We're excited to announce a major update to Private Inference Access (PIA), our local-first digital product pack designed to simplify your self-hosted AI workfl
We're excited to announce a major update to Private Inference Access (PIA), our local-first digital product pack designed to simplify your self-hosted AI workflow. This release addresses some of the pain points you've shared with us, and we're confident it will help you work more efficiently and effectively.
One of the most significant changes in this update is the introduction of a revamped automation system. Our team has worked tirelessly to make PIA's workflows more flexible and adaptable to your unique needs. You can now create custom automations using our intuitive workflow editor, which includes features like conditional logic, state machines, and reusable tasks.
For example, let's say you have a series of health checks that need to run on a regular schedule. With the new automation system, you can define a single workflow that runs all these health checks in sequence, with automatic retries and alerts when something goes wrong.
### Automation Example
```yml
---
name: Daily Health Check Workflow
tasks:
action: shell
script: |
#!/bin/bash
```
This is just a taste of what's possible with our new automation system. We've also added support for more advanced features like queueing, prioritization, and workflow dependencies.
We know that documentation can be a major pain point when working with complex tools like PIA. That's why we've invested significant time and effort into improving our documentation. Our updated guides now include more examples, code snippets, and visual walkthroughs to help you get up and running faster.
For instance, our new guide on "Running Health Checks" includes a step-by-step tutorial on how to configure your health checks using PIA's API.
### Code Snippet
```python
import pia
health_check = pia.HealthCheck(
name="CPU Usage",
interval=60, # Run every 1 minute
metric="cpu_usage"
)
workflow.add_task(health_check)
```
We've also made several enhancements to our GPU optimization tools, which are essential for running high-performance AI workloads on your local machine. Our new optimization framework now includes support for more advanced techniques like gradient checkpointing and mixed precision training.
These features will help you squeeze even more performance out of your GPUs, without sacrificing accuracy or reliability.
### Optimization Example
```bash
optimization --gpu 0 --max-iter 10000 --batch-size 32
```
We're thrilled with the progress we've made so far, and we can't wait to hear your feedback and suggestions. As always, we're committed to making PIA an indispensable tool for local AI operators like you.
If you're interested in learning more about Private Inference Access or getting started with our product pack, be sure to check out our [getting started guide](https://accessiblemadeflexible.com). We'll see you on the other side of this update!
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