I've tried to change Google Colab's runtime type to python >> GPU but it only gives me 68 gb of free space instead of 358GB.
You can use GCP to run Colab, using method in this article.
https://medium.com/#senthilnathangautham/colab-gcp-compute-how-to-link-them-together-98747e8d940e
You can specify any disk space, ram, etc. But it is not free.
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.
So I am using Google Colab because I have some functions I need to execute that take far too long on my cpu. I have set the runtime to the GPU accelrator, however when I run the cell, I still get this message: 'Warning: You are connected to a GPU runtime, but not utilizing the GPU'.
I understand that this means the code I am running is just using my cpu. However using my cpu, the function takes hours to execute. This is why I want to utilise Colab's GPU, however, even when I change runtime, it still uses my cpu... How do I specifically force Colab to utilise the GPU for executing a certain cell/function in Colab???
Edit: I have just found out apparently Colab uses GPU only when the package being used is a package specifically made for GPU usage. Is there some sort of external package I can use that forces a function to find a GPU to use before executing the function?
Edit: (The package I am using for the long calculation is Network X if that makes any difference)
Check out cuGraph, which lets you do the same graph calculations on the gpu as networkx. A medium post on compatibility between cuGraph and networkx graphs.
You only need to do a couple of things to get cuGraph working on Google Colab. As the Google Colab demo from this medium post suggests:
Use pynvml to confirm Colab allocated you a Tesla T4 GPU.
Install most recent Miniconda release compatible with Google Colab's Python install (3.6.7)
Install RAPIDS libraries
Copy RAPIDS .so files into current working directory, a workaround for conda/colab interactions
Update env variables so Python can find and use RAPIDS artifacts
!wget -nc https://github.com/rapidsai/notebooks-
extended/raw/master/utils/rapids-colab.sh
!bash rapids-colab.sh
import sys, os
sys.path.append('/usr/local/lib/python3.6/site-packages/')
os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'
os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'
And then you can do the same calculations on the gpu:
pagerank = cugraph.pagerank(G)
instead of
pagerank = nx.pagerank(G)
Could any body guide me the GPU memory memory provide by Colab pro +. I have been using colab pro but my ram is getting crashed when i try to train my model. Does Colab Pro+ GPU provides more memory than colab pro. Colab pro provides 12-15 gb memory depends on the GPU type.
Thanks
Please have a look at the image below to find the difference between Google Colab Pro and Colab Pro+.
As you can see, Colab Pro+ provides more memory with better performance than other subscriptions.
I've been using Google Colaboratory to do practice simple Python coding, and then today, my Google Colab crashed because it says I'm running out of RAM, only 0.77 GB out of 25 GB left. I moved a lot of my notebooks to trash and that didn't help. Can someone please help! Thank you!
RAM isn't consumed by Colab notebooks in Drive. Rather, RAM is consumed by Python variables you define in your notebook.
Can you share a self-contained notebook that reproduces the problem?
I have a USB TPU and would like to use it as LOCAL RUNTIME in Google Colab.
I was not able to find any resources on this topic.
You can use a local Runtime (local Jupyter) and it is explained here :
https://research.google.com/colaboratory/local-runtimes.html
Do I need to install all the TPU libraries in my local Jupyter and then connect to local Jupyter as local runtime to start using my USB TPU in Colab?
I'm not familiar with Google Colab, but looks like it allows you to expose your model on your hardware. You'll then need to locate your model in order to run inference with it. There are multiple ways that you can choose to run it which are all listed here:
https://coral.withgoogle.com/docs/edgetpu/inference/