diff options
Diffstat (limited to 'misc/py-sagemaker-serve')
| -rw-r--r-- | misc/py-sagemaker-serve/Makefile | 29 | ||||
| -rw-r--r-- | misc/py-sagemaker-serve/distinfo | 3 | ||||
| -rw-r--r-- | misc/py-sagemaker-serve/files/patch-pyproject.toml | 44 | ||||
| -rw-r--r-- | misc/py-sagemaker-serve/pkg-descr | 11 |
4 files changed, 87 insertions, 0 deletions
diff --git a/misc/py-sagemaker-serve/Makefile b/misc/py-sagemaker-serve/Makefile new file mode 100644 index 000000000000..cf7cb024e6ee --- /dev/null +++ b/misc/py-sagemaker-serve/Makefile @@ -0,0 +1,29 @@ +PORTNAME= sagemaker-serve +DISTVERSION= 1.0 +CATEGORIES= misc python # machine-learning +MASTER_SITES= PYPI +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} +DISTNAME= ${PORTNAME:S/-/_/}-${PORTVERSION} + +MAINTAINER= yuri@FreeBSD.org +COMMENT= SageMaker: Library for training & deploying models on Amazon SageMaker +WWW= https://sagemaker.readthedocs.io/en/stable/ \ + https://github.com/aws/sagemaker-python-sdk + +LICENSE= APACHE20 + +BUILD_DEPENDS= ${PY_SETUPTOOLS} \ + ${PYTHON_PKGNAMEPREFIX}wheel>0:devel/py-wheel@${PY_FLAVOR} +RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}boto3>=1.35.75<2.0:www/py-boto3@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}botocore>=1.35.75<2.0:devel/py-botocore@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}psutil>=0:sysutils/py-psutil@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}sagemaker-core>=2.0.0:misc/py-sagemaker-core@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}sagemaker-train>=0.1.0:misc/py-sagemaker-train@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}tqdm>=0:misc/py-tqdm@${PY_FLAVOR} + +USES= python +USE_PYTHON= pep517 autoplist + +NO_ARCH= yes + +.include <bsd.port.mk> diff --git a/misc/py-sagemaker-serve/distinfo b/misc/py-sagemaker-serve/distinfo new file mode 100644 index 000000000000..5af71bb8afb4 --- /dev/null +++ b/misc/py-sagemaker-serve/distinfo @@ -0,0 +1,3 @@ +TIMESTAMP = 1764171850 +SHA256 (sagemaker_serve-1.0.tar.gz) = f5aeaf376e2cb41d476c5b1a9c06c347868acb0c5e5ffa67c2da373ac54f97d4 +SIZE (sagemaker_serve-1.0.tar.gz) = 148682 diff --git a/misc/py-sagemaker-serve/files/patch-pyproject.toml b/misc/py-sagemaker-serve/files/patch-pyproject.toml new file mode 100644 index 000000000000..9ee202738eec --- /dev/null +++ b/misc/py-sagemaker-serve/files/patch-pyproject.toml @@ -0,0 +1,44 @@ +Removed dependencies: +- deepdiff: declared but never imported or used in the codebase +- mlflow: optional, only used conditionally when sagemaker_mlflow is installed +- sagemaker_schema_inference_artifacts: optional, used in try/except fallback +- pytest: test-only dependency, not needed at runtime +- tqdm, psutil: handled via RUN_DEPENDS in Makefile +- tritonclient[http]: optional, used in try/except for Triton validation +- onnx, onnxruntime, torch: not available on FreeBSD; torch has a module-level + import in app.py but that module is only used for specific serving scenarios + +--- pyproject.toml.orig 2025-11-20 20:42:14 UTC ++++ pyproject.toml +@@ -1,5 +1,5 @@ + [build-system] +-requires = ["setuptools>=64", "wheel"] ++requires = ["setuptools", "wheel"] + build-backend = "setuptools.build_meta" + + [project] +@@ -23,16 +23,6 @@ dependencies = [ + "sagemaker-train>=0.1.0", + "boto3>=1.35.75,<2.0", + "botocore>=1.35.75,<2.0", +- "deepdiff", +- "mlflow", +- "sagemaker_schema_inference_artifacts", +- "pytest", +- "tqdm", +- "psutil", +- "tritonclient[http]", +- "onnx", +- "onnxruntime", +- "torch>=2.0.0" + ] + + [project.optional-dependencies] +@@ -49,7 +39,6 @@ dev = [ + ] + + [tool.setuptools] +-package-dir = {"" = "src"} + include-package-data = true + + [tool.setuptools.packages.find] diff --git a/misc/py-sagemaker-serve/pkg-descr b/misc/py-sagemaker-serve/pkg-descr new file mode 100644 index 000000000000..16dad05472d1 --- /dev/null +++ b/misc/py-sagemaker-serve/pkg-descr @@ -0,0 +1,11 @@ +sagemaker-train is a part of the SageMaker Python SDK. + +SageMaker Python SDK is an open source library for training and deploying +machine learning models on Amazon SageMaker. + +With the SDK, you can train and deploy models using popular deep learning +frameworks Apache MXNet and TensorFlow. You can also train and deploy +models with Amazon algorithms, which are scalable implementations of core +machine learning algorithms that are optimized for SageMaker and GPU training. +If you have your own algorithms built into SageMaker compatible Docker +containers, you can train and host models using these as well. |