LabML

Guide

  • Installation & Setup
    • Lab
      • Create .labml.yaml file
    • Dashboard
  • Logger
    • Logging with colors
    • Logging debug info
  • Monitor
    • Iterators & Enumerators
    • Sections
    • Loop
  • Configs
    • Define a configuration class
    • Calculated configurations
    • Inheriting and re-using configuration classes
    • Defining configurations options
    • Calculating with predefined functions of lambdas
    • Aggregates
    • Hyper-parameters
    • Running the experiment
  • Tracker
    • Indicator settings
  • Experiment
  • Analytics
    • Load runs
    • Distribution plot
    • Scatter plot
    • Loading artifacts
    • Histogram
    • Binned heatmap

Python API

  • Lab
  • Configs
  • Experiment
  • Logger
  • Monitor
  • Tracker
  • Utils
  • Helpers
    • Installation
    • Modules
      • Datasets
  • Analytics
  • Manage

Links

  • 🧠 LabML NN: A collection of PyTorch implementations of neural network architectures and layers.
LabML
  • Docs »
  • Overview: module code

All modules for which code is available

  • labml.analytics
  • labml.configs
  • labml.experiment
  • labml.internal.configs.dynamic_hyperparam
  • labml.internal.util.colors
  • labml.lab
  • labml.logger
  • labml.manage
  • labml.monit
  • labml.tracker
  • labml.utils.pytorch
  • labml_helpers.datasets.cifar10
  • labml_helpers.datasets.mnist
  • labml_helpers.device
  • labml_helpers.seed
  • labml_helpers.train_valid
  • labml_helpers.training_loop

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