This is ml-projects-services, the managed-learn component responsible for creating projects within the managed learn services.
- Install any IDE in your system(eg: VScode etc..)
- Install nodejs from : https://nodejs.org/en/download/
- Install mongoDB: https://docs.mongodb.com/manual/installation/
- Install Robo 3T: https://robomongo.org/
Basic understanding of git and github is recommended.
- https://www.youtube.com/watch?v=RGOj5yH7evk&t=2s
- https://git-scm.com/book/en/v2/Getting-Started-What-is-Git%3F
- Create a new folder where you want to clone the repository.
- Navigate to that directory using the terminal.
- Execute the git commands to clone the repository using the provided link from the code tab.
Git link
https://github.com/shikshalokam/ml-projects-service.git
command to clone
git clone https://github.com/shikshalokam/ml-projects-service.git
Create a file named .env
and copy the environment-specific data corresponding to that service into the .env
file.
# Ml project service configurations file
APPLICATION_PORT = "3000" // Application port number
APPLICATION_ENV = "development" // Application running environment
INTERNAL_ACCESS_TOKEN = "Fg*************yr" // Internal access token for accessing internal service APIs
# DB
MONGODB_URL = "mongodb://localhost:27017/sl-assessment" // Mongodb connection url
# ML Core Service
ML_CORE_SERVICE_URL = "http://ml-core-service:3000" // ML Core Service URL
# ML Reports Service
ML_REPORTS_SERVICE_URL = "http://ml-reports-service:3000" // ML Reports Service URL
# ML Survey Service
ML_SURVEY_SERVICE_URL = "http://ml-survey-service:3000" // ML Survey Service URL
# OFFLINE VALIDATION
KEYCLOAK_PUBLIC_KEY_PATH = "keycloak-public-keys" // Keycloak public key path
# KAFKA Configurations
KAFKA_COMMUNICATIONS_ON_OFF = "ON/OFF" // Kafka enable or disable communication flag
KAFKA_URL = "172.31.0.4:9092" // IP address of kafka server with port without HTTP
KAFKA_GROUP_ID = "projects" // Kafka group id
# SUBMISSION TOPIC
SUBMISSION_TOPIC = "dev.sl.projects.submissions" // Kafka topic name for pushing projects submissions
PROJECT_SUBMISSION_TOPIC = "dev.sl.projects.submissions" // project submission topic
# SUNBIRD LOCATION AND USER READ
USER_SERVICE_URL = "http://user-service:3000" // service used for user profile read location search are using this base url
#service name
SERVICE_NAME = ml-project-service // ml-project service name
# sunbird-rc service
CERTIFICATE_SERVICE_URL = http://registry-service:8081 // sunbird-RC registry service URL
PROJECT_CERTIFICATE_ON_OFF = "ON/OFF" // Project certificate enable or disable flag
To install dependencies from a package.json
file in Visual Studio Code, you can use the integrated terminal. Here are the steps:
- Open the integrated terminal by going to View > Terminal or using the shortcut Ctrl+` (backtick).
- In the terminal, navigate to the directory where the package.json file is located.
- Run the command
npm install
oryarn install
, depending on your preferred package manager. - The package manager will read the package.json file and install all the dependencies specified in it.
- Wait for the installation process to complete. You should see progress indicators or a success message for each installed dependency.
- Once the installation is finished, the dependencies listed in the package.json file will be installed in a node_modules directory in your project.
- Create a folder on service directory named:
keycloak-public-keys
- Inside that folder create a file
GRxxx....................xxxxx60fA
for keycloak file please contact Backend Team
Before proceeding with these steps, ensure that you have MongoDB installed on your computer. For a graphical user interface (GUI) for MongoDB, you can choose to install Robo 3T.
-
Obtain the latest database dump from the backend team.
-
Restore the database in your local environment using the following command:
For Windows/Linux:
mongorestore <name you want to give the db> <directory or file to restore>
For macOS:
mongorestore -d <name you want to give the db> <directory or file to restore>
Note: Add <name you want to give the db>
to mongoDB url in .env
file.
The schema serves as a blueprint for creating and maintaining the database that supports the ML projects services data storage and retrieval operations.
Click here for DB schema and corresponding examples in a PDF format.
The ML Projects Service Postman Collection is a comprehensive resource for interacting with the ML Core Service. It includes organized endpoints, detailed documentation, and example workflows, providing a valuable reference for developers. Leverage this collection to enhance productivity and collaboration in ML Services.
Always work on branches. Never make changes to master.
Creating a branch from master.
For more information on git you can use :
https://education.github.com/git-cheat-sheet-education.pdf