ARIA

/recommend-next-experiment

Recommendation: run one “longer-training + held-out-eval” experiment
before changing model architecture or sweeping LR.

Use the current best setup, but train longer:

hyperparameter_config = {
    "learning_rate": 0.1,          # keep fixed
    "epochs": 2000,                # was 1000
    "model_type": "Multivariate_neural_network_classifier",
    "torch_seed": 0,               # add for reproducibility
}
Also add logging for the existing held-out split every epoch or
every 10–50 epochs:

train/train_loss
train/train_acc
test/test_loss
test/test_acc

and save the best checkpoint by test/test_loss, not only
by training loss.

Why this is the most promising next experiment
The project has 16 finished runs, but only 4 are actual training runs;
all four used the same training config:

learning_rate = 0.1
epochs = 1000
same model: Multivariate_neural_network_classifier

So there is no real hyperparameter comparison yet. The
strongest signal is from the learning curves: all four
training losses are still improving at the final epoch, and
the minimum training loss occurs at epoch 1000 in every run.

I’d run:

learning_rate: 0.1
epochs: 2000
model_type: unchanged
random_state: keep existing dataset split, 42
add torch.manual_seed(0) and log it as torch_seed
evaluate on X_test, y_test each epoch or every 10 epochs
checkpoint on lowest test/test_loss

Run that experiment for me.

What patterns do you see across my runs?

I’m considering running a state breakdown scan with counts by state using
specific server filters. I’ll look at the array of states: ['finished',
'running', 'crashed', 'failed', 'killed', 'pending', 'preempting'] and
determine the length of each. Summing these metrics could provide
useful insights. I should also focus on identifying objective metrics
from sample keys while excluding those that start with "_" or "system/".
It’s important to compute the best min/max for numeric summary metrics too.

Can you help me update my Python script to also log validation metrics?

I want to conduct a sweep to improve lower my training model's loss. Do you have suggestions on what hyperparameters I should try?

 The user is looking for insights to improve training loss, so I
 should suggest ranges for learning rate, optimizer, batch size,
 weight decay, and other relevant parameters.

Since the user wants to conduct a sweep, I need to show the code or
instrumentation with a clear yet concise YAML or Python configuration.
I might also suggest using random or Bayesian techniques for more
effective hyperparameter selection.

Create a report to share my findings with my team.

I’m looking to query the recent 50 runs, summarizing key frequencies 
and counts by using api.runs to slice them down. I'll be counting
lengths with lazy evaluation and weaving together the stats
for a proper report.  I need to include a description that might
require importing different report libraries.