PerceptiLabs
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v0.13
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  • Welcome
  • ⚡Getting Started
    • Quickstart Guide
      • Requirements
      • Installation
      • Video Tutorials
    • Load and Pre-process Data
    • Build Models
    • Train Models
    • Evaluate Models
    • Export and Deploy Models
    • Manage, Version, and Track Models
  • 🙏How to Contribute
    • Datasets
    • Models
    • Components
  • 👨‍🍳Tutorials
    • Basic Image Recognition
    • Basic Image Segmentation
  • 🛠️Advanced
    • Common Testing Issues
    • Common Training Issues
    • Components
      • Input and Target
      • Processing
      • Deep Learning
      • Operations
      • Custom
    • CSV File Format
    • Debugging and Diagnostic Features
    • How PerceptiLabs Works With TensorFlow
    • Included Packages
    • Types of Tests
    • UI Overview
      • Data Wizard
      • Overview Screen
      • Model Training Settings
      • Modeling Tool
      • Training View
      • Evaluate View
      • Deploy View
    • Using the Exported/Deployed Model
  • 💡Use Cases
    • General
      • A Guide to Using U-Nets for Image Segmentation
      • A Voice Recognition model using Image Recognition
    • Environmental
      • Automated Weather Analysis Using Image Recognition
      • Wildfire Detection
    • Healthcare & Medical
      • Brain Tumor Detection
      • Breast Cancer Detection
      • Classifying Chest X-Rays to Detect Pneumonia
      • Classifying Ways to Wear a Face Mask
      • Detecting Defective Pills
      • Highlighting Blood Cells in Dark-field Microscopy Using Image Segmentation
      • Ocular Disease Recognition
      • Retinal OCT
      • Skin Cancer Classification
    • Industrial IoT & Manufacturing
      • Air Conditioner Piston Check
      • Classifying Fruit
      • Classifying Wood Veneers Into Dry and Wet
      • Defect Detection in Metal Surfaces
      • Fabric Stain Classification
  • 📖Support
    • FAQs
    • Changelog
  • Code of Conduct
  • Marketing Site
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  1. Getting Started
  2. Quickstart Guide

Installation

PerceptiLabs is distributed as a free Python package (hosted on PyPI) for everyone to use.

This topic describes how to install and run the free version of Perceptilabs.

Step 1: Install and Run PerceptiLabs

Follow the steps below to install and run the free version of PerceptiLabs:

Free Version

1. Install a version of Python that meets our Python Requirements. Since multiple versions of Python and PerceptiLabs can be installed concurrently, we recommend you install them in a virtual Python environment such as:

  • pipenv

  • poetry

  • venv

  • virtualenv

  • conda

The steps below show how to set up a simple Conda environment:

  1. Download Anaconda and install it

  2. Open a terminal window.

  3. Run: conda create -n myenv python=3.8

  4. Run: conda activate myenv

2. Run the following command to install PerceptiLabs:

pip install perceptilabs

Note

Alternatively, you can install our latest nightly build with pip install pl-nightly. Note that the nightly build may not be as stable as the latest official release.

3. Run perceptilabs. The app will open in your default web browser.

Tip

If you're using Chrome, first ensure that hardware acceleration is enabled to ensure the best performance of PerceptiLabs on this browser.

To enable this, click on Chrome's triple-dot menu and select Settings. Then navigate to Advanced > System and enable Use hardware acceleration when available; you may need to relaunch your browser as well.

Step 2: Sign in

Free Version

Within PerceptiLabs, sign up for a free PerceptiLabs account and then sign in to start building your machine learning models with PerceptiLabs!

Step 3: Load and Pre-process Your Data

Now that you've installed and signed into Perceptilabs, proceed to Load and Pre-process Data to learn how to get started building a model.

PreviousRequirementsNextVideo Tutorials

Last updated 3 years ago

Was this helpful?

⚡
  • Step 1: Install and Run PerceptiLabs
  • Free Version
  • Step 2: Sign in
  • Free Version
  • Step 3: Load and Pre-process Your Data