Does using a GPU on jupyter require needing a GPU on your own laptop or is it similar to how Google colabs does theirs.
Related
Every time I need to train a 'large' deep learning model I do it from Google Collab, as it allows you to use GPU acceleration.
My pc has a dedicated GPU, I was wondering if it is possible to use it to run my notebooks locally in a fast way. Is it possible to train models using my pc GPU? In that case, how?
I am open to work with DataSpell, VSCode or any other IDE.
Nicholas Renotte has a great 'Getting Started' video that goes through the entire process of setting up GPU accelerated notebooks on your PC. The stuff you're interested starts around the 12 minute mark.
Yes, it is possible to run .ipynb notebooks locally using GPU acceleration. To do so, you will need to install the necessary libraries and frameworks such as TensorFlow, PyTorch, or Keras. Depending on the IDE you choose, you will need to install the relevant plugins and packages for GPU acceleration.
In terms of IDEs, DataSpell, VSCode, PyCharm, and Jupyter Notebook are all suitable for running notebooks locally with GPU acceleration.
Once the necessary libraries and frameworks are installed, you will then need to install the appropriate drivers for your GPU and configure the environment for GPU acceleration.
Finally, you will need to modify the .ipynb notebook to enable GPU acceleration and specify the number of GPUs you will be using. Once all the necessary steps have been taken, you will then be able to run the notebook locally with GPU acceleration.
I've coded a Neural Network from scratch in Python and I am using Google Colaboratory to train it. However, if I enable GPU or TPU acceleration, the training is not faster.
When you search for examples online, all of them use Tensorflow and other libraries, and their training times are shorter with GPU than without it.
Am I doing it correctly or am I missing something and the GPU is not being used?
Just enabling GPU or TPU won't help your problem, you need to explicitly code them to run on GPU if you are not using any frameworks or libraries.
I'm new to Kaggle Notebooks I've been working with Google colab when I want access to cloud GPU/TPU I've been trying to set a notebook with GPU In Kaggle but I don't see any settings for GPU environment.
This is my new notebook.
Can someone show me how I can set a notebook with GPU on Kaggle kennels.
I am working with tensorflow 2.0 beta, and while i managed to get my GPU working on anaconda through a few youtube tutorials I am unable to get my gpu running in google colab. I know google has the option to enable a gpu from one of their servers but My GTX 1070 is much faster, and i need to run off colab and not just Jupyter exclusively.
So I read the documentation like a good boy and the only thing i think i could have done wrong is my path settings I have screenshots bellow.
I followed several different youtube tutorials faithfully until the final one here gave me a way to install it to jupyter. Which is great, but I also need it to run on google colab as well.
I've been trying this since Friday and it's now tuesday and I'm losing my mind over this. Help me stackoverflow, you're my only hope.
https://imgur.com/a/8WibGWT
If you can get it running on your own Jupyter server then you can point colab to that local server.
Full instructions here: https://research.google.com/colaboratory/local-runtimes.html but edited highlights are:
install jupyter_http_over_ws:
pip install jupyter_http_over_ws
jupyter serverextension enable --py jupyter_http_over_ws
start your local server allowing colab domain:
jupyter notebook \
--NotebookApp.allow_origin='https://colab.research.google.com' \
--port=8888 \
--NotebookApp.port_retries=0
Click 'connect to local runtime' in colab
I am training a CNN on GCP's notebook using a Tesla V100. I've trained a simple yolo on my own custom data and it was pretty fast but not very accurate. So, I decided to write my own code from scratch to solve the specific aspects of the problem that I want to tackle.
I have tried to run my code on Google Colab prior to GCP, and it went well. Tensorflow detects the GPU and is able to use it whether it was a Tesla K80 or T4.
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
tf.test.is_gpu_available() #>>> True
My problem is that, this same function returns a False on GCP notebook, as if Tensorflow is unable to use the GPU it detected on GCP VM. I don't know of any command that forces Tensorflow to use the GPU over CPU, since it does that automatically.
I have already tried to install or uninstall and then install some versions of tensorflow, tensorflow-gpu and tf-nightly-gpu (1.13 and 2.0dev for instance) but it yielded nothing.
output of nvidia-smi
Have you tried using GCP's AI Platform Notebooks instead? They offer VMs that are pre-configured with Tensorflow and have all required GPU drivers installed.