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Note

This article refers to BaseModel accessed via Docker container. Please refer to Snowflake Native App section if you are using BaseModel as SF GUI application.

Controlling the model size

Parameters in memory_constraining_params block determine the size of the model:

• explicitly by setting model architecture,
• implicitly by steering the size of the input.

Depending on your infrastructure and data, you may want to reduce the model size or, conversely, increase it.

• hidden_dim : int
  default: NUM_LAYERS_DEFAULT
  The size of the hidden layers. NUM_LAYERS_DEFAULT constant sets it to 2048.
  
  
• num_layers : int
  default: NUM_LAYERS_DEFAULT
  The number of hidden layers. NUM_LAYERS_DEFAULT constant sets it to 4.
  
  
• emde_quality : float
  default: 1.0
  The quality of the features' density estimation. The lower the quality, the smaller the sketches and therefore the input to the model.
  
  

hidden_dim: 1024
emde_quality: 0.8

Optimizing query

The settings here control the degree of parallelization.

• num_query_chunks : int, optional
  default: 1
  This parameter represents the number of segments a query should be divided into. Splitting the query into smaller pieces can help reduce memory consumption on the database end, which is particularly useful for queries that require significant memory resources.
  
  
• num_cpus : int, optional
  default: 0
  The number of CPUs used for fit phase. If left unchanged at 0, all available CPUs will be used.
  
  
• sampling_params : SamplingParams, optional
  default: SamplingParams
  Sampling parameters used to change the size of the sample obtained from the dataset. Sample will be used to train proper model features. Changing default values is not recommended. Keyword arguments are:
  • num_entities : int, optional
    Maximal number of entities to sample. If not provided, optimal number of samples will be calculated automatically.
  • history_limit: int, optional
    Maximak number of events per entity to sample. If not provided, optimal number of samples will be calculated automatically.

query_optimization:
  num_query_chunks: 4
  num_cpus: 10