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Today is Sunday. Vancouver is sunny. It’s been quite a while that I haven’t written anything. It took me a couple of weeks to have my tax reported finally. Hmmm… Anyway, finally, I’ve got some time to talk about Supercomputer:

This is going to be a series of 3 blogs.

1. Compare All Raspberry Pi Variants

Refer to: Comparison of All Raspberry Pi Variants.

2. Four Raspberry Pis

Here, please guarantee the 4 Raspberry Pis are respectively designated with the following hostnames:

  • pi01
  • pi02
  • pi03
  • pi04

2.1 pi01: Raspberry Pi 4 Model B Rev 1.4 8GB with Raspberry Pi OS (32-bit) with desktop 2020-05-27

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pi@pi01:~ $ hostname
pi01
pi@pi01:~ $ uname -a
Linux pi01 4.19.118-v7l+ #1311 SMP Mon Apr 27 14:26:42 BST 2020 armv7l GNU/Linux
pi@pi01:~ $ lsb_release -a
No LSB modules are available.
Distributor ID: Raspbian
Description: Raspbian GNU/Linux 10 (buster)
Release: 10
Codename: buster
pi@pi01:~ $ cat /proc/meminfo
MemTotal: 8104404 kB
MemFree: 7275484 kB
MemAvailable: 7712212 kB
Buffers: 41552 kB
Cached: 592328 kB
SwapCached: 0 kB
Active: 326536 kB
Inactive: 373352 kB
Active(anon): 65820 kB
Inactive(anon): 9980 kB
Active(file): 260716 kB
Inactive(file): 363372 kB
Unevictable: 16 kB
Mlocked: 16 kB
HighTotal: 7405568 kB
HighFree: 6735192 kB
LowTotal: 698836 kB
LowFree: 540292 kB
SwapTotal: 102396 kB
SwapFree: 102396 kB
Dirty: 0 kB
Writeback: 0 kB
AnonPages: 66020 kB
Mapped: 70740 kB
Shmem: 11760 kB
Slab: 68352 kB
SReclaimable: 35736 kB
SUnreclaim: 32616 kB
KernelStack: 1320 kB
PageTables: 1964 kB
NFS_Unstable: 0 kB
Bounce: 0 kB
WritebackTmp: 0 kB
CommitLimit: 4154596 kB
Committed_AS: 498204 kB
VmallocTotal: 245760 kB
VmallocUsed: 0 kB
VmallocChunk: 0 kB
Percpu: 656 kB
CmaTotal: 262144 kB
CmaFree: 223228 kB
pi@pi01:~ $ cat /proc/cpuinfo
processor : 0
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 1
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 2
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 3
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

Hardware : BCM2835
Revision : d03114
Serial : 10000000bc6e6e05
Model : Raspberry Pi 4 Model B Rev 1.4

In fact, at the very beginning, I used to try Pi4 64-bit raspbian kernel, as following:

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pi@pi01:~ $ hostname
pi01
pi@pi01:~ $ uname -a
Linux pi01 5.4.44-v8+ #1320 SMP PREEMPT Wed Jun 3 16:20:05 BST 2020 aarch64 GNU/Linux
pi@pi01:~ $ lsb_release -a
No LSB modules are available.
Distributor ID: Raspbian
Description: Raspbian GNU/Linux 10 (buster)
Release: 10
Codename: buster
pi@pi01:~ $ cat /proc/meminfo
MemTotal: 7950652 kB
MemFree: 7749820 kB
MemAvailable: 7770884 kB
Buffers: 16092 kB
Cached: 105460 kB
SwapCached: 0 kB
Active: 91600 kB
Inactive: 47532 kB
Active(anon): 17832 kB
Inactive(anon): 8404 kB
Active(file): 73768 kB
Inactive(file): 39128 kB
Unevictable: 16 kB
Mlocked: 16 kB
SwapTotal: 102396 kB
SwapFree: 102396 kB
Dirty: 44 kB
Writeback: 0 kB
AnonPages: 17628 kB
Mapped: 26968 kB
Shmem: 8652 kB
KReclaimable: 16632 kB
Slab: 35720 kB
SReclaimable: 16632 kB
SUnreclaim: 19088 kB
KernelStack: 2304 kB
PageTables: 1284 kB
NFS_Unstable: 0 kB
Bounce: 0 kB
WritebackTmp: 0 kB
CommitLimit: 4077720 kB
Committed_AS: 181984 kB
VmallocTotal: 262930368 kB
VmallocUsed: 7680 kB
VmallocChunk: 0 kB
Percpu: 688 kB
CmaTotal: 262144 kB
CmaFree: 256244 kB
pi@pi01:~ $ cat /proc/cpuinfo
processor : 0
BogoMIPS : 108.00
Features : fp asimd evtstrm crc32 cpuid
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 1
BogoMIPS : 108.00
Features : fp asimd evtstrm crc32 cpuid
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 2
BogoMIPS : 108.00
Features : fp asimd evtstrm crc32 cpuid
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 3
BogoMIPS : 108.00
Features : fp asimd evtstrm crc32 cpuid
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

Hardware : BCM2835
Revision : d03114
Serial : 10000000bc6e6e05
Model : Raspberry Pi 4 Model B Rev 1.4

However, there are still quite a lot of issues about Pi4 64-bit raspbian kernel, I have to downgrade the system from Pi4 64-bit raspbian kernel to Raspberry Pi OS (32-bit) with desktop 2020-05-27 in the end.

2.2 pi02: Raspberry Pi 4 Model B Rev 1.1 4GB with Raspberry Pi OS (32-bit) with desktop 2020-05-27

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pi@pi02:~ $ hostname
pi02
pi@pi02:~ $ uname -a
Linux pi02 4.19.118-v7l+ #1311 SMP Mon Apr 27 14:26:42 BST 2020 armv7l GNU/Linux
pi@pi02:~ $ lsb_release -a
No LSB modules are available.
Distributor ID: Raspbian
Description: Raspbian GNU/Linux 10 (buster)
Release: 10
Codename: buster
pi@pi02:~ $ cat /proc/meminfo
MemTotal: 3999744 kB
MemFree: 3556984 kB
MemAvailable: 3761604 kB
Buffers: 42296 kB
Cached: 270688 kB
SwapCached: 0 kB
Active: 196952 kB
Inactive: 133824 kB
Active(anon): 18036 kB
Inactive(anon): 8376 kB
Active(file): 178916 kB
Inactive(file): 125448 kB
Unevictable: 16 kB
Mlocked: 16 kB
HighTotal: 3264512 kB
HighFree: 2965564 kB
LowTotal: 735232 kB
LowFree: 591420 kB
SwapTotal: 102396 kB
SwapFree: 102396 kB
Dirty: 40 kB
Writeback: 0 kB
AnonPages: 17848 kB
Mapped: 26708 kB
Shmem: 8624 kB
Slab: 54128 kB
SReclaimable: 27336 kB
SUnreclaim: 26792 kB
KernelStack: 1024 kB
PageTables: 1172 kB
NFS_Unstable: 0 kB
Bounce: 0 kB
WritebackTmp: 0 kB
CommitLimit: 2102268 kB
Committed_AS: 184320 kB
VmallocTotal: 245760 kB
VmallocUsed: 0 kB
VmallocChunk: 0 kB
Percpu: 656 kB
CmaTotal: 262144 kB
CmaFree: 223228 kB
pi@pi02:~ $ cat /proc/cpuinfo
processor : 0
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 1
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 2
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

processor : 3
model name : ARMv7 Processor rev 3 (v7l)
BogoMIPS : 108.00
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd08
CPU revision : 3

Hardware : BCM2835
Revision : c03111
Serial : 100000006c0c9b01
Model : Raspberry Pi 4 Model B Rev 1.1

2.3 pi03: Raspberry Pi 3 Model B Rev 1.2 1GB with Raspberry Pi OS (32-bit) with desktop 2020-05-27

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pi@pi03:~ $ hostname
pi03
pi@pi03:~ $ uname -a
Linux pi03 4.19.118-v7+ #1311 SMP Mon Apr 27 14:21:24 BST 2020 armv7l GNU/Linux
pi@pi03:~ $ lsb_release -a
No LSB modules are available.
Distributor ID: Raspbian
Description: Raspbian GNU/Linux 10 (buster)
Release: 10
Codename: buster
pi@pi03:~ $ cat /proc/meminfo
MemTotal: 895500 kB
MemFree: 375644 kB
MemAvailable: 709712 kB
Buffers: 54464 kB
Cached: 317028 kB
SwapCached: 0 kB
Active: 244084 kB
Inactive: 197180 kB
Active(anon): 70032 kB
Inactive(anon): 6488 kB
Active(file): 174052 kB
Inactive(file): 190692 kB
Unevictable: 16 kB
Mlocked: 16 kB
SwapTotal: 102396 kB
SwapFree: 102396 kB
Dirty: 20 kB
Writeback: 0 kB
AnonPages: 69816 kB
Mapped: 80468 kB
Shmem: 6752 kB
Slab: 59000 kB
SReclaimable: 28552 kB
SUnreclaim: 30448 kB
KernelStack: 1600 kB
PageTables: 3252 kB
NFS_Unstable: 0 kB
Bounce: 0 kB
WritebackTmp: 0 kB
CommitLimit: 550144 kB
Committed_AS: 837224 kB
VmallocTotal: 1163264 kB
VmallocUsed: 0 kB
VmallocChunk: 0 kB
Percpu: 640 kB
CmaTotal: 8192 kB
CmaFree: 6280 kB
pi@pi03:~ $ cat /proc/cpuinfo
processor : 0
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 1
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 2
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 3
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

Hardware : BCM2835
Revision : a02082
Serial : 000000009fcc6a22
Model : Raspberry Pi 3 Model B Rev 1.2

2.4 pi04: Raspberry Pi 3 Model B Rev 1.2 1GB with Raspberry Pi OS (32-bit) with desktop 2020-05-27

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pi@pi04:~ $ hostname
pi04
pi@pi04:~ $ uname -a
Linux pi04 4.19.118-v7+ #1311 SMP Mon Apr 27 14:21:24 BST 2020 armv7l GNU/Linux
pi@pi04:~ $ lsb_release -a
No LSB modules are available.
Distributor ID: Raspbian
Description: Raspbian GNU/Linux 10 (buster)
Release: 10
Codename: buster
pi@pi04:~ $ cat /proc/meminfo
MemTotal: 895500 kB
MemFree: 417612 kB
MemAvailable: 727524 kB
Buffers: 50624 kB
Cached: 296456 kB
SwapCached: 0 kB
Active: 225904 kB
Inactive: 174124 kB
Active(anon): 53244 kB
Inactive(anon): 5968 kB
Active(file): 172660 kB
Inactive(file): 168156 kB
Unevictable: 16 kB
Mlocked: 16 kB
SwapTotal: 102396 kB
SwapFree: 102396 kB
Dirty: 16 kB
Writeback: 0 kB
AnonPages: 52960 kB
Mapped: 65096 kB
Shmem: 6240 kB
Slab: 57688 kB
SReclaimable: 28096 kB
SUnreclaim: 29592 kB
KernelStack: 1464 kB
PageTables: 2724 kB
NFS_Unstable: 0 kB
Bounce: 0 kB
WritebackTmp: 0 kB
CommitLimit: 550144 kB
Committed_AS: 715652 kB
VmallocTotal: 1163264 kB
VmallocUsed: 0 kB
VmallocChunk: 0 kB
Percpu: 640 kB
CmaTotal: 8192 kB
CmaFree: 6152 kB
pi@pi04:~ $ cat /proc/cpuinfo
processor : 0
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 1
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 2
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 3
model name : ARMv7 Processor rev 4 (v7l)
BogoMIPS : 38.40
Features : half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32
CPU implementer : 0x41
CPU architecture: 7
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

Hardware : BCM2835
Revision : a22082
Serial : 000000003fc1b876
Model : Raspberry Pi 3 Model B Rev 1.2

3. Raspberry Pi Cluster Configuration

This section heavily refers to the following blogs:

Actually, the cluster can certainly be arbitrarily configured as you wish. A typical configuration is 1-master-3-workers, but which one should be the master? Is it really a good idea to ALWAYS designate the MOST powerful one as the master? Particularly, in my case, 4 Raspberry Pis are of different versions, so that they are of different computing capability.

3.1 Configure Hostfile

It’s always a good idea to create a hostfile on the master node. However, as reasons mentioned above, there is NO priority among ALL nodes in my case, I configured the hostfile for ALL 4 Raspberry Pis.

node hostfile
pi01 192.168.1.253 slots=4
192.168.1.251 slots=4
192.168.1.249 slots=4
192.168.1.247 slots=4
pi02 192.168.1.251 slots=4
192.168.1.253 slots=4
192.168.1.249 slots=4
192.168.1.247 slots=4
pi03 192.168.1.249 slots=4
192.168.1.253 slots=4
192.168.1.251 slots=4
192.168.1.247 slots=4
pi04 192.168.1.247 slots=4
192.168.1.253 slots=4
192.168.1.251 slots=4
192.168.1.249 slots=4

3.2 SSH-KEYGEN

In order to test multiple nodes across the cluster, we need to generate SSH keys to avoid inputting password for logging into the other nodes all the time. In such, for each Raspberry Pi, you’ll have to generate a SSH key by ssh-keygen -t rsa, and push this generated key using command ssh-copy-id onto the other 3 Raspberry Pis. Finally, for a cluster of 4 Raspberry Pis, there are 3 authorized keys (for these other 3 Raspberry Pis) stored in file /home/pi/.ssh/authorized_keys on each of the 4 Raspberry Pis.

4. Cluster Test

4.1 Command mpiexec

4.1.1 Argument: -hostfile and -n

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pi@pi01:~ $ mpiexec -hostfile hostfile -n 16 hostname 
pi01
pi01
pi01
pi01
pi02
pi02
pi02
pi03
pi03
pi03
pi02
pi03
pi04
pi04
pi04
pi04

For a cluster of 4 Raspberry Pis, there will be 4*4=16 CPUs in total. Therefore, the maximum number to specify for argument -n will be 16. Otherwise, you’ll meet the following ERROR message:

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pi@pi01:~ $ mpiexec -hostfile hostfile -n 20 hostname 
--------------------------------------------------------------------------
There are not enough slots available in the system to satisfy the 20 slots
that were requested by the application:
hostname

Either request fewer slots for your application, or make more slots available
for use.
--------------------------------------------------------------------------

4.1.2 Execute Python Example mpi4py helloworld.py

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pi@pi01:~ $ mpiexec -hostfile hostfile -n 16 python Downloads/helloworld.py 
Hello, World! I am process 1 of 16 on pi01.
Hello, World! I am process 5 of 16 on pi02.
Hello, World! I am process 6 of 16 on pi02.
Hello, World! I am process 7 of 16 on pi02.
Hello, World! I am process 4 of 16 on pi02.
Hello, World! I am process 15 of 16 on pi04.
Hello, World! I am process 12 of 16 on pi04.
Hello, World! I am process 13 of 16 on pi04.
Hello, World! I am process 14 of 16 on pi04.
Hello, World! I am process 2 of 16 on pi01.
Hello, World! I am process 0 of 16 on pi01.
Hello, World! I am process 3 of 16 on pi01.
Hello, World! I am process 9 of 16 on pi03.
Hello, World! I am process 10 of 16 on pi03.
Hello, World! I am process 11 of 16 on pi03.
Hello, World! I am process 8 of 16 on pi03.

4.2 mpi4py-examples

Run all examples with argument --hostfile ~/hostfile, namely, 16 cores in a row.

4.2.1 mpi4py-examples 01-hello-world

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pi@pi01:~/Downloads/mpi4py-examples $ mpirun --hostfile ~/hostfile ./01-hello-world 
Hello! I'm rank 1 from 16 running in total...
Hello! I'm rank 2 from 16 running in total...
Hello! I'm rank 3 from 16 running in total...
Hello! I'm rank 0 from 16 running in total...
Hello! I'm rank 6 from 16 running in total...
Hello! I'm rank 7 from 16 running in total...
Hello! I'm rank 4 from 16 running in total...
Hello! I'm rank 5 from 16 running in total...
Hello! I'm rank 12 from 16 running in total...
Hello! I'm rank 10 from 16 running in total...
Hello! I'm rank 11 from 16 running in total...
Hello! I'm rank 13 from 16 running in total...
Hello! I'm rank 9 from 16 running in total...
Hello! I'm rank 14 from 16 running in total...
Hello! I'm rank 8 from 16 running in total...
Hello! I'm rank 15 from 16 running in total...

4.2.2 mpi4py-examples 02-broadcast

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pi@pi01:~/Downloads/mpi4py-examples $ mpirun --hostfile ~/hostfile ./02-broadcast 
------------------------------------------------------------------------------
Running on 16 cores
------------------------------------------------------------------------------
[00] [0. 1. 2. 3. 4.]
[04] [0. 1. 2. 3. 4.]
[03] [0. 1. 2. 3. 4.]
[05] [0. 1. 2. 3. 4.]
[07] [0. 1. 2. 3. 4.]
[01] [0. 1. 2. 3. 4.]
[15] [0. 1. 2. 3. 4.]
[13] [0. 1. 2. 3. 4.]
[12] [0. 1. 2. 3. 4.]
[11] [0. 1. 2. 3. 4.]
[08] [0. 1. 2. 3. 4.]
[09] [0. 1. 2. 3. 4.]
[02] [0. 1. 2. 3. 4.]
[10] [0. 1. 2. 3. 4.]
[06] [0. 1. 2. 3. 4.]
[14] [0. 1. 2. 3. 4.]

4.2.3 mpi4py-examples 03-scatter-gather

Sometimes, without specifying the parameter btl_tcp_if_include, the running program will hang:

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pi@pi01:~/Downloads/mpi4py-examples $ mpirun --np 16 --hostfile ~/hostfile  03-scatter-gather
------------------------------------------------------------------------------
Running on 16 cores
------------------------------------------------------------------------------
After Scatter:
[0] [0. 1. 2. 3.]
[1] [4. 5. 6. 7.]
[pi03][[1597,1],8][btl_tcp_endpoint.c:626:mca_btl_tcp_endpoint_recv_connect_ack] received unexpected process identifier [[1597,1],10]
[pi01][[1597,1],0][btl_tcp_endpoint.c:626:mca_btl_tcp_endpoint_recv_connect_ack] received unexpected process identifier [[1597,1],3]
[2] [ 8. 9. 10. 11.]
^C^Z
[1]+ Stopped mpirun --np 16 --hostfile ~/hostfile 03-scatter-gather

Please refer to the explanation TCP: unexpected process identifier in connect_ack. Now, let’s specify the parameter as --mca btl_tcp_if_include "192.168.1.251/24,192.168.1.249/24,192.168.1.247/24".

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pi@pi01:~/Downloads/mpi4py-examples $ mpirun --np 16 --hostfile ~/hostfile --mca btl_tcp_if_include "192.168.1.251/24,192.168.1.249/24,192.168.1.247/24"  03-scatter-gather
------------------------------------------------------------------------------
Running on 16 cores
------------------------------------------------------------------------------
After Scatter:
[0] [0. 1. 2. 3.]
[1] [4. 5. 6. 7.]
[2] [ 8. 9. 10. 11.]
[3] [12. 13. 14. 15.]
[4] [16. 17. 18. 19.]
[5] [20. 21. 22. 23.]
[6] [24. 25. 26. 27.]
[7] [28. 29. 30. 31.]
[8] [32. 33. 34. 35.]
[9] [36. 37. 38. 39.]
[10] [40. 41. 42. 43.]
[11] [44. 45. 46. 47.]
[12] [48. 49. 50. 51.]
[13] [52. 53. 54. 55.]
[14] [56. 57. 58. 59.]
[15] [60. 61. 62. 63.]
After Allgather:
[0] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[1] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[2] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[3] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[4] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[5] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[6] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[7] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[8] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[9] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[10] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[11] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[12] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[13] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[14] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]
[15] [ 0. 2. 4. 6. 8. 10. 12. 14. 16. 18. 20. 22. 24. 26.
28. 30. 32. 34. 36. 38. 40. 42. 44. 46. 48. 50. 52. 54.
56. 58. 60. 62. 64. 66. 68. 70. 72. 74. 76. 78. 80. 82.
84. 86. 88. 90. 92. 94. 96. 98. 100. 102. 104. 106. 108. 110.
112. 114. 116. 118. 120. 122. 124. 126.]

4.2.4 mpi4py-examples 04-image-spectrogram

4.2.5 mpi4py-examples 05-pseudo-whitening

4.2.6 NULL

4.2.7 mpi4py-examples 07-matrix-vector-product

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pi@pi01:~/Downloads/mpi4py-example $ mpirun --np 16 --hostfile ~/hostfile --mca btl_tcp_if_include "192.168.1.251/24,192.168.1.249/24,192.168.1.247/24"  07-matrix-vector-product
============================================================================
Running 16 parallel MPI processes
20 iterations of size 10000 in 1.14s: 17.50 iterations per second
============================================================================

4.2.8 mpi4py-examples 08-matrix-matrix-product.py

4.2.9 mpi4py-examples 09-task-pull.py

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pi@pi01:~/Downloads/mpi4py-examples $ mpirun --hostfile ~/hostfile python ./09-task-pull.py 
Master starting with 15 workers
I am a worker with rank 1 on pi01.
I am a worker with rank 2 on pi01.
I am a worker with rank 3 on pi01.
I am a worker with rank 4 on pi02.
I am a worker with rank 5 on pi02.
I am a worker with rank 6 on pi02.
I am a worker with rank 7 on pi02.
Sending task 0 to worker 2
Sending task 1 to worker 1
Sending task 2 to worker 3
Got data from worker 2
Sending task 3 to worker 2
Got data from worker 3
Sending task 4 to worker 3
Got data from worker 1
Got data from worker 2
Sending task 5 to worker 1
Sending task 6 to worker 2
Got data from worker 3
Sending task 7 to worker 3
Got data from worker 1
Got data from worker 2
Sending task 8 to worker 1
Sending task 9 to worker 2
Got data from worker 3
Sending task 10 to worker 3
Got data from worker 1
Got data from worker 2
Sending task 11 to worker 1
Sending task 12 to worker 2
Got data from worker 3
Sending task 13 to worker 3
Got data from worker 1
Got data from worker 2
Sending task 14 to worker 1
Sending task 15 to worker 2
Got data from worker 3
Sending task 16 to worker 3
Got data from worker 1
Got data from worker 2
Sending task 17 to worker 1
Sending task 18 to worker 2
Got data from worker 3
Sending task 19 to worker 3
Got data from worker 1
Sending task 20 to worker 1
Got data from worker 2
Sending task 21 to worker 2
Got data from worker 3
Sending task 22 to worker 3
Got data from worker 1
Sending task 23 to worker 1
Got data from worker 2
Got data from worker 3
Sending task 24 to worker 2
Sending task 25 to worker 3
Got data from worker 2
Got data from worker 1
Sending task 26 to worker 2
Got data from worker 3
Sending task 27 to worker 3
Got data from worker 2
Sending task 28 to worker 1
Sending task 29 to worker 2
Got data from worker 3
Sending task 30 to worker 3
Got data from worker 2
Got data from worker 1
Sending task 31 to worker 2
Got data from worker 2
Got data from worker 3
Worker 2 exited.
Worker 1 exited.
Worker 3 exited.
I am a worker with rank 15 on pi04.
I am a worker with rank 12 on pi04.
I am a worker with rank 8 on pi03.
I am a worker with rank 13 on pi04.
I am a worker with rank 9 on pi03.
I am a worker with rank 14 on pi04.
I am a worker with rank 10 on pi03.
I am a worker with rank 11 on pi03.
Worker 5 exited.
Worker 4 exited.
Worker 6 exited.
Worker 7 exited.
Worker 15 exited.
Worker 8 exited.
Worker 9 exited.
Worker 10 exited.
Worker 11 exited.
Worker 12 exited.
Worker 13 exited.
Worker 14 exited.
Master finishing

4.2.10 mpi4py-examples 10-task-pull-spawn.py

4.3 Example mpi4py prime.py

4.3.1 Computing Capability For Each CPU

Here, we’re taking mpi4py prime.py as our example.

Hostname Computing Time
pi01
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pi@pi01:~ $ mpiexec -n 1 python prime.py 100000
Find all primes up to: 100000
Nodes: 1
Time elasped: 214.86 seconds
Primes discovered: 9592
pi02
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pi@pi02:~ $ mpiexec -n 1 python prime.py 100000
Find all primes up to: 100000
Nodes: 1
Time elasped: 212.2 seconds
Primes discovered: 9592
pi03
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pi@pi03:~ $ mpiexec -n 1 python prime.py 100000
Find all primes up to: 100000
Nodes: 1
Time elasped: 665.24 seconds
Primes discovered: 9592
pi04
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pi@pi04:~ $ mpiexec -n 1 python prime.py 100000
Find all primes up to: 100000
Nodes: 1
Time elasped: 684.64 seconds
Primes discovered: 9592

Clearly, the computing capability of each CPU on pi01/pi02 is roughly 3 times faster than the CPU on pi03/pi04, which can be easily estimated from the parameter BogoMIPS:
$$ 108.00 (pi01/pi02) / 38.40 (pi03/pi04) \approx 3 $$

4.3.2 Computing Capability For Each Raspberry Pi

Clearly, on each of my Raspberry Pi, including

there are 4 CPUs. So, let’s take a look at the result when specify argument -n 4.

Master Worker Computing Time
pi01 pi02
pi03
pi04
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pi@pi01:~ $ mpiexec -n 4 python prime.py 100000
Find all primes up to: 100000
Nodes: 4
Time elasped: 50.92 seconds
Primes discovered: 9592
pi02 pi01
pi03
pi04
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pi@pi02:~ $ mpiexec -n 4 python prime.py 100000
Find all primes up to: 100000
Nodes: 4
Time elasped: 52.83 seconds
Primes discovered: 9592
pi03 pi01
pi02
pi04
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pi@pi03:~ $ mpiexec -n 4 python prime.py 100000
Find all primes up to: 100000
Nodes: 4
Time elasped: 171.81 seconds
Primes discovered: 9592
pi04 pi01
pi02
pi03
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pi@pi04:~ $ mpiexec -n 4 python prime.py 100000
Find all primes up to: 100000
Nodes: 4
Time elasped: 171.7 seconds
Primes discovered: 9592

Clearly, to make full use of 4 CPUs -n 4 is roughly 4 times faster than just to use 1 CPU -n 1.

4.3.3 Computing Capability For The cluster

I carried out 2 experiments:

hostfile on master Computing Time
192.168.1.253 slots=4
192.168.1.251 slots=4
192.168.1.249 slots=4
192.168.1.247 slots=4
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pi@pi01:~ $ mpiexec -np 16 --hostfile hostfile --mca btl_tcp_if_include "192.168.1.251/24,192.168.1.249/24,192.168.1.247/24" python prime.py 100000
Find all primes up to: 100000
Nodes: 16
Time elasped: 42.22 seconds
Primes discovered: 9592
192.168.1.253 slots=4
192.168.1.251 slots=4
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pi@pi01:~ $ mpiexec -np 8 --hostfile hostfile --mca btl_tcp_if_include "192.168.1.251/24" python prime.py 100000
Find all primes up to: 100000
Nodes: 8
Time elasped: 29.56 seconds
Primes discovered: 9592

The results are obviously telling:

  • to calculate using a cluster of 4 Raspberry Pis with 16 CPUs is ALWAYS faster than running on a single node with 4 CPUs.
    $$ 42.22 \le 50 $$
  • to calculate using 2 fastest nodes is even faster than running on a cluster of 4 nodes. This clearly hints the importance of Load Balancing.
    $$ 29.56 \le 42.22 $$
  • the speed in Experiment 2 is roughly doubled as that using a single node of pi03 or pi04.
    $$ 52 (pi01/pi02) / 29.56 (Experiment 2) \approx 2 $$

In the end of this blog, as for Load Balancing, I may talk about it some time in the future.

Today’s concert: ONE WORLD : TOGETHER AT HOME. Yup, today, I’ve my previous blog updated. A lot of modifications. Khadas VIM3 is really a good product. With Amlogic‘s A311D with 5.0 TOPS NPU, the board itself comes with a super powerful AI inference capability.

What a sunny day after the **FIRST** snow in this winter. Let me show you 3 pictures in the first row, and 3 videos in the second. We need to enjoy both R&D and life…
Green Timers Lake 1 Green Timers Lake 2 Green Timers Park
Green Timers Lake 1 Green Timers Lake 2 Green Timers Park
A Pair of Swans A Group of Ducks A Little Stream In The Snow

After a brief break, I started investigating Khadas VIM3 again.

1. About Khadas VIM3

Khadas VIM3 is a super computer based on Amlogic A311D. Before we start, let’s carry out several simple comparisons.

1.1 Raspberry Pi 4 Model B vs. Khadas VIM3 vs. Jetson Nano Developer Kit

Please refer to:

1.2 Amlogic A311D & S922X-B vs. Rockchip RK3399 (Pro) vs. Amlogic S912

Please refer to:

2. Install Prebuilt Operating System To EMMC Via Krescue

2.1 WIRED Connection Preferred

As mentioned in VIM3 Beginners Guide, Krescue is a Swiss Army knife. As of January 2020, Krescue can download and install OS images directly from the web via wired Ethernet.

2.2 Flash Krescue Onto SD Card

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➜  Krescue sudo dd bs=4M if=VIM3.krescue-d41d8cd98f00b204e9800998ecf8427e-1587199778-67108864-279c13890fa7253d5d2b76000769803e.sd.img of=/dev/mmcblk0 conv=fsync 
[sudo] password for longervision:
16+0 records in
16+0 records out
67108864 bytes (67 MB, 64 MiB) copied, 4.03786 s, 16.6 MB/s

2.3 Setup Wifi From Within Krescue Shell

If you really don’t like the WIRED connection, boot into Krescue shell, and use the following commands to set up Wifi:

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root@Krescue:~# wifi.config WIFI_NAME WIFI_PASSWORD
root@Krescue:~# wifi.client
root@Krescue:~# wifi.status
DRIVER=brcmfmac
OF_NAME=wifi
OF_FULLNAME=/soc/sd@ffe03000/wifi@1
OF_COMPATIBLE_0=brcm,bcm4329-fmac
OF_COMPATIBLE_N=1
SDIO_CLASS=00
SDIO_ID=02D0:4359
MODALIAS=sdio:c00v02D0d4359
[i] iw dev
phy#0
Interface wlan0
ifindex 4
wdev 0x1
addr xx:xx:xx:xx:xx:xx
ssid XXXXXXXXXX
type managed
channel 161 (5805 MHz), width: 80 MHz, center1: 5775 MHz
txpower 31.00 dBm
[i] CLIENT mode active 6754
Selected interface 'wlan0'
bssid=xx:xx:xx:xx:xx:xx
freq=5805
ssid=XXXXXXXXXX
id=0
mode=station
pairwise_cipher=CCMP
group_cipher=CCMP
key_mgmt=WPA2-PSK
wpa_state=COMPLETED
ip_address=192.168.1.110
address=xx:xx:xx:xx:xx:xx
uuid=xxxxxxxxxxxxxxxxxxxxxxxxxxxx
ieee80211ac=1

2.4 SSH Into Krescue Via Wireless Connection

Now, let’s try to connect Khadas VIM3 board remotely.

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➜  ~ ping 192.168.1.110
PING 192.168.1.110 (192.168.1.110) 56(84) bytes of data.
64 bytes from 192.168.1.110: icmp_seq=1 ttl=64 time=140 ms
64 bytes from 192.168.1.110: icmp_seq=2 ttl=64 time=54.0 ms
64 bytes from 192.168.1.110: icmp_seq=3 ttl=64 time=13.1 ms
^C
--- 192.168.1.110 ping statistics ---
3 packets transmitted, 3 received, 0% packet loss, time 2001ms
rtt min/avg/max/mdev = 13.191/69.145/140.193/52.936 ms
➜ ~ ssh root@192.168.1.110
The authenticity of host '192.168.1.110 (192.168.1.110)' can't be established.
RSA key fingerprint is SHA256:0t0PZw/24nWc8hWaCJkltYtwCduMMSlRuux2Nn865Os.
Are you sure you want to continue connecting (yes/no)? yes
Warning: Permanently added '192.168.1.110' (RSA) to the list of known hosts.


BusyBox v1.28.4 () built-in shell (ash)

OpenWrt 18.06.3, r7798-97ae9e0ccb

__ _____ Khadas ## hyphop ##
/ //_/ _ \___ ___ ______ _____
/ ,< / , _/ -_|_-</ __/ // / -_)
/_/|_/_/|_|\__/___/\__/\_,_/\__/

extreme tiny and fast rescue system

BUILD: Sat Apr 18 08:49:28 UTC 2020

[i] POST_CONFIG: ap_ssid=Krescue ap_passw=12345678 wifi_mode=2g script=sd:launcher.sh eth_hw=C0:4A:00:C0:3F:DB booted= hwver=VIM3.V12

=== WARNING! =====================================
There is no root password defined on this device!
Use the passwd command to set up a new password
in order to prevent unauthorized SSH logins.
--------------------------------------------------
root@Krescue:~# uname -a
Linux Krescue 5.4.5 #4 SMP PREEMPT Thu Apr 9 22:07:48 +09 2020 aarch64 GNU/Linux

2.5 Flash OS onto EMMC (WIRED Connection Preferred)

Let’s take a look at the SD card device:

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root@Krescue:~# ls /dev/mmcblk*
/dev/mmcblk1 /dev/mmcblk1p1 /dev/mmcblk1p2 /dev/mmcblk2 /dev/mmcblk2boot0 /dev/mmcblk2boot1 /dev/mmcblk2rpmb

2.5.1 Install OS Using Shell Command

Please refer to the Shell Commands Examples.

curl -sfL dl.khadas.com/.mega | sh -s - -Y -X > /dev/mmcblk? should do.

2.5.2 Install OS Using Krescue GUI

Let’s bring back Krescue GUI by command krescue, and select VIMx.Ubuntu-xfce-bionic_Linux-4.9_arm64_V20191231.emmc.kresq and have it flashed onto EMMC.

Krescue Default Image Write To EMMC
Krescue Default Image Write To EMMC
Select Prebuilt OS Start Downloading OS
Select Prebuilt OS Start Downloading OS
Start Installation Installation Complete
Start Installation Installation Complete
Krescue Reboot Ubuntu XFCE Desktop
Krescue Reboot Ubuntu XFCE Desktop

2.6. Boot From EMMC

Actually, the 8th image in the above just showed Ubuntu XFCE desktop. We can also SSH into it after configuring Wifi successfully.

2.6.1 SSH Into Khadas VIM3

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➜  ~ ssh khadas@192.168.1.95
The authenticity of host '192.168.1.95 (192.168.1.95)' can't be established.
ECDSA key fingerprint is SHA256:Q59XrIX7bSWsphZCpgHBSnVH5ETgCY9iLfDEuvRKtOw.
Are you sure you want to continue connecting (yes/no)? yes
Warning: Permanently added '192.168.1.95' (ECDSA) to the list of known hosts.
khadas@192.168.1.95's password:

Welcome to Fenix 0.8.1 Ubuntu 18.04.3 LTS Linux 4.9.206
_ ___ _ __ _____ __ __ _____
| |/ / |__ __ _ __| | __ _ ___ \ \ / /_ _| \/ |___ /
| ' /| '_ \ / _` |/ _` |/ _` / __| \ \ / / | || |\/| | |_ \
| . \| | | | (_| | (_| | (_| \__ \ \ V / | || | | |___) |
|_|\_\_| |_|\__,_|\__,_|\__,_|___/ \_/ |___|_| |_|____/


* Website: https://www.khadas.com
* Documentation: https://docs.khadas.com
* Forum: https://forum.khadas.com

To run a command as administrator (user "root"), use "sudo <command>".
See "man sudo_root" for details.

2.6.2 Specs For Khadas VIM3

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khadas@Khadas:~$ uname -a
Linux Khadas 4.9.206 #13 SMP PREEMPT Tue Dec 31 00:37:47 CST 2019 aarch64 aarch64 aarch64 GNU/Linux
khadas@Khadas:~$ cat /proc/cpuinfo
processor : 0
BogoMIPS : 48.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 1
BogoMIPS : 48.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd03
CPU revision : 4

processor : 2
BogoMIPS : 48.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd09
CPU revision : 2

processor : 3
BogoMIPS : 48.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd09
CPU revision : 2

processor : 4
BogoMIPS : 48.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd09
CPU revision : 2

processor : 5
BogoMIPS : 48.00
Features : fp asimd evtstrm aes pmull sha1 sha2 crc32
CPU implementer : 0x41
CPU architecture: 8
CPU variant : 0x0
CPU part : 0xd09
CPU revision : 2

Serial : 290b1000010c1900000437304e424e50
Hardware : Khadas VIM3
khadas@Khadas:~$ clinfo
Number of platforms 1
Platform Name ARM Platform
Platform Vendor ARM
Platform Version OpenCL 2.0 git.c8adbf9.ad00b04c1b60847de257177231dc1a53
Platform Profile FULL_PROFILE
Platform Extensions cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_3d_image_writes cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_fp16 cl_khr_icd cl_khr_egl_image cl_khr_image2d_from_buffer cl_khr_depth_images cl_khr_create_command_queue cl_arm_core_id cl_arm_printf cl_arm_thread_limit_hint cl_arm_non_uniform_work_group_size cl_arm_import_memory cl_arm_shared_virtual_memory
Platform Extensions function suffix ARM

Platform Name ARM Platform
Number of devices 1
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Device Name <printDeviceInfo:0: get CL_DEVICE_NAME size : error -6>
Device Vendor ARM
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Device Vendor ID <printDeviceInfo:2: get CL_DEVICE_VENDOR_ID : error -6>
Device Version OpenCL 2.0 git.c8adbf9.ad00b04c1b60847de257177231dc1a53
Driver Version 2.0
Device OpenCL C Version OpenCL C 2.0 git.c8adbf9.ad00b04c1b60847de257177231dc1a53
Device Type GPU
Device Profile FULL_PROFILE
Device Available Yes
Compiler Available Yes
Linker Available Yes
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Max compute units <printDeviceInfo:17: get CL_DEVICE_MAX_COMPUTE_UNITS : error -6>
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Max clock frequency <printDeviceInfo:21: get CL_DEVICE_MAX_CLOCK_FREQUENCY : error -6>
Device Partition (core)
Max number of sub-devices 0
Supported partition types None
Max work item dimensions 3
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Max work item sizes <printDeviceInfo:36: get number of CL_DEVICE_MAX_WORK_ITEM_SIZES : error -6>
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Max work group size <printDeviceInfo:37: get CL_DEVICE_MAX_WORK_GROUP_SIZE : error -6>
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Preferred work group size multiple <getWGsizes:671: create context : error -6>
Preferred / native vector sizes
char 16 / 4
short 8 / 2
int 4 / 1
long 2 / 1
half 8 / 2 (cl_khr_fp16)
float 4 / 1
double 0 / 0 (n/a)
Half-precision Floating-point support (cl_khr_fp16)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Single-precision Floating-point support (core)
Denormals Yes
Infinity and NANs Yes
Round to nearest Yes
Round to zero Yes
Round to infinity Yes
IEEE754-2008 fused multiply-add Yes
Support is emulated in software No
Correctly-rounded divide and sqrt operations No
Double-precision Floating-point support (n/a)
Address bits 64, Little-Endian
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Global memory size <printDeviceInfo:74: get CL_DEVICE_GLOBAL_MEM_SIZE : error -6>
Error Correction support No
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Max memory allocation <printDeviceInfo:80: get CL_DEVICE_MAX_MEM_ALLOC_SIZE : error -6>
Unified memory for Host and Device Yes
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Shared Virtual Memory (SVM) capabilities <printDeviceInfo:83: get CL_DEVICE_SVM_CAPABILITIES : error -6>
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Shared Virtual Memory (SVM) capabilities (ARM) <printDeviceInfo:84: get CL_DEVICE_SVM_CAPABILITIES_ARM : error -6>
Minimum alignment for any data type 128 bytes
Alignment of base address 1024 bits (128 bytes)
Preferred alignment for atomics
SVM 0 bytes
Global 0 bytes
Local 0 bytes
Max size for global variable 65536 (64KiB)
Preferred total size of global vars 0
Global Memory cache type Read/Write
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Global Memory cache size <printDeviceInfo:97: get CL_DEVICE_GLOBAL_MEM_CACHE_SIZE : error -6>
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Global Memory cache line size <printDeviceInfo:98: get CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE : error -6>
Image support Yes
Max number of samplers per kernel 16
Max size for 1D images from buffer 65536 pixels
Max 1D or 2D image array size 2048 images
Base address alignment for 2D image buffers 32 bytes
Pitch alignment for 2D image buffers 64 pixels
Max 2D image size 65536x65536 pixels
Max 3D image size 65536x65536x65536 pixels
Max number of read image args 128
Max number of write image args 64
Max number of read/write image args 64
Max number of pipe args 16
Max active pipe reservations 1
Max pipe packet size 1024
Local memory type Global
Local memory size 32768 (32KiB)
Max number of constant args 8
Max constant buffer size 65536 (64KiB)
Max size of kernel argument 1024
Queue properties (on host)
Out-of-order execution Yes
Profiling Yes
Queue properties (on device)
Out-of-order execution Yes
Profiling Yes
Preferred size 2097152 (2MiB)
Max size 16777216 (16MiB)
Max queues on device 1
Max events on device 1024
Prefer user sync for interop No
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
Profiling timer resolution <printDeviceInfo:145: get CL_DEVICE_PROFILING_TIMER_RESOLUTION : error -6>
Execution capabilities
Run OpenCL kernels Yes
Run native kernels No
printf() buffer size 1048576 (1024KiB)
Built-in kernels
Device Extensions cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_3d_image_writes cl_khr_int64_base_atomics cl_khr_int64_extended_atomics cl_khr_fp16 cl_khr_icd cl_khr_egl_image cl_khr_image2d_from_buffer cl_khr_depth_images cl_khr_create_command_queue cl_arm_core_id cl_arm_printf cl_arm_thread_limit_hint cl_arm_non_uniform_work_group_size cl_arm_import_memory cl_arm_shared_virtual_memory

NULL platform behavior
clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...) ARM Platform
clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...) Success [ARM]
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
clCreateContext(NULL, ...) [default] <checkNullCtx:2694: create context with device from default platform : error -6>
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT) <checkNullCtxFromType:2737: create context from type CL_DEVICE_TYPE_DEFAULT : error -6>
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU) No devices found in platform
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU) <checkNullCtxFromType:2737: create context from type CL_DEVICE_TYPE_GPU : error -6>
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR) No devices found in platform
clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM) No devices found in platform
ERROR: The DDK (built for 0x70030000 r0p0 status range [0..15]) is not compatible with this Mali GPU device, /dev/mali0 detected as 0x7212 r0p0 status 0.
Failed creating base context during DDK compatibility check.
clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL) <checkNullCtxFromType:2737: create context from type CL_DEVICE_TYPE_ALL : error -6>

ICD loader properties
ICD loader Name OpenCL ICD Loader
ICD loader Vendor OCL Icd free software
ICD loader Version 2.2.11
ICD loader Profile OpenCL 2.1
khadas@Khadas:~$ cat /proc/partitions
major minor #blocks name

1 0 4096 ram0
1 1 4096 ram1
1 2 4096 ram2
1 3 4096 ram3
1 4 4096 ram4
1 5 4096 ram5
1 6 4096 ram6
1 7 4096 ram7
1 8 4096 ram8
1 9 4096 ram9
1 10 4096 ram10
1 11 4096 ram11
1 12 4096 ram12
1 13 4096 ram13
1 14 4096 ram14
1 15 4096 ram15
179 0 30535680 mmcblk0
179 1 4096 mmcblk0p1
179 2 65536 mmcblk0p2
179 3 8192 mmcblk0p3
179 4 8192 mmcblk0p4
179 5 32768 mmcblk0p5
179 6 30351360 mmcblk0p6
179 96 4096 mmcblk0rpmb
179 64 4096 mmcblk0boot1
179 32 4096 mmcblk0boot0
251 1 262144 zram1
251 2 262144 zram2
251 3 262144 zram3
251 4 262144 zram4
khadas@Khadas:~$ df -h
Filesystem Size Used Avail Use% Mounted on
udev 1.9G 0 1.9G 0% /dev
tmpfs 371M 15M 357M 4% /run
/dev/rootfs 29G 3.2G 25G 12% /
tmpfs 1.9G 0 1.9G 0% /dev/shm
tmpfs 5.0M 4.0K 5.0M 1% /run/lock
tmpfs 1.9G 0 1.9G 0% /sys/fs/cgroup
tmpfs 1.9G 24K 1.9G 1% /tmp
tmpfs 371M 8.0K 371M 1% /run/user/1000

2.6.3 Package Versions

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khadas@Khadas:~$ gcc --version
gcc (Ubuntu/Linaro 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

khadas@Khadas:~$ python --version
Python 3.6.9
khadas@Khadas:~$ ls /usr/lib/libopencv*
libopencv_calib3d.so libopencv_dnn.so.3.4.3 libopencv_highgui.so.3.4 libopencv_ml.so libopencv_photo.so.3.4.3 libopencv_superres.so.3.4 libopencv_videostab.so
libopencv_calib3d.so.3.4 libopencv_features2d.so libopencv_highgui.so.3.4.3 libopencv_ml.so.3.4 libopencv_shape.so libopencv_superres.so.3.4.3 libopencv_videostab.so.3.4
libopencv_calib3d.so.3.4.3 libopencv_features2d.so.3.4 libopencv_imgcodecs.so libopencv_ml.so.3.4.3 libopencv_shape.so.3.4 libopencv_video.so libopencv_videostab.so.3.4.3
libopencv_core.so libopencv_features2d.so.3.4.3 libopencv_imgcodecs.so.3.4 libopencv_objdetect.so libopencv_shape.so.3.4.3 libopencv_video.so.3.4
libopencv_core.so.3.4 libopencv_flann.so libopencv_imgcodecs.so.3.4.3 libopencv_objdetect.so.3.4 libopencv_stitching.so libopencv_video.so.3.4.3
libopencv_core.so.3.4.3 libopencv_flann.so.3.4 libopencv_imgproc.so libopencv_objdetect.so.3.4.3 libopencv_stitching.so.3.4 libopencv_videoio.so
libopencv_dnn.so libopencv_flann.so.3.4.3 libopencv_imgproc.so.3.4 libopencv_photo.so libopencv_stitching.so.3.4.3 libopencv_videoio.so.3.4
libopencv_dnn.so.3.4 libopencv_highgui.so libopencv_imgproc.so.3.4.3 libopencv_photo.so.3.4 libopencv_superres.so libopencv_videoio.so.3.4.3
khadas@Khadas:~$ cat /usr/lib/pkgconfig/opencv.pc
# Package Information for pkg-config

prefix=/usr/local/nick
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir_old=${prefix}/include/opencv
includedir_new=${prefix}/include

Name: OpenCV
Description: Open Source Computer Vision Library
Version: 3.4.3
Libs: -L${exec_prefix}/lib -lopencv_dnn -lopencv_ml -lopencv_objdetect -lopencv_shape -lopencv_stitching -lopencv_superres -lopencv_videostab -lopencv_calib3d -lopencv_features2d -lopencv_highgui -lopencv_videoio -lopencv_imgcodecs -lopencv_video -lopencv_photo -lopencv_imgproc -lopencv_flann -lopencv_core
Libs.private: -ldl -lm -lpthread -lrt
Cflags: -I${includedir_old} -I${includedir_new}
khadas@Khadas:~$ sudo apt remove opencv3
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following package was automatically installed and is no longer required:
libllvm8
Use 'sudo apt autoremove' to remove it.
The following packages will be REMOVED:
opencv3
0 upgraded, 0 newly installed, 1 to remove and 0 not upgraded.
After this operation, 1024 B disk space will be freed.
Do you want to continue? [Y/n] Y
(Reading database ... 118978 files and directories currently installed.)
Removing opencv3 (3.4.3-2) ...
khadas@Khadas:~$ ls /usr/lib/libopencv*
ls: cannot access '/usr/lib/libopencv*': No such file or directory
khadas@Khadas:~$ cat /usr/lib/pkgconfig/opencv.pc
cat: /usr/lib/pkgconfig/opencv.pc: No such file or directory

It looks current OpenCV on current **VIM3_Ubuntu-xfce-bionic_Linux-4.9_arm64_EMMC_V20191231.img is a kind of outdated. Let’s just remove package **opencv3 and have OpenCV-4.3.0 installed manually.

3. Install Manjaro To TF/SD Card

As one of my dreamed operating systems, Manjaro has already provided 2 operating systems for Khadas users to try out.

To flash either of the above systems onto a TF/SD card is simple. However, both are ONLY for SD-USB, instead of EMMC. For instancen:

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➜  Manjaro burn-tool -b VIM3 -i ./Manjaro-ARM-xfce-vim3-20.04.img 
Try to burn Amlogic image...
ERROR: Try to burn to eMMC storage, but the image installation type is 'SD-USB', please use 'EMMC' image!

Before moving on, let’s cite the following word from Boot Images from External Media:

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WARNING: Don’t use your PC as the USB-Host to supply the electrical power, otherwise it will fail to activate Multi-Boot!

4. NPU

In this section, we’re testing the computing capability of Khadas VIM3‘s NPU.

Before everything starts, make sure you have the galcore module loaded, by using command modinfo galcore.

4.1 Obtain aml_npu_sdk From Khadas

Extract the obtained aml_npu_sdk.tgz on your local host. Bear in mind that it is your local host, BUT NOT Khadas VIM3. Relative issues can be found at:

4.2 Model Conversion on Host

Afterwards, the models applicable on Khadas VIM3 can be obtained by following Model Conversion. Anyway, on my laptop, I obtained the converted model as follows:

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➜  nbg_unify_inception_v3 ll
total 28M
-rwxrwxrwx 1 longervision longervision 577 Apr 27 14:10 BUILD
-rwxrwxrwx 1 longervision longervision 28M Apr 27 14:10 inception_v3.nb
-rwxrwxrwx 1 longervision longervision 13K Apr 27 14:10 inceptionv3.vcxproj
-rwxrwxrwx 1 longervision longervision 5.8K Apr 27 14:10 main.c
-rwxrwxrwx 1 longervision longervision 2.0K Apr 27 14:10 makefile.linux
-rwxrwxrwx 1 longervision longervision 358 Apr 27 14:10 vnn_global.h
-rwxrwxrwx 1 longervision longervision 7.1K Apr 27 14:10 vnn_inceptionv3.c
-rwxrwxrwx 1 longervision longervision 985 Apr 27 14:10 vnn_inceptionv3.h
-rwxrwxrwx 1 longervision longervision 3.5K Apr 27 14:10 vnn_post_process.c
-rwxrwxrwx 1 longervision longervision 464 Apr 27 14:10 vnn_post_process.h
-rwxrwxrwx 1 longervision longervision 20K Apr 27 14:10 vnn_pre_process.c
-rwxrwxrwx 1 longervision longervision 1.3K Apr 27 14:10 vnn_pre_process.h

Do I need to emphasize that I’m using Tensorflow 2.1.0 ? Anyway, check the following:

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➜  ~ python
Python 3.6.9 (default, Apr 18 2020, 01:56:04)
[GCC 8.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2020-04-29 03:11:24.272348: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.2
>>> tf.__version__
'2.1.0'

4.3 Build Case Code

4.3.1 Cross-build on Host

You can of course cross-build the case code on your local host, instead of Khadas VIM3 by referring to Compile the Case Code. (The document seems NOT updated yet.) Instead of using 1 argument, we specify 2 auguments, one for aml_npu_sdk, the other for Fenix.

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➜  nbg_unify_inception_v3 ./build_vx.sh ....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4 ....../fenix 
aarch64-linux-gnu-gcc -c -DLINUX -Wall -D_REENTRANT -fno-strict-aliasing -mtune=cortex-a53 -march=armv8-a -O2 -DgcdENABLE_3D=1 -DgcdENABLE_2D=0 -DgcdENABLE_VG=0 -DgcdUSE_VX=1 -DUSE_VDK=1 -DgcdMOVG=0 -DEGL_API_FB -DgcdSTATIC_LINK=0 -DgcdFPGA_BUILD=0 -DGC_ENABLE_LOADTIME_OPT=1 -DgcdUSE_VXC_BINARY=0 -DgcdGC355_MEM_PRINT=0 -DgcdGC355_PROFILER=0 -DVIVANTE_PROFILER=1 -DVIVANTE_PROFILER_CONTEXT=1 -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include/HAL -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/sdk/inc -I./ -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/utils -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/client -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/ops -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/third-party/jpeg-9b -o bin_r/vnn_pre_process.o vnn_pre_process.c
aarch64-linux-gnu-gcc -c -DLINUX -Wall -D_REENTRANT -fno-strict-aliasing -mtune=cortex-a53 -march=armv8-a -O2 -DgcdENABLE_3D=1 -DgcdENABLE_2D=0 -DgcdENABLE_VG=0 -DgcdUSE_VX=1 -DUSE_VDK=1 -DgcdMOVG=0 -DEGL_API_FB -DgcdSTATIC_LINK=0 -DgcdFPGA_BUILD=0 -DGC_ENABLE_LOADTIME_OPT=1 -DgcdUSE_VXC_BINARY=0 -DgcdGC355_MEM_PRINT=0 -DgcdGC355_PROFILER=0 -DVIVANTE_PROFILER=1 -DVIVANTE_PROFILER_CONTEXT=1 -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include/HAL -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/sdk/inc -I./ -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/utils -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/client -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/ops -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/third-party/jpeg-9b -o bin_r/vnn_inceptionv3.o vnn_inceptionv3.c
vnn_inceptionv3.c: In function ‘vnn_CreateInceptionV3’:
vnn_inceptionv3.c:139:29: warning: unused variable ‘data’ [-Wunused-variable]
uint8_t * data;
^~~~
At top level:
vnn_inceptionv3.c:91:17: warning: ‘load_data’ defined but not used [-Wunused-function]
static uint8_t* load_data
^~~~~~~~~
aarch64-linux-gnu-gcc -c -DLINUX -Wall -D_REENTRANT -fno-strict-aliasing -mtune=cortex-a53 -march=armv8-a -O2 -DgcdENABLE_3D=1 -DgcdENABLE_2D=0 -DgcdENABLE_VG=0 -DgcdUSE_VX=1 -DUSE_VDK=1 -DgcdMOVG=0 -DEGL_API_FB -DgcdSTATIC_LINK=0 -DgcdFPGA_BUILD=0 -DGC_ENABLE_LOADTIME_OPT=1 -DgcdUSE_VXC_BINARY=0 -DgcdGC355_MEM_PRINT=0 -DgcdGC355_PROFILER=0 -DVIVANTE_PROFILER=1 -DVIVANTE_PROFILER_CONTEXT=1 -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include/HAL -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/sdk/inc -I./ -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/utils -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/client -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/ops -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/third-party/jpeg-9b -o bin_r/main.o main.c
aarch64-linux-gnu-gcc -c -DLINUX -Wall -D_REENTRANT -fno-strict-aliasing -mtune=cortex-a53 -march=armv8-a -O2 -DgcdENABLE_3D=1 -DgcdENABLE_2D=0 -DgcdENABLE_VG=0 -DgcdUSE_VX=1 -DUSE_VDK=1 -DgcdMOVG=0 -DEGL_API_FB -DgcdSTATIC_LINK=0 -DgcdFPGA_BUILD=0 -DGC_ENABLE_LOADTIME_OPT=1 -DgcdUSE_VXC_BINARY=0 -DgcdGC355_MEM_PRINT=0 -DgcdGC355_PROFILER=0 -DVIVANTE_PROFILER=1 -DVIVANTE_PROFILER_CONTEXT=1 -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/include/HAL -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/sdk/inc -I./ -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/utils -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/client -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include/ops -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/include -I....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/third-party/jpeg-9b -o bin_r/vnn_post_process.o vnn_post_process.c
aarch64-linux-gnu-gcc -mtune=cortex-a53 -march=armv8-a -Wl,-rpath-link ....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/drivers bin_r/vnn_pre_process.o bin_r/vnn_inceptionv3.o bin_r/main.o bin_r/vnn_post_process.o -o bin_r/inceptionv3 -L....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/build/sdk/drivers -l OpenVX -l OpenVXU -l CLC -l VSC -lGAL ....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/lib/libjpeg.a -L....../aml_npu_sdk/linux_sdk/linux_sdk_6.3.3.4/acuity-ovxlib-dev/lib -l ovxlib -L....../fenix/build/toolchains/gcc-linaro-aarch64-linux-gnu/aarch64-linux-gnu/libc/lib -lm -lrt
aarch64-linux-gnu-strip bin_r/inceptionv3
make: Nothing to be done for 'all'.
➜ nbg_unify_inception_v3 ll bin_r
total 164K
-rwxrwxrwx 1 longervision longervision 126K Apr 27 14:22 inceptionv3
-rwxrwxrwx 1 longervision longervision 6.3K Apr 27 14:22 main.o
-rwxrwxrwx 1 longervision longervision 3.9K Apr 27 14:22 vnn_inceptionv3.o
-rwxrwxrwx 1 longervision longervision 3.5K Apr 27 14:22 vnn_post_process.o
-rwxrwxrwx 1 longervision longervision 17K Apr 27 14:22 vnn_pre_process.o

inceptionv3 now should be ready to use, but in my case, it’s NOT working properly. It’s probably because Fenix is NOT able to provide/represent the correct cross-compile toolchains for my installed VIMx.Ubuntu-xfce-bionic_Linux-4.9_arm64_V20191231.emmc.kresq. Anyway, this is NOT my preference.

4.3.2 Directly Build on Khadas VIM3

Let’s leave this for the next section 4.4 Run Executable on Khadas VIM3.

4.4 Run Executable on Khadas VIM3

4.4.1 Step 1: Install aml-npu

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khadas@Khadas:~$ sudo apt install aml-npu
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
libllvm8 libssh-dev
Use 'sudo apt autoremove' to remove them.
The following NEW packages will be installed:
aml-npu
0 upgraded, 1 newly installed, 0 to remove and 1 not upgraded.
Need to get 0 B/3318 kB of archives.
After this operation, 1024 B of additional disk space will be used.
Selecting previously unselected package aml-npu.
(Reading database ... 136037 files and directories currently installed.)
Preparing to unpack .../aml-npu_6.4.0.3_arm64.deb ...
Unpacking aml-npu (6.4.0.3) ...
Setting up aml-npu (6.4.0.3) ...

And with command line dpkg -L aml-npu, you’ll see what’s been installed by aml-npu. However, due to its commercial license, I may NOT be allowed to show anything here in my blog.

😶

4.4.2 Step 2: Install aml-npu-demo and Run Demo

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khadas@Khadas:~$ sudo apt install aml-npu-demo
Reading package lists... Done
Building dependency tree
Reading state information... Done
The following packages were automatically installed and are no longer required:
libllvm8 libssh-dev
Use 'sudo apt autoremove' to remove them.
The following NEW packages will be installed:
aml-npu-demo
0 upgraded, 1 newly installed, 0 to remove and 1 not upgraded.
Need to get 0 B/19.7 MB of archives.
After this operation, 1024 B of additional disk space will be used.
Selecting previously unselected package aml-npu-demo.
(Reading database ... 136098 files and directories currently installed.)
Preparing to unpack .../aml-npu-demo_6.3.3.4_arm64.deb ...
Unpacking aml-npu-demo (6.3.3.4) ...
Setting up aml-npu-demo (6.3.3.4) ...

Where is the sample to run? /usr/share/npu/inceptionv3.

Alright, let’s try it.

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khadas@Khadas:~$ cd /usr/share/npu/inceptionv3
khadas@Khadas:/usr/share/npu/inceptionv3$ ./inceptionv3 ./inception_v3.nb ./dog_299x299.jpg
D [setup_node:368]Setup node id[0] uid[4294967295] op[NBG]
D [print_tensor:136]in(0) : id[ 1] vtl[0] const[0] shape[ 3, 299, 299, 1 ] fmt[u8 ] qnt[ASM zp=137, scale=0.007292]
D [print_tensor:136]out(0): id[ 0] vtl[0] const[0] shape[ 1001, 1 ] fmt[f16] qnt[NONE]
D [optimize_node:312]Backward optimize neural network
D [optimize_node:319]Forward optimize neural network
I [compute_node:261]Create vx node
Create Neural Network: 37ms or 37726us
I [vsi_nn_PrintGraph:1421]Graph:
I [vsi_nn_PrintGraph:1422]***************** Tensors ******************
D [print_tensor:146]id[ 0] vtl[0] const[0] shape[ 1001, 1 ] fmt[f16] qnt[NONE]
D [print_tensor:146]id[ 1] vtl[0] const[0] shape[ 3, 299, 299, 1 ] fmt[u8 ] qnt[ASM zp=137, scale=0.007292]
I [vsi_nn_PrintGraph:1431]***************** Nodes ******************
I [vsi_nn_PrintNode:159]( NBG)node[0] [in: 1 ], [out: 0 ] [10587cb0]
I [vsi_nn_PrintGraph:1440]******************************************
I [vsi_nn_ConvertTensorToData:750]Create 268203 data.
Verify...
Verify Graph: 1ms or 1811us
Start run graph [1] times...
Run the 1 time: 28ms or 28075us
vxProcessGraph execution time:
Total 28ms or 28091us
Average 28.09ms or 28091.00us
I [vsi_nn_ConvertTensorToData:750]Create 2002 data.
--- Top5 ---
208: 0.819824
209: 0.040344
223: 0.009354
185: 0.002956
268: 0.002829
I [vsi_nn_ConvertTensorToData:750]Create 2002 data.

The program runs smoothly.

😏

4.4.3 Step 3: Build Your Own Executable and Run

Clearly, ALL (really???) required development files have been provided by aml-npu, in such, we should be able to build this demo inceptionv3 out by ourselves.

4.4.3.1 You STILL Need aml_npu_sdk from Khadas

Besides aml-npu from repo, in order to have the demo inceptionv3 fully and successfully built, you still need aml_npu_sdk from Khadas. In my case, you do need acuity-ovxlib-dev, and let’s do export ACUITY_OVXLIB_DEV=path_to_acuity-ovxlib-dev.

4.4.3.2 Build inceptionv3 from Source

We don’t need to copy the entire aml_npu_sdk onto Khadas VIM3, but ONLY demo/inceptionv3. Here in my case, ONLY demo/inceptionv3 is copied under ~/Programs.

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khadas@Khadas:~/Programs/inceptionv3$ ll
total 28236
drwxr-xr-x 3 khadas khadas 4096 Apr 29 09:23 ./
drwxrwxr-x 5 khadas khadas 4096 Apr 29 09:22 ../
-rwxr-xr-x 1 khadas khadas 577 Apr 29 09:22 BUILD*
drwxr-xr-x 2 khadas khadas 4096 Apr 29 09:22 bin_demo/
-rwxr-xr-x 1 khadas khadas 9878 Apr 29 09:22 build_vx.sh*
-rwxr-xr-x 1 khadas khadas 28807168 Apr 29 09:23 inception_v3.nb*
-rwxr-xr-x 1 khadas khadas 12691 Apr 29 09:22 inceptionv3.vcxproj*
-rwxr-xr-x 1 khadas khadas 5869 Apr 29 09:23 main.c*
-rwxr-xr-x 1 khadas khadas 2000 Apr 29 09:23 makefile.linux*
-rwxr-xr-x 1 khadas khadas 358 Apr 29 09:23 vnn_global.h*
-rwxr-xr-x 1 khadas khadas 7191 Apr 29 09:23 vnn_inceptionv3.c*
-rwxr-xr-x 1 khadas khadas 985 Apr 29 09:23 vnn_inceptionv3.h*
-rwxr-xr-x 1 khadas khadas 3566 Apr 29 09:23 vnn_post_process.c*
-rwxr-xr-x 1 khadas khadas 464 Apr 29 09:23 vnn_post_process.h*
-rwxr-xr-x 1 khadas khadas 20385 Apr 29 09:23 vnn_pre_process.c*
-rwxr-xr-x 1 khadas khadas 1294 Apr 29 09:23 vnn_pre_process.h*

This is almost the same as folder nbg_unify_inception_v3 shown in 4.2 Model Conversion on Host.

Now, The MOST important part is to modify makefile.

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khadas@Khadas:~/Programs/inceptionv3$ cp makefile.linux makefile

My makefile is modified as follows.

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khadas@Khadas:~/Programs/inceptionv3$ cat makefile
CFLAGS += -I${ACUITY_OVXLIB_DEV}/include

################################################################################
# Supply necessary libraries.
LIBS += -L/lib -lOpenVX -lOpenVXU -lCLC -lVSC -lGAL -lovxlib -lm -ljpeg

#############################################################################
# Macros.
PROGRAM = 1
TARGET_NAME = inceptionv3
CUR_SOURCE = ${wildcard *.c}
#############################################################################
# Objects.
OBJECTS = ${patsubst %.c, $(OBJ_DIR)/%.o, $(CUR_SOURCE)}

# installation directory
INSTALL_DIR := ./

################################################################################
# Include the common makefile.

include ${VIVANTE_SDK}/common.target

In fact, you still need to modify common.target a little bit accordingly. However, to disclose it in this blog is still NOT allowed I think. Anyway, after the modification, let’s make it.

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khadas@Khadas:~/Programs/inceptionv3$ make
cc -c -I/opt/acuity-ovxlib-dev/include -o bin_r/vnn_pre_process.o vnn_pre_process.c
cc -c -I/opt/acuity-ovxlib-dev/include -o bin_r/vnn_inceptionv3.o vnn_inceptionv3.c
cc -c -I/opt/acuity-ovxlib-dev/include -o bin_r/main.o main.c
cc -c -I/opt/acuity-ovxlib-dev/include -o bin_r/vnn_post_process.o vnn_post_process.c
cc -Wl,-rpath-link /opt/vivante_sdk/drivers bin_r/vnn_pre_process.o bin_r/vnn_inceptionv3.o bin_r/main.o bin_r/vnn_post_process.o -o bin_r/inceptionv3 -L/lib -lOpenVX -lOpenVXU -lCLC -lVSC -lGAL -lovxlib -lm -ljpeg -lrt
bin_r/inceptionv3
Usage: bin_r/inceptionv3 data_file inputs...
/opt/vivante_sdk/common.target:64: recipe for target 'bin_r/inceptionv3' failed
make: *** [bin_r/inceptionv3] Error 255

Don’t worry about the error. It just failed to run the demo, but the executable inceptionv3 has already been successfully built under folder bin_r.

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khadas@Khadas:~/Programs/inceptionv3$ ll bin_r
total 92
drwxrwxr-x 2 khadas khadas 4096 Apr 29 09:46 ./
drwxr-xr-x 4 khadas khadas 4096 Apr 29 09:46 ../
-rwxrwxr-x 1 khadas khadas 34864 Apr 29 09:46 inceptionv3*
-rw-rw-r-- 1 khadas khadas 6864 Apr 29 09:46 main.o
-rw-rw-r-- 1 khadas khadas 5128 Apr 29 09:46 vnn_inceptionv3.o
-rw-rw-r-- 1 khadas khadas 4456 Apr 29 09:46 vnn_post_process.o
-rw-rw-r-- 1 khadas khadas 21976 Apr 29 09:46 vnn_pre_process.o

4.4.3.3 Run inceptionv3

Let’s run inceptionv3 under folder bin_demo.

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khadas@Khadas:~/Programs/inceptionv3$ cd bin_demo/
khadas@Khadas:~/Programs/inceptionv3/bin_demo$ ll
total 28384
drwxr-xr-x 2 khadas khadas 4096 Apr 29 09:52 ./
drwxr-xr-x 4 khadas khadas 4096 Apr 29 09:57 ../
-rwxr-xr-x 1 khadas khadas 15981 Apr 29 09:52 dog_299x299.jpg*
-rwxr-xr-x 1 khadas khadas 88322 Apr 29 09:52 goldfish_299x299.jpg*
-rwxr-xr-x 1 khadas khadas 10479 Apr 29 09:52 imagenet_slim_labels.txt*
-rwxr-xr-x 1 khadas khadas 28807168 Apr 29 09:52 inception_v3.nb*
-rwxr-xr-x 1 khadas khadas 129568 Apr 29 09:52 inceptionv3*

This is the original status of ALL files under bin_demo. Let’s copy and paste our built bin_r/inceptionv3 into this folder bin_demo. The size of the executable seems to be dramatically decreased.

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khadas@Khadas:~/Programs/inceptionv3/bin_demo$ cp ../bin_r/inceptionv3 ./
khadas@Khadas:~/Programs/inceptionv3/bin_demo$ ll inceptionv3
-rwxr-xr-x 1 khadas khadas 34864 Apr 29 09:58 inceptionv3*

Now, let’s copy the built inception_v3.nb from host to Khadas VIM3. It seems inception_v3.nb built by Tensorflow 2.1.0 on host is of the same size as provided by Khadas.

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khadas@Khadas:~/Programs/inceptionv3/bin_demo$ ll inception_v3.nb 
-rwxr-xr-x 1 khadas khadas 28807168 Apr 29 10:06 inception_v3.nb*

Finally, let’s run the demo.

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khadas@Khadas:~/Programs/inceptionv3/bin_demo$ ./inceptionv3 ./inception_v3.nb ./dog_299x299.jpg 
D [setup_node:368]Setup node id[0] uid[4294967295] op[NBG]
D [print_tensor:136]in(0) : id[ 1] vtl[0] const[0] shape[ 3, 299, 299, 1 ] fmt[u8 ] qnt[ASM zp=137, scale=0.007292]
D [print_tensor:136]out(0): id[ 0] vtl[0] const[0] shape[ 1001, 1 ] fmt[f16] qnt[NONE]
D [optimize_node:312]Backward optimize neural network
D [optimize_node:319]Forward optimize neural network
I [compute_node:261]Create vx node
Create Neural Network: 58ms or 58961us
I [vsi_nn_PrintGraph:1421]Graph:
I [vsi_nn_PrintGraph:1422]***************** Tensors ******************
D [print_tensor:146]id[ 0] vtl[0] const[0] shape[ 1001, 1 ] fmt[f16] qnt[NONE]
D [print_tensor:146]id[ 1] vtl[0] const[0] shape[ 3, 299, 299, 1 ] fmt[u8 ] qnt[ASM zp=137, scale=0.007292]
I [vsi_nn_PrintGraph:1431]***************** Nodes ******************
I [vsi_nn_PrintNode:159]( NBG)node[0] [in: 1 ], [out: 0 ] [a56c0cb0]
I [vsi_nn_PrintGraph:1440]******************************************
I [vsi_nn_ConvertTensorToData:750]Create 268203 data.
Verify...
Verify Graph: 1ms or 1959us
Start run graph [1] times...
Run the 1 time: 29ms or 29038us
vxProcessGraph execution time:
Total 29ms or 29063us
Average 29.06ms or 29063.00us
I [vsi_nn_ConvertTensorToData:750]Create 2002 data.
--- Top5 ---
208: 0.828613
209: 0.040771
223: 0.008278
268: 0.002737
185: 0.002396
I [vsi_nn_ConvertTensorToData:750]Create 2002 data.
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khadas@Khadas:~/Programs/inceptionv3/bin_demo$ ./inceptionv3 ./inception_v3.nb ./goldfish_299x299.jpg 
D [setup_node:368]Setup node id[0] uid[4294967295] op[NBG]
D [print_tensor:136]in(0) : id[ 1] vtl[0] const[0] shape[ 3, 299, 299, 1 ] fmt[u8 ] qnt[ASM zp=137, scale=0.007292]
D [print_tensor:136]out(0): id[ 0] vtl[0] const[0] shape[ 1001, 1 ] fmt[f16] qnt[NONE]
D [optimize_node:312]Backward optimize neural network
D [optimize_node:319]Forward optimize neural network
I [compute_node:261]Create vx node
Create Neural Network: 64ms or 64785us
I [vsi_nn_PrintGraph:1421]Graph:
I [vsi_nn_PrintGraph:1422]***************** Tensors ******************
D [print_tensor:146]id[ 0] vtl[0] const[0] shape[ 1001, 1 ] fmt[f16] qnt[NONE]
D [print_tensor:146]id[ 1] vtl[0] const[0] shape[ 3, 299, 299, 1 ] fmt[u8 ] qnt[ASM zp=137, scale=0.007292]
I [vsi_nn_PrintGraph:1431]***************** Nodes ******************
I [vsi_nn_PrintNode:159]( NBG)node[0] [in: 1 ], [out: 0 ] [6df47cb0]
I [vsi_nn_PrintGraph:1440]******************************************
I [vsi_nn_ConvertTensorToData:750]Create 268203 data.
Verify...
Verify Graph: 2ms or 2951us
Start run graph [1] times...
Run the 1 time: 28ms or 28835us
vxProcessGraph execution time:
Total 28ms or 28873us
Average 28.87ms or 28873.00us
I [vsi_nn_ConvertTensorToData:750]Create 2002 data.
--- Top5 ---
2: 0.832520
795: 0.008316
974: 0.003586
408: 0.002302
393: 0.002016
I [vsi_nn_ConvertTensorToData:750]Create 2002 data.

By comparing to imagenet_slim_labels.txt under current folder, let’s take a look at our inference results. Only the FIRST inference is qualified because of the probability.

Index Result for dog_299x299.jpg Result for goldfish_299x299.jpg
N/A dog_299x299.jpg goldfish_299x299.jpg
1 208: ‘curly-coated retriever’, 2: ‘tench’,
2 209: ‘golden retriever’, 795: ‘shower cap’,
3 223: ‘Irish water spaniel’, 974: ‘cliff’,
4 268: ‘miniature poodle’, 408: ‘altar’,
5 185: ‘Kerry blue terrier’, 393: ‘coho’,
😘

5. Dual Boot From Manjaro

5.1 How to Boot Images from External Media?

There are clearly 2 options:

  • dual boot by selecting devices: EMMC or TF/SD Card. On Boot Images from External Media, it’s recommended as Via Keys mode (Side-Buttons) - the easiest and fastest way, which is the FIRST option on page How To Boot Into Upgrade Mode. Therefore, by following 4 steps as follows(cited from How To Boot Into Upgrade Mode), we should be able to boot into SD-USB. 1. Power on VIM3. 2. Long press the POWER key without releasing it. 3. Short press the ‘Reset’ key and release it. 4. Count for 2 to 3 seconds, then release the POWER key to enter into Upgrade Mode. You will see the sys-led turn ON when you’ve entered Upgrade Mode.
  • multiple boot via grub: Reasonably speaking, 2 operating systems may even have a chance to be installed onto a SINGLE EMMC

5.2 How to flash Manjaro XFCE for Khadas Vim 3 from TF/SD card to EMMC?

ONLY 1 operating system is preferred. Why??? Khadas VIM3 board comes with a large EMMC of size 32G.

After a VERY long time struggling, I would really like to emphasize the quality of Type C cable and power adaptor again. Try to buy things NOT from Taobao.

😢 😭

Finally, I had Manjaro XFCE for Khadas Vim 3 on SD card booted and running, as follows:

Manjaro

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➜  ~ ssh khadas@192.168.1.95
khadas@192.168.1.95's password:
Welcome to Manjaro-ARM
~~Website: https://manjaro.org
~~Forum: https://forum.manjaro.org/c/manjaro-arm
~~IRC: #manjaro-arm on irc.freenode.net
~~Matrix: #manjaro-arm-public:matrix.org
Last login: Sun Apr 19 03:24:46 2020 from 192.168.1.200
[khadas@manjaro ~]$ ls
Desktop Documents Downloads Music Pictures Public Templates Videos
[khadas@manjaro ~]$ pwd
/home/khadas
[khadas@manjaro ~]$ uname -a
Linux manjaro 5.6.0-0.6 #1 SMP PREEMPT Wed Apr 1 19:25:06 +03 2020 aarch64 GNU/Linux
[khadas@manjaro ~]$ lsb_release -a
LSB Version: n/a
Distributor ID: Manjaro-ARM
Description: Manjaro ARM Linux
Release: 20.04
Codename: n/a
[khadas@manjaro ~]$ gcc --version
-bash: gcc: command not found
[khadas@manjaro ~]$ g++ --version
-bash: g++: command not found
[khadas@manjaro ~]$ sudo apt install gcc g++ automake autoconfig libtool

We trust you have received the usual lecture from the local System
Administrator. It usually boils down to these three things:

#1) Respect the privacy of others.
#2) Think before you type.
#3) With great power comes great responsibility.

[sudo] password for khadas:
sudo: apt: command not found
[khadas@manjaro ~]$ pamac help
Available actions:
pamac --version
pamac --help,-h [action]
pamac clean [options]
pamac checkupdates [options]
pamac update,upgrade [options]
pamac search [options] <package(s)>
pamac info [options] <package(s)>
pamac list [options] <package(s)>
pamac install [options] <package(s)>
pamac reinstall [options] <package(s)>
pamac clone [options] <package(s)>
pamac build [options] [package(s)]
pamac remove [options] [package(s)]
[khadas@manjaro ~]$

It seems Arch Linux is totally different from Debian. What can I say? Go to bed.

Today, April 17, 2020, I’ve got 2 big NEWS for me.

  • China Airline cancelled my flight back to China in May.
  • I received an Email from balena to encourage us to contribute our spare computing power (PCs, laptops, single-board devices) to [Rosetta@Home](https://boinc.bakerlab.org/) and support vital COVID-19 research.

Well, having been using balenaEtcher for quite a while, I of course will support Baker Laboratory at W - University of Washington. There are 2 points need to be emphasized here:

  • W used to be my dreamed university, but it’s STILL in my dream so far.

    😂
  • Baker Laboratory seems to be really good at bakery.

Alright, let’s taste how they bake this COVID-19. 2 manuals to follow:

Make sure one thing: Wired Connection.

Now, it’s your choice to visit either http://foldforcovid.local/ or IP address of this Raspberry Pi 4, you will see your Raspberry Pi 4 is up and running, and you are donating the compute capacity to support COVID-19.

foldforcovid.local 192.168.1.111
foldforcovid.local 192.168.1.111

Finally, 2 additional things:

Let me update a bit: Besides this Fold for Covid, there are so many activities ongoing:

Visited Green Timbers Lake again.

The Grassland Ducks In the Lake A Pair of Ducks In The Lake
The Grassland Ducks In the Lake A Pair of Ducks In The Lake
The Lake Me In Facial Mask for COVID-19 The Lake - The Other Side
The Lake Me In Facial Mask for COVID-19 The Lake - The Other Side

1. About Tensorspace

2. Let’s Have Some Fun

We FIRST create an empty project folder, here, named as WebTensorspace.

2.1 Follow Towards Data Science Blog FIRST

Let’s strictly follow this part of Towards Data Science Blog (cited from Towards Data Science Blog).

Finally we need to create .html file which will output the result. Not to spend time on setting-up TensorFlow.js and JQuery I encourage you just to use my template at TensorSpace folder. The folder structure looks as following:
  • index.html — out html file to run visualization
  • lib/ — folder storing all the dependencies
  • data/ — folder containing .json file with network inputs
  • model/ — folder containing exported model

For our html file we need to first import dependecies and write a TensorSpace script.

Now, let’s take a look at our project.

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➜  WebTensorspace ls
data index.html lib model

2.1.1 lib

Three resource is referred to download ALL required libraries.

Some required libraries suggested by me:

Now, let’s take a look at what’s under folder lib.

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➜  WebTensorspace ls lib
Chart.min.js signature_pad.min.js tensorspace.min.js tf.min.js.map TrackballControls.js
jquery.min.js stats.min.js tf.min.js three.min.js tween.cjs.js

2.1.2 model

Let’s just use tf_keras_model.h5 provided by TensorSpace as our example. You may have to click on the Download button to have this tf_keras_model.h5 downloaded into folder model.

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➜  WebTensorspace ls model 
tf_keras_model.h5

2.1.2.1 tensorspacejs_converter Failed to Run

Now, let’s try to run the following command:

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➜  WebTensorspace tensorspacejs_converter \                              
--input_model_from="tensorflow" \
--input_model_format="tf_keras" \
--output_layer_names="padding_1,conv_1,maxpool_1,conv_2,maxpool_2,dense_1,dense_2,softmax" \
./model/tf_keras_model.h5 \
./model/converted
Traceback (most recent call last):
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 584, in _build_master
ws.require(__requires__)
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 901, in require
needed = self.resolve(parse_requirements(requirements))
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 792, in resolve
raise VersionConflict(dist, req).with_context(dependent_req)
pkg_resources.ContextualVersionConflict: (tensorboard 2.2.1 (~/.local/lib/python3.6/site-packages), Requirement.parse('tensorboard<2.2.0,>=2.1.0'), {'tensorflow'})

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "~/.local/bin/tensorspacejs_converter", line 6, in <module>
from pkg_resources import load_entry_point
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 3258, in <module>
@_call_aside
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 3242, in _call_aside
f(*args, **kwargs)
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 3271, in _initialize_master_working_set
working_set = WorkingSet._build_master()
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 586, in _build_master
return cls._build_from_requirements(__requires__)
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 599, in _build_from_requirements
dists = ws.resolve(reqs, Environment())
File "~/.local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 787, in resolve
raise DistributionNotFound(req, requirers)
pkg_resources.DistributionNotFound: The 'tensorflow-estimator<2.2.0,>=2.1.0' distribution was not found and is required by tensorflow

Clearly, we can downgrade tensorflow-estimator from 2.2.0 to 2.1.0.

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➜  WebTensorspace pip show tensorflow_estimator
Name: tensorflow-estimator
Version: 2.1.0
Summary: TensorFlow Estimator.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: UNKNOWN
License: Apache 2.0
Location: ~/.local/lib/python3.6/site-packages
Requires:
Required-by: tensorflow

Now, we try to re-run the above tensorspacejs_converter command:

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➜  WebTensorspace tensorspacejs_converter \                     
--input_model_from="tensorflow" \
--input_model_format="tf_keras" \
--output_layer_names="padding_1,conv_1,maxpool_1,conv_2,maxpool_2,dense_1,dense_2,softmax" \
./model/tf_keras_model.h5 \
./model/converted
Using TensorFlow backend.
Traceback (most recent call last):
File "~/.local/bin/tensorspacejs_converter", line 11, in <module>
load_entry_point('tensorspacejs==0.2.0', 'console_scripts', 'tensorspacejs_converter')()
File "~/.local/lib/python3.6/site-packages/tensorspacejs-0.2.0-py3.6.egg/tensorspacejs/tsp_converters.py", line 167, in main
flags.output_layer_names
File "~/.local/lib/python3.6/site-packages/tensorspacejs-0.2.0-py3.6.egg/tensorspacejs/tf/tensorflow_conversion.py", line 35, in preprocess_tensorflow_model
output_node_names
File "~/.local/lib/python3.6/site-packages/tensorspacejs-0.2.0-py3.6.egg/tensorspacejs/tf/keras_model.py", line 15, in preprocess_hdf5_combined_model
with K.get_session():
File "~/.local/lib/python3.6/site-packages/Keras-2.3.1-py3.6.egg/keras/backend/tensorflow_backend.py", line 379, in get_session
'`get_session` is not available '
RuntimeError: `get_session` is not available when using TensorFlow 2.0.

2.1.2.2 Install tfjs-converter

Please checkout tfjs and enter tfjs-converter, and then have the python package installed tfjs-converter/python

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➜  python git:(master) ✗ pwd
....../tfjs/tfjs-converter/python
➜ python git:(master) ✗ python setup.py install --user
running install
running bdist_egg
running egg_info
writing tensorflowjs.egg-info/PKG-INFO
writing dependency_links to tensorflowjs.egg-info/dependency_links.txt
writing entry points to tensorflowjs.egg-info/entry_points.txt
writing requirements to tensorflowjs.egg-info/requires.txt
writing top-level names to tensorflowjs.egg-info/top_level.txt
file tensorflowjs.py (for module tensorflowjs) not found
file tensorflowjs/converters.py (for module tensorflowjs.converters) not found
package init file 'tensorflowjs/op_list/__init__.py' not found (or not a regular file)
reading manifest template 'MANIFEST.in'
writing manifest file 'tensorflowjs.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
file tensorflowjs.py (for module tensorflowjs) not found
file tensorflowjs/converters.py (for module tensorflowjs.converters) not found
file tensorflowjs.py (for module tensorflowjs) not found
file tensorflowjs/converters.py (for module tensorflowjs.converters) not found
creating build/bdist.linux-x86_64/egg
creating build/bdist.linux-x86_64/egg/tensorflowjs
creating build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/common.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/converter.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/fold_batch_norms.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/fuse_depthwise_conv2d.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/fuse_prelu.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/graph_rewrite_util.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/keras_h5_conversion.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/keras_tfjs_loader.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/tf_saved_model_conversion_v2.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/wizard.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
copying build/lib/tensorflowjs/converters/__init__.py -> build/bdist.linux-x86_64/egg/tensorflowjs/converters
creating build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/arithmetic.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/basic_math.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/control.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/convolution.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/creation.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/dynamic.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/evaluation.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/graph.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/image.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/logical.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/matrices.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/normalization.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/reduction.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/slice_join.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/spectral.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/op_list/transformation.json -> build/bdist.linux-x86_64/egg/tensorflowjs/op_list
copying build/lib/tensorflowjs/quantization.py -> build/bdist.linux-x86_64/egg/tensorflowjs
copying build/lib/tensorflowjs/read_weights.py -> build/bdist.linux-x86_64/egg/tensorflowjs
copying build/lib/tensorflowjs/resource_loader.py -> build/bdist.linux-x86_64/egg/tensorflowjs
copying build/lib/tensorflowjs/version.py -> build/bdist.linux-x86_64/egg/tensorflowjs
copying build/lib/tensorflowjs/write_weights.py -> build/bdist.linux-x86_64/egg/tensorflowjs
copying build/lib/tensorflowjs/__init__.py -> build/bdist.linux-x86_64/egg/tensorflowjs
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/common.py to common.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/converter.py to converter.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/fold_batch_norms.py to fold_batch_norms.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/fuse_depthwise_conv2d.py to fuse_depthwise_conv2d.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/fuse_prelu.py to fuse_prelu.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/graph_rewrite_util.py to graph_rewrite_util.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/keras_h5_conversion.py to keras_h5_conversion.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/keras_tfjs_loader.py to keras_tfjs_loader.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/tf_saved_model_conversion_v2.py to tf_saved_model_conversion_v2.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/wizard.py to wizard.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/converters/__init__.py to __init__.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/quantization.py to quantization.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/read_weights.py to read_weights.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/resource_loader.py to resource_loader.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/version.py to version.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/write_weights.py to write_weights.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorflowjs/__init__.py to __init__.cpython-36.pyc
creating build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorflowjs.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorflowjs.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorflowjs.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorflowjs.egg-info/entry_points.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorflowjs.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorflowjs.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
zip_safe flag not set; analyzing archive contents...
tensorflowjs.__pycache__.resource_loader.cpython-36: module references __file__
creating 'dist/tensorflowjs-1.7.0-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing tensorflowjs-1.7.0-py3.6.egg
creating ~/.local/lib/python3.6/site-packages/tensorflowjs-1.7.0-py3.6.egg
Extracting tensorflowjs-1.7.0-py3.6.egg to ~/.local/lib/python3.6/site-packages
Adding tensorflowjs 1.7.0 to easy-install.pth file
Installing tensorflowjs_converter script to ~/.local/bin
Installing tensorflowjs_wizard script to ~/.local/bin

Installed ~/.local/lib/python3.6/site-packages/tensorflowjs-1.7.0-py3.6.egg
Processing dependencies for tensorflowjs==1.7.0
Searching for PyInquirer==1.0.3
Best match: PyInquirer 1.0.3
Adding PyInquirer 1.0.3 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Not found: prompt_toolkit==1.0.14
Not found: Pygments>=2.2.0
Not found: regex>=2016.11.21
Searching for gast==0.3.3
Best match: gast 0.3.3
Adding gast 0.3.3 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for tensorflow-hub==0.8.0
Best match: tensorflow-hub 0.8.0
Adding tensorflow-hub 0.8.0 to easy-install.pth file
Installing make_image_classifier script to ~/.local/bin
Installing make_nearest_neighbour_index script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for tensorflow==2.1.0
Best match: tensorflow 2.1.0
Adding tensorflow 2.1.0 to easy-install.pth file
Installing estimator_ckpt_converter script to ~/.local/bin
Installing saved_model_cli script to ~/.local/bin
Installing tensorboard script to ~/.local/bin
Installing tf_upgrade_v2 script to ~/.local/bin
Installing tflite_convert script to ~/.local/bin
Installing toco script to ~/.local/bin
Installing toco_from_protos script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for six==1.14.0
Best match: six 1.14.0
Adding six 1.14.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for numpy==1.18.2
Best match: numpy 1.18.2
Adding numpy 1.18.2 to easy-install.pth file
Installing f2py script to ~/.local/bin
Installing f2py3 script to ~/.local/bin
Installing f2py3.6 script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for h5py==2.10.0
Best match: h5py 2.10.0
Adding h5py 2.10.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for prompt-toolkit==1.0.14
Best match: prompt-toolkit 1.0.14
Processing prompt_toolkit-1.0.14-py3.6.egg
prompt-toolkit 1.0.14 is already the active version in easy-install.pth

Using ~/.local/lib/python3.6/site-packages/prompt_toolkit-1.0.14-py3.6.egg
Searching for regex==2020.4.4
Best match: regex 2020.4.4
Adding regex 2020.4.4 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for Pygments==2.6.1
Best match: Pygments 2.6.1
Adding Pygments 2.6.1 to easy-install.pth file
Installing pygmentize script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for protobuf==3.11.3
Best match: protobuf 3.11.3
Adding protobuf 3.11.3 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for grpcio==1.28.1
Best match: grpcio 1.28.1
Adding grpcio 1.28.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for opt-einsum==3.2.1
Best match: opt-einsum 3.2.1
Adding opt-einsum 3.2.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for Keras-Preprocessing==1.1.0
Best match: Keras-Preprocessing 1.1.0
Adding Keras-Preprocessing 1.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for wrapt==1.12.1
Best match: wrapt 1.12.1
Adding wrapt 1.12.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for scipy==1.4.1
Best match: scipy 1.4.1
Adding scipy 1.4.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for tensorflow-estimator==2.1.0
Best match: tensorflow-estimator 2.1.0
Adding tensorflow-estimator 2.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for tensorboard==2.1.1
Best match: tensorboard 2.1.1
Processing tensorboard-2.1.1-py3.6.egg
tensorboard 2.1.1 is already the active version in easy-install.pth
Installing tensorboard script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages/tensorboard-2.1.1-py3.6.egg
Searching for astunparse==1.6.3
Best match: astunparse 1.6.3
Adding astunparse 1.6.3 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for google-pasta==0.2.0
Best match: google-pasta 0.2.0
Adding google-pasta 0.2.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for termcolor==1.1.0
Best match: termcolor 1.1.0
Adding termcolor 1.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for absl-py==0.9.0
Best match: absl-py 0.9.0
Adding absl-py 0.9.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for wheel==0.34.2
Best match: wheel 0.34.2
Adding wheel 0.34.2 to easy-install.pth file
Installing wheel script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for wcwidth==0.1.9
Best match: wcwidth 0.1.9
Adding wcwidth 0.1.9 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for setuptools==46.1.3
Best match: setuptools 46.1.3
Adding setuptools 46.1.3 to easy-install.pth file
Installing easy_install script to ~/.local/bin
Installing easy_install-3.8 script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for Werkzeug==1.0.1
Best match: Werkzeug 1.0.1
Adding Werkzeug 1.0.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for requests==2.23.0
Best match: requests 2.23.0
Adding requests 2.23.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for Markdown==3.2.1
Best match: Markdown 3.2.1
Adding Markdown 3.2.1 to easy-install.pth file
Installing markdown_py script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for google-auth==1.14.0
Best match: google-auth 1.14.0
Adding google-auth 1.14.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for google-auth-oauthlib==0.4.1
Best match: google-auth-oauthlib 0.4.1
Adding google-auth-oauthlib 0.4.1 to easy-install.pth file
Installing google-oauthlib-tool script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for urllib3==1.25.9
Best match: urllib3 1.25.9
Adding urllib3 1.25.9 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for idna==2.9
Best match: idna 2.9
Adding idna 2.9 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for certifi==2020.4.5.1
Best match: certifi 2020.4.5.1
Adding certifi 2020.4.5.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for chardet==3.0.4
Best match: chardet 3.0.4
Adding chardet 3.0.4 to easy-install.pth file
Installing chardetect script to ~/.local/bin

Using /usr/lib/python3/dist-packages
Searching for pyasn1-modules==0.2.8
Best match: pyasn1-modules 0.2.8
Adding pyasn1-modules 0.2.8 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for cachetools==4.1.0
Best match: cachetools 4.1.0
Adding cachetools 4.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for rsa==4.0
Best match: rsa 4.0
Adding rsa 4.0 to easy-install.pth file
Installing pyrsa-decrypt script to ~/.local/bin
Installing pyrsa-encrypt script to ~/.local/bin
Installing pyrsa-keygen script to ~/.local/bin
Installing pyrsa-priv2pub script to ~/.local/bin
Installing pyrsa-sign script to ~/.local/bin
Installing pyrsa-verify script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for requests-oauthlib==1.3.0
Best match: requests-oauthlib 1.3.0
Adding requests-oauthlib 1.3.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for pyasn1==0.4.8
Best match: pyasn1 0.4.8
Adding pyasn1 0.4.8 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for oauthlib==3.1.0
Best match: oauthlib 3.1.0
Adding oauthlib 3.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Finished processing dependencies for tensorflowjs==1.7.0
➜ python git:(master) ✗

2.1.2.3 Install tensorspace-converter

Please checkout my modified tensorspace-converter and have the python package installed. Please also keep an eye on my PR.

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➜  tensorspace-converter git:(master) python setup.py install --user
running install
running bdist_egg
running egg_info
writing tensorspacejs.egg-info/PKG-INFO
writing dependency_links to tensorspacejs.egg-info/dependency_links.txt
writing entry points to tensorspacejs.egg-info/entry_points.txt
writing requirements to tensorspacejs.egg-info/requires.txt
writing top-level names to tensorspacejs.egg-info/top_level.txt
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*' found under directory 'tensorspacejs/tfjs/node_modules'
warning: no previously-included files matching '*' found under directory 'tensorspacejs/tf/pb2json/node_modules'
writing manifest file 'tensorspacejs.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
creating build/bdist.linux-x86_64/egg
creating build/bdist.linux-x86_64/egg/tensorspacejs
copying build/lib/tensorspacejs/install.py -> build/bdist.linux-x86_64/egg/tensorspacejs
creating build/bdist.linux-x86_64/egg/tensorspacejs/krs
copying build/lib/tensorspacejs/krs/keras_conversion.py -> build/bdist.linux-x86_64/egg/tensorspacejs/krs
copying build/lib/tensorspacejs/krs/keras_model.py -> build/bdist.linux-x86_64/egg/tensorspacejs/krs
copying build/lib/tensorspacejs/krs/__init__.py -> build/bdist.linux-x86_64/egg/tensorspacejs/krs
creating build/bdist.linux-x86_64/egg/tensorspacejs/tf
copying build/lib/tensorspacejs/tf/frozen_model.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tf
copying build/lib/tensorspacejs/tf/keras_model.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tf
creating build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json
copying build/lib/tensorspacejs/tf/pb2json/package.json -> build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json
copying build/lib/tensorspacejs/tf/pb2json/pb2json_conversion.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json
copying build/lib/tensorspacejs/tf/pb2json/README.md -> build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json
creating build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json/tools
copying build/lib/tensorspacejs/tf/pb2json/tools/compiled_api.js -> build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json/tools
copying build/lib/tensorspacejs/tf/pb2json/tools/pb2json_converter.ts -> build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json/tools
copying build/lib/tensorspacejs/tf/pb2json/__init__.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json
copying build/lib/tensorspacejs/tf/saved_model.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tf
copying build/lib/tensorspacejs/tf/tensorflow_conversion.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tf
copying build/lib/tensorspacejs/tf/__init__.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tf
creating build/bdist.linux-x86_64/egg/tensorspacejs/tfjs
creating build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/app
copying build/lib/tensorspacejs/tfjs/app/Converter.js -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/app
copying build/lib/tensorspacejs/tfjs/app/Summary.js -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/app
copying build/lib/tensorspacejs/tfjs/main.js -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs
copying build/lib/tensorspacejs/tfjs/package.json -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs
copying build/lib/tensorspacejs/tfjs/tfjs_conversion.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs
creating build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/utils
copying build/lib/tensorspacejs/tfjs/utils/Utils.js -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/utils
creating build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/wrapper
copying build/lib/tensorspacejs/tfjs/wrapper/ModelWrapper.js -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/wrapper
copying build/lib/tensorspacejs/tfjs/__init__.py -> build/bdist.linux-x86_64/egg/tensorspacejs/tfjs
copying build/lib/tensorspacejs/tsp_converters.py -> build/bdist.linux-x86_64/egg/tensorspacejs
creating build/bdist.linux-x86_64/egg/tensorspacejs/utility
copying build/lib/tensorspacejs/utility/file_utility.py -> build/bdist.linux-x86_64/egg/tensorspacejs/utility
copying build/lib/tensorspacejs/utility/__init__.py -> build/bdist.linux-x86_64/egg/tensorspacejs/utility
copying build/lib/tensorspacejs/version.py -> build/bdist.linux-x86_64/egg/tensorspacejs
copying build/lib/tensorspacejs/__init__.py -> build/bdist.linux-x86_64/egg/tensorspacejs
creating build/bdist.linux-x86_64/egg/tensorspacejs/__pycache__
copying build/lib/tensorspacejs/__pycache__/version.cpython-36.pyc -> build/bdist.linux-x86_64/egg/tensorspacejs/__pycache__
copying build/lib/tensorspacejs/__pycache__/__init__.cpython-36.pyc -> build/bdist.linux-x86_64/egg/tensorspacejs/__pycache__
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/install.py to install.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/krs/keras_conversion.py to keras_conversion.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/krs/keras_model.py to keras_model.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/krs/__init__.py to __init__.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tf/frozen_model.py to frozen_model.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tf/keras_model.py to keras_model.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json/pb2json_conversion.py to pb2json_conversion.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tf/pb2json/__init__.py to __init__.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tf/saved_model.py to saved_model.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tf/tensorflow_conversion.py to tensorflow_conversion.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tf/__init__.py to __init__.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/tfjs_conversion.py to tfjs_conversion.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tfjs/__init__.py to __init__.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/tsp_converters.py to tsp_converters.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/utility/file_utility.py to file_utility.cpython-36.pyc
byte-compiling build/bdist.linux-x86_64/egg/tensorspacejs/utility/__init__.py to __init__.cpython-36.pyc
creating build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorspacejs.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorspacejs.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorspacejs.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorspacejs.egg-info/entry_points.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorspacejs.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
copying tensorspacejs.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO
zip_safe flag not set; analyzing archive contents...
tensorspacejs.__pycache__.install.cpython-36: module references __file__
tensorspacejs.__pycache__.tsp_converters.cpython-36: module references __file__
tensorspacejs.tf.pb2json.__pycache__.pb2json_conversion.cpython-36: module references __file__
tensorspacejs.tfjs.__pycache__.tfjs_conversion.cpython-36: module references __file__
creating 'dist/tensorspacejs-0.6.1-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it
removing 'build/bdist.linux-x86_64/egg' (and everything under it)
Processing tensorspacejs-0.6.1-py3.6.egg
creating ~/.local/lib/python3.6/site-packages/tensorspacejs-0.6.1-py3.6.egg
Extracting tensorspacejs-0.6.1-py3.6.egg to ~/.local/lib/python3.6/site-packages
Adding tensorspacejs 0.6.1 to easy-install.pth file
Installing tensorspacejs_converter script to ~/.local/bin

Installed ~/.local/lib/python3.6/site-packages/tensorspacejs-0.6.1-py3.6.egg
Processing dependencies for tensorspacejs==0.6.1
Searching for tensorflow==2.1.0
Best match: tensorflow 2.1.0
Adding tensorflow 2.1.0 to easy-install.pth file
Installing estimator_ckpt_converter script to ~/.local/bin
Installing saved_model_cli script to ~/.local/bin
Installing tensorboard script to ~/.local/bin
Installing tf_upgrade_v2 script to ~/.local/bin
Installing tflite_convert script to ~/.local/bin
Installing toco script to ~/.local/bin
Installing toco_from_protos script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for Keras==2.3.1
Best match: Keras 2.3.1
Processing Keras-2.3.1-py3.6.egg
Keras 2.3.1 is already the active version in easy-install.pth

Using ~/.local/lib/python3.6/site-packages/Keras-2.3.1-py3.6.egg
Searching for tensorflowjs==1.7.2
Best match: tensorflowjs 1.7.2
Processing tensorflowjs-1.7.2-py3.6.egg
tensorflowjs 1.7.2 is already the active version in easy-install.pth
Installing tensorflowjs_converter script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages/tensorflowjs-1.7.2-py3.6.egg
Searching for tensorboard==2.1.1
Best match: tensorboard 2.1.1
Processing tensorboard-2.1.1-py3.6.egg
tensorboard 2.1.1 is already the active version in easy-install.pth
Installing tensorboard script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages/tensorboard-2.1.1-py3.6.egg
Searching for Keras-Preprocessing==1.1.0
Best match: Keras-Preprocessing 1.1.0
Adding Keras-Preprocessing 1.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for grpcio==1.28.1
Best match: grpcio 1.28.1
Adding grpcio 1.28.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for termcolor==1.1.0
Best match: termcolor 1.1.0
Adding termcolor 1.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for six==1.14.0
Best match: six 1.14.0
Adding six 1.14.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for protobuf==3.11.3
Best match: protobuf 3.11.3
Adding protobuf 3.11.3 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for wrapt==1.12.1
Best match: wrapt 1.12.1
Adding wrapt 1.12.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for opt-einsum==3.2.1
Best match: opt-einsum 3.2.1
Adding opt-einsum 3.2.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for google-pasta==0.2.0
Best match: google-pasta 0.2.0
Adding google-pasta 0.2.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for scipy==1.4.1
Best match: scipy 1.4.1
Adding scipy 1.4.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for h5py==2.10.0
Best match: h5py 2.10.0
Adding h5py 2.10.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for absl-py==0.9.0
Best match: absl-py 0.9.0
Adding absl-py 0.9.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for wheel==0.34.2
Best match: wheel 0.34.2
Adding wheel 0.34.2 to easy-install.pth file
Installing wheel script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for gast==0.3.3
Best match: gast 0.3.3
Adding gast 0.3.3 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for numpy==1.18.2
Best match: numpy 1.18.2
Adding numpy 1.18.2 to easy-install.pth file
Installing f2py script to ~/.local/bin
Installing f2py3 script to ~/.local/bin
Installing f2py3.6 script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for tensorflow-estimator==2.1.0
Best match: tensorflow-estimator 2.1.0
Adding tensorflow-estimator 2.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for astunparse==1.6.3
Best match: astunparse 1.6.3
Adding astunparse 1.6.3 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for Keras-Applications==1.0.8
Best match: Keras-Applications 1.0.8
Adding Keras-Applications 1.0.8 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for PyYAML==5.3.1
Best match: PyYAML 5.3.1
Adding PyYAML 5.3.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for tensorflow-hub==0.8.0
Best match: tensorflow-hub 0.8.0
Adding tensorflow-hub 0.8.0 to easy-install.pth file
Installing make_image_classifier script to ~/.local/bin
Installing make_nearest_neighbour_index script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for Werkzeug==1.0.1
Best match: Werkzeug 1.0.1
Adding Werkzeug 1.0.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for setuptools==46.1.3
Best match: setuptools 46.1.3
Adding setuptools 46.1.3 to easy-install.pth file
Installing easy_install script to ~/.local/bin
Installing easy_install-3.8 script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for requests==2.23.0
Best match: requests 2.23.0
Adding requests 2.23.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for Markdown==3.2.1
Best match: Markdown 3.2.1
Adding Markdown 3.2.1 to easy-install.pth file
Installing markdown_py script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for google-auth==1.14.0
Best match: google-auth 1.14.0
Adding google-auth 1.14.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for google-auth-oauthlib==0.4.1
Best match: google-auth-oauthlib 0.4.1
Adding google-auth-oauthlib 0.4.1 to easy-install.pth file
Installing google-oauthlib-tool script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for chardet==3.0.4
Best match: chardet 3.0.4
Adding chardet 3.0.4 to easy-install.pth file
Installing chardetect script to ~/.local/bin

Using /usr/lib/python3/dist-packages
Searching for certifi==2020.4.5.1
Best match: certifi 2020.4.5.1
Adding certifi 2020.4.5.1 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for idna==2.9
Best match: idna 2.9
Adding idna 2.9 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for urllib3==1.25.9
Best match: urllib3 1.25.9
Adding urllib3 1.25.9 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for cachetools==4.1.0
Best match: cachetools 4.1.0
Adding cachetools 4.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for rsa==4.0
Best match: rsa 4.0
Adding rsa 4.0 to easy-install.pth file
Installing pyrsa-decrypt script to ~/.local/bin
Installing pyrsa-encrypt script to ~/.local/bin
Installing pyrsa-keygen script to ~/.local/bin
Installing pyrsa-priv2pub script to ~/.local/bin
Installing pyrsa-sign script to ~/.local/bin
Installing pyrsa-verify script to ~/.local/bin

Using ~/.local/lib/python3.6/site-packages
Searching for pyasn1-modules==0.2.8
Best match: pyasn1-modules 0.2.8
Adding pyasn1-modules 0.2.8 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for requests-oauthlib==1.3.0
Best match: requests-oauthlib 1.3.0
Adding requests-oauthlib 1.3.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for pyasn1==0.4.8
Best match: pyasn1 0.4.8
Adding pyasn1 0.4.8 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Searching for oauthlib==3.1.0
Best match: oauthlib 3.1.0
Adding oauthlib 3.1.0 to easy-install.pth file

Using ~/.local/lib/python3.6/site-packages
Finished processing dependencies for tensorspacejs==0.6.1

2.1.2.4 Try tensorspacejs_converter Again

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➜  WebTensorspace tensorspacejs_converter \
--input_model_from="tensorflow" \
--input_model_format="tf_keras" \
--output_layer_names="padding_1,conv_1,maxpool_1,conv_2,maxpool_2,dense_1,dense_2,softmax" \
./model/tf_keras_model.h5 \
./model/convertedModel
Using TensorFlow backend.
2020-04-17 04:45:23.473683: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-04-17 04:45:23.477188: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.477770: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1558] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 980M computeCapability: 5.2
coreClock: 1.1265GHz coreCount: 12 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 149.31GiB/s
2020-04-17 04:45:23.478004: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.2
2020-04-17 04:45:23.479508: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-17 04:45:23.480978: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-17 04:45:23.481245: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-17 04:45:23.482737: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-17 04:45:23.483660: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-17 04:45:23.487100: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-17 04:45:23.487273: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.487902: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.488310: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Adding visible gpu devices: 0
2020-04-17 04:45:23.488621: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA
2020-04-17 04:45:23.511713: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2599990000 Hz
2020-04-17 04:45:23.512333: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5bc9f30 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-17 04:45:23.512354: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-04-17 04:45:23.553407: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.553764: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5c320e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-04-17 04:45:23.553782: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 980M, Compute Capability 5.2
2020-04-17 04:45:23.553969: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.554208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1558] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 980M computeCapability: 5.2
coreClock: 1.1265GHz coreCount: 12 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 149.31GiB/s
2020-04-17 04:45:23.554271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.2
2020-04-17 04:45:23.554302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-17 04:45:23.554315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-17 04:45:23.554344: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-17 04:45:23.554389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-17 04:45:23.554413: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-17 04:45:23.554442: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-17 04:45:23.554519: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.554755: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.554956: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1700] Adding visible gpu devices: 0
2020-04-17 04:45:23.555015: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.2
2020-04-17 04:45:23.555794: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1099] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-17 04:45:23.555806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1105] 0
2020-04-17 04:45:23.555830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1118] 0: N
2020-04-17 04:45:23.555939: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.556211: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-17 04:45:23.556465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1244] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 133 MB memory) -> physical GPU (device: 0, name: GeForce GTX 980M, pci bus id: 0000:01:00.0, compute capability: 5.2)
Preprocessing hdf5 combined model...
Loading .h5 model into memory...
WARNING:tensorflow:From /home/longervision/.local/lib/python3.6/site-packages/tensorflow/python/ops/resource_variable_ops.py:1658: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
Generating multi-output encapsulated model...
Saving temp multi-output .h5 model...
Converting .h5 to web friendly format...
Deleting temp .h5 model...
Mission Complete!!!
➜ WebTensorspace ls model/convertedModel
group1-shard1of1.bin model.json

2.1.3 index.html

2.1.3.1 helloworld-empty

Let’s copy and paste TensorSpace’s Example helloworld-empty.html and do some trivial modification for 3D visualization, as follows:

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>AI Model Visualization By Tensorspace - 3D Interactive</title>
<meta name="author" content="Longer Vision / https://longervision.github.io/2020/04/17/AI/Visualization/model-visualization-tensorspace-3D-interactive/">

<script src="./lib/three.min.js"></script>
<script src="./lib/tween.cjs.js"></script>
<script src="./lib/tf.min.js"></script>
<script src="./lib/TrackballControls.js"></script>
<script src="./lib/tensorspace.min.js"></script>
<script src="./lib/jquery.min.js"></script>

<style>

html, body {
margin: 0;
padding: 0;
width: 100%;
height: 100%;
}

#container {
width: 100%;
height: 100%;
}

</style>

</head>

<body>

<div id="container"></div>

<script>

$(function() {

let modelContainer = document.getElementById( "container" );
let model = new TSP.models.Sequential( modelContainer );

model.add( new TSP.layers.GreyscaleInput( { shape: [ 28, 28 ] } ) );
model.add( new TSP.layers.Padding2d( { padding: [ 2, 2 ] } ) );
model.add( new TSP.layers.Conv2d( { kernelSize: 5, filters: 6, strides: 1 } ) );
model.add( new TSP.layers.Pooling2d( { poolSize: [ 2, 2 ], strides: [ 2, 2 ] } ) );
model.add( new TSP.layers.Conv2d( { kernelSize: 5, filters: 16, strides: 1 } ) );
model.add( new TSP.layers.Pooling2d( { poolSize: [ 2, 2 ], strides: [ 2, 2 ] } ) );
model.add( new TSP.layers.Dense( { units: 120 } ) );
model.add( new TSP.layers.Dense( { units: 84 } ) );
model.add( new TSP.layers.Output1d( {
units: 10,
outputs: [ "0", "1", "2", "3", "4", "5", "6", "7", "8", "9" ]
} ) );

model.init( function() {
console.log( "Hello World from TensorSpace!" );
} );

} );

</script>

</body>
</html>

First snow in 2020. Actually, it is ALSO the FIRST snow for the winter from 2019 to 2020.

First Snow 1 First Snow 2 First Snow 3
First Snow 1 First Snow 2 First Snow 3

Both my son and the Chinese New Year are coming. Let’s start the mode of celebrating. Today, I’m going to do the hotpot.

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Hotpot 1 Hotpot 2 Hotpot 3

It looks in 1-day time, everybody is doing the edge computing. Today, we’re going to have some fun of Google Coral.

1. Google Coral USB Accelerator

Image cited from Coral official website.

Google Coral Accelerator

To try out Google Coral USB Accelerator is comparitively simple. The ONLY thing to do is just to follow Google Doc - Get started with the USB Accelerator. Anyway, let’s test it out with the following commands.

Make sure we are able to list the device.

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➜  classification git:(master) ✗ lsusb
Bus 002 Device 003: ID 1a6e:089a Global Unichip Corp.
...

We then checkout Google Coral Edgue TPU and test the example classify_image.py.

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➜  edgetpu git:(master) pwd
/opt/google/edgetpu
➜ edgetpu git:(master) python3 examples/classify_image.py \
--model test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite \
--label test_data/inat_bird_labels.txt \
--image test_data/parrot.jpg
---------------------------
Ara macao (Scarlet Macaw)
Score : 0.6796875
---------------------------
Platycercus elegans (Crimson Rosella)
Score : 0.12109375

BTW, I’m going to discuss

sooner or later. Just keep an eye on my blog.

2. Google Coral Dev Board

In the following, we’re going to disscuss Google Coral Dev Board more. Image cited from Coral official website.

Google Coral Devboard

2.1 Mendel Installation

2.1.1 Mendel Linux Preparation

Google Corel Mendel Linux can be downloaded from https://coral.ai/software/. In our case, we are going to try Mendel Linux 4.0.

2.1.2 Connect Dev Board Via Micro-USB Serial Port

On the host, we should be able to see:

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➜  mendel-enterprise-day-13 lsusb
......
Bus 001 Device 020: ID 10c4:ea70 Cygnal Integrated Products, Inc. CP210x UART Bridge
......
➜ mendel-enterprise-day-13 dmesg | grep ttyUSB
[45021.091322] usb 1-8: cp210x converter now attached to ttyUSB0
[45021.092681] usb 1-8: cp210x converter now attached to ttyUSB1
➜ mendel-enterprise-day-13 ll /dev/ttyUSB*
crwxrwxrwx 1 root dialout 188, 0 Apr 6 21:33 /dev/ttyUSB0
crwxrwxrwx 1 root dialout 188, 1 Apr 6 21:25 /dev/ttyUSB1
➜ mendel-enterprise-day-13 screen /dev/ttyUSB0 115200
.....

Now what you see is a black screen. After having connected the Type C power cable, you should be able to see:

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......
[ 2871.285085] NOHZ: local_softirq_pending 08
[ 2871.376306] NOHZ: local_softirq_pending 08
[ 2872.117716] NOHZ: local_softirq_pending 08
[ 2874.283909] NOHZ: local_softirq_pending 08
[ 2875.286859] NOHZ: local_softirq_pending 08

U-Boot SPL 2017.03.3 (Nov 08 2019 - 22:29:30)
power_bd71837_init
pmic debug: name=BD71837
Board id: 6
check ddr4_pmu_train_imem code
check ddr4_pmu_train_imem code pass
check ddr4_pmu_train_dmem code
check ddr4_pmu_train_dmem code pass
Training PASS
Training PASS
check ddr4_pmu_train_imem code
check ddr4_pmu_train_imem code pass
check ddr4_pmu_train_dmem code
check ddr4_pmu_train_dmem code pass
Training PASS
Normal Boot
Trying to boot from MMC1
hdr read sector 300, count=1


U-Boot 2017.03.3 (Nov 08 2019 - 22:29:30 +0000)

CPU: Freescale i.MX8MQ rev2.0 1500 MHz (running at 1000 MHz)
CPU: Commercial temperature grade (0C to 95C) at 64C
Reset cause: POR
Model: Freescale i.MX8MQ Phanbell
DRAM: 1 GiB
Board id: 6
Baseboard id: 1
MMC: FSL_SDHC: 0, FSL_SDHC: 1
*** Warning - bad CRC, using default environment

In: serial
Out: serial
Err: serial

BuildInfo:
- ATF
- U-Boot 2017.03.3

flash target is MMC:0
Net:
Warning: ethernet@30be0000 using MAC address from ROM
eth0: ethernet@30be0000
Fastboot: Normal
Hit any key to stop autoboot: 0
u-boot=> [A

That is shown on the screen monitor of Google Coral Dev Board. We now need to input fastboot 0 on u-boot=> prompt. After having connected the Type C OTG cable, we should be able to see on the host:

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➜  mendel-enterprise-day-13 fastboot devices
101989d6f32efb39 fastboot

2.1.3 Flash Corel Dev Board

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➜  mendel-enterprise-day-13 ls
boot_arm64.img flash.sh partition-table-16gb.img partition-table-64gb.img partition-table-8gb.img README recovery.img rootfs_arm64.img u-boot.imx
➜ mendel-enterprise-day-13 bash flash.sh
Sending 'bootloader0' (991 KB) OKAY [ 0.055s]
Writing 'bootloader0' OKAY [ 0.190s]
Finished. Total time: 0.266s
Rebooting into bootloader OKAY [ 0.024s]
Finished. Total time: 0.125s
Sending 'gpt' (33 KB) OKAY [ 0.018s]
Writing 'gpt' OKAY [ 0.309s]
Finished. Total time: 0.346s
Rebooting into bootloader OKAY [ 0.022s]
Finished. Total time: 0.122s
Erasing 'misc' OKAY [ 0.069s]
Finished. Total time: 0.079s
Sending 'boot' (131072 KB) OKAY [ 5.321s]
Writing 'boot' OKAY [ 3.632s]
Finished. Total time: 8.972s
Sending sparse 'rootfs' 1/4 (368422 KB) OKAY [ 14.792s]
Writing 'rootfs' OKAY [ 36.191s]
Sending sparse 'rootfs' 2/4 (408501 KB) OKAY [ 16.646s]
Writing 'rootfs' OKAY [ 18.944s]
Sending sparse 'rootfs' 3/4 (389107 KB) OKAY [ 15.881s]
Writing 'rootfs' OKAY [ 37.021s]
Sending sparse 'rootfs' 4/4 (231325 KB) OKAY [ 9.482s]
Writing 'rootfs' OKAY [ 65.204s]
Finished. Total time: 214.205s
Rebooting OKAY [ 0.005s]
Finished. Total time: 0.105s
➜ mendel-enterprise-day-13

2.1.4 Boot Mendel

Screen Snapshot Reboot

After a while, you’ll see:

Screen Snapshot Login

Now, login with

  • username: mendel
  • password: mendel
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➜  ~ mdt devices
mocha-shrimp (192.168.100.2)

You will be able to see Google Coral Dev Board is NOW connected. If you don’t see the EXPECTED output mocha-shrimp (192.168.101.2), just plug out and plug in the Type C power cable again.

Unfortunately, mdt tool does NOT work properly.

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➜  mendel-enterprise-day-13 mdt shell
Waiting for a device...
Connecting to mocha-shrimp at 192.168.101.2
Key not present on mocha-shrimp -- pushing

It looks like you're trying to connect to a device that isn't connected
to your workstation via USB and doesn't have the SSH key this MDT generated.
To connect with `mdt shell` you will need to first connect to your device
ONLY via USB.

Cowardly refusing to attempt to push a key to a public machine.

This bug has been clarified on StackOverflow. By modifying file vim $HOME/.local/lib/python3.6/site-packages/mdt/sshclient.py line 86, from if not self.address.startswith('192.168.100'): to if not self.address.startswith('192.168.10'):, problem solved.

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➜  mendel-enterprise-day-13 mdt shell
Waiting for a device...
Connecting to mocha-shrimp at 192.168.101.2
Key not present on mocha-shrimp -- pushing
Linux mocha-shrimp 4.14.98-imx #1 SMP PREEMPT Fri Nov 8 23:28:21 UTC 2019 aarch64

The programs included with the Mendel GNU/Linux system are free software;
the exact distribution terms for each program are described in the
individual files in /usr/share/doc/*/copyright.

Mendel GNU/Linux comes with ABSOLUTELY NO WARRANTY, to the extent
permitted by applicable law.
Last login: Thu Feb 14 10:12:02 2019
mendel@mocha-shrimp:~$ ls
mendel@mocha-shrimp:~$ pwd
/home/mendel
mendel@mocha-shrimp:~$ uname -a
Linux mocha-shrimp 4.14.98-imx #1 SMP PREEMPT Fri Nov 8 23:28:21 UTC 2019 aarch64 GNU/Linux
mendel@mocha-shrimp:~$ lsb_release -a
No LSB modules are available.
Distributor ID: Mendel
Description: Mendel GNU/Linux 4 (Day)
Release: 10.0
Codename: day
mendel@mocha-shrimp:~$ ip -c address
1: lo: <LOOPBACK,UP,LOWER_UP> mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000
link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00
inet 127.0.0.1/8 scope host lo
valid_lft forever preferred_lft forever
inet6 ::1/128 scope host
valid_lft forever preferred_lft forever
2: eth0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc mq state DOWN group default qlen 1000
link/ether 7c:d9:5c:b1:fa:cc brd ff:ff:ff:ff:ff:ff
3: wlan0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc mq state DOWN group default qlen 3000
link/ether 7c:d9:5c:b1:fa:cd brd ff:ff:ff:ff:ff:ff
4: p2p0: <BROADCAST,MULTICAST> mtu 1500 qdisc noop state DOWN group default qlen 3000
link/ether 00:0a:f5:89:89:81 brd ff:ff:ff:ff:ff:ff
5: usb0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc pfifo_fast state DOWN group default qlen 1000
link/ether 02:22:78:0d:f6:df brd ff:ff:ff:ff:ff:ff
inet 192.168.100.2/24 brd 192.168.100.255 scope global noprefixroute usb0
valid_lft forever preferred_lft forever
inet6 fe80::cc6d:b3d4:f07e:eed1/64 scope link tentative noprefixroute
valid_lft forever preferred_lft forever
6: usb1: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc pfifo_fast state UP group default qlen 1000
link/ether 02:22:78:0d:f6:de brd ff:ff:ff:ff:ff:ff
inet 192.168.101.2/24 brd 192.168.101.255 scope global noprefixroute usb1
valid_lft forever preferred_lft forever
inet6 fe80::5bf4:c217:d9c9:859c/64 scope link noprefixroute
valid_lft forever preferred_lft forever
mendel@mocha-shrimp:~$

After activate the Internet by nmtui, we can NOW clearly see the wlan0 IP is automatically allocated.

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mendel@mocha-shrimp:~$ ip -c address
......
3: wlan0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc mq state UP group default qlen 3000
link/ether 7c:d9:5c:b1:fa:cd brd ff:ff:ff:ff:ff:ff
inet 192.168.1.110/24 brd 192.168.1.255 scope global dynamic noprefixroute wlan0
valid_lft 86367sec preferred_lft 86367sec
inet6 2001:569:7e6e:dc00:d1c4:697a:f60e:b5a4/64 scope global dynamic noprefixroute
valid_lft 7468sec preferred_lft 7168sec
inet6 fe80::e10b:9dc6:60c4:b91b/64 scope link noprefixroute
valid_lft forever preferred_lft forever
......

Of course, we can setup a static IP for this particular Google Coral Dev Board afterwards.

2.1.5 SSH into Mendel

In order to SSH into Mendel and connect remotely, we need to do Connect to a board’s shell on the host computer. You MUST pushkey before you can ssh into the board via the Internet IP instead of the virtual IP via USB, say 192.168.100.2 or 192.168.101.2.

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➜  ~ ssh -i  ~/.ssh/id_rsa_mendel.pub mendel@192.168.1.97
Connection closed by 192.168.1.97 port 22

However, for now, I’ve got NO idea why ssh NETVER works for Google Coral Dev Board any more.

From now on, a huge modification.

2.2 Flash from U-Boot on an SD card

If you get unlucky and you can't even boot your board into U-Boot, then you can recover the system by booting into U-Boot from an image on the SD card and then reflash the board from your Linux (cited from Google Coral Dev Board’s Official Doc). Now, fastboot devices from host is NOW back.

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➜  mendel-enterprise-day-13 fastboot devices
101989d6f32efb39 fastboot

The, we reflash Google Coral Dev Board.

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➜  mendel-enterprise-day-13 bash flash.sh 
Sending 'bootloader0' (991 KB) OKAY [ 0.056s]
Writing 'bootloader0' OKAY [ 0.190s]
Finished. Total time: 0.267s
Rebooting into bootloader OKAY [ 0.024s]
Finished. Total time: 0.125s
Sending 'gpt' (33 KB) OKAY [ 0.018s]
Writing 'gpt' OKAY [ 0.308s]
Finished. Total time: 0.420s
Rebooting into bootloader OKAY [ 0.022s]
Finished. Total time: 0.122s
Erasing 'misc' OKAY [ 0.069s]
Finished. Total time: 0.079s
Sending 'boot' (131072 KB) OKAY [ 5.331s]
Writing 'boot' OKAY [ 3.604s]
Finished. Total time: 8.954s
Sending sparse 'rootfs' 1/4 (368422 KB) OKAY [ 14.924s]
Writing 'rootfs' OKAY [ 35.731s]
Sending sparse 'rootfs' 2/4 (408501 KB) OKAY [ 16.079s]
Writing 'rootfs' OKAY [ 18.689s]
Sending sparse 'rootfs' 3/4 (389107 KB) OKAY [ 15.396s]
Writing 'rootfs' OKAY [ 36.536s]
Sending sparse 'rootfs' 4/4 (231325 KB) OKAY [ 9.290s]
Writing 'rootfs' OKAY [ 64.694s]
Finished. Total time: 212.891s
Rebooting OKAY [ 0.005s]
Finished. Total time: 0.105s

Now we are able to run mdt shell successfully.

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➜  mendel-enterprise-day-13 mdt shell
Waiting for a device...
Connecting to green-snail at 192.168.100.2
Key not present on green-snail -- pushing
Linux green-snail 4.14.98-imx #1 SMP PREEMPT Fri Nov 8 23:28:21 UTC 2019 aarch64

The programs included with the Mendel GNU/Linux system are free software;
the exact distribution terms for each program are described in the
individual files in /usr/share/doc/*/copyright.

Mendel GNU/Linux comes with ABSOLUTELY NO WARRANTY, to the extent
permitted by applicable law.
Last login: Mon Nov 11 18:19:48 2019

Run ssh-keygen and pushkey consequently:

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➜  .ssh ssh-keygen
Generating public/private rsa key pair.
Enter file in which to save the key (/home/longervision/.ssh/id_rsa): /home/longervision/.ssh/id_rsa_mendel
Enter passphrase (empty for no passphrase):
Enter same passphrase again:
Your identification has been saved in /home/longervision/.ssh/id_rsa_mendel.
Your public key has been saved in /home/longervision/.ssh/id_rsa_mendel.pub.
The key fingerprint is:
......
➜ .ssh mdt pushkey ~/.ssh/id_rsa_mendel.pub
Waiting for a device...
Connecting to green-snail at 192.168.100.2
Pushing /home/longervision/.ssh/id_rsa_mendel.pub
Key /home/longervision/.ssh/id_rsa_mendel.pub pushed.

Then, with mdt shell, run command nmtui to activate wlan0.

Let’s briefly summarize:

2.3 Demonstration

2.3.1 edgetpu_demo –device & edgetpu_demo –stream

Let’s ignore edgetpu_demo –device for I ALMOST NEVER work with a GUI mode. The demo video is on my youtube channel, please refer to:

On console, it just displays as:

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mendel@deft-orange:~$ edgetpu_demo --stream
Press 'q' to quit.
Press 'n' to switch between models.

(edgetpu_detect_server:9991): Gtk-WARNING **: 07:56:57.725: Locale not supported by C library.
Using the fallback 'C' locale.
INFO:edgetpuvision.streaming.server:Listening on ports tcp: 4665, web: 4664, annexb: 4666
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:37536
INFO:edgetpuvision.streaming.server:Number of active clients: 1
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:37538
INFO:edgetpuvision.streaming.server:[192.168.1.200:37536] Rx thread finished
INFO:edgetpuvision.streaming.server:[192.168.1.200:37536] Tx thread finished
INFO:edgetpuvision.streaming.server:Number of active clients: 2
INFO:edgetpuvision.streaming.server:[192.168.1.200:37536] Stopping...
INFO:edgetpuvision.streaming.server:[192.168.1.200:37536] Stopped.
INFO:edgetpuvision.streaming.server:Number of active clients: 1
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:37540
INFO:edgetpuvision.streaming.server:Number of active clients: 2
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:37542
INFO:edgetpuvision.streaming.server:Number of active clients: 3
INFO:edgetpuvision.streaming.server:[192.168.1.200:37538] Rx thread finished
INFO:edgetpuvision.streaming.server:[192.168.1.200:37540] Rx thread finished
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:37544
INFO:edgetpuvision.streaming.server:[192.168.1.200:37538] Tx thread finished
INFO:edgetpuvision.streaming.server:[192.168.1.200:37542] Rx thread finished
INFO:edgetpuvision.streaming.server:[192.168.1.200:37542] Tx thread finished
INFO:edgetpuvision.streaming.server:Number of active clients: 4
......

2.3.2 Classification

Refer to Install the TensorFlow Lite library.

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mendel@green-snail:~/.local$ pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_aarch64.whl
Collecting tflite-runtime==2.1.0.post1 from https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_aarch64.whl
Downloading https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_aarch64.whl (1.9MB)
100% |████████████████████████████████| 1.9MB 203kB/s
Requirement already satisfied: numpy>=1.12.1 in /usr/lib/python3/dist-packages (from tflite-runtime==2.1.0.post1) (1.16.2)
Installing collected packages: tflite-runtime
Successfully installed tflite-runtime-2.1.0.post1
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mendel@green-snail:~/Downloads/tflite/python/examples/classification$ python3 classify_image.py   --model models/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite   --labels models/inat_bird_labels.txt   --input images/parrot.jpg
----INFERENCE TIME----
Note: The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory.
13.5ms
3.5ms
2.7ms
3.0ms
3.0ms
-------RESULTS--------
Ara macao (Scarlet Macaw): 0.77734

2.3.3 Camera

2.3.3.1 Google Coral camera

The Google Coral camera can be detected as a video device:

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mendel@mocha-shrimp:~$ v4l2-ctl --list-formats-ext --device /dev/video0
ioctl: VIDIOC_ENUM_FMT
Type: Video Capture

[0]: 'YUYV' (YUYV 4:2:2)
Size: Discrete 640x480
Interval: Discrete 0.033s (30.000 fps)
Size: Discrete 720x480
Interval: Discrete 0.033s (30.000 fps)
Size: Discrete 1280x720
Interval: Discrete 0.033s (30.000 fps)
Size: Discrete 1920x1080
Interval: Discrete 0.067s (15.000 fps)
Interval: Discrete 0.033s (30.000 fps)
Size: Discrete 2592x1944
Interval: Discrete 0.067s (15.000 fps)
Size: Discrete 0x0

2.3.3.2 Face Detection Using Google TPU

My youtube real-time face detection video clearly shows Google TPU is seriously powerful.

On console, it displays:

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mendel@deft-orange:~$ edgetpu_detect_server \
> --model ${DEMO_FILES}/mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite

(edgetpu_detect_server:4081): Gtk-WARNING **: 10:40:45.436: Locale not supported by C library.
Using the fallback 'C' locale.
INFO:edgetpuvision.streaming.server:Listening on ports tcp: 4665, web: 4664, annexb: 4666
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:33950
INFO:edgetpuvision.streaming.server:[192.168.1.200:33950] Rx thread finished
INFO:edgetpuvision.streaming.server:[192.168.1.200:33950] Tx thread finished
INFO:edgetpuvision.streaming.server:Number of active clients: 1
INFO:edgetpuvision.streaming.server:[192.168.1.200:33950] Stopping...
INFO:edgetpuvision.streaming.server:[192.168.1.200:33950] Stopped.
INFO:edgetpuvision.streaming.server:Number of active clients: 0
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:33952
INFO:edgetpuvision.streaming.server:Number of active clients: 1
INFO:edgetpuvision.streaming.server:[192.168.1.200:33952] Rx thread finished
INFO:edgetpuvision.streaming.server:[192.168.1.200:33952] Tx thread finished
INFO:edgetpuvision.streaming.server:New web connection from 192.168.1.200:33954
INFO:edgetpuvision.streaming.server:Number of active clients: 2
INFO:edgetpuvision.streaming.server:[192.168.1.200:33952] Stopping...
INFO:edgetpuvision.streaming.server:[192.168.1.200:33954] Rx thread finished
INFO:edgetpuvision.streaming.server:[192.168.1.200:33952] Stopped.
INFO:edgetpuvision.streaming.server:Number of active clients: 1
......

2.3.4 Bugs

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mendel@green-snail:~$ edgetpu_demo --stream
Press 'q' to quit.
Press 'n' to switch between models.
Unable to init server: Could not connect: Connection refused

(edgetpu_detect_server:8391): Gtk-WARNING **: 20:18:07.433: Locale not supported by C library.
Using the fallback 'C' locale.
Unable to init server: Could not connect: Connection refused
Unable to init server: Could not connect: Connection refused

(edgetpu_detect_server:8391): Gtk-WARNING **: 20:18:07.473: cannot open display:
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mendel@green-snail:~/Downloads/edgetpu/test_data$ edgetpu_detect_server --model ./mobilenet_ssd_v2_face_quant_postprocess_edgetpu.tflite
Unable to init server: Could not connect: Connection refused

(edgetpu_detect_server:8382): Gtk-WARNING **: 20:16:43.553: Locale not supported by C library.
Using the fallback 'C' locale.
Unable to init server: Could not connect: Connection refused
Unable to init server: Could not connect: Connection refused
Unable to init server: Could not connect: Connection refused

(edgetpu_detect_server:8382): Gtk-WARNING **: 20:16:44.967: cannot open display: :0

To solve this problem, run the following command:

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mendel@green-snail:~$ sudo systemctl restart weston

Beautiful Sakura 1 Beautiful Sakura 2 Beautiful Sakura 3
Beautiful Sakura 1 Beautiful Sakura 2 Beautiful Sakura 3
Beautiful Sakura 4 Beautiful Sakura 5 Beautiful Sakura 6
Beautiful Sakura 4 Beautiful Sakura 5 Beautiful Sakura 6
Beautiful Sakura 7 Beautiful Sakura 8 The Willow
Beautiful Sakura 7 Beautiful Sakura 8 Beautiful Willow

Spent some time with the beautiful flowers.

😆

Okay, today, let’s briefly talk about PlotNeuralNet for 3D visualization of various AI model architectures. We just follow PlotNeuralNet and generate some results for fun.

1. pyexamples

1.2 test_simple

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➜  pyexamples git:(master) ✗ ../tikzmake.sh test_simple  

\documentclass[border=8pt, multi, tikz]{standalone}
\usepackage{import}
\subimport{../layers/}{init}
\usetikzlibrary{positioning}
\usetikzlibrary{3d} %for including external image


\def\ConvColor{rgb:yellow,5;red,2.5;white,5}
\def\ConvReluColor{rgb:yellow,5;red,5;white,5}
\def\PoolColor{rgb:red,1;black,0.3}
\def\UnpoolColor{rgb:blue,2;green,1;black,0.3}
\def\FcColor{rgb:blue,5;red,2.5;white,5}
\def\FcReluColor{rgb:blue,5;red,5;white,4}
\def\SoftmaxColor{rgb:magenta,5;black,7}


\newcommand{\copymidarrow}{\tikz \draw[-Stealth,line width=0.8mm,draw={rgb:blue,4;red,1;green,1;black,3}] (-0.3,0) -- ++(0.3,0);}

\begin{document}
\begin{tikzpicture}
\tikzstyle{connection}=[ultra thick,every node/.style={sloped,allow upside down},draw=\edgecolor,opacity=0.7]
\tikzstyle{copyconnection}=[ultra thick,every node/.style={sloped,allow upside down},draw={rgb:blue,4;red,1;green,1;black,3},opacity=0.7]


\pic[shift={(0,0,0)}] at (0,0,0)
{Box={
name=conv1,
caption= ,
xlabel={{64, }},
zlabel=512,
fill=\ConvColor,
height=64,
width=2,
depth=64
}
};


\pic[shift={ (0,0,0) }] at (conv1-east)
{Box={
name=pool1,
caption= ,
fill=\PoolColor,
opacity=0.5,
height=32,
width=1,
depth=32
}
};


\pic[shift={(1,0,0)}] at (pool1-east)
{Box={
name=conv2,
caption= ,
xlabel={{64, }},
zlabel=128,
fill=\ConvColor,
height=32,
width=2,
depth=32
}
};


\draw [connection] (pool1-east) -- node {\midarrow} (conv2-west);


\pic[shift={ (0,0,0) }] at (conv2-east)
{Box={
name=pool2,
caption= ,
fill=\PoolColor,
opacity=0.5,
height=28,
width=1,
depth=28
}
};


\pic[shift={(3,0,0)}] at (pool1-east)
{Box={
name=soft1,
caption=SOFT,
xlabel={{" ","dummy"}},
zlabel=10,
fill=\SoftmaxColor,
opacity=0.8,
height=3,
width=1.5,
depth=25
}
};


\draw [connection] (pool2-east) -- node {\midarrow} (soft1-west);


\end{tikzpicture}
\end{document}

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[][][][][]\OT1/cmr/bx/n/10 SOFT[]
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(see the transcript file for additional information)</usr/share/texlive/texmf-d
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rm: cannot remove '*.vscodeLog': No such file or directory
➜ pyexamples git:(master) ✗

The following is to show .pdf file is able to be displayed in hexo:

test_simple.png

1.2 unet

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➜  pyexamples git:(master) ✗ ../tikzmake.sh unet

\documentclass[border=8pt, multi, tikz]{standalone}
\usepackage{import}
\subimport{../layers/}{init}
\usetikzlibrary{positioning}
\usetikzlibrary{3d} %for including external image


\def\ConvColor{rgb:yellow,5;red,2.5;white,5}
\def\ConvReluColor{rgb:yellow,5;red,5;white,5}
\def\PoolColor{rgb:red,1;black,0.3}
\def\UnpoolColor{rgb:blue,2;green,1;black,0.3}
\def\FcColor{rgb:blue,5;red,2.5;white,5}
\def\FcReluColor{rgb:blue,5;red,5;white,4}
\def\SoftmaxColor{rgb:magenta,5;black,7}


\newcommand{\copymidarrow}{\tikz \draw[-Stealth,line width=0.8mm,draw={rgb:blue,4;red,1;green,1;black,3}] (-0.3,0) -- ++(0.3,0);}

\begin{document}
\begin{tikzpicture}
\tikzstyle{connection}=[ultra thick,every node/.style={sloped,allow upside down},draw=\edgecolor,opacity=0.7]
\tikzstyle{copyconnection}=[ultra thick,every node/.style={sloped,allow upside down},draw={rgb:blue,4;red,1;green,1;black,3},opacity=0.7]


\node[canvas is zy plane at x=0] (temp) at (-3,0,0) {\includegraphics[width=8cm,height=8cm]{../examples/fcn8s/cats.jpg}};


\pic[shift={ (0,0,0) }] at (0,0,0)
{RightBandedBox={
name=ccr_b1,
caption= ,
xlabel={{ 64, 64 }},
zlabel=500,
fill=\ConvColor,
bandfill=\ConvReluColor,
height=40,
width={ 2 , 2 },
depth=40
}
};


\pic[shift={ (0,0,0) }] at (ccr_b1-east)
{Box={
name=pool_b1,
caption= ,
fill=\PoolColor,
opacity=0.5,
height=32,
width=1,
depth=32
}
};


\pic[shift={ (1,0,0) }] at (pool_b1-east)
{RightBandedBox={
name=ccr_b2,
caption= ,
xlabel={{ 128, 128 }},
zlabel=256,
fill=\ConvColor,
bandfill=\ConvReluColor,
height=32,
width={ 3.5 , 3.5 },
depth=32
}
};


\pic[shift={ (0,0,0) }] at (ccr_b2-east)
{Box={
name=pool_b2,
caption= ,
fill=\PoolColor,
opacity=0.5,
height=24,
width=1,
depth=24
}
};


\draw [connection] (pool_b1-east) -- node {\midarrow} (ccr_b2-west);


\pic[shift={ (1,0,0) }] at (pool_b2-east)
{RightBandedBox={
name=ccr_b3,
caption= ,
xlabel={{ 256, 256 }},
zlabel=128,
fill=\ConvColor,
bandfill=\ConvReluColor,
height=25,
width={ 4.5 , 4.5 },
depth=25
}
};


\pic[shift={ (0,0,0) }] at (ccr_b3-east)
{Box={
name=pool_b3,
caption= ,
fill=\PoolColor,
opacity=0.5,
height=19,
width=1,
depth=19
}
};


\draw [connection] (pool_b2-east) -- node {\midarrow} (ccr_b3-west);


\pic[shift={ (1,0,0) }] at (pool_b3-east)
{RightBandedBox={
name=ccr_b4,
caption= ,
xlabel={{ 512, 512 }},
zlabel=64,
fill=\ConvColor,
bandfill=\ConvReluColor,
height=16,
width={ 5.5 , 5.5 },
depth=16
}
};


\pic[shift={ (0,0,0) }] at (ccr_b4-east)
{Box={
name=pool_b4,
caption= ,
fill=\PoolColor,
opacity=0.5,
height=12,
width=1,
depth=12
}
};


\draw [connection] (pool_b3-east) -- node {\midarrow} (ccr_b4-west);


\pic[shift={ (2,0,0) }] at (pool_b4-east)
{RightBandedBox={
name=ccr_b5,
caption=Bottleneck,
xlabel={{ 1024, 1024 }},
zlabel=32,
fill=\ConvColor,
bandfill=\ConvReluColor,
height=8,
width={ 8 , 8 },
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{Box={
name=end_b9,
caption= ,
xlabel={{64, }},
zlabel=512,
fill=\ConvColor,
height=40,
width=2.5,
depth=40
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➜ pyexamples git:(master) ✗

unet.png

Pretty neat, isn’t it?

2. Provided Examples

Let’s FIRST take a look how many LETEX files are there under the folder examples.

1
2
3
4
5
6
7
8
9
➜  PlotNeuralNet git:(master) ✗ cd examples 
➜ examples git:(master) ✗ find . -depth -name "*.tex"
./fcn32s/fcn32.tex
./fcn8s/fcn8.tex
./HED/HED.tex
./SoftmaxLoss/SoftmaxLoss.tex
./Unet/Unet.tex
./Unet_Ushape/Unet_ushape.tex
./VGG16/vgg16.tex

We then enter each subfolder and run command ../../tikzmake.sh example_name, for instance:

1
2
➜  examples git:(master) ✗ cd fcn32s
➜ fcn32s git:(master) ✗ ../../tikzmake.sh fcn32

It’s WEIRD that so far, each .tex file is removed after the .pdf file for AI model architecture has been generated. Anyway, let’s take a look at all generated files in .png format.

2.1 fcn32

fcn32.png

2.2 fcn8

fcn8.png

2.1 HED

HED.png

2.2 SoftmaxLoss

SoftmaxLoss.png

2.1 Unet

Unet.png

2.2 Unet_ushape

Unet_ushape.png

2.1 vgg16

vgg16.png