export HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxx"

export WANDB_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# Use only GPU 0
export CUDA_VISIBLE_DEVICES=0

# Use GPUs 0 and 1
export CUDA_VISIBLE_DEVICES=0,1

# Disable GPU (CPU only)
export CUDA_VISIBLE_DEVICES=""

# Limit memory fragmentation and enable expandable segments
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512,expandable_segments:True

# Force CPU training (override GPU detection)
export AUTOTRAIN_FORCE_NUM_GPUS=0

# Force single GPU
export AUTOTRAIN_FORCE_NUM_GPUS=1

# Disable MPS (force CPU on Mac)
export AUTOTRAIN_DISABLE_MPS=1

# Force enable MPS even with incompatible settings
export AUTOTRAIN_ENABLE_MPS=1

# Disable trust_remote_code for all model loading (more secure)
export ALLOW_REMOTE_CODE=false

export WANDB_PROJECT=my-project
export WANDB_ENTITY=my-team
export WANDB_MODE=offline  # Don't sync to cloud

export HF_USERNAME=my-username

export HF_HOME=/path/to/cache
export TRANSFORMERS_CACHE=/path/to/cache

export HF_HUB_DISABLE_TELEMETRY=1

export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1

#!/bin/bash

# Authentication
export HF_TOKEN="hf_your_token_here"
export WANDB_API_KEY="your_wandb_key"

# GPU settings
export CUDA_VISIBLE_DEVICES=0,1

# Cache
export HF_HOME=~/.cache/huggingface

# W&B project settings
export WANDB_PROJECT=my-project

source setup_env.sh
aitraining llm --train ...

HF_TOKEN=hf_xxxxx
WANDB_API_KEY=xxxxx
CUDA_VISIBLE_DEVICES=0

export $(cat .env | xargs)
aitraining --config training.yaml