I’ve run thousands of simulations in Llekomiss and I still see the same mistakes trip people up.
You’re here because your simulation either failed to execute or you’re trying to avoid that happening in the first place. Both are smart reasons to be reading this.
Here’s the reality: llekomiss run code is powerful but unforgiving. One misplaced parameter and you’re staring at error messages instead of results.
I analyzed execution patterns across different simulation types to figure out what actually works. Not what should work in theory. What runs without breaking.
This guide walks you through the exact workflow for executing code in Llekomiss. You’ll learn the setup steps, the commands that matter, and how to configure your environment so things actually run.
We’ve tested these protocols extensively. The workflow I’m sharing here comes from real runs, not documentation that sounds good but falls apart when you try it.
You’ll get the precise syntax you need, the configuration settings that prevent failures, and the troubleshooting steps for when something does go wrong.
No theory. Just the execution process that works.
Prerequisites & Environment Setup: A Pre-Flight Checklist
You need to get your environment right before you run anything. The ideas here carry over into Llekomiss Run Code, which is worth reading next.
I see people skip this step all the time. Then they spend hours debugging issues that never should have happened in the first place.
Let me walk you through what actually matters.
Check Your Software Version
Open your terminal and run llekomiss --version right now.
This guide works with version 3.5 and later. If you’re running something older, you’re going to hit compatibility problems. Not maybe. You will.
Version mismatches cause more headaches than any other setup issue I’ve seen.
Get Your Dependencies Sorted
You need three things installed:
Python 3.9 or newer. The GCC 11.2+ compiler toolchain. And if you’re using GPU acceleration (which speeds things up considerably), you’ll need the CUDA libraries.
Use your package manager to verify everything’s there. Don’t assume.
The simulation.yml File Explained
This configuration file controls your entire project.
Think of it as the blueprint. If the blueprint’s wrong, nothing else matters.
You need to check three paths in particular: source_directory, output_path, and data_assets. I’m serious about this. Wrong paths cause FileNotFound errors more than anything else.
Go look at your simulation.yml file right now. Make sure those paths point to real locations on your system.
Set Up Your Workspace Structure
Create a main /project/ directory.
Inside that, make four subdirectories: /code, /configs, /data, and /results.
This keeps everything organized. More importantly, it prevents pathing conflicts when you run llekomiss run.
Clean directory structure means fewer stupid errors. It also makes managing multiple runs way easier down the line.
If you run into issues after setup, check out the problem on llekomiss software guide for troubleshooting steps.
Pro tip: Take five minutes now to document your exact setup in a text file. Future you will thank present you when something breaks at 2 AM.
The Core Execution Command: A Step-by-Step Breakdown
Most people overcomplicate this part.
They see command line syntax and assume it’s harder than it actually is. But running a simulation with lleko_run is pretty straightforward once you know what each piece does.
Let me break it down.
The basic command looks like this: lleko_run --config <path_to_config.yml> --script <your_main_script.py>.
That’s it. Three components working together.
Now, some developers prefer GUI-based simulators where you click buttons and drag files around. They say command line tools are outdated and unnecessarily complex. And sure, GUIs look friendlier at first glance.
But here’s what they don’t tell you.
Command line execution gives you control. You know exactly what’s running and where your files are. No hidden processes or mysterious background operations.
Understanding Each Component
The lleko_run executable is your starting point. Think of it as the engine that fires up the whole simulation kernel. You need to call it from your project’s root directory or things get messy fast.
The --config flag is mandatory. Not optional. This tells the simulator where to find your master configuration file (usually something like simulation.yml). Without it, the kernel won’t even start. It’ll just throw an error and quit.
Then you’ve got --script. This points to your main Python file where all your custom logic lives. The simulator needs to know what code to actually run.
A Real Example
Here’s what it looks like in practice: lleko_run --config ./configs/simulation.yml --script ./code/main_simulation.py.
Notice the file paths. I’m being specific about where everything lives. That’s not by accident.
How to Know It Worked
After you hit enter, watch your terminal. You should see three key messages pop up:
• [INFO] Kernel Initialized.
• [INFO] Reading parameters from ./configs/simulation.yml.
• [INFO] Executing script: main_simulation.py.
If you see those lines, you’re good. The simulation is running.
If you don’t? Check your file paths first. That’s where most errors come from (trust me on this one).
Advanced Execution: Customizing Runs with Command-Line Flags
I’ll be honest with you.
The first time I tried to run a complex simulation on my old quad-core machine, I sat there for THREE HOURS waiting for it to finish. Just staring at the progress bar like it owed me money. I walk through this step by step in Problem on Llekomiss Software.
Turns out I was running everything on a single thread like an idiot.
That’s when I learned that knowing how to actually run your simulations matters just as much as writing good configuration files.
Overriding Configuration Parameters
Here’s something most people don’t realize. You don’t need to edit simulation.yml every time you want to test a small change.
Use the --param flag instead.
Say you want to test a different timestep without messing up your main config. Just run llekomiss run --param:simulation_timestep=0.01 and you’re done. That value only applies to that specific run.
I do this constantly when I’m debugging. It’s faster and you don’t risk forgetting to change your config back.
Running with Multiple Threads
This one changed everything for me.
If you’ve got a multi-core system (and you probably do), use the --threads flag. Something like --threads=8 will split the work across eight CPU threads.
That simulation that took me three hours? Now it finishes in about twenty minutes.
Some people say parallel processing adds complexity you don’t need. That it’s overkill for simple simulations. And sure, if you’re running a ten-second test, maybe they’re right.
But the second you’re working with real data or complex models, you NEED this.
Debugging with Verbose Output
When things go wrong and you can’t figure out why, add the --verbose or -v flag.
This prints detailed state information at each timestep. Every variable value. Every calculation. All of it dumped to your console.
It’s messy. But it works.
I’ve caught so many weird edge cases this way. Values that should’ve been positive going negative. Timesteps where everything suddenly explodes. You can’t see that stuff without verbose logging.
Headless Mode for Servers
Last thing. If you’re running simulations on a server or in a CI/CD pipeline, use --headless.
This stops the simulator from trying to render a GUI. Because trust me, watching your deployment fail because it tried to open a window on a headless server is not fun (learned that one the hard way).
Just add the flag and your simulations run clean in non-graphical environments.
Troubleshooting Common Execution Errors
You run your script and boom. Error message.
I see this all the time. You’ve written clean code but something breaks during execution. The error messages look cryptic and you’re not sure where to start.
Let me walk you through the three most common errors I run into and what you should actually do about them.
Error: ConfigFileNotFound
This one’s simple. Your path is wrong or you’re running the command from the wrong spot.
Here’s what I recommend. Stop using relative paths when you run llekomiss run code with the --config flag. Just use the full absolute path instead (something like /home/user/projects/myproject/config.yaml).
Or make sure you’re sitting in your project’s root directory before you execute anything.
Error: DependencyMismatch
This happens when your script needs a specific version of NumPy or Pandas but the simulator already loaded a different version internally.
The fix? Create a separate virtual environment for your project. Use venv or Conda to keep everything isolated. That way your dependencies won’t clash with what’s already running under the hood.
Error: SegmentationFault (core dumped)
Now this one’s serious. You’ve hit a memory wall.
First thing I’d try is cutting down your batch sizes or simplifying your model. Sometimes you’re just asking too much from the available RAM.
If that doesn’t work, you need to give the process more breathing room. Add the --memory_limit '8G' flag when you execute to bump up the allocation.
Most execution errors boil down to these three issues. Fix the path, isolate your dependencies, or give yourself more memory.
From Command Line to Actionable Insight
You came here to run code in Llekomiss without hitting walls.
Now you have a framework that works. Setup to debugging, all mapped out.
I’ve shown you the structured approach that gets results. Verify your prerequisites first. Use precise syntax. Apply the right flags when you need them.
This isn’t about fighting with execution errors anymore. It’s about getting to your results faster.
Your simulation just finished running. You’ve got output files waiting.
Here’s what you do next: Navigate to your output directory right now. Open those .csv and .log files. Start pulling the insights you were after.
The llekomiss run code command did its job. Your data is ready.
Stop wrestling with execution friction. The tools work when you use them correctly, and you just proved that.
Your next move is analysis. Go find what you were looking for in those files.