Request $100 in credits from Amazon Web Services or Google Cloud for your computing costs. Please allow 3 business days for your request to be reviewed. Credits will be provided to verified registrants until the supplies run out.
At Google, AI is in our DNA. Google was built on pioneering AI research and the principle of making the world’s information useful to people and businesses everywhere. AI powers Google products used and loved by billions, such as Search, Maps, Ads, and YouTube. We have leveraged this expertise to deliver a new, unified AI experience in our data cloud that gives every data scientist, data analyst, and ML engineer access to the same toolkit Google uses, to drive business outcomes at any scale. And Google’s unified AI platform, Vertex AI, is open and integrated across our entire technology stack.
Google Cloud redemption page: console.cloud.google.com/billing/redeem. Coupon codes are redeemable by both new users of GCP and existing customers.
New customers will need to create a billing account in order to redeem credits (which can be done at console.cloud.google.com/billing/redeem).
Existing customers should have an existing billing account.
Any issues with billing / coupon redemption can be raised directly with the billing team at https://cloud.google.com/support/billing
- Getting started with PyTorch on GCP tutorial
- AI Modeling MLOps Technical Tutorial videos
- Scalable ML Deployment Using PyTorch and Kubeflow Pipelines video
Amazon Web Services
AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure, putting machine learning in the hands of every developer, data scientist and expert practitioner. Every ML project is different, and with AWS, you can choose the infrastructure for your performance and budget needs and cloud services that best suit your ML workflow management requirements.
You can get started with PyTorch on AWS using Amazon SageMaker, a fully managed machine learning service that makes it easy and cost-effective to build, train, and deploy PyTorch models at scale. If you prefer to manage the infrastructure yourself, you can use the AWS Deep Learning AMIs or the AWS Deep Learning Containers, which come built from source and optimized for performance with the latest version of PyTorch to quickly deploy custom machine learning environments.
- AWS Credit redemption page: https://aws.amazon.com/awscredits/
- New customers will have to create a new AWS account. You will need a credit card to open your AWS account.
- Existing customers should have an existing billing account. You can log into your AWS account using this link.
- For additional information, see additional documentation and this page about AWS credits.
- Getting started: PyTorch in Deep Learning Containers
- Deep Learning AMI
- Using PyTorch with SageMaker
- Using PyTorch with AWS Inferentia and with AWS Neuron compiler.
- We have a SageMaker deep dive video series here.
- Learn about how you can get 2.3X higher throughput for your ML inference with Amazon EC2 Inf1 instances powered by AWS Inferentia here.