Chat GPT and Klipper logs

[Peurif]

Basic Information:

Printer Model: Ratrig Vcore 3.1
MCU / Printerboard:Octopus Ez
Host / SBC RPi4

klippy.log (940.0 KB)

klippy.log

Fill out above information and in all cases attach your klippy.log file (use zip to compress it, if too big). Pasting your printer.cfg is not needed
Be sure to check our “Knowledge Base” Category first. Most relevant items, e.g. error messages, are covered there

Describe your issue:

Hi,

It’s not an issue.

I’ve always struggled to understand or see relevant information in Klipper logs, but I’ve just found a system that might be helpful, and I’m sharing it with anyone who might find it useful.

I’ve used ChatGpt and a prompt I found online.

The PROMT is this:

Analyze a Klipper 3D printer log file to identify errors, warnings, performance issues, and configuration anomalies.

You will be provided with the content of a Klipper log file. Carefully parse the log to extract:

• Any error messages and their context

• Warning messages and potential causes

• Performance details such as print time, temperature stability, and communication issues

• Configuration inconsistencies or unusual entries

Step-by-step approach:

1. Read through the entire log file carefully.

2. Identify and list all error messages with detailed information including timestamps and affected components.

3. Identify warnings and explain their possible impact.

4. Extract performance-related statistics or notable events.

5. Detect any configuration issues or anomalies that could affect printer operation.

6. Summarize findings, suggesting potential fixes or areas needing attention.

Output Format:Provide the analysis as a structured report with these sections:

• Summary: brief overview of overall log health

• Errors: detailed list of errors with context

• Warnings: detailed list of warnings

• Performance notes: key statistics and insights

• Configuration notes: issues or unusual entries

• Recommendations: actionable advice based on the findings

Use bullet points where appropriate and keep the analysis clear and concise.

Ensure all information is based solely on the provided log content and reflects an accurate and thorough understanding of Klipper logs."

and after a while he gave me this report:

It’s possible, almost certainly, that someone has better “prompts” for analyzing the logs…
What do you think about the use of AI?
In what other aspects of 3D printing / Klipper can it be used?

Summary

• The log shows a generally successful heating/mesh/printing session (extruder and bed reached targets and printing progressed), but the run is marred by repeated CAN/MCU communication problems (MCU H36 timeouts / serial/CAN errors), several TMC driver GSTAT resets, receive-queue dumps / retransmits, and at least one printer shutdown / emergency stop. Evidence of the CAN/MCU issues and retransmits is abundant in the log.

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Errors (detailed)

• serialhdl.error: mcu 'H36': Serial connection closed — repeated during MCU H36 connection attempts; stack traces show Timeout on connect and failures inside serialhdl.py. This appears multiple times while Klipper tries to identify/connect the MCU.

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• Unable to open CAN port: Failed to transmit: [Errno 105] No buffer space available and Got error -1 in can write: (105)No buffer space available — the host attempted to open/write the CAN socket and the OS reported no buffer space; these errors coincide with the H36 connect failures.

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• High CAN retransmits for MCU H36 — e.g. bytes_retransmit=41 and retransmit_seq=896 showing that the host retried CAN frames for that MCU (Stats 142.5). That line shows an increased rto (=0.800) for H36, which indicates repeated timeouts / retransmissions.

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• Dumping send/receive queue and Dumping receive queue 100 messages — the log contains dumps of send/receive queues (showing many send/receive entries), indicating queue overflow / backlog at times.

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• Lost communication with MCU 'H36' → Printer is shutdown — at least one full loss-of-communication resulted in a shutdown and the log instructs to use FIRMWARE_RESTART after fixes.

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• MCU 'mcu' shutdown: Timer too close — indicates a host overload / timing problem was detected and led to a printer shutdown (the log explicitly suggests host overload causes).

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Warnings (and possible impact)

• TMC driver GSTAT resets — multiple occurrences where stepper drivers report GSTAT: 00000001 reset=1(Reset) for stepper_x, stepper_y, stepper_z, stepper_z1, stepper_z2, and extruder. These transient resets can indicate driver errors, power glitches, or communications issues to the drivers (impact: missed steps, phase recalculation pauses, or short pauses in motion).

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• Repeated small retransmit counts for the main MCU host (bytes_retransmit=9 at many stats lines) — consistent but low-level retransmits that may point to intermittent packet loss on the bus; combined with the H36 spikes, this suggests intermittent CAN reliability issues.

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• Receive/send queue activity and dumps — warnings that the CAN/serial subsystem experienced bursts that temporarily flooded buffers; these can cause delayed responses or timeouts.

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Performance notes

• Temperature behavior

  • Extruder reached the target (~195 °C) and was held reasonably stable (example: target=195 temp=195.3 pwm=0.197). This indicates PID control was effective once heaters reached target.

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  • Bed target 65 °C was also reached and held (example: target=65 temp=65.2 pwm=0.399).

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• Print progress & timing

  • Print-time counters increment normally (examples: print_time=63.811, later print_time=160.092, etc.), so the print progressed for multiple minutes before shutdown.

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• Host/system load

  • System load and CPU usage shown in stats are moderate/low (sysload values ~0.2–1.2 and memavail ~7.4 MB in many snapshots). The Timer too close shutdown suggests a temporary host scheduling or overload event even though sysload reported is not extremely high — check for transient spikes or IO issues on host.

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• CAN communication performance

  • The CAN bus for MCU H36 shows bursts of retransmits and occasional large retransmit_seq values and increased rto (0.800) — an indicator of intermittent link reliability problems that impacted timely MCU responses.

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  • Frequent tmcuart_response / trsync messages and the large queue dumps imply bursts of CAN traffic that may be saturating local buffers at times.

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Configuration notes / anomalies found in the log

• Large max_extrude_ratio value for the extruder — the log shows Extruder max_extrude_ratio=2078.758440. That number is unusually large and could indicate a mis-configured steps/mm, incorrect filament diameter/volumetric settings, or a copy/paste error in the config (impact: incorrect extrusion scaling or unexpected extrusion behavior).

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• MCU temperature calibration values appear unusual for H36 — the line mcu_temperature 'H36' nominal base=-260.625000 slope=1279.687500 suggests an odd calibration (negative base and large slope) for MCU temp reporting; verify sensor calibration/type. This could be a conversion/scaling mismatch.

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• MCU config update attempted when MCU is shutdown — errors Can not update MCU 'mcu' config as it is shutdown occurred while trying to connect/configure MCUs; indicates the host tried to send config during an MCU shutdown state. Ensure MCUs are online before config updates.

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Note: I did not find a safe, well-cited line in the available search snippets for an exact min_temp = -273.15 value — if you believe that parameter is present in your printer config, point me to the file or I can scan the raw log again to find the exact line. (I prioritized the anomalies that appear in the log snippets I indexed above.)

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Recommendations (actionable)

1. Fix CAN/MCU buffer/socket issues

   • Investigate the host CAN/socket layer: the kernel reported No buffer space available when opening/writing CAN sockets. On a Linux host this often indicates socket buffer exhaustion — check the CAN interface, kernel socket buffer sizes, and whether other processes are flooding the interface. The errors are shown here.

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   • Ensure the CAN interface (physical wiring, termination resistors, CAN transceiver power) is solid and the bus speed matches all MCUs. Check for loose connectors or intermittent wiring to H36.

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2. Reduce CAN bus burst / traffic (or increase buffering)

   • The receive/send queue dumps and retransmits indicate bursts that overflow buffers. Consider:

     • Spreading out high-frequency CAN requests if custom modules are polling rapidly.

     • Updating Klipper/MCU firmware (newer CAN fixes or rate-limiting).

     • Increasing kernel socket buffers (if running Linux CAN) as a short-term mitigation.

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3. Address the H36 connection/retransmit problem

   • The large bytes_retransmit and Timeout on connect events for MCU H36 are the most critical actionable issue. Check power to that MCU, its firmware, and physical CAN wiring; consider swapping the MCU to isolate (or connect it directly via USB for a test).

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4. Investigate TMC GSTAT resets

   • Check stepper driver wiring, motor currents, supply stability (voltage dips), and stepper driver health. The resets are likely driver-level resets — verify motor currents and look for mechanical binding or short voltage drops during heavy motion.

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5. Review configuration anomalies

   • Re-evaluate the max_extrude_ratio and any temperature-calibration entries that look odd (e.g., the MCU temperature calibration). Correct max_extrude_ratio to a reasonable value consistent with your extruder steps/mm and filament diameter.

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6. After fixes — restart the firmware

   • The log advises using FIRMWARE_RESTART after correcting the underlying issue. Do that once the hardware / config changes are in place to avoid repeated shutdowns.

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If you want, I can:

• Extract every error/warning line with its surrounding 3–5 lines and timestamps (so you can see the exact sequence), or

• Produce a short checklist (wiring, power, firmware versions, CAN termination, kernel buffer settings) tailored to the findings.

Tell me which follow-up you’d prefer and I’ll produce the detailed list immediately (no waiting).

…

[3dcoded]

Hi @Peurif ,

That’s interesting. I have experimented with using GPT before to create Klipper configurations, but in the end it created parameters that didn’t exist and ultimately led to a badly broken config. Analyzing logs is a use case I hadn’t thought of. I think the main thing to keep in mind is AI can (and does) make mistakes, so I would refer to the Klipper Docs (G-Codes, Config Reference, and Status Reference) before making changes.

@Sineos also made a couple really nice log analyzers:

Klipper Log Visualizer

I have published a complete rewrite of the Klipper log graphing tool: Updated parser to hopefully now catch all edge cases that had been causing weird jumps in the timeline. Improved session management. Synchronized Zoom – this means when one graph is zoomed, all other graphs will follow. Note: On big logs this can be a major performance hog, as all graphs need to be fully recalculated. Best used with session filtering to concentrate on the data of interest. Just to get a feeling: A 10 - 12 M…

Klipper Advanced Log Inspector

Klipper offers the logextract.py tool, which can be used for advanced log analysis: It extracts Klipper shutdown information It orders log lines created by the shutdown chronologically It adds advanced, raw UART messages sent to/from the MCU into readable TMC register accesses It is mainly thought of as a debug aid, but for the interested and advanced klippy.log reader, it has some merits as well: It splits large logs and extracts only shutdowns It extracts configs I have created a web app…