clearml-docs/docs/clearml_data/data_management_examples/data_man_python.md

---
title: Data Management with Python
---

The [dataset_creation.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/dataset_creation.py) and 
[data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) scripts
together demonstrate how to use ClearML's [`Dataset`](../../references/sdk/dataset.md) class to create a dataset and 
subsequently ingest the data. 

## Dataset Creation

The [dataset_creation.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/dataset_creation.py) script 
demonstrates how to do the following:
* Create a dataset and add files to it
* Upload the dataset to the ClearML Server
* Finalize the dataset


### Downloading the Data

You first need to obtain a local copy of the CIFAR dataset. 
The code below downloads the data and `dataset_path` contains the path to the downloaded data: 

```python
from clearml import StorageManager

manager = StorageManager()
dataset_path = manager.get_local_copy(
    remote_url="https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
)
```


### Creating the Dataset

The following code creates a data processing task called `cifar_dataset` in the `dataset examples` project, which
can be viewed in the [WebApp](../../webapp/datasets/webapp_dataset_viewing.md).

```python
from clearml import Dataset

dataset = Dataset.create(
    dataset_name="cifar_dataset", 
    dataset_project="dataset examples"
)
 ```


### Adding Files

Add the downloaded files to the current dataset:

```python
dataset.add_files(path=dataset_path)
```

### Uploading the Files

Upload the dataset: 

```python
dataset.upload()
```

By default, the dataset is uploaded to the ClearML file server. The dataset's destination can be changed by specifying the 
target storage with the `output_url` parameter of the [`upload`](../../references/sdk/dataset.md#upload) method. 

### Finalizing the Dataset

Run the [`finalize`](../../references/sdk/dataset.md#finalize) command to close the dataset and set that dataset's tasks
status to *completed*. The dataset can only be finalized if it doesn't have any pending uploads. 

```python
dataset.finalize()
```

After a dataset has been closed, it can no longer be modified. This ensures future reproducibility. 

Information about the dataset can be viewed in the WebApp, in the dataset's [details panel](../../webapp/datasets/webapp_dataset_viewing.md#version-details-panel). 
In the panel's **CONTENT** tab, you can see a table summarizing version contents, including file names, file sizes, and hashes.

![Dataset content tab](../../img/examples_data_management_cifar_dataset.png)

## Data Ingestion

Now that a new dataset is registered, you can consume it!

The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) script 
demonstrates data ingestion using the dataset created in the first script.

The following script gets the dataset and uses [`Dataset.get_local_copy()`](../../references/sdk/dataset.md#get_local_copy) 
to return a path to the cached, read-only local dataset. 

```python
dataset_name = "cifar_dataset"
dataset_project = "dataset_examples"

dataset_path = Dataset.get(
    dataset_name=dataset_name, 
    dataset_project=dataset_project
).get_local_copy()
```

If you need a modifiable copy of the dataset, use the following code: 
```python
Dataset.get(dataset_name, dataset_project).get_mutable_local_copy("path/to/download")
```

The script then creates a neural network to train a model to classify images from the dataset that was
created above.