gputil | Python module for getting the GPU status | GPU library

by anderskm Python Version: 1.4.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | gputil Summary

gputil is a Python library typically used in Hardware, GPU, Deep Learning, Pytorch, Tensorflow applications. gputil has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install gputil' or download it from GitHub, PyPI.

GPUtil is a Python module for getting the GPU status from NVIDA GPUs using nvidia-smi. GPUtil locates all GPUs on the computer, determines their availablity and returns a ordered list of available GPUs. Availablity is based upon the current memory consumption and load of each GPU. The module is written with GPU selection for Deep Learning in mind, but it is not task/library specific and it can be applied to any task, where it may be useful to identify available GPUs.

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gputil has a medium active ecosystem.

It has 967 star(s) with 106 fork(s). There are 12 watchers for this library.

It had no major release in the last 12 months.

There are 13 open issues and 15 have been closed. On average issues are closed in 64 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of gputil is 1.4.0

Quality

gputil has 0 bugs and 0 code smells.

Security

gputil has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gputil code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gputil is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

gputil releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

gputil saves you 94 person hours of effort in developing the same functionality from scratch.

It has 241 lines of code, 7 functions and 4 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed gputil and discovered the below as its top functions. This is intended to give you an instant insight into gputil implemented functionality, and help decide if they suit your requirements.

Prints information about the GPU
Return a list of all the GPUs available on the system
Safely cast string to float
Return a list of available GPUs
Returns the number of available GPUs
Returns the first available GPU
Return available GPUs

Get all kandi verified functions for this library.

gputil Key Features

No Key Features are available at this moment for gputil.

gputil Examples and Code Snippets

No Code Snippets are available at this moment for gputil.

Community Discussions

Trending Discussions on gputil

Wild discrepancies between training DeepLab ResNet V3 on Google Colab versus on local machine

Display GPU Usage While Code is Running in Colab

How to run two or more functions at the same time in Python3?

Google Colaboratory session abruptly ends when filling up shuffle buffer

Google colab pro GPU running extremely slow

QUESTION

Wild discrepancies between training DeepLab ResNet V3 on Google Colab versus on local machine

Asked 2021-Apr-21 at 20:24

I am attempting to train Deeplab Resnet V3 to perform semantic segmentation on a custom dataset. I had been working on my local machine however my GPU is just a small Quadro T1000 so I decided to move my model onto Google Colab to take advantage of their GPU instances and get better results.

Whilst I get the speed increase I was hoping for, I am getting wildly different training losses on colab compared to my local machine. I have copied and pasted the exact same code so the only difference I can find would be in the dataset. I am using the exact same dataset except for the one on colab is a copy of the local dataset on Google Drive. I have noticed that Drive orders file differently on Windows but I can't see how this is a problem since I randomly shuffle the dataset. I understand that these random splitting can cause small differences in the outputs however a difference of about 10x in the training losses doesn't make sense.

I have also tried running the version on colab with different random seeds, different batch sizes, different train_test_split parameters, and changing the optimizer from SGD to Adam, however, this still causes the model to converge very early at a loss of around 0.5.

Here is my code:

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ANSWER

Answered 2021-Mar-09 at 09:24

I fixed this problem by unzipping the training data to Google Drive and reading the files from there instead of using the Colab command to unzip the folder to my workspace directly. I have absolutely no idea why this was causing the problem; a quick visual inspection at the images and their corresponding tensors looks fine, but I can't go through each of the 6,000 or so images to check every one. If anyone knows why this was causing a problem, please let me know!

Source https://stackoverflow.com/questions/66529577

QUESTION

Display GPU Usage While Code is Running in Colab

Asked 2020-Dec-26 at 22:49

I have a program running on Google Colab in which I need to monitor GPU usage while it is running. I am aware that usually you would use nvidia-smi in a command line to display GPU usage, but since Colab only allows one cell to run at once at any one time, this isn't an option. Currently, I am using GPUtil and monitoring GPU and VRAM usage with GPUtil.getGPUs()[0].load and GPUtil.getGPUs()[0].memoryUsed but I can't find a way for those pieces of code to execute at the same time as the rest of my code, thus the usage numbers are much lower than they actually should be. Is there any way to print the GPU usage while other code is running?

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ANSWER

Answered 2020-Jun-30 at 09:14

Used wandb to log system metrics:

Source https://stackoverflow.com/questions/62620268

QUESTION

How to run two or more functions at the same time in Python3?

Asked 2020-Dec-17 at 02:35

I want to run functions at the same time.

Also I want to know how to get return values when process ended.

Here is my codes ↓

[function 1 : training model]

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ANSWER

Answered 2020-Dec-17 at 02:35

I solved the problem using 'Thread' not multiprocessing.

Because of GIL(Global Interpreter Lock) multiprocessing doesn't work.

This is my fixed code below.

Source https://stackoverflow.com/questions/65319458

QUESTION

Google Colaboratory session abruptly ends when filling up shuffle buffer

Asked 2020-Oct-26 at 20:02

I am using Google Colaboratory to train an image recognition algorithm, using TensorFlow 1.15. I have uploaded all needed files into Google Drive, and have gotten the code to run until the shuffle buffer finishes running. However, I get a "^C" in the dialog box, and cannot figure out what is going on.

Note: I have previously tried to train the algorithm on my PC, and did not delete the checkpoint files that were generated from the previous training session. Could that perhaps be the problem?

Code:

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ANSWER

Answered 2020-Oct-26 at 20:02

I can't run your code because you use some files in it. But I can tell you it is probably because you are using TF 1 with GPU, and in Colab downgrading is not easy when it comes to GPU.

For example, I don't see in your code that you've downgraded CUDA (to the version you want) like this:

Source https://stackoverflow.com/questions/64419191

QUESTION

Google colab pro GPU running extremely slow

Asked 2020-Mar-22 at 18:40

I am running a Convnet on colab Pro GPU. I have selected GPU in my runtime and can confirm that GPU is available. I am running exactly the same network as yesterday evening, but it is taking about 2 hours per epoch... last night it took about 3 minutes per epoch... nothing has changed at all. I have a feeling colab may have restricted my GPU usage but I can't work out how to tell if this is the issue. Does GPU speed fluctuate much depending on time of day etc? Here are some diagnostics which I have printed, does anyone know how I can investigate deeper what the root cause of this slow behaviour is?

I also tried changing to accelerator in colab to 'None', and my network was the same speed as with 'GPU' selected, implying that for some reason i am no longer training on GPU, or resources have been severely limited. I am using Tensorflow 2.1.

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ANSWER

Answered 2020-Mar-22 at 13:06

From Colab's FAQ:

The types of GPUs that are available in Colab vary over time. This is necessary for Colab to be able to provide access to these resources for free. The GPUs available in Colab often include Nvidia K80s, T4s, P4s and P100s. There is no way to choose what type of GPU you can connect to in Colab at any given time. Users who are interested in more reliable access to Colab’s fastest GPUs may be interested in Colab Pro.

If the code did not change, the issue is likely related to performance characteristics of the GPU types you happened to be connected to.

Source https://stackoverflow.com/questions/60798910

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install gputil

Open a terminal (Ctrl+Shift+T)
Type pip install gputil
Test the installation Open a terminal in a folder other than the GPUtil folder Start a python console by typing python in the terminal In the newly opened python console, type: import GPUtil GPUtil.showUtilization() Your output should look something like following, depending on your number of GPUs and their current usage: ID GPU MEM -------------- 0 0% 0%

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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