krunic 0.1.5

Hyperparameter search for image classifiers using Ray Tune + SkyPilot

krunic

Automated hyperparameter search for image classifiers - from dataset to tuned model with one command. Distributed across GPUs and across hosts, locally and on the cloud (AWS).

krunic uses off-the-shelf models and packages, so you won't get SOTA performance. That said, it can get surprisingly close, with almost zero effort. Useful as a baseline, or experimentation with architectures, GPUs etc.

Built on Ray Tune, Optuna, timm, and SkyPilot.

Install (Mac and Linux)

$ pipx install krunic

This installs three commands: tunic (local training), krunic (cloud launcher), and tunic-plotter (results visualizer). The command takes a couple of minutes.

Quick start

Local:

$ tunic --data /path/to/dataset --model resnet50 --n_trials 30 --epochs 30 --output results.json

Cloud (AWS):

This requires, obviously, an AWS account. The image data must be copied to S3 prior to the run, for example like this:

$ aws s3 sync ~/image_data/tin s3://image.data/tin

$ krunic \
  --cluster skya \
  --s3-path my-dataset \
  --model resnet50 \
  --accelerator T4:4 \
  --num-nodes 4 \
  --n-trials 48 \
  --n-epochs 50 \
  --prefix kaws

SkyPilot creates the cluster, Ray distributes the load across the GPUs. In my experiments, it achieves near-perfect utilization:

Upon completion, get the best model hyperparameters:

$ aws s3 cp s3://image.data/ray-results/tin6/kaws_results.json .

Plot metric per trial:

$ tunic-plotter kaws_results.json

Remember to take down the cluster after downloading the results.

$ yes | sky down skya

Train final model from tuning results:

$ tunic --final kaws_results.json --data /path/to/dataset --epochs 50 --amp

Results on common benchmarks

Dataset	Model	Metric	Validation	Test	SOTA
PCam	ResNet18	AUROC	0.96	0.96	0.96
TinyImageNet	ViT-Small	Accuracy	0.87		0.91
ChestMNIST	ResNet18	AUROC	0.75	0.75	0.77
TissueMNIST	ResNet18	AUROC	0.92	0.94	0.93

All runs use generic off-the-shelf models with no domain-specific modifications.

Search space

Parameter	Range
Optimizer	AdamW, SGD
Learning rate	1e-5 – 1e-1 (log)
Weight decay	1e-6 – 1e-1 (log)
Label smoothing	0 – 0.3
Dropout rate	0 – 0.5
RandAugment magnitude	1 – 15
RandAugment num ops	1 – 4
Mixup alpha	0 – 0.5
CutMix alpha	0 – 1.0

Override any part with a YAML file via --search-space.

tunic - local hyperparameter search

tunic --data PATH --model MODEL [options]

Flag	Default	Description
--data	required	Dataset root (ImageFolder or WebDataset)
--model	required	Any timm model name
--n_trials	80	Number of Optuna trials
--epochs	30	Training epochs per trial (also used for --final)
--tune-metric	val_auroc	Metric for trial selection and pruning
--training_fraction	1.0	Fraction of training data (val always uses 1.0)
--batch-size	32	Batch size per trial
--amp		Enable automatic mixed precision
--resume		Warm-start from a previous experiment directory
--final		Skip tuning; train final model from results JSON
--combine		Train final model on train+val combined
--final-model	tunic_final.pt	Output path for final model weights
--final-stats		Output path for final model stats (JSON)
--device	auto	auto, cuda, mps, or cpu
--smoke-test		Quick end-to-end test with synthetic data

krunic - cloud launcher

krunic generates a SkyPilot YAML and launches the job. The dataset is S3-mounted (or copied); results are uploaded to S3 when the job completes.

Prerequisites

1. AWS credentials

aws configure

Prompts for your Access Key ID, Secret Access Key, and region (e.g. us-east-1). Your IAM user needs EC2 and S3 permissions. SkyPilot uses these credentials directly — no separate SkyPilot account or configuration needed.

2. Verify SkyPilot sees AWS

sky check

Should show AWS: enabled.

3. Dataset in S3

aws s3 sync ~/image_data/my-dataset s3://my-bucket/my-dataset

Monitor and tear down

krunic launches the cluster and streams logs. Once the job completes, download results and tear down:

sky status                          # check cluster state
sky logs my-cluster 1               # stream logs (job ID increments with each run)
aws s3 cp s3://my-bucket/ray-results/prefix/prefix_results.json .
yes | sky down my-cluster           # terminate cluster

--workdir defaults to the installed package directory (contains tunic.py and requirements.txt). Override it only if you are developing from a local source checkout and want to test unpublished changes.

krunic --cluster NAME --workdir DIR --s3-path PATH --model MODEL [options]

Flag	Default	Description
--cluster	required	SkyPilot cluster name
--workdir	package dir	Local directory synced to the cluster. Used for development
--s3-path	required	Dataset path within the S3 bucket
--model	required	Any timm model name
--accelerator	T4:4	GPU spec (e.g. T4:4, A10G:1, A100:8)
--num-nodes	1	Number of cluster nodes
--n-trials	30	Number of Optuna trials
--n-epochs	30	Training epochs per trial
--batch-size	32	Batch size per trial
--training-fraction	1.0	Fraction of training data per trial
--tune-metric	val_auroc	Metric for trial selection and pruning
--bucket	image.data	S3 bucket name
--prefix	tunic	Prefix for output files and S3 paths
--spot	—	Use spot instances (with retry-until-up)
--copy	—	Copy data from S3 to local disk instead of mounting
--idle-minutes	60	Auto-stop cluster after N idle minutes
--no-autostop	—	Disable auto-stop

Results are uploaded to s3://<bucket>/ray-results/<prefix>/<prefix>_results.json.

tunic-plotter - visualize results

tunic-plotter results.json                  # plots val_auroc and val_acc
tunic-plotter results.json --metric val_acc # single metric
tunic-plotter results.json --trial_sort     # keep original trial order, show running best

Saves PNG files alongside the results JSON.

Dataset format

tunic auto-detects the dataset format:

• ImageFolder - standard split/class/image.ext layout
• WebDataset - sharded TAR files; detected when wds/dataset_info.json exists

Scaling

Concurrent trials = total GPUs: --num-nodes 4 --accelerator T4:4 --> 16 concurrent trials.

Optuna's TPE needs ~20 trials before it outperforms random search. 32–64 trials is a practical range for most problems.

Output format

{
  "model": "resnet18",
  "best_val_auroc": 0.963,
  "best_val_acc": 0.891,
  "best_params": {
    "optimizer": "AdamW",
    "lr": 0.0028,
    "weight_decay": 3.6e-06,
    "label_smoothing": 0.058,
    "drop_rate": 0.183
  },
  "n_trials": 48,
  "completed_trials": 48,
  "epochs": 50,
  "all_trials": [...]
}