The next generation of ML development

PerceptiLabs is a dataflow driven, visual API for TensorFlow, carefully designed to make machine learning (or deep learning) modeling as intuitive as possible.

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Get production ready models in minutes

Model
management

  • Overview with control over all models
  • Easily see model status, start training, run tests or export models in one view

Infrastructure

  • Distribute workloads over multiple machines and GPUs
  • Secure and air-gapped

Tests and
explainability

  • Run tests on your models for evaluation
  • Compare several models

Components in PerceptiLabs

The components have settings that you can change and tune

A component automatically generates a visualization of the output

The visualizations update in real-time when you change the component settings

The components auto-generate TensorFlow low-level code, which you can view and edit

  • The components have settings that you can change and tune
  • A component automatically generates a visualization of the output
  • The visualizations update in real-time when you change the component settings
  • The components auto-generate TensorFlow low-level code, which you can view and edit
Learn more

Graph creation

The dataflow is defined by connecting the components

The model architecture is visualized as you are building your model output

The shape of the inputs and outputs are automatically calculated, and configs/hyperparameters suggested, to quickly get you up and running

  • The dataflow is defined by connecting the components
  • The model architecture is visualized as you are building your model output
  • The shape of the inputs and outputs are automatically calculated, and configs/hyperparameters suggested, to quickly get you up and running
Learn more

Debugging

PerceptiLabs shows you tips, warnings and errors while you are building your model

Quickly see where something is wrong and fix it

Debug the model and the code

Get instant feedback to better understand your model

  • PerceptiLabs shows you tips, warnings and errors while you are building your model
  • Quickly see where something is wrong and fix it
  • Debug the model and the code
  • Get instant feedback to better understand your model
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Advanced statistics view during training

Real-time granular visualizations

Get an overview of the model performance and predictions

Visualize each components variables, such as weights, biases, gradients and outputs

Real-time debugging example:
Understand the gradients if it is vanishing or exploding and where in the model it happens

  • Real-time granular visualizations
  • Get an overview of the model performance and predictions
  • Visualize each components variables, such as weights, biases, gradients and outputs
  • Real-time debugging example:
    Understand the gradients if it is vanishing or exploding and where in the model it happens
Learn more