This is a very simple HTTP server for Unix, using fork(). It's very easy to use
- include header
httpd.h
- write your route method, handling requests.
- call
serve_forever("12913")
to start serving on port 12913
Show all images -> docker images | |
list of images.... | |
<your_image> | |
.... | |
Remove image -> docker rmi <image_name> (-f for force removal> | |
Build image -> docker build -t ts_image:1.2 <directory containing Dockerfile/current dir as ' . ' > | |
Start container with 8080/81 port exposed to outerworld/localhost -> docker run --rm -it -p 8080:8080 -p 8081:8081 <your_image>:1.2 |
1. Copy your local python folder/package to directory containing Dockerfile e.g. | |
../mydocker | |
- Dockerfile | |
- <mylocalpythonpackage> | |
- config.propertis | |
- .. | |
2. Modify your Dockerfile to add local python package to docker image as : | |
ADD /<your local python package/dir.> /<same_package_name_as_local> e.g. ADD /test /test |
import com.google.gson.Gson; | |
import com.google.gson.GsonBuilder; | |
import com.google.gson.JsonObject; | |
class User { | |
public String getName() { | |
return name; | |
} | |
public void setName(String name) { |
-> How to create simple test plan for http server performance testing | |
Open jmeter gui [using jmeter/jmeter.sh] | |
Under new test plan | |
Add thread group | |
In thread group | |
Add http request | |
Add counter [which can be used in http request body/param/header etc.] | |
Add event listener as result tree | |
Now you should be able to execute and see result in result tree | |
Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.
>> Free NLP Course from Huggingface | |
https://huggingface.co/learn/nlp-course/en/chapter1/1 | |
>> LLMs from Huggingface | |
https://huggingface.co/docs/transformers/en/llm_tutorial | |
>> A Data Science Practitioner’s Guide - This is 3 part blog which talks about ideation, modeling and deployment | |
https://medium.com/geekculture/a-data-science-practitioners-guide-f459eb915b5a | |
>> Data science in action - Customer churn prediction |