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.

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.

​
Including Files in Deployments

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

[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.

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.

  1. Upload files with cerebrium cp:

    # 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:

    # List root contents
cerebrium ls

# List specific folder
cerebrium ls sub_folder/

  3. Remove files with cerebrium rm:

    # Remove a file
cerebrium rm file_name.txt

# Remove a directory
cerebrium rm folder_name

  4. Download files with cerebrium download:

    # 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:

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:

# 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")

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.

Should you require additional storage capacity, please reach out to us through support.