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.
Related
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?
Okay, so I've worked on a bunch of Deep Learning projects and internships now and I've never had to do heavy training. But lately I've been thinking of doing some Transfer Learning for which I'll need to run my code on a GPU. Now I have a system with Windows 10 and a dedicated NVIDIA GeForce 940M GPU. I've been doing a lot of research online, but I'm still a bit confused. I haven't installed the NVIDIA Cuda Toolkit or cuDNN or tensorflow-gpu on my system yet. I currently use tensorflow and pytorch to train my DL models. Here are my queries -
When I define a tensor in tf or pytorch, it is a cpu tensor by default. So, all the training I've been doing so far has been on the CPU. So, if I make sure to install the correct versions of Cuda and cuDNN and tensorflow-gpu (specifically for tensorflow), I can run my models on my GPU using tf-gpu and pytorch and that's it? (I'm aware of the torch.cuda.is_available() in pytorch to ensure pytorch can access my GPU and the device_lib module in tf to check if my gpu is visible to tensorflow)(I'm also aware of the fact that tf doesnt support all Nvidia GPUs)
Why does tf have a separate module for GPU support? PyTorch doesnt seem to have that and all you need to do is cast your tensor from cpu() to cuda() to switch between them.
Why install cuDNN? I know it is a high-level API CUDA built for support to train Deep Neural Nets on the GPU. But do tf-gpu and torch use these in the backend while training on the gpu?
After tf == 1.15, did they combine CPU and GPU support all into one package?
First of all unfortunately 940M is a kinda weak GPU for training. I suggest you use Google colab for faster training but of course, it would be faster than the CPU. So here my answers to your four questions.
1-) Yes if you install the requirements correctly, then you can run on GPU. You can manually place your data to your GPU as well. You can check implementations on TensorFlow. In PyTorch, you should specify the device that you want to use. As you said you should do device = torch.device("cuda" if args.cuda else "cpu") then for models and data you should always call .to(device) Then it will automatically use GPU if available.
2-) PyTorch also needs extra installation (module) for GPU support. However, with recent updates both TF and PyTorch are easy to use for GPU compatible code.
3-) Both Tensorflow and PyTorch is based on cuDNN. You can use them without cuDNN but as far as I know, it hurts the performance but I'm not sure about this topic.
4-) No they are still different packages. tensorflow-gpu==1.15 and tensorflow==1.15 what they did with tf2, was making the tensorflow more like Keras. So it is more simplified then 1.15 or before.
Rest was already answered by regarding 3) cudNN optimizes layer and such operations on hardware level and those implementations are pure black magic. It is incredibly hard to write CUDA code that properly utilizes your GPU (how load data into the GPU, how to actually perform them using matrices etc. )
We have a DGX-1 in Lab.
I see many tasks are running on different GPU.
For MLperf docker application, I can use NV_GPU=x to assign which GPU to use.
However, I have a python Keras/TensorFlow code, I used this same way, the loading doesn't go to the specified GPU.
You could use CUDA_VISIBLE_DEVICES to specify the GPU to be used by your model:
import os
os.environ["CUDA_VISIBLE_DEVICES"] = 0,1 #Will assign GPUs 0 and 1 to the model
Simply in tensorflow we can run our project on CPU or GPU. I have a GTX-1080 with 2500 cores. when we use a GPU in tensorflow all of the cores and memory of the graphic card is involved.
How can I use a part or certain number of my graphic cores in tensorflow ?
Reading :
https://www.tensorflow.org/versions/r0.10/resources/faq.html it states :
Does TensorFlow make use of all the devices (GPUs and CPUs) available
on my machine?
TensorFlow supports multiple GPUs and CPUs. See the how-to
documentation on using GPUs with TensorFlow for details of how
TensorFlow assigns operations to devices, and the CIFAR-10 tutorial
for an example model that uses multiple GPUs.
Note that TensorFlow only uses GPU devices with a compute capability
greater than 3.5.
Does this mean Tensorflow can automatically make use of all CPU's on given machine or does it ned to be explicitly configured ?
CPUs are used via a "device" which is just a threadpool. You can control the number of threads if you feel like you need more:
sess = tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=NUM_THREADS))