AWS batch documentation change

Service: batch · 2026-04-19 · Documentation low

File: batch/latest/userguide/submit-job-quota-share.md

Summary

Completely restructured document from AWS CLI focus to comprehensive guide on submitting service jobs to quota shares, adding prerequisites and SageMaker Python SDK examples

Security assessment

This is a major documentation restructuring and expansion for quota management features. While it mentions IAM permissions in prerequisites, the primary focus is on functionality and usage patterns, not security vulnerability fixes or new security features.

Diff

diff --git a/batch/latest/userguide/submit-job-quota-share.md b/batch/latest/userguide/submit-job-quota-share.md
index 7ebcf2c3f..d0bbc0cdd 100644
--- a//batch/latest/userguide/submit-job-quota-share.md
+++ b//batch/latest/userguide/submit-job-quota-share.md
@@ -7 +7 @@
-Submit a job using the AWS CLI
+PrerequisitesSubmit a service job to a quota share
@@ -13 +13,58 @@ Quota management job queues require that all jobs specify a quota share at job s
-## Submit a job using the AWS CLI
+## Prerequisites
+
+Before submitting jobs to a quota share, ensure you have:
+
+  * **Quota management resources** – A scheduling policy, service environment, and job queue configured for quota management. For more information, see [Create quota management resources](./create-quota-management-resources.html).
+
+  * **Quota share** – At least one quota share created on the job queue. For more information, see [Creating quota shares](./create-quota-shares.html).
+
+  * **IAM permissions** – Permissions to submit jobs to AWS Batch. For more information, see [AWS Batch IAM policies, roles, and permissions](./IAM_policies.html).
+
+
+
+
+## Submit a service job to a quota share
+
+The table below shows how to submit a service job to a quota share using either the SageMaker Python SDK or the AWS CLI: 
+
+Submit using the SageMaker Python SDK
+    
+
+The [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/v3-examples/training-examples/aws_batch/sm-training-queues_quota-management.html) has built-in support for submitting jobs to a quota management enabled job queue. The following examples show how to create a model trainer, create a training queue, and submit jobs to a quota share. For a complete example, see the [full sample notebook](https://github.com/aws/sagemaker-python-sdk/blob/master/v3-examples/training-examples/aws_batch/sm-training-queues_quota-management.ipynb) on GitHub.
+
+Create a `ModelTrainer` that defines the training job configuration.
+    
+    
+    from sagemaker.train.model_trainer import ModelTrainer
+    from sagemaker.train.configs import SourceCode, Compute, StoppingCondition
+    
+    source_code = SourceCode(command="echo 'Hello World'")
+    
+    model_trainer = ModelTrainer(
+        training_image="123456789012.dkr.ecr.us-east-1.amazonaws.com/pytorch-training:2.5-gpu-py311",
+        source_code=source_code,
+        base_job_name="my-training-job",
+        compute=Compute(instance_type="ml.g5.xlarge", instance_count=1),
+        stopping_condition=StoppingCondition(max_runtime_in_seconds=300),
+    )
+
+Create a `TrainingQueue` object that references your quota management enabled job queue by name.
+    
+    
+    from sagemaker.train.aws_batch.training_queue import TrainingQueue
+    
+    queue = TrainingQueue("my-sagemaker-job-queue")
+
+Submit jobs to a quota share by calling `queue.submit` and specifying the `quota_share_name`. You should set a `priority` to influence job ordering within the quota share. A real-world `ModelTrainer` will require `inputs` so that it has data to train on.
+    
+    
+    job = queue.submit(
+        job_name="my-training-job",
+        training_job=model_trainer,
+        quota_share_name="my_quota_share",
+        priority=3,
+        inputs=None,
+    )
+
+Submit using the AWS CLI
+