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

# inferless init

Use this command to initialize a new model import.

**Usage**:

```console theme={null}
$ inferless init [OPTIONS] COMMAND [ARGS]...
```

**Options**:

* `-n, --name TEXT`: Denotes the name of the model.
* `-s, --source TEXT`: Not needed if local, else provide Github/Gitlab. \[default: local]
* `-u, --url TEXT`: Denotes the URL of the repo. required if source is not local.
* `-b, --branch TEXT`: Denotes the branch where the model is located. required if source is not local.
* `-a, --autobuild`: Enable autobuild for the model. will be False for local source.

**Commands**:

* `docker`: Initialize with Docker.
* `file`: Import a PyTorch, ONNX, or TensorFlow file...
* `hf`: Load a model from Hugging Face.
* `pythonic`: (Default) Deploy a Python workflow.

### `inferless init`

(Default) Deploy a Python workflow.

**Usage**:

```console theme={null}
$ inferless init [OPTIONS]
```

**Options**:

* `-n, --name TEXT`: Denotes the name of the model. \[required]
* `-s, --source TEXT`: Not needed if local, else provide Github/Gitlab. \[default: local]
* `-u, --url TEXT`: Denotes the URL of the repo. required if source is not local.
* `-b, --branch TEXT`: Denotes the branch where the model is located. required if source is not local.
* `-a, --autobuild`: Enable autobuild for the model. will be False for local source.

### Example usage

You can run the command

```bash theme={null}
inferless init -n inferless-onboarding
```

Then create the below files

### Example app.py

```python theme={null}

from diffusers import StableDiffusionPipeline
import torch
from io import BytesIO
import base64

class InferlessPythonModel:
    def initialize(self):
        self.pipe = StableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2-1", use_safetensors=True,
            torch_dtype=torch.float16, device_map='auto'
        )

    def infer(self, inputs):
        prompt = inputs["prompt"]
        image = self.pipe(prompt).images[0]
        buff = BytesIO()
        image.save(buff, format="JPEG")
        return { "generated_image_base64" : base64.b64encode(buff.getvalue()).decode() }

```

### Example input\_schema.py

```python theme={null}
# input_schema.py
INPUT_SCHEMA = {
    "prompt": {
        'datatype': 'STRING',
        'required': True,
        'shape': [1],
        'example': ["There is a fine house in the forest"]
    }
}
```

```bash theme={null}
inferless deploy --gpu T4
```

## Sub Commands

### Hugging Face.

This command creates new files called app.py and input\_schema.py using the hugging face model name in you active dir

**Usage**:

```console theme={null}
$ inferless init hf [OPTIONS]
```

**Options**:

* `-n, --name TEXT`: Denotes the name of the model. \[required]
* `-m, --hfmodelname TEXT`: Name of the Hugging Face repo. \[required]
* `-t, --modeltype TEXT`: Type of the model (transformer/diffuser). \[required]
* `-k, --tasktype TEXT`: Task type of the model (text-generation). \[required]

**Transformers options:**

* audio-classification
* automatic-speech-recognition
* conversational
* depth-estimation
* document-question-answering
* feature-extraction
* fill-mask
* image-classification
* image-segmentation
* image-to-text
* object-detection
* question-answering
* summarization
* table-question-answering
* text-classification
* text-generation
* text2text-generation
* token-classification
* translation
* video-classification
* visual-question-answering
* zero-shot-classification
* zero-shot-image-classification
* zero-shot-object-detection

**Diffusers options:**

* Depth-to-Image
* Image-Variation
* Image-to-Image
* Inpaint
* InstructPix2Pix
* Stable-Diffusion-Latent-Upscaler

Once init is complete you will see the below files created

```bash theme={null}
./
├── app.py
├── input_schema.py
└── inferless.yaml
```

* `input_schema.py `This file defines the structure and validation rules for the input data that a model expects. This file is crucial for ensuring that the data fed into the model is in the correct format and meets all necessary requirements.

* `inferless-runtime-config.yaml`This file will have all the software packages and the Python packages required for the model inferencing.

* `inferless.yaml`This file will have all the configurations required for the deployment. Users can update this file according to their requirements.

### Docker

**Usage**:

```console theme={null}
$ inferless init docker [OPTIONS]
```

**Options**:

* `-n, --name TEXT`: Denotes the name of the model. \[required]
* `-t, --type TEXT`: Type for import: dockerimage/dockerfile. \[required]
* `-p, --provider TEXT`: Provider for the model dockerimage = (dockerhub/ecr) dockerfile = (github/gitlab). \[required]
* `-u, --url TEXT`: Docker image URL or GitHub/GitLab URL. \[required]
* `-b, --branch TEXT`: Branch for Dockerfile import (GitHub/GitLab). required if type is dockerfile.
* `-d, --dockerfilepath TEXT`: Path to the Dockerfile. required if type is dockerfile.
* `-h, --healthapi TEXT`: Health check API endpoint. \[required]
* `-i, --inferapi TEXT`: Inference API endpoint. \[required]
* `-s, --serverport INTEGER`: Server port. \[required]
* `-a, --autobuild`: Enable autobuild for the model.

### File ( PyTorch/ ONNX /TensorFlow ) inference with Triton server.

The folder structure for the zip file should be as follows:

.

├── config.pbtxt (optional)

├── input.json

├── output.json

├── 1/

│ ├── model.xxx (pt/onnx/savedmodel)

**Usage**:

```console theme={null}
$ inferless init file [OPTIONS]
```

**Options**:

* `-n, --name TEXT`: Denotes the name of the model. \[required]
* `-f, --framework TEXT`: Framework of the model. \[pytorch, onnx, tensorflow] \[default: pytorch]
* `-p, --provider TEXT`: Provider for the model (local/gcs/s3). \[default: local]
* `--url TEXT`: Provider URL. required if provider is not local.