Getting Started with NVIDIA Jetson Nano
Table of Contents
- Intent
- Hardware
- Software
- Preparing Your Jetson Nano
- Flashing SD card image
- Vefifying Docker Binaries
Intent
Everything and anything you want to know about NVIDIA Jetson Nano, Docker & K3s support
Hardware
- Jetson Nano
- A Camera Module
- A 5V 4Ampere Charger
- 64GB SD card
Software
- Jetson SD card image from https://developer.nvidia.com/embedded/downloads
- Etcher software installed on your system
Preparing Your Jetson Nano
1. Preparing Your Raspberry Pi Flashing Jetson SD Card Image
- Unzip the SD card image
- Insert SD card into your system.
- Bring up Etcher tool and select the target SD card to which you want to flash the image.
sudo lshw -C system
pico2
description: Computer
product: NVIDIA Jetson Nano Developer Kit
serial: 1422919082257
width: 64 bits
capabilities: smp cp15_barrier setend swp
CUDA Compiler and Libraries
ajeetraina@ajeetraina-desktop:~/meetup$ nvcc --version
-bash: nvcc: command not found
ajeetraina@ajeetraina-desktop:~/meetup$ export PATH=${PATH}:/usr/local/cuda/bin
ajeetraina@ajeetraina-desktop:~/meetup$ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64
ajeetraina@ajeetraina-desktop:~/meetup$ source ~/.bashrc
ajeetraina@ajeetraina-desktop:~/meetup$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_21:14:42_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
DeviceQuery
$ pwd
/usr/local/cuda/samples/1_Utilities/deviceQuery
sudo make
ajeetraina@ajeetraina-desktop:/usr/local/cuda/samples/1_Utilities/deviceQuery$ sudo make
/usr/local/cuda-10.2/bin/nvcc -ccbin g++ -I../../common/inc -m64 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_32,code=sm_32 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o deviceQuery.o -c deviceQuery.cpp
/usr/local/cuda-10.2/bin/nvcc -ccbin g++ -m64 -gencode arch=compute_30,code=sm_30 -gencode arch=compute_32,code=sm_32 -gencode arch=compute_53,code=sm_53 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_62,code=sm_62 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_72,code=sm_72 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_75,code=compute_75 -o deviceQuery deviceQuery.o
mkdir -p ../../bin/aarch64/linux/release
cp deviceQuery ../../bin/aarch64/linux/release
ajeetraina@ajeetraina-desktop:/usr/local/cuda/samples/1_Utilities/deviceQuery$ ls
Makefile NsightEclipse.xml deviceQuery deviceQuery.cpp deviceQuery.o readme.txt
ajeetraina@ajeetraina-desktop:/usr/local/cuda/samples/1_Utilities/deviceQuery$ ./deviceQuery
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA Tegra X1"
CUDA Driver Version / Runtime Version 10.2 / 10.2
CUDA Capability Major/Minor version number: 5.3
Total amount of global memory: 3956 MBytes (4148387840 bytes)
( 1) Multiprocessors, (128) CUDA Cores/MP: 128 CUDA Cores
GPU Max Clock rate: 922 MHz (0.92 GHz)
Memory Clock rate: 13 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 262144 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: Yes
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: No
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
2. Verifying if it is shipped with Docker Binaries
ajeetraina@ajeetraina-desktop:~$ sudo docker version
[sudo] password for ajeetraina:
Client:
Version: 19.03.6
API version: 1.40
Go version: go1.12.17
Git commit: 369ce74a3c
Built: Fri Feb 28 23:47:53 2020
OS/Arch: linux/arm64
Experimental: false
Server:
Engine:
Version: 19.03.6
API version: 1.40 (minimum version 1.12)
Go version: go1.12.17
Git commit: 369ce74a3c
Built: Wed Feb 19 01:06:16 2020
OS/Arch: linux/arm64
Experimental: false
containerd:
Version: 1.3.3-0ubuntu1~18.04.2
GitCommit:
runc:
Version: spec: 1.0.1-dev
GitCommit:
docker-init:
Version: 0.18.0
GitCommit:
3. Checking Docker runtime
Starting with JetPack 4.2, NVIDIA has introduced a container runtime with Docker integration. This custom runtime enables Docker containers to access the underlying GPUs available in the Jetson family.
pico@pico1:/tmp/docker-build$ sudo nvidia-docker version
NVIDIA Docker: 2.0.3
Client:
Version: 19.03.6
API version: 1.40
Go version: go1.12.17
Git commit: 369ce74a3c
Built: Fri Feb 28 23:47:53 2020
OS/Arch: linux/arm64
Experimental: false
Server:
Engine:
Version: 19.03.6
API version: 1.40 (minimum version 1.12)
Go version: go1.12.17
Git commit: 369ce74a3c
Built: Wed Feb 19 01:06:16 2020
OS/Arch: linux/arm64
Experimental: false
containerd:
Version: 1.3.3-0ubuntu1~18.04.2
GitCommit:
runc:
Version: spec: 1.0.1-dev
GitCommit:
docker-init:
Version: 0.18.0
GitCommit:
Installing Docker Compose on NVIDIA Jetson Nano
Jetson Nano doesnt come with Docker Compose installed by default. You will need to install it first:
export DOCKER_COMPOSE_VERSION=1.27.4
sudo apt-get install libhdf5-dev
sudo apt-get install libssl-dev
sudo pip3 install docker-compose=="${DOCKER_COMPOSE_VERSION}"
apt install python3
apt install python3-pip
pip install docker-compose
docker-compose version
docker-compose version 1.26.2, build unknown
docker-py version: 4.3.1
CPython version: 3.6.9
OpenSSL version: OpenSSL 1.1.1 11 Sep 2018
Next, add default runtime for NVIDIA:
Edit /etc/docker/daemon.json
{
"runtimes": {
"nvidia": {
"path": "/usr/bin/nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia",
"node-generic-resources": [ "NVIDIA-GPU=0" ]
}
Restart the Docker Daemon
systemctl restart docker
Identify the Jetson board
pico@pico1:~$ git clone https://github.com/jetsonhacks/jetsonUtilities
Cloning into 'jetsonUtilities'...
remote: Enumerating objects: 123, done.
remote: Counting objects: 100% (39/39), done.
remote: Compressing objects: 100% (30/30), done.
remote: Total 123 (delta 15), reused 23 (delta 8), pack-reused 84
Receiving objects: 100% (123/123), 32.87 KiB | 5.48 MiB/s, done.
Resolving deltas: 100% (49/49), done.
pico@pico1:~$ cd jetson
-bash: cd: jetson: No such file or directory
pico@pico1:~$ cd jetsonUtilities/
pico@pico1:~/jetsonUtilities$ ls
LICENSE README.md jetsonInfo.py scripts
pico@pico1:~/jetsonUtilities$ python3 jetsonInfo.py
NVIDIA Jetson Nano (Developer Kit Version)
L4T 32.4.4 [ JetPack 4.4.1 ]
Ubuntu 18.04.5 LTS
Kernel Version: 4.9.140-tegra
CUDA 10.2.89
CUDA Architecture: 5.3
OpenCV version: 4.1.1
OpenCV Cuda: NO
CUDNN: 8.0.0.180
TensorRT: 7.1.3.0
Vision Works: 1.6.0.501
VPI: 4.4.1-b50
Vulcan: 1.2.70
Install the latest version of CUDA
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/sbsa/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda-repo-ubuntu1804-11-3-local_11.3.1-465.19.01-1_arm64.deb
sudo dpkg -i cuda-repo-ubuntu1804-11-3-local_11.3.1-465.19.01-1_arm64.deb
sudo apt-key add /var/cuda-repo-ubuntu1804-11-3-local/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda
Verify Docker runtime
docker info | grep runtime
Runtimes: nvidia runc io.containerd.runc.v2 io.containerd.runtime.v1.linux
Testing GPU Support
We’ll use the deviceQuery NVIDIA test application (included in L4T) to check that we can access the GPU in the cluster. First, we’ll create a Docker image with the appropriate software, run it directly as Docker, then run it using containerd ctr and finally on the Kubernetes cluster itself.
Running deviceQuery on Docker with GPU support
Create a directory
mkdir test
cd test
Copy the sample files
Copy the demos where deviceQuery is located to the working directory where the Docker image will be created:
cp -R /usr/local/cuda/samples .
Create a Dockerfile
FROM nvcr.io/nvidia/l4t-base:r32.5.0
RUN apt-get update && apt-get install -y --no-install-recommends make g++
COPY ./samples /tmp/samples
WORKDIR /tmp/samples/1_Utilities/deviceQuery
RUN make clean && make
CMD ["./deviceQuery"]
sudo docker build -t ajeetraina/jetson_devicequery . -f Dockerfile
pico@pico2:~/test$ sudo docker run --rm --runtime nvidia ajeetraina/jetson_devicequery:latest
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA Tegra X1"
CUDA Driver Version / Runtime Version 10.2 / 10.2
CUDA Capability Major/Minor version number: 5.3
Total amount of global memory: 3963 MBytes (4155383808 bytes)
( 1) Multiprocessors, (128) CUDA Cores/MP: 128 CUDA Cores
GPU Max Clock rate: 922 MHz (0.92 GHz)
Memory Clock rate: 13 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 262144 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: Yes
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: No
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
Test 2: Running deviceQuery on containerd with GPU support
Since K3s uses containerd as its runtime by default, we will use the ctr command line to test and deploy the deviceQuery image we pushed on containerd with this script:
#!/bin/bash
IMAGE=ajeetraina/jetson_devicequery:latest
export KUBECONFIG=/etc/rancher/k3s/k3s.yaml
ctr i pull docker.io/${IMAGE}
ctr run --rm --gpus 0 --tty docker.io/${IMAGE} deviceQuery
Execute the script
sudo sh usectr.sh
sudo sh usectr.sh
docker.io/ajeetraina/jetson_devicequery:latest: resolved |++++++++++++++++++++++++++++++++++++++|
manifest-sha256:dfeaad4046f78871d3852e5d5fb8fa848038c57c34c6554c6c97a00ba120d550: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:4438ebff930fb27930d802553e13457783ca8a597e917c030aea07f8ff6645c0: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:b1cdeb9e69c95684d703cf96688ed2b333a235d5b33f0843663ff15f62576bd4: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:bf60857fb4964a3e3ce57a900bbe47cd1683587d6c89ecbce4af63f98df600aa: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:0aac5305d11a81f47ed76d9663a8d80d2963b61c643acfce0515f0be56f5e301: done |++++++++++++++++++++++++++++++++++++++|
config-sha256:37987db6d6570035e25e713f41e665a6d471d25056bb56b4310ed1cb1d79a100: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:f0f57d03cad8f8d69b1addf90907b031ccb253b5a9fc5a11db83c51aa311cbfb: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:08c23323368d4fde5347276d543c500e1ff9b712024ca3f85172018e9440d8b0: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:04da93b342eb651d6b94c74a934a3290697573a907fa0a06067b538095601745: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:f84ceb6e8887e9b3b454813459ee97c2b9730869dbd37d4cca4051958b7a5a36: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:93752947af53e2a3225e145b359b956df36e20521b5dde0fe6d3fb92fd2a9538: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:b235194751dee33624fc154603f7e25ecdfbb02538fb7d55fa796df9afa95fee: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:905b1329c1d473c79650e33b882d980b3522fb72e58ecd3456c4fb3c4039fe92: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:8931d5ba88b488c949f77f990e8f9198b153ceb71afd0369eac9c39beb38f2d6: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:cfb2938be99fb944fe31165bdf44532a5536865ce53b12eb7758d1e2a51ad33e: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:606a67bb8db9a1111022bdc6406442e11c1a66653136c5c777114bf67b61038a: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:2f37138d1c8ac71d9314a0f8996ba69579bbc6ee6a57440557bc7eef486ed292: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:9ce7ce1da17c2b8149573d1d73132f61a73083f0cd498eeb7a0da404fd77db14: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:a36863a728ec9221c83c745f40511946dfd63beca0f10c9afcc774ef7a98e420: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:86dd6e5994e2c15f2783d8d543327479ccee7f3b20023dd962fdb9a211071e16: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:f5299db1221c515de91f59d84b79f2f839f9c94a5d0cc7fad04134e23ec9b88a: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:15a5811e1a7bf377cbac066b04e0b36b4c1a41ca63eb3c67c17b734577f6beea: done |++++++++++++++++++++++++++++++++++++++|
layer-sha256:cb893097de39451407d7167b312ec56eaea80baa041877af8239dbe833fa044b: done |++++++++++++++++++++++++++++++++++++++|
elapsed: 81.4s total: 305.5 (3.8 MiB/s)
unpacking linux/arm64/v8 sha256:dfeaad4046f78871d3852e5d5fb8fa848038c57c34c6554c6c97a00ba120d550...
done
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "NVIDIA Tegra X1"
CUDA Driver Version / Runtime Version 10.2 / 10.2
CUDA Capability Major/Minor version number: 5.3
Total amount of global memory: 3963 MBytes (4155383808 bytes)
( 1) Multiprocessors, (128) CUDA Cores/MP: 128 CUDA Cores
GPU Max Clock rate: 922 MHz (0.92 GHz)
Memory Clock rate: 13 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 262144 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
Total amount of constant memory: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total number of registers available per block: 32768
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 1 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: Yes
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: No
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.2, CUDA Runtime Version = 10.2, NumDevs = 1
Result = PASS
Test 3: Running deviceQuery on the K3s cluster
pico@pico2:~/test$ cat pod_deviceQuery.yaml
apiVersion: v1
kind: Pod
metadata:
name: devicequery
spec:
containers:
- name: nvidia
image: ajeetraina/jetson_devicequery:latest
command: [ "./deviceQuery" ]
pico@pico2:~/test$
sudo KUBECONFIG=/etc/rancher/k3s/k3s.yaml kubectl apply -f ./pod_deviceQuery.yaml
pod/devicequery created
pico@pico2:~/test$ sudo KUBECONFIG=/etc/rancher/k3s/k3s.yaml kubectl describe pod devicequery
Name: devicequery
Namespace: default
Priority: 0
Node: pico4/192.168.1.163
Start Time: Sun, 13 Jun 2021 09:16:44 -0700
Labels: <none>
Annotations: <none>
Status: Pending
IP:
IPs: <none>
Containers:
nvidia:
Container ID:
Image: ajeetraina/jetson_devicequery:latest
Image ID:
Port: <none>
Host Port: <none>
Command:
./deviceQuery
State: Waiting
Reason: ContainerCreating
Ready: False
Restart Count: 0
Environment: <none>
Mounts:
/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-mcrmv (ro)
Conditions:
Type Status
Initialized True
Ready False
ContainersReady False
PodScheduled True
Volumes:
kube-api-access-mcrmv:
Type: Projected (a volume that contains injected data from multiple sources)
TokenExpirationSeconds: 3607
ConfigMapName: kube-root-ca.crt
ConfigMapOptional: <nil>
DownwardAPI: true
QoS Class: BestEffort
Node-Selectors: <none>
Tolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 78s default-scheduler Successfully assigned default/devicequery to pico4
Normal Pulling 77s kubelet Pulling image "ajeetraina/jetson_devicequery:latest"
pico@pico2:~/test$
cat pod_deviceQuery_jetson4.yaml
apiVersion: v1
kind: Pod
metadata:
name: devicequery
spec:
nodeName: pico4
containers:
- name: nvidia
image: ajeetraina/jetson_devicequery:latest
command: [ "./deviceQuery" ]
pico@pico2:~/test$
pico@pico2:~/test$ sudo KUBECONFIG=/etc/rancher/k3s/k3s.yaml kubectl describe pod devicequery
Name: devicequery
Namespace: default
Priority: 0
Node: pico4/192.168.1.163
Start Time: Sun, 13 Jun 2021 09:16:44 -0700
Labels: <none>
Annotations: <none>
Status: Running
IP: 10.42.1.3
IPs:
IP: 10.42.1.3
Containers:
nvidia:
Container ID: containerd://fd502d6bfa55e2f80b2d50bc262e6d6543fd8d09e9708bb78ecec0b2e09621c3
Image: ajeetraina/jetson_devicequery:latest
Image ID: docker.io/ajeetraina/jetson_devicequery@sha256:dfeaad4046f78871d3852e5d5fb8fa848038c57c34c6554c6c97a00ba120d550
Port: <none>
Host Port: <none>
Command:
./deviceQuery
State: Waiting
Reason: CrashLoopBackOff
Last State: Terminated
Reason: Error
Exit Code: 1
Started: Sun, 13 Jun 2021 09:21:50 -0700
Finished: Sun, 13 Jun 2021 09:21:50 -0700
Ready: False
Restart Count: 5
Environment: <none>
Mounts:
/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-mcrmv (ro)
Conditions:
Type Status
Initialized True
Ready False
ContainersReady False
PodScheduled True
Volumes:
kube-api-access-mcrmv:
Type: Projected (a volume that contains injected data from multiple sources)
TokenExpirationSeconds: 3607
ConfigMapName: kube-root-ca.crt
ConfigMapOptional: <nil>
DownwardAPI: true
QoS Class: BestEffort
Node-Selectors: <none>
Tolerations: node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal Scheduled 7m51s default-scheduler Successfully assigned default/devicequery to pico4
Normal Pulled 5m45s kubelet Successfully pulled image "ajeetraina/jetson_devicequery:latest" in 2m5.699757621s
Normal Pulled 5m43s kubelet Successfully pulled image "ajeetraina/jetson_devicequery:latest" in 1.000839703s
Normal Pulled 5m29s kubelet Successfully pulled image "ajeetraina/jetson_devicequery:latest" in 967.072951ms
Normal Pulled 4m59s kubelet Successfully pulled image "ajeetraina/jetson_devicequery:latest" in 1.025604394s
Normal Created 4m59s (x4 over 5m45s) kubelet Created container nvidia
Normal Started 4m59s (x4 over 5m45s) kubelet Started container nvidia
Warning BackOff 4m20s (x8 over 5m42s) kubelet Back-off restarting failed container
Normal Pulling 2m47s (x6 over 7m51s) kubelet Pulling image "ajeetraina/jetson_devicequery:latest"
pico@pico2:~/test$ sudo KUBECONFIG=/etc/rancher/k3s/k3s.yaml kubectl apply -f ./pod_deviceQuery_jetson4.yaml
pod/devicequery configured