@spec _base_init(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module._base_init.

Returns

• term()

@spec _set_lm_usage(
  SnakeBridge.Ref.t(),
  %{optional(String.t()) => term()},
  term(),
  keyword()
) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module._set_lm_usage.

Parameters

• tokens (%{optional(String.t()) => term()})
• output (term())

Returns

• term()

@spec acall(SnakeBridge.Ref.t(), [term()], keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.acall.

Parameters

• args (term())
• kwargs (term())

Returns

• term()

@spec batch(SnakeBridge.Ref.t(), [term()], [term()], keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Processes a list of dspy.Example instances in parallel using the Parallel module.

Parameters

• examples - List of dspy.Example instances to process.
• num_threads - Number of threads to use for parallel processing.
• max_errors - Maximum number of errors allowed before stopping execution. If None, inherits from dspy.settings.max_errors.
• return_failed_examples - Whether to return failed examples and exceptions.
• provide_traceback - Whether to include traceback information in error logs.
• disable_progress_bar - Whether to display the progress bar.
• timeout - Seconds before a straggler task is resubmitted. Set to 0 to disable.
• straggler_limit - Only check for stragglers when this many or fewer tasks remain.

Returns

• term()

@spec deepcopy(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Deep copy the module.

This is a tweak to the default python deepcopy that only deep copies self.parameters(), and for other attributes, we just do the shallow copy.

Returns

• term()

@spec dump_state(SnakeBridge.Ref.t(), [term()], keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.dump_state.

Parameters

• json_mode (term() default: True)

Returns

• term()

@spec get_lm(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.get_lm.

Returns

• term()

@spec inspect_history(SnakeBridge.Ref.t(), [term()], keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.inspect_history.

Parameters

• n (integer() default: 1)

Returns

• term()

@spec load(SnakeBridge.Ref.t(), term(), [term()], keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Load the saved module. You may also want to check out dspy.load, if you want to

load an entire program, not just the state for an existing program.

Parameters

• path - Path to the saved state file, which should be a .json or a .pkl file (type: String.t())
• allow_pickle - If True, allow loading .pkl files, which can run arbitrary code. This is dangerous and should only be used if you are sure about the source of the file and in a trusted environment. (type: boolean())

Returns

• term()

@spec load_state(SnakeBridge.Ref.t(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.load_state.

Parameters

• state (term())

Returns

• term()

@spec map_named_predictors(SnakeBridge.Ref.t(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Applies a function to all named predictors.

Parameters

• func (term())

Returns

• term()

@spec named_parameters(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Unlike PyTorch, handles (non-recursive) lists of parameters too.

Returns

• term()

@spec named_predictors(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.named_predictors.

Returns

• term()

@spec named_sub_modules(SnakeBridge.Ref.t(), [term()], keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Find all sub-modules in the module, as well as their names.

Say self.children[4]['key'].sub_module is a sub-module. Then the name will be children[4]['key'].sub_module. But if the sub-module is accessible at different paths, only one of the paths will be returned.

Parameters

• type_ (term() default: None)
• skip_compiled (term() default: False)

Returns

• term()

@spec new(
  [term()],
  keyword()
) :: {:ok, SnakeBridge.Ref.t()} | {:error, Snakepit.Error.t()}

Initialize self. See help(type(self)) for accurate signature.

Parameters

• callbacks (term() default: None)

@spec parameters(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.parameters.

Returns

• term()

@spec predictors(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.predictors.

Returns

• term()

@spec reset_copy(
  SnakeBridge.Ref.t(),
  keyword()
) :: {:ok, term()} | {:error, Snakepit.Error.t()}

Deep copy the module and reset all parameters.

Returns

• term()

@spec save(SnakeBridge.Ref.t(), term(), [term()], keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Save the module.

Save the module to a directory or a file. There are two modes:

• save_program=False: Save only the state of the module to a json or pickle file, based on the value of the file extension.
• save_program=True: Save the whole module to a directory via cloudpickle, which contains both the state and architecture of the model.

If save_program=True and modules_to_serialize are provided, it will register those modules for serialization with cloudpickle's register_pickle_by_value. This causes cloudpickle to serialize the module by value rather than by reference, ensuring the module is fully preserved along with the saved program. This is useful when you have custom modules that need to be serialized alongside your program. If None, then no modules will be registered for serialization.

We also save the dependency versions, so that the loaded model can check if there is a version mismatch on critical dependencies or DSPy version.

Parameters

• path - Path to the saved state file, which should be a .json or .pkl file when save_program=False, and a directory when save_program=True. (type: String.t())
• save_program - If True, save the whole module to a directory via cloudpickle, otherwise only save the state. (type: boolean())
• modules_to_serialize - A list of modules to serialize with cloudpickle's register_pickle_by_value. If None, then no modules will be registered for serialization. (type: list())

Returns

• term()

@spec set_lm(SnakeBridge.Ref.t(), term(), keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}

Python method Module.set_lm.

Parameters

• lm (term())

Returns

• term()