Quick Start Guide

1. Create a Model Repository

• Navigate to Inference → Model Repository in the TIR Dashboard
• Click CREATE REPOSITORY
• Enter a repository name (e.g., my-llm-model)
• Select model type:
  • PyTorch - For PyTorch models (.pth, .pt, Hugging Face models)
  • Triton - For Triton Inference Server model repositories
  • Custom - For other model formats
• Choose storage type:
  • New EOS Bucket - Automatically provisions a new bucket (recommended)
  • Existing EOS Bucket - Use an existing bucket
  • External EOS Bucket - Connect to an external bucket with credentials
• Click Create

2: Configure Access

After creation, you'll receive:

• Bucket name
• Access key
• Secret key
• Endpoint URL (typically https://objectstore.e2enetworks.net)

tip

Model Repositories are backed by E2E Object Storage (EOS). If you have not used EOS storage before, please read Object Storage first.

3: Upload Model Files

Method 1: Using Minio CLI (Recommended)

Install Minio CLI:

# macOS
brew install minio/stable/mc

# Linux
wget https://dl.min.io/client/mc/release/linux-amd64/mc
chmod +x mc
sudo mv mc /usr/local/bin/

Configure the client:

mc config host add <alias-name> https://objectstore.e2enetworks.net <access-key> <secret-key>

Upload model files:

# Upload entire directory
mc cp -r /path/to/model/* <alias-name>/<bucket-name>/

# Upload specific version
mc cp -r /path/to/model/* <alias-name>/<bucket-name>/v1/

# Upload Hugging Face model snapshot
mc cp -r ~/.cache/huggingface/hub/<model-name>/* <alias-name>/<bucket-name>/

Method 2: Using Python SDK

Step 1: Install the SDK

pip install e2enetworks

Step 2: Initialize the SDK

from e2enetworks.cloud import tir
from e2enetworks.constants import DELHI_LOCATION, CHENNAI_LOCATION

# Choose the required location
location = DELHI_LOCATION

# Initialize TIR SDK
# Get API Key and Access Token from Projects → API Tokens
tir.init(
    api_key="<API_KEY>",
    access_token="<ACCESS_TOKEN>",
    location=location
)

Step 3: Create a Model Repository client

model_repo_client = tir.Models(
    project=<PROJECT_ID>
)

Step 4: Upload a model

# Upload all files from a local directory to the model repository
model_repo_client.push_model(
    model_path="./model-dir",
    prefix="v1",
    model_id=<MODEL_ID>
)

Method 3: Using TIR Notebooks

In a TIR Notebook, use the SDK or CLI directly:

Step 1: Install the SDK

pip install e2enetworks

Step 2: Run commands in a TIR Notebook

from e2enetworks.cloud import tir

# Set location
location = "Delhi"

# Create model repository client
model_repo_client = tir.Models(location=location)

# Upload a model version from a local directory
model_repo_client.push_model(
    model_path="<LOCAL_MODEL_DIRECTORY>",
    prefix="<VERSION_PREFIX>",
    model_id=2297
)

# Download model files from the repository
model_repo_client.download_model(
    model_id=2297,
    local_path="<LOCAL_DOWNLOAD_PATH>",
    prefix="<VERSION_PREFIX>"
)

4: Verify Upload

1. Navigate to your Model Repository in the dashboard
2. Click on the Model Files tab
3. Verify your files are uploaded correctly

5: Use with Model Endpoints

1. Create a Model Endpoint (Inference → Model Endpoints)
2. Select Link with Model Repository
3. Choose your repository from the list
4. Configure the model path (e.g., / for root, /v1 for versioned models)
5. Launch the endpoint

The model files will be automatically downloaded to the endpoint container during startup.

---

Supported Frameworks

Model Repositories support storing models from various ML frameworks:

Framework	File Formats	Use Case
PyTorch	.pth, .pt, .pkl	PyTorch checkpoints, Hugging Face models, custom PyTorch models
TensorFlow	SavedModel, .h5, .pb	TensorFlow SavedModels, Keras models
Triton Inference Server	Triton model repository structure	Triton models with configuration files
Custom Formats	Any file structure	ONNX models, TensorRT engines, or any custom format

Example Directory Structures

PyTorch

my-pytorch-model/
├── config.json
├── pytorch_model.bin
├── tokenizer.json
└── tokenizer_config.json

TensorFlow

my-tensorflow-model/
├── saved_model.pb
├── variables/
│   ├── variables.data-00000-of-00001
│   └── variables.index
└── assets/

Triton

my-triton-model/
├── config.pbtxt
└── 1/
    └── model.graphdef

Custom

my-custom-model/
├── model.onnx
├── config.yaml
└── assets/

---