TitanML Documentation
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  • ๐Ÿ’กOverview
    • Guide to TitanML...
    • Need help?
  • ๐ŸฆฎGuides
  • Getting started
    • Installing iris
    • Sign up & sign in
    • Iris commands
      • Using iris upload
      • iris API
  • ๐Ÿ›ซTitan Takeoff ๐Ÿ›ซ: Inference Server
    • When should I use the Takeoff Server?
    • Getting started
    • Supported models
    • Using the Takeoff API (Client-side)
    • Chat and Playground UI
    • Shutting down
    • Using a local model
    • Generation Parameters
  • ๐ŸŽ“Titan Train ๐ŸŽ“: Finetuning Service
    • Quickstart
    • Supported models & Tasks
    • Using iris finetune
      • Benchmark experiments for finetuning
      • A closer look at iris finetune arguments
      • Evaluating the model performance
    • Deploying and Inferencing the model
    • When should I use Titan Train?
  • โœจTitan Optimise โœจ: Knowledge Distillation
    • When should I use Titan Optimise?
    • How to get the most out of Titan Optimise
    • Supported models & Tasks
    • Using iris distil
      • Benchmark experiments for knowledge distillation
      • A closer look at iris distil arguments
      • Monitoring progress
    • Evaluating and selecting a model
    • Deploying the optimal model
      • Which hardware should I deploy to?
      • Pulling the model
      • Inferencing the model
  • ๐Ÿค“Other bits!! ๐Ÿค“
    • Iris roadmap
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  1. Getting started

Sign up & sign in

Signing up to the TitanML platform

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Last updated 1 year ago

These docs are outdated! Please check out for the latest information on the TitanML platform. If there's anything that's not covered there, please contact us on our .

Sign up

To signup, navigate to the . There, you should be presented with a login box, below which you should find a link to sign up.

Login

The Titan product consists of two parts: the iris command-line interface (CLI), and our experiment tracking frontend. If you're using TitanML's training platform, this web interface will let you track the progress of your training jobs and compare the capabilities of your finetuned & optimized models.

  • To log into the frontend, navigate to and click 'login', entering your username and password into the login page when prompted. Successfully signing in will return you to the Titan frontend, where you can view, edit, and analyse your training jobs and optimised models

  • To log into the command line and dispatch new jobs, or use the takeoff server, use the iris login command. You should receive the following prompt:

1. On your computer or mobile device navigate to:  https://titanml.eu.auth0.com/activate?user_code=#8char code goes here
2. Enter the following code:  # 8 char code goes here
Logged in as # email address goes here
  • Navigate to the provided link. After checking that the code printed on the login screen matches that presented by the CLI, enter your login details and click login. The CLI should register your login and welcome you to the Titan platform.

If you have logged in successfully then you should be able to run iris get and obtain the following response, indicating that you have no running experiments.


โ•’โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•คโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ••
โ”‚ status   โ”‚   total โ”‚ experiments   โ”‚
โ•žโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ชโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•ก
โ”‚ success  โ”‚       0 โ”‚ []            โ”‚
โ•˜โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•งโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•›

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