# Retrieve and update your assets

## Working with data assets

### List all your data assets

If you want to retrieve all the data assets you saved for one experiment, here is how :

```python
from picsellia.client import Client

api_token = '4d388e237d10b8a19a93517ffbe7ea32ee7f4787'
project_token = '9c68b4ae-691d-4c3a-9972-8fe49ffb2799'

experiment = Client.Experiment(
    api_token=api_token,
    project_token=project_token
    )
experiment.checkout('my_new_experiment')
experiment.list_data()
```

```python
 [
  {
   'id': 72,
    'date_created': '2021-02-09T12:32:18.293746Z',
    'last_update': '2021-02-09T12:32:18.293556Z',
    'name': 'parameters',
    'data': {'steps': 200000,
     'nb_gpu': 1,
     'batch_size': 8,
     'learning_rate': 0.005,
     'annotation_type': 'classification'
     },
    'type': 'table'
   }
  ]
```

### Delete all your data assets

If you think that your experiments needs a bit of cleansing, you can delete all the data assets you saved at once like this :

```python
experiment.delete_all_data()
```

### Retrieve an asset

If you want to retrieve a particular data asset, let's say the parameters of your last training, here is how you can do it :

{% hint style="info" %}
You will only return what is stored in the data field of the data asset, not all the information about the asset.
{% endhint %}

```python
experiment.get_data('parameters')
```

```python
{'steps': 200000,
 'nb_gpu': 1,
 'batch_size': 8,
 'learning_rate': 0.005,
 'annotation_type': 'classification'}
```

### Updating an asset

Now let's say that you want to change the value list of parameters for this training, here is how :

```python
parameters = {
 'steps': 5e6,
 'nb_gpu': 8,
 'batch_size': 64,
 'learning_rate': 0.0055,
 'annotation_type': 'detection'
}
experiment.update_data('parameters', data=parameters)
```

The method return the updated object

```python
{
 'id': 72,
 'date_created': '2021-02-09T12:32:18.293746Z',
 'last_update': '2021-02-09T12:32:18.293556Z',
 'name': 'parameters',
 'data': {'steps': 5000000.0,
  'nb_gpu': 8,
  'batch_size': 64,
  'learning_rate': 0.0055,
  'annotation_type': 'detection'},
 'type': 'table'
 }
```

### Delete an asset

If you want to completely remove a data asset from all your visualizations, here is how :

```python
experiment.delete_data('parameters')
```

## Working with file assets

### List all your file assets

If you want to retrieve all the file assets you saved for one experiment, here is how :

```python
from picsellia.client import Client

api_token = '4d388e237d10b8a19a93517ffbe7ea32ee7f4787'
project_token = '9c68b4ae-691d-4c3a-9972-8fe49ffb2799'

experiment = Client.Experiment(
    api_token=api_token,
    project_token=project_token
    )
experiment.checkout('my_new_experiment')
experiment.list_files()
```

The method return the list of files

```python
[{'id': 22,
  'date_created': '2021-02-09T12:32:18.022694Z',
  'last_update': '2021-02-09T12:32:18.022465Z',
  'large': False,
  'name': 'config',
  'object_name': '9a141ede-03dc-4e6c-a695-38661d9a97c3/pipeline.config'},
 {'id': 23,
  'date_created': '2021-02-09T12:32:18.068900Z',
  'last_update': '2021-02-09T12:32:18.068723Z',
  'large': True,
  'name': 'model-latest',
  'object_name': '9a141ede-03dc-4e6c-a695-38661d9a97c3/0/saved_model.zip'},
 {'id': 24,
  'date_created': '2021-02-09T12:32:18.112582Z',
  'last_update': '2021-02-09T12:32:18.112410Z',
  'large': True,
  'name': 'checkpoint-data-latest',
  'object_name': '9a141ede-03dc-4e6c-a695-38661d9a97c3/ckpt-0.data-00000-of-00001'},
 {'id': 25,
  'date_created': '2021-02-09T12:32:18.156945Z',
  'last_update': '2021-02-09T12:32:18.156767Z',
  'large': False,
  'name': 'checkpoint-index-latest',
  'object_name': '9a141ede-03dc-4e6c-a695-38661d9a97c3/ckpt-0.index'}]
```

### Delete all your file assets

f you think that your experiments needs a bit of cleansing, you can delete all the file assets you saved at once like this :

```python
experiment.delete_all_files()
```

### Retrieve and download a file asset

If you only need to retrieve informations about a file you can use the following method

```python
experiment.get_file('config')
```

```python
{'id': 22,
 'date_created': '2021-02-09T12:32:18.022694Z',
 'last_update': '2021-02-09T12:32:18.022465Z',
 'large': False,
 'name': 'config',
 'object_name': '9a141ede-03dc-4e6c-a695-38661d9a97c3/pipeline.config'}
```

If you need to download the file you can use the following method

```python
experiment.download('config')
```

{% hint style="warning" %}
If the file size exceeds 5Mb or if you get errors while using `download`, you might need to set the `large` parameter to `True`
{% endhint %}

```python
experiment.download('config', large=True)
```

Alternatively, you can download the file to a specified folder, just set the `path` parameter to the path of the folder (must be an existing folder)

```python
experiment.download('config', path='training')
```

### Update a file asset

The update function is there if you need to perform update on file information such as its name.

```python
experiment.update_file('config', name='new-config')
```

{% hint style="warning" %}
If you call the store method again, it will automatically erase the old file and replace if with the new one
{% endhint %}

### Delete a file asset

If you want to remove a file asset from one of your experiment, here is how to do it.

```python
from picsellia.client import Client

api_token = '4d388e237d10b8a19a93517ffbe7ea32ee7f4787'
project_token = '9c68b4ae-691d-4c3a-9972-8fe49ffb2799'

experiment = Client.Experiment(
    api_token=api_token,
    project_token=project_token
    name='my_new_experiment'
    )

experiment.delete_file('model-latest')
```

{% hint style="danger" %}
**Be aware that this will also permanently delete  the file from our storage.** 
{% endhint %}