Account preparation checklist (quotas, Bedrock, tags)

Everything that has to be requested or configured on the class AWS account before the workshop, sized for a class of up to 100 students (100 for one-per-student resources, 200 for anything a student can have two of at once). GPU quota increases often take days and sometimes a support exchange, so file them three to four weeks ahead. Instance usage per episode is taken from the code in the episodes; if you change an instance type in an episode, change the quota here.

Region

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The shared workshop account runs in US East (Ohio), us-east-2. Quotas are per region, so file them there; the nuke config targets it; learners are told the region in the sign-in email and the setup page’s access check. The lesson’s price tables quote us-east-1 list prices (the AWS price list of 2026-10-16); Ohio prices are the same or within a few percent, which does not change any decision in the lesson. All notebook code reads the region from the session rather than hard-coding it, so the lesson also works unchanged in any other region a self-paced learner picks.

SageMaker service quotas

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Console: Service Quotas → AWS services → Amazon SageMaker, in the region the class uses. Names below are the quota names as the console lists them.

Quota	Request	Used by
ml.t3.medium for notebook instance usage	100	controller notebook, every episode
ml.g6e.xlarge for notebook instance usage	0 (optional)	only the optional self-study GPU-notebook episode; not needed for class
Total number of notebook instances	100	one controller per student
ml.m5.large for training job usage	200	XGBoost episode; PyTorch part 2; tuning (2 parallel jobs each)
ml.m5.xlarge for training job usage	200	PyTorch part 2 runs instance_count=2
ml.g4dn.xlarge for training job usage	100	PyTorch part 2 GPU comparison
ml.g5.xlarge for training job usage	100	fine-tuning episode (one job per student at once)
ml.g6e.xlarge for training job usage	100	optional: fine-tuning Exercise 2
Number of instances across all training jobs	250 (already applied)	account-level ceiling; 250 covers 100 students
ml.g5.xlarge for processing job usage	200	Processing Jobs episode (both jobs) and fine-tuning homework (two jobs per student, possibly at once)
ml.g6e.xlarge for processing job usage	100 (optional)	only the Processing Jobs episode’s 27B variant. Default is usually 0 and the increase takes days; the episode runs Qwen3-8B on ml.g5.xlarge without it
Number of instances across all processing jobs	250 (already applied)	account-level ceiling

Also check ml.g5.xlarge for training warm pool usage is left at its default of 0; the episodes do not use warm pools and a nonzero quota lets a student leave an idle GPU billing.

Learner access

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Learners sign in through IAM Identity Center with their university login. Before the workshop, add every registered user to the group that carries the workshop permission set, then email them the Identity Center console link for that account and permission set (the .../start/#/console?account_id=...&role_name=... deep link), together with the account name and region to expect. The link is deliberately not printed in the public lesson: it is not a credential, but a public start URL plus role name is the raw material for a convincing phishing page, and the lesson is reused outside UW. Ask learners to run the “verify your access” steps in the setup page; a missing assignment is the most common day-one problem and it cannot be fixed from the classroom in a hurry.

Permission set guard rails

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Learners reach the account through the console, the SageMaker SDK in notebooks, and, whether or not you teach it, the AWS CLI: the Identity Center portal offers “Command line or programmatic access” next to every account. All three are the same API, so the only control that holds across them is the permission set. The lesson tells learners that the account refuses endpoints, EC2, out-of-list instance types and untagged jobs; this section is what makes that true. Attach it as an inline policy on the workshop permission set alongside the SageMaker allow policy, replacing the instance-type list with the one your quota table allows.

JSON

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "NothingThatRunsUntilDeleted",
      "Effect": "Deny",
      "Action": [
        "sagemaker:CreateEndpoint",
        "sagemaker:CreateEndpointConfig",
        "sagemaker:CreateInferenceComponent",
        "ec2:RunInstances"
      ],
      "Resource": "*"
    },
    {
      "Sid": "OnlyLessonInstanceTypes",
      "Effect": "Deny",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateProcessingJob",
        "sagemaker:CreateHyperParameterTuningJob",
        "sagemaker:CreateTransformJob",
        "sagemaker:CreateNotebookInstance",
        "sagemaker:UpdateNotebookInstance"
      ],
      "Resource": "*",
      "Condition": {
        "ForAnyValue:StringNotEquals": {
          "sagemaker:InstanceTypes": [
            "ml.t3.medium",
            "ml.m5.large", "ml.m5.xlarge",
            "ml.g4dn.xlarge", "ml.g5.xlarge", "ml.g6.xlarge", "ml.g6e.xlarge", "ml.g6e.2xlarge"
          ]
        }
      }
    },
    {
      "Sid": "ProjectTagRequired",
      "Effect": "Deny",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateProcessingJob",
        "sagemaker:CreateHyperParameterTuningJob",
        "sagemaker:CreateNotebookInstance"
      ],
      "Resource": "*",
      "Condition": { "Null": { "aws:RequestTag/Project": "true" } }
    },
    {
      "Sid": "NameTagRequired",
      "Effect": "Deny",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateProcessingJob",
        "sagemaker:CreateHyperParameterTuningJob",
        "sagemaker:CreateNotebookInstance"
      ],
      "Resource": "*",
      "Condition": { "Null": { "aws:RequestTag/Name": "true" } }
    },
    {
      "Sid": "PurposeTagRequired",
      "Effect": "Deny",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateProcessingJob",
        "sagemaker:CreateHyperParameterTuningJob",
        "sagemaker:CreateNotebookInstance"
      ],
      "Resource": "*",
      "Condition": { "Null": { "aws:RequestTag/Purpose": "true" } }
    }
  ]
}

What each statement does, and what it costs you:

• NothingThatRunsUntilDeleted blocks the three resource kinds with no runtime cap. Nothing in the lesson creates any of them, so no episode changes. This is the statement that would have stopped the participant who spent a year’s credits from a terminal.
• OnlyLessonInstanceTypes refuses any job or notebook whose instance type is not on the list. The list must match the quota table above exactly, including the capacity fallbacks; a type you forget here fails for every learner at once, from the console too. Keep ml.g6e.xlarge on the list only if you want the optional GPU-notebook episode to be possible.
• The three tag statements refuse a job or notebook created without each of the cost-allocation keys. Every estimator, processor and tuner in the lesson passes all three, and the console’s notebook-instance form has the tags field the S3 and controllers episodes fill in, so nothing in the lesson is affected; a learner who deletes the tags= line gets an AccessDeniedException instead of an unattributed bill. Tuning-job child jobs are created by the SageMaker service with the tuner’s tags propagated, so they pass.

Three cautions. Test the policy with one job of each kind (training, processing, tuning, notebook creation from the console) a week before class, from a learner-level sign-in, not an administrator one. Second, the role the notebook instance assumes (ml-sagemaker-bedrock-use) is a separate identity from the permission set; what the SDK does from inside a notebook is governed by that role’s policy, so attach the same denies there, or the guard rail only covers the laptop. Third, set the permission set’s session duration to the length of a class day (8 hours) so a CLI login does not expire mid-episode; it is on the permission set’s General settings.

Bedrock

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Console: Service Quotas → Amazon Bedrock, and Bedrock → Model access.

• Enable model access for the two models the Bedrock episode uses: deepseek.v3-v1:0 (generation) and amazon.titan-embed-text-v2:0 (embeddings).
• Check the requests per minute and tokens per minute quotas for both (Service Quotas → Amazon Bedrock, search the model name). The class load, if fifty learners run the episode in the same quarter hour: the generation step is about 120 DeepSeek calls per learner (the class sample of 60 questions, two calls each) at ~3,000 input tokens each, so roughly 6,000 requests and 18 million input tokens per minute at the peak; the embedding step is about 9,000 Titan calls and 3 million tokens per learner, ten minutes for one learner alone at the 300,000 tokens-per-minute quota, so in class everyone pulls the staged copy of the matrix instead (use_instructor_embeddings = True, the default; see the Shared data bucket page). Ask for what the console lets you (many Bedrock quotas adjust in place, some need a support case); the notebook’s client retries with backoff, so a quota below the peak slows the run rather than failing it. The cross-region profile us.deepseek.v3-v1:0 usually carries a higher quota than the in-region ID and is a one-line swap in the episode. Splitting the room into a RAG track and a fine-tuning track halves the peak as well.
• The learner execution role needs Bedrock permissions, and the default SageMaker role has none: the Bedrock episode’s first Bedrock call fails with AccessDeniedException ... bedrock:ListInferenceProfiles until they are added. On the UW account the policy below is attached as the inline policy BedrockAccess to ml-sagemaker-use, the role the controller-notebook episode tells learners to pick, and to ml-sagemaker-bedrock-use, so either works; it went onto the default role after the console’s role dropdown kept saving the default instead of the chosen one, which cost a class session. Elsewhere, attach the policy below as an inline policy on whatever role the notebooks run with, with <account-id> filled in; aws iam put-role-policy --role-name <role> --policy-name BedrockAccess --policy-document file://bedrock-policy.json does it, and a policy change reaches running instances within a minute. InvokeModel must be allowed on both the application inference profile ARN and the underlying foundation-model ARN, or calls through the profile are denied; DeleteInferenceProfile is for the cleanup episode.

JSON

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "BedrockCallModels",
      "Effect": "Allow",
      "Action": ["bedrock:InvokeModel", "bedrock:InvokeModelWithResponseStream", "bedrock:Converse", "bedrock:ConverseStream"],
      "Resource": [
        "arn:aws:bedrock:*::foundation-model/*",
        "arn:aws:bedrock:*:<account-id>:application-inference-profile/*",
        "arn:aws:bedrock:*:<account-id>:inference-profile/*"
      ]
    },
    {
      "Sid": "BedrockTaggedProfiles",
      "Effect": "Allow",
      "Action": ["bedrock:CreateInferenceProfile", "bedrock:GetInferenceProfile", "bedrock:ListInferenceProfiles",
                 "bedrock:DeleteInferenceProfile", "bedrock:TagResource", "bedrock:ListTagsForResource"],
      "Resource": [
        "arn:aws:bedrock:*:<account-id>:application-inference-profile/*",
        "arn:aws:bedrock:*:<account-id>:inference-profile/*"
      ]
    },
    {
      "Sid": "BedrockCatalog",
      "Effect": "Allow",
      "Action": ["bedrock:GetFoundationModel", "bedrock:ListFoundationModels", "bedrock:ListInferenceProfiles"],
      "Resource": "*"
    }
  ]
}

Cost allocation tags

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• Confirm with the Public Cloud team that Name, Project, Purpose are active cost allocation keys on the account (KB 72454). The lesson uses exactly those three everywhere.
• Set a Budgets alert on the account at the planned class total, and a Cost Explorer saved report filtered on Project.

Data and files

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• One shared read-only bucket holds the WattBot inputs (metadata, questions, PDF corpus) and the Badger Scribe data plus your full fine-tune outputs. Staging commands and the bucket policy: Shared data bucket: staging before each term. Learners create one bucket each in the S3 episode and write all outputs there.

Capacity fallbacks to have on a slide

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• Processing Jobs episode: if ml.g6e.xlarge capacity is short, ml.g6e.2xlarge ($2.80/h, same GPU) is the only same-memory alternative; a 24 GB instance cannot hold the 27B model.
• Fine-tuning episode: ml.g6.xlarge ($1.13/h, L4, 24 GB) runs the mini job at about the same speed as ml.g5.xlarge.