This setup below gives us as much RAM as possible with 12 CPU cores in GCE (without paying for extended memory).
You can change the zone, if there are no ressources available.Depending on the size of your dataset, finetuning usually only takes a few hours. At the time of writing, this configuration only costs about $1.28 / hour in GCE, when using preemptible. You can remove the -preemptible flag from the command below, but keeping it reduces your cost to about 1/3 and allows Google to shut down your instance at any point.Replace YOURPROJECTID in the command below with the project id from your GCE project.Log in and initialize the cloud sdk with gcloud auth login and gcloud init and follow the steps until you are set up.The UI changed a bit and looks now like this. Request a quota limit increase for "GPU All Regions" to 1.Register a Google Cloud Account, create a project and set up billing (only once you set up billing, you can use the $300 dollar sign up credit for GPUs).
Stata mp only using 1.5 gb of ram install#
Install the Google Cloud SDK: Click Here.If you use your own server and not the setup described here, you will need to install CUDA and Pytorch on it. The GPT-NEO model needs at least 70 GB RAM. Note: The GPT2-xl model does run on any server with a GPU with at least 16 GB VRAM and 60 GB RAM. (Optional) Setup VM with V100 in Google Compute Engine