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_dir="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_dir="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.

If you are using MegadetectorLite to select frames (which is the default for the official models we ship with), you can also decrease the size of the frame used at this stage by setting frame_selection_height and frame_selection_width.

YAML file

video_loader_config:
    frame_selection_height: 400  # if using megadetectorlite
    frame_selection_width: 600  # if using megadetectorlite
    model_input_height: 100
    model_input_width: 100
    total_frames: 16 # total_frames is always required

Python

video_loader_config = VideoLoaderConfig(
    frame_selection_height=400, frame_selection_width=600,  # if using megadetectorlite
    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. We recommend trying 1 if 3 is too many.

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_dir="example_vids/", num_workers=1
)

Logging

To check that videos are getting loaded and cached as expected, set your environment variabe LOG_LEVEL to debug. The default log level is info. For example:

$ LOG_LEVEL=debug zamba predict --data-dir example_vids/