-DARM_COMPUTE_AARCH64_V8A -DNO_DOT_IN_TOOLCHAIN -DEMBEDDED_KERNELS
-Iinclude -I. -I. examples/graph_temp_scheduler2.cpp
g++ -o build/release/examples/graph_temp_scheduler2
-Wl,--allow-shlib-undefined
build/release/examples/graph_temp_scheduler2.o
build/release/utils/Utils.o build/release/utils/GraphUtils.o
build/release/utils/CommonGraphOptions.o -Lbuild/release -L.
-lpthread
-larm_compute_graph -larm_compute -larm_compute_core
After that, I gave commands:
$make all
$sudo LD_LIBRARY_PATH=/home/shunya/ComputeLibrary1/build/release
./build/release/examples/graph_mobilenet
--instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
but, I could not see the outputs with time taken by each kernel. The
path
to library in the above command has libarm_compute.so,
libarm_compute_core.so, libarm_compute_graph.so,
libarm_compute_core-static.a, libarm_compute_graph-static.a and
libarm_compute-static.a. Could you please help me know where I am
going
wrong?
Have a good day.
Sincerely,
Shraddha Dahal
On Thu, Jun 17, 2021 at 7:10 AM developer-compute @.***>
wrote:

Hi Shraddha,
If you build ACL with the option benchmark_examples=1 you can then
run the network and use the instruments to see how much time each
kernel
is taking:
@.***_hikey_dev:~/tmp/pabtel01#
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./acl_neon+cl_release/
./benchmark_graph_mobilenet --instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
This will produce output like below:
OpenCLTimestamps/Now OpenCL: AVG=1121307039 ms
OpenCLTimestamps/Now Wall clock: AVG=1623931494169279 us

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[8,3136,1] LWS[4,1,1] #2: AVG=1121306920 ms
OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/im2col3x3_nhwc
GWS[2,12544,1] #1: AVG=1121306919 ms

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/reshape_to_columns

GWS[3,3,3] #0: AVG=1121306918 ms

OpenCLTimestamps/[end]Conv2d_10_depthwise/depthwise+Conv2d_10_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #36: AVG=1121306986 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #40: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #39: AVG=1121306989 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #38: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #37: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_11_depthwise/depthwise+Conv2d_11_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #41: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #45: AVG=1121307003 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #44: AVG=1121306998 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #43: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #42: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_12_depthwise/depthwise+Conv2d_12_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[512,7,7] #46: AVG=1121307004 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #50: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,10,1] #49: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,128,1] #48: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #47: AVG=1121307007 ms

OpenCLTimestamps/[end]Conv2d_13_depthwise/depthwise+Conv2d_13_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[1024,4,4] #51: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #55: AVG=1121307025 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[256,10,1] #54: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,256,1] #53: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/reshape_to_columns

GWS[1024,1,1] #52: AVG=1121307016 ms

OpenCLTimestamps/[end]Conv2d_1_depthwise/depthwise+Conv2d_1_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[32,56,56] #3: AVG=1121306921 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[16,28,112] LWS[4,1,1] #5: AVG=1121306924 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/reshape_to_columns

GWS[32,1,1] #4: AVG=1121306922 ms

OpenCLTimestamps/[end]Conv2d_2_depthwise/depthwise+Conv2d_2_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[64,56,56] #6: AVG=1121306926 ms

OpenCLTimestamps/[end]Conv2d_2_pointwise/Conv2D+Conv2d_2_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[32,14,56] LWS[4,1,1] #8: AVG=1121306929 ms
Another alternative is to use Armnn's ExecuteNetwork to run a
tfllite
model and use the -e option which will make the tool output the
time
consumed by each kernel
For more information about ExecuteNetwork see:

https://github.com/ARM-software/armnn/tree/branches/armnn_21_02/tests/ExecuteNetwork

Hope this helps.

From: Shraddhaa1 @.>
Sent: 14 June 2021 19:33
To: ARM-software/ComputeLibrary @.>
Cc: Subscribed @.***>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
I had contacted you a few weeks ago regarding the Execution of
Inference
Workloads on Hikey970 with layer splitting. Could you please help
me
know
if there is any function specific in ARM-CL which would let me
calculate
inference time taken by each layer of a neural network?
Thank you.
Sincerely,
Shraddha Dahal
On Fri, Apr 9, 2021 at 12:06 PM Shraddha Dahal @.***>
wrote:

Hello Sir,
Thank you for the reply. That really helped.
Have a good day.
Sincerely,
Shraddha Dahal
On Fri, Mar 26, 2021 at 5:44 AM Pablo Marquez Tello <
@.***> wrote:

Closed #882 <
https://github.com/ARM-software/ComputeLibrary/issues/882
.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#event-4513101747

,

or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC2KQYG5FVP7OF54Z53TFRQQVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-860902526

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISSHV4V7LSD3ZI2TQSTTSZDQXANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments
are
confidential and may also be privileged. If you are not the
intended
recipient, please notify the sender immediately and do not
disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-863185227

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6MGOTC7JHJOXE7NFTTTHQ2XANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865332541

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISU4USITBC3ODKTNV4LTT6QIRANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865664696

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC7EFF42VJLI3ECM4LDTUA2TTANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866187809

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISUBGWRVL4IE2NWXNG3TUDCIJANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866297436

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6ZEHGM54QQBSQEDCLTUDURVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866479229

,

or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISRK34AZAZJCC7G3GLDTUFDKBANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-869021541
,
or unsubscribe
<
https://github.com/notifications/unsubscribe-auth/ARRDMC3JFBHEPA7T7A67TWTTUXZVRANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-877414890>,
or unsubscribe<
https://github.com/notifications/unsubscribe-auth/AGNXISSW55NW4TS74O3QJXDTW5GATANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878299935,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARRDMC7BOHEULDPMRSA2MDTTXLX4XANCNFSM4ZHEPDJQ
.

Hi,
We cannot see the images, could you please open an issue on github to discuss this?

From: Shraddhaa1 @.>
Sent: 12 July 2021 16:22
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the response. It worked. I am also currently working on
accessing performance monitoring counters such as cache misses, IPC and
memory bandwidth. I was trying to check how memory intensive each neural
network is. I was using perf tool for CPU profiling but the above
mentioned parameters are not supported in HiKey970 board. This can be
observed from the image attached below:
[image: perf_result.png]
The parameters were not on the perf list:
[image: perf_list.png]
Could you please help me know if there are tools which would help me obtain
these parameters when running a neural network?
Thanks again.
Have a good day.

From: Shraddhaa1 @.>
Sent: 09 July 2021 20:39
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
I was working on obtaining time taken by each kernel with target CL for the
available networks under development repo, as you suggested in previous
emails. Could you please help me know if there are any such instruments
available, which would help me know the time taken by each kernel when
assigning benchmark networks under CPU? I am currently using the command
mentioned below for target CL:
sudo LD_LIBRARY_PATH=/home/shunya/ComputeLibrary/build
./build/tests/benchmark_graph_mobilenet_v2
--instruments=OPENCL_TIMESTAMPS_MS --example_args='--target=CL'
Could you please help me know how I can obtain similar information with
target NEON?
Have a good day.
Sincerely,
Shraddha Dahal
On Thu, Jul 1, 2021 at 2:34 PM Shraddha Dahal @.***>
wrote:

Hello Sir,
Thank you for the response. It really helped.
Have a good day.
Sincerely,
Shraddha Dahal
On Sat, Jun 26, 2021 at 10:52 AM developer-compute <
@.***> wrote:

Hi,
The graph api in ACL does not support that level of granularity, you
must
run all the workloads on the backend specified with the --target option.
You cannot have some workloads on the CPU and others on the GPU.
Hope this helps.

From: Shraddhaa1 @.>
Sent: 23 June 2021 03:46
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the reply. I can now observe time taken by each kernel of
the
neural networks. Could you please help me know if there has been any
update
on how specific layers of a neural network can be specified to either
CPU
or GPU? With the repo:
https://github.com/adityagupta1089/ComputeLibrary.git
I could mix the use of CPU and GPU, but I am trying to specify layers to
CPU or GPU. Could you please help me understand how it can be obtained?
Have a good evening.
Sincerely,
Shraddha Dahal
On Tue, Jun 22, 2021 at 3:07 PM developer-compute @.***>
wrote:

Hi,
The benchmark examples are only available in the development repo:
https://review.mlplatform.org/admin/repos/ml%2FComputeLibrary
Try: git clone "https://review.mlplatform.org/ml/ComputeLibrary"
Then build with benchmark_examples=1
Hope this helps

From: Shraddhaa1 @.>
Sent: 22 June 2021 18:31
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the reply. The file benchmark_graph_mobilenet, is not
generated anywhere inside the Compute Library. Could you please help
me
know if the way I used to build ARM-CL with benchmarks_examples=1
correct?
Have a good day.
Sincerely,
Shraddha Dahal
On Tue, Jun 22, 2021 at 2:20 AM developer-compute @.***>
wrote:

Hi,
Please try running benchmark_graph_mobilenet instead of
graph_mobilenet.
Hope this helps.

From: Shraddhaa1 @.>
Sent: 21 June 2021 21:46
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the response. I tried the first method you mentioned
in
the
previous email.
In the makefile.arm file, I added benchmark_examples:=1 such as:
BUILD:=native
NEON:=1
OPENCL:=1
ARCH:=arm64-v8a
all: release
CFLAGS:=-std=c++14
benchmark_examples:=1
debug:
scons -j8 -Q arch=$(ARCH) build=$(BUILD) neon=$(NEON)
opencl=$(OPENCL)
build_dir=debug debug=1 extra_cxx_flags=$(CFLAGS)
release:
scons -j8 -Q arch=$(ARCH) build=$(BUILD) neon=$(NEON)
opencl=$(OPENCL)
build_dir=release debug=0 extra_cxx_flags=$(CFLAGS)
sched:
g++ -o build/release/examples/graph_temp_scheduler2.o -c
-Wno-deprecated-declarations -Wall -DARCH_ARM -Wextra
-Wno-unused-parameter
-pedantic -Wdisabled-optimization -Wformat=2 -Winit-self
-Wstrict-overflow=2 -Wswitch-default -fpermissive -std=gnu++11
-Wno-vla
-Woverloaded-virtual -Wctor-dtor-privacy -Wsign-promo -Weffc++
-Wno-format-nonliteral -Wno-overlength-strings -Wno-strict-overflow
-Wlogical-op -Wnoexcept -Wstrict-null-sentinel
-Wno-implicit-fallthrough
-march=armv8-a -Wno-ignored-attributes -Werror -O3 -ftree-vectorize
-std=c++14 -D_GLIBCXX_USE_NANOSLEEP -DARM_COMPUTE_CPP_SCHEDULER=1
-DARM_COMPUTE_AARCH64_V8A -DNO_DOT_IN_TOOLCHAIN -DEMBEDDED_KERNELS
-Iinclude -I. -I. examples/graph_temp_scheduler2.cpp
g++ -o build/release/examples/graph_temp_scheduler2
-Wl,--allow-shlib-undefined
build/release/examples/graph_temp_scheduler2.o
build/release/utils/Utils.o build/release/utils/GraphUtils.o
build/release/utils/CommonGraphOptions.o -Lbuild/release -L.
-lpthread
-larm_compute_graph -larm_compute -larm_compute_core
After that, I gave commands:
$make all
$sudo LD_LIBRARY_PATH=/home/shunya/ComputeLibrary1/build/release
./build/release/examples/graph_mobilenet
--instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
but, I could not see the outputs with time taken by each kernel. The
path
to library in the above command has libarm_compute.so,
libarm_compute_core.so, libarm_compute_graph.so,
libarm_compute_core-static.a, libarm_compute_graph-static.a and
libarm_compute-static.a. Could you please help me know where I am
going
wrong?
Have a good day.
Sincerely,
Shraddha Dahal
On Thu, Jun 17, 2021 at 7:10 AM developer-compute @.***>
wrote:

Hi Shraddha,
If you build ACL with the option benchmark_examples=1 you can then
run the network and use the instruments to see how much time each
kernel
is taking:
@.***_hikey_dev:~/tmp/pabtel01#
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./acl_neon+cl_release/
./benchmark_graph_mobilenet --instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
This will produce output like below:
OpenCLTimestamps/Now OpenCL: AVG=1121307039 ms
OpenCLTimestamps/Now Wall clock: AVG=1623931494169279 us

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[8,3136,1] LWS[4,1,1] #2: AVG=1121306920 ms
OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/im2col3x3_nhwc
GWS[2,12544,1] #1: AVG=1121306919 ms

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/reshape_to_columns

GWS[3,3,3] #0: AVG=1121306918 ms

OpenCLTimestamps/[end]Conv2d_10_depthwise/depthwise+Conv2d_10_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #36: AVG=1121306986 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #40: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #39: AVG=1121306989 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #38: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #37: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_11_depthwise/depthwise+Conv2d_11_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #41: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #45: AVG=1121307003 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #44: AVG=1121306998 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #43: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #42: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_12_depthwise/depthwise+Conv2d_12_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[512,7,7] #46: AVG=1121307004 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #50: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,10,1] #49: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,128,1] #48: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #47: AVG=1121307007 ms

OpenCLTimestamps/[end]Conv2d_13_depthwise/depthwise+Conv2d_13_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[1024,4,4] #51: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #55: AVG=1121307025 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[256,10,1] #54: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,256,1] #53: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/reshape_to_columns

GWS[1024,1,1] #52: AVG=1121307016 ms

OpenCLTimestamps/[end]Conv2d_1_depthwise/depthwise+Conv2d_1_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[32,56,56] #3: AVG=1121306921 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[16,28,112] LWS[4,1,1] #5: AVG=1121306924 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/reshape_to_columns

GWS[32,1,1] #4: AVG=1121306922 ms

OpenCLTimestamps/[end]Conv2d_2_depthwise/depthwise+Conv2d_2_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[64,56,56] #6: AVG=1121306926 ms

OpenCLTimestamps/[end]Conv2d_2_pointwise/Conv2D+Conv2d_2_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[32,14,56] LWS[4,1,1] #8: AVG=1121306929 ms
Another alternative is to use Armnn's ExecuteNetwork to run a
tfllite
model and use the -e option which will make the tool output the
time
consumed by each kernel
For more information about ExecuteNetwork see:

https://github.com/ARM-software/armnn/tree/branches/armnn_21_02/tests/ExecuteNetwork

Hope this helps.

From: Shraddhaa1 @.>
Sent: 14 June 2021 19:33
To: ARM-software/ComputeLibrary @.>
Cc: Subscribed @.***>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
I had contacted you a few weeks ago regarding the Execution of
Inference
Workloads on Hikey970 with layer splitting. Could you please help
me
know
if there is any function specific in ARM-CL which would let me
calculate
inference time taken by each layer of a neural network?
Thank you.
Sincerely,
Shraddha Dahal
On Fri, Apr 9, 2021 at 12:06 PM Shraddha Dahal @.***>
wrote:

Hello Sir,
Thank you for the reply. That really helped.
Have a good day.
Sincerely,
Shraddha Dahal
On Fri, Mar 26, 2021 at 5:44 AM Pablo Marquez Tello <
@.***> wrote:

Closed #882 <
https://github.com/ARM-software/ComputeLibrary/issues/882
.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#event-4513101747

,

or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC2KQYG5FVP7OF54Z53TFRQQVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-860902526

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISSHV4V7LSD3ZI2TQSTTSZDQXANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments
are
confidential and may also be privileged. If you are not the
intended
recipient, please notify the sender immediately and do not
disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-863185227

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6MGOTC7JHJOXE7NFTTTHQ2XANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865332541

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISU4USITBC3ODKTNV4LTT6QIRANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865664696

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC7EFF42VJLI3ECM4LDTUA2TTANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866187809

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISUBGWRVL4IE2NWXNG3TUDCIJANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866297436

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6ZEHGM54QQBSQEDCLTUDURVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866479229

,

or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISRK34AZAZJCC7G3GLDTUFDKBANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-869021541
,
or unsubscribe
<
https://github.com/notifications/unsubscribe-auth/ARRDMC3JFBHEPA7T7A67TWTTUXZVRANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-877414890>,
or unsubscribe<
https://github.com/notifications/unsubscribe-auth/AGNXISSW55NW4TS74O3QJXDTW5GATANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878299935,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARRDMC7BOHEULDPMRSA2MDTTXLX4XANCNFSM4ZHEPDJQ
.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHubhttps://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878371095, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AGNXISUD7XXGGA5OMHUKX43TXMCDHANCNFSM4ZHEPDJQ.
IMPORTANT NOTICE: The contents of this email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please notify the sender immediately and do not disclose the contents to any other person, use it for any purpose, or store or copy the information in any medium. Thank you.

Hello Sir,
Thank you for the reply. I will open an issue on github soon to discuss it.
However, I have a question regarding the time taken by each kernel when
assigning benchmark networks under CPU and GPU. By running the benchmarks
on CPU and GPU, I observed that the time taken by each kernel is much more
for target CL than that with target NEON. I was not expecting such a big
time difference. Could you please help me understand why kernels are taking
less time with target NEON?
Thank you.
Sincerely,
Shraddha Dahal
On Tue, Jul 13, 2021 at 3:50 AM developer-compute @.***>
wrote:

Hi,
We cannot see the images, could you please open an issue on github to
discuss this?

From: Shraddhaa1 @.>
Sent: 12 July 2021 16:22
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the response. It worked. I am also currently working on
accessing performance monitoring counters such as cache misses, IPC and
memory bandwidth. I was trying to check how memory intensive each neural
network is. I was using perf tool for CPU profiling but the above
mentioned parameters are not supported in HiKey970 board. This can be
observed from the image attached below:
[image: perf_result.png]
The parameters were not on the perf list:
[image: perf_list.png]
Could you please help me know if there are tools which would help me obtain
these parameters when running a neural network?
Thanks again.
Have a good day.

From: Shraddhaa1 @.>
Sent: 09 July 2021 20:39
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
I was working on obtaining time taken by each kernel with target CL for
the
available networks under development repo, as you suggested in previous
emails. Could you please help me know if there are any such instruments
available, which would help me know the time taken by each kernel when
assigning benchmark networks under CPU? I am currently using the command
mentioned below for target CL:
sudo LD_LIBRARY_PATH=/home/shunya/ComputeLibrary/build
./build/tests/benchmark_graph_mobilenet_v2
--instruments=OPENCL_TIMESTAMPS_MS --example_args='--target=CL'
Could you please help me know how I can obtain similar information with
target NEON?
Have a good day.
Sincerely,
Shraddha Dahal
On Thu, Jul 1, 2021 at 2:34 PM Shraddha Dahal @.***>
wrote:

Hello Sir,
Thank you for the response. It really helped.
Have a good day.
Sincerely,
Shraddha Dahal
On Sat, Jun 26, 2021 at 10:52 AM developer-compute <
@.***> wrote:

Hi,
The graph api in ACL does not support that level of granularity, you
must
run all the workloads on the backend specified with the --target
option.
You cannot have some workloads on the CPU and others on the GPU.
Hope this helps.

From: Shraddhaa1 @.>
Sent: 23 June 2021 03:46
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the reply. I can now observe time taken by each kernel
of
the
neural networks. Could you please help me know if there has been any
update
on how specific layers of a neural network can be specified to either
CPU
or GPU? With the repo:
https://github.com/adityagupta1089/ComputeLibrary.git
I could mix the use of CPU and GPU, but I am trying to specify layers
to
CPU or GPU. Could you please help me understand how it can be
obtained?
Have a good evening.
Sincerely,
Shraddha Dahal
On Tue, Jun 22, 2021 at 3:07 PM developer-compute @.***>
wrote:

Hi,
The benchmark examples are only available in the development repo:
https://review.mlplatform.org/admin/repos/ml%2FComputeLibrary
Try: git clone "https://review.mlplatform.org/ml/ComputeLibrary"
Then build with benchmark_examples=1
Hope this helps

From: Shraddhaa1 @.>
Sent: 22 June 2021 18:31
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the reply. The file benchmark_graph_mobilenet, is not
generated anywhere inside the Compute Library. Could you please help
me
know if the way I used to build ARM-CL with benchmarks_examples=1
correct?
Have a good day.
Sincerely,
Shraddha Dahal
On Tue, Jun 22, 2021 at 2:20 AM developer-compute @.***>
wrote:

Hi,
Please try running benchmark_graph_mobilenet instead of
graph_mobilenet.
Hope this helps.

From: Shraddhaa1 @.>
Sent: 21 June 2021 21:46
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the response. I tried the first method you mentioned
in
the
previous email.
In the makefile.arm file, I added benchmark_examples:=1 such as:
BUILD:=native
NEON:=1
OPENCL:=1
ARCH:=arm64-v8a
all: release
CFLAGS:=-std=c++14
benchmark_examples:=1
debug:
scons -j8 -Q arch=$(ARCH) build=$(BUILD) neon=$(NEON)
opencl=$(OPENCL)
build_dir=debug debug=1 extra_cxx_flags=$(CFLAGS)
release:
scons -j8 -Q arch=$(ARCH) build=$(BUILD) neon=$(NEON)
opencl=$(OPENCL)
build_dir=release debug=0 extra_cxx_flags=$(CFLAGS)
sched:
g++ -o build/release/examples/graph_temp_scheduler2.o -c
-Wno-deprecated-declarations -Wall -DARCH_ARM -Wextra
-Wno-unused-parameter
-pedantic -Wdisabled-optimization -Wformat=2 -Winit-self
-Wstrict-overflow=2 -Wswitch-default -fpermissive -std=gnu++11
-Wno-vla
-Woverloaded-virtual -Wctor-dtor-privacy -Wsign-promo -Weffc++
-Wno-format-nonliteral -Wno-overlength-strings
-Wno-strict-overflow
-Wlogical-op -Wnoexcept -Wstrict-null-sentinel
-Wno-implicit-fallthrough
-march=armv8-a -Wno-ignored-attributes -Werror -O3
-ftree-vectorize
-std=c++14 -D_GLIBCXX_USE_NANOSLEEP -DARM_COMPUTE_CPP_SCHEDULER=1
-DARM_COMPUTE_AARCH64_V8A -DNO_DOT_IN_TOOLCHAIN -DEMBEDDED_KERNELS
-Iinclude -I. -I. examples/graph_temp_scheduler2.cpp
g++ -o build/release/examples/graph_temp_scheduler2
-Wl,--allow-shlib-undefined
build/release/examples/graph_temp_scheduler2.o
build/release/utils/Utils.o build/release/utils/GraphUtils.o
build/release/utils/CommonGraphOptions.o -Lbuild/release -L.
-lpthread
-larm_compute_graph -larm_compute -larm_compute_core
After that, I gave commands:
$make all
$sudo LD_LIBRARY_PATH=/home/shunya/ComputeLibrary1/build/release
./build/release/examples/graph_mobilenet
--instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
but, I could not see the outputs with time taken by each kernel.
The
path
to library in the above command has libarm_compute.so,
libarm_compute_core.so, libarm_compute_graph.so,
libarm_compute_core-static.a, libarm_compute_graph-static.a and
libarm_compute-static.a. Could you please help me know where I am
going
wrong?
Have a good day.
Sincerely,
Shraddha Dahal
On Thu, Jun 17, 2021 at 7:10 AM developer-compute @.***>
wrote:

Hi Shraddha,
If you build ACL with the option benchmark_examples=1 you can
then
run the network and use the instruments to see how much time
each
kernel
is taking:
@.***_hikey_dev:~/tmp/pabtel01#
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./acl_neon+cl_release/
./benchmark_graph_mobilenet --instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
This will produce output like below:
OpenCLTimestamps/Now OpenCL: AVG=1121307039 ms
OpenCLTimestamps/Now Wall clock: AVG=1623931494169279 us

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[8,3136,1] LWS[4,1,1] #2: AVG=1121306920 ms
OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/im2col3x3_nhwc
GWS[2,12544,1] #1: AVG=1121306919 ms

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/reshape_to_columns

GWS[3,3,3] #0: AVG=1121306918 ms

OpenCLTimestamps/[end]Conv2d_10_depthwise/depthwise+Conv2d_10_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #36: AVG=1121306986 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #40: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #39: AVG=1121306989 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #38: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #37: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_11_depthwise/depthwise+Conv2d_11_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #41: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #45: AVG=1121307003 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #44: AVG=1121306998 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #43: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #42: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_12_depthwise/depthwise+Conv2d_12_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[512,7,7] #46: AVG=1121307004 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #50: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,10,1] #49: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,128,1] #48: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #47: AVG=1121307007 ms

OpenCLTimestamps/[end]Conv2d_13_depthwise/depthwise+Conv2d_13_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[1024,4,4] #51: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #55: AVG=1121307025 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[256,10,1] #54: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,256,1] #53: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/reshape_to_columns

GWS[1024,1,1] #52: AVG=1121307016 ms

OpenCLTimestamps/[end]Conv2d_1_depthwise/depthwise+Conv2d_1_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[32,56,56] #3: AVG=1121306921 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[16,28,112] LWS[4,1,1] #5: AVG=1121306924 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/reshape_to_columns

GWS[32,1,1] #4: AVG=1121306922 ms

OpenCLTimestamps/[end]Conv2d_2_depthwise/depthwise+Conv2d_2_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[64,56,56] #6: AVG=1121306926 ms

OpenCLTimestamps/[end]Conv2d_2_pointwise/Conv2D+Conv2d_2_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[32,14,56] LWS[4,1,1] #8: AVG=1121306929 ms
Another alternative is to use Armnn's ExecuteNetwork to run a
tfllite
model and use the -e option which will make the tool output the
time
consumed by each kernel
For more information about ExecuteNetwork see:

https://github.com/ARM-software/armnn/tree/branches/armnn_21_02/tests/ExecuteNetwork

Hope this helps.

From: Shraddhaa1 @.>
Sent: 14 June 2021 19:33
To: ARM-software/ComputeLibrary @.>
Cc: Subscribed @.***>
Subject: Re: [ARM-software/ComputeLibrary] Execution of
Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
I had contacted you a few weeks ago regarding the Execution of
Inference
Workloads on Hikey970 with layer splitting. Could you please
help
me
know
if there is any function specific in ARM-CL which would let me
calculate
inference time taken by each layer of a neural network?
Thank you.
Sincerely,
Shraddha Dahal
On Fri, Apr 9, 2021 at 12:06 PM Shraddha Dahal @.***>
wrote:

Hello Sir,
Thank you for the reply. That really helped.
Have a good day.
Sincerely,
Shraddha Dahal
On Fri, Mar 26, 2021 at 5:44 AM Pablo Marquez Tello <
@.***> wrote:

Closed #882 <
https://github.com/ARM-software/ComputeLibrary/issues/882
.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#event-4513101747

,

or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC2KQYG5FVP7OF54Z53TFRQQVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you are subscribed to this
thread.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-860902526

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISSHV4V7LSD3ZI2TQSTTSZDQXANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments
are
confidential and may also be privileged. If you are not the
intended
recipient, please notify the sender immediately and do not
disclose
the
contents to any other person, use it for any purpose, or store
or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-863185227

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6MGOTC7JHJOXE7NFTTTHQ2XANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865332541

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISU4USITBC3ODKTNV4LTT6QIRANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments
are
confidential and may also be privileged. If you are not the
intended
recipient, please notify the sender immediately and do not
disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865664696

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC7EFF42VJLI3ECM4LDTUA2TTANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866187809

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISUBGWRVL4IE2NWXNG3TUDCIJANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866297436

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6ZEHGM54QQBSQEDCLTUDURVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866479229

,

or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISRK34AZAZJCC7G3GLDTUFDKBANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-869021541

,

or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC3JFBHEPA7T7A67TWTTUXZVRANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-877414890

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISSW55NW4TS74O3QJXDTW5GATANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878299935
,
or unsubscribe
<
https://github.com/notifications/unsubscribe-auth/ARRDMC7BOHEULDPMRSA2MDTTXLX4XANCNFSM4ZHEPDJQ
.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878371095>,
or unsubscribe<
https://github.com/notifications/unsubscribe-auth/AGNXISUD7XXGGA5OMHUKX43TXMCDHANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878901912,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARRDMC67KAAMOBFCL5ZA7DDTXP46HANCNFSM4ZHEPDJQ
.

Hi Shraddha,
Usually, the first iteration is slower than the next ones, this is particularly true for the CL backend since the OpenCL source implementing the kernels must be compiled at runtime.
Try to use the option --iterations as shown below to have a better sense of the performance.
It's difficult to speculate about the performance without knowing more details:

What GPU/CPU are you using?
How big is the difference?

From: Shraddhaa1 @.>
Sent: 24 August 2021 05:32
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the reply. I will open an issue on github soon to discuss it.
However, I have a question regarding the time taken by each kernel when
assigning benchmark networks under CPU and GPU. By running the benchmarks
on CPU and GPU, I observed that the time taken by each kernel is much more
for target CL than that with target NEON. I was not expecting such a big
time difference. Could you please help me understand why kernels are taking
less time with target NEON?
Thank you.
Sincerely,
Shraddha Dahal
On Tue, Jul 13, 2021 at 3:50 AM developer-compute @.***>
wrote:

Hi,
We cannot see the images, could you please open an issue on github to
discuss this?

From: Shraddhaa1 @.>
Sent: 12 July 2021 16:22
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the response. It worked. I am also currently working on
accessing performance monitoring counters such as cache misses, IPC and
memory bandwidth. I was trying to check how memory intensive each neural
network is. I was using perf tool for CPU profiling but the above
mentioned parameters are not supported in HiKey970 board. This can be
observed from the image attached below:
[image: perf_result.png]
The parameters were not on the perf list:
[image: perf_list.png]
Could you please help me know if there are tools which would help me obtain
these parameters when running a neural network?
Thanks again.
Have a good day.

From: Shraddhaa1 @.>
Sent: 09 July 2021 20:39
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
I was working on obtaining time taken by each kernel with target CL for
the
available networks under development repo, as you suggested in previous
emails. Could you please help me know if there are any such instruments
available, which would help me know the time taken by each kernel when
assigning benchmark networks under CPU? I am currently using the command
mentioned below for target CL:
sudo LD_LIBRARY_PATH=/home/shunya/ComputeLibrary/build
./build/tests/benchmark_graph_mobilenet_v2
--instruments=OPENCL_TIMESTAMPS_MS --example_args='--target=CL'
Could you please help me know how I can obtain similar information with
target NEON?
Have a good day.
Sincerely,
Shraddha Dahal
On Thu, Jul 1, 2021 at 2:34 PM Shraddha Dahal @.***>
wrote:

Hello Sir,
Thank you for the response. It really helped.
Have a good day.
Sincerely,
Shraddha Dahal
On Sat, Jun 26, 2021 at 10:52 AM developer-compute <
@.***> wrote:

Hi,
The graph api in ACL does not support that level of granularity, you
must
run all the workloads on the backend specified with the --target
option.
You cannot have some workloads on the CPU and others on the GPU.
Hope this helps.

From: Shraddhaa1 @.>
Sent: 23 June 2021 03:46
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the reply. I can now observe time taken by each kernel
of
the
neural networks. Could you please help me know if there has been any
update
on how specific layers of a neural network can be specified to either
CPU
or GPU? With the repo:
https://github.com/adityagupta1089/ComputeLibrary.git
I could mix the use of CPU and GPU, but I am trying to specify layers
to
CPU or GPU. Could you please help me understand how it can be
obtained?
Have a good evening.
Sincerely,
Shraddha Dahal
On Tue, Jun 22, 2021 at 3:07 PM developer-compute @.***>
wrote:

Hi,
The benchmark examples are only available in the development repo:
https://review.mlplatform.org/admin/repos/ml%2FComputeLibrary
Try: git clone "https://review.mlplatform.org/ml/ComputeLibrary"
Then build with benchmark_examples=1
Hope this helps

From: Shraddhaa1 @.>
Sent: 22 June 2021 18:31
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the reply. The file benchmark_graph_mobilenet, is not
generated anywhere inside the Compute Library. Could you please help
me
know if the way I used to build ARM-CL with benchmarks_examples=1
correct?
Have a good day.
Sincerely,
Shraddha Dahal
On Tue, Jun 22, 2021 at 2:20 AM developer-compute @.***>
wrote:

Hi,
Please try running benchmark_graph_mobilenet instead of
graph_mobilenet.
Hope this helps.

From: Shraddhaa1 @.>
Sent: 21 June 2021 21:46
To: ARM-software/ComputeLibrary @.>
Cc: developer-compute @.>; Comment @.>
Subject: Re: [ARM-software/ComputeLibrary] Execution of Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
Thank you for the response. I tried the first method you mentioned
in
the
previous email.
In the makefile.arm file, I added benchmark_examples:=1 such as:
BUILD:=native
NEON:=1
OPENCL:=1
ARCH:=arm64-v8a
all: release
CFLAGS:=-std=c++14
benchmark_examples:=1
debug:
scons -j8 -Q arch=$(ARCH) build=$(BUILD) neon=$(NEON)
opencl=$(OPENCL)
build_dir=debug debug=1 extra_cxx_flags=$(CFLAGS)
release:
scons -j8 -Q arch=$(ARCH) build=$(BUILD) neon=$(NEON)
opencl=$(OPENCL)
build_dir=release debug=0 extra_cxx_flags=$(CFLAGS)
sched:
g++ -o build/release/examples/graph_temp_scheduler2.o -c
-Wno-deprecated-declarations -Wall -DARCH_ARM -Wextra
-Wno-unused-parameter
-pedantic -Wdisabled-optimization -Wformat=2 -Winit-self
-Wstrict-overflow=2 -Wswitch-default -fpermissive -std=gnu++11
-Wno-vla
-Woverloaded-virtual -Wctor-dtor-privacy -Wsign-promo -Weffc++
-Wno-format-nonliteral -Wno-overlength-strings
-Wno-strict-overflow
-Wlogical-op -Wnoexcept -Wstrict-null-sentinel
-Wno-implicit-fallthrough
-march=armv8-a -Wno-ignored-attributes -Werror -O3
-ftree-vectorize
-std=c++14 -D_GLIBCXX_USE_NANOSLEEP -DARM_COMPUTE_CPP_SCHEDULER=1
-DARM_COMPUTE_AARCH64_V8A -DNO_DOT_IN_TOOLCHAIN -DEMBEDDED_KERNELS
-Iinclude -I. -I. examples/graph_temp_scheduler2.cpp
g++ -o build/release/examples/graph_temp_scheduler2
-Wl,--allow-shlib-undefined
build/release/examples/graph_temp_scheduler2.o
build/release/utils/Utils.o build/release/utils/GraphUtils.o
build/release/utils/CommonGraphOptions.o -Lbuild/release -L.
-lpthread
-larm_compute_graph -larm_compute -larm_compute_core
After that, I gave commands:
$make all
$sudo LD_LIBRARY_PATH=/home/shunya/ComputeLibrary1/build/release
./build/release/examples/graph_mobilenet
--instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
but, I could not see the outputs with time taken by each kernel.
The
path
to library in the above command has libarm_compute.so,
libarm_compute_core.so, libarm_compute_graph.so,
libarm_compute_core-static.a, libarm_compute_graph-static.a and
libarm_compute-static.a. Could you please help me know where I am
going
wrong?
Have a good day.
Sincerely,
Shraddha Dahal
On Thu, Jun 17, 2021 at 7:10 AM developer-compute @.***>
wrote:

Hi Shraddha,
If you build ACL with the option benchmark_examples=1 you can
then
run the network and use the instruments to see how much time
each
kernel
is taking:
@.***_hikey_dev:~/tmp/pabtel01#
LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./acl_neon+cl_release/
./benchmark_graph_mobilenet --instruments=OPENCL_TIMESTAMPS_MS
--example_args='--target=CL'
This will produce output like below:
OpenCLTimestamps/Now OpenCL: AVG=1121307039 ms
OpenCLTimestamps/Now Wall clock: AVG=1623931494169279 us

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[8,3136,1] LWS[4,1,1] #2: AVG=1121306920 ms
OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/im2col3x3_nhwc
GWS[2,12544,1] #1: AVG=1121306919 ms

OpenCLTimestamps/[end]Conv2d_0+Conv2d_0/BatchNorm/reshape_to_columns

GWS[3,3,3] #0: AVG=1121306918 ms

OpenCLTimestamps/[end]Conv2d_10_depthwise/depthwise+Conv2d_10_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #36: AVG=1121306986 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #40: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #39: AVG=1121306989 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #38: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_10_pointwise/Conv2D+Conv2d_10_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #37: AVG=1121306988 ms

OpenCLTimestamps/[end]Conv2d_11_depthwise/depthwise+Conv2d_11_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[512,7,7] #41: AVG=1121306995 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[128,64,1] #45: AVG=1121307003 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,40,1] #44: AVG=1121306998 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[128,128,1] #43: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_11_pointwise/Conv2D+Conv2d_11_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #42: AVG=1121306997 ms

OpenCLTimestamps/[end]Conv2d_12_depthwise/depthwise+Conv2d_12_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[512,7,7] #46: AVG=1121307004 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #50: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[128,10,1] #49: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,128,1] #48: AVG=1121307008 ms

OpenCLTimestamps/[end]Conv2d_12_pointwise/Conv2D+Conv2d_12_pointwise/BatchNorm/reshape_to_columns

GWS[512,1,1] #47: AVG=1121307007 ms

OpenCLTimestamps/[end]Conv2d_13_depthwise/depthwise+Conv2d_13_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[1024,4,4] #51: AVG=1121307012 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_mm_reshaped_lhs_nt_rhs_t

GWS[256,16,1] #55: AVG=1121307025 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_lhs_matrix_nt

GWS[256,10,1] #54: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/gemm_reshape_rhs_matrix_t

GWS[256,256,1] #53: AVG=1121307018 ms

OpenCLTimestamps/[end]Conv2d_13_pointwise/Conv2D+Conv2d_13_pointwise/BatchNorm/reshape_to_columns

GWS[1024,1,1] #52: AVG=1121307016 ms

OpenCLTimestamps/[end]Conv2d_1_depthwise/depthwise+Conv2d_1_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc_stride1

GWS[32,56,56] #3: AVG=1121306921 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[16,28,112] LWS[4,1,1] #5: AVG=1121306924 ms

OpenCLTimestamps/[end]Conv2d_1_pointwise/Conv2D+Conv2d_1_pointwise/BatchNorm/reshape_to_columns

GWS[32,1,1] #4: AVG=1121306922 ms

OpenCLTimestamps/[end]Conv2d_2_depthwise/depthwise+Conv2d_2_depthwise/BatchNorm/depthwise_convolution_3x3_nhwc

GWS[64,56,56] #6: AVG=1121306926 ms

OpenCLTimestamps/[end]Conv2d_2_pointwise/Conv2D+Conv2d_2_pointwise/BatchNorm/gemm_mm_floating_point_f32_bifrost

GWS[32,14,56] LWS[4,1,1] #8: AVG=1121306929 ms
Another alternative is to use Armnn's ExecuteNetwork to run a
tfllite
model and use the -e option which will make the tool output the
time
consumed by each kernel
For more information about ExecuteNetwork see:

https://github.com/ARM-software/armnn/tree/branches/armnn_21_02/tests/ExecuteNetwork

Hope this helps.

From: Shraddhaa1 @.>
Sent: 14 June 2021 19:33
To: ARM-software/ComputeLibrary @.>
Cc: Subscribed @.***>
Subject: Re: [ARM-software/ComputeLibrary] Execution of
Inference
Workloads on Hikey970 with layer splitting (#882)
Hello Sir,
I had contacted you a few weeks ago regarding the Execution of
Inference
Workloads on Hikey970 with layer splitting. Could you please
help
me
know
if there is any function specific in ARM-CL which would let me
calculate
inference time taken by each layer of a neural network?
Thank you.
Sincerely,
Shraddha Dahal
On Fri, Apr 9, 2021 at 12:06 PM Shraddha Dahal @.***>
wrote:

Hello Sir,
Thank you for the reply. That really helped.
Have a good day.
Sincerely,
Shraddha Dahal
On Fri, Mar 26, 2021 at 5:44 AM Pablo Marquez Tello <
@.***> wrote:

Closed #882 <
https://github.com/ARM-software/ComputeLibrary/issues/882
.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#event-4513101747

,

or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC2KQYG5FVP7OF54Z53TFRQQVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you are subscribed to this
thread.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-860902526

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISSHV4V7LSD3ZI2TQSTTSZDQXANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments
are
confidential and may also be privileged. If you are not the
intended
recipient, please notify the sender immediately and do not
disclose
the
contents to any other person, use it for any purpose, or store
or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-863185227

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6MGOTC7JHJOXE7NFTTTHQ2XANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865332541

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISU4USITBC3ODKTNV4LTT6QIRANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments
are
confidential and may also be privileged. If you are not the
intended
recipient, please notify the sender immediately and do not
disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-865664696

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC7EFF42VJLI3ECM4LDTUA2TTANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866187809

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISUBGWRVL4IE2NWXNG3TUDCIJANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or
copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866297436

,
or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC6ZEHGM54QQBSQEDCLTUDURVANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-866479229

,

or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISRK34AZAZJCC7G3GLDTUFDKBANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose
the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-869021541

,

or unsubscribe
<

https://github.com/notifications/unsubscribe-auth/ARRDMC3JFBHEPA7T7A67TWTTUXZVRANCNFSM4ZHEPDJQ

.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<

https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-877414890

,
or unsubscribe<

https://github.com/notifications/unsubscribe-auth/AGNXISSW55NW4TS74O3QJXDTW5GATANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy
the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878299935
,
or unsubscribe
<
https://github.com/notifications/unsubscribe-auth/ARRDMC7BOHEULDPMRSA2MDTTXLX4XANCNFSM4ZHEPDJQ
.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHub<
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878371095>,
or unsubscribe<
https://github.com/notifications/unsubscribe-auth/AGNXISUD7XXGGA5OMHUKX43TXMCDHANCNFSM4ZHEPDJQ

.

IMPORTANT NOTICE: The contents of this email and any attachments are
confidential and may also be privileged. If you are not the intended
recipient, please notify the sender immediately and do not disclose the
contents to any other person, use it for any purpose, or store or copy the
information in any medium. Thank you.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-878901912,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARRDMC67KAAMOBFCL5ZA7DDTXP46HANCNFSM4ZHEPDJQ
.

—
You are receiving this because you commented.
Reply to this email directly, view it on GitHubhttps://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-904314984, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AGNXISQYCIIFIWHEG3LJCGTT6MOFPANCNFSM4ZHEPDJQ.
Triage notifications on the go with GitHub Mobile for iOShttps://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Androidhttps://play.google.com/store/apps/details?id=com.github.android&utm_campaign=notification-email.
IMPORTANT NOTICE: The contents of this email and any attachments are confidential and may also be privileged. If you are not the intended recipient, please notify the sender immediately and do not disclose the contents to any other person, use it for any purpose, or store or copy the information in any medium. Thank you.

@Shraddhaa1 I am also working on using ARM CL with HiKey 970. Would you like to discuss this?

Hello Chang,
Thank you for the reply, and I would like to discuss this more. Have you
gone through the CPU + GPU utilization from ComputeLibrary? I have attached
link to the github below:
https://github.com/adityagupta1089/ComputeLibrary.git
Could you please help me understand how the number of images to be
processed is decided by CPU and GPU?
Is it possible to modify the code so that I could assign a specific number
of images to CPU and GPU?
Have a good weekend.
Sincerely,
Shraddha Dahal
On Fri, Sep 3, 2021 at 9:03 AM Hung-Yang Chang @.***>
wrote:

@Shraddhaa1 https://github.com/Shraddhaa1 I am also working on using
ARM CL with HiKey 970. Would you like to discuss this?
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-912564215,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARRDMC5GJO3G4ZBSHSVEC63UADISVANCNFSM4ZHEPDJQ
.
Triage notifications on the go with GitHub Mobile for iOS
https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675
or Android
https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

Hello @Shraddhaa1
I indeed went through the GitHub you share above, but for my work I will only focus on using CPU.
Here is the reference github: https://github.com/Ehsan-aghapour/ARMCL-pipe-all.
Btw now I have moved to ARM NN, because ARM NN is built on top of ARM CL. you can check ARM NN for more info.

Hello Chang,
I am currently working on the repo that you mentioned in the previous
email: https://github.com/Ehsan-aghapour/ARMCL-pipe-all.
I ran the networks separately on GPU, CPU Big and CPU Little. However, I
observed that CPU Big performed better than the GPU in all of the networks.
Also, when I split layers of ResNet50 amongst GPU, CPU Big and CPU Little,
I could see that the inference time of GPU is lesser than that of CPU Big
and CPU Little. The command I used for it was:
sudo LD_LIBRARY_PATH=/home/shunya/ARMCL-pipe-all-pipe-all/build
./graph_resnet50_all_pipe_sync --threads=4 --threads2=2 --total_cores=6
--partition_point=8 --partition_point2=12 --order=G-L-B --n=50
Could you please help me know why CPU Big is better than GPU when I assign
all of the layers to it?
Also, for ResNet50, I could see that the total parts are given as 18.
First partition point:8
Second partition point:12
Total parts:18
Should it not be 50 for ResNet50?
Have a good weekend.
Sincerely,
Shraddha Dahal
On Tue, Sep 14, 2021 at 10:42 AM Shraddha Dahal @.***>
wrote:

Hello Chang,
Thank you for the response. I will go through the GitHub link that you
have mentioned, and email you again with some queries.
Have a good day.
Sincerely,
Shraddha Dahal
On Mon, Sep 13, 2021 at 9:35 AM Hung-Yang Chang @.***>
wrote:

Hello @Shraddhaa1 https://github.com/Shraddhaa1
I indeed went through the GitHub you share above, but for my work I will
only focus on using CPU.
Here is the reference github:
https://github.com/Ehsan-aghapour/ARMCL-pipe-all.
Btw now I have moved to ARM NN, because ARM NN is built on top of ARM CL.
you can check ARM NN for more info.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/ARM-software/ComputeLibrary/issues/882#issuecomment-918257531,
or unsubscribe
https://github.com/notifications/unsubscribe-auth/ARRDMC7DLIMGUCVGUBADYJTUBYD2FANCNFSM4ZHEPDJQ
.
Triage notifications on the go with GitHub Mobile for iOS
https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675
or Android
https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.


ConstructorInvoker: Drop code

@PascalSchumacher what kind of problem is  this PR addressing ?

It removes unnecessary and unused code. I thought that was obvious from the commit comments, sorry.

thanks for the explanation, that's what I thought but I wanted to double check


Relabel Files tab to Staging

Fixes #79
I left references to the tab as "files" in the code and simply changed the tab label.  I thought this would be the least disruptive way to fix this.  I can go through and rename the references in the code if need be, but this small change introduces the minimum risk.

Fixed in 8fae79be399185b9beb237d6fce579aa969d8860.


Different commands depending activities

I want to use a same command "Pause" for example but the http order sent will be different depending the activity launched, putting in pause the music for exemple if music is the current activity or pause the movie if movie is the running activity. Is there a way to do that actually, i search but didn't find, or could you think to add it in future releases.

First, I don't think "Pause" will work with the echo to control the philips hue api as it is a lighting interface. To test this, you would have to have a bridge device called "Pause" and I'm sure that would be a reserved word for the echo. Try it to control a light on or off, if it works maybe we can do something.

I can attest that "pause" is a reserved Alexa word.  You may be able to say, "Alexa, turn on the pause light" with the word light specifically in you phrase.  If you find a device name that works, a hack might be to add all Harmony controlled device pause commands in one ha-bridge Harmony button.  This way the command works no matter which playback device is on.

Right i did not figure out that Echo could have reserved words such as Pause. To be honest i'm anticipating a little bit my configuration, i still not have my echo, i hope receiving it this week. But i totally understand the limitation. So about my specific question, to be more precise what i need is to send a pause command to the same machine my Home Cinema Computer, one command will be to send pause command to Foobar for pausing the music and Sending pause to Kodi when activity is Kodi. But understanding the limitation you've mentioned, the solution could be to create to device called "Pause music" and "Pause movie" and call them telling "Alexa Turn On Pause music" or "Alexa Turn On Pause movie". I've got to try these when i'll have my Echo. Thanks for your replies, i close the topic.


Icons missing for theme using atom:// protocol

Some of the icons that I'm using seem to be missing when using the ATOM_HOME variable in my shell.
The theme doesn't seem to be the issue, it seems to be the way Atom is routing requests coming from the atom:// protocol.
I get the following errors in the console:
Failed to load resource: net::ERR_FILE_NOT_FOUND    atom://seti-ui/stylesheets/_fonts/icomoon.woff
Failed to load resource: net::ERR_FILE_NOT_FOUND    atom://seti-ui/stylesheets/_fonts/icomoon.ttf

Looking through the code, I found this line that may be pointing to the wrong directory, or rather, not respecting the ATOM_HOME variable.
Atom: 0.178.0
OS: Mac OS X Yosemite 10.10.2

Thanks so much for reporting this, it will be fixed in the next Atom release 0.179

That was fast! I was just looking into how I might be able to help. Great job

0.179 is now available.


Search method for a range.

Instead of searching linearly, we can use binary search.
Find the key which is just lower than the lowerBound variable, and find the key which is just greater than the upperBound variable. And then iterate in this range linearly.
https://github.com/shandysulen/B-Plus-Tree/blob/40d3944257d466f1c7c0c8fcf5a8b7b5de41bc04/bplustree.java#L665

Hey Ayush, appreciate the issue! However, I'm not currently working on this repo, so the best path forward might be to fork this repo to implement your binary search idea.


How to import drawflow graph after postback

hello @jerosoler, When button been clicked will trigger page postback,then the drawflow graph will lost. How can I rebind drawflow graph after postback?

Hello
With import method to lo load data.
editor.import(exportdata);

Why is the graph lost?

I write editor.import(exportdata) in document.ready method but after postback
Drawflow graph get lost.

Show your code to review


Batch API Sample using v2.0 endpoints causes 2.1 WebHooks to be unusable

Describe the bug
The sample for batch transcription needs to be updated for v2.1 of the API.
To Reproduce
Steps to reproduce the behavior:

Setup a webhook using v2.1 of API (https://eastus.cris.ai/api/speechtotext/v2.1/transcriptions/hooks)
Start a batch transcription using v2.0 (as shown in the demo in this repo), notice webhook never fires!

Expected behavior
My Webhook should fire or sample should be updated to include 2.1 BatchClient code
Version of the Cognitive Services Speech SDK
Which version of the SDK are you using.
Platform, Operating System, and Programming Language

OS: Windows
Hardware - x64
Programming language: C#

Additional context

Error messages, log information, stack trace, ...
If you report an error for a specific service interaction, please report the SessionId and time (incl. timezone) of the reported incidents. The SessionId is reported in all call-backs/events you receive.
Any other additional information

some API changes and improvements to the batch docs are in progress
sorry for the delay
Wolfgang

When can we expect an update? I'm looking to demo a prototype solution for this ASAP.

hi everyone, Just a quick note to say that the webhook functionality will be exposed publicly through v3 API which is under development. 2.1 is not public and we will not likley make it so.  I would suggest you stick with 2.0.


Have cargo add --optional <dep> create a <dep> = "dep:<dep> feature

Problem
This is inspired by my running cargo add and seeing a lot of features that might be there only because people have not updated to the new dep: syntax to disable unintended implicit features.  For my crates, I'm aware of the syntax and plan to transition my crates.  For others, they might not be aware the feature exists and by us cargo add turning it explicit, it would help raise visibility on these features to help lead more people to using it.
In addition,  https://github.com/rust-lang/cargo/issues/10556 mentions the idea of cargo fix doing

possibly define a feature named "package" if it doesn't already exist and dep:package isn't already used

ie by convention moving away from implicit feature declarations.
Proposed Solution
cargo add --optional <dep> would create a <dep> = "dep:<dep> feature iff

rust-version is unset or is new enough for the syntax
dep:<dep> doesn't already exist

Problem
This is inspired by my running cargo add and seeing a lot of features that might be there only because people have not updated to the new dep: syntax to disable unintended implicit features.  For my crates, I'm aware of the syntax and plan to transition my crates.  For others, they might not be aware the feature exists and by us cargo add turning it explicit, it would help raise visibility on these features to help lead more people to using it.
In addition,  https://github.com/rust-lang/cargo/issues/10556 mentions the idea of cargo fix doing

possibly define a feature named "package" if it doesn't already exist and dep:package isn't already used

ie by convention moving away from implicit feature declarations.
Proposed Solution
cargo add --optional <dep> would create a <dep> = "dep:<dep> feature iff

rust-version is unset or is new enough for the syntax
dep:<dep> doesn't already exist

This will be needed for #12826 once we no longer allow optional dependencies to be unreferenced.  I'd propose we go ahead and implement it now to help raise awareness of dep: and prepare for that future.

The contrib guide will soon have some guidance on how we should be handling formatting.  See #12836

Looks like nobody's handling it. I'd like to try it. @rustbot claim


Unable to create segments: Exception in thread "main" java.lang.ClassNotFoundException: SegmentCreation.class

"hadoop jar pinot-hadoop-0.016.jar SegmentCreation job.properties" is not working
I found a class SegmentCreationJob.class but it does not have main method

"hadoop jar pinot-hadoop-0.016.jar SegmentCreation job.properties" is not working
I found a class SegmentCreationJob.class but it does not have main method

You can jar it in sentences
mvn  license:format
mvn clean install -DskipTests -Pbuild-shaded-jar，
Then use pinot-hadoop/target/pinot-hadoop-0.016-shaded.jar
Finally, use the following statement
/usr/local/hadoop-2.8.2/bin/hadoop pinot-hadoop-0.016-shaded.jar SegmentCreation job.properties
/usr/local/hadoop-2.8.2/bin/hadoop jar lib/pinot-hadoop-0.016-shaded.jar SegmentTarPush job.properties
I wonder if I can solve your problem

Does this work for you? https://github.com/apache/incubator-pinot/wiki/Offline-Index-Creation
You can try generating the shaded jar.


Enable check style for streampipes-integration-tests

Purpose
Discussion: #815
Master issue: #820
Enable check style for streampipes-integration-tests.
This is the first try to enable checkstyle for the repo. I use the module streampipes-integration-tests as the example here.
If we are good with this PR, then we can apply it to all other modules gradually.
Approach

Add the Indentation convention to the checkstyle.xml. I saw that we use the different Indentation convention in the repo. Here  I use the google_checks.xml as the Indentation convention, which works for most of our codes.
Change checkstyle-header to make it compatible with most of codes.
Reformat the code in streampipes-integration-tests

Validation

Make sure you are at the root path of the project
Run maven validate -f streampipes-integration-tests

Samples

Remarks

Do we want to create an issue to track the progress with validating all modules?

Great idea. I have created an issue to track it: https://github.com/apache/streampipes/issues/820


Zipkin/tracing should be configurable

Zipkin address is currently hardcoded, and the component is not easy to disable.
We should allow:

[ ] changing zipkin for pre-installed zipkin if it is available
[ ] allow disabling tracing altogether
[ ] changing zipkin in bootstrap configuration

cc @linsun @mandarjog

Agreed on the issue.  Reference to work of exploratory design work at the API level: https://github.com/istio/api/pull/363.
Note also: with the trace template in Mixer (and coming OpenCensus adapter work), there will be an additional knob available for controlling trace reporting.

@bianpengyuan can speak to issues related to zipkin-to-stackdriver. IIRC, there was a related issue opened just recently.


Use str.splitlines where appropriate

Before (only tested github-merge):
[pull/17218/local-merge e3ecb347e4] Merge #17218: Replace the LogPrint function with a macro
 Date: Fri Nov 1 10:08:05 2019 -0400
#17218 Replace the LogPrint function with a macro into master
* 8734c856f85cb506fa97596383dd7e7b9edd7e03 Replace the LogPrint function with a macro (Jeffrey Czyz) (pull/17218/head)
ACKs:
* I've thought about it a bit and I think for the reason I gave above I'd
want to NACK this change.

If there are motivating examples for not evaluating the calls, they should
be fixed one by one.

It would also seem like perfect forwarding and inline can help with some of
these concerns too to nudge the compiler to defer constructing strings or
whatever.

On Tue, Oct 22, 2019, 1:47 PM Jeffrey Czyz<EMAIL_ADDRESS>wrote:

> *@jkczyz* commented on this pull request.
> ------------------------------
>
> In src/logging.h
> <https://github.com/bitcoin/bitcoin/pull/17218#discussion_r337741803>:
>
> > @@ -155,12 +155,10 @@ static inline void LogPrintf(const char* fmt, const Args&... args)
>      }
>  }
>
> -template <typename... Args>
> -static inline void LogPrint(const BCLog::LogFlags& category, const Args&... args)
> -{
> -    if (LogAcceptCategory((category))) {
> -        LogPrintf(args...);
> -    }
> -}
> +#define LogPrint(category, ...) do { \
>
> Done in 8734c85
> <https://github.com/bitcoin/bitcoin/commit/8734c856f85cb506fa97596383dd7e7b9edd7e03>
> .
>
> —
> You are receiving this because you commented.
> Reply to this email directly, view it on GitHub
> <https://github.com/bitcoin/bitcoin/pull/17218?email_source=notifications&email_token=AAGYN6ZCEVD7RSKFT4WLQPTQP5RGBA5CNFSM4JDTSZXKYY3PNVWWK3TUL52HS4DFWFIHK3DMKJSXC5LFON2FEZLWNFSXPKTDN5WW2ZLOORPWSZGOCI2YLLA#discussion_r337741803>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAGYN636IOUCRCYGJ2VKPFDQP5RGBANCNFSM4JDTSZXA>
> .
>
 (JeremyRubin)
* ACK 8734c856f85cb506fa97596383dd7e7b9edd7e03 (jnewbery)
Type 's' to sign off on the above merge, or 'x' to reject and exit. x

After:
[pull/17218/local-merge 74208557d0] Merge #17218: Replace the LogPrint function with a macro
 Date: Fri Nov 1 10:14:07 2019 -0400
#17218 Replace the LogPrint function with a macro into master
* 8734c856f85cb506fa97596383dd7e7b9edd7e03 Replace the LogPrint function with a macro (Jeffrey Czyz) (pull/17218/head)
ACKs:
* ACK 8734c856f85cb506fa97596383dd7e7b9edd7e03 (jnewbery)
Type 's' to sign off on the above merge, or 'x' to reject and exit. s

See https://docs.python.org/3/library/stdtypes.html#str.splitlines

Concept ACK


Jesse: Watch mpomm-01.com

Jesse requests the watch of the watch_keyword mpomm-01\.com. See the MS search here and the Stack Exchange search in text, in URLs, and in code.

mpomm-01\.com has been seen in 0 true positives, 0 false positives, and 0 NAAs.

Approved by Jeff Schaller in Charcoal HQ


Changeset 124718243 incorrectly showing no changes

I'm submitting a bug report
Brief Description
Navigating to https://osmcha.org/changesets/124718243/ causes the spinning wheel (indicating a loading process) to spin for an unusually long amount of time and then leads OSMCha to indicate that basically nothing was changed as part of this Changeset even though there actually were changes (as can be checked on https://www.openstreetmap.org/changeset/124718243).
What  is the current behaviour, (attach relevant screenshots) ?

https://osmcha.org/changesets/124718243/ takes quite long to load.
OSMCha does not report any nodes being created as part of this Changeset under "Other Features"

What  is the expected behaviour ?

OSMCha should report the nodes that had been created as part of the Changeset under "Other Features".
OSMCha should load the Changeset faster (i.e. similarly quickly like most other ones of this size)

When does this occur ?
Only found Changeset 124718243 as the trigger for this behavior.
How do we replicate the issue ?

Navigate to https://www.openstreetmap.org/changeset/124718243
Note that Nodes<PHONE_NUMBER>-9944358851 all have version 1, i.e. were created as part of this Changeset
Navigate to https://osmcha.org/changesets/124718243/
Note that it takes an unusually long amount of time until loading has been completed
Click "Other Features"
Note that OSMCha does not report any nodes being created as part of this Changeset

Please tell us about your environment:

Google Chrome 104.0.5112.81 (Official Build) (64-bit) (cohort: Stable)
Windows 11

Other Information / context:
While I'm using Nodes not being listed by OSMCha as an example, please also note that the other OSMCha functions like "Tag Changes" etc. also incorrectly don't show any data.

The issue is caused by a timeout after 180s in the underlying engine providing the data. It is caused by a fairly large bounding box, a rather frequent issue when people map on a continent or global scale.
However, for testing purposes you can resort to either https://dev.overpass-api.de/changeset-map/#124718243 or https://dev.overpass-api.de/achavi/index.html?relations=true&changeset=124718243
Most of the changes were made in Finland close to a place called Nastola.

Thanks for the information. I've updated the title of this bug report accordingly.
With this new information, there could be a "second best" way to address this problem (in case solving the root cause of the timeout is non-trivial): Showing a proper error message that explains why no data is shown for the respective Changeset.


Brew files are large

Hello!
Recently, I have added Homebrew to a docker image of mine, and have realized that the homebrew install layer (only downloads Homebrew, not any apps through brew), and this layer is a huge (for docker images) ~300 MB.
If possible, I think the install script should run something like git gc --aggressive, in order to conserve space.
Any feedback on this proposal is welcome 😃
Thanks in advance!

How much space does git gc save? I think we would consider a pull request for this but benchmarks that it does indeed save a lot of space would be necessary.
For tips on how to save space in Docker images see this Discourse post.

Please always fill in the issue template.


Generate project files for a single subproject only

We have a build with a large number of subprojects (~30) and running sbt eclipse can take a substantial amount of time (most of it due to dependency resolution). It would be nice to have the capability in sbt eclipse to only (re)generate project files for a single subproject at a time. One possibility would be for the eclipse command by default to only generate files for the currently-selected project by default. If in the root project of the build, it would continue to generate project files for all subprojects, as it currently does. Another option would be to control this behavior with an argument passed to the "eclipse" command.

Current version is 5.1.0. Is this implemented already?


Review tests associated with water rights

Review the tests for water rights, in particular for REST web services.

Update tests for HydroBase water rights queries.  Close the issue.


Add eslint to check frontend codes style, closes #1463

closes #1463

Add eslint to the front end, if wordwrap appears in the node_modules folder, please do not add eslint

Add eslint to the front end, if wordwrap appears in the node_modules folder, please do not add eslint

Your information is out of date, the license issue of word-wrap has already been fixed[1], please refer to the mailing list[2] for complete context
[1] https://github.com/substack/node-wordwrap/issues/21
[2] https://lists.apache.org/thread.html/346ea8ac79c1cdd91c6d7c6d5acb2360a1d84d3a3e8966f91d260cbf%40<legal-discuss.apache.org>

Codecov Report

Merging #2580 into dev will decrease coverage by 0.13%.
The diff coverage is n/a.

@@             Coverage Diff              @@
##                dev    #2580      +/-   ##
============================================
- Coverage     36.19%   36.05%   -0.14%
+ Complexity     2428     2420       -8
============================================
  Files           424      424
  Lines         19624    19624
  Branches       2395     2395
============================================
- Hits           7102     7076      -26
- Misses        11893    11918      +25
- Partials        629      630       +1

Impacted Files
Coverage Δ
Complexity Δ

.../server/worker/processor/TaskExecuteProcessor.java
10.71% <0.00%> (-28.58%)
1.00% <0.00%> (-2.00%)

...he/dolphinscheduler/api/service/LoggerService.java
82.60% <0.00%> (-8.70%)
9.00% <0.00%> (-1.00%)

...nscheduler/server/entity/TaskExecutionContext.java
73.26% <0.00%> (-5.95%)
53.00% <0.00%> (-3.00%)

...e/dolphinscheduler/remote/NettyRemotingClient.java
51.07% <0.00%> (-2.88%)
9.00% <0.00%> (-2.00%)

...he/dolphinscheduler/common/thread/ThreadUtils.java
74.60% <0.00%> (+3.17%)
14.00% <0.00%> (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 c94748a...8d90270. Read the comment docs.

frontEnd +1

frontEnd +1

What do you mean by saying "frontEnd +1"? It's better to follow the GitHub flow, use the review comment to indicate your opinion, like this

This PR is opened for a long time, is there any maintainer who is able to review and give me some feedback? @dailidong @chenxingchun


inspect: print input and output tensor too

Codecov Report

Merging #12 (779df50) into master (906f163) will decrease coverage by 3.07%.
The diff coverage is 82.14%.

@@            Coverage Diff             @@
##           master      #12      +/-   ##
==========================================
- Coverage   91.27%   88.19%   -3.08%
==========================================
  Files           9        9
  Lines         401      432      +31
==========================================
+ Hits          366      381      +15
- Misses         35       51      +16

Impacted Files
Coverage Δ

onnxcli/extract.py
92.63% <0.00%> (ø)

onnxcli/inspect.py
82.73% <82.08%> (-7.78%)
:arrow_down:

onnxcli/draw.py
96.29% <100.00%> (ø)

onnxcli/optimize.py
87.50% <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 906f163...779df50. Read the comment docs.


Please add methods for Jettons

I could not find methods getData for JettonWallet, so it's impossible to get Balance of specific jettons.
And please add methods for getting JettonWallet from wallet address. (I have my wallet address -> how to get my jetton address if I know jetton minter address)

We are planning to implement it soon. Thank you for feed back!


Scene selection

Hi,
Would be wonderful, if you can add scene selection to your plugin.
KR
HdA

same here ! i found another plugin ho can add that, but it doesn't work :(

Hi,
Would be wonderful, if you can add scene selection to your plugin.
KR
HdA

it work's on this plugin ! https://github.com/NorthernMan54/homebridge-yamaha-home

Would be great to have a scene selector that functions just like the input selector in this plugin, as I currently have to use another plugin alongside this one to be able to switch scenes - and the way that plugin handles it is not very elegant. It creates an on/off switch for every scene, so I have eight different on/off buttons to interact with instead of a simple scroller like this plugin has for inputs.

how would you think is best to implement?
It's not something I can put as an input as it will interrupt with the input selection

Hello
I subscribe to this feature request too, it would complement this great plugin.
ON/OFF switches for each selected scene as the other plugin (homebridge-yamaha-home) would be awesome !
Do you think it would take a lot of work ?
Thanks :)


feat: overlay layers

This switches overlay from a bool to an int everywhere so you can utilize layer height composition.
Untested and produces one compiler warning due to the int casting implicitly to a bool.
Submitting sooner rather than later, take a look if you will.

Tested, functional, just needs an intelligent way to cast int.

Thanks for the patch!


minTime and maxTime in the same day and hour fix

Applies for "date-hour-min" user interface.
Not tested with other UI picker variants.
Passing minTime and maxTime parameters that are in the same day causes maxTime hours and minutes to be set to the logical maximum i.e. 23/59.
Passing minTime and maxTime parameters that are in the same hour causes minTime hours and minutes to be set to minimum hour from the parameter and minutes to be set to '0'.
This fix includes:
Correctly set items in list for hour and minutes when minTime and maxTime are in the same day.
Correctly set items in list for hour and minutes when minTime and maxTime are in the same hour.

Hi there what does your fix do i don't understand


Renaming boxes

Acceptance criteria:
Given that the user is identified and owns the given boxes
Ensure that the boxes can be renamed.

Merged with #17.


No more syncing for the frontend

Depends on a new cut of mutiny-wasm.
It's behaving much more fluid now. Excited about this.

Probably want to remove the syncing indicator on the on-chain balance too

Probably want to remove the syncing indicator on the on-chain balance too. Can make @futurepaul do that tho

Handled that in here.


Crashes on the opening of large files.

This is a duplicate of https://github.com/atom/atom/issues/307 – please subscribe there for updates. If you have any further information please feel free to update this issue and it could be reopened.


Links with parens

Common in Wikipedia links.
Example: From https://en.wikipedia.org/wiki/Join_(SQL) link is recognized as https://en.wikipedia.org/wiki/Join_.

Maybe as additional info for @nschloe:
The regular expression in _main.py https://github.com/nschloe/deadlink/blob/a86802242e0cd12ccdb346c80863d0f2545f6d42/src/deadlink/_main.py#L19-L21 could be extended by parantheses by just adding them after the last =. While the function in _get_urls_from_file() at https://github.com/nschloe/deadlink/blob/a86802242e0cd12ccdb346c80863d0f2545f6d42/src/deadlink/_main.py#L26-L32 correctly finds links with parantheses when doing so, the links break along the way when using deadlink to check for links with parens.