> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runcomfy.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

**Trainer API** provides HTTP endpoints to run **AI Toolkit LoRA training**. You bring your dataset and an **AI Toolkit YAML** config, and RunComfy runs the job on GPUs, then returns the outputs you need (e.g. LoRA checkpoints, resolved config, and sample images) so you can automate end-to-end fine-tuning in code.

After training, you can take the resulting LoRA and run **inference** via **[Model APIs](/model-apis/quickstart)** (on-demand) or deploy it with **[Serverless API (LoRA)](/serverless-lora/introduction)**. Not sure which one to use for inference? Start with **[Choose a LoRA Inference API](/serverless-lora/api-types)**.

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## Key concepts

Trainer API revolves around two objects:

### Dataset

A **Dataset** is the training data you upload for a LoRA run. You **create** it with the Dataset API, then **upload** its files until it's `READY`. It typically includes:

* images/videos
* caption `.txt` files (same base filename as the media)

Datasets have a lifecycle (`DRAFT` → `UPLOADING` → `READY`).\
Only datasets in `READY` can be mounted by a training job.

### Training Job

A **Training Job** is an async run that executes your **AI Toolkit YAML** on a GPU. When a job starts, RunComfy:

* mounts your `READY` dataset into the training container (under `/app/ai-toolkit/datasets/{dataset_name}`)
* runs the YAML config you provide
* produces artifacts such as **LoRA checkpoints (`.safetensors`)**, resolved config, and sample outputs

A training job references its input dataset by `dataset_name` (from the Dataset API).

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## Typical workflow

1. **Create a dataset** (metadata)
2. **Upload dataset files**, then wait until it becomes `READY`
3. **Submit a training job** with your AI Toolkit YAML config (and `gpu_type`)
4. **Poll status** and **download results** (`checkpoints`, `samples`, `config`)
5. **Run inference** with the trained LoRA:
   * as a dedicated endpoint via **[Serverless API (LoRA)](/serverless-lora/introduction)**, or
   * on-demand via **[Model APIs](/model-apis/quickstart)**

Next step: **[Quickstart](/trainer-apis/quickstart)**