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:
```bash
#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 <module>
    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**:
```bash
sudo apt-get update
sudo apt-get install -y libgl1-mesa-glx libglib2.0-0
```

For **CentOS/RHEL**:
```bash
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:

```bash
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:

```bash
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:

```bash
# 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.

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*