Python SDK Notebook Examples | Clarifai Docs
Learn how to use the Clarifai Python SDK
Here are comprehensive step-by-step walkthroughs within Jupyter or Colab notebooks that showcase how to harness the power of the Clarifai SDKs.
| Notebook | Description | Open in Colab |
|---|---|---|
| Basics | Create, manage, update, and delete Clarifai resources, including apps, datasets, inputs, and models | |
| CLI | Clarifai provides a user-friendly command line interface (CLI) that simplifies various tasks | |
| Compute Orchestration | Use our Compute Orchestration system to create, get, list, and delete compute clusters, nodepools, and model deployments. | |
| Data Utils | Get a range of multimedia data utilities designed to streamline your data management and processing operations. - Image annotation loader — A comprehensive framework for loading, exporting, and analyzing different annotated datasets. - Data ingestion pipelines — Load text files (PDF, DOC, etc.), transform and chunk the content, and upload it to the Clarifai platform. - Multimodal dataloader - Ready-to-use foundational pipelines |
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| RAG | Use Retrieval Augmented Generation (RAG) to improve Large Language Models (LLMs) | |
| Concept management | Establish a hierarchical relationship between concepts using concept relations | |
| Datasets basics | Merge datasets and list inputs of a dataset | |
| Dataset export | Export datasets from a Clarifai app | |
| Dataset upload | Upload datasets into a Clarifai app | |
| Inputs upload | Upload inputs with various types of data, such as metadata, geo info, or bounding box annotations, into a Clarifai app | |
| Models predict (inference) | Get predictions with text, image, video, and audio inputs with different types of models | |
| Evaluation for embedding classification | Evaluate the performance of embedding classifier models | |
| Evaluation for text classification | Evaluate the performance of text classifier models | |
| Evaluation for visual classification | Evaluate the performance of visual classifier models | |
| Evaluation for visual detection | Evaluate the performance of visual detector models | |
| Training for image classification | Train image classifier models | |
| Training for image detection | Train image detector models | |
| Training for image segmentation | Train image segmentation models | |
| Training for text classification | Train text classifier models | |
| Training for transfer learn | Train transfer learn models | |
| Model upload | - Upload image classifiers - Upload image detectors - Upload LLMs - Upload speech recognition models |
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| Cross-modal search | Perform vector search over your own data | |
| Create workflows | Create various types of workflows | |
| Export workflows | Download a YAML file representing your workflow | |
| Patch workflows | Perform patch operations by merging, removing, or overwriting data |