You train models on GPU in the SageMaker ecosystem via 2 different components:
You can instantiate a GPU-powered SageMaker Notebook Instance, for example
p2.xlarge(NVIDIA K80) orp3.2xlarge(NVIDIA V100). This is convenient for interactive development - you have the GPU right under your notebook and can run code on the GPU interactively and monitor the GPU vianvidia-smiin a terminal tab - a great development experience. However when you develop directly from a GPU-powered machine, there are times when you may not use the GPU. For example when you write code or browse some documentation. All that time you pay for a GPU that sits idle. In that regard, it may not be the most cost-effective option for your use-case.Another option is to use a SageMaker Training Job running on a GPU instance. This is a preferred option for training, because training metadata (data and model path, hyperparameters, cluster specification, etc) is persisted in the SageMaker metadata store, logs and metrics stored in Cloudwatch and the instance automatically shuts down itself at the end of training. Developing on a small CPU instance and launching training tasks using SageMaker Training API will help you make the most of your budget, while helping you retain metadata and artifacts of all your experiments. You can see here a well documented TensorFlow example
amazon web services - AWS SageMaker on GPU - Stack Overflow
Why not all compute instance in sagemaker have gpus? how does a llm model supposed to run without it? and also Someone please explain the cost estimation
Which GPU instances are supported by the sagemaker algorithm forecasting-deepar?
no gpu runtime available for sagemaker studio lab every time i try?
Videos
You train models on GPU in the SageMaker ecosystem via 2 different components:
You can instantiate a GPU-powered SageMaker Notebook Instance, for example
p2.xlarge(NVIDIA K80) orp3.2xlarge(NVIDIA V100). This is convenient for interactive development - you have the GPU right under your notebook and can run code on the GPU interactively and monitor the GPU vianvidia-smiin a terminal tab - a great development experience. However when you develop directly from a GPU-powered machine, there are times when you may not use the GPU. For example when you write code or browse some documentation. All that time you pay for a GPU that sits idle. In that regard, it may not be the most cost-effective option for your use-case.Another option is to use a SageMaker Training Job running on a GPU instance. This is a preferred option for training, because training metadata (data and model path, hyperparameters, cluster specification, etc) is persisted in the SageMaker metadata store, logs and metrics stored in Cloudwatch and the instance automatically shuts down itself at the end of training. Developing on a small CPU instance and launching training tasks using SageMaker Training API will help you make the most of your budget, while helping you retain metadata and artifacts of all your experiments. You can see here a well documented TensorFlow example
If you want to train your model in a Sagemaker Studio notebook make sure you choose both a GPU instance type and GPU Image type: https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-images.html https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-available-instance-types.html
For example for Tensorflow GPU:
There literally are so many options to choose from, i am getting confused, would be helpful to get someones advice. Why do i see that many instances offered in aws sagemaker dont even have gpu?? i does that work? how is a llm model running without gpus?, only some models have gpu. some have more than 1. Any explaination on that will be helpful.