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changing the requirements
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classifier-e2e/requirements.txt

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zenml[server]>=0.55.2
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zenml[server]>=0.70.0
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notebook
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scikit-learn<1.3
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s3fs>2022.3.0,<=2023.4.0

customer-satisfaction/requirements.txt

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@@ -5,6 +5,6 @@ optuna==2.10.0
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streamlit==1.29.0
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xgboost==2.0.3
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markupsafe==1.1.1
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zenml>=0.52.0
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zenml>=0.70.0
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scikit-learn>=1.3.2
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altair

databricks-demo/requirements.txt

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zenml[server]
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zenml[server]>=0.70.0

end-to-end-computer-vision/requirements.txt

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zenml[server]>=0.55.3
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zenml[server]>=0.70.0
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notebook
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scikit-learn<1.3
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pyarrow

explainability-shap/requirements.txt

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shap
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matplotlib
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scipy
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zenml
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zenml>=0.70.0
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pyarrow
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fastparquet

label_studio_annotation/README.md

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Studio.
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```shell
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pip install "zenml[server]" torchvision
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pip install "zenml[server]>=0.70.0" torchvision
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# clone the ZenML repository
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git clone https://github.com/zenml-io/zenml-projects.git

llm-finetuning-simple/README.md

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pip install -r requirements.txt
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# Install ZenML and Lightning integrations
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pip install zenml
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pip install "zenml>=0.70.0"
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zenml integration install lightning s3 aws -y
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# Initialize and connect to a deployed ZenML server

llm-finetuning-simple/requirements.txt

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nltk
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accelerate>=0.30.0
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urllib3<2
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zenml>=0.62.0
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zenml>=0.70.0
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torch>=2.2.0
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sentencepiece
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huggingface_hub

native-experiment-tracking/requirements.txt

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zenml[server]>=0.67.0
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zenml[server]>=0.70.0
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notebook
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scikit-learn
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pyarrow

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