MajorWiki/01-linux/distro-specific/wsl2-rebuild-fedora43-training-env.md

---
title: WSL2 Fedora 43 Training Environment Rebuild
domain: linux
category: distro-specific
tags:
  - wsl2
  - fedora
  - unsloth
  - pytorch
  - cuda
  - majorrig
  - majortwin
status: published
created: '2026-03-16'
updated: '2026-03-16'
---

# WSL2 Fedora 43 Training Environment Rebuild

How to rebuild the MajorTwin training environment from scratch on MajorRig after a WSL2 loss. Covers Fedora 43 install, Python 3.11 via pyenv, PyTorch with CUDA, Unsloth, and llama.cpp for GGUF conversion.

## The Short Answer

```bash
# 1. Install Fedora 43 and move to D:
wsl --install -d FedoraLinux-43 --no-launch
wsl --export FedoraLinux-43 D:\WSL\fedora43.tar
wsl --unregister FedoraLinux-43
wsl --import FedoraLinux-43 D:\WSL\Fedora43 D:\WSL\fedora43.tar

# 2. Set default user
echo -e "[boot]\nsystemd=true\n[user]\ndefault=majorlinux" | sudo tee /etc/wsl.conf
useradd -m -G wheel majorlinux && passwd majorlinux
echo "%wheel ALL=(ALL) ALL" | sudo tee /etc/sudoers.d/wheel

# 3. Install Python 3.11 via pyenv, PyTorch, Unsloth
# See full steps below
```

## Step 1 — System Packages

```bash
sudo dnf update -y
sudo dnf install -y git curl wget tmux screen htop rsync unzip \
    python3 python3-pip python3-devel gcc gcc-c++ make cmake \
    ninja-build pkg-config openssl-devel libffi-devel \
    gawk patch readline-devel sqlite-devel
```

## Step 2 — Python 3.11 via pyenv

Fedora 43 ships Python 3.13. Unsloth requires 3.11. Use pyenv:

```bash
curl https://pyenv.run | bash

# Add to ~/.bashrc
export PYENV_ROOT="$HOME/.pyenv"
[[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init - bash)"

source ~/.bashrc
pyenv install 3.11.9
pyenv global 3.11.9
```

The tkinter warning during install is harmless — it's not needed for training.

## Step 3 — Training Virtualenv + PyTorch

```bash
mkdir -p ~/majortwin/{staging,datasets,outputs,scripts}
python -m venv ~/majortwin/venv
source ~/majortwin/venv/bin/activate

pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

# Verify GPU
python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"
```

Expected output: `True NVIDIA GeForce RTX 3080 Ti`

## Step 4 — Unsloth + Training Stack

```bash
source ~/majortwin/venv/bin/activate

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install transformers datasets accelerate peft trl bitsandbytes \
    sentencepiece protobuf scipy einops

# Pin transformers for unsloth-zoo compatibility
pip install "transformers<=5.2.0"

# Verify
python -c "import unsloth; print('Unsloth OK')"
```

> [!warning] Never run `pip install -r requirements.txt` from inside llama.cpp while the training venv is active. It installs CPU-only PyTorch and downgrades transformers, breaking the CUDA setup.

## Step 5 — llama.cpp (CPU-only for GGUF conversion)

CUDA 12.8 is incompatible with Fedora 43's glibc for compiling llama.cpp (math function conflicts in `/usr/include/bits/mathcalls.h`). Build CPU-only — it's sufficient for GGUF conversion, which doesn't need GPU:

```bash
# Install GCC 14 (CUDA 12.8 doesn't support GCC 15 which Fedora 43 ships)
sudo dnf install -y gcc14 gcc14-c++

cd ~/majortwin
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

cmake -B build \
    -DGGML_CUDA=OFF \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_C_COMPILER=/usr/bin/gcc-14 \
    -DCMAKE_CXX_COMPILER=/usr/bin/g++-14

cmake --build build --config Release -j$(nproc) 2>&1 | tee /tmp/llama_build.log &
tail -f /tmp/llama_build.log
```

Verify:
```bash
ls ~/majortwin/llama.cpp/build/bin/llama-quantize && echo "OK"
ls ~/majortwin/llama.cpp/build/bin/llama-cli && echo "OK"
```

## Step 6 — Shell Environment

```bash
cat >> ~/.bashrc << 'EOF'
# MajorInfrastructure Paths
export VAULT="/mnt/c/Users/majli/Documents/MajorVault"
export MAJORANSIBLE="/mnt/d/MajorAnsible"
export MAJORTWIN_D="/mnt/d/MajorTwin"
export MAJORTWIN_WSL="$HOME/majortwin"
export LLAMA_CPP="$HOME/majortwin/llama.cpp"

# Venv
alias mtwin='source $MAJORTWIN_WSL/venv/bin/activate && cd $MAJORTWIN_WSL'
alias vault='cd $VAULT'
alias ll='ls -lah --color=auto'

# SSH Fleet Aliases
alias majorhome='ssh majorlinux@100.120.209.106'
alias dca='ssh root@100.104.11.146'
alias majortoot='ssh root@100.110.197.17'
alias majorlinuxvm='ssh root@100.87.200.5'
alias majordiscord='ssh root@100.122.240.83'
alias majorlab='ssh root@100.86.14.126'
alias majormail='ssh root@100.84.165.52'
alias teelia='ssh root@100.120.32.69'
alias tttpod='ssh root@100.84.42.102'
alias majorrig='ssh majorlinux@100.98.47.29'  # port 2222 retired 2026-03-25, fleet uses port 22

# DNF5
alias update='sudo dnf upgrade --refresh'
alias install='sudo dnf install'
alias clean='sudo dnf clean all'

# MajorTwin helpers
stage_dataset() {
    cp "$VAULT/20-Projects/MajorTwin/03-Datasets/$1" "$MAJORTWIN_WSL/datasets/"
    echo "Staged: $1"
}
export_gguf() {
    cp "$MAJORTWIN_WSL/outputs/$1" "$MAJORTWIN_D/models/"
    echo "Exported: $1 → $MAJORTWIN_D/models/"
}
EOF

source ~/.bashrc
```

## Key Rules

- **Always activate venv before pip installs:** `source ~/majortwin/venv/bin/activate`
- **Never train from /mnt/c or /mnt/d** — stage files in `~/majortwin/staging/` first
- **Never put ML artifacts inside MajorVault** — models, venvs, artifacts go on D: drive
- **Max viable training model:** 7B at QLoRA 4-bit (RTX 3080 Ti, 12GB VRAM)
- **Current base model:** Qwen2.5-7B-Instruct (ChatML format — stop token: `<|im_end|>` only)
- **Transformers must be pinned:** `pip install "transformers<=5.2.0"` for unsloth-zoo compatibility

## D: Drive Layout

```
D:\MajorTwin\
  models\          ← finished GGUFs
  datasets\        ← dataset archives
  artifacts\       ← training run artifacts
  training-runs\   ← logs, checkpoints
D:\WSL\
  Fedora43\        ← WSL2 VHDX
  Backups\         ← weekly WSL2 backup tars
```

## See Also

- [WSL2 Instance Migration](wsl2-instance-migration-fedora43.md)
- [WSL2 Backup via PowerShell](wsl2-backup-powershell.md)