mlflow.transformers

MLFlow module for HuggingFace/transformer support.

mlflow.transformers.autolog(log_input_examples=False, log_model_signatures=False, log_models=False, log_datasets=False, disable=False, exclusive=False, disable_for_unsupported_versions=False, silent=False, extra_tags=None)[source]: Note

Experimental: This function may change or be removed in a future release without warning.

Note

Autologging is known to be compatible with the following package versions: 4.25.1 <= transformers <= 4.33.1. Autologging may not succeed when used with package versions outside of this range.

This autologging integration is solely used for disabling spurious autologging of irrelevant sub-models that are created during the training and evaluation of transformers-based models. Autologging functionality is not implemented fully for the transformers flavor.

mlflow.transformers.generate_signature_output(pipeline, data, inference_config=None)[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Utility for generating the response output for the purposes of extracting an output signature for model saving and logging. This function simulates loading of a saved model or pipeline as a pyfunc model without having to incur a write to disk.

Parameters

pipeline – A transformers pipeline object. Note that component-level or model-level inputs are not permitted for extracting an output example.
data – An example input that is compatible with the given pipeline
inference_config – Any additional inference configuration, provided as kwargs, to inform the format of the output type from a pipeline inference call.

Returns

The output from the pyfunc pipeline wrapper’s predict method

mlflow.transformers.get_default_conda_env(model)[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Returns: The default Conda environment for MLflow Models produced with the transformers flavor, based on the model instance framework type of the model to be logged.

mlflow.transformers.get_default_pip_requirements(model) → List[str][source]

Note

Experimental: This function may change or be removed in a future release without warning.

Parameters: model – The model instance to be saved in order to provide the required underlying deep learning execution framework dependency requirements. Note that this must be the actual model instance and not a Pipeline.
Returns: A list of default pip requirements for MLflow Models that have been produced with the transformers flavor. Calls to save_model() and log_model() produce a pip environment that contain these requirements at a minimum.

mlflow.transformers.is_gpu_available()[source]

mlflow.transformers.load_model(model_uri: str, dst_path: str = None, return_type='pipeline', device=None, **kwargs)[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Note

The ‘transformers’ MLflow Models integration is known to be compatible with the following package version ranges: 4.25.1 - 4.33.1. MLflow Models integrations with transformers may not succeed when used with package versions outside of this range.

Load a transformers object from a local file or a run.

Parameters

model_uri –
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 – 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.
return_type –
A return type modifier for the stored transformers object. If set as “components”, the return type will be a dictionary of the saved individual components of either the Pipeline or the pre-trained model. The components for NLP-focused models will typically consist of a return representation as shown below with a text-classification example:
```
{"model": BertForSequenceClassification, "tokenizer": BertTokenizerFast}
```
Vision models will return an ImageProcessor instance of the appropriate type, while multi-modal models will return both a FeatureExtractor and a Tokenizer along with the model. Returning “components” can be useful for certain model types that do not have the desired pipeline return types for certain use cases. If set as “pipeline”, the model, along with any and all required Tokenizer, FeatureExtractor, Processor, or ImageProcessor objects will be returned within a Pipeline object of the appropriate type defined by the task set by the model instance type. To override this behavior, supply a valid task argument during model logging or saving. Default is “pipeline”.
device – The device on which to load the model. Default is None. Use 0 to load to the default GPU.
kwargs – Optional configuration options for loading of a transformers object. For information on parameters and their usage, see transformers documentation.

Returns

A transformers model instance or a dictionary of components

mlflow.transformers.log_model(transformers_model, artifact_path: str, processor=None, task: Optional[str] = None, model_card=None, inference_config: Optional[Dict[str, Any]] = None, code_paths: Optional[List[str]] = None, registered_model_name: str = None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes]] = None, await_registration_for=300, pip_requirements: Optional[Union[List[str], str]] = None, extra_pip_requirements: Optional[Union[List[str], str]] = None, conda_env=None, metadata: Dict[str, Any] = None, **kwargs)[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Note

The ‘transformers’ MLflow Models integration is known to be compatible with the following package version ranges: 4.25.1 - 4.33.1. MLflow Models integrations with transformers may not succeed when used with package versions outside of this range.

Log a transformers object as an MLflow artifact for the current run.

Parameters

transformers_model –

A trained transformers Pipeline or a dictionary that maps required components of a pipeline to the named keys of [“model”, “image_processor”, “tokenizer”, “feature_extractor”]. The model key in the dictionary must map to a value that inherits from PreTrainedModel, TFPreTrainedModel, or FlaxPreTrainedModel. All other component entries in the dictionary must support the defined task type that is associated with the base model type configuration.

An example of supplying component-level parts of a transformers model is shown below:

from transformers import MobileBertForQuestionAnswering, AutoTokenizer

architecture = "csarron/mobilebert-uncased-squad-v2"
tokenizer = AutoTokenizer.from_pretrained(architecture)
model = MobileBertForQuestionAnswering.from_pretrained(architecture)

with mlflow.start_run():
    components = {
        "model": model,
        "tokenizer": tokenizer,
    }
    mlflow.transformers.log_model(
        transformers_model=components,
        artifact_path="my_model",
    )

An example of submitting a Pipeline from a default pipeline instantiation:

from transformers import pipeline

qa_pipe = pipeline("question-answering", "csarron/mobilebert-uncased-squad-v2")

with mlflow.start_run():
    mlflow.transformers.log_model(
        transformers_model=qa_pipe,
        artifact_path="my_pipeline",
    )

artifact_path – Local path destination for the serialized model to be saved.
processor –
An optional Processor subclass object. Some model architectures, particularly multi-modal types, utilize Processors to combine text encoding and image or audio encoding in a single entrypoint.

Note

If a processor is supplied when logging a model, the model will be unavailable for loading as a Pipeline or for usage with pyfunc inference.
task – The transformers-specific task type of the model. These strings are utilized so that a pipeline can be created with the appropriate internal call architecture to meet the needs of a given model. If this argument is not specified, the pipeline utilities within the transformers library will be used to infer the correct task type. If the value specified is not a supported type within the version of transformers that is currently installed, an Exception will be thrown.
model_card –
An Optional ModelCard instance from huggingface-hub. If provided, the contents of the model card will be saved along with the provided transformers_model. If not provided, an attempt will be made to fetch the card from the base pretrained model that is provided (or the one that is included within a provided Pipeline).

Note

In order for a ModelCard to be fetched (if not provided), the huggingface_hub package must be installed and the version must be >=0.10.0

inference_config –

A dict of valid overrides that can be applied to a pipeline instance during inference. These arguments are used exclusively for the case of loading the model as a pyfunc Model or for use in Spark. These values are not applied to a returned Pipeline from a call to mlflow.transformers.load_model()

Warning

If the key provided is not compatible with either the Pipeline instance for the task provided or is not a valid override to any arguments available in the Model, an Exception will be raised at runtime. It is very important to validate the entries in this dictionary to ensure that they are valid prior to saving or logging.

An example of providing overrides for a question generation model:

from transformers import pipeline, AutoTokenizer

task = "text-generation"
architecture = "gpt2"

sentence_pipeline = pipeline(
    task=task, tokenizer=AutoTokenizer.from_pretrained(architecture), model=architecture
)

# Validate that the overrides function
prompts = ["Generative models are", "I'd like a coconut so that I can"]

# validation of config prior to save or log
inference_config = {
    "top_k": 2,
    "num_beams": 5,
    "max_length": 30,
    "temperature": 0.62,
    "top_p": 0.85,
    "repetition_penalty": 1.15,
}

# Verify that no exceptions are thrown
sentence_pipeline(prompts, **inference_config)

with mlflow.start_run():
    mlflow.transformers.log_model(
        transformers_model=sentence_pipeline,
        artifact_path="my_sentence_generator",
        task=task,
        inference_config=inference_config,
    )

code_paths – 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.
registered_model_name – This argument may change or be removed in a future release without warning. If given, create a model version under registered_model_name, also creating a registered model if one with the given name does not exist.

signature –

A Model Signature object that describes the input and output Schema of the model. The model signature can be inferred using infer_signature function of mlflow.models.signature. Example:

from mlflow.models import infer_signature
from mlflow.transformers import generate_signature_output
from transformers import pipeline

en_to_de = pipeline("translation_en_to_de")

data = "MLflow is great!"
output = generate_signature_output(en_to_de, data)
signature = infer_signature(data, output)

with mlflow.start_run() as run:
    mlflow.transformers.log_model(
        transformers_model=en_to_de,
        artifact_path="english_to_german_translator",
        signature=signature,
        input_example=data,
    )

model_uri = f"runs:/{run.info.run_id}/english_to_german_translator"
loaded = mlflow.pyfunc.load_model(model_uri)

print(loaded.predict(data))
# MLflow ist großartig!

If an input_example is provided and the signature is not, a signature will be inferred automatically and applied to the MLmodel file iff the pipeline type is a text-based model (NLP). If the pipeline type is not a supported type, this inference functionality will not function correctly and a warning will be issued. In order to ensure that a precise signature is logged, it is recommended to explicitly provide one.

input_example – 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.
await_registration_for – 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 – Either an iterable of pip requirement strings (e.g. ["transformers", "-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 model’s conda environment (conda.yaml) file.
extra_pip_requirements –
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 user’s 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 model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t 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.
conda_env –
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 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": [
                "transformers==x.y.z"
            ],
        },
    ],
}
```
metadata –
Custom metadata dictionary passed to the model and stored in the MLmodel file.

Note

Experimental: This parameter may change or be removed in a future release without warning.
kwargs – Additional arguments for mlflow.models.model.Model

mlflow.transformers.save_model(transformers_model, path: str, processor=None, task: Optional[str] = None, model_card=None, inference_config: Optional[Dict[str, Any]] = None, code_paths: Optional[List[str]] = None, mlflow_model: Optional[mlflow.models.model.Model] = None, signature: Optional[mlflow.models.signature.ModelSignature] = None, input_example: Optional[Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list, csr_matrix, csc_matrix, str, bytes]] = None, pip_requirements: Optional[Union[List[str], str]] = None, extra_pip_requirements: Optional[Union[List[str], str]] = None, conda_env=None, metadata: Dict[str, Any] = None, **kwargs) → None[source]

Note

Experimental: This function may change or be removed in a future release without warning.

Note

The ‘transformers’ MLflow Models integration is known to be compatible with the following package version ranges: 4.25.1 - 4.33.1. MLflow Models integrations with transformers may not succeed when used with package versions outside of this range.

Save a trained transformers model to a path on the local file system.

Parameters

transformers_model –

A trained transformers Pipeline or a dictionary that maps required components of a pipeline to the named keys of [“model”, “image_processor”, “tokenizer”, “feature_extractor”]. The model key in the dictionary must map to a value that inherits from PreTrainedModel, TFPreTrainedModel, or FlaxPreTrainedModel. All other component entries in the dictionary must support the defined task type that is associated with the base model type configuration.

An example of supplying component-level parts of a transformers model is shown below:

from transformers import MobileBertForQuestionAnswering, AutoTokenizer

architecture = "csarron/mobilebert-uncased-squad-v2"
tokenizer = AutoTokenizer.from_pretrained(architecture)
model = MobileBertForQuestionAnswering.from_pretrained(architecture)

with mlflow.start_run():
    components = {
        "model": model,
        "tokenizer": tokenizer,
    }
    mlflow.transformers.save_model(
        transformers_model=components,
        path="path/to/save/model",
    )

An example of submitting a Pipeline from a default pipeline instantiation:

from transformers import pipeline

qa_pipe = pipeline("question-answering", "csarron/mobilebert-uncased-squad-v2")

with mlflow.start_run():
    mlflow.transformers.save_model(
        transformers_model=qa_pipe,
        path="path/to/save/model",
    )

path – Local path destination for the serialized model to be saved.
processor –
An optional Processor subclass object. Some model architectures, particularly multi-modal types, utilize Processors to combine text encoding and image or audio encoding in a single entrypoint.

Note

If a processor is supplied when saving a model, the model will be unavailable for loading as a Pipeline or for usage with pyfunc inference.
task – The transformers-specific task type of the model. These strings are utilized so that a pipeline can be created with the appropriate internal call architecture to meet the needs of a given model. If this argument is not specified, the pipeline utilities within the transformers library will be used to infer the correct task type. If the value specified is not a supported type within the version of transformers that is currently installed, an Exception will be thrown.
model_card –
An Optional ModelCard instance from huggingface-hub. If provided, the contents of the model card will be saved along with the provided transformers_model. If not provided, an attempt will be made to fetch the card from the base pretrained model that is provided (or the one that is included within a provided Pipeline).

Note

In order for a ModelCard to be fetched (if not provided), the huggingface_hub package must be installed and the version must be >=0.10.0

inference_config –

A dict of valid overrides that can be applied to a pipeline instance during inference. These arguments are used exclusively for the case of loading the model as a pyfunc Model or for use in Spark. These values are not applied to a returned Pipeline from a call to mlflow.transformers.load_model()

Warning

If the key provided is not compatible with either the Pipeline instance for the task provided or is not a valid override to any arguments available in the Model, an Exception will be raised at runtime. It is very important to validate the entries in this dictionary to ensure that they are valid prior to saving or logging.

An example of providing overrides for a question generation model:

from transformers import pipeline, AutoTokenizer

task = "text-generation"
architecture = "gpt2"

sentence_pipeline = pipeline(
    task=task, tokenizer=AutoTokenizer.from_pretrained(architecture), model=architecture
)

# Validate that the overrides function
prompts = ["Generative models are", "I'd like a coconut so that I can"]

# validation of config prior to save or log
inference_config = {
    "top_k": 2,
    "num_beams": 5,
    "max_length": 30,
    "temperature": 0.62,
    "top_p": 0.85,
    "repetition_penalty": 1.15,
}

# Verify that no exceptions are thrown
sentence_pipeline(prompts, **inference_config)

mlflow.transformers.save_model(
    transformers_model=sentence_pipeline,
    path="/path/for/model",
    task=task,
    inference_config=inference_config,
)

code_paths – 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.
mlflow_model – An MLflow model object that specifies the flavor that this model is being added to.

signature –

A Model Signature object that describes the input and output Schema of the model. The model signature can be inferred using infer_signature function of mlflow.models.signature. Example:

from mlflow.models import infer_signature
from mlflow.transformers import generate_signature_output
from transformers import pipeline

en_to_de = pipeline("translation_en_to_de")

data = "MLflow is great!"
output = generate_signature_output(en_to_de, data)
signature = infer_signature(data, output)

mlflow.transformers.save_model(
    transformers_model=en_to_de,
    path="/path/to/save/model",
    signature=signature,
    input_example=data,
)

loaded = mlflow.pyfunc.load_model("/path/to/save/model")
print(loaded.predict(data))
# MLflow ist großartig!

If an input_example is provided and the signature is not, a signature will be inferred automatically and applied to the MLmodel file iff the pipeline type is a text-based model (NLP). If the pipeline type is not a supported type, this inference functionality will not function correctly and a warning will be issued. In order to ensure that a precise signature is logged, it is recommended to explicitly provide one.

input_example – 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 – Either an iterable of pip requirement strings (e.g. ["transformers", "-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 model’s conda environment (conda.yaml) file.
extra_pip_requirements –
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 user’s 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 model’s conda environment (conda.yaml) file.
Warning

The following arguments can’t 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.
conda_env –
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 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": [
                "transformers==x.y.z"
            ],
        },
    ],
}
```
metadata –
Custom metadata dictionary passed to the model and stored in the MLmodel file.

Note

Experimental: This parameter may change or be removed in a future release without warning.
kwargs – Optional additional configurations for transformers serialization.

Returns

None