This is the first and a necessary step in building your machine learning project. We recommend that you use the full power JupyterLab IDE for your project but you can also work from CLI on your local machine.
Create a new project
In rorodata, a project is unit workspace for app you are building. While you can deploy multiple apps/APIs from a single project, we recommend that each project should focus one specific objective.
Dashboard is the most simple way of getting started with a new project. Click on the
New Project button in the dashboard and get started.
You can also start a new project from the CLI of your local machine. Just use the
roro create <project-name> command to get started. Ensure that you have the rorodata CLI toolbelt installed - use
pip to install the latest version. Please login to your account from CLI using
roro login before creating a project.
# Create a new project from CLI $ roro create my-new-project Created project: my-new-project # Get a list of existing projects $ roro projects my-new-project
The platform expects each project name to be unique. You cannot recycle old project names, even if the project is deleted.
Your machine learing model code resides on your local machine or a repo cloned to your JupyterLab IDE. To run experiments with model you need to create a simple configuration file
roro.yml. The configuration file contains
project name, your choosen
runtime enviroment and other details of training, hardware to use etc. This configuration file should be present in the root of your project. Each project has it's own
roro.yml configuration file. Below is a sample configuration file for a
project: style-transfer-demo runtime: python3-keras services: - name: default function: StyleTransfer.style_transfer # File_Name.function_name cors_allow_origins: "*" # CORS support for the RESTFul API size: S2
The services section specifies
function to be deployed as an API or
command or script that needs to be executed. In the above case we have function
style_transfer from file
StyleTransfer.py in the project folder. You can add as many services to the configuration file.
Additional library dependencies not present in the runtime can be added to the project context using a simple
requirements.txt placed in the project alongside
To run a script, use
command, followed by the command you want to execute, instead of
function, checkout the following example:
project: style-transfer-demo runtime: python3-keras services: - name: default command: python train.py size: S2 - name: single-page command: python -m http.server 8080 size: S2
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