# Full pipelines > Run a Full Pipeline in AI Center to train and evaluate an ML model in a single operation, with optional data preprocessing. A Full Pipeline is used to train a new machine learning model and evaluate the performance of this new model, all in one go. Additionally, a preprocessing step is run before training, allowing data manipulation/training a trained machine learning model. To use this pipeline, the package must contain code to process data, train, evaluate and save a model (the `process_data()`, `train ()`, `evaluate()` and `save()` functions in the **train.py** file). This code, together with a dataset or sub-folder within a dataset, and optionally an evaluation set, produce a new package version, a score (the return of the `evaluate()` function for the new version of the model) and any arbitrary outputs the user would like to persist in addition to the score. ## Creating full pipelines Create a new full pipeline and make sure to provide the following full pipeline specific information: * In the **Pipeline type** field, select **Full Pipeline run**. * In the **Choose input dataset** field, select a dataset or folder from which you want to import data for full training. All files in this dataset/folder should be available locally during the runtime of the pipeline at the path stored in `data_directory` variable. * Optionally, in the **Choose evaluation dataset** field, select a dataset or folder from which you want to import data for evaluation. All files in this dataset/folder should be available locally during the runtime of the pipeline at the path stored in `test_data_directory` variable. If no folder is selected, it is expected that your pipeline writes something to the directory `test_data_directory` variable in `process_data` function. If you do not select a folder, and your `process_data` does not write to `test_data_directory` then the directory passed to the evaluate function will be empty. * In the **Enter parameters** section, enter the environment variables defined and used by your pipeline, if any. The environment variables are: + `training_data_directory`, with default value **dataset/training**: Defines where the training data is accessible locally for the pipeline. This directory is used as input for the `train()` function. Most users will never have to override this through the UI and can just write data into `os.environ['training_data_directory']` in the `process_data` function and can just expect that the argument *data_directory* in `train(self, data_directory` will be called with `os.environ['training_data_directory']`. + `test_data_directory` with default value **dataset/test**: Defines where the test data is accessible locally for the pipeline. This directory is used as input to the `evaluate()` function. Most users will never have to override this through the UI and can just write data into `os.environ['test_data_directory']` in the `process_data` function and can just expect that the argument *data_directory* in `evaluate(self, data_directory` will be called with `os.environ['test_data_directory']`. + `artifacts_directory`, with default value **artifacts**: This defines the path to a directory that will be persisted as ancillary data related to this pipeline. Most, if not all users, will never have the need to override this through the UI. Anything can be saved during pipeline execution including images, pdfs, and subfolders. Concretely, any data your code writes in the directory specified by the path `os.environ['artifacts_directory']` will be uploaded at the end of the pipeline run and will be viewable from the **Pipeline details** page. + `save_training_data`, with default value **true**: If set to **true**, `training_data_directory` folder will be uploaded at the end of the pipeline run as an output of the pipeline under directory `training_data_directory`. + `save_test_data`, with default value **true**: If set to **true**, `test_data_directory` folder will be uploaded at the end of the pipeline run as an output of the pipeline under directory `test_data_directory`. Depending on your choice to select or not an evaluation dataset, you can create full pipelines: * Explicitly selecting evaluation data Watch the following video to learn how to create a full pipeline with the newly trained package version 1.1. Be sure to select the same dataset (in our example, **tutorialdataset**) both as the input dataset and as the evaluation dataset. * Without explicitly selecting evaluation data Watch the following video to learn how to create a full pipeline with the newly trained package version 1.1. Select the input dataset, but leave the evaluation dataset unselected. ### Results of a full pipeline run execution :::note The pipeline execution might take some time. Check back to it after a while to see its status. ::: After the pipeline was executed, in the **Pipelines** page, the pipeline's status changed to **Successful**. The **Pipeline Details** page displays the arbitrary files and folders related to the pipeline run. * The `train()` function is trained on **train.csv**, and not on the unaltered contents of the data folder (**example1.txt**, and **example2.txt**). The `process_data` can be used to dynamically split data based on any user-defined parameters. * The first full pipeline runs evaluations on a directory with **example1.txt**,**example2.txt**, and **test.csv**. The second full pipeline runs evaluations on a directory with only **test.csv**. This is the difference in not selecting an evaluation set explicitly when creating the second full pipeline run. This way you can have evaluations on new data from UiPath® Robots, as well as dynamically split data already in your project. * Each individual component can write arbitrary artifacts as part of a pipeline (histograms, tensorboard logs, distribution plots, etc.). * The ML Package zip file is the new package version automatically generated by the training pipeline. * Artifacts folder, only visible if not empty, is the folder regrouping all the artifacts generated by the pipeline, and it is saved under `artifacts_directory` folder. * Training folder, only visible if `save_training_data` was set to **true**, is a copy of the `training_data_directory` folder. * Test folder, only visible if `save_training_data` was set to **true**, is a copy of the `test_data_directory` folder. ## Analogy for building your own full pipelines This example is a conceptually analogous execution of a full pipeline on some package, for example version 1.1, the output of a training pipeline on version 1.0. :::important This is a simplified example. Its purpose is to illustrate how datasets and packages interact in a full pipeline. The steps are merely conceptual and do not represent how the platform works. ::: 1. Copy package version 1.1 into `~/mlpackage`. 2. Create a directory called `./dataset`. 3. Copy the contents of the input dataset into `./dataset`. 4. If the user set something in the **Choose evaluation dataset** field, copy that evaluation dataset and put it in `./dataset/test`. 5. Set environment variables `training_data_directory=./dataset/training` and `test_data_directory=./dataset/test`. 6. Execute the following python code: ``` from train import Main m = Main() m.process_data('./dataset') m.evaluate(os.environ['test_data_directory']) m.train(os.environ['training_data_directory']) m.evaluate(os.environ['test_data_directory']) ``` 7. Persist the contents of `~/mlpackage` as package version 1.2. Persist artifacts if written, snapshot data if `save_data` is set to **true**. :::note The existence of the environment variables `training_data_directory` and `test_data_directory` mean that `process_data` can use these variables to split data dynamically. ::: ## Pipeline outputs The `_results.json` file contains a summary of the pipeline run execution, exposing all inputs/outputs and execution times for a full pipeline. ``` { "parameters": { "pipeline": "< Pipeline_name >", "inputs": { "package": "", "version": "", "input_data": "", "evaluation_data": "/None", "gpu": "True/False" }, "env": { "key": "value", ... } }, "run_summary": { "execution_time":