# Platform Features

<details>

<summary>What types of AI workloads can Yotta Labs run?</summary>

Yotta Labs can run all types of AI workloads, including model training, inference, data processing, and experimentation.

</details>

<details>

<summary>What frameworks and models are supported？</summary>

Yotta Labs supports major AI frameworks including PyTorch and integrates seamlessly with Hugging Face Transformers and other popular pretrained models. Users can train, fine-tune, or run inference on a wide range of models without extra configuration. You can create your own by launching private templates as well. See our [**Templates**](https://console.yottalabs.ai/compute/templates).

</details>

<details>

<summary>What logging and monitoring tools are included?</summary>

Yotta Labs provides built‑in logs at the container and system level accessible via the console or API, helping users debug and monitor workloads.

</details>

<details>

<summary>What APIs are available and how do I authenticate?</summary>

* Yotta Labs provides APIs for ssh connection to manage resources, submit workloads, and monitor jobs.
* Authentication is handled via API keys, which can be generated from the Yotta console and passed in request headers.

</details>

<details>

<summary>Is data persistent across sessions?</summary>

Persistence depends on your storage choice; for long-term datasets/models use persistent storage options.See Pods-> Deploy->Persistent Storage.

</details>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.yottalabs.ai/company/faqs/platform-features.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.