ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

cursor-chats/20251002_191725_#!_bin_bash.md

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+# 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)*