PyTorch

running pytorch on vast ai a complete guide introduction this guide walks you through setting up and running pytorch workloads on vast ai, a marketplace for renting gpu compute power whether you're training large models or running inference, this guide will help you get started efficiently prerequisites a vast ai account basic familiarity with pytorch install tls certificate for jupyter (optional) ssh client installed on your local machine and ssh public key added in account tab at cloud vast ai (optional) install and use vast cli (optional) docker knowledge for custom environments setting up your environment 1\ selecting pytorch template navigate to the templates tab to view available templates before choosing a specific instance, you'll need to select the appropriate pytorch template for your needs choose recommended pytorch template a container is built on the vast ai base image, inheriting its core functionality it provides a flexible development environment with pre configured libraries pytorch is pre installed at /venv/main/ for immediate use supports for both amd64 and arm64 (grace) architectures, especially on cuda 12 4+ you can select specific pytorch versions via the version tag selector 2\ choosing an instance click the play button to select the template and see gpus you can rent for pytorch workloads, consider gpu memory minimum 8gb for most models cuda version pytorch 2 0+ works best with cuda 11 7 or newer disk space minimum 50gb for datasets and checkpoints internet speed look for instances with >100 mbps for dataset downloads rent the gpu of your choice 3\ connecting to your instance click blue button on instance card in instances tab when it says "open" to access jupyter setting up your pytorch environment 1\ basic environment check open python's interactive shell in the jupyter terminal verify your setup by executing these commands in python's interactive shell in a jupyter terminal import torch print(f"pytorch version {torch version }") print(f"cuda available {torch cuda is available()}") print(f"gpu device {torch cuda get device name(0)}") 2\ data management for efficient data handling a) fast local storage mkdir /workspace/data cd /workspace/data b) dataset downloads \# using wget wget your dataset url \# using git lfs for larger files https //git lfs com/ sudo apt get install git lfs git lfs install git clone your dataset repo training best practices checkpoint management always save checkpoints to prevent data loss checkpoint dir = '/workspace/checkpoints' os makedirs(checkpoint dir, exist ok=true) checkpoint = { 'epoch' epoch, 'model state dict' model state dict(), 'optimizer state dict' optimizer state dict(), 'loss' loss, } torch save(checkpoint, f'{checkpoint dir}/checkpoint {epoch} pt') resource monitoring monitor gpu usage watch n 1 nvidia smi or in python def print gpu utilization() print(torch cuda memory allocated() / 1024 2, "mb allocated") print(torch cuda memory reserved() / 1024 2, "mb reserved") cost optimization instance selection use vast cli search offers command to search for machines that fit your budget monitor your spending in vast ai's billing tab resource utilization use appropriate batch sizes to maximize gpu utilization enable gradient checkpointing for large models implement early stopping to avoid unnecessary compute time troubleshooting common issues and solutions out of memory (oom) errors reduce batch size enable gradient checkpointing use mixed precision training from torch cuda amp import autocast, gradscaler scaler = gradscaler() with autocast() outputs = model(inputs) loss = criterion(outputs, labels) scaler scale(loss) backward() slow training check gpu utilization verify data loading pipeline consider using torch compile() for pytorch 2 0+ model = torch compile(model) connection issues use tmux or screen for persistent sessions set up automatic reconnection in your ssh config best practices environment management document your setup and requirements keep track of software versions data management use data versioning tools implement proper data validation set up efficient data loading pipelines training management implement logging (e g , wandb, tensorboard) set up experiment tracking use configuration files for hyperparameters advanced topics multi gpu training for distributed training model = torch nn dataparallel(model) mixed precision training enable amp for faster training from torch cuda amp import autocast with autocast() outputs = model(inputs) custom docker images create a custom docker image from your own dockerfile and create your own template as needed from pytorch/pytorch 2 1 0 cuda11 8 cudnn8 runtime \# install additional dependencies run pip install wandb tensorboard \# add your custom requirements copy requirements txt run pip install r requirements txt conclusion running pytorch on vast ai provides a cost effective way to rent cheap gpus and accelerate deep learning workloads by following this guide and best practices, you can efficiently set up and manage your pytorch workloads while optimizing costs and performance additional resources pytorch documentation vast ai documentation pytorch performance tuning guide