Debugging

Before kicking off a full run of inference or model training, we recommend testing your code with a "dry run". If you are generating predictions, this will run one batch of inference to quickly detect any bugs. If you are trainig a model, this will run one training and validation batch for one epoch. If the dry run completes successfully, predict and train away!

CLI

$ zamba predict --data-dir example_vids/ --dry-run

$ zamba train --data-dir example_vids/ --labels example_labels.csv --dry-run

Python

In Python, add dry_run=True to PredictConfig or TrainConfig:

predict_config = PredictConfig(
    data_directory="example_vids/", dry_run=True
)

GPU memory errors

The dry run will also catch any GPU memory errors. If you hit a GPU memory error, there are a couple fixes.

Reducing the batch size

CLI

zamba train --data-dir example_vids/ --labels example_labels.csv --batch-size 1

Python

In Python, add batch_size to PredictConfig or TrainConfig:

predict_config = PredictConfig(
    data_directory="example_vids/", batch_size=1
)

Decreasing video size

Resize video frames to be smaller before they are passed to the model. The default for all three models is 240x426 pixels. model_input_height and model_input_width cannot be passed directly to the command line, so if you are using the CLI these must be specified in a YAML file.

YAML file

video_loader_config:
    model_input_height: 100
    model_input_width: 100
    total_frames: 16 # total_frames is always required

Python

video_loader_config = VideoLoaderConfig(
    model_input_height=100, model_input_width=100, total_frames=16
) # total_frames is always required

Reducing num_workers

Reduce the number of workers (subprocesses) used for data loading. By default num_workers will be set to 3. The minimum value is 0, which means that the data will be loaded in the main process, and the maximum is one less than the number of CPUs in the system.

CLI

$ zamba predict --data-dir example_vids/ --num-workers 1

$ zamba train --data-dir example_vids/ --labels example_labels.csv --num-workers 1

Python

In Python, add num_workers to PredictConfig or TrainConfig:

predict_config = PredictConfig(
    data_directory="example_vids/", num_workers=1
)