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  • 2020-09-19 10:23:27

    显存占满、而GPU利用率为0的情况

    1、经查阅官方文档得知“在GPU上,tf.Variable操作只支持实数型(float16 float32 double)的参数。不支持整数型参数”

    2、可能是定义在图中的op只能在CPU中运行,GPU不支持。加上代码中设置的参数allow_soft_placement = True,那么此时代码将模型加载到CPU上进行计算,因此会出现,参数加载到GPU中,但是GPU的利用率为0,CPU的利用率则很高。

    更多相关内容
  • ** 待解决 ** 今天在Kaggle上试一下TextCNN,涉及到TensorFlow。 但是训练模型时发现GPU利用率为0,而且整个过程非常慢。 隔壁的CPU都到顶了… 查了很多方法,说是和cuda版本不对应 ...

    **

    待解决

    **
    今天在Kaggle上试一下TextCNN,涉及到TensorFlow。
    但是训练模型时发现GPU利用率为0,而且整个过程非常慢。
    在这里插入图片描述

    隔壁的CPU都到顶了…
    查了很多方法,说是和cuda版本不对应

    1. https://stackoverflow.com/questions/60208936/cannot-dlopen-some-gpu-libraries-skipping-registering-gpu-devices
    2. https://www.codenong.com/cs108869930/
    3. https://blog.csdn.net/u010165147/article/details/106354671

    But,cuda也不是pip能装上的
    在这里插入图片描述
    官方说需要配置,区分Ubuntu和Windows,解决方法不一样,但是都要CUDA。
    软件包版本:
    在这里插入图片描述
    软件要求:
    在这里插入图片描述

    试了TensorFlow-gpu 1.15.0 和TensorFlow 2.x 都有一下信息…
    在这里插入图片描述

    2022-02-19 14:27:11.312590: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
    Skipping registering GPU devices...
    2022-02-19 14:27:11.312782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
    2022-02-19 14:27:11.312820: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 
    2022-02-19 14:27:11.312836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N 
    

    整体信息是这样的:
    在这里插入图片描述
    不知道怎么肥四>_>

    下面这位朋友也是这样:
    kaggle中使用低版本的tensorflow时GPU占用率很低
    在这里插入图片描述
    Kaggle社区的Feedback

    展开全文
  • 【debug】tensorflow训练GPU利用率为0

    千次阅读 2021-01-20 10:15:22
    在跑一个基于tensorflow的代码时,发现指定了GPU后,只占了很小的显存,且GPU利用率为0。 经核查发现是tensorflow-gpu版本和cuda版本没对上,(也没报错。。gucci) tensorflow-gpu版本1.15.0 原cuda:10.1 更改10.0后...

    在跑一个基于tensorflow的代码时,发现指定了GPU后,只占了很小的显存,且GPU利用率为0。如图卡1:
    在这里插入图片描述

    经核查发现是tensorflow-gpu版本和cuda版本没对上,(也没报错。。gucci)
    tensorflow-gpu版本1.15.0
    原cuda:10.1
    更改为10.0后问题解决。

    展开全文
  • 这两天给服务器装了Anaconda,重装了cuda,都是为了GPU加速的问题 结果。。。发现并没有解决,每次跑代码都会卡住,然后出一大段报错 ssh://zhanglei@223.2.43.124:22/home/zhanglei/conda/envs/tensorflow-gpu/...

    这两天给服务器装了Anaconda,重装了cuda,都是为了GPU加速的问题

    结果。。。发现并没有解决,每次跑代码都会卡住,然后出一大段报错

    ssh://zhanglei@223.2.43.124:22/home/zhanglei/conda/envs/tensorflow-gpu/bin/python3.6 -u /mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py
    WARNING:tensorflow:From /mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py:65: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
    
    WARNING:tensorflow:From /mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py:41: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.
    
    WARNING:tensorflow:From /mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py:61: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
    
    WARNING:tensorflow:From /mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py:95: The name tf.log is deprecated. Please use tf.math.log instead.
    
    WARNING:tensorflow:From /mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py:96: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.
    
    WARNING:tensorflow:From /mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py:103: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
    
    2020-12-21 21:19:02.928666: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
    2020-12-21 21:19:02.969208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
    name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.71
    pciBusID: 0000:03:00.0
    2020-12-21 21:19:02.969407: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
    2020-12-21 21:19:02.970403: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
    2020-12-21 21:19:02.971277: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
    2020-12-21 21:19:02.971500: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
    2020-12-21 21:19:02.972680: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
    2020-12-21 21:19:02.973553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
    2020-12-21 21:19:02.976372: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
    2020-12-21 21:19:02.978442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
    2020-12-21 21:19:02.978726: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
    2020-12-21 21:19:02.983634: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3597800000 Hz
    2020-12-21 21:19:02.983988: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55e02caddc70 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
    2020-12-21 21:19:02.984003: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
    2020-12-21 21:19:03.102321: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55e02c4eb3b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
    2020-12-21 21:19:03.102370: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 3090, Compute Capability 8.6
    2020-12-21 21:19:03.107308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: 
    name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.71
    pciBusID: 0000:03:00.0
    2020-12-21 21:19:03.107391: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
    2020-12-21 21:19:03.107432: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
    2020-12-21 21:19:03.107468: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
    2020-12-21 21:19:03.107556: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
    2020-12-21 21:19:03.107617: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
    2020-12-21 21:19:03.107651: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
    2020-12-21 21:19:03.107684: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
    2020-12-21 21:19:03.114101: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
    2020-12-21 21:19:03.114148: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
    2020-12-21 21:19:03.117098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
    2020-12-21 21:19:03.117140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 
    2020-12-21 21:19:03.117158: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N 
    2020-12-21 21:19:03.135120: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22797 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3090, pci bus id: 0000:03:00.0, compute capability: 8.6)
    WARNING:tensorflow:From /home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/util/tf_should_use.py:198: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
    Instructions for updating:
    Use `tf.global_variables_initializer` instead.
    2020-12-21 21:23:57.073416: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
    2020-12-21 21:25:21.265230: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
    2020-12-21 21:41:28.961104: E tensorflow/stream_executor/cuda/cuda_blas.cc:428] failed to run cuBLAS routine: CUBLAS_STATUS_EXECUTION_FAILED
    Traceback (most recent call last):
      File "/home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 1365, in _do_call
        return fn(*args)
      File "/home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 1350, in _run_fn
        target_list, run_metadata)
      File "/home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 1443, in _call_tf_sessionrun
        run_metadata)
    tensorflow.python.framework.errors_impl.InternalError: Blas GEMM launch failed : a.shape=(150, 6), b.shape=(1000, 6), m=150, n=1000, k=6
    	 [[{{node gradients/MatMul_1_grad/MatMul}}]]
    
    During handling of the above exception, another exception occurred:
    
    Traceback (most recent call last):
      File "/mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py", line 110, in <module>
        _, c = session.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y})
      File "/home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 956, in run
        run_metadata_ptr)
      File "/home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 1180, in _run
        feed_dict_tensor, options, run_metadata)
      File "/home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 1359, in _do_run
        run_metadata)
      File "/home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/client/session.py", line 1384, in _do_call
        raise type(e)(node_def, op, message)
    tensorflow.python.framework.errors_impl.InternalError: Blas GEMM launch failed : a.shape=(150, 6), b.shape=(1000, 6), m=150, n=1000, k=6
    	 [[node gradients/MatMul_1_grad/MatMul (defined at home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py:1748) ]]
    
    Original stack trace for 'gradients/MatMul_1_grad/MatMul':
      File "mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py", line 96, in <module>
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/training/optimizer.py", line 403, in minimize
        grad_loss=grad_loss)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/training/optimizer.py", line 512, in compute_gradients
        colocate_gradients_with_ops=colocate_gradients_with_ops)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/gradients_impl.py", line 158, in gradients
        unconnected_gradients)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/gradients_util.py", line 679, in _GradientsHelper
        lambda: grad_fn(op, *out_grads))
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/gradients_util.py", line 350, in _MaybeCompile
        return grad_fn()  # Exit early
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/gradients_util.py", line 679, in <lambda>
        lambda: grad_fn(op, *out_grads))
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py", line 1585, in _MatMulGrad
        grad_a = gen_math_ops.mat_mul(grad, b, transpose_b=True)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/gen_math_ops.py", line 6136, in mat_mul
        name=name)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 794, in _apply_op_helper
        op_def=op_def)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func
        return func(*args, **kwargs)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 3357, in create_op
        attrs, op_def, compute_device)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 3426, in _create_op_internal
        op_def=op_def)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1748, in __init__
        self._traceback = tf_stack.extract_stack()
    
    ...which was originally created as op 'MatMul_1', defined at:
      File "mnt/ba3b04da-ce1b-4c21-ad1b-3aff7d337cdf/wangxing/WISDM/WISDM-master/WISDN_CSDN_change.py", line 93, in <module>
        y_ = tf.nn.softmax(tf.matmul(f, out_weights) + out_biases)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/util/dispatch.py", line 180, in wrapper
        return target(*args, **kwargs)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_ops.py", line 2754, in matmul
        a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/gen_math_ops.py", line 6136, in mat_mul
        name=name)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/op_def_library.py", line 794, in _apply_op_helper
        op_def=op_def)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func
        return func(*args, **kwargs)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 3357, in create_op
        attrs, op_def, compute_device)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 3426, in _create_op_internal
        op_def=op_def)
      File "home/zhanglei/.local/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 1748, in __init__
        self._traceback = tf_stack.extract_stack()
    
    
    Process finished with exit code 1
    

    先介绍一下我的环境,tf1.15,cuda10,一张3090,一张2080ti

    通过nvidia-smi一看,乖乖,显存快占满了,GPU利用率却是0%

    本来想去细究为啥会有这一段报错。。。但是,随手一个举动,居然直接解决问题了。

    我把

    os.environ['CUDA_VISIBLE_DEVICES'] = '0'

    换成

    os.environ['CUDA_VISIBLE_DEVICES'] = '1'

    就解决问题了,也就是把GPU由3090换成2080ti(我不知道,为啥顺序是反的),可能是cudatoolkit10无法支持3090,或者tf1不支持3090的原因

    导致了这一大段的报错

    展开全文
  • ![图片说明]... ...什么我在gpu上训练模型但是gpu利用率为0且运行速度还是很慢? 模型主要利用的是tensorflow和keras 已经安装了tensorflow-gpu和cuda
  • GPU显存占满利用率GPU-util为0

    千次阅读 多人点赞 2021-09-10 18:41:30
    运行程序的时候提醒显存不够,查看了一下nvidia-smi,确实显存占满了,但是GPU-Util,gpu利用率有三个都是0,只有一个是56% 搜索后发现这个现象的原因还比较普遍,但是似乎没有几个可以很好解决这个问题, 参考: ...
  • 如果torch.cuda.is_available()True,说明可以使用GPU,其实运行时是使用的GPU,可以用如下方法观察 法一:把GPU观察模式调整CUDA 法二:按win+R→输入cmd→输入nvidia-smi
  • 采用了网上很多方法都没有解决我的问题,比如修改batch_size,num_workers等,都没有解决我的问题。 修改超参的这些方法都是针对GPU...而能跑,但是GPU利用率为0,说明是修改的数据集部分的bug,比如train_set,val_
  • 1:进入anoconda prompt,输入以下代码,如果结果是True,表示GPU可用,若Flase,无GPU可用。 import tensorflow as tf print(tf.test.is_gpu_available()) 2:用如下代码可以查看cpu,gpu配置情况:
  • 近日配置环境后终于可以开始跑GAN了,但是运行时发现GPU利用率为0。上网百度了一下,有几种方法,尝试后发现可以这样做,在此记录。 实时动态监测GPU 在终端输入: watch -n 10 nvidia-smi 指定GPU 我只有一块0号...
  • 在深度学习模型训练过程中,在服务器端或者本地pc端,输入nvidia-smi来观察显卡的GPU内存占用率(Memory-Usage),显卡的GPU利用率(GPU-util),然后采用top来查看CPU的线程数(PID数)和利用率(%CPU)。...
  • 利用Resnet进行训练,发现训练速度不够快,因此查看了任务管理器里面的CPU和GPU使用率,发现CPU接近爆满状态,而GPU利用率只有个位数的百分比,就很奇怪,明明把模型和计算问题都送进GPU了,GPU才这么点利用率,因此...
  • 问题描述:在使用Fluid进行模型训练时,使用了GPU进行训练,但发现GPU的利用率...通常如果训练数据比较大,而模型计算量有比较小,这就会导致GPU大部分时间都拷贝数据,造成GPU利用率为0的现象。 解决方法: 如...
  • 将NVIDIA GPU利用率记录到文件的示例 此存储库包含一些小代码示例,这些示例说明如何使用nvidia-smi将GPU使用率记录到CSV文件中,以及如何使用python脚本来绘制结果。 使用脚本log_gpu_utilization.sh开始记录gpu...
  • 状况如标题 解决方法: 1、卸载原来的tensorflow-gpu版本 2、重新安装稍微高级一些的...Notes:本人CUDA9.0,tensorflow-gpu=1.8.0升级成tensorflow-gpu=1.12.0,此问题网上暂无相同案例,纯属本人侥幸成果 ...
  • ​ 在不适用cond虚拟环境的情况下,重新安装tensorflow-gpu和keras。 ​ 卸载之前的版本: ​ conda uninstall tensorflow-gpu ​ conda uninstall keras ​ 安装新的版本: ​ 先执行: conda install tensorflow...
  • 但我在运行如下代码训练模型时,通过任务管理器看到 cpu 占用 20%左右,gpu占用为0%。我以为此时仍是 cpu 在训练模型(其实已经是 gpu 在训练模型了)。 history = model.fit( train_generator, steps_per_epoch=...
  • 极智开发 | 谈谈 GPU 利用率

    千次阅读 2022-01-28 20:24:57
    大家好,我是极智视界,本文主要谈谈 GPU 利用率,以 Nvidia GPU 例。
  • 在跑代码的时候,运行cmd,输入nvidia-smi,发现这样一个问题,显存的占比高特别低但是GPU利用率特别低。 解决方法 这个跟自己写的代码有关系,我的主要问题在于,读取磁盘中数据的时候,是依靠CPU在读取数据(此时...
  • 聊聊GPU利用率那些事

    千次阅读 2021-07-27 17:41:17
    众所周知,GPU本身的计算能力是越来越强大,特别是新一代的NVIDIA AMPERE架构发布之后,又一次刷新了大家对AI算力的认知。目前,确实有不少大规模分布式训练对更大算力的渴求是极其强烈的,比如语音、自然语言处理等...
  • linux gpu显存满了但是使用率为0

    千次阅读 2019-04-26 20:31:01
    在跑openpose的demo时,...查看gpu使用情况,发现gpu显存(memory-usage)满了,但是使用gpu-util)是0。 下面的进程可以看到有用了非常多显存的进程,记下PID kill -9 PID 把对应的进程杀掉就ok了。 gpu...
  • pytorch 提高gpu利用率

    千次阅读 2021-04-08 10:19:09
    pytorch跑Unet代码,gpu利用率0%-20%闪现,主要问题是GPU一直在等cpu处理的数据传输过去。利用top查看cup的利用率也是从0省道100%且显然cup的线程并不多,能处理出的数据也不多。在一般的程序中,除了加载从...
  • Pytorch GPU利用率

    2022-05-19 14:34:24
    GPU 利用率低常见原因分析及优化 - 知乎 GPU利用率低的解决办法_Data_Designer的博客-CSDN博客_gpu利用率低 https://www.csdn.net/tags/NtjaIg1sNTk3NjMtYmxvZwO0O0OO0O0O.html
  • 实时查看gpu利用率

    千次阅读 2022-01-26 08:56:07
    windows: 每隔1s查看一次 nvidia-smi -l 1 linux 每隔1s查看一次 watch -n 1 nvidia-smi 只查看一次 nvidia-smi
  • 尤其是训练ResNet和VGG的时候更加发现了这个问题,使用nvidia-smi查看了一下结果如下: 显然GPU利用率为0就很不正常,但是有显存占用说明模型应该是在跑的。后来既然GPU利用不起来,我干脆同时跑多个模型,想充分...
  • TensorFlow训练时合理安排GPU利用率

    千次阅读 2022-04-06 19:43:08
    gpus = tf.config.experimental.list_physical_devices(device_type='GPU') for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) 将其插入训练文件中,例如下图
  • GPU利用率与使用率

    万次阅读 2019-02-28 14:52:28
    GPU利用率 广义的GPU利用是指对GPU利用率效率,包括GPU空间和时间上的利用效率。 狭义的GPU利用率是指GPU时间片上的利用率; GPU可用的物理资源有哪些? GPU可利用资源:SM (计算单元)MEM(存储) Encoder...
  • GPU利用率不高问题记录

    千次阅读 2019-10-15 13:00:23
    上次被老板教授了好久,出现西安卡利用率一直很低的情况千万不能认为它不是问题,而一定要想办法解决。比如可以在加载训练图像的过程中(__getitem__方法中)设定数据增强过程中每个步骤的时间点,对每个步骤的时间...
  • Nvidia GPU监视器 使用nvidia-smi帮助监视Nvidia GPU利用率。 请检查下面的完整文档。 目录 安装及使用 使用npm: $ npm install --save nvidia-gpu-monitor 使用纱: $ yarn add nvidia-gpu-monitor

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