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

​
Including Files in Deployments

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

[cerebrium]
include = [
    "src/*.py",         # Python files in src
    "config/*.json",    # JSON files in config
    "requirements.txt"  # Specific files
]

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

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 three commands for working with persistent storage:

  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 entire directory
cerebrium cp dir_name sub_folder/
  1. List files with cerebrium ls:
# View root contents
cerebrium ls

# View specific folder
cerebrium ls sub_folder/
  1. Remove files with cerebrium rm:
# Remove a file
cerebrium rm file_name.txt

# Remove a directory
cerebrium rm sub_folder/

​
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)

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

Custom Web ServersVercel Integration