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Your agent queues training jobs, and a worker picks them up and runs them. There are four kinds:
  • Browser worker: a tab at train.auremi.ai. Browser workers are a great way to start training quickly, but they have memory limits. For larger jobs, install the desktop app or train on cloud compute.
  • Desktop app: a TensorFlow.js or Python worker without the browser’s memory limits. It runs unattended.
  • Python worker (coming soon): a command-line worker for a GPU machine. It will run PyTorch model repos, with no memory limits beyond the machine’s own.
  • Cloud compute: SageMaker or Vertex AI in your own cloud account.

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Pair a browser worker

1

Open the worker

On the training machine, open train.auremi.ai.
2

Request pairing

Enter your Auremi account email and an optional Device name, then click Pair this worker. Copy the eight-character pairing code it shows.
3

Approve it in the console

Sign in to the Auremi console and open Training Workers from the sidebar. Under Approve a training worker, enter the Pairing code and a Worker name. Leave Eligible jobs on Any compatible job, pick an Authorization length, and click Approve worker.
The worker starts by itself. On Training Workers its status reads available. If the code expires first (the time is shown under it), pair again. Authorization ends after the length you picked. Tick Extend authorization while this worker is healthy to keep it running, or click Renew on the worker later.

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Install the desktop app

Use the desktop app when a job needs more memory than a browser tab allows, or on a machine that should train with no browser open. It runs TensorFlow.js and Python workers.
  • Windows
  • Mac: coming soon
  • Linux: coming soon
Open the app and pair it the same way: enter your email, click Pair this worker, and approve the code on Training Workers. Then leave the app running.

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Run a worker from Python

Coming soon. The Python worker will run PyTorch model repos on a GPU machine from a terminal or a service manager, with no browser memory limits.

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Train on SageMaker or Vertex

Connect your cloud account once in the Auremi console. After that, ask your agent to run jobs there. It builds and uploads the training image itself the first time.

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SageMaker

1

Sign in to Auremi and open the setup guide

Sign in to the Auremi console. The setup guide and the AWS policy JSON you need are only shown there. Open Cloud Compute, stay on the GPU tab, choose SageMaker, click How do I set this up?, and pick Allow Coding Agent To Push Images.
2

Set up AWS

In AWS, using the policy JSON from that setup guide:
  • Create an IAM user or role for your coding agent, with the policy under See image upload policy JSON, so it can push the runner image to ECR.
  • On the machine where your agent runs, install Docker and sign the AWS CLI in with that identity. Check it with aws sts get-caller-identity. The agent builds and pushes the image with these, not with anything stored in Auremi.
  • Create the Auremi access role. Auremi assumes it to start, check, and stop training jobs, so they run and bill in your AWS account. Give it the trust policy under See Auremi access role trust policy JSON, which already contains your external ID, and the permissions under See SageMaker submit policy JSON.
  • Create the SageMaker execution role. Give it the trust policy under See SageMaker trust policy JSON and the permissions under See SageMaker runtime policy JSON.
3

Fill in the form

Back in the Auremi console, fill in the SageMaker form:
  • Display name, AWS account id, and Region
  • Execution role ARN: the execution role you just created.
  • Auremi access role ARN: the access role you just created.
  • ECR image: <account-id>.dkr.ecr.<region>.amazonaws.com/auremi/runner:latest. This is where your agent pushes the image.
  • The instance type (for example ml.g5.xlarge), Instance count, Max spend, Max runtime minutes, and Storage GB
Click Save SageMaker.
4

Ask your agent

Train on SageMaker

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Vertex AI

1

Sign in to Auremi and open the setup guide

Sign in to the Auremi console. Open Cloud Compute, choose Vertex, and click How do I set this up?. The guide lists what to set up in Google Cloud.
2

Set up Google Cloud

Following that guide:
  • Enable the Vertex AI, Artifact Registry, and IAM Service Account Credentials APIs.
  • Create a service account for Auremi’s jobs, and give it Vertex AI User, plus Service Account User on itself.
  • Create a Workload Identity Federation pool with an OIDC provider whose issuer is Auremi. The guide shows the issuer URL. Then grant your Auremi account Workload Identity User on the service account, using the exact subject the guide shows. Auremi starts, checks, and stops jobs only by acting as that service account, so they run and bill in your project.
  • On the machine where your agent runs, install Docker and sign gcloud in. The agent builds and pushes the image with these, not with anything stored in Auremi.
3

Fill in the form

Back in the Auremi console, fill in the Vertex form:
  • Display name, GCP project, GCP project number, and Region
  • Service account and Workload identity provider
  • Container image: an Artifact Registry address, such as us-central1-docker.pkg.dev/<gcp-project>/<repo>/auremi-runner:latest. This is where your agent pushes the image.
  • Machine type, Accelerator type, Accelerator count, Max spend, Max runtime minutes, and Boot disk GB
Click Save Vertex.
4

Ask your agent

Train on Vertex

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Manage workers

Each worker has a card on Training Workers. A worker shows offline when its tab or app is closed or asleep.
  • Drain finishes the current job and takes no new ones.
  • Stop pauses the worker.
  • Revoke removes its access immediately.