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b/portal/docs/2_Installation/docker-desktop-for-linux.md @@ -0,0 +1,15 @@ +# To install Docker Desktop for Linux on Ubuntu, you can follow these steps: + +1. Download the .deb package from the official Docker website. + +2. Install the package using the command: + +```sudo dpkg -i .deb``` + +3. Start the Docker Desktop service: + +```sudo systemctl start docker``` + +4. Verify that Docker is running by checking its status: + +```sudo systemctl status docker``` diff --git a/portal/docs/2_Installation/docker-desktop-for-mac.md b/portal/docs/2_Installation/docker-desktop-for-mac.md new file mode 100644 index 000000000..e3e57e061 --- /dev/null +++ b/portal/docs/2_Installation/docker-desktop-for-mac.md @@ -0,0 +1,25 @@ +# Installing Docker Desktop for Mac + +Docker Desktop is free for small businesses (fewer than 250 employees AND less than $10 million in annual revenue), personal use, education, and non-commercial open-source projects. Otherwise, it requires a paid subscription for professional use. Paid subscriptions are also required for government entities. When downloading and installing Docker Desktop, you are asked to agree to the Docker Subscription Service Agreement + + +## Download Docker Desktop for Mac + +- [Apple Silicon](https://desktop.docker.com/mac/main/arm64/Docker.dmg?utm_source=docker&utm_medium=webreferral&utm_campaign=dd-smartbutton&utm_location=module) +- [Intel Chip](https://desktop.docker.com/mac/main/amd64/Docker.dmg?utm_source=docker&utm_medium=webreferral&utm_campaign=dd-smartbutton&utm_location=module) + +## Install interactively + +1. Double-click `Docker.dmg` to open the installer, then drag the Docker icon to + the Applications folder. + + +2. Double-click `Docker.app` in the **Applications** folder to start Docker. + +3. The Docker menu (whale menu) displays the Docker Subscription Service Agreement window. + + + +4. Select **Accept** to continue. Docker Desktop starts after you accept the terms. + +![docker desktop](image/docker%20desktop.png) \ No newline at end of file diff --git a/portal/docs/2_Installation/docker-desktop-for-windows.md b/portal/docs/2_Installation/docker-desktop-for-windows.md new file mode 100644 index 000000000..e69de29bb diff --git a/portal/docs/2_Installation/docker-on-jetson-nano.md b/portal/docs/2_Installation/docker-on-jetson-nano.md new file mode 100644 index 000000000..ca592ff5a --- /dev/null +++ b/portal/docs/2_Installation/docker-on-jetson-nano.md @@ -0,0 +1,661 @@ +# Getting Started with NVIDIA Jetson Nano + +![image](https://user-images.githubusercontent.com/34368930/212855112-8895c2f4-6aba-4724-9185-b866e369de95.png) + + + +## Table of Contents + +1. [Intent](#Intent) +2. [Hardware](#hardware) +3. [Software](#Software) +4. [Preparing Your Jetson Nano](#preparing-your-jetson-nano) + - [Flashing SD card image](#1-flashing-sd-card-image) + - [Vefifying Docker Binaries](#2-verifying-if-it-is-shipped-with-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. + +![image](https://user-images.githubusercontent.com/34368930/212854917-8ead3f07-03f4-4189-8eb0-dbbcaed4ee7f.png) + + +``` +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: +Annotations: +Status: Pending +IP: +IPs: +Containers: + nvidia: + Container ID: + Image: ajeetraina/jetson_devicequery:latest + Image ID: + Port: + Host Port: + Command: + ./deviceQuery + State: Waiting + Reason: ContainerCreating + Ready: False + Restart Count: 0 + Environment: + 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: + DownwardAPI: true +QoS Class: BestEffort +Node-Selectors: +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: +Annotations: +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: + Host Port: + 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: + 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: + DownwardAPI: true +QoS Class: BestEffort +Node-Selectors: +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 +``` + diff --git a/portal/docs/2_Installation/docker-on-raspberrypi.md b/portal/docs/2_Installation/docker-on-raspberrypi.md new file mode 100644 index 000000000..e69de29bb diff --git a/portal/docs/2_Installation/image/docker desktop.png b/portal/docs/2_Installation/image/docker desktop.png new file mode 100644 index 0000000000000000000000000000000000000000..812727a49c9fc358b7b00b8156d3c6334bbb9442 GIT binary patch literal 416317 zcmZ^~1z1#Hw?9s&G)Oa)gd(ZZjC4wfbV*Btw8YRz2uLePh=53kGz{GWiZs%lLk}=> z&L6+;d+&Y!_qo5B=R9Z6-utu9+54=s)?VwgCPrIRnS_Xe2m=FyL{&xMIR*yqCI$xf 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z=ouZ76^-sAPLo>ZnV^eq*=7tGbC(3Ow#I1VSY^5ZXarzy?#56uY_p^j75QMb^4B!T zfD=!*aS3*1EY)j z?SJ|r6}oUn56u0R3b!Y!8o2=EdQ9>|QNsu`@!JDga#nS!Z|eCC-dX`S8wPGOmwgbw zyF7H=R1-0p8}P62Cx2n~lHJap6RE8;Ysz}zbL!z*yFv~o&xUYVwuGzNhqmQsvC~k= zFm8myB?pV@eu%}r7YNnHBPFaE0fFYg_9A5JJg?&?3Z`T2WJ7yPx? z*-D*|HAN_l)%v>;s2vTB?ti=cYiq1FT3}trTIFkR4SE&3S3T_Wuk(!AmIO=4CGC55 zcZHmOpUqzE`&w1dII0t-XJbklvo4E5;Grn{GR&q)T4USq}QJJKoXI7QS2u|Hj;_ zrzU^g)JRjk#RWCAMB})YE!-bnu_Wd0UejqpNZM2R|w05dZ)H literal 0 HcmV?d00001 diff --git a/portal/docs/3_Docker-For-Beginners/1_Getting-Started-with-Docker-Hub/docker-hub.md b/portal/docs/3_Docker-For-Beginners/1_Getting-Started-with-Docker-Hub/docker-hub.md new file mode 100644 index 000000000..1ba7e62f6 --- /dev/null +++ b/portal/docs/3_Docker-For-Beginners/1_Getting-Started-with-Docker-Hub/docker-hub.md @@ -0,0 +1,21 @@ +# How to create Dockerhub Account + +* Open [https://hub.docker.com](https://dockr.ly/3Q0iiPA) and click on "Sign Up" for DockerHub + +![My image](https://raw.githubusercontent.com/collabnix/dockerlabs/master/workshop/docker/dockerhub1.png) + +* Enter your username as DockerID and provide your email address( I would suggest you to provide your Gmail ID) + +![My image](https://raw.githubusercontent.com/collabnix/dockerlabs/master/workshop/docker/dockerhub2.png) + +* Example + +I have added ajeetraina as my userID as shown below. Please note that we will require this userID at the later point of time during the workshop. Hence, do keep it handy. + +![My image](https://raw.githubusercontent.com/collabnix/dockerlabs/master/workshop/docker/dockerhub3.png) + +* That's it. Head over to your Email account to validate this account. + + +![My image](https://raw.githubusercontent.com/collabnix/dockerlabs/master/workshop/docker/dockerhub4.png) + diff --git a/portal/docs/3_Docker-For-Beginners/2_Getting-Started-With-Docker-Image/building-first-alpine.md b/portal/docs/3_Docker-For-Beginners/2_Getting-Started-With-Docker-Image/building-first-alpine.md new file mode 100644 index 000000000..0bdb8ecc0 --- /dev/null +++ b/portal/docs/3_Docker-For-Beginners/2_Getting-Started-With-Docker-Image/building-first-alpine.md @@ -0,0 +1,84 @@ +# How to build Your First Alpine Docker Image and Push it to DockerHub + +## Tested Infrastructure + +| **Platform** | **Number of Instance** | **Reading Time** | +| -------------------- | ---------------------- | ---------------- | +| **Play with Docker** | **1** | **5 min** | + +## Pre-requisite + +Create an account with [DockerHub](https://hub.docker.com/) + +Under this tutorial we will see how to build our own first alpine based Docker Image. + +`docker run -dit alpine sh` + +![alpine run](image/first-alpine-image/run_alpine.png) + +- -d: This option tells Docker to run the container in detached mode, which means the container will run in the background and the command prompt will be returned immediately. + +- -i: This option tells Docker to keep the standard input (STDIN) open, even if not attached. This allows you to interact with the container, for example by running commands inside it. + +- -t: This option tells Docker to allocate a pseudo-TTY, which is a terminal in the container. This allows you to run interactive commands inside the container, for example by starting a shell. + +![docker ps](image/first-alpine-image/docker_ps.png) + +![docker ps](image/first-alpine-image/attach.png) + +# Updating APK Packages + +`apk update` + +![apk update](image/first-alpine-image/apk_update.png) + +`apk add git` + +![apk update](image/first-alpine-image/add_git.png) + +# Now lets come out of it by Ctrl+P+Q and commit the changes + +`docker commit -m "Added GIT" 620 ajeetraina/alpine-git` + +![commit](image/first-alpine-image/commit.png) + +`docker images` + +![images](image/first-alpine-image/images.png) + +There you see a new image just created. + +# Time to tag the image + +`docker tag --help` + +![images](image/first-alpine-image/tag_help.png) + +`docker tag ajeetraina/alpine-git:latest ajeetraina/alpine-git:1.0` + +![images](image/first-alpine-image/tag.png) + +# Pushing it to DockerHub + +``` +$ docker login +Login with your Docker ID to push and pull images from Docker Hub. If you don't have a Docker ID, head over to https://hub.docker +.com to create one. +Username: ajeetraina +Password: +WARNING! Your password will be stored unencrypted in /root/.docker/config.json. +Configure a credential helper to remove this warning. See +https://docs.docker.com/engine/reference/commandline/login/#credentials-store + +Login Succeeded +[node4] (local) root@192.168.0.20 ~ +``` + +``` +$ docker push ajeetraina/alpine-git:1.0 +The push refers to repository [docker.io/ajeetraina/alpine-git] +3846235f8c17: Pushed +bcf2f368fe23: Mounted from library/alpine +1.0: digest: sha256:85d50f702e930db9e5b958387e667b7e26923f4de340534085cea184adb8411e size: 740 +[node4] (local) root@192.168.0.20 ~ +``` diff --git a/portal/docs/3_Docker-For-Beginners/2_Getting-Started-With-Docker-Image/hello-world.md b/portal/docs/3_Docker-For-Beginners/2_Getting-Started-With-Docker-Image/hello-world.md new file mode 100644 index 000000000..c85859542 --- /dev/null +++ b/portal/docs/3_Docker-For-Beginners/2_Getting-Started-With-Docker-Image/hello-world.md @@ -0,0 +1,50 @@ +# Demonstrating Hello World Example + +## Tested Infrastructure + +| Platform | Tested on | Reading Time | +| :------------: | :-------: | :----------: | +| Docker Desktop | MacOS | 5 min | + +## Pre-requisite + +- Create an account with [DockerHub](https://hub.docker.com) + +## Running Hello World Example + +``` +$ docker run hello-world + +``` + +![](image/hello-world/hello_world.png) + +## Explanation + +This image is a prime example of using the scratch image effectively. See hello.c in https://github.com/docker-library/hello-world for the source code of the hello binary included in this image. + +So what’s happened here? We’ve called the docker run command, which is responsible for launching containers. + +The argument hello-world is the name of the image someone created on dockerhub for us. It will first search for "hello-world" image locally and then search in Dockerhub. + +Once the image has been downloaded, Docker turns the image into a running container and executes it. + +## Did you Know? + +- The Hello World Docker Image is only 1.84 KB size. + +![](image/hello-world/docker_images.png) + +- While running `docker ps` command, it doesn't display any running container. 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