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.

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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.

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Managing Persistent Storage

The CLI provides four 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 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 sub_folder/

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

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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")