Rakesh Kumar

Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running? Root cause of this error message is only that DOCKER_HOST variable has not been exported to all the users. In my case it was working fine while I was running docker commands as root user but when I tried the same commands from Jenkins, didn't work. To understand this issue in detail let me create such environment and see troubleshooting steps. Docker Host - [root@server106 ~]# hostname server106.example.com Docker Client - [root@server109 ~]# hostname server109.example.com [root@server109 ~]# docker ps -a CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES [root@server109 ~]# docker ps -a Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running? Solution:- [root@server109 ~]# echo "export DOCKER_HOST=192.168.99.100:4243"

docker: Error response from daemon:  OCI runtime create create failed: container_linux.go:348: starting container process caused "exec: \"/usr/sbin/init\": stat /usr/sbin/init: no such file or directory": unknown. If you are getting above error message trying to launch a container. Simple fix of this error message is to set SELinux in permissive mode. [root@server109 docker-ws]# setenforce 0

Since last few weekend I am learning Docker. A great initiative from Linux word under IIEC Rise and guidance of Vimal Daga. At the completion of this training I have implemented container technology and created my own monitoring system(Moatis). Project steps:- Pull centos:7 image I created a volume for persistent storage  Launched a container Exposed it to public Login into newly created centos container Deployed Nagios Setup monitoring on few hosts Tested if each and everything is working fine or not. Please check below for all the commands which I have executed to complete this project. Commands I have executed on Container Host -    176  podman container ls   177  podman container ls -a   178  podman image ls   179  podman volume create mymoatis   180  podman volume inspect mymoatis   181  cd /var/lib/containers/storage/mymoatis/_data/   182  ls   183  podman volume rm mymoatis   184  podman volume ls   185  podman volume create moatis_sto

" Failed to get D-Bus connection: Operation not permitted " - systemctl command is not working in Docker container. If systemctl command is not working in your container and giving subjected error message then simple solution of this error is, create container with -- privileged option and also provide init file full path  /usr/sbin/init [root@server109 ~]# docker container run -dit --privileged --name systemctl_not_working_centos1 centos:7 /usr/sbin/init For detailed explanation and understanding I am writing more about it, please have look below. If we have a daemon based program(httpd, sshd, jenkins, docker etc.) running inside a container and we would like to start/stop or check status of daemon inside docker then it becomes difficult for us to perform such operations , because by default systemctl and service  commands don't work inside docker. Normally we run below commands to check services status in Linux systems. [root@server109 ~]# systemctl status

Dockerfle is a very important concept to create you own container image. However there are lots of images available on docker hub but these are standard images and creators of these images are not aware about our requirement and environment. In this situation we need our own images. There are two way to create your own container images. docker commit - We can create our own images using 'docker commit' command but only limited feature available in this approach. In below command centos  is my existing container name and webserver:v1 is my new image name and version. [root@server109 docker-ws]# docker pull centos [root@server109 docker-ws]# docker container commit centos webserver:v1 docker build - A powerful approach to create a container image, We can customize our image as more as customization required. This approach is also known as Dockerfile approach. As of now I have below images available in my environment but I need an image which has  net-tools, httpd, py

We have understood basics of CNN in detail in my  previous post  post now this is time to write our own code and start create our own CNN model. Please have a look on below images to understand high level CNN architecture. We shown in picture we need multiple layers in CNN so we will create multiple layers for different purpose. So let's start writing code - First layer - Convolution layer Second layer - pooling layer (For minimize the size) Third layer - Flatten layer (To convert data from 2D to 1D) Fourth layer - Dense layer (For neural network) from keras.layers import Convolution2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense model = Sequential() model.add(Convolution2D(filters=32,  kernel_size=(3,3),  activation='relu',  input_shape=(64,64,3)  ) ) model.summary() Model: "sequential_3" _________________________________________________________________ Layer (type)

  If you have knowledge of a subject, it is always great but if know how and where you can utilize it became your talent. Likewise if you have knowledge of some technologies and also know how to use them. In other words I would like to say, you know how to integrate these technologies? Just by doing this you can create your own product, you can become a creator. I have lots of examples of it, but one of them is OpenStack (Collection of lots of services like Glance, shift, cinder, nova, neutron, Gnocchi etc).   I am also writing this post to show you an interesting demo. In which I'll integrate multiple tools like Git, GitHub, Jenkins, Docker, Linux etc. and I will try to solve a use case. We have a web app developed by multiple developers will be first of all deploy in test environment, Then it will be checked by UAT team and if everything is fine and approved all the requirement from QAT team then automatically deployed in production environment. So lets start with the

  We have studied lots of things about traditional(old) ML and DL but if we would like to summarize our learning then we can say that ML learning is approach to find correct weight and  bias , but biggest difficulty to find correct  weight  and  bias is to find correct feature. Because weight and bias both are dependent of correct feature.   So finally we want a correct feature. Also I have written multiple posts on feature selection approach. But in short if data is small(small data means number of rows can be any number but number of columns are limited) we can use manual approach for feature selection and it can be done using traditional ML. But if we have Big Data(Big Data mean unlimited number of columns) we can't select feature manually and it is impossible. So for this we have new machine learning approach which is known as DL(Deep learning). In deep learning NN(Neural Network) help to select features automatically.   One of great example of Big Data is image/video/voic