Welcome to TIR AI Platform Documentation!

TIR is an modern AI Development Platform designed to tackle the friction of training and serving large AI models.

We do so by using highly optimised GPU containers (NGC), pre-configured environments (pytorch, tensorflow, triton), automated API generation for model serving, shared notebook storage, and much more.

• Introduction
  • Components of TIR Platform
  • Why AI Model Development is so hard?
  • Key Features of TIR Platform
• Getting Started
  • Notebooks
  • Datasets
  • Using Datasets with Notebooks
  • Model and Model Endpoints
• Introduction
• How to create Project ?
• Notebook
  • Notebook Details
    • Overview
    • Disk Size
    • Metrics
    • Associated Datasets
  • Notebook Actions
    • Launch Notebook
    • Stop
    • Update Notebook
    • Delete Notebook
  • Launch Notebook from Sidebar
• Datasets
  • How does it work?
  • Benefits
  • Usage
    • WebUI
    • SDK
  • Getting Started
    • Prequisites
    • Create a new dataset
    • Copy data into Dataset
  • Working with Datasets
    • Import Data from local machine
    • Import Data from Amazon S3
    • Import Data from GCS
    • Upload data from TIR Jupyter Notebook
    • Permenantly Migrate from other Cloud
  • How to Create Datasets ?
  • Delete DataSet
• Models
  • Uploading weights to TIR Models
  • Downloading weights from TIR models
• Model Endpoints
  • Pre-built Images
    • API format supported by Pre-built containers
    • Pytorch Inference (Torchserve)
• Model Development (Tutorials)
  • Fine-tune LLMA with Multiple GPUs
    • Steps
    • Conclusion
  • Deploy Inference for LLMA 2
  • Deploy Inference for Codellama
    • Model Endpoint creation for Codellama-7b using prebuilt container
    • Creating Model endpoint with custom model weights
      • Supported Parameters
  • Deploy Inference for Stable Diffusion v2.1
    • A guide on Model Endpoint creation & Image generation
    • Creating Model endpoint with custom model weights
    • Supported parameters for image generation
  • Deploy Inference Endpoint For MPT-7B-CHAT
  • Creating a ready-to-use Model endpoint
  • Creating Model endpoint with custom model weights
    • INFERENCE:
    • Supported parameters
  • Custom Containers in TIR
• API Token
• Analytics
  • Activity
  • Resource usage
• Team Management
  • How to add Team members in a team?
• Settings
• Billing
  • Notebooks
  • Free-Tier Notebooks
  • Notebook Disks (PVC)
  • Datasets