Configuration Options

from lightly.api import ApiWorkflowClient

# Create the Lightly client to connect to the API.
client = ApiWorkflowClient(token="MY_LIGHTLY_TOKEN", dataset_id="MY_DATASET_ID")

client.schedule_compute_worker_run(
    worker_config={
        # Enable training of the self-supervised model.
        "enable_training": False,
      
      	"training": {
          # Prediction task name. For example "lightly_pretagging" or "my_predictions".
          # Set to train the embedding model on object crops. Must be an object detection or
          # keypoint detection task.
        	"task_name": "",
        },

        # Provide a checkpoint for the self-supervised model.
        "checkpoint": "https://my-checkpoint-read-url",

        # Enable pretagging. This detects objects in the input images/videos and makes
        # them available for selection. See https://docs.lightly.ai/docs/lightly-pretagging for details.
        "pretagging": False,

        # Path to a file containing filenames to run the Lightly Worker on a subset of the
        # files in the input datasource. See https://docs.lightly.ai/docs/relevant-filenames
        # on how to specify relevant filenames.
        "relevant_filenames_file": "",

        # Sequence length for sequence selection on videos.
        # See https://docs.lightly.ai/docs/sequence-selection for details.
        "selected_sequence_length": 1,

        # Datasource settings. See https://docs.lightly.ai/docs/input-files-and-folder-struture
        # for details on how to configure a datasource.
        "datasource": {
            # If False then only new samples in the datasource are processed, that were 
            # not yet processed by an earlier Lightly Worker run. Set to True to reprocess
            # all samples in the datasource.
            "process_all": False,

            # Set to False to disable uploading the selected samples to the Lightly
            # Platform. This will keep your dataset unchanged and can be useful for
            # dry-runs of the Lightly Worker.
            "enable_datapool_update": True,

            # Bypass the verification of read/write access to the datasource.
            "bypass_verify": False,
        },

        # Image format for selected video frames that are uploaded to the bucket.
        "output_image_format": "png",
      
        # Number of data loading processes. If -1, then one process per CPU core
        # is created. Set to 0 to load data in the main process. Set to low number
        # to reduce memory usage at cost of slower processing.
        "num_processes": -1,
      
        # Number of data loading threads. If -1, then two threads per CPU core
        # are created. Is always at least one.
        "num_threads": -1,

        # Path to a file containing custom embeddings for the images in your input datasource.
        # The file must be stored in the .lightly/embeddings/ directory in your Lightly
        # datasource. The path in the config must be relative to the .lightly/embeddings
        # directory. See https://docs.lightly.ai/docs/custom-embeddings for details.
        "embeddings": "",
    },

    # Selection settings. See https://docs.lightly.ai/docs/selection for details.
    selection_config={
        # Absolute number of samples to select. When using a datapool, n_samples additional
      	# samples will be added to the datapool independent of the datapool size.
        "n_samples": None,

        # Number of samples to select relative to the number of input samples. If set to
        # 0.1 then 10% of the input samples are selected. When using a datapool, proportion_samples
        # of the new input samples will be added to the datapool independent of the datapool size.
        "proportion_samples": None,

        # List of selection strategy configurations.
        "strategies": [
            {
                # See https://docs.lightly.ai/docs/selection#selection-input on how to
                # set the input configuration.
                "input": {
                    # Input type. For example "EMBEDDINGS".
                    "type": None,

                    # Prediction task name. For example "lightly_pretagging" or "my_predictions".
                    # Only used if input type is "EMBEDDINGS", "PREDICTIONS" or "SCORES".
                    "task": None,

                    # Active learning score name. For example "uncertainty_entropy".
                    # Only used if input type is "SCORES".
                    "score": None,

                    # Metadata key. For example "lightly.sharpness" or "weather.temperature".
                    # Only used if input type is "METADATA".
                    "key": None,

                    # Must be set to "CLASS_DISTRIBUTION" if input type is "PREDICTIONS".
                    # Otherwise unused.
                    "name": None,

                    # Dataset id from which similarity search query embeddings are loaded.
                    # Only used if input type is "EMBEDDINGS".
                    "dataset_id": None,

                    # Tag name from which similarity search query embeddings are loaded.
                    # Only used if input type is "EMBEDDINGS".
                    "tag_name": None,
                },

                # See https://docs.lightly.ai/docs/selection#selection-strategy on how
                # to set the strategy configuration.
                "strategy": {
                    # Strategy type. For example "DIVERSITY".
                    "type": None,

                    # Minimum distance between chosen samples. For example 0.1. 
                    # Only used if strategy type is "DIVERSITY". Value should be between
                    # 0 and 2. Increasing the distance results in fewer selected samples.
                    "stopping_condition_minimum_distance": None,

                    # Selection threshold. For example 20. Only used if strategy type is
                    # "THRESHOLD".
                    "threshold": None,

                    # Threshold operation. For example "BIGGER_EQUAL". Only used if
                    # strategy type is "THRESHOLD".
                    "operation": None,

                    # Balancing target. Must be dictionary from target name to target
                    # ratio. For example {"Amulance": 0.4, "Bus": 0.6}. Only used if
                    # strategy type is "BALANCE".
                    "target": None,
                  	
                  	# Strength of this strategy relative to other strategies. Value must
                  	# be in [-1e9, 1e9].
                  	"strength": 1.0,
                },
            },
        ],
    },
    lightly_config={
        # Dataloader Settings.
        "loader": {
            # The number of processes and number of threads to use for data loading.
            # This is deprecated, please use "num_processes" and "num_threads" instead.
            "num_workers": -1,

            # Batch size used by the Lightly Worker. Reduce to lower memory usage.
            # We recommend to not reduce the batch size if training is enabled.
            "batch_size": 16,

            # Whether to reshuffle data after each epoch.
            "shuffle": True,
        },

        # Trainer Settings.
        "trainer": {
            # Number of GPUs to use for training. Set to 0 to use CPU instead.
            # Using more than one GPU is not yet supported.
            "gpus": 1,

            # Number of training epochs.
            "max_epochs": 100,

            # Floating point precision. Set to 16 for faster processing with half-precision.
            "precision": 32,
        },

        # Model Settings.
        "model": {
            # Name of the model, currently supports popular variants:
            # resnet-18, resnet-34, resnet-50, resnet-101, resnet-152.
            "name": 'resnet-18',

            # Dimensionality of output on which self-supervised loss is calculated.
            "out_dim": 128,

            # Dimensionality of feature vectors (embedding size).
            "num_ftrs": 32,

            # Width of the resnet.
            "width": 1,
        },

        # Training Loss Settings.
        "criterion": {
            # Temperature by which logits are divided in self-supervised loss.
            "temperature": 0.5,
        },

        # Training Optimizser Settings.
        "optimizer": {
            # Learning rate of the optimizer.
            "lr": "1.",

            # L2 penalty.
            "weight_decay": 0.00001,
        },

        # Training Augmentation Settings.
        "collate": {
            # Size of the input images in pixels.
            "input_size": 64,

            # Probability that color jitter is applied.           
            "cj_prob": 0.8,

            # How much to jitter brightness.
            "cj_bright": 0.7,

            # How much to jitter contrast.
            "cj_contrast": 0.7,

            # How much to jitter saturation.
            "cj_sat": 0.7,

            # How much to jitter hue.
            "cj_hue": 0.2,

            # Minimum size of random crop relative to input_size.
            "min_scale": 0.15,

            # Probability that image is converted to grayscale.
            "random_gray_scale": 0.2,

            # Probability that gaussian blur is applied.
            "gaussian_blur": 0.5,

            # Kernel size of gaussian blur relative to input_size.
            "kernel_size": 0.1,

            # Probability that vertical flip is applied.
            "vf_prob": 0.0,

            # Probability that horizontal flip is applied.
            "hf_prob": 0.5,

            # Probability that random rotation is applied.
            "rr_prob": 0.0,

            # Range of degrees to select from for random rotation.
            # If rr_degrees is None, images are rotated by 90 degrees.
            # If rr_degrees is a [min, max] list, images are rotated
            # by a random angle in [min, max]. All rotations are counter-clockwise.
            "rr_degrees": None,
        },

        "checkpoint_callback": {
            # If True, the checkpoint from the last epoch is saved.
            "save_last": True,
        },

        # Random Seed.
        "seed": 1,
    }
)

docker run --shm-size="1024m" --gpus all --rm -it \
	-e LIGHTLY_TOKEN={MY_LIGHTLY_TOKEN} \
	-e LIGHTLY_WORKER_ID={MY_WORKER_ID} \
	lightly/worker:latest \
	worker.force_start=True \
	sanity_check=False