Managing Downloaded Models with torch.hub.set_dir() in PyTorch

Purpose

• By default, PyTorch Hub uses a directory within your system's cache to store these downloaded files.
• Controls the location where PyTorch Hub downloads pre-trained models and weights.

Usage

import torch.hub

# Set a custom directory for downloaded models
custom_dir = "/path/to/your/model/directory"
torch.hub.set_dir(custom_dir)

# Now, when you load models using torch.hub.load(), they'll be downloaded to the specified directory
model = torch.hub.load("pytorch/vision:v0.10.0", "resnet50", pretrained=True)

1. Import: Begin by importing the torch.hub module.
2. Set Directory: Call torch.hub.set_dir() with the desired path (custom_dir) as an argument. This informs PyTorch Hub where to store downloaded models and weights for future use.

Default Behavior (if set_dir() is not called)

• PyTorch Hub employs a hierarchical approach to determine the download location:
  • Priority 1: Environment Variable TORCH_HOME: If set, this location is used.
  • Priority 2: $XDG_CACHE_HOME/torch/hub: If the TORCH_HOME environment variable is not defined, PyTorch Hub falls back to this system-specific cache directory.
  • Priority 3: ~/.cache/torch/hub: As a final resort, PyTorch Hub utilizes this hidden directory within your user's home directory.

Benefits of Using set_dir()

• Visibility: By specifying a custom directory, you can easily locate and manage downloaded models outside of hidden system cache locations.
• Customization: You gain control over where downloaded models reside. This is useful for organizing your project's files or managing storage space.

• If you plan to share your project with others, you might need to adjust the custom directory path to accommodate different environments or operating systems. Consider using environment variables or relative paths for better portability.
• Ensure the provided directory (custom_dir) exists and has the necessary permissions for PyTorch Hub to write files.

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Checking the Current Hub Directory

import torch.hub

current_dir = torch.hub.get_dir()
print(f"Current PyTorch Hub directory: {current_dir}")

This code retrieves the current directory where PyTorch Hub stores downloaded models using the get_dir() function.

Creating a Custom Directory and Setting It

import torch.hub
import os

# Create a new directory if it doesn't exist
custom_dir = "/path/to/your/models"
os.makedirs(custom_dir, exist_ok=True)  # Ensures directory creation

# Set the custom directory for PyTorch Hub
torch.hub.set_dir(custom_dir)

# Now, downloaded models will be stored here
model = torch.hub.load("pytorch/vision:v0.10.0", "resnet50", pretrained=True)
print(f"Downloaded model saved in: {custom_dir}")

This code creates a new directory (custom_dir) if it doesn't exist and then sets it as the PyTorch Hub directory using set_dir(). Finally, it loads a model and prints the directory where it's saved (which should be custom_dir).

import torch.hub
import os

# Define an environment variable for the custom directory
os.environ["CUSTOM_HUB_DIR"] = "/path/to/shared/models"

# Check if the environment variable is set
if "CUSTOM_HUB_DIR" in os.environ:
    custom_dir = os.environ["CUSTOM_HUB_DIR"]
    torch.hub.set_dir(custom_dir)
    print(f"Using custom directory from environment variable: {custom_dir}")
else:
    print("CUSTOM_HUB_DIR environment variable not set. Using default location.")

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Manually Downloading Models

• Use torch.hub.load() with the url argument pointing to the downloaded file path instead of the online model name.
• Download the model weights file (often a .pth file) to your preferred location.

Example

import torch
from pathlib import Path

# Download the model weights file from the PyTorch Hub repository (replace with actual URL)
model_url = "https://download.pytorch.org/models/resnet50.pth"
model_path = Path("/path/to/your/downloaded/model.pth")
model_path.parent.mkdir(parents=True, exist_ok=True)  # Create directory if needed
torch.hub.download_url_to_file(model_url, str(model_path))

# Load the model using the downloaded file path
model = torch.hub.load("pytorch/vision:v0.10.0", "resnet50", model_path=str(model_path), pretrained=False)

Considerations

• You need to track downloaded file paths for future use.
• This approach requires manual download management.

Using a Custom Caching Mechanism

• This approach offers more control over the caching process but requires additional development effort.
• If you have specific caching requirements, you could develop a custom caching mechanism using libraries like filelock or cachetools to manage downloads and avoid conflicts.

Leveraging Alternative Pre-Trained Model Sources

• This option depends on the availability and compatibility of models on other sources.
• Explore other platforms or repositories that offer pre-trained models in PyTorch format. These platforms might provide their own download mechanisms or allow specifying custom download locations.

• For pre-trained models not available on PyTorch Hub, explore alternative sources.
• If you require more control over caching or have specific requirements, consider developing a custom caching mechanism.
• For simple use cases with occasional model downloads, manually downloading or using torch.hub.set_dir() are often sufficient.