Saving and Loading a Model

This section describes how to save and load models using R, Python, and Flow.

In R and Python

In R and Python, you can save a model locally or to HDFS using the h2o.saveModel (R) or h2o.save_model (Python) function . This function accepts the model object and the file path. If no path is specified, then the model will be saved to the current working directory. After the model is saved, you can load it using the h2o.loadModel (R) or h2o.load_model (Python) function.

# build the model
model <- h2o.deeplearning(params)

# save the model
model_path <- h2o.saveModel(object=model, path=getwd(), force=TRUE)

print(model_path)
/tmp/mymodel/DeepLearning_model_R_1441838096933

# load the model
saved_model <- h2o.loadModel(model_path)

# build the model
model = H2ODeepLearningEstimator(params)
model.train(params)

# save the model
model_path = h2o.save_model(model=model, path="/tmp/mymodel", force=True)

print model_path
/tmp/mymodel/DeepLearning_model_python_1441838096933

# load the model
saved_model = h2o.load_model(model_path)

Note: When saving to HDFS, you must prepend the save directory with hdfs://. For example:

# build the model
model <- h2o.glm(model params)

# save the model to HDFS
hdfs_name_node <- "node-1"
hdfs_tmp_dir <- "/tmp/runit”
model_path <- sprintf("hdfs://%s%s", hdfs_name_node, hdfs_tmp_dir)
h2o.saveModel(model, dir=model_path, name="mymodel")

# build the model
h2o_glm = H2OGeneralizedLinearEstimator(model params)
h2o_glm.train(training params)

# save the model to HDFS
hdfs_name_node = "node-1"
hdfs_model_path = sprintf("hdfs://%s%s", hdfs_name_node, hdfs_tmp_dir)
new_model_path = h2o.save_model(h2o_glm, "hdfs://" + hdfs_name_node + "/" + hdfs_model_path)

In Flow

The steps for saving and loading models in Flow are described in the Using Flow - H2O’s Web UI section. Specifically, refer to Exporting and Importing Models for information about loading models into Flow.