I want to finetune llama2-13b in my 48G A6000 GPU(GPUid:1) of single GPU mode. Though I have already set CUDA_VISIBLE_DEVICES=1, the finetune process is still run on my 24G A5000 GPU(GPUid:0), which is have limited memory to run the process. ...
- Set the `CUDA_VISIBLE_DEVICES` environment variable to `-1` in `nn_classification`. This fixes a bug where the `nn_classification` module would fail to run when a GPU was available and the input had a single sequence. ## [1.7.4] - 2023-12-08 ### Fixed 1 change: 1 addition ...
遇到“RuntimeError: environment variable CUDA_VISIBLE_DEVICES is not set correctly”这个错误时,通常意味着CUDA环境变量CUDA_VISIBLE_DEVICES没有正确设置。这个环境变量用于指定哪些GPU设备对CUDA程序可见。以下是一些解决步骤: 确认CUDA环境已正确安装并配置: 确保你的系统上已经安装了NVIDIA的CUDA Toolkit,并且驱动也...
方案1:添加环境变量 由于我是docker容器启动项目,所以进入容器后安装完vim,然后在~/.bashrc最后添加了一下内容。 export CUDA_VISIBLE_DEVICES=0 由于在构建容器的时候选的显卡编号为0,所以上面我配置的编号为0。 重启容器后检查$ CUDA_VISIBLE_DEVICES输出正常,但是没有解决问题,报错依旧。 方案2:代码添加环境变量 ...
{ "source": "ABSOLUTE_PATH_TO_PROJECT_NETWORK_SPECS_DIRECTORY", "destination": "/workspace/tao-experiments/faster_rcnn/specs" } ], "Envs": [ { "variable": "CUDA_VISIBLE_DEVICES", "value": "0" } ], "DockerOptions": { "shm_size": "16G", "ulimits": { "memlock": -1, "...
1. 1.~/.bashrc中添加export CUDA_VISIBLE_DEVICES=02.代码中加入 AI检测代码解析 import os os.environ['CUDA_VISIBLE_DEVICES'] =‘0’ 1. 2. 3.重启服务器
注意:使用‘tf.config.experimental.list_physical_devices('GPU’)'来确认TensorFlow正在使用GPU。 1. 在一台或多台机器上,在多GPU上运行模型的最简单方法是使用分布策略(Distribution Strategies)。 本指南适用于那些尝试过这些方法并发现需要对TensorFlow如何使用GPU进行微粒度控制的用户。
1 llama2 repository:here dataset mmlu dataset structure RESULT command CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \ --model_name_or_path ../llama/models_hf/7B \ --adapter_name_or_path ./FINE/llama2-7b-chat-alpaca_gpt4_single/checkpoint-20000 \ ...
changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. 解决方式 1.重启系统2.sudo apt-get install nvidia-modprobe文章标签: 并行计算 Python 关键词: Python error Python笔记 Set env error Set env Python env ...
What happened + What you expected to happen Setting a GPU to a fractional value appears to cause RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES to be ignored when using TorchTrainer, as demonstrated below: I’m using Ray 2.24, and this works...