected behavior
Same as in v4 where it worked. Should work same as in v5 alternative:
var query = Query.And(Query.EQ("ConnectorId", connector.Id), Query.EQ("Enabled", true))
var logs = GetLogs(query);
        public IEnumerable<Log> GetLogs(BsonExpression expression)
        {
            var connectors = database.GetCollection<Log>(Database.Types.Log.TableName);
            return connectors.Find(expression).OrderByDescending(x => x.LogDate);
        }

Screenshots/Stacktrace
System.NotSupportedException: Method Invoke not available to convert to BsonExpression (value(ConnectorMonitor.Models.Providers.LiteDbLogProvider+<>c__DisplayClass5_0).function.Invoke(x)).
   at LiteDB.LinqExpressionVisitor.VisitMethodCall(MethodCallExpression node)
   at System.Linq.Expressions.MethodCallExpression.Accept(ExpressionVisitor visitor)
   at System.Linq.Expressions.ExpressionVisitor.Visit(Expression node)
   at System.Linq.Expressions.ExpressionVisitor.VisitLambda[T](Expression`1 node)
   at LiteDB.LinqExpressionVisitor.VisitLambda[T](Expression`1 node)
   at System.Linq.Expressions.Expression`1.Accept(ExpressionVisitor visitor)
   at System.Linq.Expressions.ExpressionVisitor.Visit(Expression node)
   at LiteDB.LinqExpressionVisitor.Resolve(Boolean predicate)
   at LiteDB.LiteCollection`1.Find(Expression`1 predicate, Int32 skip, Int32 limit)
   at ConnectorMonitor.Models.Providers.LiteDbLogProvider.GetLogs(Func`2 function) in C:\Work\ConnectorMonitor\ConnectorMonitor\Models\Providers\LiteDbLogProvider.cs:line 61

Additional context
Can add rest of the code if needed. Bug looks to be easily reproduced with code i provided
ConnectorId is PK System.Guid

Hi @SlaskoCZ, in v4, when you write an LINQ expressions that can't be converted into database query, LiteDB just execute as LINQ to Object (after result from database). It's just IEnumerable LINQ command.
In v5 (current 5.0.2 version) this feature does not exists. All LINQ expressions must be converted into BsonExpression. When is not possible convert a error message is throw. This will be "fix" in next v5.1 version...

Ok, thank, should i close this, or keep it open as reference for future feature?

I link this issue with my project boarding for v5.1. I will close when finish next version.


Flash Of Unstyled Content (FOUC) in Dev?

First off, thanks so much for creating this starter kit, it looks awesome!
I booted it up in dev and noticed every page refresh shows about 500ms of unstyled HTML.  Is anyone else experiencing this?  Any tips to prevent this?

I also experience this but until now I dont take it serious because it only happens in dev mod.
I think it's because of inline styles that rendered on client. Server cannot handle css unless you use webpack-isomorphic-tools package. This package is used in react-redux-isomorphic-app by erikras.

Yup, only happens in DEV as @ZeroCho explained. Even with a massive background-image, zero hiccups w/ rendering on Heroku. Really smooth w/ <ReactCSSTransitionGroup> too, was thinking of creating a PR for that, and also to add react-router-scroll (so pages don't load 1/2 way scrolled down).
This is more on the rookie side-of-things, but also beware of default State, sometimes you'll see a flicker of text and realize you just need to tweak your display logic a little bit to keep it clean until a fetch completes, or whatever..

This is happening to me in production. Any reason why?

This happens to me in production mode as well.

Ah, I've identified my issue. If you run npm run start after running npm run dev, your files in public/assets will be still be in dev mode.
Other webpack noobies like me might have similar problems in the future. Perhaps we can make webpack build in different folders within public depending on the stage? So webpack.config.prod.js will build in public/prod/assets and webpack.config.dev.js build in public/dev/assets?

@rclmenezes This makes sense.
@choonkending This can be solved by adding npm prestart script

Alternatively, could we make npm run start build prior to actually starting?
I like this idea! Sorry for the lack of clearer documentation - we're working towards a better + cleaner repository so hopefully a lot of the configuration will become simpler over time.

This can be solved by adding npm prestart script

Did not know this even existed lol. Sure I think we should add that in!

We're cleaning up issues on the project and since this is an old issue we're closing it. Moreover, the code has changed quite a lot since this issue was opened.
If you still experience this or have an issue, please comment here or open a new one


API v1 Methods Library wiki update

Hi, on V1 Library Remove API it says return 200 for succeeds operation, but apparently it's 201

The twist is I accidentally updated upstream wiki instead of mine to correct one :) Please let me know if i need to revert the change.

I'm afraid I don't follow.  Can you elaborate what the issue is?

Not issue on the application but the wiki documentation, and then i accidentally updated it. So i'm just asking if that's okay.

If the wiki docs were incorrect, there's no issue with fixing it.  It's a wiki, anyone can edit it?

Yup everyone can, in good and bad way. So it's correct I'll leave it as it then.


Init README, add license, extract archives, disassemble objects.

Thank you very much for your pull request!
For receiving the free PineCone, please sign up at this link.
(If there will be any issues with signing up, please let me know here).

You're welcome. Thanks for the PineCone!

Sure, no problem, take your time :)

@gamelaster Can you confirm my entry was added? (It was unclear if after the form invalidated itself with time.)

@WildCryptoFox I checked it and your entry isn't here. Did you filled your email, name, and shipping address?
Thx

@gamelaster Resubmitted. I did. I loaded the page when you presented the link, logged into GitHub and soon before my last message, I submitted the form. The form did not show any errors, it only returned me to the "login with GitHub" page. This time it appears to have worked.
Thank you. Still no email, though I don't know if I should expect an automated or manual email.


gantt.overlap not working in plunker environment

Hi I've been wanting to play around in the plunker environment to see if this plugin is perfect to sue in the project I'm involved in.
However when I add "gantt.overlap" as a angular module dependency it throws an exception. Would really like to see how the overlap looks on the tool

It works in the demo, if you move a task on another. It's possible to customize the look with CSS.


Use mapillary js 2.x

We probably need to do this at some point, and it maybe slightly non-trivial: https://github.com/mapillary/mapillary-js/blob/master/MIGRATING.md
@srividyacb can you mention a bit why we need this?
@ajithranka is it possible to get a sense of how much work this might be / how much is it worth doing / and if we need to prioritize / figure out team bandwidth to get this done?

@batpad @ajithranka
We have been seeing many incorrect detections in Mapillary Vector layer - as mentioned here. Per conversation with Mapillary folks in mapping ticket, these incorrect detections are due to older version of Mapillary JS in navigation-map - here.
To get the right detection on the navigation-map and to add right turn-restriction to OpenStreetMap, we might have to get this migrated to new Mapillary JS which will have better signage detection layer and reduce false detection during mapping.
cc @maning


[Unity] Add R.quantize and R.dequantize ops.

This commit adds 2 new operations (R.quantize and R.dequantize) and supports them in LegalizeOps pass.

cc @masahi @quic-sanirudh


Add logic to not mount docker binary from host to admiral container

https://github.com/Shippable/pm/issues/10352

this is done, closing


[Feature Request] - Just pressed and released info for Input Actions

Hey, there. So, there's no "Just Pressed" or "Released" functions for input Actions; I think they would be really useful to assist in programming in input. Interestingly, there's isEcho() for InputEvents which could help, but there's no tutorial or help on using them.

Yeah, but there's no tutorials on using them - when and how would I get a non-generated input event?

https://github.com/okamstudio/godot/wiki/tutorial_input_events
On Wed, Jan 7, 2015 at 12:07 PM, Sol<EMAIL_ADDRESS>wrote:

Yeah, but there's no tutorials on using them - when and how would I get a
non-generated input event?
—
Reply to this email directly or view it on GitHub
https://github.com/okamstudio/godot/issues/1137#issuecomment-69034034.

SolarLune,
for that exact same reason I have written my own input class, which just uses the actions.
Take a look at this example project. This is exactly what you need :D
I have mapped it to 0,1,2,3. So its the same as in the bge.
Here is an example how to is it later:
if (btn.check() in [1,2]):
do stuff
https://www.dropbox.com/s/x5trykq1t4t6mp0/input_states.zip?dl=0

:+1: for this feature... I also am trying to implement this to pause the game and go to the menu if I "just realeased" the ESC button... but (let's say 1 is pressed and 0 is not pressed) when I tap escape for as fast as I could the function I made to check if it is pressed returns 101 or 11101 in just milliseconcs apart which is frustrating...
hope it gets implemented...
:)

Here is also a link to a github project where I use my input states:
https://github.com/ndee85/gBot/blob/master/projectfolder/scripts/input_states.gd

uhm... can you explain how the script works? not how to integrate it but why this line works and that line works... :)

I don't want to spam that topic here. But in short it works that way:
You create to variables. One which stores the old button state, and a second which stores the current state. That is done in the check() function. (line 18 and 19)
Once you have the old button state and the current button state you just need to compare both.
From there you can conclude if you just pressed or just the released the button. And that showed with the digits 0,1,2,3. (from line 23)
So you just have to see what the check() functions returns. Thats it.
In the gbot project you can see how to use that class in your project. Fairly simple.

What is a "non-generated" input event?
to do what you need, you need to call "set_process_input(true)" on your
"_ready()" method (or whenever you're ready to start receiving input). Then
create a _input(event) method with something like this:
func _input(event):
if event.is_action("pause") && event.is_pressed() && !event.is_echo():
# show pause menu here
Create an input event called "pause", assign "esc" key to it (also the
joystick start button :p ). This will show pause menu even if you press and
release the key between 2 frames.
On Mon, Jan 12, 2015 at 8:05 AM, ndee85<EMAIL_ADDRESS>wrote:

I don't want to spam that topic here. But in short it works that way:
You create to variables. One which stores the old button state, and a
second which stores the current state. That is done in the check()
function. (line 18 and 19)
Once you have the old button state and the current button state you just
need to compare both.
From there you can conclude if you just pressed or just the released the
button. And that showed with the digits 0,1,2,3. (from line 23)
So you just have to see what the check() functions returns. Thats it.
In the gbot project you can see how to use that class in your project.
Fairly simple.
—
Reply to this email directly or view it on GitHub
https://github.com/okamstudio/godot/issues/1137#issuecomment-69535102.

^ OK, I think that's what I was missing. I meant "non-user-generated input event"; it seemed like only user-created input events were outlined in the docs (how to make the game think a key was pressed). Thanks for the example, @punto-.

It looks to me like is_echo() is all that is needed to address this feature request, or am I missing something?


color map for lidar points?

Hi all,
Dose anyone knows how to get a colorful lidar points map, like following:

I know the code is here:
https://github.com/uber/xviz/blob/e11f245a9cab6d5627a2c0113effb967bdb7e294/examples/converters/kitti/src/parsers/parse-lidar-points.js#L47
but i dont know the function between RGB  3 channels and the height(z axis) of lidar points?
Thanks !

This is actually a builtin style for point data.
See in the example https://github.com/uber/streetscape.gl/blob/master/examples/get-started/src/constants.js#L53
And the docs at
https://github.com/uber/xviz/blob/master/docs/protocol-schema/style-specification.md#point


Add a case for 64-bit OS X in config

This makes it possible to just run ./config on a x86_64 machine with
no extra fuss.
RT#4356

Ping?  Someone?

+1 and long overdue - although this change may surprise some as default building is changing

Good point, is it worthy of a note in CHANGES?

A note in changes would be appropriate IMHO.

Note in CHANGES added.  Still +1?

+1 thanks

Thank you!  Merged.


Dalton on-premise release

Good morning,
Are there any plans to release a version for deployment in on-premise or virtualization environments?
Regards

Dalton is just software and can be installed wherever you like (within reason).  Its current form makes it easy since the system can be built as a set of Docker containers which can be done wherever there is a docker engine, including on bare metal (commodity or appliance-grade hardware), virtual machines (including virtual machines running virtual machines), and the cloud.  I've run it on all those.
For simple instructions on a quick install in AWS see https://github.com/secureworks/dalton/blob/master/INSTALL-AWS.rst


Audit log: what should Maintainers of projects see?

Regarding #5863:

Each user who maintains at least one project on PyPI will be able to view a log of sensitive actions (performed by ANY user) relevant to projects they act in the Owner Role on.

Just Owners, or also Maintainers? Maybe the Maintainers should be able to see a subset of the things Owners can see?
cc @dstufft @nlhkabu @di
(This is not urgent right now but will come up as @woodruffw implements the audit log in the future.)

@brainwane Yes, off the bat I agree that Maintainers should be able to see only a subset of the things Owners can see, but I'm not familiar with all of the things Owners can see, so we maybe we can start there!


Add: recommanded Kubernetes labels

What this PR does / why we need it:
Add recommanded labels into manifests files
Cf: https://kubernetes.io/docs/concepts/overview/working-with-objects/common-labels/
Which issue this PR fixes (optional, in fixes #<issue number>(, fixes #<issue_number>, ...) format, will close that issue when PR gets merged): fixes #
Special notes for your reviewer:
Checklist:

[ ] Fixes #
[ ] Labelled this PR & related issue with documentation tag
[ ] PR messages has document related information
[ ] Labelled this PR & related issue with breaking-changes tag
[ ] PR messages has breaking changes related information
[ ] Labelled this PR & related issue with requires-upgrade tag
[ ] PR messages has upgrade related information
[ ] Commit has unit tests
[ ] Commit has integration tests

Thanks for the PR!!


Asrc 243 - Adding a range to all numerical variables

so that they can be inclusive or exclusive. Error codes and exceptions are updated.

I’ll look at this today but we cannot merge until I can upload the final documents and tag the release.
David
From: jamesbigmac<EMAIL_ADDRESS>Sent: Tuesday, February 24, 2015 12:05 PM
To: VHAINNOVATIONS/ASRCM
Subject: [ASRCM] Asrc 243 - Adding a range to all numerical variables (#21)
so that they can be inclusive or exclusive. Error codes and exceptions are updated.

You can view, comment on, or merge this pull request online at:
https://github.com/VHAINNOVATIONS/ASRCM/pull/21
Commit Summary

Numerical and discrete numerical variables now have a numerical range
Adding error codes for both inclusive and exclusive cases for numerical
Fixed the exception constructors and tests in InputParserVisitorTest to

File Changes

M install/resources/create_database.sqlhttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-0 (4)
M srcalc/src/main/java/gov/va/med/srcalc/domain/variable/AbstractNumericalVariable.javahttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-1 (68)
M srcalc/src/main/java/gov/va/med/srcalc/domain/variable/ValueTooHighException.javahttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-2 (31)
M srcalc/src/main/java/gov/va/med/srcalc/domain/variable/ValueTooLowException.javahttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-3 (31)
M srcalc/src/main/resources/messages.propertieshttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-4 (6)
M srcalc/src/test/java/gov/va/med/srcalc/domain/variable/DiscreteNumericalValueTest.javahttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-5 (18)
M srcalc/src/test/java/gov/va/med/srcalc/domain/variable/NumericalValueTest.javahttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-6 (18)
M srcalc/src/test/java/gov/va/med/srcalc/web/view/InputParserVisitorTest.javahttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-7 (9)
M srcalc/src/test/resources/sql/insert_base_data.sqlhttps://github.com/VHAINNOVATIONS/ASRCM/pull/21/files#diff-8 (8)

Patch Links:

https://github.com/VHAINNOVATIONS/ASRCM/pull/21.patch
https://github.com/VHAINNOVATIONS/ASRCM/pull/21.diff

—
Reply to this email directly or view it on GitHubhttps://github.com/VHAINNOVATIONS/ASRCM/pull/21.

Approved. The GitHub v0.4 release is tagged so you can merge at will.


[Feature] The cache-max-entry-count working off percentages makes it difficult to setup multiple servers

Motivation
  --cache-max-entry-count CACHE_MAX_ENTRY_COUNT
                        The percentage of free gpu memory occupied by the k/v cache, excluding weights . Default: 0.8. Type: float

I have a DGX machine, and I want to run multiple models on it. If I launched multiple servers concurrently (with tp=8), it would be ambiguous how much memory they would actually take up. By that, consider what would happen if I spun up a 1gb model with this set to 0.5, and then spun up a 100gb model with this set to 0.5.
The 1gb model would get half of the 640gb of memory that the machine has, which is 320gb, but the large model would only allocate 110gb for it's kv cache.
If I lauched them in the opposite order, the large machine would get 270gb, and the smaller one would get ~135gb.
For that reason, it'd be more convenient to be able to put in cache sizes in absolute amounts.
Related resources
No response
Additional context
No response

I wish a single api server could serve multiple models and share the kv cache between them. That would be the best solution to this.

We will not do multiple models sharing kv cache.
Instead, we will make --cache-max-entry-count referring to the number of cached kv tokens shared by the inference instances of a model.
For example,
lmdeploy serve api_server InternLM/internlm2_5-7b-chat --cache-max-entry-count 10000
lmdeploy serve api_server Qwen/Qwen2.5-7B-Instruct --cache-max-entry-count 10000

It serves two models, each model owns a kv cache, which at most save 10000 tokens, respectively.

I think that would be better, though I think I'd prefer to specify it in absolute byte amounts or even percentages before (and not after) the model is loaded. Right now, the way the fractions are used are very awkward. If I put in 1.0 or 0.99 I get an out of memory error. If I put in 0.96, it actually silently freezes and doesn't load the API server properly (at least on the nvidia/Llama-3.1-Nemotron-70B-Instruct-HF on the HGX machine that I am testing it on.) With 0.95 it works.
I can't use multiple models without making use of containers and allocating them the specific GPU ids that I want them to use.

It serves two models, each model owns a kv cache, which at most save 10000 tokens, respectively.

On review, I think your suggestion would be the most precise, but if you commit to it, do give the user a way of getting how much memory each token takes.


Test reminding other users

This is just a test issue.

/remind @steveoh to test out this issue tomorrow

works great!


Support for react v15.0

React just release major v15.0 with full svg support.
https://facebook.github.io/react/blog/.
I suppose this could have large impact on react-canvas?

Hi! Since React-Canvas already uses ReactDOM.render to render components, and they use React.createElement/Document.createElement, this is much unlikely. However, you can see that this library hasn't been updated for a while. It uses regular var requires and dependencies hasn't been updated. I'll put up a updated PR later on today, if that helps this issue. :smile:

The only thing that I can see now that is affected by v15 is var assign = require('react/lib/Object.assign'); as react has changed this

When i try use react-canvas with react v15, it not working. The react throw exception "Uncaught TypeError: Cannot read property 'props' of undefined" on the function mountComponent of Group component.
Any idea for fix this bug?

I was starting to work on a fix and it's more fundamental than "just fix the assign". there are bunch of problem and I don't have time for this. react-canvas uses bunch of internal React API that have all changed (mountComponent signature, and bunch of other signatures).
so I recommend you guys to use react-art that is highly maintain and always up to date.

Maybe put up a warning at the top of the README...

I am having the following issue when running an app that is importing 'react-canvas':
Uncaught Error: ENOENT: no such file or directory, open '//classes.trie'
I am currently using react v15 and while npm installing I get
<EMAIL_ADDRESS>requires a peer of<EMAIL_ADDRESS>but none was installed.
Is this a compatibility issue or is something wrong on my end ? I only installed the package and used a very basic Surface + Image.

It's a compatibility issue. The current implementation is far from working with React 15.
Current implementation rely on some react 0.14 internal APIs that have changed.

Oh ok got it! Thanks

It's possible for someone like me to maybe downgrade to react 0.14 if I knew 15.x support was coming.
Is there any information on this anywhere?

#159


Pulsar doesn't build on Windows 10 or on Mac with Maven 3.6.3

Describe the bug
Maven is reporting a successful build without actually building anything.
To Reproduce

Clone Pulsar master
Run mvn clean install at project root
Maven downloads dependencies but does not produce build output
Again, run mvn clean install
The result is this:

(base) C:\Users\devin\src\pulsar>mvn clean install
[INFO] Scanning for projects...
[INFO] ------------------------------------------------------------------------
[INFO] Detecting the operating system and CPU architecture
[INFO] ------------------------------------------------------------------------
[INFO] os.detected.name: windows
[INFO] os.detected.arch: x86_64
[INFO] os.detected.classifier: windows-x86_64
[INFO]
[INFO] ----------------------< org.apache.pulsar:pulsar >----------------------
[INFO] Building Pulsar 2.7.0-SNAPSHOT
[INFO] --------------------------------[ pom ]---------------------------------
[INFO]
[INFO] --- maven-clean-plugin:2.4.1:clean (default-clean) @ pulsar ---
[INFO] Deleting C:\Users\devin\src\pulsar\target
[INFO]
[INFO] --- maven-enforcer-plugin:3.0.0-M2:enforce (enforce-maven) @ pulsar ---
[INFO]
[INFO] --- git-commit-id-plugin:3.0.0:revision (git-info) @ pulsar ---
[INFO]
[INFO] --- maven-remote-resources-plugin:1.5:process (process-resource-bundles) @ pulsar ---
[INFO]
[INFO] --- maven-site-plugin:3.5.1:attach-descriptor (attach-descriptor) @ pulsar ---
[INFO]
[INFO] --- maven-install-plugin:2.5.2:install (default-install) @ pulsar ---
[INFO] Installing C:\Users\devin\src\pulsar\pom.xml to C:\Users\devin.m2\repository\org\apache\pulsar\pulsar\2.7.0-SNAPSHOT\pulsar-2.7.0-SNAPSHOT.pom
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time:  10.486 s
[INFO] Finished at: 2020-08-04T13:45:21-06:00
[INFO] ------------------------------------------------------------------------

Hence, the build succeeds but gives no output and doesn't run tests.

We don't work with maven 3.6.3 ,  try 3.6.1

As the modules moved to profiles, you should tick "main" or "core-modules" checkbox in Maven panel.

Because the modules have been moved to profile section after #7566 , see the pom.xml in the project directory:
    <profile>
      <id>core-modules</id>
      <modules>
        <module>buildtools</module>
        <!-- more modules... -->

So you need run mvn install -Pcore-modules if you only want to install the modules of core-modules profile. For indices generation of Intellj Idea, you should click the associated checkbox in profile to choose the modules:

Thanks for the help on this.
I rolled back to Maven 3.6.0, and I was able to build Pulsar successfully.
However, I'm having a new problem now which is that I'm having trouble debugging the function for my PR here: https://github.com/apache/pulsar/pull/7704
When I set a breakpoint in IntelliJ and run the tests from the Maven runner, it runs the tests, but my breakpoint is never hit, even if I run the tests in debug mode like this:

If I try to run the test as I would in the past, like this:

then I get this build error:

So, how do I debug my test now?

It turns out that I just needed to turn back on the Lombok annotations. Somehow, they got turned off.

Closed as stale. It seems the original issue is resolved.


Compara test db homologies

Habemus testis !
I have added a test database that can cover gene-trees and homologies. Currently, only the /homology/id/ endpoint is tested but I will add the other ones soon.
At the same time, I fixed a couple of bugs

Looks useful. Approved

Urk. DBD::mysql::db do failed: Specified key was too long; max key length is 1000 bytes
Suggests a missing character set assignment.

The charset is defined in my original database, but is missing from the database dump. Is it stripped out by dump_mysql.pl ?

Indeed the charset definition is removed when --testcompatible is given, which I used (following https://www.ebi.ac.uk/seqdb/confluence/display/ENSCORE/Creating+a+test+database)

It wouldn't come up if people didn't have massive keys. I suppose we'll have to change our guide.

I think the problem comes from our meta table.
CREATE TABLE IF NOT EXISTS meta (

  meta_id                     INT NOT NULL AUTO_INCREMENT,
  species_id                  INT UNSIGNED DEFAULT 1,
  meta_key                    VARCHAR(40) NOT NULL,
  meta_value                  TEXT NOT NULL,

  PRIMARY   KEY (meta_id),
  UNIQUE    KEY species_key_value_idx (species_id, meta_key, meta_value(255)),
            KEY species_value_idx (species_id, meta_value(255))

);

It's different from what you guys have in the core schema
CREATE TABLE IF NOT EXISTS meta (

  meta_id                     INT NOT NULL AUTO_INCREMENT,
  species_id                  INT UNSIGNED DEFAULT 1,
  meta_key                    VARCHAR(40) NOT NULL,
  meta_value                  VARCHAR(255) BINARY,

  PRIMARY   KEY (meta_id),
  UNIQUE    KEY species_key_value_idx (species_id, meta_key, meta_value),
            KEY species_value_idx (species_id, meta_value)

);

Didn't work ... I'll try to reproduce this locally to identify the offending keys (rather than blindly adding a charset everywhere)

external_db is a good candidate
UNIQUE KEY db_name_db_release_idx (db_name,db_release)
with
db_name varchar(100) NOT NULL,
db_release varchar(255) DEFAULT NULL,

but otherwise, thanks for the tests, it looks promising 👍

Yeah ! Thank you @magaliruffier for the tip !


CUDA Error when fine tuning GPT-J for CasualLM

Hello, I am trying to finetune GPT-J for text generation by adapting this notebook. However, when I run trainer.train I get a CUDA error that states the following,  RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling 'cublasCreate(handle)'
The error seems to originating from ./peft/src/peft/tuners/lora.py line 277 from the  traceback. Any ideas why this is happening or how to fix it?
The full traceback is below :

RuntimeError                              Traceback (most recent call last)
Cell In[14], line 1
----> 1 trainer.train()

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:1543, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1538     self.model_wrapped = self.model
   1540 inner_training_loop = find_executable_batch_size(
   1541     self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
   1542 )
-> 1543 return inner_training_loop(
   1544     args=args,
   1545     resume_from_checkpoint=resume_from_checkpoint,
   1546     trial=trial,
   1547     ignore_keys_for_eval=ignore_keys_for_eval,
   1548 )

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/accelerate/utils/memory.py:124, in find_executable_batch_size.<locals>.decorator(*args, **kwargs)
    122     raise RuntimeError("No executable batch size found, reached zero.")
    123 try:
--> 124     return function(batch_size, *args, **kwargs)
    125 except Exception as e:
    126     if should_reduce_batch_size(e):

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:1791, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1789         tr_loss_step = self.training_step(model, inputs)
   1790 else:
-> 1791     tr_loss_step = self.training_step(model, inputs)
   1793 if (
   1794     args.logging_nan_inf_filter
   1795     and not is_torch_tpu_available()
   1796     and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
   1797 ):
   1798     # if loss is nan or inf simply add the average of previous logged losses
   1799     tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:2539, in Trainer.training_step(self, model, inputs)
   2536     return loss_mb.reduce_mean().detach().to(self.args.device)
   2538 with self.compute_loss_context_manager():
-> 2539     loss = self.compute_loss(model, inputs)
   2541 if self.args.n_gpu > 1:
   2542     loss = loss.mean()  # mean() to average on multi-gpu parallel training

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:2571, in Trainer.compute_loss(self, model, inputs, return_outputs)
   2569 else:
   2570     labels = None
-> 2571 outputs = model(**inputs)
   2572 # Save past state if it exists
   2573 # TODO: this needs to be fixed and made cleaner later.
   2574 if self.args.past_index >= 0:

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

***** Running training *****
  Num examples = 36139
  Num Epochs = 6
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 32
  Gradient Accumulation steps = 4
  Total optimization steps = 6774
  Number of trainable parameters = 7340032
`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...
~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[14], line 1
----> 1 trainer.train()

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:1543, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1538     self.model_wrapped = self.model
   1540 inner_training_loop = find_executable_batch_size(
   1541     self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
   1542 )
-> 1543 return inner_training_loop(
   1544     args=args,
   1545     resume_from_checkpoint=resume_from_checkpoint,
   1546     trial=trial,
   1547     ignore_keys_for_eval=ignore_keys_for_eval,
   1548 )

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/accelerate/utils/memory.py:124, in find_executable_batch_size.<locals>.decorator(*args, **kwargs)
    122     raise RuntimeError("No executable batch size found, reached zero.")
    123 try:
--> 124     return function(batch_size, *args, **kwargs)
    125 except Exception as e:
    126     if should_reduce_batch_size(e):

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:1791, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1789         tr_loss_step = self.training_step(model, inputs)
   1790 else:
-> 1791     tr_loss_step = self.training_step(model, inputs)
   1793 if (
   1794     args.logging_nan_inf_filter
   1795     and not is_torch_tpu_available()
   1796     and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
   1797 ):
   1798     # if loss is nan or inf simply add the average of previous logged losses
   1799     tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:2539, in Trainer.training_step(self, model, inputs)
   2536     return loss_mb.reduce_mean().detach().to(self.args.device)
   2538 with self.compute_loss_context_manager():
-> 2539     loss = self.compute_loss(model, inputs)
   2541 if self.args.n_gpu > 1:
   2542     loss = loss.mean()  # mean() to average on multi-gpu parallel training

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/trainer.py:2571, in Trainer.compute_loss(self, model, inputs, return_outputs)
   2569 else:
   2570     labels = None
-> 2571 outputs = model(**inputs)
   2572 # Save past state if it exists
   2573 # TODO: this needs to be fixed and made cleaner later.
   2574 if self.args.past_index >= 0:

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File ~/nlp/peft/src/peft/peft_model.py:502, in PeftModelForCausalLM.forward(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, **kwargs)
    490 def forward(
    491     self,
    492     input_ids=None,
   (...)
    499     **kwargs,
    500 ):
    501     if not isinstance(self.peft_config, PromptLearningConfig):
--> 502         return self.base_model(
    503             input_ids=input_ids,
    504             attention_mask=attention_mask,
    505             inputs_embeds=inputs_embeds,
    506             labels=labels,
    507             output_attentions=output_attentions,
    508             output_hidden_states=output_hidden_states,
    509             return_dict=return_dict,
    510             **kwargs,
    511         )
    513     batch_size = input_ids.shape[0]
    514     if attention_mask is not None:
    515         # concat prompt attention mask

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/models/gptj/modeling_gptj.py:813, in GPTJForCausalLM.forward(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)
    805 r"""
    806 labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
    807     Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
    808     `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
    809     are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
    810 """
    811 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
--> 813 transformer_outputs = self.transformer(
    814     input_ids,
    815     past_key_values=past_key_values,
    816     attention_mask=attention_mask,
    817     token_type_ids=token_type_ids,
    818     position_ids=position_ids,
    819     head_mask=head_mask,
    820     inputs_embeds=inputs_embeds,
    821     use_cache=use_cache,
    822     output_attentions=output_attentions,
    823     output_hidden_states=output_hidden_states,
    824     return_dict=return_dict,
    825 )
    826 hidden_states = transformer_outputs[0]
    828 # Set device for model parallelism

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/models/gptj/modeling_gptj.py:660, in GPTJModel.forward(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)
    656             return module(*inputs, use_cache, output_attentions)
    658         return custom_forward
--> 660     outputs = torch.utils.checkpoint.checkpoint(
    661         create_custom_forward(block),
    662         hidden_states,
    663         None,
    664         attention_mask,
    665         head_mask[i],
    666     )
    667 else:
    668     outputs = block(
    669         hidden_states,
    670         layer_past=layer_past,
   (...)
    674         output_attentions=output_attentions,
    675     )

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/utils/checkpoint.py:249, in checkpoint(function, use_reentrant, *args, **kwargs)
    246     raise ValueError("Unexpected keyword arguments: " + ",".join(arg for arg in kwargs))
    248 if use_reentrant:
--> 249     return CheckpointFunction.apply(function, preserve, *args)
    250 else:
    251     return _checkpoint_without_reentrant(
    252         function,
    253         preserve,
    254         *args,
    255         **kwargs,
    256     )

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/utils/checkpoint.py:107, in CheckpointFunction.forward(ctx, run_function, preserve_rng_state, *args)
    104 ctx.save_for_backward(*tensor_inputs)
    106 with torch.no_grad():
--> 107     outputs = run_function(*args)
    108 return outputs

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/models/gptj/modeling_gptj.py:656, in GPTJModel.forward.<locals>.create_custom_forward.<locals>.custom_forward(*inputs)
    654 def custom_forward(*inputs):
    655     # None for past_key_value
--> 656     return module(*inputs, use_cache, output_attentions)

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/models/gptj/modeling_gptj.py:302, in GPTJBlock.forward(self, hidden_states, layer_past, attention_mask, head_mask, use_cache, output_attentions)
    300 residual = hidden_states
    301 hidden_states = self.ln_1(hidden_states)
--> 302 attn_outputs = self.attn(
    303     hidden_states,
    304     layer_past=layer_past,
    305     attention_mask=attention_mask,
    306     head_mask=head_mask,
    307     use_cache=use_cache,
    308     output_attentions=output_attentions,
    309 )
    310 attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
    311 outputs = attn_outputs[1:]

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/transformers/models/gptj/modeling_gptj.py:203, in GPTJAttention.forward(self, hidden_states, attention_mask, layer_past, head_mask, use_cache, output_attentions)
    190 def forward(
    191     self,
    192     hidden_states: Optional[torch.FloatTensor],
   (...)
    200     Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
    201 ]:
--> 203     query = self.q_proj(hidden_states)
    204     key = self.k_proj(hidden_states)
    205     value = self.v_proj(hidden_states)

File ~/miniconda3/envs/ltstrsf/lib/python3.8/site-packages/torch/nn/modules/module.py:1194, in Module._call_impl(self, *input, **kwargs)
   1190 # If we don't have any hooks, we want to skip the rest of the logic in
   1191 # this function, and just call forward.
   1192 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1193         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1194     return forward_call(*input, **kwargs)
   1195 # Do not call functions when jit is used
   1196 full_backward_hooks, non_full_backward_hooks = [], []

File ~/nlp/peft/src/peft/tuners/lora.py:277, in Linear.forward(self, x)
    275 def forward(self, x: torch.Tensor):
    276     if self.r > 0 and not self.merged:
--> 277         result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
    278         if self.r > 0:
    279             result += self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling

RuntimeError: CUDA error: CUBLAS_STATUS_NOT_INITIALIZED when calling `cublasCreate(handle)`

I can also confirm that finetuning a Seq2SeqLM model works fine, and there are no problems when I test PyTorch functions that uses CUBLAS such as the following snippet:
import torch
import numpy as np
import time

flatten_masks = np.random.random((800, 60800))

flatten_masks = torch.from_numpy(flatten_masks).cuda(device=0)
print()
t1 = time.time()
i = 0
while i < 2500:
    if i == 500:
        t1 = time.time()

    inter_matrix = torch.mm(flatten_masks, flatten_masks.transpose(1, 0))

    i += 1
t2 = time.time()
print(t2-t1)

Hi @JohnnyRacer
Thanks for your interest in this! Would be able to share with us the full notebook so that we can try to reproduce?
Also can you run the script on CPU first, I suspect there is some label mismatching / index out of range at some point

Hi @JohnnyRacer Thanks for your interest in this! Would be able to share with us the full notebook so that we can try to reproduce? Also can you run the script on CPU first, I suspect there is some label mismatching / index out of range at some point (see a similar issue here )
i.e. something badly configured when initialized the LoRA layers
Maybe you can just share the way you have initialized your model with peft ?

@younesbelkada
Hello, here is the link to the Colab notebook . Unfortunately my system has insufficient RAM to load the model for training on the CPU, for some reason peft requires much more system RAM than VRAM (not sure if this intentional or a bug).  Also if it's any help, my transformers version is 4.26.1 and peft is version 0.1.0.dev0, the code I am using to initialize the model with peft  is below:
from peft import LoraConfig, get_peft_model,TaskType

def print_trainable_parameters(model):
    """
    Prints the number of trainable parameters in the model.
    """
    trainable_params = 0
    all_param = 0
    for _, param in model.named_parameters():
        all_param += param.numel()
        if param.requires_grad:
            trainable_params += param.numel()
    print(
        f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
    )

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

model = get_peft_model(model, lora_config)
print_trainable_parameters(model)

Hello, I'm able to run it with INT8 training without any issues. Here is the colab notebook:https://colab.research.google.com/drive/1mYBWjqwHDRGz0cxlmV4IyGOr9CxqN9MF?usp=sharing

Hi @pacman100 ,
In the colab that you shared appears 'No log' for the training loss.
Why does that happen?


Adding Field to the Profile table from registration form fails when User model is Overriden

It fails if I override the dektrium User model.
The below is the error given.
Declaration of app\models\RegistrationForm::loadAttributes(app\models\User $user) should be compatible with dektrium\user\models\RegistrationForm::loadAttributes(dektrium\user\models\User $user)
Here is the overridden model
<?php
namespace app\models;
use dektrium\user\models\User as BaseUser;

class User extends BaseUser
{

}

hi, do you solve this problem?

Nope skipped it by not overriding the user model.

How you solve it?

Like I did NOT solve it. I used the model that was given by dektrium. Check out the last lesson changing view file in Yii user module in doingiteasychannel. I use the dektrium user class.

OK ,Thanks a lot.

well, you are ovveride user and not profile... look my model:
use dektrium\user\models\Profile as ProfileUser;
 class Profile extends ProfileUser
{

Okay, thank you, I try to do

You should not change the signature of the loadAttributes method, so it should look like this:
function loadAttributes(\dektrium\user\models\User $user)


Upgrade and add missing rules

comma-dangle
no-return-await
no-useless-return
prefer-promise-reject-errors
require-await
prefer-destructuring

Just a test

First/nSecond

First
Second

First
Second

Warnings:

First
Second

Warnings:
First
Second

Warnings:

First
Second

First
This is the first message/n/n## Second ###
This is the first message

First
This is the first message
Second
This is the first message

First
This is the first message
Second
This is the first message
Created by PR-Check

First
This is the first message
Second
This is the first message
Created by PR-Check_

First
This is the first message
Second
This is the first message
Created by PR-Check

First
This is the first message
Second
This is the second message
Third
This is the third message

Created by PR-Check

We ought to explain why we've added some of these rules. I'm sure they're valuable but the first thing devs are going to notice is potential errors whilst linting a project.

I agree @jdmunro, the more I thought about it the more I saw the overlap with the handbook, I think we should add to the handbook and make sure everyone agrees before adding the ESLint rules.
I added these over the weekend, maybe someone could add reasons to the handbook


Final milestone: Project completed

In this milestone, I achieved the following...

I choose an Financial modeling prep API to base the development of my web app.
I followed the design guidelines created by Nelson Sakwa on Behance.
I implemented the home page, such that when the page loads, the web app shows the list of categories that could be filtered out.
I displayed some numeric values per category that come from the API, such as the stock worth, the loss, and gains
When the user clicks (or taps) on a category item, the application navigates to the details page.
On the details page, the web app retrieves data from the API to show detailed data in the category.
The user can navigate to the home page from detail page by clicking on the back icon.
I also carried out the testing for my project

Thanks for reviewing and approving my project, I appreciate it.


Provide a maven artifact

Hi Florian,
would it be possible to provide a maven artifact on maven central or any other maven repository? I like to use your great library in my https://github.com/philipparndt/groheondus-to-mqtt-gw project. Currently I've uploaded the latest build to my own packagecloud.io repository but I would prefer a repository from you so that others can use this too.
Thanks for your great work!

Hi!
Great that you think this library is useful and that you want to use it in your project :)
I think you will be happy to hear, that there's already a released maven artifact for this library. It is on jcenter, a maven central mirror from bintray. You can find the releases here:
https://bintray.com/floriansw/java-libraries/grohe-ondus-api
Just add the jcenter repository into your project and the following dependency and it should work out of the box:
<dependency>
  <groupId>org.grohe</groupId>
  <artifactId>ondus-api</artifactId>
  <version>0.0.9</version>
  <type>pom</type>
</dependency>

Thank you, that's great!


[C++][Parquet] Would Arrow::FileReader support filter evaluating and optimize by BloomFilter

Describe the usage question you have. Please include as many useful details as  possible.
Parquet cpp has implemented BloomFilter however Arrow FileReader or any one else never call it  during reading.
I am confused and want to figure out:
(1) Does Arrow::FileReader has plan to support filter push down, and when?
(2) When and how BloomFilter will be use by Arrow::FileReader?
Looking forward to reply，thx a lot!
Component(s)
Parquet

I'm a little bit lost here as well. I see that the Parquet 12.0.0 release notes contain your new BF contributions, but that doesn't mean if checking for value equality, the pyarrow.parquet.read_table() would actually use it. Also pyarrow doesn't write it either, right?
Are there any issues / draft PRs open for tracking the BF support? Searching GitHub issues didn't come up with good results.

Support for reading bloom filters from parquet files into memory was added in 12.0.0.  There is an open issue for using this feature to do pushdown filtering here: https://github.com/apache/arrow/issues/27277
The datasets feature was already doing some pushdown using the parquet file statistics.  That issue asks to also use the bloom filter for pushdown filtering for datasets.
The parquet reader itself hasn't done pushdown in the past, but I'd be generally in favor of moving the pushdown filtering out of the datasets layer and into the file reader layer itself if someone was motivated to do the work.  That would be more complex than just adding bloom filter filtering support to the datasets layer though because you'd have to figure out how to formulate filter expressions (you could add a dependency on arrow expressions but I'm not sure if that makes sense in the parquet layer).

I'll finish it this month if no other people interested in it

Regarding the expressions: Maybe it’s an overkill but would using the filter subset of substrait work?

All depends on data distribution and user's query. Maybe it could make query faster. The worst case may make query slower

I'll finish the dataset scanner part it this month if no other people interested in it @westonpace

That would be great, thanks.

Maybe it’s an overkill but would using the filter subset of substrait work?

That probably is overkill though it would work if someone had a desire.  I believe bloom filters are only useful for equality / inequality.  The statistics support comparison.  So you probably just need =,!=,<,>,<=,>=.  The simplest thing to do might be to do what we used to do for the old python datasets and accept disjunctive normal form:

Predicates are expressed using an Expression or using the disjunctive normal form (DNF), like [[('x', '=', 0), ...], ...]. DNF allows arbitrary boolean logical combinations of single column predicates. The innermost tuples each describe a single column predicate. The list of inner predicates is interpreted as a conjunction (AND), forming a more selective and multiple column predicate. Finally, the most outer list combines these filters as a disjunction (OR).

Sorry for late reply because I'm a bit busy these days. I found a problem that bloom filter is not trival, it might enhance the performance, and might not. Should I add an use_bloom_filter options in ParquetFragmentScanOptions ?
/// \brief Per-scan options for Parquet fragments
class ARROW_DS_EXPORT ParquetFragmentScanOptions : public FragmentScanOptions {
 public:
  ParquetFragmentScanOptions();
  std::string type_name() const override { return kParquetTypeName; }

  /// Reader properties. Not all properties are respected: memory_pool comes from
  /// ScanOptions.
  std::shared_ptr<parquet::ReaderProperties> reader_properties;
  /// Arrow reader properties. Not all properties are respected: batch_size comes from
  /// ScanOptions. Additionally, dictionary columns come from
  /// ParquetFileFormat::ReaderOptions::dict_columns.
  std::shared_ptr<parquet::ArrowReaderProperties> arrow_reader_properties;
};

@westonpace

I've two problem about this:
First, I found it a bit hard to implement it using current framework. Our input is an expression tree, bloom filter can transform:

("eq", field_ref, exact_value)
("is_in", field_ref, exact_values...)

To an "false" in row-group expressions. So I need to:

match these expressions
If I found we can turn them to false, transform it.

Can you help me what expression handling method can I use?
Second, ParquetFileFragment::Subset should ensure metadata already in, so maybe I should add an options, and load all bloomfilter in memory. I guess I would be time-consuming. Should I load them all at first, or loading them by requirement?
@westonpace .

Sorry for the slow response.  For (2) I think you will want to do something similar to SimplifyWithGuarantee: https://github.com/apache/arrow/blob/9736dde84bb2e6996d1d12f6a044c33398e3c3a3/cpp/src/arrow/compute/expression.cc#L1332
I don't think you can use SimplifyWithGuarantee directly because the bloom filter is a weird sort of guarantee.  So I think you will need a new method.  Specifically for (2) I think you want to:

Replace the is_in/eq in the AST with literal(false).
Run Canonicalize
Run FoldConstants

Got it, I'll have a try on it

I see this is still open and I have the same original question: can bloom filters be utilized through Arrow Read API?
@mapleFU

Generally not implemented yet. I'll repick that after https://github.com/apache/arrow/pull/37400/files is merged