LogoLogo
  • Tensorleap
  • Examples
    • Semantic Segmentation
    • Image Analysis
    • Sentiment Analysis
    • MNIST Project Walkthrough
    • IMDB Project Walkthrough
  • Quickstart using CLI
  • Guides
    • Full Guides
      • MNIST Guide
        • Dataset Integration
        • Model Integration
        • Model Perception Analysis
        • Advanced Metrics
      • IMDB Guide
        • Dataset Integration
        • Model Integration
        • Model Perception Analysis
        • Advanced Metrics
    • Integration Script
      • Preprocess Function
      • Input Encoder
      • Ground Truth Encoder
      • Metadata Function
      • Visualizer Function
      • Prediction
      • Custom Metrics
      • Custom Loss Function
      • Custom Layers
      • Unlabeled Data
      • Examples
        • CelebA Object Detection (YoloV7)
        • Wikipedia Toxicity (using Tensorflow Datasets)
        • Confusion Matrix
        • CelebA Classification (using GCS)
  • Platform
    • Resources Management
    • Project
    • Dataset
    • Secret Manager
    • Network
      • Dataset Node
      • Layers
      • Loss and Optimizer
      • Visualizers
      • Import Model
      • Metrics
    • Evaluate / Train Model
    • Metrics Dashboard
    • Versions
    • Issues
    • Tests
    • Analysis
      • helpers
        • detection
          • YOLO
    • Team management
    • Insights
  • API
    • code_loader
      • leap_binder
        • add_custom_metric
        • set_preprocess
        • set_unlabeled_data_preprocess
        • set_input
        • set_ground_truth
        • set_metadata
        • add_prediction
        • add_custom_loss
        • set_visualizer
      • enums
        • DatasetMetadataType
        • LeapDataType
      • datasetclasses
        • PreprocessResponse
      • visualizer_classes
        • LeapImage
        • LeapImageWithBBox
        • LeapGraph
        • LeapText
        • LeapHorizontalBar
        • LeapImageMask
        • LeapTextMask
  • Tips & Tricks
    • Import External Code
  • Legal
    • Terms of Use
    • Privacy Policy
Powered by GitBook
On this page
  • Introduction
  • Full Guides
  • Dataset Script

Was this helpful?

Guides

PreviousQuickstart using CLINextFull Guides

Last updated 2 years ago

Was this helpful?

Introduction

To get started with Tensorleap, we provided a few step-by-step guides, describing an integration of datasets and models, in addition to various analyses.

Integrating the Dataset and Model

The first step to getting started with Tensorleap is to integrate your datasets and models. This can be done through the Integration Script, which defines how to read each sample and metadata.

There are multiple ways to integrate your model:

  • Pre-Trained - You can import a pre-trained model through the Import Model feature or through the CLI Model Integration. After the model and dataset are imported, you must Evaluate the Model (inference) in order to get the metrics and analyses.

  • Built from Scratch - Some users prefer building the model and altering it in the Network View. Then the model is Trained within the platform.

In the full-guides below, we present both cases for each use-case.

Tensorleap is now available for a free trial! For more info, see Try Tensorleap for Free.

Full Guides

Guides are designed to get you started quickly with Tensorleap.

Choose one to get started:

  • MNIST - Image Classification - integration, training, analysis and metrics.

  • IMDB - Semantic Text classification - integration, training, analysis and metrics.

Dataset Script

Guide

A guide about the Dataset Script can be found here.

Examples

  • Wikipedia Toxicity (using Tensorflow Datasets)

  • CelebA (using Google Cloud Storage)

The Reference documentation for the Tensorleap user interface (UI) and Command Line Interface (CLI) may prove useful when going through these tutorials.

Sample's Details
Fetch Similars
Similar Samples Heat Map
Heat map for Negative Output
Metrics Dashboard