> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/fpt-gpu-cloud/gpu-container/tutorials/how-to-manage-template.md).
# How to Manage template?
Templates are used to launch images as containers and define the required container disk size, volume, volume paths, and ports needed. You can also define environment variables and startup commands within the template.
#### Built-in Templates
These templates are created and maintained by FPT AI Factory. We now offer 6 built-in templates:
1. **Jupyter Notebook**
* Intended Use: This template provides Jupyter Notebook to adopt remote development for AI/Data Scientists without local hardware limitations.
* Environment Variables
Some more useful environment variables are provided for container customization.
| Variable | Type | Default | Description |
| -------- | ------ | ------- | -------------------------------------------------------------------------------- |
| USERNAME | string | admin | Username to access Jupyter Notebook |
| PASSWORD | string | | - Password to access Jupyter Notebook
- Generate by system
|
* Port
| Type | Port |
| ---- | ---- |
| HTTP | 8000 |
| TCP | 22 |
2. **Ollama WebUI**
* Intended Use: This template supports running various large language model (LLM) programs, including Ollama and APIs compatible with OpenAI, making it easy for users to customize based on workflow.
* Port:
| Type | Port |
| ---- | ---- |
| HTTP | 8080 |
| TCP | 22 |
3. **Ollama**
* Intended Use: This template enables high-throughput inference using GPU resources with a state-of-the-art engine.
* Environment Variables
Some more useful environment variables are provided for container customization.
| Variable | Type | Default | Description |
| ---------- | ------ | ------- | ------------------------------------------------------------------------------------- |
| API\_TOKEN | string | | - Auto-authenticate with external services
- Generate by system
|
* Port:
| HTTP | 8000 |
| ---- | ---- |
4. **vLLM**
* Intended Use: This vLLM container image is built and maintained by AI Factory. This template enables high-throughput model inference using GPU resources with a state-of-the-art engine.
* Environment Variables
Some more useful environment variables are provided for container customization.
| Variable | Type | Default | Description |
| ------------------------- | ------ | ------- | ----------------------------------- |
| HUGGING\_FACE\_HUB\_TOKEN | string | | Your Hugging Face User Access Token |
* Port:
| Type | Port |
| ---- | ---- |
| HTTP | 8000 |
5. **Code Server**
* Intended Use: This template offers cloud-based VS Code with GPU to train, test, and debug AI models remotely with full IDE capabilities.
* Environment Variables
Some more useful environment variables are provided for container customization.
| **Variable** | **Type** | **Default** | **Description** |
| ------------------ | -------- | --------------------- | ------------------------------------------------- |
| PUID | int | 0 | UserID |
| PGID | int | 0 | GroupID |
| TZ | string | Etc/UTC | Your timezone |
| PROXY\_DOMAIN | string | code-server.my.domain | The domain will be proxied for subdomain proxying |
| DEFAULT\_WORKSPACE | string | / | Default folder opened when accessing code-server |
| PASSWORD | string | | Generate by system |
* Port:
| Type | Port |
| ---- | ---- |
| HTTP | 8443 |
| TCP | 22 |
6. **Ubuntu**
| Type | Port |
| ---- | ---- |
| TCP | 22 |
* Intended Use: This is a minimal Ubuntu CLI virtual machine with several useful additions to improve your user experience. While the root account is available as usual, we have created a normal system user for your convenience.
* Development tools pre-installed: SSH access.
#### Custom Templates
You can use your own **Docker image** by clicking **Custom Template** and overriding your own image:tag. If your image is from a private Docker repository, make sure to provide your **username and password** for authentication.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://ai-docs.fptcloud.com/fpt-gpu-cloud/gpu-container/tutorials/how-to-manage-template.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.