fix: validation script fixes

Author: weklund

CHANGELOG.md

@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### **Added**
 
 ### **Changed**
+- updated GitHub repo creation in `sagemaker-templates` module to support GitHub organizations
 
 ## v2.1.0
 

modules/sagemaker/sagemaker-templates/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.ruff]
 line-length = 120
-target-version = "py38"
+target-version = "py310"
 extend-include = ["*.ipynb"]
 
 exclude = [
@@ -24,7 +24,7 @@ select = ["F", "I", "E", "W"]
 fixable = ["ALL"]
 
 [tool.mypy]
-python_version = "3.8"
+python_version = "3.10"
 strict = true
 ignore_missing_imports = true
 disallow_untyped_decorators = false

modules/sagemaker/sagemaker-templates/templates/finetune_llm_evaluation/pipeline_constructs/build_pipeline_construct.py

@@ -228,8 +228,7 @@ def __init__(
                     "sagemaker:ListTags",
                 ],
                 resources=[
-                    f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/"
-                    f"{sagemaker_pipeline_name}",
+                    f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/{sagemaker_pipeline_name}",
                     f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/"
                     f"{sagemaker_pipeline_name}/execution/*",
                 ],

modules/sagemaker/sagemaker-templates/templates/finetune_llm_evaluation/seed_code/build_app/source_scripts/evaluate.py

@@ -77,7 +77,7 @@ def evaluate_model(args: Any) -> None:
         os.makedirs(args.model_dir, exist_ok=True)
         tar_file.extractall(args.model_dir)
 
-    logger.info(f"Decompressed Model assets: {os.listdir( args.model_dir )}")
+    logger.info(f"Decompressed Model assets: {os.listdir(args.model_dir)}")
 
     # Load test dataset
     test_dataset = load_from_disk(args.test_data_dir)
@@ -88,7 +88,7 @@ def evaluate_model(args: Any) -> None:
     # ensure that we do not have any trailing/leading whitespaces, this can lead to issues during inference
     test_data = test_dataset.map(lambda sample: {"prompt": sample["prompt"].strip()})
     logger.info("Loading test dataset")
-    logger.info(f"Test dataset has {len( test_data )} samples")
+    logger.info(f"Test dataset has {len(test_data)} samples")
 
     model = AutoModelForCausalLM.from_pretrained(args.model_dir, device_map="auto", torch_dtype=torch.float16)
 

modules/sagemaker/sagemaker-templates/templates/finetune_llm_evaluation/seed_code/build_app/source_scripts/train.py

@@ -71,7 +71,7 @@ def create_peft_model(model: Any, gradient_checkpointing: bool = True, bf16: boo
 
     # get lora target modules
     modules = find_all_linear_names(model)
-    logger.info(f"Found {len( modules )} modules to quantize: {modules}")
+    logger.info(f"Found {len(modules)} modules to quantize: {modules}")
 
     peft_config = LoraConfig(
         r=64,

modules/sagemaker/sagemaker-templates/templates/model_deploy/product_stack.py

@@ -111,8 +111,7 @@ def __init__(
         dev_region: str = Aws.REGION
 
         model_package_arn = (
-            f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:model-package/"
-            f"{model_package_group_name}/*"
+            f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:model-package/{model_package_group_name}/*"
         )
         model_package_group_arn = (
             f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:model-package-group/"

modules/sagemaker/sagemaker-templates/templates/xgboost_abalone/pipeline_constructs/build_pipeline_construct.py

@@ -262,8 +262,7 @@ def __init__(
                     "sagemaker:ListTags",
                 ],
                 resources=[
-                    f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/"
-                    f"{sagemaker_pipeline_name}",
+                    f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/{sagemaker_pipeline_name}",
                     f"arn:{Aws.PARTITION}:sagemaker:{Aws.REGION}:{Aws.ACCOUNT_ID}:pipeline/"
                     f"{sagemaker_pipeline_name}/execution/*",
                 ],