geniusrise 0.0.31

An LLM framework

Documentation

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About

Geniusrise is a modular, loosely-coupled AgentOps / MLOps framework designed for the era of Large Language Models, offering flexibility, inclusivity, and standardization in designing networks of AI agents.

It seamlessly integrates tasks, state management, data handling, and model versioning, all while supporting diverse infrastructures and user expertise levels. With its plug-and-play architecture, Geniusrise empowers teams to build, share, and deploy AI agent workflows across various platforms.

TLDR 🙄

1. Install geniusrise

pip install geniusrise
pip install geniusrise-huggingface

2. Create genius.yaml

version: '1'
bolts:
  http_classifier:
    name: HuggingFaceClassificationFineTuner
    method: fine_tune
    args:
      model_name: bert-base-uncased
      tokenizer_name: bert-base-uncased
      num_train_epochs: 2
      per_device_train_batch_size: 2
      model_class: BertForSequenceClassification
      tokenizer_class: BertTokenizer
      data_masked: true
      hf_repo_id: your/hf_model_repo
      hf_commit_message: 'say hello to geniusrise!'
      hf_create_pr: true
      hf_token: hf_mykey
    state:
      type: none
    input:
      type: batch
      args:
        bucket: my-s3-bucket
        folder: training_data
    output:
      type: batch
      args:
        bucket: my-s3-bucket
        folder: model
    deploy:
      type: k8s
      args:
        kind: deployment
        name: classifier
        context_name: arn:aws:eks:us-east-1:genius-dev:cluster/geniusrise-dev
        namespace: geniusrise
        image: geniusrise/geniusrise
        kube_config_path: ~/.kube/config

        cpu: 16
        memory: 50G
        storage: 250Gb
        gpu: 1

3. Deploy

genius rise up

🙄 This was not even tip of the iceberg. Please see docs.

Links

• Website: geniusrise.ai
• Docs: docs.geniusrise.ai
• Hub: geniusrise.com [coming soon]