Welcome to TIR AI Platform Documentation!

TIR is a 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
  • 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
• How-to Guides
  • Create notebooks instance with your own container
    • 1. Build a container image
      • 1.1 Patching using the Dockerfile
      • 1.2 Patching an instance of the container and Saving the Image
      • 1.3 Using Image Builder Utility
    • 2. Push the container image
      • 2.1 Setup Registry Integration
      • 2.2 Setup Registry Credential on your local system
      • 2.3 Push the image
    • 3. Create Notebook
  • List of TIR-provided Images
    • TIR-provided Images
    • Image Descriptions & Versions
      • Diffusers
      • Fast.AI
      • Jupyter
      • NVIDIA Cuda
      • NVIDIA Nemo
      • NVIDIA RAPIDS
      • PyTorch
      • SciPy
      • TensorFlow
      • Transformers
      • Ubuntu
  • Vector Embeddings Generation
    • 1. Install our python client package
    • 2. Set the required variables
    • 3. Generate the vector embeddings
      • How to insert the generated vector embeddings into a Vector database
    • Qdrant
    • Installing the Python Client
    • Using the Qdrant Client
      • Importing the Client
      • Connecting to the Qdrant Instance
      • Creating a Collection
      • Adding Vectors
      • Getting detailed info on a Collection
      • Whats Next?
• Projects
• Notebooks
  • Getting Started
  • Notebook options
  • Notebook Statuses
  • How to create Notebook ??
    • Notebook Details
      • Overview
      • Disk Size
      • Metrics
      • Associated Datasets
      • SSH Key
      • Update SSH Key
      • Add SSH Key After Notebook Creation
    • Notebook Actions
      • Launch Notebook
      • Stop
      • Update Notebook
      • Delete Notebook
    • Launch Notebook from Sidebar
• Committed Notebook
  • Creation of committed notebook
    • Getting Started
      • Auto-Renewal
      • Hourly Billing
      • Auto Deletion
  • Updating a hourly notebook to committed notebook
  • Updating a committed notebook to committed notebook
  • Delete Committed Notebook
• GPU H100 Plans
  • Notebook options
  • Steps to create GPU H100 notebook
    • Notebook Details
      • Overview
      • Disk Size
      • Metrics
      • Associated Datasets
      • SSH Key
    • Here are the Steps to verify the configuration of the GPU H100 notebook using the terminal
    • Or you can verify the configuration of the GPU H100 notebook using the Lab URL
    • Notebook Actions
      • Launch Notebook
      • Stop
      • Delete Notebook
• Datasets
  • How does it work?
  • Benefits
  • Usage
    • WebUI
    • SDK
  • Getting Started
    • Prequisites
    • Create a new dataset
      • Setup Minio CLI
      • Setup S3cmd
      • Dataset Details
      • Setup Minio CLI
      • Setup S3cmd
      • Dataset Details
      • Setup
      • Overview
      • Data Objects
    • Delete Dataset
• Model Repository
  • Uploading weights to TIR Models
  • Downloading weights from TIR models
  • How to create Model Repositories in TIR dashboard?
    • Using SDk
    • Using TIR Notebook
    • Using CLI
    • Using SDk
    • Using TIR Notebook
    • Using CLI
    • Using SDk
    • Using TIR Notebook
    • Using CLI
    • Delete Model Repository
• Model Endpoints
  • Pre-built Containers
    • TorchServe
    • NVIDIA Triton
    • LLAMA2
    • CodeLLMA
    • Stable Diffusion
    • Gemma
    • LLAMA 3 8B-IT
  • Custom Containers
    • Create Model Endpoints
    • Resource Details
    • Endpoint Details
    • Environment Variables
• Pipeline
  • Introduction
  • What is Pipeline
    • How to create a Pipeline
      • Create a new pipeline
      • Pipeline Versions
      • Actions
      • Create a new pipeline version under an existing pipeline
      • Create Run
      • View Run
      • Pipeline Versions Actions
      • Actions
• Run
  • Introduction
    • How to create a Run
      • Actions
• Scheduled Run
  • Introduction
    • How to perform Schedule Run
      • Actions
      • Delete Schedule Run
• Fine Tuning Models
  • Introduction
    • What is Fine-Tuning ?
    • How to Create a Fine-Tuning Job ?
    • How to define Dataset-Preparation ?
      • Choose a Dataset
    • How to define a Hyperparameter Configuration ?
    • Viewing your Job parameters and Finetuned models ?
• Guide to Fine Tune Mistral-7B
  • Introduction
    • What is Fine-Tuning ?
    • How to Create a Fine-Tuning Job ?
    • How to define Dataset-Preparation ?
      • Choosing a Custom Dataset
    • How to define a Hyperparameter Configuration ?
    • Viewing your Job parameters and Finetuned models ?
    • Running infernece on Finetuned mistral model ?
      • Deployment of finetuned Mistral-7B
      • Running inference on the Model Endpoint
• Guide to Fine Tune Google Gemma-7b
  • Introduction
    • What is Fine-Tuning ?
    • How to Create a Fine-Tuning Job ?
    • How to define Dataset-Preparation ?
      • Choosing a Custom Dataset
    • How to define a Hyperparameter Configuration ?
    • Viewing your Job parameters and Finetuned models ?
    • Running infernece on Finetuned Gemma-7b model ?
      • Deployment of finetuned Gemma-7b
      • Running inference on the Model Endpoint
• Model Playground
  • Introduction
    • What is GenAI API?
    • TIR GenAI API AI Studio helps in two ways:
    • Key features of a GenAI API include:
    • How to use GenAI API?
    • “Llama-2-13b-chat” model
    • “Stable Diffusion 2.1” model
    • “Code Llama 13b” model
    • “Whisper-large-v3” model
    • “e5-mistral-7b-instruct” model
• Samples and 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 for Gemma
    • Model Endpoint creation for Gemma 2B-IT using prebuilt container
      • Supported Parameters
  • Deploy Inference for LLAMA 3 8B-Instruct
    • Model Endpoint creation for LLAMA-3-8B-IT using prebuilt container
    • Creating Model endpoint with custom model weights
      • Supported Parameters
  • Fine-tune Stable Diffusion model on TIR
    • About Training methods
      • Dreambooth
      • Textual Inversion
    • Fine tuning the model
      • Step-1: Launch a Notebook on TIR
      • Step-2: Initial Setup
      • Step-3: Settings for teaching the new concept
      • Step-4: Teach the model the new concept (Fine-tuning with the training method)
      • Step-5: Run Inference with the newly trained Model
      • Step-6: Save the newly created concept
      • Step-7: Create Inference Server against our newly trained model
    • Conclusion
  • Deploy Inference 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
  • Fine Tuning Bloom
    • Introduction
      • What Is Fine-Tuning?
      • Why Fine-Tune BLOOM?
      • Benefits of Fine-Tuning BLOOM
    • Requirements
      • Python Libraries
      • Launch Your GPU-Backed Notebook
      • Packages and Libraries
      • BLOOM Model and Tokenizer
        • Training Data
        • Example
      • Training the BLOOM Model
        • Load the BLOOM model and tokenizer
        • Create the validation dataset
        • Define the training arguments
        • Create the trainer
      • Evaluation
        • Saving and Loading
        • Example 1:
        • Example 2:
        • Example 3:
      • Conclusion
  • Natural Language Queries to SQL with Code Llama
    • Introduction
      • Technologies
      • LLama 2 architecture used for Code llama
      • Prerequisites
      • How to Use and Querying Examples
      • Challenges and Solutions
      • Conclusion
• API Tokens
  • API TOKENS
• SSH Key
  • Introduction
  • Manage SSH Keys
    • Logging into E2E Networks ‘TIR AI PLATEFORM’
    • Navigate to SSH Keys
    • Add/Delete SSH Keys
    • ADD SSH Keys
    • Delete SSH Keys
    • Using an SSH Key
• Container
  • Getting Started
  • Container options
  • Container Status
  • How to create Container ?
    • Container Details
      • Overview
      • Disk Size
      • Metrics
      • Associated Datasets
      • SSH Key
      • Update SSH Key
      • Container
      • Add SSH Key After Container Creation
    • Container Actions
      • Launch Notebook
      • Stop Container
      • Update Container
      • Delete Container
• Vector Database
  • Qdrant
    • Getting Started
      • What is Qdrant?
      • Basic Terminology
      • What’s Next?
    • Creating New Qdrant Instance
      • Getting Started
      • Choosing Plans
      • Finalizing Qdrant
      • Qdrant Details
      • Qdrant Metrics
      • Connect to Qdrant
      • Resizing Storage Capacity
      • What’s Next?
    • Qdrant Python Client
      • Installation
        • Installing Python
        • Creating a Virtual Environment
        • Installing the Python Client
      • Using the Qdrant Client
        • Importing the Client
        • Connecting to the Qdrant Database
        • Creating a Collection
        • Listing Collections
        • Getting detailed info on a Collection
        • Adding Vectors
        • Searching for Vectors
        • Deleting the collection
        • Closing the connection
      • What’s Next?
    • Qdrant HTTP API
      • Pre-requisites
      • Creating a Collection
      • Listing Collections
      • Getting detailed info on a Collection
      • Adding Vectors
      • Searching for Vectors
        • Deleting the collection
          • What’s Next?
    • Qdrant Dashboard
      • Dashboard Landing Page
      • Overview of the Dashboard
      • Tutorial Section
      • Dataset Section
      • Console Section
      • Collections Section
      • Whats Next?
• Team Features
  • To create a Team click on create team.
  • How to add Team members in a team?
    • You can remove the member by clicking on Remove.
    • To delete the team, choose the team and then click on the delete icon.
• Settings
• Analytics
  • Activity
  • Resource usage
• Billing
  • Notebooks
    • Free-Tier Notebooks
    • Notebook Disks (PVC)
    • Datasets
    • Model Playground