tracel-ai/burn · error

Can save learning rate scheduler checkpoint.

Error message

Can save learning rate scheduler checkpoint.

What it means

On CheckpointingAction::Save, the learning-rate scheduler record is written via its checkpointer and unwrapped with expect(). Failure to serialize or write the scheduler checkpoint panics with this message.

Source

Thrown at crates/burn-train/src/learner/base.rs:184

                        .delete(epoch)
                        .expect("Can delete model checkpoint.");
                    self.optim
                        .delete(epoch)
                        .expect("Can delete optimizer checkpoint.");
                    self.lr_scheduler
                        .delete(epoch)
                        .expect("Can delete learning rate scheduler checkpoint.");
                }
                CheckpointingAction::Save => {
                    self.model
                        .save(epoch, learner.model.clone().into_record())
                        .expect("Can save model checkpoint.");
                    self.optim
                        .save(epoch, learner.optim.to_record())
                        .expect("Can save optimizer checkpoint.");
                    self.lr_scheduler
                        .save(epoch, learner.lr_scheduler.to_record())
                        .expect("Can save learning rate scheduler checkpoint.");
                }
            }
        }
    }

    /// Load a training checkpoint.
    ///
    /// No device is taken: checkpoints are device-free burnpack records (file-backed bytes). On
    /// load, the model keeps the device of the learner's existing parameters, and the optimizer
    /// state is migrated to each parameter's device on the next step. The training device is fixed
    /// earlier, when the learner's model is created/forked.
    pub fn load_checkpoint(&self, mut learner: Learner<M>, epoch: usize) -> Learner<M> {
        let record = self
            .model
            .restore(epoch)
            .expect("Can load model checkpoint.");
        learner.load_model(record);

View on GitHub (pinned to d16f7ba2ed)

Solutions

1. Verify the scheduler FileCheckpointer's directory matches the other checkpointers and is writable.
2. Check free disk space — saves happen sequentially, so an almost-full disk often fails on the last component.
3. Create the directory up front (create_dir_all) before training starts.
4. If a partial save happened, delete that epoch's three files and re-run the Save action.
5. Keep all three checkpointers pointed at the same base directory to spot misconfiguration quickly.

Example fix

// before
let sched_cp = FileCheckpointer::new(Recorder, "/checkpoint/sched", "scheduler"); // wrong dir
// after
let base = "/checkpoints/run-1";
std::fs::create_dir_all(base)?;
let model_cp = FileCheckpointer::new(Recorder, base, "model");
let optim_cp = FileCheckpointer::new(Recorder, base, "optim");
let sched_cp = FileCheckpointer::new(Recorder, base, "scheduler");

let dir = Path::new(&checkpoint_dir);
std::fs::create_dir_all(dir).expect("create checkpoint dir");
assert!(dir.metadata().unwrap().permissions().readonly() == false, "checkpoint dir read-only");

fn dir_writable(dir: &str) -> bool {
    let p = Path::new(dir).join(".probe");
    std::fs::write(&p, b"1").is_ok() && std::fs::remove_file(p).is_ok()
}

let result = std::panic::catch_unwind(AssertUnwindSafe(|| checkpointer.checkpoint(actions)));
if result.is_err() { eprintln!("scheduler checkpoint save failed; check dir/disk"); }

When it happens

Trigger: Calling checkpoint() with Save when the LR scheduler record cannot be written: unwritable/missing checkpoint directory, disk full, or serialization error for the scheduler record.

Common situations: Scheduler checkpointer configured with a different (typo'd) path than the other checkpointers; permissions changed on the dir after startup; disk filled during the save sequence (model and optimizer succeeded, scheduler failed).

Related errors

• Can delete learning rate scheduler checkpoint.
• Failed to load module from file
• Can delete model checkpoint.
• Can delete optimizer checkpoint.
• Can save model checkpoint.

AI-assisted analysis of tracel-ai/burn@d16f7ba2ed (2026-09-05). Data as JSON: /api/errors/e824a998a7d30b79. Report an issue: GitHub.