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Creating an Account

You must have a valid Google account to use the trainML platform. To ensure you are creating an account with the correct google account, make sure you are signed in to Google using that account on the browser you use to login to trainML.

Login with Google

  1. Click the Login/Signup button in the upper right corner
  2. Click the Continue with Google button.
  3. If you have multiple Google accounts, you may be prompted to select the account to use. Select the appropriate account and complete your Google login
  4. Once you are redirected to trainML, you should be able to update your profile information. Click Next.
  5. If you accept the terms of use, your new account will be created.

Updating Your Profile

If you need to update your profile information at any time, click your name/profile picture on the top right next to the Logout button. This will navigate you to the Account Profile page. Here you can update your profile information, configure new job defaults, and add third-party keys to use in training jobs.

Managing Third-Party Keys

In order to enable training job workers to utilize other cloud-provider data stores or services, you can configure access credentials here that you can later attach to job workers. Although configuring a third-party key is not mandatory for using a training job, it is the most secure way of transferring data to and from the job workers.


Once you configure a third-party key, you will no longer be able to retrieve the key's secret. If a configured key's secret is blank when revisiting the page, this does not mean the secret is lost. However, if you edit the key and click save without re-entering the key secret, it will be erased.

Currently, the trainML platform supports AWS IAM keys, GCP Service Account keys, and a generated SSH key to use with private Git repositories.

AWS Keys


Never provide trainML (or anyone for that matter) credentials for an IAM user with admin privileges

Create a new IAM user for the trainML platform to use. It is not recommended to reuse an existing IAM user. Follow the AWS documentation for this step. While creating the user, ensure you create a very specific policy that allows access only to the specific data or services needed to complete the training job. As an example, the following policy allows the user to download a dataset a specific path of one bucket, and allows the ability to upload the final output to a specific path in another bucket.

"Version": "2012-10-17",
"Statement": [
"Sid": "Stmt0",
"Effect": "Allow",
"Action": ["s3:GetObject", "s3:ListBucket"],
"Resource": [
"arn:aws:s3:::<name of bucket with data>",
"arn:aws:s3:::<name of bucket with data>/path/to/data/*"
"Sid": "Stmt1",
"Effect": "Allow",
"Action": ["s3:PutObject"],
"Resource": [
"arn:aws:s3:::<name of bucket for model outputs>/path/to/outputs/*"

Once the user is created, go back to the trainML Account Profile page, and select AWS from the Add menu under Third-Party Keys. Copy the Access Key ID and the Secret Key from the AWS console to the relevant fields and click the check button.

Git SSH Key

If you want to run training jobs using private git repositories, you must create an SSH key for the trainML platform to use when connecting to your repository. Click the Generate button to create a new key. Once the key is created, copy the entire public key starting from ssh-ed25519 and up to and including and attach this key user in your private repository. For example, the instructions for adding a SSH key to your Github account can be found here

API Keys

API keys are used to secure programmatic access to the trainML platform when using the trainML CLI and SDK. To create a new API key, click the Create button in thi section. This will automatically download a credentials.json file. This file can only be generated once per API key.


Treat this file as a password, as anyone with access to your API key will have the ability to create and control resources in your trainML account.

You can deactivate any API key by clicking the Remove button.

For more information on how to use the API keys, review the details on SDK authentication.

Automatic Top-Up

By default, trainML will not bill your credit card unless you manually purchase credits. If you run out of credits while a job is running, the job will be stopped. To prevent this, you can enabled Automatic Top-ups by clicking the Set Automatic Top-Up button. Check the Enable automatic Top-up checkbox to enable the form. By default, every time an automatic top-up is performed, you will receive an email notification. If you wish to disable this, uncheck the Send me an email notification when a top-up occurs box. The Top-up Credits Threshold is the level of credits your account must dip below before an automatic top-up is triggered and the Top-up Credits Amount is the amount of credits to purchase once that threshold is passed. For example, if you set the Top-up Credits Threshold to 10 and the Top-up Credits Amount to 30 and you have a job running that consumes 1.5 credits per hour with a current account balance of 11, when the job bills you for an additional hour your credits drop to will drop to 9.5. This is below the credits threshold of 10 and will therefore trigger a top-up. Your default payment method will automatically be charged for 30 credits (your Top-up Credits Amount) and, if the payment is successful, your credit balance will be 39.5.

Default Job Settings

Notebook Environment - The default job environment to use for new Notebooks. This setting can be overridden on a per job basis.

Training Environment - The default job environment to use for new Training Jobs. This setting can be overridden on a per job basis.

Inference Environment - The default job environment to use for new Inference Jobs. This setting can be overridden on a per job basis.