from SO this allows copy.copy(df) to work properly by definition 'always' deep __copy__ = copy __deepcopy__ = copy
Fix crash ingptManagerBenchmark#649 Fix Blip2 build error #695 Add pickle support forInferenceRequest#701 Fix Mixtral-8x7b build failure with custom_all_reduce #825 Fix INT8 GEMM shape #935 Minor bug fixes Performance [BREAKING CHANGES]Increase defaultfreeGpuMemoryFractionparameter from 0.85 to ...
) model = pickle.load(open("xgboost-model", "rb")) test_path = "/opt/ml/processing/test/test.csv" df = pd.read_csv(test_path, header=None) y_test = df.iloc[:, 0].to_numpy() df.drop(df.columns[0], axis=1, inplace=True) X_test = xgboost.DMatrix(df.values) predictions ...
You can generate your JSON pipeline definition using either the SageMaker Python SDK or the visual drag-and-drop Pipeline Designer feature in Amazon SageMaker Studio. The following image is a representation of the pipeline DAG that you create in this tutorial: The pipeline that you define in the...
Fix crash ingptManagerBenchmark#649 Fix Blip2 build error #695 Add pickle support forInferenceRequest#701 Fix Mixtral-8x7b build failure with custom_all_reduce #825 Fix INT8 GEMM shape #935 Minor bug fixes Performance [BREAKING CHANGES]Increase defaultfreeGpuMemoryFractionparameter from 0.85 to ...
Fix crash in gptManagerBenchmark #649 Fix Blip2 build error #695 Add pickle support for InferenceRequest #701 Fix Mixtral-8x7b build failure with custom_all_reduce #825 Fix INT8 GEMM shape #935 Minor bug fixes Performance [BREAKING CHANGES] Increase default freeGpuMemoryFraction parameter fro...
) model = pickle.load(open("xgboost-model", "rb")) test_path = "/opt/ml/processing/test/test.csv" df = pd.read_csv(test_path, header=None) y_test = df.iloc[:, 0].to_numpy() df.drop(df.columns[0], axis=1, inplace=True) X_test = xgboost.DMatrix(df.values) predictions ...