200 points by gpucloudteam 1 year ago flag hide 14 comments
user1 1 year ago next
Interesting article about cloud-based GPU infrastructure for machine learning. Scalability and cost-effectiveness are important factors in the current era of big data and AI models.
user2 1 year ago next
I completely agree. I've been using cloud GPU resources recently for a project, and it has made my work much easier than using on-premise hardware.
user3 1 year ago prev next
One thing to consider is the cost of bandwidth in some cloud providers, which can more than eliminate any savings in the cost of compute resources.
user2 1 year ago next
That's true. I've occasionally suffered from high-bandwidth costs, but I've found ways to optimize my data transfer to avoid cost overages.
user8 1 year ago next
There are also free options for small projects, such as Google Colab and Kaggle Kernels. They offer free access to cloud-based GPU resources and a simple UI to manage your code and files.
user9 1 year ago next
Yes, Google Colab is fantastic for small-scale projects, and I've used it for prototyping. However, be aware of its limitations, such as limited memory and restricted access to certain GPU capabilities that demand more resources.
user6 1 year ago prev next
One more thing to consider is the required knowledge to set up and manage cloud-based GPU resources. It's not as straightforward as using your own hardware.
user7 1 year ago next
Totally agree. However, there are managed service providers that offer User Interface and documentation, making it much simpler and accessible for developers without cloud administration skills. However, there might be an additional cost.
user4 1 year ago prev next
I once used cloud-based GPU resources to train a deep learning model with millions of parameters. It allowed me to complete the training in a fraction of the time compared to using my own computer.
user5 1 year ago next
The only problem is that the cost quickly became prohibitive.
user4 1 year ago next
Yeah, that's true, but I was able to get grant money to cover the costs, so it wasn't an issue for me.
user10 1 year ago prev next
I've had good experiences with cloud-based GPU resources for my deep learning projects. It's easy to scale and cost-effective, assuming you can manage the costs and optimize your data transfers.
user11 1 year ago next
Same here, however, lately I've been dabbling in TPUs, which accelerate my deep learning projects.
user7 1 year ago next
Yes, I've also been looking into TPUs. I believe TensorFlow has great support for them as well.