python-docker | Writing Dockerfiles for Python Web Applications | Continuous Deployment library

I am currently following docker's doc on how to build an image using python : My "bug" could therefore be reproduced by following the doc. I have checked & triple-checked my actions, and everything is exactly as the doc shows. Also, i have looked at quite a few similar posts here, but none helped me resolve my problem. For those that do not wish to look at the link, this is what i did: The commands used :

I am trying to set an environment variable for a container that is readable from my python script.

The python script is run via a containerized cron job. When running the script directly in the container, without the cronjob, I am able to read my environment variables.

My Dockerfile is

I am trying to build a Docker Img for a python program(It's a telegram Bot from FreeCodeCamp), Now the code runs perfectly but when I try to build this Dockerfile

Use-Case:

• when we first connect to a remote host/s, the remote host will already have some python version installed - the auto-discovery feature will find it
• now we install ansible-docker on the remote host
• from this time on: the ansible-docker docs suggest to use ansible_python_interpreter=/usr/bin/env python-docker

We connect to the same host/s again, but now we must use the /usr/bin/env python-docker python interpreter

What is the best way to do this?

Currently we set ansible_python_interpreter on the playbook level of Playbook 2:

I'm hoping to get my pip install instructions inside my docker builds as fast as possible.

I've read many posts explaining how adding your requirements.txt before the rest of the app helps you take advantage of Docker's own image cache if your requirements.txt hasn't changed. But this is no help at all when dependencies do change, even slightly.

The next step would be if we could use a consistent pip cache directory. By default, pip will cache downloaded packages in ~/.cache/pip (on Linux), and so if you're ever installing the same version of a module that has been installed before anywhere on the system, it shouldn't need to go and download it again, but instead simply use the cached version. If we could leverage a shared cache directory for docker builds, this could help speed up dependency installs a lot.

However, there doesn't appear to be any simple way to mount a volume while running docker build. The build environment seems to be basically impenetrable. I found one article suggesting a genius but complex method of running an rsync server on the host and then, with a hack inside the build to get the host IP, rsyncing the pip cache in from the host. But I'm not relishing the idea of running an rsync server in Jenkins (which isn't the most secure platform at the best of times).

Does anyone know if there's any other way to achieve a shared cache volume more simply?

in my Kubuntu 18.04 I installed docker-ce and running LAMP instance suffer error that python not found:

I am trying to install tensorflow in docker image for my application.

I have 3 files in the folder which i am using to build image.Dockerfile, index.py and requirements.txt

Contents of these files are

Dockerfile

I'm having the issue needing to use docker compose in python (for using the docker_service functionality in Ansible), but its not possible to install using pip because of a network policy of the company (the VM has no network access only acces to a RPM). I although can use a yum repository that contains docker compose.

What I tried is to install "docker compose" (version 1.18.0) using yum. Although python is not recognizing docker compose and suggest me to use pip: "Unable to load docker-compose. Try pip install docker-compose

Since in most cases I can solve this issue by installing this using yum install python-, I already looked the web for a package called python-docker-compose but no result :(

minimalistic ansible script for test:

I am not able to install docker compose on my Linux system.getting below error after running installation command: