kandi X-RAY | nvitop Summary
kandi X-RAY | nvitop Summary
An interactive NVIDIA-GPU process viewer, the one-stop solution for GPU process management. (screenshots). Monitor mode of nvitop. (TERM: GNOME Terminal / OS: Ubuntu 16.04 LTS (over SSH) / Locale: en_US.UTF-8).
Top functions reviewed by kandi - BETA
- Draw the screen
- Return the mean value of the metric
- Return the maximum value
- Iterate over the values of the histogram
- Select a list of devices
- Return a list of physical devices
- Check if the device is a leaf device
- Parse arguments
- Color a string
- Start the curses console
- Returns a mapping of GpuProcesses to GpuProcess instances
- Draw the machine
- Color text
- Return a copy of the CUDAError class
- Prints a progress bar
- Set the snapshot information
- Initializes the CUDA library
- Draw the tree
- Draws the screen
- Renders the display
- Draw the bar
- Takes a list of devices
- Print the snapshot information
- Renders the progress bar
- Prints the history
- Return a snapshot of the process
- Sends a signal to all processes
nvitop Key Features
nvitop Examples and Code Snippets
Trending Discussions on GPU
In my understanding, VkPhysicalDevice represents an implementation of Vulkan, which could be represented as a GPU and its drivers. We are supposed to record commands with VkCommandBuffers and send them through queues to, potentially, multithread the work we send to the gpu. That is why I understand the fact there can be multiple queues. I understand as well that QueueFamilies groups queues depending on the features they can do (the extensions available for them e.g. presentation, as well as graphics computations, transfer, etc).
However, if a GPU is able to do Graphics work, why are there queues unable to do so? I heard that using queues with less features could be faster, but why? What is a queue concretely? Is it only tied to vulkan implementation? Or is it related to hardware specific things?
I just don't understand why queues with different features exist, and even after searching through the Vulkan doc, StackOverflow, vulkan-tutorial and vkguide, the only thing I found was "Queues in Vulkan are an “execution port” for GPUs.", which I don't really understand and on which I can't find anything on google.
Thank you in advance for your help!...
ANSWERAnswered 2022-Apr-03 at 21:56
A queue is a thing that consumes and executes commands, such that each queue (theoretically) executes separately from every other queue. You can think of a queue as a mouth, with commands as food.
Queues within a queue family typically execute commands using the same underlying hardware to process them. This would be like a creature with multiple mouths but all of them connect to the same digestive tract. How much food they can eat is separate from how much food they can digest. Food eaten by one mouth may have to wait for food previously eaten by another to pass through the digestive tract.
Queues from different families may (or may not) have distinct underlying execution hardware. This would be like a creature with multiple mouths and multiple digestive tracts. If a mouth eats, that food need not wait for food from a different mouth to digest.
Of course, distinct underlying execution hardware is typically distinct for a reason. Several GPUs have specialized DMA hardware for doing copies to/from device-local memory. Such hardware will typically expose a queue family that only allows transfer operations, and those transfer operations may be restricted in their byte alignment compared to transfers done on graphics-capable queues.
Note that these are general rules. Sometimes queues within a family do execute on different hardware, and sometimes queues between families use much of the same hardware. The API and implementations don't always make this clear, so you may have to benchmark different circumstances.
- Does OpenCL local memory really exist on Mali/Adreno GPU or they only exist in some special mobile phones?
- If they exist, in which case should we use local memory, such as GEMM/Conv or other cl kernel?
ANSWERAnswered 2022-Mar-24 at 15:27
Interesting question. OpenCL defines a number of conceptual memories including local memory, constant memory, global memory, and private memory. And physically as you know, the hardware implementation of these memories is hardware dependent. For instance, some may emulate local memory using cache or system memory instead of having physical memory.
AFAIK, ARM Mali GPU does not have local memory, whereas Qualcomm Adreno GPU does have local memory.
For instance below table shows the definition of each memory in OpenCL and their relative latency and physical locations in Adreno GPU cited from OpenCL Optimization and Best Practices for Qualcomm Adreno GPUs∗
as commented by SK-logic below, Mali6xx have a local memory (shared with cache).
I use JavaFX with Java 8 and i set this properties before launching my app
The verbose mode for prism gives me this :
ANSWERAnswered 2022-Mar-09 at 05:23
For those who are trying to solve a similar issue, it might be coming from the java.exe executable not using the gpu you want as a default device, you can change that in Windows' settings.
I'm trying to setup a Google Kubernetes Engine cluster with GPU's in the nodes loosely following these instructions, because I'm programmatically deploying using the Python client.
I've already installed the NVIDIA DaemonSet with this yaml file: https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml
For some reason those 2 lines always seem to be in status "ContainerCreating" and "PodInitializing". They never flip green to status = "Running". How can I get the GPU's in the NodePool to become available in the node(s)?Update:
Based on comments I ran the following commands on the 2 NVIDIA pods;
kubectl describe pod POD_NAME --namespace kube-system.
To do this I opened the UI KUBECTL command terminal on the node. Then I ran the following commands:
gcloud container clusters get-credentials CLUSTER-NAME --zone ZONE --project PROJECT-NAME
Then, I called
kubectl describe pod nvidia-gpu-device-plugin-UID --namespace kube-system and got this output:
ANSWERAnswered 2022-Mar-03 at 08:30
According the docker image that the container is trying to pull (
gke-nvidia-installer:fixed), it looks like you're trying use Ubuntu daemonset instead of
You should run
kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml
This will apply the right daemonset for your
cos node pool, as stated here.
In addition, please verify your node pool has the
https://www.googleapis.com/auth/devstorage.read_only scope which is needed to pull the image. You can should see it in your node pool page in GCP Console, under Security -> Access scopes (The relevant service is Storage).
my computer has only 1 GPU.
Below is what I get the result by entering someone's code...
ANSWERAnswered 2021-Oct-12 at 08:52
For the benefit of community providing solution here
This problem is because when keras run with gpu, it uses almost all
vram. So we needed to give
memory_limitfor each notebook as shown below
I've run into an issue while attempting to use SSBOs as follows:...
ANSWERAnswered 2022-Feb-10 at 13:25
GLSL structs and C++ structs have different rules on alignment. For structs, the spec states:
If the member is a structure, the base alignment of the structure is N, where N is the largest base alignment value of any of its members, and rounded up to the base alignment of a vec4. The individual members of this substructure are then assigned offsets by applying this set of rules recursively, where the base offset of the first member of the sub-structure is equal to the aligned offset of the structure. The structure may have padding at the end; the base offset of the member following the sub-structure is rounded up to the next multiple of the base alignment of the structure.
Let's analyze the struct:
Consider the following kernel, which reduces along the rows of a 2-D matrix...
ANSWERAnswered 2022-Jan-21 at 18:57
Here is the code:
I'm working on Convolution Tasnet, model size I made is about 5.05 million variables.
I want to train this using custom training loops, and the problem is,...
ANSWERAnswered 2022-Jan-07 at 11:08
Gradient tape triggers automatic differentiation which requires tracking gradients on all your weights and activations. Autodiff requires multiple more memory. This is normal. You'll have to manually tune your batch size until you find one that works, then tune your LR. Usually, the tune just means guess & check or grid search. (I am working on a product to do all of that for you but I'm not here to plug it).
I've installed Windows 10 21H2 on both my desktop (AMD 5950X system with RTX3080) and my laptop (Dell XPS 9560 with i7-7700HQ and GTX1050) following the instructions on https://docs.nvidia.com/cuda/wsl-user-guide/index.html:
- Install CUDA-capable driver in Windows
- Update WSL2 kernel in PowerShell:
- Install CUDA toolkit in Ubuntu 20.04 in WSL2 (Note that you don't install a CUDA driver in WSL2, the instructions explicitly tell that the CUDA driver should not be installed.):
ANSWERAnswered 2021-Nov-18 at 19:20
Turns out that Windows 10 Update Assistant incorrectly reported it upgraded my OS to 21H2 on my laptop.
Checking Windows version by running
winver reports that my OS is still 21H1.
Of course CUDA in WSL2 will not work in Windows 10 without 21H2.
After successfully installing 21H2 I can confirm CUDA works with WSL2 even for laptops with Optimus NVIDIA cards.
I tried to train a model using PyTorch on my Macbook pro. It uses the new generation apple M1 CPU. However, PyTorch couldn't recognize my GPUs....
ANSWERAnswered 2021-Nov-18 at 03:08
It looks like PyTorch support for the M1 GPU is in the works, but is not yet complete.
From @soumith on GitHub:
So, here's an update. We plan to get the M1 GPU supported. @albanD, @ezyang and a few core-devs have been looking into it. I can't confirm/deny the involvement of any other folks right now.
So, what we have so far is that we had a prototype that was just about okay. We took the wrong approach (more graph-matching-ish), and the user-experience wasn't great -- some operations were really fast, some were really slow, there wasn't a smooth experience overall. One had to guess-work which of their workflows would be fast.
So, we're completely re-writing it using a new approach, which I think is a lot closer to your good ole PyTorch, but it is going to take some time. I don't think we're going to hit a public alpha in the next ~4 months.
We will open up development of this backend as soon as we can.
TL;DR: a public beta is at least 4 months out.
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