diff --git a/_sources/features/custom_scripts.md.txt b/_sources/features/custom_scripts.md.txt index f4a8b6669..14ab4ba22 100644 --- a/_sources/features/custom_scripts.md.txt +++ b/_sources/features/custom_scripts.md.txt @@ -51,8 +51,8 @@ Use `my_script.py` with Olive workflow configuration json file(sub_types name sh "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ], "user_config":{ "post_processing_func": "post_process", @@ -103,8 +103,8 @@ Use `script_dir` and `my_script.py` with Olive workflow configuration json file: "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ] "user_config":{ "post_processing_func": "post_process", diff --git a/_sources/overview/options.md.txt b/_sources/overview/options.md.txt index ae6b31e50..9870fa72e 100644 --- a/_sources/overview/options.md.txt +++ b/_sources/overview/options.md.txt @@ -233,7 +233,7 @@ information of the evaluator contains following items: - `type: [str]` The type of the metric. The supported types are `accuracy`, `latency`, `throughput` and `custom`. - `backend: [str]` The type of metrics' backend. Olive implement `torch_metrics` and `huggingface_metrics` backends. The default value is `torch_metrics`. - - `torch_metrics` backend uses `torchmetrics` library to compute metrics. It supports `accuracy_score`, `f1_score`, `precision`, `recall` and `auc` metrics. + - `torch_metrics` backend uses `torchmetrics`(>=0.1.0) library to compute metrics. It supports `accuracy_score`, `f1_score`, `precision`, `recall` and `auroc` metrics which are used for `binary` task (equal to `metric_config:{"task": "binary"}`) by default. You need alter the `task` if needed. Please refer to [torchmetrics](https://lightning.ai/docs/torchmetrics/stable/) for more details. - `huggingface_metrics` backend uses huggingface `evaluate` library to compute metrics. The supported metrics can be found at [huggingface metrics](https://huggingface.co/metrics). - `subtypes: [List[Dict]]` The subtypes of the metric. Cannot be null or empty. Each subtype is a dictionary that contains following items: @@ -311,8 +311,8 @@ information of the evaluator contains following items: "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ], "user_config":{ "post_processing_func": "post_process", diff --git a/_sources/tutorials/configure_metrics.rst.txt b/_sources/tutorials/configure_metrics.rst.txt index ff45262e1..882272cc7 100644 --- a/_sources/tutorials/configure_metrics.rst.txt +++ b/_sources/tutorials/configure_metrics.rst.txt @@ -19,8 +19,8 @@ Accuracy Metric "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ], "user_config": { "post_processing_func": "post_process", @@ -227,8 +227,8 @@ If you have multiple metrics to evaluate, you can configure them in the followin "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ] }, { diff --git a/features/custom_scripts.html b/features/custom_scripts.html index 86510ad66..7a7a24374 100644 --- a/features/custom_scripts.html +++ b/features/custom_scripts.html @@ -184,8 +184,8 @@

Examples"type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ], "user_config":{ "post_processing_func": "post_process", @@ -230,8 +230,8 @@

Examples "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ] "user_config":{ "post_processing_func": "post_process", diff --git a/overview/options.html b/overview/options.html index 19be5205c..39626277d 100644 --- a/overview/options.html +++ b/overview/options.html @@ -433,7 +433,7 @@

Evaluators Information

type: [str] The type of the metric. The supported types are accuracy, latency, throughput and custom.

  • backend: [str] The type of metrics’ backend. Olive implement torch_metrics and huggingface_metrics backends. The default value is torch_metrics.
    • @@ -507,8 +507,8 @@

    Example "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ], "user_config":{ "post_processing_func": "post_process", diff --git a/searchindex.js b/searchindex.js index 10e6ed8d2..f77493018 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api/engine", "api/evaluator", "api/metric", "api/models", "api/passes", "api/resource_path", "api/search-algorithms", "api/systems", "examples", "extending_olive/design", "extending_olive/how_to_add_optimization_pass", "features/azureml_integration", "features/custom_scripts", "features/huggingface_model_optimization", "features/model_transformations_and_optimizations", "features/packaging_output_models", "features/passes/onnx", "features/passes/openvino", 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Implement the run function": [[10, "implement-the-run-function"]], "Azure ML integration": [[11, "azure-ml-integration"]], "Olive Core": [[11, "olive-core"]], "Using AzureML registered model": [[11, "using-azureml-registered-model"]], "Using AzureML curated model": [[11, "using-azureml-curated-model"]], "Using model stored in AzureML datastore": [[11, "using-model-stored-in-azureml-datastore"]], "Using a model from an AzureML job output": [[11, "using-a-model-from-an-azureml-job-output"]], "Using data stored in AzureML datastore": [[11, "using-data-stored-in-azureml-datastore"]], "Using Azure ML compute as host or target": [[11, "using-azure-ml-compute-as-host-or-target"]], "Connect your own device to Azure ML as target or host by Azure Arc": [[11, "connect-your-own-device-to-azure-ml-as-target-or-host-by-azure-arc"]], "Azure ML Helper Scripts": [[11, "azure-ml-helper-scripts"]], "Custom Scripts": [[12, "custom-scripts"]], "user_script": [[12, "user-script"]], "Examples": [[12, "examples"], [12, "id1"]], "script_dir": [[12, "script-dir"]], "Huggingface Model Optimization": [[13, "huggingface-model-optimization"]], "Introduction": [[13, "introduction"]], "Model config and dataset config": [[13, "model-config-and-dataset-config"]], "Metric config": [[13, "metric-config"]], "Custom components config": [[13, "custom-components-config"]], "Script example": [[13, "script-example"]], "E2E example": [[13, "e2e-example"]], "Model Transformations and Optimizations": [[14, "model-transformations-and-optimizations"]], "Packaging Olive artifacts": [[15, "packaging-olive-artifacts"]], "What is Olive Packaging": [[15, "what-is-olive-packaging"]], "Zipfile": [[15, "zipfile"]], "CandidateModels": [[15, "candidatemodels"]], "Inference config file": [[15, "inference-config-file"]], "SampleCode": [[15, "samplecode"]], "How to package Olive artifacts": [[15, "how-to-package-olive-artifacts"]], "ONNX": [[16, "onnx"]], "Model Conversion": [[16, "model-conversion"], [17, "model-conversion"], [19, "model-conversion"]], "Example Configuration": [[16, "example-configuration"], [16, "id1"], [16, "id2"], [16, "id3"], [16, "id4"], [16, "id5"], [16, "id6"], [16, "id7"], [16, "id8"], [17, "example-configuration"], [17, "id1"], [18, "example-configuration"], [18, "id1"], [18, "id2"], [18, "id3"], [18, "id4"], [19, "example-configuration"], [19, "id1"]], "Model Optimizer": [[16, "model-optimizer"]], "ORT Transformers Optimization": [[16, "ort-transformers-optimization"]], "Append Pre/Post Processing Ops": [[16, "append-pre-post-processing-ops"]], "Insert Beam Search Op": [[16, "insert-beam-search-op"]], "Post Training Quantization (PTQ)": [[16, "post-training-quantization-ptq"], [17, "post-training-quantization-ptq"], [19, "post-training-quantization-ptq"]], "Quantize with onnxruntime": [[16, "quantize-with-onnxruntime"]], "Quantize with Intel\u00ae Neural Compressor": [[16, "quantize-with-intel-neural-compressor"]], "Quantize with AMD Vitis AI Quantizer": [[16, "quantize-with-amd-vitis-ai-quantizer"]], "ORT Performance Tuning": [[16, "ort-performance-tuning"]], "Float16 Conversion": [[16, "float16-conversion"]], "Mixed Precision Conversion": [[16, "mixed-precision-conversion"]], "OpenVINO": [[17, "openvino"]], "Prerequisites": [[17, "prerequisites"], [19, "prerequisites"], [21, "prerequisites"], [23, "prerequisites"], [35, "prerequisites"], [35, "id2"]], "Option 1: install Olive with OpenVINO extras": [[17, "option-1-install-olive-with-openvino-extras"]], "Option 2: Install OpenVINO Runtime and OpenVINO Development Tools from Pypi": [[17, "option-2-install-openvino-runtime-and-openvino-development-tools-from-pypi"]], "PyTorch": [[18, "pytorch"]], "Quantization Aware Training": [[18, "quantization-aware-training"]], "SNPE": [[19, "snpe"]], "Download and unzip SNPE SDK": [[19, "download-and-unzip-snpe-sdk"]], "Install SDK system dependencies": [[19, "install-sdk-system-dependencies"]], "Configure Olive SNPE": [[19, "configure-olive-snpe"]], "Installation": [[20, "installation"]], "Install with pip": [[20, "install-with-pip"]], "Install from source": [[20, "install-from-source"]], "Editable install": [[20, "editable-install"]], "Optional Dependencies": [[20, "optional-dependencies"]], "BERT optimization with PTQ on CPU": [[21, "bert-optimization-with-ptq-on-cpu"]], "Pip requirements": [[21, "pip-requirements"], [23, "pip-requirements"]], "Run sample using config. The optimization techniques to run are specified in bert_ptq_cpu.json": [[21, "run-sample-using-config-the-optimization-techniques-to-run-are-specified-in-bert-ptq-cpu-json"]], "Optimize model automatically without selecting any optimization technique.": [[21, "optimize-model-automatically-without-selecting-any-optimization-technique"]], "Quickstart Examples": [[22, "quickstart-examples"]], "ResNet optimization with PTQ on CPU": [[23, "resnet-optimization-with-ptq-on-cpu"]], "Prepare data and model": [[23, "prepare-data-and-model"]], "Run sample using config": [[23, "run-sample-using-config"]], "Welcome to Olive\u2019s documentation!": [[24, "welcome-to-olive-s-documentation"]], "OVERVIEW": [[24, null]], "GET STARTED": [[24, null]], "EXAMPLES": [[24, null]], "FEATURES": [[24, null]], "EXTENDING OLIVE": [[24, null]], "TUTORIALS": [[24, null]], "API REFERENCE": [[24, null]], "Olive": [[25, "olive"]], "Contents": [[25, "contents"]], "Olive Options": [[26, "olive-options"]], "Azure ML Client": [[26, "azure-ml-client"]], "azureml_client with aml_config_path:": [[26, "azureml-client-with-aml-config-path"]], "aml_config.json:": [[26, "aml-config-json"]], "azureml_client:": [[26, "azureml-client"]], "azureml_client with azureml config fields:": [[26, "azureml-client-with-azureml-config-fields"]], "Input Model Information": [[26, "input-model-information"]], "Data Information": [[26, "data-information"]], "Local Examples": [[26, "local-examples"]], "Azureml Examples": [[26, "azureml-examples"]], "Systems Information": [[26, "systems-information"]], "Evaluators Information": [[26, "evaluators-information"]], "Passes Information": [[26, "passes-information"]], "Pass Flows Information": [[26, "pass-flows-information"]], "Engine Information": [[26, "engine-information"]], "Quick Tour": [[27, "quick-tour"]], "Information needed to accelerate a model": [[27, "information-needed-to-accelerate-a-model"]], "Input Model": [[27, "input-model"], [28, "input-model"]], "Performance Requirement": [[27, "performance-requirement"]], "Passes to apply": [[27, "passes-to-apply"]], "Olive Engine": [[27, "olive-engine"]], "Advanced User Tour": [[28, "advanced-user-tour"]], "Host and Target Systems": [[28, "host-and-target-systems"]], "Register Passes": [[28, "register-passes"]], "Run the engine": [[28, "run-the-engine"]], "Self-hosted Kubernetes cluster": [[29, "self-hosted-kubernetes-cluster"]], "Create Kubernetes cluster": [[29, "create-kubernetes-cluster"]], "Add local Kubernetes cluster to Azure Arc": [[29, "add-local-kubernetes-cluster-to-azure-arc"]], "Add AML extension to cluster": [[29, "add-aml-extension-to-cluster"]], "Link Azure Arc Kubernetes cluster to Azure Machine Learning": [[29, "link-azure-arc-kubernetes-cluster-to-azure-machine-learning"]], "Azure ML scripts": [[30, "azure-ml-scripts"]], "Scripts list": [[30, "scripts-list"]], "manage_compute_instance": [[30, "manage-compute-instance"]], "Usage": [[30, "usage"]], "How To Configure Data": [[31, "how-to-configure-data"]], "Supported Data Config Template": [[31, "supported-data-config-template"]], "Generic Data Config": [[31, "generic-data-config"]], "Title": [[31, "id5"]], "Configs with built-in component:": [[31, "configs-with-built-in-component"]], "Configs with customized component:": [[31, "configs-with-customized-component"]], "How To Configure Metric": [[32, "how-to-configure-metric"]], "Metric Types": [[32, "metric-types"]], "Accuracy Metric": [[32, "accuracy-metric"]], "Latency Metric": [[32, "latency-metric"]], "Throughput Metric": [[32, "throughput-metric"]], "Custom Metric": [[32, "custom-metric"]], "Multi Metrics configuration": [[32, "multi-metrics-configuration"]], "How To Set Model Path": [[33, "how-to-set-model-path"]], "Local Model Path": [[33, "local-model-path"]], "Local File": [[33, "local-file"]], "Local Folder": [[33, "local-folder"]], "String Name": [[33, "string-name"]], "Remote Model Path": [[33, "remote-model-path"]], "AzureML Model": [[33, "azureml-model"]], "AzureML Registry Model": [[33, "azureml-registry-model"]], "AzureML Datastore": [[33, "azureml-datastore"]], "AzureML Job Output": [[33, "azureml-job-output"]], "How To Configure Pass": [[34, "how-to-configure-pass"]], "How To Configure System": [[35, "how-to-configure-system"]], "Local System": [[35, "local-system"]], "AzureML System": [[35, "azureml-system"]], "System Configuration": [[35, "system-configuration"], [35, "id3"]], "AzureML Readymade Systems": [[35, "azureml-readymade-systems"]], "Docker System": [[35, "docker-system"]], "Python Environment System": [[35, "python-environment-system"]]}, "indexentries": {"engine (class in olive.engine)": [[0, "olive.engine.Engine"]], "searchstrategy (class in olive.strategy.search_strategy)": [[0, "olive.strategy.search_strategy.SearchStrategy"]], "azureml_client_config (olive.engine.engineconfig attribute)": [[0, "olive.engine.EngineConfig.azureml_client_config"]], "cache_dir (olive.engine.engineconfig attribute)": 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(olive.systems.system_alias.surfaceprosystem1807 attribute)": [[7, "olive.systems.system_alias.SurfaceProSystem1807.system_type"]], "type (olive.systems.system_config.systemconfig attribute)": [[7, "olive.systems.system_config.SystemConfig.type"]]}}) \ No newline at end of file diff --git a/tutorials/configure_metrics.html b/tutorials/configure_metrics.html index 3ee6e9829..ecb601f77 100644 --- a/tutorials/configure_metrics.html +++ b/tutorials/configure_metrics.html @@ -153,8 +153,8 @@

    Accuracy Metric "type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ], "user_config": { "post_processing_func": "post_process", @@ -341,8 +341,8 @@

    Multi Metrics configuration"type": "accuracy", "sub_types": [ {"name": "accuracy_score", "priority": 1, "goal": {"type": "max-degradation", "value": 0.01}}, - {"name": "f1_score", "metric_config": {"multiclass": false}}, - {"name": "auroc", "metric_config": {"num_classes": 2}} + {"name": "f1_score"}, + {"name": "auroc"} ] }, {