mlflow.gluon
-
mlflow.gluon.
autolog
()[source] Note
Experimental: This method may change or be removed in a future release without warning.
Enable automatic logging from Gluon to MLflow. Logs loss and any other metrics specified in the fit function, and optimizer data as parameters. Model checkpoints are logged as artifacts to a ‘models’ directory.
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mlflow.gluon.
get_default_conda_env
()[source] - Returns
The default Conda environment for MLflow Models produced by calls to
save_model()
andlog_model()
.
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mlflow.gluon.
load_model
(model_uri, ctx)[source] Note
Experimental: This method may change or be removed in a future release without warning.
Load a Gluon model 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
models:/<model_name>/<model_version>
models:/<model_name>/<stage>
For more information about supported URI schemes, see Referencing Artifacts.
ctx – Either CPU or GPU.
- Returns
A Gluon model instance.
-
mlflow.gluon.
log_model
(gluon_model, artifact_path, conda_env=None, registered_model_name=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = None)[source] Note
Experimental: This method may change or be removed in a future release without warning.
Log a Gluon model as an MLflow artifact for the current run.
- Parameters
gluon_model – Gluon model to be saved. Must be already hybridized.
artifact_path – Run-relative artifact path.
conda_env –
Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in
get_default_conda_env()
. IfNone
, the defaultmlflow.gluon.get_default_conda_env()
environment is added to the model. The following is an example dictionary representation of a Conda environment:{ 'name': 'mlflow-env', 'channels': ['defaults'], 'dependencies': [ 'python=3.7.0', 'mxnet=1.5.0' ] }
registered_model_name – (Experimental) 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 –
(Experimental)
ModelSignature
describes model input and outputSchema
. The model signature can beinferred
from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:from mlflow.models.signature import infer_signature train = df.drop_column("target_label") predictions = ... # compute model predictions signature = infer_signature(train, predictions)
input_example – (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
from mxnet.gluon import Trainer from mxnet.gluon.contrib import estimator from mxnet.gluon.loss import SoftmaxCrossEntropyLoss from mxnet.gluon.nn import HybridSequential from mxnet.metric import Accuracy import mlflow # Build, compile, and train your model net = HybridSequential() with net.name_scope(): ... net.hybridize() net.collect_params().initialize() softmax_loss = SoftmaxCrossEntropyLoss() trainer = Trainer(net.collect_params()) est = estimator.Estimator(net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer) # Log metrics and log the model with mlflow.start_run(): est.fit(train_data=train_data, epochs=100, val_data=validation_data) mlflow.gluon.log_model(net, "model")
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mlflow.gluon.
save_model
(gluon_model, path, mlflow_model=None, conda_env=None, signature: mlflow.models.signature.ModelSignature = None, input_example: Union[pandas.core.frame.DataFrame, numpy.ndarray, dict, list] = None)[source] Note
Experimental: This method may change or be removed in a future release without warning.
Save a Gluon model to a path on the local file system.
- Parameters
gluon_model – Gluon model to be saved. Must be already hybridized.
path – Local path where the model is to be saved.
mlflow_model – MLflow model config this flavor is being added to.
conda_env –
Either a dictionary representation of a Conda environment or the path to a Conda environment yaml file. If provided, this decribes the environment this model should be run in. At minimum, it should specify the dependencies contained in
get_default_conda_env()
. IfNone
, the defaultmlflow.gluon.get_default_conda_env()
environment is added to the model. The following is an example dictionary representation of a Conda environment:{ 'name': 'mlflow-env', 'channels': ['defaults'], 'dependencies': [ 'python=3.7.0', 'mxnet=1.5.0' ] }
signature –
(Experimental)
ModelSignature
describes model input and outputSchema
. The model signature can beinferred
from datasets with valid model input (e.g. the training dataset with target column omitted) and valid model output (e.g. model predictions generated on the training dataset), for example:from mlflow.models.signature import infer_signature train = df.drop_column("target_label") predictions = ... # compute model predictions signature = infer_signature(train, predictions)
input_example – (Experimental) Input example provides one or several instances of valid model input. The example can be used as a hint of what data to feed the model. The given example will be converted to a Pandas DataFrame and then serialized to json using the Pandas split-oriented format. Bytes are base64-encoded.
from mxnet.gluon import Trainer from mxnet.gluon.contrib import estimator from mxnet.gluon.loss import SoftmaxCrossEntropyLoss from mxnet.gluon.nn import HybridSequential from mxnet.metric import Accuracy import mlflow # Build, compile, and train your model gluon_model_path = ... net = HybridSequential() with net.name_scope(): ... net.hybridize() net.collect_params().initialize() softmax_loss = SoftmaxCrossEntropyLoss() trainer = Trainer(net.collect_params()) est = estimator.Estimator(net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer) est.fit(train_data=train_data, epochs=100, val_data=validation_data) # Save the model as an MLflow Model mlflow.gluon.save_model(net, gluon_model_path)