remove requires_permission_to_run (#8552)

Author: TomeHirata

docs/docs/api/optimizers/MIPROv2.md

@@ -47,7 +47,6 @@ gsm8k = GSM8K()
 optimized_program = teleprompter.compile(
     dspy.ChainOfThought("question -> answer"),
     trainset=gsm8k.train,
-    requires_permission_to_run=False,
 )
 
 # Save optimize program for future use

docs/docs/cheatsheet.md

@@ -307,7 +307,6 @@ optimized_program = teleprompter.compile(
     trainset=trainset,
     max_bootstrapped_demos=3,
     max_labeled_demos=4,
-    requires_permission_to_run=False,
 )
 
 # Save optimize program for future use
@@ -337,7 +336,6 @@ optimized_program = teleprompter.compile(
     trainset=trainset,
     max_bootstrapped_demos=0,
     max_labeled_demos=0,
-    requires_permission_to_run=False,
 )
 
 # Save optimize program for future use

docs/docs/tutorials/entity_extraction/index.ipynb

@@ -567,7 +567,6 @@
         "    people_extractor,\n",
         "    trainset=train_set,\n",
         "    max_bootstrapped_demos=4,\n",
-        "    requires_permission_to_run=False,\n",
         "    minibatch=False\n",
         ")"
       ]

docs/docs/tutorials/games/index.ipynb

@@ -572,7 +572,7 @@
     "optimizer = dspy.MIPROv2(metric=metric, auto=\"light\", num_threads=16, prompt_model=gpt4o)\n",
     "\n",
     "config = dict(max_bootstrapped_demos=1, max_labeled_demos=0, minibatch_size=40)\n",
-    "optimized_4o = optimizer.compile(agent_4o, trainset=trainset, **config, requires_permission_to_run=False)"
+    "optimized_4o = optimizer.compile(agent_4o, trainset=trainset, **config)"
    ]
   },
   {

docs/docs/tutorials/math/index.ipynb

@@ -414,7 +414,7 @@
     "kwargs = dict(num_threads=THREADS, teacher_settings=dict(lm=gpt4o), prompt_model=gpt4o_mini)\n",
     "optimizer = dspy.MIPROv2(metric=dataset.metric, auto=\"medium\", **kwargs)\n",
     "\n",
-    "kwargs = dict(requires_permission_to_run=False, max_bootstrapped_demos=4, max_labeled_demos=4)\n",
+    "kwargs = dict(max_bootstrapped_demos=4, max_labeled_demos=4)\n",
     "optimized_module = optimizer.compile(module, trainset=dataset.train, **kwargs)"
    ]
   },

docs/docs/tutorials/multihop_search/index.ipynb

@@ -574,7 +574,7 @@
     "models = dict(prompt_model=gpt4o, teacher_settings=dict(lm=gpt4o))\n",
     "tp = dspy.MIPROv2(metric=top5_recall, auto=\"medium\", num_threads=16, **models)\n",
     "\n",
-    "kwargs = dict(minibatch_size=40, minibatch_full_eval_steps=4, requires_permission_to_run=False)\n",
+    "kwargs = dict(minibatch_size=40, minibatch_full_eval_steps=4)\n",
     "optimized = tp.compile(Hop(), trainset=trainset, max_bootstrapped_demos=4, max_labeled_demos=4, **kwargs)"
    ]
   },

docs/docs/tutorials/rag/index.ipynb

@@ -1143,8 +1143,7 @@
     "tp = dspy.MIPROv2(metric=metric, auto=\"medium\", num_threads=24)  # use fewer threads if your rate limit is small\n",
     "\n",
     "optimized_rag = tp.compile(RAG(), trainset=trainset,\n",
-    "                           max_bootstrapped_demos=2, max_labeled_demos=2,\n",
-    "                           requires_permission_to_run=False)"
+    "                           max_bootstrapped_demos=2, max_labeled_demos=2)"
    ]
   },
   {

dspy/teleprompt/mipro_optimizer_v2.py

@@ -1,8 +1,5 @@
 import logging
 import random
-import sys
-import textwrap
-import time
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Callable, Literal
 
@@ -112,7 +109,7 @@ def compile(
         view_data_batch_size: int = 10,
         tip_aware_proposer: bool = True,
         fewshot_aware_proposer: bool = True,
-        requires_permission_to_run: bool = True,
+        requires_permission_to_run: bool = True, # deprecated
         provide_traceback: bool | None = None,
     ) -> Any:
         effective_max_errors = (
@@ -162,20 +159,6 @@ def compile(
         if minibatch and minibatch_size > len(valset):
             raise ValueError(f"Minibatch size cannot exceed the size of the valset. Valset size: {len(valset)}.")
 
-        # Estimate LM calls and get user confirmation
-        if requires_permission_to_run:
-            if not self._get_user_confirmation(
-                student,
-                num_trials,
-                minibatch,
-                minibatch_size,
-                minibatch_full_eval_steps,
-                valset,
-                program_aware_proposer,
-            ):
-                logger.info("Compilation aborted by the user.")
-                return student  # Return the original student program
-
         # Initialize program and evaluator
         program = student.deepcopy()
         evaluate = Evaluate(
@@ -336,80 +319,6 @@ def _estimate_lm_calls(
 
         return prompt_model_line, task_model_line
 
-    def _get_user_confirmation(
-        self,
-        program: Any,
-        num_trials: int,
-        minibatch: bool,
-        minibatch_size: int,
-        minibatch_full_eval_steps: int,
-        valset: list,
-        program_aware_proposer: bool,
-    ) -> bool:
-        prompt_model_line, task_model_line = self._estimate_lm_calls(
-            program,
-            num_trials,
-            minibatch,
-            minibatch_size,
-            minibatch_full_eval_steps,
-            valset,
-            program_aware_proposer,
-        )
-
-        user_message = textwrap.dedent(
-            f"""\
-            {YELLOW}{BOLD}Projected Language Model (LM) Calls{ENDC}
-
-            Based on the parameters you have set, the maximum number of LM calls is projected as follows:
-
-            {prompt_model_line}
-            {task_model_line}
-
-            {YELLOW}{BOLD}Estimated Cost Calculation:{ENDC}
-
-            {YELLOW}Total Cost = (Number of calls to task model * (Avg Input Token Length per Call * Task Model Price per Input Token + Avg Output Token Length per Call * Task Model Price per Output Token)
-                        + (Number of program calls * (Avg Input Token Length per Call * Task Prompt Price per Input Token + Avg Output Token Length per Call * Prompt Model Price per Output Token).{ENDC}
-
-            For a preliminary estimate of potential costs, we recommend you perform your own calculations based on the task
-            and prompt models you intend to use. If the projected costs exceed your budget or expectations, you may consider:
-
-            {YELLOW}- Reducing the number of trials (`num_trials`), the size of the valset, or the number of LM calls in your program.{ENDC}
-            {YELLOW}- Using a cheaper task model to optimize the prompt.{ENDC}
-            {YELLOW}- Setting `minibatch=True` if you haven't already.{ENDC}\n"""
-        )
-
-        user_confirmation_message = textwrap.dedent(
-            f"""\
-            To proceed with the execution of this program, please confirm by typing {BLUE}'y'{ENDC} for yes or {BLUE}'n'{ENDC} for no.
-            If no input is received within 20 seconds, the program will proceed automatically.
-
-            If you would like to bypass this confirmation step in future executions, set the {YELLOW}`requires_permission_to_run`{ENDC} flag to {YELLOW}`False`{ENDC} when calling compile.
-
-            {YELLOW}Awaiting your input...{ENDC}
-        """
-        )
-
-        print(f"{user_message}\n{user_confirmation_message}\nDo you wish to continue? (y/n): ", end="", flush=True)
-
-        # Wait for input with timeout
-        start_time = time.time()
-        while time.time() - start_time < 20:
-            if sys.platform == "win32":
-                import msvcrt
-                if msvcrt.kbhit():
-                    user_input = msvcrt.getch().decode("utf-8").strip().lower()
-                    print(user_input)  # Echo the input
-                    return user_input == "y"
-            else:
-                import select
-                if select.select([sys.stdin], [], [], 0.1)[0]:
-                    user_input = sys.stdin.readline().strip().lower()
-                    return user_input == "y"
-            time.sleep(0.1)
-
-        print("\nNo input received within 20 seconds. Proceeding with execution...")
-        return True
-
     def _bootstrap_fewshot_examples(self, program: Any, trainset: list, seed: int, teacher: Any) -> list | None:
         logger.info("\n==> STEP 1: BOOTSTRAP FEWSHOT EXAMPLES <==")
         if self.max_bootstrapped_demos > 0: