run_1gpu.sh

#!/usr/bin/env bash
set -euo pipefail

export MASTER_ADDR=$(hostname -I | awk '{print $1}')
export MASTER_PORT=30001

export NNODES=1
export NPROC_PER_NODE=1
export CUDA_VISIBLE_DEVICES="0"
export NODE_RANK=0

CONFIG_FILE="./dinov3/configs/train/vith16plus.yaml"
OUTPUT_DIR="./output_vith16plus_1gpu"
RESUME="False"

REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)"
export DINOV3_RUN_SCRIPT="${REPO_ROOT}/$(basename "${BASH_SOURCE[0]}")"
export PYTHONPATH="${REPO_ROOT}${PYTHONPATH:+:${PYTHONPATH}}"
export PATH="$HOME/.local/bin:$PATH"

if [[ "${RESUME}" == "True" ]]; then
  echo "Resume enabled; preserving ${OUTPUT_DIR}"
  RESUME_FLAG=""
else
  echo "Resume disabled; cleaning ${OUTPUT_DIR}"
  rm -rf "${OUTPUT_DIR}"
  RESUME_FLAG="--no-resume"
fi
mkdir -p "${OUTPUT_DIR}"

echo "[Master Node] Starting training..."
echo "MASTER_ADDR=${MASTER_ADDR}"
echo "MASTER_PORT=${MASTER_PORT}"
echo "NNODES=${NNODES}, NPROC_PER_NODE=${NPROC_PER_NODE}"
echo "CONFIG_FILE=${CONFIG_FILE}"
echo "OUTPUT_DIR=${OUTPUT_DIR}"
echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES}"

uv run torchrun \
  --nnodes "${NNODES}" \
  --nproc_per_node "${NPROC_PER_NODE}" \
  --node_rank "${NODE_RANK}" \
  --master_addr "${MASTER_ADDR}" \
  --master_port "${MASTER_PORT}" \
  dinov3/train/train.py \
  --config-file "${CONFIG_FILE}" \
  --output-dir "${OUTPUT_DIR}" \
  ${RESUME_FLAG}