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### (A) Create a New Project
The function **create\_new\_project** creates a new project directory, required subdirectories, and a basic project configuration file. Each project is identified by the name of the project (e.g. Reaching), name of the experimenter (e.g. YourName), as well as the date at creation.
Thus, this function requires the user to input the enter the name of the project, the name of the experimenter, and the full path of the videos that are (initially) used to create the training dataset.
Optional arguments specify the working directory, where the project directory will be created, and if the user wants to copy the videos (to the project directory). If the optional argument working\_directory is unspecified, the project directory is created in the current working directory, and if copy\_videos is unspecified symbolic links for the videos are created in the videos directory. Each symbolic link creates a reference to a video and thus eliminates the need to copy the entire video to the video directory (if the videos remain at that original location).
deeplabcut.create_project(`Name of the project',`Name of the experimenter', [`Full path of video 1',`Full path of video2',`Full path of video3'], working_directory=`Full path of the working directory',copy_videos=True/False)
(TIP: you can also place ``config_path`` in front of ``deeplabcut.create_project`` to create a vriable that holds the path to the config.yaml file, i.e. ``config_path=deeplabcut.create_project(...)``)
This set of arguments will create a project directory with the name **Name of the project+name of the experimenter+date of creation of the project** in the **Working directory** and creates the symbolic links to videos in the **videos** directory. The project directory will have subdirectories: **dlc-models**, **labeled-data**, **training-datasets**, and **videos**. All the outputs generated during the course of a project will be stored in one of these subdirectories, thus allowing each project to be curated in separation from other projects. The purpose of the subdirectories is as follows:
**dlc-models:** This directory contains the subdirectories *test* and *train*, each of which holds the meta information with regard to the parameters of the feature detectors in configuration file. The configuration files are YAML files, a common human-readable data serialization language. These files can be opened and edited with standard text editors. The subdirectory *train* will store checkpoints (called snapshots in TensorFlow) during training of the model. These snapshots allow the user to reload the trained model without re-training it, or to pick-up training from a particular saved checkpoint, in case the training was interrupted.
**labeled-data:** This directory will store the frames used to create the training dataset. Frames from different videos are stored in separate subdirectories. Each frame has a filename related to the temporal index within the corresponding video, which allows the user to trace every frame back to its origin.