ai-agent-book/cursor-chats/20251002_191725_#!_bin_bash.md

Cursor Chat: ai-agent-book

Metadata

• Project: ai-agent-book
• Path: /Users/boj
• Date: 2025-10-02 19:17:25
• Session ID: e83eea6b-7378-4571-9e83-241f98f6befc

Conversation

👤 You

#!/bin/bash

#SBATCH --job-name=TrainGP #SBATCH --mail-user=user@gmail.com #SBATCH --mail-type=ALL #SBATCH --nodes=1 # Total number of nodes requested #SBATCH --ntasks-per-node=8 # Total number of task requested #SBATCH --cpus-per-task=8 # Total number of cores requested #SBATCH --mem=1000G #SBATCH --time=96:00:00 # Time limit (hh:mm:ss) #SBATCH --gpus-per-node=8
#SBATCH --output=./slurm_logs/train_%A_%a.out #SBATCH --output=./slurm_logs/train_%A_%a.err #SBATCH --partition=gpu

LR=1e-7 save_every=1 save_model=False # disable running saving. one checkpoint ~30GB

CKPT_NAME="YOUR_MODEL_PATH" # official init model: tianzhechu/GP-L-Init PORT=$((RANDOM % 10000 + 1000))

download from our huggingface dataset repo tianzhechu/SFTvsRL_Data

ROUTE_INFO="YOUR_ROUTE_INFO_PATH" # .json GPS_TO_PANO="YOUR_GPS_TO_PANO_MAPPING_PATH" # .pkl STREETVIEWS="YOUR_STREETVIEWS_PATH" # folder of images

DS_SKIP_CUDA_CHECK=1 TOKENIZERS_PARALLELISM=false
accelerate launch
--config_file scripts/config_zero2_8gpu.yaml
--main_process_port ${PORT} -m rl.launcher
-f rl/configs/llama_virl_vl.yaml
--output_dir=train_ckpt/virl_vl/
--optimizer_config.init_lr=${LR}
--optimizer_config.lr_max_steps=20
--prompt_config.enable_verification=True
--num_updates=15
--run_name=virl_vl_training
--num_steps=256
--model_path=${CKPT_NAME}
--save_ckpt=${save_model}
--save_every=${save_every}
--env_config.route_info_path=${ROUTE_INFO}
--env_config.platform_cfg.OFFLINE.PANORAMA_DIR=${STREETVIEWS}
--env_config.platform_cfg.OFFLINE.GPS_TO_PANO_PATH=${GPS_TO_PANO}

-- How many GPUs do I need to run this experiment

---

🤖 Cursor Assistant

Based on the SLURM script, you need 8 GPUs to run this experiment.

This is specified by the line:

#SBATCH --gpus-per-node=8

Additional resource requirements:

• 1 node (single machine)
• 8 GPUs on that node
• 1TB of RAM (--mem=1000G)
• 64 CPU cores total (8 tasks × 8 CPUs per task)
• 96 hours of runtime

The script also confirms this with the accelerate configuration file config_zero2_8gpu.yaml, which indicates it's using DeepSpeed ZeRO-2 optimization distributed across 8 GPUs.

Important notes:

• The model checkpoints are ~30GB each (according to the comment)
• This appears to be a vision-language RL training job for a navigation model
• You'll need access to a GPU partition with nodes that have 8 GPUs available (typically A100 or H100 nodes)
• Make sure you replace the placeholder paths (YOUR_MODEL_PATH, YOUR_ROUTE_INFO_PATH, etc.) with actual paths before running

---

👤 You

File "/root/miniconda3/lib/python3.13/site-packages/cv2/__init__.py", line 181, in bootstrap() ~~~~~~~~~^^ File "/root/miniconda3/lib/python3.13/site-packages/cv2/__init__.py", line 153, in bootstrap native_module = importlib.import_module("cv2") File "/root/miniconda3/lib/python3.13/importlib/__init__.py", line 88, in import_module return _bootstrap._gcd_import(name[level:], package, level) ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ImportError: libGL.so.1: cannot open shared object file: No such file or directory

-- what should i do

---

🤖 Cursor Assistant

This is a common OpenCV error in headless Linux environments (like HPC clusters or Docker containers). The issue is that OpenCV's Python package requires OpenGL libraries, but they're not installed on your system.

Here are the solutions:

Solution 1: Install OpenGL libraries (Recommended for SLURM/HPC)

For Ubuntu/Debian:

sudo apt-get update
sudo apt-get install -y libgl1-mesa-glx libglib2.0-0

For CentOS/RHEL:

sudo yum install -y mesa-libGL

If you don't have sudo access on an HPC cluster, contact your system administrator to install these packages.

Solution 2: Use headless OpenCV

Install the headless version of OpenCV which doesn't require GUI libraries:

pip uninstall opencv-python opencv-contrib-python opencv-python-headless
pip install opencv-python-headless

This is often the best solution for server environments where you don't need display functionality.

Solution 3: Set environment variable (temporary workaround)

If you only need basic OpenCV functionality without GUI:

export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libffi.so.7

Or add this to your SLURM script before the accelerate launch command.

For your SLURM script specifically:

I'd recommend adding this to your script before the training command:

# Add after the variable definitions, before accelerate launch
pip install opencv-python-headless --upgrade --force-reinstall

DS_SKIP_CUDA_CHECK=1 TOKENIZERS_PARALLELISM=false \
    accelerate launch \
    ...

Best approach: Use opencv-python-headless since you're running a vision-based RL training job on an HPC cluster where GUI libraries typically aren't available or needed.

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Exported from Cursor View