dspy-production-deployment

# DSPy Production Deployment ## Goal Prepare a DSPy program for repeatable, observable, scalable, and safer production execution. ## Cache Hardening DSPy enables memory and disk caches by default. Disk cache deserialization uses pickle unless restricted. Enable the allowlist mode in production: ```python import dspy dspy.configure_cache(restrict_pickle=True) ``` Register trusted custom cache types only when needed: ```python dspy.configure_cache( restrict_pickle=True, safe_types=[MyResult, Metadata], ) ``` Disable a cache layer explicitly when a deployment cannot persist data or requires fresh model responses: ```python dspy.configure_cache( enable_disk_cache=False, enable_memory_cache=True, ) ``` ## Save and Load Prefer state-only JSON for readable, safer artifacts: ```python compiled.save("./artifacts/program.json", save_program=False) loaded = MyProgram() loaded.load("./artifacts/program.json") ``` Use whole-program save only for trusted artifacts. It uses cloudpickle: ```python compiled.save("./artifacts/program/", save_program=True) loaded = dspy.load("./artifacts/program/") ``` Keep the DSPy major version compatible when loading saved programs. ## Usage Tracking ```python dspy.configure( lm=dspy.LM("openai/gpt-4o-mini"), track_usage=True, ) prediction = program(question="What is DSPy?") print(prediction.get_lm_usage()) ``` Cached calls return no new token usage. ## Async Execution Most built-in modules support `acall()`: `...