sample_settings.yaml

project:
  debug: true
  json_serializer: default # or complex. If "complex", FlowCept will deal with complex python dicts that may contain JSON unserializable values
  replace_non_json_serializable: true
  performance_logging: false
  register_workflow: true
  enrich_messages: true
  db_flush_mode: online   # or offline

log:
  log_path: "default"
  log_file_level: error # use 'disable' to disable logs
  log_stream_level: error

telemetry_capture:
  gpu: ~ # ~ means None. This is a list that should specify which metrics of the GPU that should be monitored
  cpu: true
  per_cpu: true
  process_info: true
  mem: true
  disk: true
  network: true
  machine_info: true

instrumentation:
  enabled: true
  torch:
    what: parent_and_children # parent_only, parent_and_children, ~
    children_mode: lightweight   # lightweight, tensor_inspection, telemetry, telemetry_and_tensor_inspection
    save_models: True

experiment:
  user: root
  campaign_id: super_campaign

mq:
  type: redis  # or kafka; Please adjust the port (kafka's default is 9092; redis is 6379)
  host: localhost
  # instances: ["localhost:6379"] # We can have multiple redis instances being accessed by the consumers but each interceptor will currently access one single redis.
  port: 6379
  channel: interception
  buffer_size: 50
  insertion_buffer_time_secs: 5
  chunk_size: 10  # use can use 0 or -1 to disable this. Or simply omit this from the config file.

kv_db:
  host: localhost
  port: 6379

web_server:
  host: 0.0.0.0
  port: 5000

sys_metadata:
  environment_id: "laptop"

extra_metadata:
  place_holder: ""

analytics:
  sort_orders:
    generated.loss: minimum_first
    generated.accuracy: maximum_first

buffer:
  adaptive_buffer_size: true
  insertion_buffer_time_secs: 5
  max_buffer_size: 50
  min_buffer_size: 10
  remove_empty_fields: false

databases:

  lmdb:
    enabled: true
    path: flowcept_lmdb

  mongodb:
    enabled: true
    host: localhost
    port: 27017
    db: flowcept
    create_collection_index: true

adapters:
  # For each key below, you can have multiple instances. Like mlflow1, mlflow2; zambeze1, zambeze2. Use an empty dict, {}, if you won't use any adapter.
  zambeze:
    kind: zambeze
    host: localhost
    port: 5672
    queue_names:
      - hello
      - hello2
#    key_values_to_filter:
#      - key: activity_status
#        value: CREATED

  mlflow:
    kind: mlflow
    file_path: mlflow.db
    log_params: ['*']
    log_metrics: ['*']
    watch_interval_sec: 2

  tensorboard:
    kind: tensorboard
    file_path: tensorboard_events
    log_tags: ['scalars', 'hparams', 'tensors']
    log_metrics: ['accuracy']
    watch_interval_sec: 5

  dask:
    kind: dask
    worker_should_get_input: true
    scheduler_should_get_input: true
    worker_should_get_output: true
    scheduler_create_timestamps: true
    worker_create_timestamps: false