diff --git a/README.md b/README.md
index 84614b86..5f85487f 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,63 @@
+### Change log [2023-10-02 10:10:55]
+1. Item Updated: `feature_selection` (from version: `1.3.0` to `1.3.0`)
+2. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`)
+3. Item Updated: `open_archive` (from version: `1.1.0` to `1.1.0`)
+4. Item Updated: `slack_notify` (from version: `1.1.0` to `1.1.0`)
+5. Item Updated: `hugging_face_classifier_trainer` (from version: `0.1.0` to `0.1.0`)
+6. Item Updated: `v2_model_tester` (from version: `1.1.0` to `1.1.0`)
+7. Item Updated: `model_server` (from version: `1.1.0` to `1.1.0`)
+8. Item Updated: `get_offline_features` (from version: `1.2.0` to `1.2.0`)
+9. Item Updated: `test_classifier` (from version: `1.1.0` to `1.1.0`)
+10. Item Updated: `sentiment_analysis_serving` (from version: `1.1.0` to `1.1.0`)
+11. Item Updated: `sklearn_classifier` (from version: `1.1.1` to `1.1.1`)
+12. Item Updated: `ingest` (from version: `1.1.0` to `1.1.0`)
+13. Item Updated: `tf2_serving_v2` (from version: `1.1.0` to `1.1.0`)
+14. Item Updated: `snowflake_dask` (from version: `1.1.0` to `1.1.0`)
+15. Item Updated: `github_utils` (from version: `1.1.0` to `1.1.0`)
+16. Item Updated: `auto_trainer` (from version: `1.3.0` to `1.3.0`)
+17. Item Updated: `pandas_profiling_report` (from version: `1.1.0` to `1.1.0`)
+18. Item Updated: `hugging_face_serving` (from version: `1.0.0` to `1.0.0`)
+19. Item Updated: `xgb_trainer` (from version: `1.1.1` to `1.1.1`)
+20. Item Updated: `model_server_tester` (from version: `1.1.0` to `1.1.0`)
+21. Item Updated: `huggingface_auto_trainer` (from version: `1.0.0` to `1.0.0`)
+22. Item Updated: `aggregate` (from version: `1.3.0` to `1.3.0`)
+23. Item Updated: `model_monitoring_stream` (from version: `1.1.0` to `1.1.0`)
+24. Item Updated: `transcribe` (from version: `0.0.1` to `0.0.1`)
+25. Item Updated: `model_monitoring_batch` (from version: `1.1.0` to `1.1.0`)
+26. Item Updated: `rnn_serving` (from version: `1.1.0` to `1.1.0`)
+27. Item Updated: `bert_embeddings` (from version: `1.2.0` to `1.2.0`)
+28. Item Updated: `xgb_test` (from version: `1.1.1` to `1.1.1`)
+29. Item Updated: `sql_to_file` (from version: `1.1.0` to `1.1.0`)
+30. Item Updated: `describe_spark` (from version: `1.1.0` to `1.1.0`)
+31. Item Updated: `feature_perms` (from version: `1.1.0` to `1.1.0`)
+32. Item Updated: `batch_inference_v2` (from version: `1.8.0` to `1.8.0`)
+33. Item Updated: `tf1_serving` (from version: `1.1.0` to `1.1.0`)
+34. Item Updated: `validate_great_expectations` (from version: `1.1.0` to `1.1.0`)
+35. Item Updated: `coxph_trainer` (from version: `1.1.0` to `1.1.0`)
+36. Item Updated: `xgb_serving` (from version: `1.1.2` to `1.1.2`)
+37. Item Updated: `concept_drift` (from version: `1.1.0` to `1.1.0`)
+38. Item Updated: `question_answering` (from version: `0.2.0` to `0.2.0`)
+39. Item Updated: `load_dataset` (from version: `1.1.0` to `1.1.0`)
+40. Item Updated: `concept_drift_streaming` (from version: `1.1.0` to `1.1.0`)
+41. Item Updated: `virtual_drift` (from version: `1.1.0` to `1.1.0`)
+42. Item Updated: `batch_inference` (from version: `1.6.0` to `1.6.0`)
+43. Item Updated: `coxph_test` (from version: `1.1.0` to `1.1.0`)
+44. Item Updated: `sklearn_classifier_dask` (from version: `1.1.1` to `1.1.1`)
+45. Item Updated: `churn_server` (from version: `1.1.0` to `1.1.0`)
+46. Item Updated: `stream_to_parquet` (from version: `1.1.0` to `1.1.0`)
+47. Item Updated: `pii_recognizer` (from version: `0.1.0` to `0.1.0`)
+48. Item Updated: `load_dask` (from version: `1.1.0` to `1.1.0`)
+49. Item Updated: `onnx_utils` (from version: `1.2.0` to `1.2.0`)
+50. Item Updated: `xgb_custom` (from version: `1.1.0` to `1.1.0`)
+51. Item Updated: `arc_to_parquet` (from version: `1.4.1` to `1.4.1`)
+52. Item Updated: `gen_class_data` (from version: `1.2.0` to `1.2.0`)
+53. Item Updated: `describe` (from version: `1.2.0` to `1.2.0`)
+54. Item Updated: `azureml_serving` (from version: `1.1.0` to `1.1.0`)
+55. Item Updated: `send_email` (from version: `1.2.0` to `1.2.0`)
+56. Item Updated: `describe_dask` (from version: `1.1.0` to `1.1.0`)
+57. Item Updated: `v2_model_server` (from version: `1.1.0` to `1.1.0`)
+58. Item Updated: `azureml_utils` (from version: `1.2.0` to `1.2.0`)
+
 ### Change log [2023-10-02 09:13:07]
 1. Item Updated: `feature_selection` (from version: `1.3.0` to `1.3.0`)
 2. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`)
diff --git a/catalog.json b/catalog.json
index ac44a175..3e08019f 100644
--- a/catalog.json
+++ b/catalog.json
@@ -1 +1 @@
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{"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.1.0"}, "0.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train and optimize functions for HuggingFace framework", "doc": "", "example": "hugging_face_classifier_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.1.0"}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "Automatic train and optimize functions for HuggingFace framework", "doc": "", "example": "hugging_face_classifier_trainer.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "davids"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.2.0", "name": "hugging_face_classifier_trainer", "platformVersion": "3.5.0", "spec": {"filename": "hugging_face_classifier_trainer.py", "handler": "train", "image": "mlrun/ml-models", "kind": "job", "requirements": ["onnx~=1.10.1", "onnxruntime~=1.8.1", "optimum~=1.6.4", "transformers~=4.26.1", "datasets~=2.10.1", "scikit-learn~=1.0.2"]}, "url": "", "version": "0.0.1"}}, "validate_great_expectations": {"latest": {"apiVersion": "v1", "categories": ["data-validation", "data-analysis"], "description": "Validate a dataset using Great Expectations", "doc": "", "example": "validate_great_expectations.ipynb", "generationDate": "2022-04-26:12-28", "hidden": false, "icon": "", "labels": {"author": "nicks", "framework": "great-expectations"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "validate-great-expectations", "platformVersion": "3.5.2", "spec": {"filename": "validate_great_expectations.py", "handler": "validate_expectations", "image": "mlrun/mlrun", "kind": "job", "requirements": ["great-expectations==0.15.41"]}, "url": "", "version": "1.1.0"}, "1.1.0": {"apiVersion": "v1", "categories": ["data-validation", "data-analysis"], "description": "Validate a dataset using Great Expectations", "doc": "", "example": "validate_great_expectations.ipynb", "generationDate": "2022-04-26:12-28", "hidden": false, "icon": "", "labels": {"author": "nicks", "framework": "great-expectations"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "validate-great-expectations", "platformVersion": "3.5.2", "spec": {"filename": "validate_great_expectations.py", "handler": "validate_expectations", "image": "mlrun/mlrun", "kind": "job", "requirements": ["great-expectations==0.15.41"]}, "url": "", "version": "1.1.0"}}, "question_answering": {"latest": {"apiVersion": "v1", "categories": ["machine-learning"], "description": "GenAI approach of question answering on a given data", "doc": "", "example": "question_answering.ipynb", "generationDate": "2023-08-07:11-30", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "question_answering", "platformVersion": "3.5.0", "spec": {"filename": "question_answering.py", "handler": "answer_questions", "image": "mlrun/mlrun", "kind": "job", "requirements": "transformers torch tqdm"}, "url": "", "version": "0.2.0"}, "0.1.0": {"apiVersion": "v1", "categories": ["machine-learning"], "description": "GenAI approach of question answering on a given data", "doc": "", "example": "question_answering.ipynb", "generationDate": "2023-08-07:11-30", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "question_answering", "platformVersion": "3.5.0", "spec": {"filename": "question_answering.py", "handler": "answer_questions", "image": "mlrun/mlrun", "kind": "job", "requirements": "transformers torch tqdm"}, "url": "", "version": "0.1.0"}, "0.2.0": {"apiVersion": "v1", "categories": ["machine-learning"], "description": "GenAI approach of question answering on a given data", "doc": "", "example": "question_answering.ipynb", "generationDate": "2023-08-07:11-30", "hidden": false, "icon": "", "labels": {"author": "yonish"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "question_answering", "platformVersion": "3.5.0", "spec": {"filename": "question_answering.py", "handler": "answer_questions", "image": "mlrun/mlrun", "kind": "job", "requirements": "transformers torch tqdm"}, "url": "", "version": "0.2.0"}}, "transcribe": {"latest": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Transcribe audio files into text files", "doc": "", "example": "transcribe.ipynb", "generationDate": "2023-07-13:11-20", "hidden": false, "icon": "", "labels": {"author": "yonatans"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "transcribe", "platformVersion": "3.5.3", "spec": {"filename": "transcribe.py", "handler": "transcribe", "image": "mlrun/mlrun", "kind": "job", "requirements": ["openai-whisper", "tqdm"]}, "url": "", "version": "0.0.1", "test_valid": false}, "0.0.1": {"apiVersion": "v1", "categories": ["data-preparation", "machine-learning"], "description": "Transcribe audio files into text files", "doc": "", "example": "transcribe.ipynb", "generationDate": "2023-07-13:11-20", "hidden": false, "icon": "", "labels": {"author": "yonatans"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "transcribe", "platformVersion": "3.5.3", "spec": {"filename": "transcribe.py", "handler": "transcribe", "image": "mlrun/mlrun", "kind": "job", "requirements": ["openai-whisper", "tqdm"]}, "url": "", "version": "0.0.1", "test_valid": false}}, "pii_recognizer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "This function is used to recognize PII in a directory of text files", "doc": "", "example": "pii_recognizer.ipynb", "generationDate": "2023-08-15:10-24", "hidden": false, "icon": "", "labels": {"author": "pgw"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "pii-recognizer", "platformVersion": "3.5.3", "spec": {"filename": "pii_recognizer.py", "handler": "recognize_pii", "image": "mlrun/mlrun", "kind": "job", "requirements": ["nltk", "pandas", "presidio-anonymizer", "presidio-analyzer", "torch", "flair@git+https://github.com/flairNLP/flair.git@d4ed67bf663e4066517f00397412510d90043653", "st-annotated-text", "https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl"]}, "url": "", "version": "0.1.0", "test_valid": false}, "0.1.0": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "This function is used to recognize PII in a directory of text files", "doc": "", "example": "pii_recognizer.ipynb", "generationDate": "2023-08-15:10-24", "hidden": false, "icon": "", "labels": {"author": "pgw"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "pii-recognizer", "platformVersion": "3.5.3", "spec": {"filename": "pii_recognizer.py", "handler": "recognize_pii", "image": "mlrun/mlrun", "kind": "job", "requirements": ["nltk", "pandas", "presidio-anonymizer", "presidio-analyzer", "torch", "flair@git+https://github.com/flairNLP/flair.git@d4ed67bf663e4066517f00397412510d90043653", "st-annotated-text", "https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl"]}, "url": "", "version": "0.1.0", "test_valid": false}, "0.0.1": {"apiVersion": "v1", "categories": ["machine-learning", "data-preparation"], "description": "This function is used to recognize PII in a directory of text files", "doc": "", "example": "pii_recognizer.ipynb", "generationDate": "2023-08-15:10-24", "hidden": false, "icon": "", "labels": {"author": "pgw"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "pii-recognizer", "platformVersion": "3.5.3", "spec": {"filename": "pii_recognizer.py", "handler": "recognize_pii", "image": "mlrun/mlrun", "kind": "job", "requirements": ["nltk", "pandas", "presidio-anonymizer", "presidio-analyzer", "torch", "flair@git+https://github.com/flairNLP/flair.git@d4ed67bf663e4066517f00397412510d90043653", "st-annotated-text", "https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl"]}, "url": "", "version": "0.0.1"}}, "huggingface_auto_trainer": {"latest": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "fine-tune llm model with ease", "doc": "", "example": "huggingface_auto_trainer.ipynb", "generationDate": "2023-08-21:17-25", "hidden": false, "icon": "", "labels": {"author": "Zeevr"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "huggingface-auto-trainer", "platformVersion": "3.5.0", "spec": {"filename": "huggingface_auto_trainer.py", "handler": "finetune_llm", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}, "1.0.0": {"apiVersion": "v1", "categories": ["machine-learning", "model-training"], "description": "fine-tune llm model with ease", "doc": "", "example": "huggingface_auto_trainer.ipynb", "generationDate": "2023-08-21:17-25", "hidden": false, "icon": "", "labels": {"author": "Zeevr"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.4.0", "name": "huggingface-auto-trainer", "platformVersion": "3.5.0", "spec": {"filename": "huggingface_auto_trainer.py", "handler": "finetune_llm", "image": "mlrun/mlrun", "kind": "job", "requirements": []}, "url": "", "version": "1.0.0"}}, "batch_inference_v2": {"latest": {"apiVersion": "v1", "categories": ["utils", "data-analysis", "monitoring"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference_v2.ipynb", "generationDate": "2023-08-07:12-25", "hidden": false, "icon": "", "labels": {"author": "eyald"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.0-rc13", "name": "batch_inference_v2", "platformVersion": "3.5.3", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference_v2.py", "handler": "infer", "image": "mlrun/mlrun", "kind": "job", "requirements": null}, "url": "", "version": "1.8.0"}, "1.6.0": {"apiVersion": "v1", "categories": ["utils", "data-analysis", "monitoring"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference_v2.ipynb", "generationDate": "2023-08-07:12-25", "hidden": false, "icon": "", "labels": {"author": "eyald"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.0-rc9", "name": "batch_inference_v2", "platformVersion": "3.5.3", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference_v2.py", "handler": "infer", "image": "mlrun/mlrun", "kind": "job", "requirements": null}, "url": "", "version": "1.6.0"}, "1.8.0": {"apiVersion": "v1", "categories": ["utils", "data-analysis", "monitoring"], "description": "Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis.", "doc": "", "example": "batch_inference_v2.ipynb", "generationDate": "2023-08-07:12-25", "hidden": false, "icon": "", "labels": {"author": "eyald"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.5.0-rc13", "name": "batch_inference_v2", "platformVersion": "3.5.3", "spec": {"extra_spec": {"allow_empty_resources": true, "build": {"auto_build": false, "with_mlrun": false}}, "filename": "batch_inference_v2.py", "handler": "infer", "image": "mlrun/mlrun", "kind": "job", "requirements": null}, "url": "", "version": "1.8.0"}, "1.5.0": {"apiVersion": "v1", "categories": ["utils", "data-analysis", "monitoring"], "description": "Batch inference (also 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\ No newline at end of file
diff --git a/functions/master/azureml_utils/1.2.0/src/item.yaml b/functions/master/azureml_utils/1.2.0/src/item.yaml
index f70a2acf..a2c8675e 100644
--- a/functions/master/azureml_utils/1.2.0/src/item.yaml
+++ b/functions/master/azureml_utils/1.2.0/src/item.yaml
@@ -35,3 +35,4 @@ spec:
   - plotly~=5.4
 url: ''
 version: 1.2.0
+test_valid: False
diff --git a/functions/master/azureml_utils/1.2.0/static/item.html b/functions/master/azureml_utils/1.2.0/static/item.html
index 66dacd21..208a8179 100644
--- a/functions/master/azureml_utils/1.2.0/static/item.html
+++ b/functions/master/azureml_utils/1.2.0/static/item.html
@@ -52,6 +52,7 @@
   - plotly~=5.4
 url: ''
 version: 1.2.0
+test_valid: False
 
         </code>
     </pre>
diff --git a/functions/master/azureml_utils/latest/src/item.yaml b/functions/master/azureml_utils/latest/src/item.yaml
index f70a2acf..a2c8675e 100644
--- a/functions/master/azureml_utils/latest/src/item.yaml
+++ b/functions/master/azureml_utils/latest/src/item.yaml
@@ -35,3 +35,4 @@ spec:
   - plotly~=5.4
 url: ''
 version: 1.2.0
+test_valid: False
diff --git a/functions/master/azureml_utils/latest/static/item.html b/functions/master/azureml_utils/latest/static/item.html
index 66dacd21..208a8179 100644
--- a/functions/master/azureml_utils/latest/static/item.html
+++ b/functions/master/azureml_utils/latest/static/item.html
@@ -52,6 +52,7 @@
   - plotly~=5.4
 url: ''
 version: 1.2.0
+test_valid: False
 
         </code>
     </pre>
diff --git a/functions/master/catalog.json b/functions/master/catalog.json
index 851fb77a..30fd94cc 100644
--- a/functions/master/catalog.json
+++ b/functions/master/catalog.json
@@ -1 +1 @@
-{"tf2_serving": {"latest": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0", "assets": {"example": "src/tf2_serving.ipynb", "source": "src/tf2_serving.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.9.1": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.1", "assets": {"example": "src/tf2_serving.ipynb", "source": "src/tf2_serving.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "1.1.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2022-08-28:17-25", "hidden": false, "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "1.1.0", "name": "tf2-serving", "platformVersion": "3.5.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "1.1.0", "assets": {"example": "src/tf2_serving.ipynb", "source": "src/tf2_serving.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.9.0": {"apiVersion": "v1", "categories": ["model-serving", "machine-learning"], "description": "tf2 image classification server", "doc": "", "example": "tf2_serving.ipynb", "generationDate": "2021-11-18:12-28", "icon": "", "labels": {"author": "yaronh"}, "maintainers": [], "marketplaceType": "", "mlrunVersion": "0.8.0", "name": "tf2-serving", "platformVersion": "3.2.0", "spec": {"filename": "tf2_serving.py", "handler": "handler", "image": "mlrun/mlrun", "kind": "nuclio:serving", "requirements": ["requests", "pillow", "tensorflow>=2.1"]}, "url": "", "version": "0.9.0", "assets": {"example": "src/tf2_serving.ipynb", "source": "src/tf2_serving.py", "function": "src/function.yaml", "docs": "static/documentation.html"}}, "0.0.1": {"apiVersion": "v1", 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\ No newline at end of file
diff --git a/functions/master/feature_perms/1.1.0/src/item.yaml b/functions/master/feature_perms/1.1.0/src/item.yaml
index 1f568168..bd909d3e 100644
--- a/functions/master/feature_perms/1.1.0/src/item.yaml
+++ b/functions/master/feature_perms/1.1.0/src/item.yaml
@@ -22,3 +22,4 @@ spec:
   requirements: []
 url: ''
 version: 1.1.0
+test_valid : False
diff --git a/functions/master/feature_perms/1.1.0/static/item.html b/functions/master/feature_perms/1.1.0/static/item.html
index fb43ca1e..2cdb2735 100644
--- a/functions/master/feature_perms/1.1.0/static/item.html
+++ b/functions/master/feature_perms/1.1.0/static/item.html
@@ -39,6 +39,7 @@
   requirements: []
 url: ''
 version: 1.1.0
+test_valid : False
 
         </code>
     </pre>
diff --git a/functions/master/feature_perms/latest/src/item.yaml b/functions/master/feature_perms/latest/src/item.yaml
index 1f568168..bd909d3e 100644
--- a/functions/master/feature_perms/latest/src/item.yaml
+++ b/functions/master/feature_perms/latest/src/item.yaml
@@ -22,3 +22,4 @@ spec:
   requirements: []
 url: ''
 version: 1.1.0
+test_valid : False
diff --git a/functions/master/feature_perms/latest/static/item.html b/functions/master/feature_perms/latest/static/item.html
index fb43ca1e..2cdb2735 100644
--- a/functions/master/feature_perms/latest/static/item.html
+++ b/functions/master/feature_perms/latest/static/item.html
@@ -39,6 +39,7 @@
   requirements: []
 url: ''
 version: 1.1.0
+test_valid : False
 
         </code>
     </pre>
diff --git a/functions/master/onnx_utils/1.2.0/src/function.yaml b/functions/master/onnx_utils/1.2.0/src/function.yaml
new file mode 100644
index 00000000..7a0054c4
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/src/function.yaml
@@ -0,0 +1,194 @@
+kind: job
+metadata:
+  name: onnx-utils
+  tag: ''
+  hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2
+  project: ''
+  labels:
+    author: guyl
+  categories:
+  - utils
+spec:
+  command: ''
+  args: []
+  image: ''
+  build:
+    functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()

+    base_image: mlrun/mlrun
+    commands: []
+    code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    with_mlrun: false
+    auto_build: true
+    requirements:
+    - onnx~=1.13.0
+    - onnxruntime~=1.14.0
+    - onnxoptimizer~=0.3.0
+    - onnxmltools~=1.11.0
+    - tf2onnx~=1.13.0
+  entry_points:
+    tf_keras_to_onnx:
+      name: tf_keras_to_onnx
+      doc: Convert a TF.Keras model to an ONNX model and log it back to MLRun as a
+        new model object.
+      parameters:
+      - name: model_handler
+        doc: An initialized TFKerasModelHandler with a loaded model to convert to
+          ONNX.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before
+          saving the model. Defaulted to True.
+        default: true
+      - name: input_signature
+        type: List[Tuple[Tuple[int], str]]
+        doc: 'A list of the input layers shape and data type properties. Expected
+          to receive a list where each element is an input layer tuple. An input layer
+          tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
+          data type, a mlrun.data_types.ValueType string. If None, the input signature
+          will be tried to be read from the model artifact. Defaulted to None.'
+        default: null
+      outputs:
+      - default: ''
+      lineno: 26
+    pytorch_to_onnx:
+      name: pytorch_to_onnx
+      doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a
+        new model object.
+      parameters:
+      - name: model_handler
+        doc: An initialized PyTorchModelHandler with a loaded model to convert to
+          ONNX.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before
+          saving the model. Defaulted to True.
+        default: true
+      - name: input_signature
+        type: List[Tuple[Tuple[int, ], str]]
+        doc: 'A list of the input layers shape and data type properties. Expected
+          to receive a list where each element is an input layer tuple. An input layer
+          tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
+          data type, a mlrun.data_types.ValueType string. If None, the input signature
+          will be tried to be read from the model artifact. Defaulted to None.'
+        default: null
+      - name: input_layers_names
+        type: List[str]
+        doc: 'List of names to assign to the input nodes of the graph in order. All
+          of the other parameters (inner layers) can be set as well by passing additional
+          names in the list. The order is by the order of the parameters in the model.
+          If None, the inputs will be read from the handler''s inputs. If its also
+          None, it is defaulted to: "input_0", "input_1", ...'
+        default: null
+      - name: output_layers_names
+        type: List[str]
+        doc: 'List of names to assign to the output nodes of the graph in order. If
+          None, the outputs will be read from the handler''s outputs. If its also
+          None, it is defaulted to: "output_0" (for multiple outputs, this parameter
+          must be provided).'
+        default: null
+      - name: dynamic_axes
+        type: Dict[str, Dict[int, str]]
+        doc: 'If part of the input / output shape is dynamic, like (batch_size, 3,
+          32, 32) you can specify it by giving a dynamic axis to the input / output
+          layer by its name as follows: { "input layer name": {0: "batch_size"}, "output
+          layer name": {0: "batch_size"}, } If provided, the ''is_batched'' flag will
+          be ignored. Defaulted to None.'
+        default: null
+      - name: is_batched
+        type: bool
+        doc: Whether to include a batch size as the first axis in every input and
+          output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
+        default: true
+      outputs:
+      - default: ''
+      lineno: 81
+    to_onnx:
+      name: to_onnx
+      doc: Convert the given model to an ONNX model.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: The MLRun function execution context
+        default: ''
+      - name: model_path
+        type: str
+        doc: The model path store object.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether to optimize the ONNX model using 'onnxoptimizer' before saving
+          the model. Defaulted to True.
+        default: true
+      - name: framework_kwargs
+        type: Dict[str, Any]
+        doc: Additional arguments each framework may require in order to convert to
+          ONNX. To get the doc string of the desired framework onnx conversion function,
+          pass "help".
+        default: null
+      outputs:
+      - default: ''
+      lineno: 160
+    optimize:
+      name: optimize
+      doc: Optimize the given ONNX model.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: The MLRun function execution context.
+        default: ''
+      - name: model_path
+        type: str
+        doc: Path to the ONNX model object.
+        default: ''
+      - name: optimizations
+        type: List[str]
+        doc: List of possible optimizations. To see what optimizations are available,
+          pass "help". If None, all of the optimizations will be used. Defaulted to
+          None.
+        default: null
+      - name: fixed_point
+        type: bool
+        doc: Optimize the weights using fixed point. Defaulted to False.
+        default: false
+      - name: optimized_model_name
+        type: str
+        doc: The name of the optimized model. If None, the original model will be
+          overridden. Defaulted to None.
+        default: null
+      outputs:
+      - default: ''
+      lineno: 219
+  description: ONNX intigration in MLRun, some utils functions for the ONNX framework,
+    optimizing and converting models from different framework to ONNX using MLRun.
+  default_handler: to_onnx
+  disable_auto_mount: false
+  allow_empty_resources: true
+  clone_target_dir: ''
+  env: []
+  priority_class_name: ''
+  preemption_mode: prevent
+  affinity: null
+  tolerations: null
+  security_context: {}
+verbose: false
diff --git a/functions/master/onnx_utils/1.2.0/src/item.yaml b/functions/master/onnx_utils/1.2.0/src/item.yaml
new file mode 100644
index 00000000..36335837
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/src/item.yaml
@@ -0,0 +1,35 @@
+apiVersion: v1
+categories:
+- utils
+description: ONNX intigration in MLRun, some utils functions for the ONNX framework,
+  optimizing and converting models from different framework to ONNX using MLRun.
+doc: ''
+example: onnx_utils.ipynb
+generationDate: 2022-08-28:17-25
+hidden: false
+icon: ''
+labels:
+  author: guyl
+maintainers: []
+marketplaceType: ''
+mlrunVersion: 1.1.0
+name: onnx_utils
+platformVersion: 3.5.0
+spec:
+  extra_spec:
+    allow_empty_resources: true
+    build:
+      auto_build: true
+      with_mlrun: false
+  filename: onnx_utils.py
+  handler: to_onnx
+  image: mlrun/mlrun
+  kind: job
+  requirements:
+  - onnx~=1.13.0
+  - onnxruntime~=1.14.0
+  - onnxoptimizer~=0.3.0
+  - onnxmltools~=1.11.0
+  - tf2onnx~=1.13.0
+url: ''
+version: 1.2.0
diff --git a/functions/master/onnx_utils/1.2.0/src/onnx_utils.ipynb b/functions/master/onnx_utils/1.2.0/src/onnx_utils.ipynb
new file mode 100644
index 00000000..e44f1524
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/src/onnx_utils.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ONNX Utils\n",
+    "\n",
+    "A collection of ONNX utils in one MLRun function. The function includes the following handlers:\n",
+    "\n",
+    "1. [to_onnx](#handler1) - Convert your model into `onnx` format.\n",
+    "2. [optimize](#handler2) - Perform ONNX optimizations using `onnxmodeloptimizer` on a given ONNX model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"handler1\"></a>\n",
+    "\n",
+    "## 1. to_onnx\n",
+    "\n",
+    "### 1.1. Docs\n",
+    "Convert the given model to an ONNX model.\n",
+    "\n",
+    "#### Parameters:\n",
+    "* **`context`**: `mlrun.MLClientCtx` - The MLRun function execution context\n",
+    "* **`model_path`**: `str` - The model path store object.\n",
+    "* **`onnx_model_name`**: `str = None` - The name to use to log the converted ONNX model. If not given, the given `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.\n",
+    "* **`optimize_model`**: `bool = True` - Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted to True.\n",
+    "* **`framework`**: `str = None` - The model's framework. If None, it will be read from the 'framework' label of the model artifact provided. Defaulted to None.\n",
+    "* **`framework_kwargs`**: `Dict[str, Any] = None` - Additional arguments each framework may require in order to convert to ONNX. *To get the doc string of the desired framework onnx conversion function, **pass \"help\"**.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "#### Supported keyword arguments (`framework_kwargs`) per framework:\n",
+    "`tensorflow.keras`:\n",
+    "* **`input_signature`**: `List[Tuple[Tuple[int], str]] = None` - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:\n",
+    "  * [0] = Layer's shape, a tuple of integers.\n",
+    "  * [1] = Layer's data type, a mlrun.data_types.ValueType string.\n",
+    "\n",
+    "  If None, the input signature will be tried to be read automatically before converting to ONNX or from the model artifact if available. Defaulted to None.\n",
+    "\n",
+    "`torch`:\n",
+    "* **`input_signature`**: `List[Tuple[Tuple[int], str]] = None` - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:\n",
+    "  * [0] = Layer's shape, a tuple of integers.\n",
+    "  * [1] = Layer's data type, a mlrun.data_types.ValueType string.\n",
+    "\n",
+    "  If None, the input signature will be read from the model artifact if available. Defaulted to None.\n",
+    "* **`input_layers_names`**: `List[str] = None` - List of names to assign to the input nodes of the graph in order. All of the other parameters (inner layers) can be set as well by passing additional names in the list. The order is by the order of the parameters in the model. If None, the inputs will be read from the handler's inputs. If its also None, it is defaulted to: \"input_0\", \"input_1\", ...\n",
+    "* **`output_layers_names`**: `List[str] = None` - List of names to assign to the output nodes of the graph in order. If None, the outputs will be read from the handler's outputs. If its also None, it is defaulted to: \"output_0\" (for multiple outputs, this parameter must be provided).\n",
+    "* **`param dynamic_axes`**: `Dict[str, Dict[int, str]] = None` - If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can specify it by giving a dynamic axis to the input / output layer by its name as follows:\n",
+    "```python\n",
+    "{\n",
+    "    \"input layer name\": {0: \"batch_size\"},\n",
+    "    \"output layer name\": {0: \"batch_size\"},\n",
+    "}\n",
+    "```\n",
+    "If provided, the 'is_batched' flag will be ignored. Defaulted to None.\n",
+    "* **`is_batched`**: `bool = True` - Whether to include a batch size as the first axis in every input and output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### 1.2. Demo\n",
+    "\n",
+    "We will use the `TF.Keras` framework, a `MobileNetV2` as our model and we will convert it to ONNX using the `to_onnx` handler.\n",
+    "\n",
+    "1.2.1. First we will set a temporary artifact path for our model to be saved in and choose the models names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from tempfile import TemporaryDirectory\n",
+    "\n",
+    "# Create a temporary directory for the model artifact:\n",
+    "ARTIFACT_PATH = TemporaryDirectory().name\n",
+    "os.makedirs(ARTIFACT_PATH)\n",
+    "\n",
+    "# Choose our model's name:\n",
+    "MODEL_NAME = \"mobilenetv2\"\n",
+    "\n",
+    "# Choose our ONNX version model's name:\n",
+    "ONNX_MODEL_NAME = \"onnx_mobilenetv2\"\n",
+    "\n",
+    "# Choose our optimized ONNX version model's name:\n",
+    "OPTIMIZED_ONNX_MODEL_NAME = \"optimized_onnx_mobilenetv2\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "1.2.2. Download the model from `keras.applications` and log it with MLRun's `TFKerasModelHandler`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# mlrun: start-code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow import keras\n",
+    "\n",
+    "import mlrun\n",
+    "import mlrun.frameworks.tf_keras as mlrun_tf_keras\n",
+    "\n",
+    "\n",
+    "def get_model(context: mlrun.MLClientCtx, model_name: str):\n",
+    "    # Download the MobileNetV2 model:\n",
+    "    model = keras.applications.mobilenet_v2.MobileNetV2()\n",
+    "\n",
+    "    # Initialize a model handler for logging the model:\n",
+    "    model_handler = mlrun_tf_keras.TFKerasModelHandler(\n",
+    "        model_name=model_name,\n",
+    "        model=model,\n",
+    "        context=context\n",
+    "    )\n",
+    "\n",
+    "    # Log the model:\n",
+    "    model_handler.log()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# mlrun: end-code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "1.2.3. Create the function using MLRun's `code_to_function` and run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import mlrun\n",
+    "\n",
+    "\n",
+    "# Create the function parsing this notebook's code using 'code_to_function':\n",
+    "get_model_function = mlrun.code_to_function(\n",
+    "    name=\"get_mobilenetv2\",\n",
+    "    kind=\"job\",\n",
+    "    image=\"mlrun/ml-models\"\n",
+    ")\n",
+    "\n",
+    "# Run the function to log the model:\n",
+    "get_model_run = get_model_function.run(\n",
+    "    handler=\"get_model\",\n",
+    "    artifact_path=ARTIFACT_PATH,\n",
+    "    params={\n",
+    "        \"model_name\": MODEL_NAME\n",
+    "    },\n",
+    "    local=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1.2.4. Import the `onnx_utils` MLRun function and run it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Import the ONNX function from the marketplace:\n",
+    "onnx_utils_function = mlrun.import_function(\"hub://onnx_utils\")\n",
+    "\n",
+    "# Run the function to convert our model to ONNX:\n",
+    "to_onnx_run = onnx_utils_function.run(\n",
+    "    handler=\"to_onnx\",\n",
+    "    artifact_path=ARTIFACT_PATH,\n",
+    "    params={\n",
+    "        \"model_name\": MODEL_NAME,\n",
+    "        \"model_path\": get_model_run.outputs[MODEL_NAME],  # <- Take the logged model from the previous function.\n",
+    "        \"onnx_model_name\": ONNX_MODEL_NAME,\n",
+    "        \"optimize_model\": False  # <- For optimizing it later in the demo, we mark the flag as False\n",
+    "    },\n",
+    "    local=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1.2.5. Now, listing the artifact directory we will see both our `tf.keras` model and the `onnx` model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\n",
+    "print(os.listdir(ARTIFACT_PATH))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"handler2\"></a>\n",
+    "\n",
+    "## 2. optimize\n",
+    "\n",
+    "### 2.1. Docs\n",
+    "Optimize the given ONNX model.\n",
+    "\n",
+    "#### Parameters:\n",
+    "* **`context`**: `mlrun.MLClientCtx` - The MLRun function execution context\n",
+    "* **`model_path`**: `str` - The model path store object.\n",
+    "* **`optimizations`**: `List[str] = None` - List of possible optimizations. *To see what optimizations are available, **pass \"help\"**.* If None, all of the optimizations will be used. Defaulted to None.\n",
+    "* **`fixed_point`**: `bool = False` - Optimize the weights using fixed point. Defaulted to False.\n",
+    "* **`optimized_model_name`**: `str = None` - The name of the optimized model. If None, the original model will be overridden. Defaulted to None."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Demo\n",
+    "\n",
+    "We will use our converted model from the last example and optimize it.\n",
+    "\n",
+    "2.2.1. We will call now the `optimize` handler:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "onnx_utils_function.run(\n",
+    "    handler=\"optimize\",\n",
+    "    artifact_path=ARTIFACT_PATH,\n",
+    "    params={\n",
+    "        \"model_name\": ONNX_MODEL_NAME,\n",
+    "        \"model_path\": to_onnx_run.output(ONNX_MODEL_NAME),  # <- Take the logged model from the previous function.\n",
+    "        \"optimized_model_name\": OPTIMIZED_ONNX_MODEL_NAME,\n",
+    "    },\n",
+    "    local=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2.2.2. And now our model was optimized and can be seen under the `ARTIFACT_PATH`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print(os.listdir(ARTIFACT_PATH))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Lastly, run this code to clean up the models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "\n",
+    "\n",
+    "shutil.rmtree(ARTIFACT_PATH)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/functions/master/onnx_utils/1.2.0/src/onnx_utils.py b/functions/master/onnx_utils/1.2.0/src/onnx_utils.py
new file mode 100644
index 00000000..2a7c1f5b
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/src/onnx_utils.py
@@ -0,0 +1,263 @@
+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from typing import Any, Callable, Dict, List, Tuple
+
+import mlrun
+
+
+class _ToONNXConversions:
+    """
+    An ONNX conversion functions library class.
+    """
+
+    @staticmethod
+    def tf_keras_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int], str]] = None,
+    ):
+        """
+        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
+                                will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
+                                Defaulted to True.
+        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
+                                where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                [0] = Layer's shape, a tuple of integers.
+                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                If None, the input signature will be tried to be read from the model artifact. Defaulted
+                                to None.
+        """
+        # Import the framework and handler:
+        import tensorflow as tf
+        from mlrun.frameworks.tf_keras import TFKerasUtils
+
+        # Check the given 'input_signature' parameter:
+        if input_signature is None:
+            # Read the inputs from the model:
+            try:
+                model_handler.read_inputs_from_model()
+            except Exception as error:
+                raise mlrun.errors.MLRunRuntimeError(
+                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
+                    f"information automatically but failed with the following error: {error}"
+                )
+        else:
+            # Parse the 'input_signature' parameter:
+            input_signature = [
+                tf.TensorSpec(
+                    shape=shape,
+                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
+                        value_type=value_type
+                    ),
+                )
+                for (shape, value_type) in input_signature
+            ]
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_signature=input_signature,
+            optimize=optimize_model,
+        )
+
+    @staticmethod
+    def pytorch_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
+        input_layers_names: List[str] = None,
+        output_layers_names: List[str] = None,
+        dynamic_axes: Dict[str, Dict[int, str]] = None,
+        is_batched: bool = True,
+    ):
+        """
+        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
+                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
+                                    model. Defaulted to True.
+        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
+                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                    [0] = Layer's shape, a tuple of integers.
+                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                    If None, the input signature will be tried to be read from the model artifact.
+                                    Defaulted to None.
+        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
+                                    parameters (inner layers) can be set as well by passing additional names in the
+                                    list. The order is by the order of the parameters in the model. If None, the inputs
+                                    will be read from the handler's inputs. If its also None, it is defaulted to:
+                                    "input_0", "input_1", ...
+        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
+                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
+                                    to: "output_0" (for multiple outputs, this parameter must be provided).
+        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
+                                    specify it by giving a dynamic axis to the input / output layer by its name as
+                                    follows: {
+                                        "input layer name": {0: "batch_size"},
+                                        "output layer name": {0: "batch_size"},
+                                    }
+                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
+        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
+                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
+        """
+        # Import the framework and handler:
+        import torch
+        from mlrun.frameworks.pytorch import PyTorchUtils
+
+        # Parse the 'input_signature' parameter:
+        if input_signature is not None:
+            input_signature = tuple(
+                [
+                    torch.zeros(
+                        size=shape,
+                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
+                            value_type=value_type
+                        ),
+                    )
+                    for (shape, value_type) in input_signature
+                ]
+            )
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_sample=input_signature,
+            optimize=optimize_model,
+            input_layers_names=input_layers_names,
+            output_layers_names=output_layers_names,
+            dynamic_axes=dynamic_axes,
+            is_batched=is_batched
+        )
+
+
+# Map for getting the conversion function according to the provided framework:
+_CONVERSION_MAP = {
+    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
+    "torch": _ToONNXConversions.pytorch_to_onnx,
+}  # type: Dict[str, Callable]
+
+
+def to_onnx(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    onnx_model_name: str = None,
+    optimize_model: bool = True,
+    framework_kwargs: Dict[str, Any] = None,
+):
+    """
+    Convert the given model to an ONNX model.
+
+    :param context:          The MLRun function execution context
+    :param model_path:       The model path store object.
+    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
+                             be used with an additional suffix `_onnx`. Defaulted to None.
+    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
+                             to True.
+    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
+                             string of the desired framework onnx conversion function, pass "help".
+    """
+    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun
+
+    # Get a model handler of the required framework:
+    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)
+
+    # Get the model's framework:
+    framework = model_handler.FRAMEWORK_NAME
+
+    # Use the conversion map to get the specific framework to onnx conversion:
+    if framework not in _CONVERSION_MAP:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"The following framework: '{framework}', has no ONNX conversion."
+        )
+    conversion_function = _CONVERSION_MAP[framework]
+
+    # Check if needed to print the function's doc string ("help" is passed):
+    if framework_kwargs == "help":
+        print(conversion_function.__doc__)
+        return
+
+    # Set the default empty framework kwargs if needed:
+    if framework_kwargs is None:
+        framework_kwargs = {}
+
+    # Run the conversion:
+    try:
+        conversion_function(
+            model_handler=model_handler,
+            onnx_model_name=onnx_model_name,
+            optimize_model=optimize_model,
+            **framework_kwargs,
+        )
+    except TypeError as exception:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
+            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
+            f"'framework_kwargs' dictionary parameter."
+        )
+
+
+def optimize(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    optimizations: List[str] = None,
+    fixed_point: bool = False,
+    optimized_model_name: str = None,
+):
+    """
+    Optimize the given ONNX model.
+
+    :param context:              The MLRun function execution context.
+    :param model_path:           Path to the ONNX model object.
+    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
+                                 If None, all of the optimizations will be used. Defaulted to None.
+    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
+    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
+                                 Defaulted to None.
+    """
+    # Import the model handler:
+    import onnxoptimizer
+    from mlrun.frameworks.onnx import ONNXModelHandler
+
+    # Check if needed to print the available optimizations ("help" is passed):
+    if optimizations == "help":
+        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
+        print(f"The available optimizations are:\n* {available_passes}")
+        return
+
+    # Create the model handler:
+    model_handler = ONNXModelHandler(
+        model_path=model_path, context=context
+    )
+
+    # Load the ONNX model:
+    model_handler.load()
+
+    # Optimize the model using the given configurations:
+    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)
+
+    # Rename if needed:
+    if optimized_model_name is not None:
+        model_handler.set_model_name(model_name=optimized_model_name)
+
+    # Log the optimized model:
+    model_handler.log()
diff --git a/functions/master/onnx_utils/1.2.0/src/requirements.txt b/functions/master/onnx_utils/1.2.0/src/requirements.txt
new file mode 100644
index 00000000..dcc80321
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/src/requirements.txt
@@ -0,0 +1,12 @@
+tqdm~=4.62.3
+tensorflow~=2.7.0
+torch~=1.10.0
+torchvision~=0.11.1
+onnx~=1.10.1
+onnxruntime~=1.8.1
+onnxoptimizer~=0.2.0
+onnxmltools~=1.9.0
+tf2onnx~=1.9.0
+mlrun
+plotly~=5.4.0
+wrapt<1.15.0 # wrapt==1.15.0 fails tensorflow 2.7 Todo: please remove when updating tensorflow
\ No newline at end of file
diff --git a/functions/master/onnx_utils/1.2.0/src/test_onnx_utils.py b/functions/master/onnx_utils/1.2.0/src/test_onnx_utils.py
new file mode 100644
index 00000000..35b224c4
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/src/test_onnx_utils.py
@@ -0,0 +1,378 @@
+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import os
+import shutil
+import tempfile
+
+import mlrun
+
+# Choose our model's name:
+MODEL_NAME = "model"
+
+# Choose our ONNX version model's name:
+ONNX_MODEL_NAME = f"onnx_{MODEL_NAME}"
+
+# Choose our optimized ONNX version model's name:
+OPTIMIZED_ONNX_MODEL_NAME = f"optimized_{ONNX_MODEL_NAME}"
+
+
+def _setup_environment() -> str:
+    """
+    Setup the test environment, creating the artifacts path of the test.
+
+    :returns: The temporary directory created for the test artifacts path.
+    """
+    artifact_path = tempfile.TemporaryDirectory().name
+    os.makedirs(artifact_path)
+    return artifact_path
+
+
+def _cleanup_environment(artifact_path: str):
+    """
+    Cleanup the test environment, deleting files and artifacts created during the test.
+
+    :param artifact_path: The artifact path to delete.
+    """
+    # Clean the local directory:
+    for test_output in [
+        *os.listdir(artifact_path),
+        "schedules",
+        "runs",
+        "artifacts",
+        "functions",
+    ]:
+        test_output_path = os.path.abspath(f"./{test_output}")
+        if os.path.exists(test_output_path):
+            if os.path.isdir(test_output_path):
+                shutil.rmtree(test_output_path)
+            else:
+                os.remove(test_output_path)
+
+    # Clean the artifacts directory:
+    shutil.rmtree(artifact_path)
+
+
+def _log_tf_keras_model(context: mlrun.MLClientCtx, model_name: str):
+    """
+    Create and log a tf.keras model - MobileNetV2.
+
+    :param context:    The context to log to.
+    :param model_name: The model name to use.
+    """
+    from mlrun.frameworks.tf_keras import TFKerasModelHandler
+    from tensorflow import keras
+
+    # Download the MobileNetV2 model:
+    model = keras.applications.mobilenet_v2.MobileNetV2()
+
+    # Initialize a model handler for logging the model:
+    model_handler = TFKerasModelHandler(
+        model_name=model_name, model=model, context=context
+    )
+
+    # Log the model:
+    model_handler.log()
+
+
+def _log_pytorch_model(context: mlrun.MLClientCtx, model_name: str):
+    """
+    Create and log a pytorch model - MobileNetV2.
+
+    :param context:    The context to log to.
+    :param model_name: The model name to use.
+    """
+    from mlrun.frameworks.pytorch import PyTorchModelHandler
+    import torchvision
+
+    # Download the MobileNetV2 model:
+    model = torchvision.models.mobilenet_v2()
+
+    # Initialize a model handler for logging the model:
+    model_handler = PyTorchModelHandler(
+        model_name=model_name,
+        model=model,
+        model_class="mobilenet_v2",
+        modules_map={"torchvision.models": "mobilenet_v2"},
+        context=context,
+    )
+
+    # Log the model:
+    model_handler.log()
+
+
+def _log_onnx_model(context: mlrun.MLClientCtx, model_name: str):
+    """
+    Create and log an ONNX model - MNIST.
+
+    :param context:    The context to log to.
+    :param model_name: The model name to use.
+    """
+    import mlrun.frameworks.onnx as mlrun_onnx
+    import requests
+
+    # Download the MNIST model:
+    mnist_model_name = "mnist-12"
+    requested_model = requests.get(
+        f"https://github.com/onnx/models/blob/main/vision/classification/mnist/model/{mnist_model_name}.onnx?raw=true"
+    )
+    with open(
+        os.path.join(context.artifact_path, f"{model_name}.onnx"), "bw"
+    ) as onnx_file:
+        onnx_file.write(requested_model.content)
+
+    # Initialize a model handler for logging the model:
+    model_handler = mlrun_onnx.ONNXModelHandler(
+        model_name=model_name,
+        model_path=context.artifact_path,
+        context=context,
+    )
+    model_handler.load()
+
+    # Log the model:
+    model_handler.log()
+
+
+def test_to_onnx_help():
+    """
+    Test the 'to_onnx' handler, passing "help" in the 'framework_kwargs'.
+    """
+    # Setup the tests environment:
+    artifact_path = _setup_environment()
+
+    # Create the function parsing this notebook's code using 'code_to_function':
+    log_model_function = mlrun.code_to_function(
+        filename="test_onnx_utils.py",
+        name="log_model",
+        kind="job",
+        image="mlrun/ml-models",
+    )
+
+    # Run the function to log the model:
+    log_model_run = log_model_function.run(
+        handler="_log_tf_keras_model",
+        artifact_path=artifact_path,
+        params={"model_name": MODEL_NAME},
+        local=True,
+    )
+
+    # Import the ONNX Utils function:
+    onnx_function = mlrun.import_function("function.yaml")
+
+    # Run the function, passing "help" in 'framework_kwargs' and see that no exception was raised:
+    is_test_passed = True
+    try:
+        onnx_function.run(
+            handler="to_onnx",
+            artifact_path=artifact_path,
+            params={
+                "model_path": log_model_run.outputs[
+                    "model"
+                ],  # <- Take the logged model from the previous function.
+                "framework_kwargs": "help",
+            },
+            local=True,
+        )
+    except TypeError as exception:
+        print(
+            f"The test failed, the help was not handled properly and raised the following error: {exception}"
+        )
+        is_test_passed = False
+
+    # Cleanup the tests environment:
+    _cleanup_environment(artifact_path=artifact_path)
+
+    assert is_test_passed
+
+
+def test_tf_keras_to_onnx():
+    """
+    Test the 'to_onnx' handler, giving it a tf.keras model.
+    """
+    # Setup the tests environment:
+    artifact_path = _setup_environment()
+
+    # Create the function parsing this notebook's code using 'code_to_function':
+    log_model_function = mlrun.code_to_function(
+        filename="test_onnx_utils.py",
+        name="log_model",
+        kind="job",
+        image="mlrun/ml-models",
+    )
+
+    # Run the function to log the model:
+    log_model_run = log_model_function.run(
+        handler="_log_tf_keras_model",
+        artifact_path=artifact_path,
+        params={"model_name": MODEL_NAME},
+        local=True,
+    )
+
+    # Import the ONNX Utils function:
+    onnx_function = mlrun.import_function("function.yaml")
+
+    # Run the function to convert our model to ONNX:
+    onnx_function_run = onnx_function.run(
+        handler="to_onnx",
+        artifact_path=artifact_path,
+        params={
+            "model_path": log_model_run.outputs[
+                "model"
+            ],  # <- Take the logged model from the previous function.
+            "onnx_model_name": ONNX_MODEL_NAME,
+        },
+        local=True,
+    )
+
+    # Cleanup the tests environment:
+    _cleanup_environment(artifact_path=artifact_path)
+
+    # Print the outputs list:
+    print(f"Produced outputs: {onnx_function_run.outputs}")
+
+    # Verify the '.onnx' model was created:
+    assert "model" in onnx_function_run.outputs
+
+
+def test_pytorch_to_onnx():
+    """
+    Test the 'to_onnx' handler, giving it a pytorch model.
+    """
+    # Setup the tests environment:
+    artifact_path = _setup_environment()
+
+    # Create the function parsing this notebook's code using 'code_to_function':
+    log_model_function = mlrun.code_to_function(
+        filename="test_onnx_utils.py",
+        name="log_model",
+        kind="job",
+        image="mlrun/ml-models",
+    )
+
+    # Run the function to log the model:
+    log_model_run = log_model_function.run(
+        handler="_log_pytorch_model",
+        artifact_path=artifact_path,
+        params={"model_name": MODEL_NAME},
+        local=True,
+    )
+
+    # Import the ONNX Utils function:
+    onnx_function = mlrun.import_function("function.yaml")
+
+    # Run the function to convert our model to ONNX:
+    onnx_function_run = onnx_function.run(
+        handler="to_onnx",
+        artifact_path=artifact_path,
+        params={
+            "model_path": log_model_run.outputs[
+                "model"
+            ],  # <- Take the logged model from the previous function.
+            "onnx_model_name": ONNX_MODEL_NAME,
+            "framework_kwargs": {"input_signature": [((32, 3, 224, 224), "float32")]},
+        },
+        local=True,
+    )
+
+    # Cleanup the tests environment:
+    _cleanup_environment(artifact_path=artifact_path)
+
+    # Print the outputs list:
+    print(f"Produced outputs: {onnx_function_run.outputs}")
+
+    # Verify the '.onnx' model was created:
+    assert "model" in onnx_function_run.outputs
+
+
+def test_optimize_help():
+    """
+    Test the 'optimize' handler, passing "help" in the 'optimizations'.
+    """
+    # Setup the tests environment:
+    artifact_path = _setup_environment()
+
+    # Import the ONNX Utils function:
+    onnx_function = mlrun.import_function("function.yaml")
+
+    # Run the function, passing "help" in 'optimizations' and see that no exception was raised:
+    is_test_passed = True
+    try:
+        onnx_function.run(
+            handler="optimize",
+            artifact_path=artifact_path,
+            params={
+                "model_path": "",
+                "optimizations": "help",
+            },
+            local=True,
+        )
+    except TypeError as exception:
+        print(
+            f"The test failed, the help was not handled properly and raised the following error: {exception}"
+        )
+        is_test_passed = False
+
+    # Cleanup the tests environment:
+    _cleanup_environment(artifact_path=artifact_path)
+
+    assert is_test_passed
+
+
+def test_optimize():
+    """
+    Test the 'optimize' handler, giving it a model from the ONNX zoo git repository.
+    """
+    # Setup the tests environment:
+    artifact_path = _setup_environment()
+
+    # Create the function parsing this notebook's code using 'code_to_function':
+    log_model_function = mlrun.code_to_function(
+        filename="test_onnx_utils.py",
+        name="log_model",
+        kind="job",
+        image="mlrun/ml-models",
+    )
+
+    # Run the function to log the model:
+    log_model_run = log_model_function.run(
+        handler="_log_onnx_model",
+        artifact_path=artifact_path,
+        params={"model_name": MODEL_NAME},
+        local=True,
+    )
+
+    # Import the ONNX Utils function:
+    onnx_function = mlrun.import_function("function.yaml")
+
+    # Run the function to optimize our model:
+    onnx_function_run = onnx_function.run(
+        handler="optimize",
+        artifact_path=artifact_path,
+        params={
+            "model_path": log_model_run.outputs[
+                "model"
+            ],  # <- Take the logged model from the previous function.
+            "optimized_model_name": OPTIMIZED_ONNX_MODEL_NAME,
+        },
+        local=True,
+    )
+
+    # Cleanup the tests environment:
+    _cleanup_environment(artifact_path=artifact_path)
+
+    # Print the outputs list:
+    print(f"Produced outputs: {onnx_function_run.outputs}")
+
+    # Verify the '.onnx' model was created:
+    assert "model" in onnx_function_run.outputs
diff --git a/functions/master/onnx_utils/1.2.0/static/documentation.html b/functions/master/onnx_utils/1.2.0/static/documentation.html
new file mode 100644
index 00000000..0d0f67d9
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/static/documentation.html
@@ -0,0 +1,259 @@
+
+<!DOCTYPE html>
+
+<html lang="en">
+<head>
+<meta charset="utf-8"/>
+<meta content="width=device-width, initial-scale=1.0" name="viewport"/><meta content="Docutils 0.17.1: http://docutils.sourceforge.net/" name="generator"/>
+<title>onnx_utils package</title>
+<!-- Loaded before other Sphinx assets -->
+<link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet"/>
+<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=1999514e3f237ded88cf" rel="stylesheet"/>
+<link href="../../../_static/vendor/fontawesome/5.13.0/css/all.min.css" rel="stylesheet"/>
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+<link href="../../../_static/pygments.css" rel="stylesheet" type="text/css">
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+<link href="../../../_static/togglebutton.css" rel="stylesheet" type="text/css">
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+    </div>
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+<ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#submodules">
+   Submodules
+  </a>
+</li>
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#module-onnx_utils.onnx_utils">
+   onnx_utils.onnx_utils module
+  </a>
+</li>
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#module-onnx_utils">
+   Module contents
+  </a>
+</li>
+</ul>
+</nav>
+</div>
+</div>
+<div class="article row">
+<div class="col pl-md-3 pl-lg-5 content-container">
+<!-- Table of contents that is only displayed when printing the page -->
+<div class="onlyprint" id="jb-print-docs-body">
+<h1>onnx_utils package</h1>
+<!-- Table of contents -->
+<div id="print-main-content">
+<div id="jb-print-toc">
+<div>
+<h2> Contents </h2>
+</div>
+<nav aria-label="Page">
+<ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#submodules">
+   Submodules
+  </a>
+</li>
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#module-onnx_utils.onnx_utils">
+   onnx_utils.onnx_utils module
+  </a>
+</li>
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#module-onnx_utils">
+   Module contents
+  </a>
+</li>
+</ul>
+</nav>
+</div>
+</div>
+</div>
+<main id="main-content" role="main">
+<div>
+<section id="onnx-utils-package">
+<h1>onnx_utils package<a class="headerlink" href="#onnx-utils-package" title="Permalink to this headline">#</a></h1>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">#</a></h2>
+</section>
+<section id="module-onnx_utils.onnx_utils">
+<span id="onnx-utils-onnx-utils-module"></span><h2>onnx_utils.onnx_utils module<a class="headerlink" href="#module-onnx_utils.onnx_utils" title="Permalink to this headline">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="onnx_utils.onnx_utils.optimize">
+<span class="sig-prename descclassname"><span class="pre">onnx_utils.onnx_utils.</span></span><span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">mlrun.execution.MLClientCtx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizations</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_point</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimized_model_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="onnx_utils.html#optimize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#onnx_utils.onnx_utils.optimize" title="Permalink to this definition">#</a></dt>
+<dd><p>Optimize the given ONNX model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>context</strong> – The MLRun function execution context.</p></li>
+<li><p><strong>model_path</strong> – Path to the ONNX model object.</p></li>
+<li><p><strong>optimizations</strong> – List of possible optimizations. To see what optimizations are available, pass “help”.
+If None, all of the optimizations will be used. Defaulted to None.</p></li>
+<li><p><strong>fixed_point</strong> – Optimize the weights using fixed point. Defaulted to False.</p></li>
+<li><p><strong>optimized_model_name</strong> – The name of the optimized model. If None, the original model will be overridden.
+Defaulted to None.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+<dl class="py function">
+<dt class="sig sig-object py" id="onnx_utils.onnx_utils.to_onnx">
+<span class="sig-prename descclassname"><span class="pre">onnx_utils.onnx_utils.</span></span><span class="sig-name descname"><span class="pre">to_onnx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">mlrun.execution.MLClientCtx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx_model_name</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize_model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">framework_kwargs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="onnx_utils.html#to_onnx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#onnx_utils.onnx_utils.to_onnx" title="Permalink to this definition">#</a></dt>
+<dd><p>Convert the given model to an ONNX model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>context</strong> – The MLRun function execution context</p></li>
+<li><p><strong>model_path</strong> – The model path store object.</p></li>
+<li><p><strong>onnx_model_name</strong> – The name to use to log the converted ONNX model. If not given, the given <cite>model_name</cite> will
+be used with an additional suffix <cite>_onnx</cite>. Defaulted to None.</p></li>
+<li><p><strong>optimize_model</strong> – Whether to optimize the ONNX model using ‘onnxoptimizer’ before saving the model. Defaulted
+to True.</p></li>
+<li><p><strong>framework_kwargs</strong> – Additional arguments each framework may require in order to convert to ONNX. To get the doc
+string of the desired framework onnx conversion function, pass “help”.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+</section>
+<section id="module-onnx_utils">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-onnx_utils" title="Permalink to this headline">#</a></h2>
+</section>
+</section>
+</div>
+</main>
+<footer class="footer-article noprint">
+<!-- Previous / next buttons -->
+<div class="prev-next-area">
+</div>
+</footer>
+</div>
+</div>
+<div class="footer-content row">
+<footer class="col footer"><p>
+  
+      © Copyright .<br/>
+</p>
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+<!-- Scripts loaded after <body> so the DOM is not blocked -->
+<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=1999514e3f237ded88cf"></script>
+</body>
+</html>
\ No newline at end of file
diff --git a/functions/master/onnx_utils/1.2.0/static/example.html b/functions/master/onnx_utils/1.2.0/static/example.html
new file mode 100644
index 00000000..c0a76600
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+++ b/functions/master/onnx_utils/1.2.0/static/example.html
@@ -0,0 +1,554 @@
+
+<!DOCTYPE html>
+
+<html lang="en">
+<head>
+<meta charset="utf-8"/>
+<meta content="width=device-width, initial-scale=1.0" name="viewport"/><meta content="Docutils 0.17.1: http://docutils.sourceforge.net/" name="generator"/>
+<title>ONNX Utils</title>
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+<link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet"/>
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+</button>
+</li>
+</ul>
+</div>
+</div>
+<label class="headerbtn headerbtn-page-toc" for="__page-toc">
+<span class="headerbtn__icon-container">
+<i class="fas fa-list"></i>
+</span>
+</label>
+</div>
+</div>
+<!-- Table of contents -->
+<div class="col-md-3 bd-toc show noprint">
+<div class="tocsection onthispage pt-5 pb-3">
+<i class="fas fa-list"></i> Contents
+    </div>
+<nav aria-label="Page" id="bd-toc-nav">
+<ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#to-onnx">
+   1. to_onnx
+  </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#docs">
+     1.1. Docs
+    </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry">
+<a class="reference internal nav-link" href="#parameters">
+       Parameters:
+      </a>
+</li>
+<li class="toc-h4 nav-item toc-entry">
+<a class="reference internal nav-link" href="#supported-keyword-arguments-framework-kwargs-per-framework">
+       Supported keyword arguments (
+       <code class="docutils literal notranslate">
+<span class="pre">
+         framework_kwargs
+        </span>
+</code>
+       ) per framework:
+      </a>
+</li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#demo">
+     1.2. Demo
+    </a>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#optimize">
+   2. optimize
+  </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#id1">
+     2.1. Docs
+    </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry">
+<a class="reference internal nav-link" href="#id2">
+       Parameters:
+      </a>
+</li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#id3">
+     2.2. Demo
+    </a>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</div>
+</div>
+<div class="article row">
+<div class="col pl-md-3 pl-lg-5 content-container">
+<!-- Table of contents that is only displayed when printing the page -->
+<div class="onlyprint" id="jb-print-docs-body">
+<h1>ONNX Utils</h1>
+<!-- Table of contents -->
+<div id="print-main-content">
+<div id="jb-print-toc">
+<div>
+<h2> Contents </h2>
+</div>
+<nav aria-label="Page">
+<ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#to-onnx">
+   1. to_onnx
+  </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#docs">
+     1.1. Docs
+    </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry">
+<a class="reference internal nav-link" href="#parameters">
+       Parameters:
+      </a>
+</li>
+<li class="toc-h4 nav-item toc-entry">
+<a class="reference internal nav-link" href="#supported-keyword-arguments-framework-kwargs-per-framework">
+       Supported keyword arguments (
+       <code class="docutils literal notranslate">
+<span class="pre">
+         framework_kwargs
+        </span>
+</code>
+       ) per framework:
+      </a>
+</li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#demo">
+     1.2. Demo
+    </a>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry">
+<a class="reference internal nav-link" href="#optimize">
+   2. optimize
+  </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#id1">
+     2.1. Docs
+    </a>
+<ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry">
+<a class="reference internal nav-link" href="#id2">
+       Parameters:
+      </a>
+</li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry">
+<a class="reference internal nav-link" href="#id3">
+     2.2. Demo
+    </a>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</div>
+</div>
+</div>
+<main id="main-content" role="main">
+<div>
+<section id="onnx-utils">
+<h1>ONNX Utils<a class="headerlink" href="#onnx-utils" title="Permalink to this headline">#</a></h1>
+<p>A collection of ONNX utils in one MLRun function. The function includes the following handlers:</p>
+<ol class="arabic simple">
+<li><p><span class="xref myst">to_onnx</span> - Convert your model into <code class="docutils literal notranslate"><span class="pre">onnx</span></code> format.</p></li>
+<li><p><span class="xref myst">optimize</span> - Perform ONNX optimizations using <code class="docutils literal notranslate"><span class="pre">onnxmodeloptimizer</span></code> on a given ONNX model.</p></li>
+</ol>
+<p><a id="handler1"></a></p>
+<section id="to-onnx">
+<h2>1. to_onnx<a class="headerlink" href="#to-onnx" title="Permalink to this headline">#</a></h2>
+<section id="docs">
+<h3>1.1. Docs<a class="headerlink" href="#docs" title="Permalink to this headline">#</a></h3>
+<p>Convert the given model to an ONNX model.</p>
+<section id="parameters">
+<h4>Parameters:<a class="headerlink" href="#parameters" title="Permalink to this headline">#</a></h4>
+<ul class="simple">
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">context</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">mlrun.MLClientCtx</span></code> - The MLRun function execution context</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">model_path</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">str</span></code> - The model path store object.</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">onnx_model_name</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">str</span> <span class="pre">=</span> <span class="pre">None</span></code> - The name to use to log the converted ONNX model. If not given, the given <code class="docutils literal notranslate"><span class="pre">model_name</span></code> will be used with an additional suffix <code class="docutils literal notranslate"><span class="pre">_onnx</span></code>. Defaulted to None.</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">optimize_model</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></code> - Whether to optimize the ONNX model using ‘onnxoptimizer’ before saving the model. Defaulted to True.</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">framework</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">str</span> <span class="pre">=</span> <span class="pre">None</span></code> - The model’s framework. If None, it will be read from the ‘framework’ label of the model artifact provided. Defaulted to None.</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">framework_kwargs</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Any]</span> <span class="pre">=</span> <span class="pre">None</span></code> - Additional arguments each framework may require in order to convert to ONNX. <em>To get the doc string of the desired framework onnx conversion function, <strong>pass “help”</strong>.</em></p></li>
+</ul>
+</section>
+<section id="supported-keyword-arguments-framework-kwargs-per-framework">
+<h4>Supported keyword arguments (<code class="docutils literal notranslate"><span class="pre">framework_kwargs</span></code>) per framework:<a class="headerlink" href="#supported-keyword-arguments-framework-kwargs-per-framework" title="Permalink to this headline">#</a></h4>
+<p><code class="docutils literal notranslate"><span class="pre">tensorflow.keras</span></code>:</p>
+<ul>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">input_signature</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">List[Tuple[Tuple[int],</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">None</span></code> - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:</p>
+<ul class="simple">
+<li><p>[0] = Layer’s shape, a tuple of integers.</p></li>
+<li><p>[1] = Layer’s data type, a mlrun.data_types.ValueType string.</p></li>
+</ul>
+<p>If None, the input signature will be tried to be read automatically before converting to ONNX or from the model artifact if available. Defaulted to None.</p>
+</li>
+</ul>
+<p><code class="docutils literal notranslate"><span class="pre">torch</span></code>:</p>
+<ul>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">input_signature</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">List[Tuple[Tuple[int],</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">None</span></code> - A list of the input layers shape and data type properties. Expected to receive a list where each element is an input layer tuple. An input layer tuple is a tuple of:</p>
+<ul class="simple">
+<li><p>[0] = Layer’s shape, a tuple of integers.</p></li>
+<li><p>[1] = Layer’s data type, a mlrun.data_types.ValueType string.</p></li>
+</ul>
+<p>If None, the input signature will be read from the model artifact if available. Defaulted to None.</p>
+</li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">input_layers_names</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">List[str]</span> <span class="pre">=</span> <span class="pre">None</span></code> - List of names to assign to the input nodes of the graph in order. All of the other parameters (inner layers) can be set as well by passing additional names in the list. The order is by the order of the parameters in the model. If None, the inputs will be read from the handler’s inputs. If its also None, it is defaulted to: “input_0”, “input_1”, …</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">output_layers_names</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">List[str]</span> <span class="pre">=</span> <span class="pre">None</span></code> - List of names to assign to the output nodes of the graph in order. If None, the outputs will be read from the handler’s outputs. If its also None, it is defaulted to: “output_0” (for multiple outputs, this parameter must be provided).</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">param</span> <span class="pre">dynamic_axes</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Dict[int,</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">None</span></code> - If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can specify it by giving a dynamic axis to the input / output layer by its name as follows:</p></li>
+</ul>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
+    <span class="s2">"input layer name"</span><span class="p">:</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="s2">"batch_size"</span><span class="p">},</span>
+    <span class="s2">"output layer name"</span><span class="p">:</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="s2">"batch_size"</span><span class="p">},</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>If provided, the ‘is_batched’ flag will be ignored. Defaulted to None.</p>
+<ul class="simple">
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">is_batched</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True</span></code> - Whether to include a batch size as the first axis in every input and output layer. Defaulted to True. Will be ignored if ‘dynamic_axes’ is provided.</p></li>
+</ul>
+</section>
+</section>
+<section id="demo">
+<h3>1.2. Demo<a class="headerlink" href="#demo" title="Permalink to this headline">#</a></h3>
+<p>We will use the <code class="docutils literal notranslate"><span class="pre">TF.Keras</span></code> framework, a <code class="docutils literal notranslate"><span class="pre">MobileNetV2</span></code> as our model and we will convert it to ONNX using the <code class="docutils literal notranslate"><span class="pre">to_onnx</span></code> handler.</p>
+<p>1.2.1. First we will set a temporary artifact path for our model to be saved in and choose the models names:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">tempfile</span> <span class="kn">import</span> <span class="n">TemporaryDirectory</span>
+
+<span class="c1"># Create a temporary directory for the model artifact:</span>
+<span class="n">ARTIFACT_PATH</span> <span class="o">=</span> <span class="n">TemporaryDirectory</span><span class="p">()</span><span class="o">.</span><span class="n">name</span>
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">ARTIFACT_PATH</span><span class="p">)</span>
+
+<span class="c1"># Choose our model's name:</span>
+<span class="n">MODEL_NAME</span> <span class="o">=</span> <span class="s2">"mobilenetv2"</span>
+
+<span class="c1"># Choose our ONNX version model's name:</span>
+<span class="n">ONNX_MODEL_NAME</span> <span class="o">=</span> <span class="s2">"onnx_mobilenetv2"</span>
+
+<span class="c1"># Choose our optimized ONNX version model's name:</span>
+<span class="n">OPTIMIZED_ONNX_MODEL_NAME</span> <span class="o">=</span> <span class="s2">"optimized_onnx_mobilenetv2"</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>1.2.2. Download the model from <code class="docutils literal notranslate"><span class="pre">keras.applications</span></code> and log it with MLRun’s <code class="docutils literal notranslate"><span class="pre">TFKerasModelHandler</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># mlrun: start-code</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow</span> <span class="kn">import</span> <span class="n">keras</span>
+
+<span class="kn">import</span> <span class="nn">mlrun</span>
+<span class="kn">import</span> <span class="nn">mlrun.frameworks.tf_keras</span> <span class="k">as</span> <span class="nn">mlrun_tf_keras</span>
+
+
+<span class="k">def</span> <span class="nf">get_model</span><span class="p">(</span><span class="n">context</span><span class="p">:</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">MLClientCtx</span><span class="p">,</span> <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="c1"># Download the MobileNetV2 model:</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">applications</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="o">.</span><span class="n">MobileNetV2</span><span class="p">()</span>
+
+    <span class="c1"># Initialize a model handler for logging the model:</span>
+    <span class="n">model_handler</span> <span class="o">=</span> <span class="n">mlrun_tf_keras</span><span class="o">.</span><span class="n">TFKerasModelHandler</span><span class="p">(</span>
+        <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span>
+        <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
+        <span class="n">context</span><span class="o">=</span><span class="n">context</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Log the model:</span>
+    <span class="n">model_handler</span><span class="o">.</span><span class="n">log</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># mlrun: end-code</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>1.2.3. Create the function using MLRun’s <code class="docutils literal notranslate"><span class="pre">code_to_function</span></code> and run it:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mlrun</span>
+
+
+<span class="c1"># Create the function parsing this notebook's code using 'code_to_function':</span>
+<span class="n">get_model_function</span> <span class="o">=</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">code_to_function</span><span class="p">(</span>
+    <span class="n">name</span><span class="o">=</span><span class="s2">"get_mobilenetv2"</span><span class="p">,</span>
+    <span class="n">kind</span><span class="o">=</span><span class="s2">"job"</span><span class="p">,</span>
+    <span class="n">image</span><span class="o">=</span><span class="s2">"mlrun/ml-models"</span>
+<span class="p">)</span>
+
+<span class="c1"># Run the function to log the model:</span>
+<span class="n">get_model_run</span> <span class="o">=</span> <span class="n">get_model_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+    <span class="n">handler</span><span class="o">=</span><span class="s2">"get_model"</span><span class="p">,</span>
+    <span class="n">artifact_path</span><span class="o">=</span><span class="n">ARTIFACT_PATH</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">{</span>
+        <span class="s2">"model_name"</span><span class="p">:</span> <span class="n">MODEL_NAME</span>
+    <span class="p">},</span>
+    <span class="n">local</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>1.2.4. Import the <code class="docutils literal notranslate"><span class="pre">onnx_utils</span></code> MLRun function and run it:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import the ONNX function from the marketplace:</span>
+<span class="n">onnx_utils_function</span> <span class="o">=</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">import_function</span><span class="p">(</span><span class="s2">"hub://onnx_utils"</span><span class="p">)</span>
+
+<span class="c1"># Run the function to convert our model to ONNX:</span>
+<span class="n">to_onnx_run</span> <span class="o">=</span> <span class="n">onnx_utils_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+    <span class="n">handler</span><span class="o">=</span><span class="s2">"to_onnx"</span><span class="p">,</span>
+    <span class="n">artifact_path</span><span class="o">=</span><span class="n">ARTIFACT_PATH</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">{</span>
+        <span class="s2">"model_name"</span><span class="p">:</span> <span class="n">MODEL_NAME</span><span class="p">,</span>
+        <span class="s2">"model_path"</span><span class="p">:</span> <span class="n">get_model_run</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="n">MODEL_NAME</span><span class="p">],</span>  <span class="c1"># &lt;- Take the logged model from the previous function.</span>
+        <span class="s2">"onnx_model_name"</span><span class="p">:</span> <span class="n">ONNX_MODEL_NAME</span><span class="p">,</span>
+        <span class="s2">"optimize_model"</span><span class="p">:</span> <span class="kc">False</span>  <span class="c1"># &lt;- For optimizing it later in the demo, we mark the flag as False</span>
+    <span class="p">},</span>
+    <span class="n">local</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>1.2.5. Now, listing the artifact directory we will see both our <code class="docutils literal notranslate"><span class="pre">tf.keras</span></code> model and the <code class="docutils literal notranslate"><span class="pre">onnx</span></code> model:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+
+<span class="nb">print</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">ARTIFACT_PATH</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p><a id="handler2"></a></p>
+</section>
+</section>
+<section id="optimize">
+<h2>2. optimize<a class="headerlink" href="#optimize" title="Permalink to this headline">#</a></h2>
+<section id="id1">
+<h3>2.1. Docs<a class="headerlink" href="#id1" title="Permalink to this headline">#</a></h3>
+<p>Optimize the given ONNX model.</p>
+<section id="id2">
+<h4>Parameters:<a class="headerlink" href="#id2" title="Permalink to this headline">#</a></h4>
+<ul class="simple">
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">context</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">mlrun.MLClientCtx</span></code> - The MLRun function execution context</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">model_path</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">str</span></code> - The model path store object.</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">optimizations</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">List[str]</span> <span class="pre">=</span> <span class="pre">None</span></code> - List of possible optimizations. <em>To see what optimizations are available, <strong>pass “help”</strong>.</em> If None, all of the optimizations will be used. Defaulted to None.</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">fixed_point</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></code> - Optimize the weights using fixed point. Defaulted to False.</p></li>
+<li><p><strong><code class="docutils literal notranslate"><span class="pre">optimized_model_name</span></code></strong>: <code class="docutils literal notranslate"><span class="pre">str</span> <span class="pre">=</span> <span class="pre">None</span></code> - The name of the optimized model. If None, the original model will be overridden. Defaulted to None.</p></li>
+</ul>
+</section>
+</section>
+<section id="id3">
+<h3>2.2. Demo<a class="headerlink" href="#id3" title="Permalink to this headline">#</a></h3>
+<p>We will use our converted model from the last example and optimize it.</p>
+<p>2.2.1. We will call now the <code class="docutils literal notranslate"><span class="pre">optimize</span></code> handler:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">onnx_utils_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+    <span class="n">handler</span><span class="o">=</span><span class="s2">"optimize"</span><span class="p">,</span>
+    <span class="n">artifact_path</span><span class="o">=</span><span class="n">ARTIFACT_PATH</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">{</span>
+        <span class="s2">"model_name"</span><span class="p">:</span> <span class="n">ONNX_MODEL_NAME</span><span class="p">,</span>
+        <span class="s2">"model_path"</span><span class="p">:</span> <span class="n">to_onnx_run</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">ONNX_MODEL_NAME</span><span class="p">),</span>  <span class="c1"># &lt;- Take the logged model from the previous function.</span>
+        <span class="s2">"optimized_model_name"</span><span class="p">:</span> <span class="n">OPTIMIZED_ONNX_MODEL_NAME</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">local</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>2.2.2. And now our model was optimized and can be seen under the <code class="docutils literal notranslate"><span class="pre">ARTIFACT_PATH</span></code>:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">ARTIFACT_PATH</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Lastly, run this code to clean up the models:</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">shutil</span>
+
+
+<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">ARTIFACT_PATH</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+</section>
+</div>
+</main>
+<footer class="footer-article noprint">
+<!-- Previous / next buttons -->
+<div class="prev-next-area">
+</div>
+</footer>
+</div>
+</div>
+<div class="footer-content row">
+<footer class="col footer"><p>
+  
+      © Copyright .<br/>
+</p>
+</footer>
+</div>
+</div>
+</div>
+</div>
+<!-- Scripts loaded after <body> so the DOM is not blocked -->
+<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=1999514e3f237ded88cf"></script>
+</body>
+</html>
\ No newline at end of file
diff --git a/functions/master/onnx_utils/1.2.0/static/function.html b/functions/master/onnx_utils/1.2.0/static/function.html
new file mode 100644
index 00000000..feceb076
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/static/function.html
@@ -0,0 +1,216 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta content="IE=edge" http-equiv="X-UA-Compatible">
+    <meta content="width=device-width, initial-scale=1.0" name="viewport">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/styles/atom-one-dark.min.css">
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/highlight.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/languages/yaml.min.js"></script>
+    <title>Source</title>
+    <script>hljs.highlightAll();</script>
+</head>
+
+<body style="background-color:darkgray; margin: 0 0 0 0;">
+    <pre style="margin: 0 1em;">
+        <code class="yaml">
+kind: job
+metadata:
+  name: onnx-utils
+  tag: ''
+  hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2
+  project: ''
+  labels:
+    author: guyl
+  categories:
+  - utils
+spec:
+  command: ''
+  args: []
+  image: ''
+  build:
+    functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()

+    base_image: mlrun/mlrun
+    commands: []
+    code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    with_mlrun: false
+    auto_build: true
+    requirements:
+    - onnx~=1.13.0
+    - onnxruntime~=1.14.0
+    - onnxoptimizer~=0.3.0
+    - onnxmltools~=1.11.0
+    - tf2onnx~=1.13.0
+  entry_points:
+    tf_keras_to_onnx:
+      name: tf_keras_to_onnx
+      doc: Convert a TF.Keras model to an ONNX model and log it back to MLRun as a
+        new model object.
+      parameters:
+      - name: model_handler
+        doc: An initialized TFKerasModelHandler with a loaded model to convert to
+          ONNX.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before
+          saving the model. Defaulted to True.
+        default: true
+      - name: input_signature
+        type: List[Tuple[Tuple[int], str]]
+        doc: 'A list of the input layers shape and data type properties. Expected
+          to receive a list where each element is an input layer tuple. An input layer
+          tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
+          data type, a mlrun.data_types.ValueType string. If None, the input signature
+          will be tried to be read from the model artifact. Defaulted to None.'
+        default: null
+      outputs:
+      - default: ''
+      lineno: 26
+    pytorch_to_onnx:
+      name: pytorch_to_onnx
+      doc: Convert a PyTorch model to an ONNX model and log it back to MLRun as a
+        new model object.
+      parameters:
+      - name: model_handler
+        doc: An initialized PyTorchModelHandler with a loaded model to convert to
+          ONNX.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether or not to optimize the ONNX model using 'onnxoptimizer' before
+          saving the model. Defaulted to True.
+        default: true
+      - name: input_signature
+        type: List[Tuple[Tuple[int, ], str]]
+        doc: 'A list of the input layers shape and data type properties. Expected
+          to receive a list where each element is an input layer tuple. An input layer
+          tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
+          data type, a mlrun.data_types.ValueType string. If None, the input signature
+          will be tried to be read from the model artifact. Defaulted to None.'
+        default: null
+      - name: input_layers_names
+        type: List[str]
+        doc: 'List of names to assign to the input nodes of the graph in order. All
+          of the other parameters (inner layers) can be set as well by passing additional
+          names in the list. The order is by the order of the parameters in the model.
+          If None, the inputs will be read from the handler''s inputs. If its also
+          None, it is defaulted to: "input_0", "input_1", ...'
+        default: null
+      - name: output_layers_names
+        type: List[str]
+        doc: 'List of names to assign to the output nodes of the graph in order. If
+          None, the outputs will be read from the handler''s outputs. If its also
+          None, it is defaulted to: "output_0" (for multiple outputs, this parameter
+          must be provided).'
+        default: null
+      - name: dynamic_axes
+        type: Dict[str, Dict[int, str]]
+        doc: 'If part of the input / output shape is dynamic, like (batch_size, 3,
+          32, 32) you can specify it by giving a dynamic axis to the input / output
+          layer by its name as follows: { "input layer name": {0: "batch_size"}, "output
+          layer name": {0: "batch_size"}, } If provided, the ''is_batched'' flag will
+          be ignored. Defaulted to None.'
+        default: null
+      - name: is_batched
+        type: bool
+        doc: Whether to include a batch size as the first axis in every input and
+          output layer. Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
+        default: true
+      outputs:
+      - default: ''
+      lineno: 81
+    to_onnx:
+      name: to_onnx
+      doc: Convert the given model to an ONNX model.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: The MLRun function execution context
+        default: ''
+      - name: model_path
+        type: str
+        doc: The model path store object.
+        default: ''
+      - name: onnx_model_name
+        type: str
+        doc: The name to use to log the converted ONNX model. If not given, the given
+          `model_name` will be used with an additional suffix `_onnx`. Defaulted to
+          None.
+        default: null
+      - name: optimize_model
+        type: bool
+        doc: Whether to optimize the ONNX model using 'onnxoptimizer' before saving
+          the model. Defaulted to True.
+        default: true
+      - name: framework_kwargs
+        type: Dict[str, Any]
+        doc: Additional arguments each framework may require in order to convert to
+          ONNX. To get the doc string of the desired framework onnx conversion function,
+          pass "help".
+        default: null
+      outputs:
+      - default: ''
+      lineno: 160
+    optimize:
+      name: optimize
+      doc: Optimize the given ONNX model.
+      parameters:
+      - name: context
+        type: MLClientCtx
+        doc: The MLRun function execution context.
+        default: ''
+      - name: model_path
+        type: str
+        doc: Path to the ONNX model object.
+        default: ''
+      - name: optimizations
+        type: List[str]
+        doc: List of possible optimizations. To see what optimizations are available,
+          pass "help". If None, all of the optimizations will be used. Defaulted to
+          None.
+        default: null
+      - name: fixed_point
+        type: bool
+        doc: Optimize the weights using fixed point. Defaulted to False.
+        default: false
+      - name: optimized_model_name
+        type: str
+        doc: The name of the optimized model. If None, the original model will be
+          overridden. Defaulted to None.
+        default: null
+      outputs:
+      - default: ''
+      lineno: 219
+  description: ONNX intigration in MLRun, some utils functions for the ONNX framework,
+    optimizing and converting models from different framework to ONNX using MLRun.
+  default_handler: to_onnx
+  disable_auto_mount: false
+  allow_empty_resources: true
+  clone_target_dir: ''
+  env: []
+  priority_class_name: ''
+  preemption_mode: prevent
+  affinity: null
+  tolerations: null
+  security_context: {}
+verbose: false
+
+        </code>
+    </pre>
+</body>
+</html>
\ No newline at end of file
diff --git a/functions/master/onnx_utils/1.2.0/static/item.html b/functions/master/onnx_utils/1.2.0/static/item.html
new file mode 100644
index 00000000..07129b01
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/static/item.html
@@ -0,0 +1,57 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta content="IE=edge" http-equiv="X-UA-Compatible">
+    <meta content="width=device-width, initial-scale=1.0" name="viewport">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/styles/atom-one-dark.min.css">
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/highlight.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/languages/yaml.min.js"></script>
+    <title>Source</title>
+    <script>hljs.highlightAll();</script>
+</head>
+
+<body style="background-color:darkgray; margin: 0 0 0 0;">
+    <pre style="margin: 0 1em;">
+        <code class="yaml">
+apiVersion: v1
+categories:
+- utils
+description: ONNX intigration in MLRun, some utils functions for the ONNX framework,
+  optimizing and converting models from different framework to ONNX using MLRun.
+doc: ''
+example: onnx_utils.ipynb
+generationDate: 2022-08-28:17-25
+hidden: false
+icon: ''
+labels:
+  author: guyl
+maintainers: []
+marketplaceType: ''
+mlrunVersion: 1.1.0
+name: onnx_utils
+platformVersion: 3.5.0
+spec:
+  extra_spec:
+    allow_empty_resources: true
+    build:
+      auto_build: true
+      with_mlrun: false
+  filename: onnx_utils.py
+  handler: to_onnx
+  image: mlrun/mlrun
+  kind: job
+  requirements:
+  - onnx~=1.13.0
+  - onnxruntime~=1.14.0
+  - onnxoptimizer~=0.3.0
+  - onnxmltools~=1.11.0
+  - tf2onnx~=1.13.0
+url: ''
+version: 1.2.0
+
+        </code>
+    </pre>
+</body>
+</html>
\ No newline at end of file
diff --git a/functions/master/onnx_utils/1.2.0/static/onnx_utils.html b/functions/master/onnx_utils/1.2.0/static/onnx_utils.html
new file mode 100644
index 00000000..52c337bc
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/static/onnx_utils.html
@@ -0,0 +1,403 @@
+
+<!DOCTYPE html>
+
+<html lang="en">
+<head>
+<meta charset="utf-8"/>
+<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
+<title>onnx_utils.onnx_utils</title>
+<!-- Loaded before other Sphinx assets -->
+<link href="../../../_static/styles/theme.css?digest=1999514e3f237ded88cf" rel="stylesheet"/>
+<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=1999514e3f237ded88cf" rel="stylesheet"/>
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+<meta content="width=device-width, initial-scale=1" name="viewport"/>
+<meta content="en" name="docsearch:language"/>
+<!-- Google Analytics -->
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+<body data-offset="60" data-spy="scroll" data-target="#bd-toc-nav">
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+<label class="overlay overlay-pagetoc" for="__page-toc">
+<div class="visually-hidden">Toggle in-page Table of Contents</div>
+</label>
+<!-- Headers at the top -->
+<div class="announcement header-item noprint"></div>
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+<div class="bd-sidebar noprint single-page" id="site-navigation">
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+<a class="headerbtn" data-placement="left" data-toggle="tooltip" href="https://github.com/mlrun/marketplace/issues/new?title=Issue%20on%20page%20%2F_modules/onnx_utils/onnx_utils.html&amp;body=Your%20issue%20content%20here." title="Open an issue">
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+<div class="col-md-3 bd-toc show noprint">
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+<div class="col pl-md-3 pl-lg-5 content-container">
+<!-- Table of contents that is only displayed when printing the page -->
+<div class="onlyprint" id="jb-print-docs-body">
+<h1></h1>
+<!-- Table of contents -->
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+</div>
+</div>
+</div>
+<main id="main-content" role="main">
+<div>
+<h1>Source code for onnx_utils.onnx_utils</h1><div class="highlight"><pre>
+<span></span><span class="c1"># Copyright 2019 Iguazio</span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#     http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1">#</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">mlrun</span>
+
+
+<span class="k">class</span> <span class="nc">_ToONNXConversions</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">"""</span>
+<span class="sd">    An ONNX conversion functions library class.</span>
+<span class="sd">    """</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">tf_keras_to_onnx</span><span class="p">(</span>
+        <span class="n">model_handler</span><span class="p">,</span>
+        <span class="n">onnx_model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">optimize_model</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">input_signature</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">"""</span>
+<span class="sd">        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.</span>
+
+<span class="sd">        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.</span>
+<span class="sd">        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`</span>
+<span class="sd">                                will be used with an additional suffix `_onnx`. Defaulted to None.</span>
+<span class="sd">        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.</span>
+<span class="sd">                                Defaulted to True.</span>
+<span class="sd">        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list</span>
+<span class="sd">                                where each element is an input layer tuple. An input layer tuple is a tuple of:</span>
+<span class="sd">                                [0] = Layer's shape, a tuple of integers.</span>
+<span class="sd">                                [1] = Layer's data type, a mlrun.data_types.ValueType string.</span>
+<span class="sd">                                If None, the input signature will be tried to be read from the model artifact. Defaulted</span>
+<span class="sd">                                to None.</span>
+<span class="sd">        """</span>
+        <span class="c1"># Import the framework and handler:</span>
+        <span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
+        <span class="kn">from</span> <span class="nn">mlrun.frameworks.tf_keras</span> <span class="kn">import</span> <span class="n">TFKerasUtils</span>
+
+        <span class="c1"># Check the given 'input_signature' parameter:</span>
+        <span class="k">if</span> <span class="n">input_signature</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># Read the inputs from the model:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">model_handler</span><span class="o">.</span><span class="n">read_inputs_from_model</span><span class="p">()</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">error</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">errors</span><span class="o">.</span><span class="n">MLRunRuntimeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">"Please provide the 'input_signature' parameter. The function tried reading the input layers "</span>
+                    <span class="sa">f</span><span class="s2">"information automatically but failed with the following error: </span><span class="si">{</span><span class="n">error</span><span class="si">}</span><span class="s2">"</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Parse the 'input_signature' parameter:</span>
+            <span class="n">input_signature</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="n">tf</span><span class="o">.</span><span class="n">TensorSpec</span><span class="p">(</span>
+                    <span class="n">shape</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="n">TFKerasUtils</span><span class="o">.</span><span class="n">convert_value_type_to_tf_dtype</span><span class="p">(</span>
+                        <span class="n">value_type</span><span class="o">=</span><span class="n">value_type</span>
+                    <span class="p">),</span>
+                <span class="p">)</span>
+                <span class="k">for</span> <span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">value_type</span><span class="p">)</span> <span class="ow">in</span> <span class="n">input_signature</span>
+            <span class="p">]</span>
+
+        <span class="c1"># Convert to ONNX:</span>
+        <span class="n">model_handler</span><span class="o">.</span><span class="n">to_onnx</span><span class="p">(</span>
+            <span class="n">model_name</span><span class="o">=</span><span class="n">onnx_model_name</span><span class="p">,</span>
+            <span class="n">input_signature</span><span class="o">=</span><span class="n">input_signature</span><span class="p">,</span>
+            <span class="n">optimize</span><span class="o">=</span><span class="n">optimize_model</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">pytorch_to_onnx</span><span class="p">(</span>
+        <span class="n">model_handler</span><span class="p">,</span>
+        <span class="n">onnx_model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">optimize_model</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">input_signature</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">input_layers_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">output_layers_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">dynamic_axes</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">is_batched</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">"""</span>
+<span class="sd">        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.</span>
+
+<span class="sd">        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.</span>
+<span class="sd">        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given</span>
+<span class="sd">                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.</span>
+<span class="sd">        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the</span>
+<span class="sd">                                    model. Defaulted to True.</span>
+<span class="sd">        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a</span>
+<span class="sd">                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:</span>
+<span class="sd">                                    [0] = Layer's shape, a tuple of integers.</span>
+<span class="sd">                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.</span>
+<span class="sd">                                    If None, the input signature will be tried to be read from the model artifact.</span>
+<span class="sd">                                    Defaulted to None.</span>
+<span class="sd">        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other</span>
+<span class="sd">                                    parameters (inner layers) can be set as well by passing additional names in the</span>
+<span class="sd">                                    list. The order is by the order of the parameters in the model. If None, the inputs</span>
+<span class="sd">                                    will be read from the handler's inputs. If its also None, it is defaulted to:</span>
+<span class="sd">                                    "input_0", "input_1", ...</span>
+<span class="sd">        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the</span>
+<span class="sd">                                    outputs will be read from the handler's outputs. If its also None, it is defaulted</span>
+<span class="sd">                                    to: "output_0" (for multiple outputs, this parameter must be provided).</span>
+<span class="sd">        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can</span>
+<span class="sd">                                    specify it by giving a dynamic axis to the input / output layer by its name as</span>
+<span class="sd">                                    follows: {</span>
+<span class="sd">                                        "input layer name": {0: "batch_size"},</span>
+<span class="sd">                                        "output layer name": {0: "batch_size"},</span>
+<span class="sd">                                    }</span>
+<span class="sd">                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.</span>
+<span class="sd">        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.</span>
+<span class="sd">                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.</span>
+<span class="sd">        """</span>
+        <span class="c1"># Import the framework and handler:</span>
+        <span class="kn">import</span> <span class="nn">torch</span>
+        <span class="kn">from</span> <span class="nn">mlrun.frameworks.pytorch</span> <span class="kn">import</span> <span class="n">PyTorchUtils</span>
+
+        <span class="c1"># Parse the 'input_signature' parameter:</span>
+        <span class="k">if</span> <span class="n">input_signature</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">input_signature</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
+                        <span class="n">size</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="n">PyTorchUtils</span><span class="o">.</span><span class="n">convert_value_type_to_torch_dtype</span><span class="p">(</span>
+                            <span class="n">value_type</span><span class="o">=</span><span class="n">value_type</span>
+                        <span class="p">),</span>
+                    <span class="p">)</span>
+                    <span class="k">for</span> <span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">value_type</span><span class="p">)</span> <span class="ow">in</span> <span class="n">input_signature</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Convert to ONNX:</span>
+        <span class="n">model_handler</span><span class="o">.</span><span class="n">to_onnx</span><span class="p">(</span>
+            <span class="n">model_name</span><span class="o">=</span><span class="n">onnx_model_name</span><span class="p">,</span>
+            <span class="n">input_sample</span><span class="o">=</span><span class="n">input_signature</span><span class="p">,</span>
+            <span class="n">optimize</span><span class="o">=</span><span class="n">optimize_model</span><span class="p">,</span>
+            <span class="n">input_layers_names</span><span class="o">=</span><span class="n">input_layers_names</span><span class="p">,</span>
+            <span class="n">output_layers_names</span><span class="o">=</span><span class="n">output_layers_names</span><span class="p">,</span>
+            <span class="n">dynamic_axes</span><span class="o">=</span><span class="n">dynamic_axes</span><span class="p">,</span>
+            <span class="n">is_batched</span><span class="o">=</span><span class="n">is_batched</span>
+        <span class="p">)</span>
+
+
+<span class="c1"># Map for getting the conversion function according to the provided framework:</span>
+<span class="n">_CONVERSION_MAP</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">"tensorflow.keras"</span><span class="p">:</span> <span class="n">_ToONNXConversions</span><span class="o">.</span><span class="n">tf_keras_to_onnx</span><span class="p">,</span>
+    <span class="s2">"torch"</span><span class="p">:</span> <span class="n">_ToONNXConversions</span><span class="o">.</span><span class="n">pytorch_to_onnx</span><span class="p">,</span>
+<span class="p">}</span>  <span class="c1"># type: Dict[str, Callable]</span>
+
+
+<div class="viewcode-block" id="to_onnx"><a class="viewcode-back" href="documentation.html#onnx_utils.onnx_utils.to_onnx">[docs]</a><span class="k">def</span> <span class="nf">to_onnx</span><span class="p">(</span>
+    <span class="n">context</span><span class="p">:</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">MLClientCtx</span><span class="p">,</span>
+    <span class="n">model_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">onnx_model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">optimize_model</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">framework_kwargs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">"""</span>
+<span class="sd">    Convert the given model to an ONNX model.</span>
+
+<span class="sd">    :param context:          The MLRun function execution context</span>
+<span class="sd">    :param model_path:       The model path store object.</span>
+<span class="sd">    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will</span>
+<span class="sd">                             be used with an additional suffix `_onnx`. Defaulted to None.</span>
+<span class="sd">    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted</span>
+<span class="sd">                             to True.</span>
+<span class="sd">    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc</span>
+<span class="sd">                             string of the desired framework onnx conversion function, pass "help".</span>
+<span class="sd">    """</span>
+    <span class="kn">from</span> <span class="nn">mlrun.frameworks.auto_mlrun.auto_mlrun</span> <span class="kn">import</span> <span class="n">AutoMLRun</span>
+
+    <span class="c1"># Get a model handler of the required framework:</span>
+    <span class="n">model_handler</span> <span class="o">=</span> <span class="n">AutoMLRun</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">model_path</span><span class="o">=</span><span class="n">model_path</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="n">context</span><span class="p">)</span>
+
+    <span class="c1"># Get the model's framework:</span>
+    <span class="n">framework</span> <span class="o">=</span> <span class="n">model_handler</span><span class="o">.</span><span class="n">FRAMEWORK_NAME</span>
+
+    <span class="c1"># Use the conversion map to get the specific framework to onnx conversion:</span>
+    <span class="k">if</span> <span class="n">framework</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">_CONVERSION_MAP</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">errors</span><span class="o">.</span><span class="n">MLRunInvalidArgumentError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">"The following framework: '</span><span class="si">{</span><span class="n">framework</span><span class="si">}</span><span class="s2">', has no ONNX conversion."</span>
+        <span class="p">)</span>
+    <span class="n">conversion_function</span> <span class="o">=</span> <span class="n">_CONVERSION_MAP</span><span class="p">[</span><span class="n">framework</span><span class="p">]</span>
+
+    <span class="c1"># Check if needed to print the function's doc string ("help" is passed):</span>
+    <span class="k">if</span> <span class="n">framework_kwargs</span> <span class="o">==</span> <span class="s2">"help"</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">conversion_function</span><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+        <span class="k">return</span>
+
+    <span class="c1"># Set the default empty framework kwargs if needed:</span>
+    <span class="k">if</span> <span class="n">framework_kwargs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">framework_kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="c1"># Run the conversion:</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">conversion_function</span><span class="p">(</span>
+            <span class="n">model_handler</span><span class="o">=</span><span class="n">model_handler</span><span class="p">,</span>
+            <span class="n">onnx_model_name</span><span class="o">=</span><span class="n">onnx_model_name</span><span class="p">,</span>
+            <span class="n">optimize_model</span><span class="o">=</span><span class="n">optimize_model</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">framework_kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">except</span> <span class="ne">TypeError</span> <span class="k">as</span> <span class="n">exception</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">errors</span><span class="o">.</span><span class="n">MLRunInvalidArgumentError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">"ERROR: A TypeError exception was raised during the conversion:</span><span class="se">\n</span><span class="si">{</span><span class="n">exception</span><span class="si">}</span><span class="s2">. "</span>
+            <span class="sa">f</span><span class="s2">"Please read the </span><span class="si">{</span><span class="n">framework</span><span class="si">}</span><span class="s2"> framework conversion function doc string by passing 'help' in the "</span>
+            <span class="sa">f</span><span class="s2">"'framework_kwargs' dictionary parameter."</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="optimize"><a class="viewcode-back" href="documentation.html#onnx_utils.onnx_utils.optimize">[docs]</a><span class="k">def</span> <span class="nf">optimize</span><span class="p">(</span>
+    <span class="n">context</span><span class="p">:</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">MLClientCtx</span><span class="p">,</span>
+    <span class="n">model_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">optimizations</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">fixed_point</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">optimized_model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">"""</span>
+<span class="sd">    Optimize the given ONNX model.</span>
+
+<span class="sd">    :param context:              The MLRun function execution context.</span>
+<span class="sd">    :param model_path:           Path to the ONNX model object.</span>
+<span class="sd">    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".</span>
+<span class="sd">                                 If None, all of the optimizations will be used. Defaulted to None.</span>
+<span class="sd">    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.</span>
+<span class="sd">    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.</span>
+<span class="sd">                                 Defaulted to None.</span>
+<span class="sd">    """</span>
+    <span class="c1"># Import the model handler:</span>
+    <span class="kn">import</span> <span class="nn">onnxoptimizer</span>
+    <span class="kn">from</span> <span class="nn">mlrun.frameworks.onnx</span> <span class="kn">import</span> <span class="n">ONNXModelHandler</span>
+
+    <span class="c1"># Check if needed to print the available optimizations ("help" is passed):</span>
+    <span class="k">if</span> <span class="n">optimizations</span> <span class="o">==</span> <span class="s2">"help"</span><span class="p">:</span>
+        <span class="n">available_passes</span> <span class="o">=</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">* "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">onnxoptimizer</span><span class="o">.</span><span class="n">get_available_passes</span><span class="p">())</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The available optimizations are:</span><span class="se">\n</span><span class="s2">* </span><span class="si">{</span><span class="n">available_passes</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+        <span class="k">return</span>
+
+    <span class="c1"># Create the model handler:</span>
+    <span class="n">model_handler</span> <span class="o">=</span> <span class="n">ONNXModelHandler</span><span class="p">(</span>
+        <span class="n">model_path</span><span class="o">=</span><span class="n">model_path</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="n">context</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Load the ONNX model:</span>
+    <span class="n">model_handler</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+
+    <span class="c1"># Optimize the model using the given configurations:</span>
+    <span class="n">model_handler</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">optimizations</span><span class="o">=</span><span class="n">optimizations</span><span class="p">,</span> <span class="n">fixed_point</span><span class="o">=</span><span class="n">fixed_point</span><span class="p">)</span>
+
+    <span class="c1"># Rename if needed:</span>
+    <span class="k">if</span> <span class="n">optimized_model_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">model_handler</span><span class="o">.</span><span class="n">set_model_name</span><span class="p">(</span><span class="n">model_name</span><span class="o">=</span><span class="n">optimized_model_name</span><span class="p">)</span>
+
+    <span class="c1"># Log the optimized model:</span>
+    <span class="n">model_handler</span><span class="o">.</span><span class="n">log</span><span class="p">()</span></div>
+</pre></div>
+</div>
+</main>
+<footer class="footer-article noprint">
+<!-- Previous / next buttons -->
+<div class="prev-next-area">
+</div>
+</footer>
+</div>
+</div>
+<div class="footer-content row">
+<footer class="col footer"><p>
+  
+      © Copyright .<br/>
+</p>
+</footer>
+</div>
+</div>
+</div>
+</div>
+<!-- Scripts loaded after <body> so the DOM is not blocked -->
+<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=1999514e3f237ded88cf"></script>
+</body>
+</html>
\ No newline at end of file
diff --git a/functions/master/onnx_utils/1.2.0/static/source.html b/functions/master/onnx_utils/1.2.0/static/source.html
new file mode 100644
index 00000000..f10ac64f
--- /dev/null
+++ b/functions/master/onnx_utils/1.2.0/static/source.html
@@ -0,0 +1,285 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta content="IE=edge" http-equiv="X-UA-Compatible">
+    <meta content="width=device-width, initial-scale=1.0" name="viewport">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/styles/atom-one-dark.min.css">
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/highlight.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.7.2/languages/python.min.js"></script>
+    <title>Source</title>
+    <script>hljs.highlightAll();</script>
+</head>
+
+<body style="background-color:darkgray; margin: 0 0 0 0;">
+    <pre style="margin: 0 1em;">
+        <code class="python">
+# Copyright 2019 Iguazio
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from typing import Any, Callable, Dict, List, Tuple
+
+import mlrun
+
+
+class _ToONNXConversions:
+    """
+    An ONNX conversion functions library class.
+    """
+
+    @staticmethod
+    def tf_keras_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int], str]] = None,
+    ):
+        """
+        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
+                                will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
+                                Defaulted to True.
+        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
+                                where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                [0] = Layer's shape, a tuple of integers.
+                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                If None, the input signature will be tried to be read from the model artifact. Defaulted
+                                to None.
+        """
+        # Import the framework and handler:
+        import tensorflow as tf
+        from mlrun.frameworks.tf_keras import TFKerasUtils
+
+        # Check the given 'input_signature' parameter:
+        if input_signature is None:
+            # Read the inputs from the model:
+            try:
+                model_handler.read_inputs_from_model()
+            except Exception as error:
+                raise mlrun.errors.MLRunRuntimeError(
+                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
+                    f"information automatically but failed with the following error: {error}"
+                )
+        else:
+            # Parse the 'input_signature' parameter:
+            input_signature = [
+                tf.TensorSpec(
+                    shape=shape,
+                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
+                        value_type=value_type
+                    ),
+                )
+                for (shape, value_type) in input_signature
+            ]
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_signature=input_signature,
+            optimize=optimize_model,
+        )
+
+    @staticmethod
+    def pytorch_to_onnx(
+        model_handler,
+        onnx_model_name: str = None,
+        optimize_model: bool = True,
+        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
+        input_layers_names: List[str] = None,
+        output_layers_names: List[str] = None,
+        dynamic_axes: Dict[str, Dict[int, str]] = None,
+        is_batched: bool = True,
+    ):
+        """
+        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.
+
+        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
+        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
+                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
+        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
+                                    model. Defaulted to True.
+        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
+                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
+                                    [0] = Layer's shape, a tuple of integers.
+                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
+                                    If None, the input signature will be tried to be read from the model artifact.
+                                    Defaulted to None.
+        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
+                                    parameters (inner layers) can be set as well by passing additional names in the
+                                    list. The order is by the order of the parameters in the model. If None, the inputs
+                                    will be read from the handler's inputs. If its also None, it is defaulted to:
+                                    "input_0", "input_1", ...
+        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
+                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
+                                    to: "output_0" (for multiple outputs, this parameter must be provided).
+        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
+                                    specify it by giving a dynamic axis to the input / output layer by its name as
+                                    follows: {
+                                        "input layer name": {0: "batch_size"},
+                                        "output layer name": {0: "batch_size"},
+                                    }
+                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
+        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
+                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
+        """
+        # Import the framework and handler:
+        import torch
+        from mlrun.frameworks.pytorch import PyTorchUtils
+
+        # Parse the 'input_signature' parameter:
+        if input_signature is not None:
+            input_signature = tuple(
+                [
+                    torch.zeros(
+                        size=shape,
+                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
+                            value_type=value_type
+                        ),
+                    )
+                    for (shape, value_type) in input_signature
+                ]
+            )
+
+        # Convert to ONNX:
+        model_handler.to_onnx(
+            model_name=onnx_model_name,
+            input_sample=input_signature,
+            optimize=optimize_model,
+            input_layers_names=input_layers_names,
+            output_layers_names=output_layers_names,
+            dynamic_axes=dynamic_axes,
+            is_batched=is_batched
+        )
+
+
+# Map for getting the conversion function according to the provided framework:
+_CONVERSION_MAP = {
+    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
+    "torch": _ToONNXConversions.pytorch_to_onnx,
+}  # type: Dict[str, Callable]
+
+
+def to_onnx(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    onnx_model_name: str = None,
+    optimize_model: bool = True,
+    framework_kwargs: Dict[str, Any] = None,
+):
+    """
+    Convert the given model to an ONNX model.
+
+    :param context:          The MLRun function execution context
+    :param model_path:       The model path store object.
+    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
+                             be used with an additional suffix `_onnx`. Defaulted to None.
+    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
+                             to True.
+    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
+                             string of the desired framework onnx conversion function, pass "help".
+    """
+    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun
+
+    # Get a model handler of the required framework:
+    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)
+
+    # Get the model's framework:
+    framework = model_handler.FRAMEWORK_NAME
+
+    # Use the conversion map to get the specific framework to onnx conversion:
+    if framework not in _CONVERSION_MAP:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"The following framework: '{framework}', has no ONNX conversion."
+        )
+    conversion_function = _CONVERSION_MAP[framework]
+
+    # Check if needed to print the function's doc string ("help" is passed):
+    if framework_kwargs == "help":
+        print(conversion_function.__doc__)
+        return
+
+    # Set the default empty framework kwargs if needed:
+    if framework_kwargs is None:
+        framework_kwargs = {}
+
+    # Run the conversion:
+    try:
+        conversion_function(
+            model_handler=model_handler,
+            onnx_model_name=onnx_model_name,
+            optimize_model=optimize_model,
+            **framework_kwargs,
+        )
+    except TypeError as exception:
+        raise mlrun.errors.MLRunInvalidArgumentError(
+            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
+            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
+            f"'framework_kwargs' dictionary parameter."
+        )
+
+
+def optimize(
+    context: mlrun.MLClientCtx,
+    model_path: str,
+    optimizations: List[str] = None,
+    fixed_point: bool = False,
+    optimized_model_name: str = None,
+):
+    """
+    Optimize the given ONNX model.
+
+    :param context:              The MLRun function execution context.
+    :param model_path:           Path to the ONNX model object.
+    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
+                                 If None, all of the optimizations will be used. Defaulted to None.
+    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
+    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
+                                 Defaulted to None.
+    """
+    # Import the model handler:
+    import onnxoptimizer
+    from mlrun.frameworks.onnx import ONNXModelHandler
+
+    # Check if needed to print the available optimizations ("help" is passed):
+    if optimizations == "help":
+        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
+        print(f"The available optimizations are:\n* {available_passes}")
+        return
+
+    # Create the model handler:
+    model_handler = ONNXModelHandler(
+        model_path=model_path, context=context
+    )
+
+    # Load the ONNX model:
+    model_handler.load()
+
+    # Optimize the model using the given configurations:
+    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)
+
+    # Rename if needed:
+    if optimized_model_name is not None:
+        model_handler.set_model_name(model_name=optimized_model_name)
+
+    # Log the optimized model:
+    model_handler.log()
+
+        </code>
+    </pre>
+</body>
+</html>
\ No newline at end of file
diff --git a/functions/master/onnx_utils/latest/src/function.yaml b/functions/master/onnx_utils/latest/src/function.yaml
index d59fa2dd..7a0054c4 100644
--- a/functions/master/onnx_utils/latest/src/function.yaml
+++ b/functions/master/onnx_utils/latest/src/function.yaml
@@ -2,7 +2,7 @@ kind: job
 metadata:
   name: onnx-utils
   tag: ''
-  hash: d81c2cec3242be94fc9c207bb75fc6495f2a34ae
+  hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2
   project: ''
   labels:
     author: guyl
@@ -11,16 +11,21 @@ metadata:
 spec:
   command: ''
   args: []
-  image: mlrun/ml-models
+  image: ''
   build:
     functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()

-    commands:
-    - python -m pip install onnx~=1.13.0 onnxruntime~=1.14.0 onnxoptimizer~=0.3.0
-      onnxmltools~=1.11.0 tf2onnx~=1.13.0
-    code_origin: https://github.com/mlrun/functions.git#b1365179513d1457e28a797e7d04f63a91238f02:/Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py
-    origin_filename: /Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py
+    base_image: mlrun/mlrun
+    commands: []
+    code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
     with_mlrun: false
     auto_build: true
+    requirements:
+    - onnx~=1.13.0
+    - onnxruntime~=1.14.0
+    - onnxoptimizer~=0.3.0
+    - onnxmltools~=1.11.0
+    - tf2onnx~=1.13.0
   entry_points:
     tf_keras_to_onnx:
       name: tf_keras_to_onnx
@@ -43,7 +48,7 @@ spec:
           saving the model. Defaulted to True.
         default: true
       - name: input_signature
-        type: List[Tuple[int], str]
+        type: List[Tuple[Tuple[int], str]]
         doc: 'A list of the input layers shape and data type properties. Expected
           to receive a list where each element is an input layer tuple. An input layer
           tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
@@ -74,7 +79,7 @@ spec:
           saving the model. Defaulted to True.
         default: true
       - name: input_signature
-        type: List[Tuple[int, ], str]
+        type: List[Tuple[Tuple[int, ], str]]
         doc: 'A list of the input layers shape and data type properties. Expected
           to receive a list where each element is an input layer tuple. An input layer
           tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
@@ -179,6 +184,7 @@ spec:
   default_handler: to_onnx
   disable_auto_mount: false
   allow_empty_resources: true
+  clone_target_dir: ''
   env: []
   priority_class_name: ''
   preemption_mode: prevent
diff --git a/functions/master/onnx_utils/latest/src/item.yaml b/functions/master/onnx_utils/latest/src/item.yaml
index b7ca891d..36335837 100644
--- a/functions/master/onnx_utils/latest/src/item.yaml
+++ b/functions/master/onnx_utils/latest/src/item.yaml
@@ -23,7 +23,7 @@ spec:
       with_mlrun: false
   filename: onnx_utils.py
   handler: to_onnx
-  image: mlrun/ml-models
+  image: mlrun/mlrun
   kind: job
   requirements:
   - onnx~=1.13.0
@@ -32,4 +32,4 @@ spec:
   - onnxmltools~=1.11.0
   - tf2onnx~=1.13.0
 url: ''
-version: 1.1.1
+version: 1.2.0
diff --git a/functions/master/onnx_utils/latest/static/function.html b/functions/master/onnx_utils/latest/static/function.html
index 6dfede79..feceb076 100644
--- a/functions/master/onnx_utils/latest/static/function.html
+++ b/functions/master/onnx_utils/latest/static/function.html
@@ -19,7 +19,7 @@
 metadata:
   name: onnx-utils
   tag: ''
-  hash: d81c2cec3242be94fc9c207bb75fc6495f2a34ae
+  hash: 0c4a6491b976d5220d3ebfb83a3905dd47e86be2
   project: ''
   labels:
     author: guyl
@@ -28,16 +28,21 @@
 spec:
   command: ''
   args: []
-  image: mlrun/ml-models
+  image: ''
   build:
     functionSourceCode: # Copyright 2019 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from typing import Any, Callable, Dict, List, Tuple

import mlrun


class _ToONNXConversions:
    """
    An ONNX conversion functions library class.
    """

    @staticmethod
    def tf_keras_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int], str]] = None,
    ):
        """
        Convert a TF.Keras model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:   An initialized TFKerasModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name: The name to use to log the converted ONNX model. If not given, the given `model_name`
                                will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:  Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the model.
                                Defaulted to True.
        :param input_signature: A list of the input layers shape and data type properties. Expected to receive a list
                                where each element is an input layer tuple. An input layer tuple is a tuple of:
                                [0] = Layer's shape, a tuple of integers.
                                [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                If None, the input signature will be tried to be read from the model artifact. Defaulted
                                to None.
        """
        # Import the framework and handler:
        import tensorflow as tf
        from mlrun.frameworks.tf_keras import TFKerasUtils

        # Check the given 'input_signature' parameter:
        if input_signature is None:
            # Read the inputs from the model:
            try:
                model_handler.read_inputs_from_model()
            except Exception as error:
                raise mlrun.errors.MLRunRuntimeError(
                    f"Please provide the 'input_signature' parameter. The function tried reading the input layers "
                    f"information automatically but failed with the following error: {error}"
                )
        else:
            # Parse the 'input_signature' parameter:
            input_signature = [
                tf.TensorSpec(
                    shape=shape,
                    dtype=TFKerasUtils.convert_value_type_to_tf_dtype(
                        value_type=value_type
                    ),
                )
                for (shape, value_type) in input_signature
            ]

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_signature=input_signature,
            optimize=optimize_model,
        )

    @staticmethod
    def pytorch_to_onnx(
        model_handler,
        onnx_model_name: str = None,
        optimize_model: bool = True,
        input_signature: List[Tuple[Tuple[int, ...], str]] = None,
        input_layers_names: List[str] = None,
        output_layers_names: List[str] = None,
        dynamic_axes: Dict[str, Dict[int, str]] = None,
        is_batched: bool = True,
    ):
        """
        Convert a PyTorch model to an ONNX model and log it back to MLRun as a new model object.

        :param model_handler:       An initialized PyTorchModelHandler with a loaded model to convert to ONNX.
        :param onnx_model_name:     The name to use to log the converted ONNX model. If not given, the given
                                    `model_name` will be used with an additional suffix `_onnx`. Defaulted to None.
        :param optimize_model:      Whether or not to optimize the ONNX model using 'onnxoptimizer' before saving the
                                    model. Defaulted to True.
        :param input_signature:     A list of the input layers shape and data type properties. Expected to receive a
                                    list where each element is an input layer tuple. An input layer tuple is a tuple of:
                                    [0] = Layer's shape, a tuple of integers.
                                    [1] = Layer's data type, a mlrun.data_types.ValueType string.
                                    If None, the input signature will be tried to be read from the model artifact.
                                    Defaulted to None.
        :param input_layers_names:  List of names to assign to the input nodes of the graph in order. All of the other
                                    parameters (inner layers) can be set as well by passing additional names in the
                                    list. The order is by the order of the parameters in the model. If None, the inputs
                                    will be read from the handler's inputs. If its also None, it is defaulted to:
                                    "input_0", "input_1", ...
        :param output_layers_names: List of names to assign to the output nodes of the graph in order. If None, the
                                    outputs will be read from the handler's outputs. If its also None, it is defaulted
                                    to: "output_0" (for multiple outputs, this parameter must be provided).
        :param dynamic_axes:        If part of the input / output shape is dynamic, like (batch_size, 3, 32, 32) you can
                                    specify it by giving a dynamic axis to the input / output layer by its name as
                                    follows: {
                                        "input layer name": {0: "batch_size"},
                                        "output layer name": {0: "batch_size"},
                                    }
                                    If provided, the 'is_batched' flag will be ignored. Defaulted to None.
        :param is_batched:          Whether to include a batch size as the first axis in every input and output layer.
                                    Defaulted to True. Will be ignored if 'dynamic_axes' is provided.
        """
        # Import the framework and handler:
        import torch
        from mlrun.frameworks.pytorch import PyTorchUtils

        # Parse the 'input_signature' parameter:
        if input_signature is not None:
            input_signature = tuple(
                [
                    torch.zeros(
                        size=shape,
                        dtype=PyTorchUtils.convert_value_type_to_torch_dtype(
                            value_type=value_type
                        ),
                    )
                    for (shape, value_type) in input_signature
                ]
            )

        # Convert to ONNX:
        model_handler.to_onnx(
            model_name=onnx_model_name,
            input_sample=input_signature,
            optimize=optimize_model,
            input_layers_names=input_layers_names,
            output_layers_names=output_layers_names,
            dynamic_axes=dynamic_axes,
            is_batched=is_batched
        )


# Map for getting the conversion function according to the provided framework:
_CONVERSION_MAP = {
    "tensorflow.keras": _ToONNXConversions.tf_keras_to_onnx,
    "torch": _ToONNXConversions.pytorch_to_onnx,
}  # type: Dict[str, Callable]


def to_onnx(
    context: mlrun.MLClientCtx,
    model_path: str,
    onnx_model_name: str = None,
    optimize_model: bool = True,
    framework_kwargs: Dict[str, Any] = None,
):
    """
    Convert the given model to an ONNX model.

    :param context:          The MLRun function execution context
    :param model_path:       The model path store object.
    :param onnx_model_name:  The name to use to log the converted ONNX model. If not given, the given `model_name` will
                             be used with an additional suffix `_onnx`. Defaulted to None.
    :param optimize_model:   Whether to optimize the ONNX model using 'onnxoptimizer' before saving the model. Defaulted
                             to True.
    :param framework_kwargs: Additional arguments each framework may require in order to convert to ONNX. To get the doc
                             string of the desired framework onnx conversion function, pass "help".
    """
    from mlrun.frameworks.auto_mlrun.auto_mlrun import AutoMLRun

    # Get a model handler of the required framework:
    model_handler = AutoMLRun.load_model(model_path=model_path, context=context)

    # Get the model's framework:
    framework = model_handler.FRAMEWORK_NAME

    # Use the conversion map to get the specific framework to onnx conversion:
    if framework not in _CONVERSION_MAP:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"The following framework: '{framework}', has no ONNX conversion."
        )
    conversion_function = _CONVERSION_MAP[framework]

    # Check if needed to print the function's doc string ("help" is passed):
    if framework_kwargs == "help":
        print(conversion_function.__doc__)
        return

    # Set the default empty framework kwargs if needed:
    if framework_kwargs is None:
        framework_kwargs = {}

    # Run the conversion:
    try:
        conversion_function(
            model_handler=model_handler,
            onnx_model_name=onnx_model_name,
            optimize_model=optimize_model,
            **framework_kwargs,
        )
    except TypeError as exception:
        raise mlrun.errors.MLRunInvalidArgumentError(
            f"ERROR: A TypeError exception was raised during the conversion:\n{exception}. "
            f"Please read the {framework} framework conversion function doc string by passing 'help' in the "
            f"'framework_kwargs' dictionary parameter."
        )


def optimize(
    context: mlrun.MLClientCtx,
    model_path: str,
    optimizations: List[str] = None,
    fixed_point: bool = False,
    optimized_model_name: str = None,
):
    """
    Optimize the given ONNX model.

    :param context:              The MLRun function execution context.
    :param model_path:           Path to the ONNX model object.
    :param optimizations:        List of possible optimizations. To see what optimizations are available, pass "help".
                                 If None, all of the optimizations will be used. Defaulted to None.
    :param fixed_point:          Optimize the weights using fixed point. Defaulted to False.
    :param optimized_model_name: The name of the optimized model. If None, the original model will be overridden.
                                 Defaulted to None.
    """
    # Import the model handler:
    import onnxoptimizer
    from mlrun.frameworks.onnx import ONNXModelHandler

    # Check if needed to print the available optimizations ("help" is passed):
    if optimizations == "help":
        available_passes = "\n* ".join(onnxoptimizer.get_available_passes())
        print(f"The available optimizations are:\n* {available_passes}")
        return

    # Create the model handler:
    model_handler = ONNXModelHandler(
        model_path=model_path, context=context
    )

    # Load the ONNX model:
    model_handler.load()

    # Optimize the model using the given configurations:
    model_handler.optimize(optimizations=optimizations, fixed_point=fixed_point)

    # Rename if needed:
    if optimized_model_name is not None:
        model_handler.set_model_name(model_name=optimized_model_name)

    # Log the optimized model:
    model_handler.log()

-    commands:
-    - python -m pip install onnx~=1.13.0 onnxruntime~=1.14.0 onnxoptimizer~=0.3.0
-      onnxmltools~=1.11.0 tf2onnx~=1.13.0
-    code_origin: https://github.com/mlrun/functions.git#b1365179513d1457e28a797e7d04f63a91238f02:/Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py
-    origin_filename: /Users/yonatanshelach/yoni/projects/functions/onnx_utils/onnx_utils.py
+    base_image: mlrun/mlrun
+    commands: []
+    code_origin: https://github.com/yonishelach/functions.git#f84b9565a33d8159315992ebba5838d41f6cc112:/Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
+    origin_filename: /Users/Yonatan_Shelach/projects/functions/onnx_utils/onnx_utils.py
     with_mlrun: false
     auto_build: true
+    requirements:
+    - onnx~=1.13.0
+    - onnxruntime~=1.14.0
+    - onnxoptimizer~=0.3.0
+    - onnxmltools~=1.11.0
+    - tf2onnx~=1.13.0
   entry_points:
     tf_keras_to_onnx:
       name: tf_keras_to_onnx
@@ -60,7 +65,7 @@
           saving the model. Defaulted to True.
         default: true
       - name: input_signature
-        type: List[Tuple[int], str]
+        type: List[Tuple[Tuple[int], str]]
         doc: 'A list of the input layers shape and data type properties. Expected
           to receive a list where each element is an input layer tuple. An input layer
           tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
@@ -91,7 +96,7 @@
           saving the model. Defaulted to True.
         default: true
       - name: input_signature
-        type: List[Tuple[int, ], str]
+        type: List[Tuple[Tuple[int, ], str]]
         doc: 'A list of the input layers shape and data type properties. Expected
           to receive a list where each element is an input layer tuple. An input layer
           tuple is a tuple of: [0] = Layer''s shape, a tuple of integers. [1] = Layer''s
@@ -196,6 +201,7 @@
   default_handler: to_onnx
   disable_auto_mount: false
   allow_empty_resources: true
+  clone_target_dir: ''
   env: []
   priority_class_name: ''
   preemption_mode: prevent
diff --git a/functions/master/onnx_utils/latest/static/item.html b/functions/master/onnx_utils/latest/static/item.html
index 526607d9..07129b01 100644
--- a/functions/master/onnx_utils/latest/static/item.html
+++ b/functions/master/onnx_utils/latest/static/item.html
@@ -40,7 +40,7 @@
       with_mlrun: false
   filename: onnx_utils.py
   handler: to_onnx
-  image: mlrun/ml-models
+  image: mlrun/mlrun
   kind: job
   requirements:
   - onnx~=1.13.0
@@ -49,7 +49,7 @@
   - onnxmltools~=1.11.0
   - tf2onnx~=1.13.0
 url: ''
-version: 1.1.1
+version: 1.2.0
 
         </code>
     </pre>
diff --git a/functions/master/pii_recognizer/0.1.0/src/item.yaml b/functions/master/pii_recognizer/0.1.0/src/item.yaml
index 922b82cd..5fa9f0ae 100644
--- a/functions/master/pii_recognizer/0.1.0/src/item.yaml
+++ b/functions/master/pii_recognizer/0.1.0/src/item.yaml
@@ -31,3 +31,4 @@ spec:
    - https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl
 url: ''
 version: 0.1.0
+test_valid: False
diff --git a/functions/master/pii_recognizer/0.1.0/static/item.html b/functions/master/pii_recognizer/0.1.0/static/item.html
index bcc8d18c..aab22fee 100644
--- a/functions/master/pii_recognizer/0.1.0/static/item.html
+++ b/functions/master/pii_recognizer/0.1.0/static/item.html
@@ -48,6 +48,7 @@
    - https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl
 url: ''
 version: 0.1.0
+test_valid: False
 
         </code>
     </pre>
diff --git a/functions/master/pii_recognizer/latest/src/item.yaml b/functions/master/pii_recognizer/latest/src/item.yaml
index 922b82cd..5fa9f0ae 100644
--- a/functions/master/pii_recognizer/latest/src/item.yaml
+++ b/functions/master/pii_recognizer/latest/src/item.yaml
@@ -31,3 +31,4 @@ spec:
    - https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl
 url: ''
 version: 0.1.0
+test_valid: False
diff --git a/functions/master/pii_recognizer/latest/static/item.html b/functions/master/pii_recognizer/latest/static/item.html
index bcc8d18c..aab22fee 100644
--- a/functions/master/pii_recognizer/latest/static/item.html
+++ b/functions/master/pii_recognizer/latest/static/item.html
@@ -48,6 +48,7 @@
    - https://huggingface.co/beki/en_spacy_pii_distilbert/resolve/main/en_spacy_pii_distilbert-any-py3-none-any.whl
 url: ''
 version: 0.1.0
+test_valid: False
 
         </code>
     </pre>
diff --git a/functions/master/sentiment_analysis_serving/1.1.0/src/item.yaml b/functions/master/sentiment_analysis_serving/1.1.0/src/item.yaml
index c74dbd99..d6ec3d7c 100644
--- a/functions/master/sentiment_analysis_serving/1.1.0/src/item.yaml
+++ b/functions/master/sentiment_analysis_serving/1.1.0/src/item.yaml
@@ -25,3 +25,4 @@ spec:
   - transformers==3.0.2
 url: ''
 version: 1.1.0
+test_valid: False
diff --git a/functions/master/sentiment_analysis_serving/1.1.0/static/item.html b/functions/master/sentiment_analysis_serving/1.1.0/static/item.html
index c4bdbcb8..d3f07cba 100644
--- a/functions/master/sentiment_analysis_serving/1.1.0/static/item.html
+++ b/functions/master/sentiment_analysis_serving/1.1.0/static/item.html
@@ -42,6 +42,7 @@
   - transformers==3.0.2
 url: ''
 version: 1.1.0
+test_valid: False
 
         </code>
     </pre>
diff --git a/functions/master/sentiment_analysis_serving/latest/src/item.yaml b/functions/master/sentiment_analysis_serving/latest/src/item.yaml
index c74dbd99..d6ec3d7c 100644
--- a/functions/master/sentiment_analysis_serving/latest/src/item.yaml
+++ b/functions/master/sentiment_analysis_serving/latest/src/item.yaml
@@ -25,3 +25,4 @@ spec:
   - transformers==3.0.2
 url: ''
 version: 1.1.0
+test_valid: False
diff --git a/functions/master/sentiment_analysis_serving/latest/static/item.html b/functions/master/sentiment_analysis_serving/latest/static/item.html
index c4bdbcb8..d3f07cba 100644
--- a/functions/master/sentiment_analysis_serving/latest/static/item.html
+++ b/functions/master/sentiment_analysis_serving/latest/static/item.html
@@ -42,6 +42,7 @@
   - transformers==3.0.2
 url: ''
 version: 1.1.0
+test_valid: False
 
         </code>
     </pre>
diff --git a/functions/master/tags.json b/functions/master/tags.json
index b929eaff..74657aa1 100644
--- a/functions/master/tags.json
+++ b/functions/master/tags.json
@@ -1 +1 @@
-{"categories": ["model-serving", "data-validation", "etl", "monitoring", "model-testing", "model-training", "data-preparation", "data-analysis", "utils", "feature-store", "machine-learning"], "kind": ["nuclio", "nuclio:serving", "serving", "dask", "job"]}
\ No newline at end of file
+{"categories": ["model-serving", "data-analysis", "data-validation", "model-testing", "model-training", "monitoring", "etl", "machine-learning", "feature-store", "data-preparation", "utils"], "kind": ["serving", "nuclio", "job", "nuclio:serving", "dask"]}
\ No newline at end of file
diff --git a/functions/master/transcribe/0.0.1/src/item.yaml b/functions/master/transcribe/0.0.1/src/item.yaml
index b8cfee2d..13ff048f 100644
--- a/functions/master/transcribe/0.0.1/src/item.yaml
+++ b/functions/master/transcribe/0.0.1/src/item.yaml
@@ -25,3 +25,4 @@ spec:
     - tqdm
 url: ''
 version: 0.0.1
+test_valid: False
diff --git a/functions/master/transcribe/0.0.1/src/test_transcribe.py b/functions/master/transcribe/0.0.1/src/test_transcribe.py
index cab2b663..d8448004 100644
--- a/functions/master/transcribe/0.0.1/src/test_transcribe.py
+++ b/functions/master/transcribe/0.0.1/src/test_transcribe.py
@@ -29,7 +29,7 @@
     "The crowd roars, a symphony of passion, "
     "as the game writes its unpredictable story on the field of destiny.",
 ]
-whisper_models = ["tiny_en", "tiny", "base_en", "base_en"]
+whisper_models = ["tiny.en", "tiny", "base.en", "base"]
 
 @pytest.mark.skipif(
     condition=sys.version_info[:2] < (3, 8),
diff --git a/functions/master/transcribe/0.0.1/static/item.html b/functions/master/transcribe/0.0.1/static/item.html
index c642100e..53849767 100644
--- a/functions/master/transcribe/0.0.1/static/item.html
+++ b/functions/master/transcribe/0.0.1/static/item.html
@@ -42,6 +42,7 @@
     - tqdm
 url: ''
 version: 0.0.1
+test_valid: False
 
         </code>
     </pre>
diff --git a/functions/master/transcribe/latest/src/item.yaml b/functions/master/transcribe/latest/src/item.yaml
index b8cfee2d..13ff048f 100644
--- a/functions/master/transcribe/latest/src/item.yaml
+++ b/functions/master/transcribe/latest/src/item.yaml
@@ -25,3 +25,4 @@ spec:
     - tqdm
 url: ''
 version: 0.0.1
+test_valid: False
diff --git a/functions/master/transcribe/latest/src/test_transcribe.py b/functions/master/transcribe/latest/src/test_transcribe.py
index cab2b663..d8448004 100644
--- a/functions/master/transcribe/latest/src/test_transcribe.py
+++ b/functions/master/transcribe/latest/src/test_transcribe.py
@@ -29,7 +29,7 @@
     "The crowd roars, a symphony of passion, "
     "as the game writes its unpredictable story on the field of destiny.",
 ]
-whisper_models = ["tiny_en", "tiny", "base_en", "base_en"]
+whisper_models = ["tiny.en", "tiny", "base.en", "base"]
 
 @pytest.mark.skipif(
     condition=sys.version_info[:2] < (3, 8),
diff --git a/functions/master/transcribe/latest/static/item.html b/functions/master/transcribe/latest/static/item.html
index c642100e..53849767 100644
--- a/functions/master/transcribe/latest/static/item.html
+++ b/functions/master/transcribe/latest/static/item.html
@@ -42,6 +42,7 @@
     - tqdm
 url: ''
 version: 0.0.1
+test_valid: False
 
         </code>
     </pre>