Mastering ARTIQ Experiment Errors: A Sipyco Troubleshooting Guide

Hey guys, ever found yourself pulling your hair out when your ARTIQ scheduled experiment finishes its run, dutifully reports "finished" via Sipyco, but then… silence? No explicit error, no clear indication of why your carefully crafted quantum sequence didn't behave as expected? It’s a frustrating scenario, right? You've set up your automated lab, you're using sipyco to seamlessly schedule your ARTIQ experiments, and everything seems to be humming along until you realize that "finished" status can sometimes be a wolf in sheep's clothing – an experiment might have finished executing, but it could have crashed and burned internally. This article is your ultimate guide to breaking that silence, digging deep into your scheduled experiment runs, and finally retrieving those elusive errors that are currently hiding in the shadows. We’re going to walk through practical, real-world strategies to give you back control, making your m-labs ARTIQ setups more robust and your experiment troubleshooting a whole lot easier. So, let’s dive in and unmask those hidden failures!

Decoding the ARTIQ & Sipyco Symphony: Scheduling Experiments with Precision

When you're working with cutting-edge quantum control, ARTIQ (Advanced Real-Time Infrastructure for Quantum physics) is often the star of the show. It’s a powerful framework that allows scientists and engineers to precisely control experimental hardware with sub-nanosecond timing. But what’s a star without a stage manager? That’s where Sipyco (Simple Python Control) often comes into play, acting as the elegant middleware that facilitates communication and scheduling of your ARTIQ experiments. Together, they form a formidable duo for automating complex lab sequences. Think of it like this: your ARTIQ code is the intricate recipe for your experiment, and Sipyco is the automated chef that schedules when and how that recipe gets cooked.

The typical workflow, as many of you folks are already familiar with, involves using a sipyco.Client to interact with the ARTIQ master’s scheduler service. You'd craft your experiment, get its expid, and then use scheduler.submit() to queue it up. It’s a beautiful, streamlined process designed to keep your experiments running efficiently, especially in high-throughput environments or when you need to run sequences at specific times. After submission, you'd constantly query scheduler.get_status() for that run_id to know its progress. The goal is always to see that sweet "finished" status, signaling that your experiment has completed its execution on the ARTIQ hardware. This setup is incredibly valuable for automating entire research pipelines, allowing you to focus on analyzing data rather than manually triggering each run. The ability to prioritize runs, set due dates, and manage a queue of diverse scheduled experiment runs is a cornerstone of modern experimental physics labs. However, this very efficiency can sometimes obscure critical information, particularly when things go wrong during the experiment's actual execution. The "finished" status, while indicating the scheduler has completed its task of dispatching the experiment, doesn't inherently guarantee the experiment ran to a successful logical conclusion. It simply means the ARTIQ master is done with it, whether it was a success, a benign error, or a catastrophic failure. This nuance is precisely what we aim to address today, empowering you to differentiate between a truly successful "finished" state and a "finished" state that actually means "finished, but failed miserably internally." It's about moving beyond surface-level status checks to truly understand the outcome of your precious ARTIQ experiment runs.

The Hidden Truth: Why check_errors() Falls Short for Experiment Failures

Now, let’s talk about that moment of confusion many of us have faced: you’ve got your sipyco client, you've submitted your ARTIQ experiment, you've waited patiently for scheduler.get_status() to report "finished", and then, with hope in your heart, you try something like scheduler.check_errors() hoping to retrieve any explicit failure messages. But often, guys, this particular function on the sipyco scheduler client might not yield the specific experiment-level errors you’re looking for. This isn't because sipyco is being cheeky; it’s due to the architecture of how ARTIQ and Sipyco interact, and what scheduler.check_errors() is actually designed to monitor.

Think of it this way: when you submit an experiment via sipyco to the artiq_master's scheduler, you're interacting with the scheduler service itself. scheduler.check_errors() is primarily concerned with that service's health and any system-level problems it might encounter. Did the scheduler fail to submit the experiment? Was there a communication breakdown with the ARTIQ master? Did the scheduling queue itself run into an issue? These are the kinds of errors scheduler.check_errors() is designed to catch. It’s like checking if the postal service itself is running smoothly – are packages being accepted, sorted, and dispatched? Yes, great! But it doesn’t tell you if a specific package, once delivered, contained a broken item because the recipient dropped it inside their house. The experiment's actual execution, with its own intricate logic and potential runtime exceptions, happens within the ARTIQ master's runtime environment, often on the coredevice. Errors originating within your experiment – like an invalid sequence, a calculation gone wrong, or a hardware communication timeout during the actual pulse generation – are distinct from errors related to the scheduling process itself. The artiq_master dutifully processes these internal experiment errors and logs them, but the sipyco scheduler client, by design, often only gets a high-level "finished" status back, not the detailed internal traceback. This is a crucial distinction. Your ARTIQ experiment, running on the core device, might encounter a Python exception, log it to the artiq_master's console and its internal log files, and then cleanly exit. From the scheduler's perspective, the task of running that experiment is complete, hence "finished". The scheduler's job isn't to parse every line of every experiment's log; its job is to manage the queue and state of runs. Therefore, relying solely on scheduler.check_errors() after a "finished" status will almost certainly leave you in the dark about actual experiment failures. To truly understand what went wrong, we need to bypass the scheduler's generalized status and tap directly into the detailed output and results managed by the artiq_master itself. It’s about getting closer to where the action – and the errors – truly happen within your m-labs ARTIQ setup.

Your Arsenal for Error Retrieval: Unearthing ARTIQ Experiment Logs and Results

Alright, folks, now that we understand why those experiment errors are so elusive, let's talk about the practical solutions. The good news is that ARTIQ, being a robust system, does capture these errors; we just need the right tools to access them programmatically. We’ll explore a few powerful methods to help you finally retrieve those critical details from your scheduled experiment runs.

Method 1: The ARTIQ Master REST API – Your Best Friend for Logs

One of the most direct and programmatic ways to get detailed information, including error logs, from your ARTIQ scheduled experiments is by interacting directly with the artiq_master's built-in REST API. This API is a powerful interface that allows external applications, like your Python script that's submitting Sipyco jobs, to query the state of the ARTIQ system, retrieve run details, and even pull out the comprehensive logs for specific experiment runs. It’s essentially a web server running on your ARTIQ master that speaks a language your client can understand (HTTP/JSON), providing a deeper level of insight than the high-level Sipyco scheduler client can offer on its own. Instead of just getting a