ONNX

Learn about ONNX conda environments.

A description of the ONNX 1.13 for CPU on Python 3.9 (version 1.0) conda environment.


Released	May 25, 2023
Description	This environment includes `oracle-ads`, `onnx`, `onnxruntime`, and their dependencies. The purpose of this environment is to offer a runtime for your ONNX model artifacts. ONNX is an open source, open model format that enables you to save a model from different machine learning (ML) libraries into a single, portable format that's independent of the training library. ONNX models can be deployed using model deployments. You can use this conda environment to convert models from different ML libraries into ONNX format, including the Oracle Labs AutoML model. Use ONNX for runtime inferencing, and plotting your model workflow. To get started with this conda environment, review the Getting Started notebook, using the Launcher.
Python Version	3.9
Slug	`onnx113_p39_cpu_v1`
Object Storage Path	`oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/ONNX_1.10_for_CPU_on_Python_3.9/1.0/onnx113_p39_cpu_v1`
Top Libraries	onnx (v1.13.0) onnxconverter-common (v1.13.0) onnxmltools (v1.11.0) onnxruntime (v1.14.1) oracle-ads (v2.8.5) scikit-learn (v1.1.1) lightgbm (v3.3.5) xgboost (v1.7.3) For a complete list of preinstalled Python libraries, see onnx113_p39_cpu_v1.txt.

A description of the ONNX 1.13 for GPU on Python 3.9 (version 1.0) conda environment.


Released	July 10, 2023
Description	This environment includes `oracle-ads`, `onnx`, `onnxruntime`, and their dependencies. The purpose of this environment is to offer a runtime for your ONNX model artifacts. ONNX is an open source, open model format that enables you to save a model from different machine learning (ML) libraries into a single, portable format that's independent of the training library. ONNX models can be deployed using model deployments. You can use this conda environment to convert models from different ML libraries into ONNX format, including the Oracle Labs AutoML model. Use ONNX for runtime inferencing, and plotting your model workflow. To get started with this conda environment, review the Getting Started notebook, using the Launcher.
Python Version	3.9
Slug	`onnx113_p39_gpu_v1`
Object Storage Path	`oci://service-conda-packs@id19sfcrra6z/service_pack/gpu/ONNX_1.10_for_GPU_on_Python_3.9/1.0/onnx113_p39_gpu_v1`
Top Libraries	onnx (v1.13.1) onnxconverter-common (v1.13.0) onnxmltools (v1.11.2) onnxruntime_gpu (v1.15.1) oracle-ads (v2.8.7) scikit-learn (v1.1.1) For a complete list of preinstalled Python libraries, see onnx110_p39_gpu_v1.txt.

A description of the ONNX 1.10 for CPU on Python 3.9 (version 1.0) conda environment.


Released	June 24, 2022
Description	This environment includes `oracle-ads`, `onnx`, `onnxruntime`, and their dependencies. The purpose of this environment is to offer a runtime for your ONNX model artifacts. ONNX is an open-source, open-model format that allows you to save a model from different machine learning (ML) libraries into a single, portable format that is independent of the training library. ONNX models can be deployed using model deployments. You can use this conda environment to convert models from different ML libraries into ONNX format, including the Oracle Labs AutoML model. Use ONNX for runtime inferencing, and plotting your model workflow. To get started with this conda environment, review the Getting Started notebook, using the Launcher.
Python Version	3.9
Slug	`onnx110_p39_cpu_v1`
Object Storage Path	`oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/ONNX_1.10_for_CPU_on_Python_3.9/1.0/onnx110_p39_cpu_v1`
Top Libraries	oracle-ads (v2.6.1) onnx (v1.10.2) onnxconverter-common (v1.9.0) onnxmltools (v1.10.0) onnxruntime (v1.10.0) For a complete list of preinstalled Python libraries, see onnx113_p39_cpu_v1.txt.

A description of the ONNX 1.10 for CPU on Python 3.8 (version 2.0) conda environment.


Released	December 1, 2022
Description	This environment is designed to test and run your ONNX model artifacts. ONNX is an open source, open model format which lets you to save a model from different machine learning (ML) libraries into a single, portable format that's independent of the training library. You can deploy ONNX models using the Data Science model deployments. Use this conda environment to convert models from most ML libraries into ONNX format. Then use the ONNX runtime to perform inferencing. Review the processing steps that your model makes by having ONNX generate a graph of the model work flow. To get started with this conda environment, review the Getting Started notebook, using the Launcher.
Python Version	3.8
Slug	`onnx110_p38_cpu_v2`
Object Storage Path	`oci://service-conda-packs@id19sfcrra6z/service_pack/cpu/ONNX_1.10_for_CPU_on_Python_3.8/2.0/onnx110_p38_cpu_v2`
Top Libraries	oracle-ads (v2.6.8) onnx (v1.10.2) onnxmltools (v1.11.0) onnxruntime (v1.12.0) For a complete list of preinstalled Python libraries, see onnx110_p38_cpu_v2.txt.