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    • Deployment Examples
      • Remote training with data on S3
      • 3LC with Docker and Kubernetes
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    • Overview
    • Data Layout
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    • Tables
      • Overview
      • Import
      • Schema
      • Rows and Samples
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      • Map Functions
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      • Creating Runs
      • Collecting Metrics
      • Combining Data and Models
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    • Column Types
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        • Categorical
      • Computer Vision
        • Overview
        • Images
        • Bounding Boxes
        • Oriented Bounding Boxes (OBBs)
        • Keypoints and Pose Estimation
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  • Python Package
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      • tlc
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          • bulk_data_url_utils
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              • predictor
        • core
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                • pacmap_table
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            • metrics_writer
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        • integration
          • detectron2
            • detectron_metrics_collection_hook
            • metrics_collection_hook
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          • hugging_face
            • features
            • table_from_hugging_face
            • trainer
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          • pytorch_lightning
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          • super_gradients
            • callbacks
              • base_callback
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            • datasets
              • detection_dataset
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      • tlcconfig
        • constants
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      • Overview
      • Deployment Guide
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    • Integrations
      • PyTorch
      • Hugging Face
      • Detectron2
      • YOLOv5
      • YOLO
      • SuperGradients
      • PyTorch Lightning
    • System Requirements
    • Dependencies
    • Release Notes
      • 3LC Python Package Version 2.21
      • 3LC Python Package Version 2.20
      • 3LC Python Package Version 2.19
      • 3LC Python Package Version 2.18
      • 3LC Python Package Version 2.17
      • 3LC Python Package Version 2.16
      • 3LC Python Package Version 2.15
      • 3LC Python Package Version 2.14
      • 3LC Python Package Version 2.13
      • 3LC Python Package Version 2.12
      • 3LC Python Package Version 2.11
      • 3LC Python Package Version 2.10
      • 3LC Python Package Version 2.9
      • 3LC Python Package Version 2.8
      • 3LC Python Package Version 2.7
      • 3LC Python Package Version 2.6
      • 3LC Python Package Version 2.5
      • 3LC Version 2.3 Release Notes
      • 3LC Version 2.2 Release Notes
      • 3LC Version 2.1 Release Notes
      • 3LC Version 2.0 Release Notes
      • 3LC Version 1.0 Release Notes
  • Dashboard
    • Overview
    • Panels
      • Overview
      • Rows
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      • Overview
      • Confusion Matrix
      • N-dimensional Travel Distance
      • User Defined Workflows
    • Settings
    • Keyboard Shortcuts
    • GPU Acceleration
    • System Requirements
    • Release Notes
      • 3LC Dashboard Version 2.7
      • 3LC Dashboard Version 2.6
      • 3LC Dashboard Version 2.5
      • 3LC Version 2.3 Release Notes
      • 3LC Version 2.2 Release Notes
      • 3LC Version 2.1 Release Notes
      • 3LC Version 2.0 Release Notes
      • 3LC Version 1.0 Release Notes
  • Examples
    • Create Tables
      • Create Custom Table
      • Ingest FHIBE Dataset
      • Create Semantic Segmentation Table
      • Create Table from Image Folder
      • Create Table from PyTorch Dataset
      • Create a Table from video frames
      • 3D
        • Create a Table from 3D points
      • Instance Segmentation
        • Create Custom Instance Segmentation Table
        • Convert Semantic to Instance Segmentation
        • Import instance segmentation dataset from multiple image masks
        • Create Table from COCO Instance Segmentation
        • Create Table from YOLO Instance Segmentation
      • Keypoints
        • Create Custom Keypoints Table
        • Create Table from COCO Keypoints
        • Create Table from YOLO Keypoints
      • Oriented Bounding Boxes
        • Create Custom Oriented Bounding Box Table
        • Create Table from YOLO - Oriented Bounding Boxes (OBB)
      • Object Detection
        • Create Custom Bounding Box Table
        • Create Table from COCO Object Detection
        • Create Table from YOLO Object Detection
    • Modify Tables
      • Weighted Table Subset Selection
      • Add uniqueness metrics to Table
      • Add image embeddings to a Table
      • Add image metrics to a Table
      • Add new data to existing Table lineage
      • Add new data to dataset with splits
      • Auto-segment images using SAM
      • Per Bounding Box Luminosity Calculation
      • Compare dimensionality reduction methods
      • Split tables
      • Convert bounding boxes to instance segmentation masks using SAM
      • Instance embeddings
        • Train a instance classifier on a 3LC Table
        • Collect and reduce classifier embeddings
    • Training And Metrics
      • Fine-tune a object detection model using Detectron2
      • Fine-tune a instance segmentation model using Detectron2
      • Collect Bounding Boxes using Detectron2
      • Collect instance segmentation metrics using Detectron2
      • Apply dimensionality reduction to multiple Tables
      • Fine-tune Hugging Face SegFormer on a custom dataset
      • Hugging Face CIFAR-100 Embeddings Example
      • Collect Instance Segmentation Metrics from Pretrained Model
      • 🤗 and 3LC example on the IMDb dataset
      • Fine-tuning a model with the 🤗 TLC Trainer API
      • Fine-tuning a SegFormer model with Pytorch Lightning
      • Training a classifier using PyTorch Lightning
      • Register Augmented Samples From a Training Loop
      • Collect metrics using a pre-trained model
      • PyTorch 3LC CIFAR-10 Sample Notebook
      • Train autoencoder for embedding extraction
      • PyTorch 3LC Fashion-MNIST Sample Notebook
      • PyTorch 3LC MNIST Sample Notebook
      • Collect SAM image embeddings
      • Train a YOLO-NAS model for pose estimation with SuperGradients
      • Train Yolo-NAS model with 3LC Metrics Collection
      • Train a YOLO model for pose estimation on a custom keypoints dataset
      • Train a YOLO segmentation model with 3LC metrics collection
      • Train a YOLO classifier with 3LC metrics collection
      • Train a YOLO model for object detection with oriented bounding boxes
    • End To End Examples
      • Scaling an Instance Segmentation Dataset with Active Labeling in 3LC
      • Improving models by fixing object detection labels
  • FAQ
  • Glossary
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Deployment Examples¶

The following pages go through full examples of how to deploy and use 3LC in different scenarios, getting the components of 3LC installed, configured and running in different ways.

  • Remote training with data on S3
  • 3LC with Docker and Kubernetes
Next
Deployment Example: Remote Training with shared S3 Data
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