> ## 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.

# Importing from Cloud Buckets

Below are the providers that are supported to directly load the model file into Inferless

* AWS S3

* Google Cloud Buckets

* Azure File Storage

Before you upload your custom model, make sure you go through and follow the prerequisites conditions mentioned below.

### File structure requirements

The below format should be kept in mind while loading your code to Inferless from cloud providers.

**The model file has to be loaded as a ZIP file.**

The structure of the model file that is to be loaded is described below based on the framework used.

The model names has to be in the format as given in the example below.

* ### **`PyTorch`**

```python PyTorch - File structure theme={null}
/
├── config.pbtxt
├── 1
│   ├── model.pt

#*Any other file can be added along with the above structure and files for any dependencies in the model*

```

* ### **`TensorFlow`**

```json TensorFlow - Structure theme={null}
/
├── 1
│   ├── model.savedmodel
│   │   ├── saved_model.pb

#*Any other file can be added along with the above structure and files for any dependencies in the model*

```

* ### **`ONNX`**

```json ONNX - File Structure theme={null}
/
├── config.pbtxt
├── 1
│   ├── model.onnx

#*Any other file can be added along with the above structure and files for any dependencies in the model*

```

Once you have made sure the model file structure is in the right format, you can upload the model from your cloud storage to Inferless using the "Import Model" option

* Click [here](/integrations/cloud-buckets-s3-gcs) for steps to upload