Product Introduction

Overview

Product Pricing

Benefits to Customers

Use Cases

Purchase Guide

Billing Overview

Purchase Mode

Renewal Instructions

Overdue Payment Instructions

Security Compliance

Data Security Protection Mechanism

Monitoring, Auditing, and Logging

Security Compliance Qualifications

Quick Start

Platform Usage Preparation

Operation Guide

Model Hub

Task-Based Modeling

Dev Machine

Model Management

Model Evaluation

Online Services

Resource Group Management

Managing Data Sources

Tikit

GPU Virtualization

Practical Tutorial

Deploying and Reasoning of LLM

LLM Training and Evaluation

Built-In Training Image List

Custom Training Image Specification

Angel Training Acceleration Feature Introduction

Implementing Resource Isolation Between Sub-users Based on Tags

API Documentation

History

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Built-In Training Image List

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Last updated: 2025-08-14 15:38:37

Overview
TI-ONE includes mainstream deep learning and machine learning frameworks such as TensorFlow, Pytorch, and PySpark. It also introduced the self-developed training acceleration framework Angel. Among them, tilearn-llm is a training acceleration component customized for large models, built into the platform's common training image, and supports both Notebook and task-based modeling.
Built-In Image List
Framework
Image Name
Supported Training Mode
Remarks
PyTorch
tilearn-llm0.9-torch2.3-py3.10-cuda12.4-gpu
DDP
Supported core libraries: Python 3.10, CUDA 12.4, jupyterlab 2.3.2, torch 2.3.0a0+40ec155e58.nv24.3, transformers 4.39.3, deepspeed 0.13.4, tilearn-llm 0.9.9, tilearn.ops 0.2.2.175, angel-vllm 0.4.2 
Supported GPU types: PNV6, H100, H800, PNV5b, L40, A100, A800, A10, V100, T4
Supported modules: Task-Based Modeling and Development Machine
﻿
tilearn-llm0.9-torch2.1-py3.10-cuda12.1-gpu
DDP
Supported core libraries: Python 3.10, CUDA 12.1, jupyterlab 2.3.2, torch 2.1.2, transformers 4.39.3, deepspeed 0.14.0, tilearn-llm 0.9.3.3, tilearn.ops 0.2.1.172, angel-vllm 0.3.4 
Supported GPU types: H100, H800, PNV5b, L40, A100, A800, A10, V100, T4
Supported modules: Task-Based Modeling and Development Machine
﻿
tilearn-llm0.8-torch2.1-py3.10-cuda12.1-gpu
DDP
Supported core libraries: Python 3.10, CUDA 12.1, jupyterlab 2.3.2, torch 2.1.0a0+b5021ba, transformers 4.31.0, deepspeed 0.10.0, tilearn-llm 0.8.3, tilearn.ops 0.2.0.1 
Supported GPU types: H100, H800, PNV5b, L40, A100, A800, A10, V100, T4
Supported modules: Task-Based Modeling and Development Machine
﻿
ti-acc2.5-torch1.9-py3.8-cuda11.1-gpu
DDP
Supported core libraries: Python 3.8, CUDA 11.1, torch 1.9.0+cu111, tiacc-training.torch 2.5.1.dev13 
Supported GPU types: A100, A10, V100, T4
Supported modules: Task-Based Modeling
﻿
torch1.9-py3.8-cuda11.1-gpu
DDP,MPI,Horovod
Supported core libraries: Python 3.8, CUDA 11.1, torch 1.9.0+cu111 
Supported GPU types: A100, A10, V100, T4
Supported modules: Task-Based Modeling
﻿
ti-acc2.5-torch1.12-tf1.15-tf2.4-pyspark2.4.5-py3.8-cuda11.3-gpu
-
Supported core libraries: jupyterlab 3.6.1, multiple conda environments, including: - pyspark: Python 3.7, spark-2.4.5-bin-hadoop2.7 - pyspark3: Python 3.8, spark-3.3.1-bin-hadoop3 - pytorch_py3: Python 3.8, CUDA 11.1, torch 1.9.0+cu111 - tiacc_pytorch_py3: Python 3.8, CUDA 11.1, torch 1.12.1+cu113, tiacc-training.torch 2.5.1.dev10 - tf_py3: Python 3.7, CUDA 10.0, tensorflow-gpu 1.15.0 - tiacc_tf_py3: Python 3.7, CUDA 10.0, tensorflow-gpu 1.15.0 - tf2_py3: Python 3.8, CUDA 11.0, tensorflow 2.4.0 Supported GPU types: V100, T4, partial kernel support for A100/A10
Supported modules: Development Machine
TensorFlow
ti-acc1.0-tf1.15-py3.6-cuda10.0-gpu
PS-Worker
-
﻿
tf1.15-py3.7-cpu
PS-Worker,MPI,Horovod
-
﻿
tf1.15-py3.7-cuda10.0-gpu
﻿
﻿
﻿
tf2.4-py3.8-cpu
﻿
﻿
﻿
tf2.4-py3.8-cuda11.1-gpu
﻿
﻿
Spark
spark2.4.5-cpu
Spark
-
PySpark
spark2.4.5-py3.6-cpu
Spark
-
Others
py3.8-cpu
-
Miniforge3, jupyterlab 4.3, tensorboard 2.18.0, Python 3.8 conda environment
Supported modules: Development Machine
﻿
py3.10-cpu
-
Miniforge3, jupyterlab 4.3, tensorboard 2.18.0, Python 3.10 conda environment
Supported modules: Development Machine
﻿

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Built-In Training Image List

Overview

Built-In Image List

Help and Support

Framework	Image Name	Supported Training Mode	Remarks
PyTorch	tilearn-llm0.9-torch2.3-py3.10-cuda12.4-gpu	DDP	Supported core libraries: Python 3.10, CUDA 12.4, jupyterlab 2.3.2, torch 2.3.0a0+40ec155e58.nv24.3, transformers 4.39.3, deepspeed 0.13.4, tilearn-llm 0.9.9, tilearn.ops 0.2.2.175, angel-vllm 0.4.2 Supported GPU types: PNV6, H100, H800, PNV5b, L40, A100, A800, A10, V100, T4 Supported modules: Task-Based Modeling and Development Machine
		tilearn-llm0.9-torch2.1-py3.10-cuda12.1-gpu	DDP	Supported core libraries: Python 3.10, CUDA 12.1, jupyterlab 2.3.2, torch 2.1.2, transformers 4.39.3, deepspeed 0.14.0, tilearn-llm 0.9.3.3, tilearn.ops 0.2.1.172, angel-vllm 0.3.4 Supported GPU types: H100, H800, PNV5b, L40, A100, A800, A10, V100, T4 Supported modules: Task-Based Modeling and Development Machine
		tilearn-llm0.8-torch2.1-py3.10-cuda12.1-gpu	DDP	Supported core libraries: Python 3.10, CUDA 12.1, jupyterlab 2.3.2, torch 2.1.0a0+b5021ba, transformers 4.31.0, deepspeed 0.10.0, tilearn-llm 0.8.3, tilearn.ops 0.2.0.1 Supported GPU types: H100, H800, PNV5b, L40, A100, A800, A10, V100, T4 Supported modules: Task-Based Modeling and Development Machine
		ti-acc2.5-torch1.9-py3.8-cuda11.1-gpu	DDP	Supported core libraries: Python 3.8, CUDA 11.1, torch 1.9.0+cu111, tiacc-training.torch 2.5.1.dev13 Supported GPU types: A100, A10, V100, T4 Supported modules: Task-Based Modeling
		torch1.9-py3.8-cuda11.1-gpu	DDP,MPI,Horovod	Supported core libraries: Python 3.8, CUDA 11.1, torch 1.9.0+cu111 Supported GPU types: A100, A10, V100, T4 Supported modules: Task-Based Modeling
		ti-acc2.5-torch1.12-tf1.15-tf2.4-pyspark2.4.5-py3.8-cuda11.3-gpu	-	Supported core libraries: jupyterlab 3.6.1, multiple conda environments, including: - pyspark: Python 3.7, spark-2.4.5-bin-hadoop2.7 - pyspark3: Python 3.8, spark-3.3.1-bin-hadoop3 - pytorch_py3: Python 3.8, CUDA 11.1, torch 1.9.0+cu111 - tiacc_pytorch_py3: Python 3.8, CUDA 11.1, torch 1.12.1+cu113, tiacc-training.torch 2.5.1.dev10 - tf_py3: Python 3.7, CUDA 10.0, tensorflow-gpu 1.15.0 - tiacc_tf_py3: Python 3.7, CUDA 10.0, tensorflow-gpu 1.15.0 - tf2_py3: Python 3.8, CUDA 11.0, tensorflow 2.4.0 Supported GPU types: V100, T4, partial kernel support for A100/A10 Supported modules: Development Machine
TensorFlow	ti-acc1.0-tf1.15-py3.6-cuda10.0-gpu	PS-Worker	-
		tf1.15-py3.7-cpu	PS-Worker,MPI,Horovod	-
		tf1.15-py3.7-cuda10.0-gpu
		tf2.4-py3.8-cpu
		tf2.4-py3.8-cuda11.1-gpu
Spark	spark2.4.5-cpu	Spark	-
PySpark	spark2.4.5-py3.6-cpu	Spark	-
Others	py3.8-cpu	-	Miniforge3, jupyterlab 4.3, tensorboard 2.18.0, Python 3.8 conda environment Supported modules: Development Machine
Others		py3.10-cpu	-	Miniforge3, jupyterlab 4.3, tensorboard 2.18.0, Python 3.10 conda environment Supported modules: Development Machine