Enables (or disables) and configures autologging from DSPy to MLflow. Currently, the MLflow DSPy flavor only supports autologging for tracing.
log_traces â If True, traces are logged for DSPy models by using. If False, no traces are collected during inference. Default to True.
log_traces_from_compile â If True, traces are logged when compiling (optimizing) DSPy programs. If False, traces are only logged from normal model inference and disabled when compiling. Default to False.
log_traces_from_eval â If True, traces are logged for DSPy models when running DSPyâs built-in evaluator. If False, traces are only logged from normal model inference and disabled when running the evaluator. Default to True.
log_compiles â If True, information about the optimization process is logged when Teleprompter.compile() is called.
log_evals â If True, information about the evaluation call is logged when Evaluate.__call__() is called.
disable â If True, disables the DSPy autologging integration. If False, enables the DSPy autologging integration.
silent â If True, suppress all event logs and warnings from MLflow during DSPy autologging. If False, show all events and warnings.
Load a Dspy model from a run.
This function will also set the global dspy settings dspy.settings by the saved settings.
model_uri â
The location, in URI format, of the MLflow model. For example:
/Users/me/path/to/local/model
relative/path/to/local/model
s3://my_bucket/path/to/model
runs:/<mlflow_run_id>/run-relative/path/to/model
mlflow-artifacts:/path/to/model
For more information about supported URI schemes, see Referencing Artifacts.
dst_path â The local filesystem path to utilize for downloading the model artifact. This directory must already exist if provided. If unspecified, a local output path will be created.
An dspy.module instance, representing the dspy model.
Log a Dspy model along with metadata to MLflow.
This method saves a Dspy model along with metadata such as model signature and conda environments to MLflow.
dspy_model â an instance of dspy.Module. The Dspy model to be saved.
artifact_path â Deprecated. Use name instead.
task â defaults to None. The task type of the model. Can only be llm/v1/chat or None for now.
model_config â keyword arguments to be passed to the Dspy Module at instantiation.
code_paths â
A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.
For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
conda_env â
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements. pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
{
"name": "mlflow-env",
"channels": ["conda-forge"],
"dependencies": [
"python=3.8.15",
{
"pip": [
"dspy==x.y.z"
],
},
],
}
signature â
an instance of the ModelSignature class that describes the modelâs inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
from mlflow.models import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
input_example â one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.
registered_model_name â defaults to None. If set, create a model version under registered_model_name, also create a registered model if one with the given name does not exist.
await_registration_for â defaults to mlflow.tracking._model_registry.DEFAULT_AWAIT_MAX_SLEEP_SECONDS. Number of seconds to wait for the model version to finish being created and is in READY status. By default, the function waits for five minutes. Specify 0 or None to skip waiting.
pip_requirements â Either an iterable of pip requirement strings (e.g. ["dspy", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the modelâs conda environment (conda.yaml) file.
extra_pip_requirements â
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the userâs current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the modelâs conda environment (conda.yaml) file.
Warning
The following arguments canât be specified at the same time:
conda_env
pip_requirements
extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata â Custom metadata dictionary passed to the model and stored in the MLmodel file.
resources â A list of model resources or a resources.yaml file containing a list of resources required to serve the model.
prompts â
A list of prompt URIs registered in the MLflow Prompt Registry, to be associated with the model. Each prompt URI should be in the form prompt:/<name>/<version>. The prompts should be registered in the MLflow Prompt Registry before being associated with the model.
This will create a mutual link between the model and the prompt. The associated prompts can be seen in the modelâs metadata stored in the MLmodel file. From the Prompt Registry UI, you can navigate to the model as well.
import mlflow
prompt_template = "Hi, {name}! How are you doing today?"
# Register a prompt in the MLflow Prompt Registry
mlflow.prompts.register_prompt("my_prompt", prompt_template, description="A simple prompt")
# Log a model with the registered prompt
with mlflow.start_run():
model_info = mlflow.pyfunc.log_model(
name=MyModel(),
name="model",
prompts=["prompt:/my_prompt/1"]
)
print(model_info.prompts)
# Output: ['prompt:/my_prompt/1']
# Load the prompt
prompt = mlflow.genai.load_prompt(model_info.prompts[0])
name â Model name.
params â A dictionary of parameters to log with the model.
tags â A dictionary of tags to log with the model.
model_type â The type of the model.
step â The step at which to log the model outputs and metrics
model_id â The ID of the model.
import dspy
import mlflow
from mlflow.models import ModelSignature
from mlflow.types.schema import ColSpec, Schema
# Set up the LM.
lm = dspy.LM(model="openai/gpt-4o-mini", max_tokens=250)
dspy.settings.configure(lm=lm)
class CoT(dspy.Module):
def __init__(self):
super().__init__()
self.prog = dspy.ChainOfThought("question -> answer")
def forward(self, question):
return self.prog(question=question)
dspy_model = CoT()
mlflow.set_tracking_uri("http://127.0.0.1:5000")
mlflow.set_experiment("test-dspy-logging")
from mlflow.dspy import log_model
input_schema = Schema([ColSpec("string")])
output_schema = Schema([ColSpec("string")])
signature = ModelSignature(inputs=input_schema, outputs=output_schema)
with mlflow.start_run():
log_model(
dspy_model,
"model",
input_example="what is 2 + 2?",
signature=signature,
)
Save a Dspy model.
This method saves a Dspy model along with metadata such as model signature and conda environments to local file system. This method is called inside mlflow.dspy.log_model().
model â an instance of dspy.Module. The Dspy model/module to be saved.
path â local path where the MLflow model is to be saved.
task â defaults to None. The task type of the model. Can only be llm/v1/chat or None for now.
model_config â keyword arguments to be passed to the Dspy Module at instantiation.
code_paths â
A list of local filesystem paths to Python file dependencies (or directories containing file dependencies). These files are prepended to the system path when the model is loaded. Files declared as dependencies for a given model should have relative imports declared from a common root path if multiple files are defined with import dependencies between them to avoid import errors when loading the model.
For a detailed explanation of code_paths functionality, recommended usage patterns and limitations, see the code_paths usage guide.
mlflow_model â an instance of mlflow.models.Model, defaults to None. MLflow model configuration to which to add the Dspy model metadata. If None, a blank instance will be created.
conda_env â
Either a dictionary representation of a Conda environment or the path to a conda environment yaml file. If provided, this describes the environment this model should be run in. At a minimum, it should specify the dependencies contained in get_default_conda_env(). If None, a conda environment with pip requirements inferred by mlflow.models.infer_pip_requirements() is added to the model. If the requirement inference fails, it falls back to using get_default_pip_requirements. pip requirements from conda_env are written to a pip requirements.txt file and the full conda environment is written to conda.yaml. The following is an example dictionary representation of a conda environment:
{
"name": "mlflow-env",
"channels": ["conda-forge"],
"dependencies": [
"python=3.8.15",
{
"pip": [
"dspy==x.y.z"
],
},
],
}
signature â
an instance of the ModelSignature class that describes the modelâs inputs and outputs. If not specified but an input_example is supplied, a signature will be automatically inferred based on the supplied input example and model. To disable automatic signature inference when providing an input example, set signature to False. To manually infer a model signature, call infer_signature() on datasets with valid model inputs, such as a training dataset with the target column omitted, and valid model outputs, like model predictions made on the training dataset, for example:
from mlflow.models import infer_signature
train = df.drop_column("target_label")
predictions = ... # compute model predictions
signature = infer_signature(train, predictions)
input_example â one or several instances of valid model input. The input example is used as a hint of what data to feed the model. It will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format, or a numpy array where the example will be serialized to json by converting it to a list. Bytes are base64-encoded. When the signature parameter is None, the input example is used to infer a model signature.
pip_requirements â Either an iterable of pip requirement strings (e.g. ["dspy", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes the environment this model should be run in. If None, a default list of requirements is inferred by mlflow.models.infer_pip_requirements() from the current software environment. If the requirement inference fails, it falls back to using get_default_pip_requirements. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the modelâs conda environment (conda.yaml) file.
extra_pip_requirements â
Either an iterable of pip requirement strings (e.g. ["pandas", "-r requirements.txt", "-c constraints.txt"]) or the string path to a pip requirements file on the local filesystem (e.g. "requirements.txt"). If provided, this describes additional pip requirements that are appended to a default set of pip requirements generated automatically based on the userâs current software environment. Both requirements and constraints are automatically parsed and written to requirements.txt and constraints.txt files, respectively, and stored as part of the model. Requirements are also written to the pip section of the modelâs conda environment (conda.yaml) file.
Warning
The following arguments canât be specified at the same time:
conda_env
pip_requirements
extra_pip_requirements
This example demonstrates how to specify pip requirements using pip_requirements and extra_pip_requirements.
metadata â Custom metadata dictionary passed to the model and stored in the MLmodel file.
resources â A list of model resources or a resources.yaml file containing a list of resources required to serve the model.
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