wehub-resource-sync
64 lines
1.6 KiB
Python
64 lines
1.6 KiB
Python
import openai
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import pandas as pd
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import mlflow
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"""
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Set environment variables for Azure OpenAI service
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export OPENAI_API_KEY="<AZURE OPENAI KEY>"
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# OPENAI_API_BASE should be the endpoint of your Azure OpenAI resource
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# e.g. https://<service-name>.openai.azure.com/
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export OPENAI_API_BASE="<AZURE OPENAI BASE>"
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# OPENAI_API_VERSION e.g. 2023-05-15
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export OPENAI_API_VERSION="<AZURE OPENAI API VERSION>"
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export OPENAI_API_TYPE="azure"
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export OPENAI_DEPLOYMENT_NAME="<AZURE OPENAI DEPLOYMENT ID OR NAME>"
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"""
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with mlflow.start_run():
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model_info = mlflow.openai.log_model(
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# Your Azure OpenAI model e.g. gpt-4o-mini
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model="<YOUR AZURE OPENAI MODEL>",
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task=openai.chat.completions,
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name="model",
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messages=[{"role": "user", "content": "Tell me a joke about {animal}."}],
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)
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# Load native OpenAI model
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native_model = mlflow.openai.load_model(model_info.model_uri)
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completion = openai.chat.completions.create(
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deployment_id=native_model["deployment_id"],
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messages=native_model["messages"],
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)
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print(completion["choices"][0]["message"]["content"])
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# Load as Pyfunc model
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model = mlflow.pyfunc.load_model(model_info.model_uri)
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df = pd.DataFrame({
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"animal": [
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"cats",
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"dogs",
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]
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})
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print(model.predict(df))
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list_of_dicts = [
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{"animal": "cats"},
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{"animal": "dogs"},
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]
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print(model.predict(list_of_dicts))
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list_of_strings = [
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"cats",
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"dogs",
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]
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print(model.predict(list_of_strings))
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list_of_strings = [
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"Let me hear your thoughts on AI",
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"Let me hear your thoughts on ML",
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]
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model = mlflow.pyfunc.load_model(model_info.model_uri)
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print(model.predict(list_of_strings))
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