https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/Recent content in MLflow Integration onHugo -- gohugo.ioSun, 11 Dec 2022 19:37:55 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/about-mlflow/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/about-mlflow/MLflow is an open-source, unified platform to manage end-to-end ML and GenAI workflows. It streamlines the machine learning process, from experimentation to production deployment, providing tools for tracking experiments, sharing models, and managing model deployment. Integrate your MLflow instance with Gathr by connecting to the MLflow Tracking Server, allowing access to MLflow models directly within Gathr’s interface. Utilize MLflow models in ETL applications with the MLflow Inference processor to generate inferences on your prediction data.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/mlflow-home/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/mlflow-home/Manage and explore MLflow models associated with your connections in Gathr on the MLflow listing page. To access this page, navigate through the Models (main menu) > MLflow Tab. Note: For models to display here, ensure at least one MLflow connection has been created in Gathr. Upon selecting a connection, MLflow models associated with it will display. You can leverage MLflow models within Gathr’s intuitive interface. The actions available on the MLflow Models listing page are explained below:https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/mlflow-inference-processor/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/mlflow-inference-processor/The MLflow Inference Processor is a versatile and advanced operator, empowering users to tackle diverse data tasks effortlessly using simple, natural language instructions. Choose from a variety of models registered on MLflow to classify data, extract specific information, summarize lengthy texts, analyze sentiment—tasks, and perform many more operations that traditionally require complex coding skills. Configure the processor parameters as explained below. Model Selection # Provide the details of the MLflow registered model to be used.https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/model-templates/Sun, 11 Dec 2022 21:01:49 +0530https://docs.gathr.ai/gathr-unlimited/7.6.0/docs/gen-ai-in-gathr/mlflow-integration/model-templates/Refer below templates to create your own model python file. (.py file). Notes: Ensure the model weight path is /opt/models/mlflowmodel/modelweight in the .py files. Below section is mandatory in any .py file: import mlflow import os tracking_uri = os.getenv('MLFLOW_TRACKING_URL') mlflow.set_tracking_uri(tracking_uri) Template For Completion Model (example: BART) # #!/usr/bin/env python # coding: utf-8 import torch from datasets import Dataset, load_dataset MODEL_NAME = "philschmid/bart-large-cnn-samsum" from transformers import ( BartForConditionalGeneration, BartTokenizer, BitsAndBytesConfig, TrainingArguments, ) model_path = "/opt/models/mlflowmodel/modelweight" # Load model model = BartForConditionalGeneration.