I'm trying to do object detection with yolo. I want to use gpu instead of cpu, but as you can see, I get the following error;
DNN MODULE WAS NOT BUILT WITH CUDA BACKEND; SWITCHING TO CPU
I am using windows 10. can you help?
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We aim to reduce CPU usage during inference by moving to GPU. However, when we run on GPU, the CPU usage is still high. Using the timeline output from RunMetadata and RunOptions class, we get the following profiling:
CPU and GPU Activity during inference
The 6th row is the CPU usage, 8th is GPU. The block with op "Unknown" have the same name as the GPU block. It is not a back-and-forth copy between host and device problem, as we can see by the first 4 rows.
What we see is that CPU does something while the GPU work. I'd like to understand why this happens. Does anyone has a clue?
We are using Tensorflow-GPU 1.14 (C++ interface) on a Jetson TX2, jetpack 4.2.3, CUDA 10.2, capabilities 6.2.
Regards
I have RTX2070 Nvidia graphic card which has tensor cores on it. I want to run my deep learning code utilizing tensor cores instead of Cuda cores. Is it possible in this graphic card? and isn't there any specific driver that I should install in order to do that?
and how should I check to know whether the model is running on tensor cores or Cuda cores?
I am using Keras framework on windows 10.
According to NVIDIA:
The container enables Tensor Core math by default
If you want to disable it you can set TF_DISABLE_CUDNN_TENSOR_OP_MATH to 1.
For example, I have a cuda tensor in pytorch, how to directly convert it to mxnet/tensorflow in gpu without copying it to cpu? Frequently copying data between cpu and gpu is very time-consuming.
The fastest way would be to use CUDA API for that.
Sharing tensors via PyTorch is only supported on CPU.
And if you would try to do some Python exhibition you will be slow because of GIL.
Knowing that you can set the max. memory amount for GPUs for Tensorflow,
I wonder how can I prevent tensorflow**.js** from using all my gpu ram. I didn't found anything in the API documentation.
Not yet. Currently you can only select to register the tensorflow backend by using tf.setBackend which will use all the available gpu.
use tensorflow backend by using
tf.setBackend('tensorflow')
use webgl or cpu in the brower by using
tf.setBackend('cpu')
tf.setBackend('webgl') // if using tfjs with the browser
I'm using tensorflow to do distributed training, I'm confused that since parameter server usaually does not use GPU, can we run parameter server on CPU only machines, I mean with no GPU, or disable GPU with something like CUDA_VISIBLE_DEVICES='', I tried this with TF object detection API, but it reports no GPU device available, I then tried per_process_gpu_memory_fraction=0.0001, it works, but this is not a good solution, as it still take around 400MB gpu memory but does not use it. can some suggest something on this issue?