Kaggle: Kaggle provides free access to NVIDIA TESLA P100 GPUs.
You can get great development experience in relation to training deep learning models with GPU running right under the notebooks. You can instantiate a GPU-powered SageMaker Notebook Instance, for example, ml.p2.xlarge (NVIDIA K80) in $1.125/hour or ml.p3.2xlarge (NVIDIA V100) in $3.825/hour. You need an AWS account setup to get started with Amazon Sagemaker.
Amazon Sagemaker: Amazon sagemaker is an Amazon service that provides Jupyter notebooks with elastic compute and sharing.
Select the hardware accelerator as “GPU” in the pop-up window.įig 1. A pop-up window will open up with a drop-down menu. While using Google Colab notebook, you need to change the runtime type by clicking on the ‘Runtime’ tab and selecting ‘Change runtime type’.
Google Colab: Google Colab provides GPU-powered notebooks for free.
Here is the list of Jupyter notebook platforms that could be used for training deep learning models: I will be writing about it in my next post. There are online GPU Linux servers available (free and paid options) that can be used to train deep learning & machine learning models. When starting with GPUs, it is recommended to use rented options available online rather than buying your own GPU servers. The list consists of both freely available and paid options of online Jupyter notebook available with GPUs. In this post, you will get information regarding the online Jupyter notebooks platform (GPU-based) which you can use to get started with both, machine learning and deep learning.