> ## Documentation Index
> Fetch the complete documentation index at: https://cerebrium-assembly-ai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Managing Files

Cerebrium offers file management through a 50GB persistent volume that's available to all apps in a project. This storage mounts at `/persistent-storage` and helps store model weights and files efficiently across deployments.

<Note>
  The `/persistent-storage` directory is not directly accessible during build
  time. You'll need to use the Cerebrium CLI commands or the mounted volume at
  runtime to manage these files.
</Note>

## Including Files in Deployments

The `cerebrium.toml` configuration file controls which files become part of the app:

```toml theme={null}
[cerebrium.deployment]
include = [
    "src/*.py",         # Python files in src.
    "config/*.json",    # JSON files in config.
    "requirements.txt"  # Specific files.
]

exclude = [
    "tests/*",          # Skip test files.
    "*.log"            # Skip log files.
]
```

Files included in deployments must be under 2GB each, with deployments working best for files under 1GB. Larger files should use persistent storage instead.

## Managing Persistent Storage

The CLI provides four commands for working with persistent storage.

<Note>
  At runtime, the volume is mounted at `/persistent-storage`. When using these
  commands, the Cerebrium CLI does not display the `/persistent-storage/`
  portion of the path.
</Note>

1. Upload files with `cerebrium cp`:

   ```bash theme={null}
   # Upload to root directory
   cerebrium cp src_file_name.txt

   # Upload to specific location
   cerebrium cp src_file_name.txt dest_file_name.txt

   # Upload to directory
   cerebrium cp dir_name sub_folder/
   ```

2. List files with `cerebrium ls`:

   ```bash theme={null}
   # List root contents
   cerebrium ls

   # List specific folder
   cerebrium ls sub_folder/
   ```

3. Remove files with `cerebrium rm`:

   ```bash theme={null}
   # Remove a file
   cerebrium rm file_name.txt

   # Remove a directory
   cerebrium rm folder_name
   ```

4. Download files with `cerebrium download`:

   ```bash theme={null}
   # Download to current directory with same filename
   cerebrium download file_name.txt

   # Download with a different local filename
   cerebrium download file_name.txt local_file_name.txt

   # Download from a subdirectory
   cerebrium download sub_folder/file_name.txt
   ```

## Using Stored Files

Here's how to work with files in persistent storage:

```python theme={null}
import os
import torch

# Load a model from persistent storage.
file_path = "/persistent-storage/segment-anything/sam_vit_h_4b8939.pth"
model = torch.jit.load(file_path)
```

During runtime on Cerebrium, your application should access files using the full `/persistent-storage/` path. For example:

```python theme={null}
# Read a file from persistent storage
with open("/persistent-storage/data/config.json", "r") as f:
    config = json.load(f)

# Write to persistent storage
with open("/persistent-storage/data/results.json", "w") as f:
    json.dump(results, f)

# Check if file exists in persistent storage
if os.path.exists("/persistent-storage/models/mymodel.pt"):
    model = torch.load("/persistent-storage/models/mymodel.pt")
```

<Note>
  Remember that while the CLI commands don't display the `/persistent-storage/`
  prefix in their output, your code must use the full path to access these files
  at runtime.
</Note>

<Warning>
  Should you require additional storage capacity, please reach out to us through
  [support](mailto:support@cerebrium.ai).
</Warning>