Installing PEFT and Accelerate compatible with Torch 1.9.0+cu111

We are embarking on a journey to seamlessly install PEFT and Accelerate to harmoniously work with Torch 1.9.0+cu111.

What will you learn?

By delving into this guide, you will master the art of installing PEFT and Accelerate on your system while ensuring they are in perfect sync with Torch 1.9.0+cu111.

Introduction to the Problem and Solution

The challenge at hand demands precise alignment of dependencies for a smooth installation process. Our goal is to create an environment where both PEFT, an energy function toolkit for protein design, and Accelerate, a high-performance computing library, can seamlessly integrate with Torch 1.9.0+cu111.

Here’s our strategy to tackle this issue:


# Create a new virtual environment (optional but recommended)
# Replace 'my_env' with your preferred environment name

conda create --name my_env python=3.x

# Activate the newly created virtual environment

conda activate my_env

# Install PyTorch based on your CUDA version (for CUDA 11)

pip install torch==1.9.0+cu111 torchvision==0.10 -f

# Install PEFT from GitHub repository using pip

pip install git+

# Lastly, install Accelerate using pip

pip install accelerate

# Copyright PHD

Explanation of each step:

  • Setting up a new virtual environment ensures isolation for our installations.
  • Activating the virtual environment is crucial for subsequent installations.
  • Installing Torch version 1.9.0 tailored to CUDA 11 guarantees compatibility.
  • Directly installing PEFT from its GitHub repository provides access to the latest features.
  • The installation of Accelerate enhances high-performance computations within our setup.


In this solution: – Isolation is maintained by creating a dedicated virtual environment. – Torch is installed according to the specified CUDA version for seamless compatibility. – Retrieving PEFT directly from GitHub ensures access to the latest updates effortlessly. – Accelerate’s installation optimizes high-performance computations efficiently.

    How do I check my current Torch version?

    To verify your installed Torch version within Python code:

    import torch
    # Copyright PHD

    Can I use a different virtual environment manager instead of Conda?

    Certainly! Tools like venv offer alternatives, but Conda simplifies package management tasks significantly.

    Is it mandatory to specify exact versions during installation?

    While recommended for reproducibility, specifying versions is optional if flexibility is preferred.

    What should I do if dependency conflicts arise during installation?

    Resolve conflicts by updating conflicting packages or seek community support through platforms like Stack Overflow.

    How can I confirm if PEFT has been installed correctly?

    After installation, import relevant modules in Python; absence of errors indicates successful setup.


    Achieving a successful installation of PEFT and Accelerate alongside Torch necessitates meticulous consideration of dependencies and version compatibility as elaborated in this comprehensive guide.

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