1. Create Project#
An Efemarai project groups together multiple datasets, models and operational domains that you use when working on a given ML problem. We recommend setting up a project per source code repository where you develop your ML solution.
To list the projects you have associated with your account, you can run
ef project list
The result should be empty, as you’ve just created your account.
Let’s create our first project.
Let’s assume this is a standard ML project structure. You have a code, data and weights folders with corresponding files within.
/first-project ├── code │ ├── intialize.sh │ ├── model.py │ ├── dataset_utils.py │ └── ├── data │ ├── train │ ├── test │ └── val ├── weights │ ├── checkpoint-1.pt │ └── checkopint-2.tf └── Dockerfile
In order to use Efemarai with it, you have to create a base file that describes the project and functionality within. This file is called
efemarai.yaml and should sit in the root of the folder tree.
This is how a minimalistic project can be defined
project: name: Traffic Sign Detection description: Implement multiple models for detecting traffic signs.
Once you have created the example yaml
efemarai.yaml you can run the following command to create it!
efemarai project create efemarai.yaml
or if you’re using the standard name as above, you can simply type
efemarai project create .
After succesfully executing either of these commands, you will be able to see the project in the home page and in the CLI with
ef project list
For all parameters that can be set for the project, see the reference.
Next, let’s create our first dataset.