Refactor ClearML Data docs (#108)

docs/clearml_data/data_management_examples/data_man_python.md

@@ -0,0 +1,94 @@
+---
+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) 
+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
+
+We first need to obtain a local copy of the CIFAR dataset.
+
+ ```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")
+```
+
+This script downloads the data and `dataset_path` contains the path to the downloaded data. 
+
+### Creating the Dataset
+
+```python
+from clearml import Dataset
+
+dataset = Dataset.create(dataset_name="cifar_dataset", dataset_project="dataset examples" )
+ ```
+
+This creates a data processing task called `cifar_dataset` in the `dataset examples` project, which
+can be viewed in the WebApp.
+
+### Adding Files
+
+```python
+dataset.add_files(path=dataset_path)
+```
+
+This adds the downloaded files to the current dataset.  
+
+### Uploading the Files
+
+```python
+dataset.upload()
+```
+This uploads the dataset to the ClearML Server by default. The dataset's destination can be changed by specifying the 
+target storage with the `output_url` parameter of the [`upload`](../../references/sdk/dataset#upload) method. 
+
+### Finalizing the Dataset
+
+Run the [`finalize`](../../references/sdk/dataset#finzalize) 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. 
+
+The information about the dataset, including a list of files and their sizes, can be viewed
+in the WebApp, in the dataset task's **ARTIFACTS** tab.
+
+![image](../../img/examples_data_management_cifar_dataset.png)
+
+## Data Ingestion
+
+Now that we have a new dataset registered, we 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.
+
+```python
+dataset_name = "cifar_dataset"
+dataset_project = "dataset_examples"
+
+dataset_path = Dataset.get(dataset_name=dataset_name, dataset_project=dataset_project).get_local_copy()
+```
+
+The script above gets the dataset and uses the [`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy) 
+method to return a path to the cached, read-only local dataset. If you need a modifiable copy of the dataset, 
+use `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.