command to start sagemaker training

include sample training

README.md

@@ -30,11 +30,16 @@ qc-cli --help
 # 1. Create config.yaml in the current directory
 qc-cli init
 
-# 2. Edit config.yaml — at minimum set s3.bucket and sagemaker.role_name
+# 2. Edit config.yaml — at minimum set s3.bucket and sagemaker.training.image_uri
 
 # 3. Provision AWS infrastructure (S3 bucket + SageMaker IAM role).
 #    This is the step that requires the AWS CDK CLI.
 qc-cli infra setup
+
+# 4. Upload training data, then submit a SageMaker training job.
+qc-cli upload ./my-dataset
+qc-cli train start
+qc-cli train status
 ```
 
 ## Configuration
@@ -51,8 +56,17 @@ s3:
 
 sagemaker:
   role_name: qc-cli-sagemaker-role
+  training:
+    image_uri: ""           # ECR URI for your training container
+    instance_type: ml.m5.xlarge
+    instance_count: 1
+    entry_point: null       # Optional: script inside source_dir
+    source_dir: null        # Optional: local dir packaged and uploaded automatically
+    hyperparameters: {}
 ```
 
+`hyperparameters` is a flat map of values passed to the training container. Valid keys depend on the selected training image and entry point.
+
 To provision an MLflow tracking server, set:
 
 ```yaml
@@ -101,6 +115,19 @@ qc-cli upload <file> --s3-key <key>  Upload a file to a custom S3 key
 
 Uploads use `s3.bucket` and `s3.data_prefix` from `config.yaml`. File uploads default to `s3://<bucket>/<data_prefix>/<filename>`. Directory uploads are recursive, preserve paths relative to the uploaded directory, and place files under `s3://<bucket>/<data_prefix>/`.
 
+### `train`
+
+```
+qc-cli train start              Submit a SageMaker training job
+qc-cli train status [job-name]  Show job status; defaults to the last submitted job
+qc-cli train list               List recent training jobs
+qc-cli train list --limit 3     Show a custom number of recent jobs
+```
+
+`train start` uses `s3://<bucket>/<data_prefix>/` as the training channel and writes outputs under `s3://<bucket>/<model_prefix>/`. If `sagemaker.training.source_dir` is set, the CLI packages that directory, uploads it beside the job output prefix, and passes `sagemaker_program`/`sagemaker_submit_directory` to the SageMaker container.
+
+The expected output artifact is SageMaker’s `model.tar.gz`, normally containing the trained model file your container writes to `/opt/ml/model`.
+
 ## AWS permissions required
 
 The IAM user or role running the CLI needs:
@@ -111,6 +138,7 @@ The IAM user or role running the CLI needs:
 | CreateRole, GetRole, DeleteRole, AttachRolePolicy, DetachRolePolicy | IAM |
 | CreateStack, UpdateStack, DeleteStack, DescribeStacks, DescribeStackEvents | CloudFormation |
 | GetCallerIdentity | STS |
+| CreateTrainingJob, DescribeTrainingJob, ListTrainingJobs | SageMaker AI |
 | CreateMlflowTrackingServer, DescribeMlflowTrackingServer, DeleteMlflowTrackingServer | SageMaker AI, when `mlflow.mode` is `create` or `existing` |
 
 `AdministratorAccess` covers all of the above.