# Getting started with Dobb·E code

In this section, we will help you get started with running all the software parts of Dobb·E. There are three major parts in the software component of Dobb-E, which are:

1. [Processing data collected with the Stick](/software/processing-collected-data.md),
2. [Training a model on the collected data](/software/readme-1/fine-tuning-policies.md),
3. [Deploying the model](/software/readme-1/deploying-a-policy-on-the-robot.md).

Optionally, you can [pre-train your own model](/software/readme-1/optional-training-your-own-home-pretrained-representations.md) similar to how we trained the Home Pretrained Representations (HPR).

We will frequently refer to the "robot", which for this part would be the Intel NUC installed in the Hello Robot Stretch, and the "machine", which should be a beefier machine with GPU(s) where you can preprocess your data and train new models.

Also, ensure that you have [mamba](https://mamba.readthedocs.io/en/latest/) installed. Mamba is generally a faster but compatible alternative to conda, which we prefer.

## Getting Started

* Clone the repository:

  ```bash
  git clone https://github.com/notmahi/dobb-e.git
  cd dobb-e/imitation-in-homes
  ```
* Set up the project environment:

  ```bash
  mamba env create -f conda_env.yaml
  ```
* Activate the environment:

  ```bash
  mamba activate home_robot
  ```
* Logging:
  * To enable logging, log in with a Weights and Biases (`wandb`) account:

    ```bash
    wandb login
    ```
  * Alternatively, disable logging altogether:

    ```bash
    export WANDB_MODE=disabled
    ```

##


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