Install.md

Install m-LoRA

Table of Contents

• Docker
• Linux
• Windows
• macOS

Docker

Requirements

• One or more NVIDIA GPUs
  • At least 16GB VRAM per card
  • Cards with Ampere or newer architecture will perform faster
• Installation of the Docker Engine
• Installation of the latest graphics driver and NVIDIA Container Toolkit

Steps

docker run --gpus all -it --rm mikecovlee/mlora

You can check all available tags from: mikecovlee/mlora/tags. Currently, we only provide a Linux image for the x86_64 (amd64) architecture.

Verification

From the command line, type:

python

then enter the following code:

import mlora
mlora.setup_logging("INFO")
mlora.backend.check_available()

Expected output:

m-LoRA: NVIDIA CUDA initialized successfully.

Linux (Ubuntu, Debian, Fedora, etc.)

Requirements

• One or more NVIDIA GPUs
  • At least 16GB VRAM per card
  • Cards with Ampere or newer architecture will perform faster
• Installation of the latest graphics driver and CUDA toolkit
• conda installed and configured
• Internet connection for automated tasks

Steps

# Clone Repository
git clone https://github.com/mikecovlee/mLoRA
cd mlora
# Optional but recommended
conda create -n mlora python=3.11
conda activate mlora
# Install requirements
pip3 install -r requirements.txt --upgrade
# Install extra requirements
pip3 install ninja
pip3 install bitsandbytes==0.43.1
pip3 install flash-attn==2.5.8 --no-build-isolation

Verification

From the command line, type:

python

then enter the following code:

import mlora
mlora.setup_logging("INFO")
mlora.backend.check_available()

Expected output:

m-LoRA: NVIDIA CUDA initialized successfully.

Microsoft Windows

Requirements

• One or more NVIDIA GPUs
  • At least 16GB VRAM per card
  • Cards with Ampere or newer architecture will perform faster
• Windows 10 or later
• Installation of the latest graphics driver and CUDA toolkit
• conda installed and configured
• Internet connection for automated tasks

Steps

# Clone Repository
git clone https://github.com/mikecovlee/mLoRA
cd mlora
# Optional but recommended
conda create -n mlora python=3.11
conda activate mlora
# Install requirements (CUDA 12.1)
pip3 install torch==2.3.1 --index-url https://download.pytorch.org/whl/cu121
pip3 install -r requirements.txt --upgrade
# Install extra requirements
pip3 install bitsandbytes==0.43.1

Verification

From the command line, type:

python

then enter the following code:

import mlora
mlora.setup_logging("INFO")
mlora.backend.check_available()

Expected output:

m-LoRA: NVIDIA CUDA initialized successfully.

Apple macOS

Requirements

• Macintosh with Apple Silicon (recommended) or AMD GPUs
• macOS 12.3 or later
• Xcode command-line tools: xcode-select --install
• conda installed and configured
• Internet connection for automated tasks

Steps

# Clone Repository
git clone https://github.com/mikecovlee/mLoRA
cd mlora
# Optional but recommended
conda create -n mlora python=3.11
conda activate mlora
# Install requirements
pip3 install -r requirements.txt --upgrade

Verification

From the command line, type:

python

then enter the following code:

import mlora
mlora.setup_logging("INFO")
mlora.backend.check_available()

Expected output:

m-LoRA: APPLE MPS initialized successfully.