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...
1. 2. export CUDA_VISIBLE_DEVICES=0,1,2,3 1. 如果没解决 这个时候可以采用sudo aptitude install cuda 来安装,这种安装aptitude会自动把所有依赖的库都帮你顺着找到,并下载好。如果aptitude还未安装,可以先使用sudo apt-get install aptitude,先将aptitude安装好。在安装过程中,在选择【Y/N】,选择 Y 就行。
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 ...
Num GPUs Available: 1 1. 2. 一、概览 TensorFlow支持在各种类型的设备上运行计算,包括CPU和GPU。它们用字符串标识符表示,例如: -“/device:CPU:0”:表示我们机器里的CPU -“/GPU:0”:简写符合指我们机器里第一个对TensorFlow可见的GPU -“/job:localhost/replica:0/task:0/device:GPU:1”:我们机器里对...
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。
{ "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, "...
environ['CUDA_VISIBLE_DEVICES'] = str(self.device) import caffe if self.device >= 0: caffe.set_mode_gpu() else: caffe.set_mode_cpu() caffe.set_random_seed(0) np.random.seed(0) self.model = CaffeModel(*self.model_info) self.model.img = np.zeros((3, 1, 1), dtype=np.float...
If I didn't set the CUDA_VISIBLE_DEVICES, the command worked on GPU 0 and 1. Is it possible to set the CUDA_VISIBLE_DEVICES in command line? cofiiwuadded thequestionlabelJun 13, 2019 stalebotadded thewontfixlabelNov 7, 2020 stalebotclosed this ascompletedNov 14, 2020 ...