Saving and Loading a Model

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

In Flow

There are a number of ways you can save your model in Flow.

  • In the web UI, click the Flow menu, then click Save Flow. Your flow is saved to the Flows tab in the Help sidebar on the right.
  • In the web UI, click the Flow menu, then click Download this Flow.... Depending on your browser and configuration, your flow is saved to the “Downloads” folder (by default) or to the location you specify in the pop-up Save As window if it appears.
  • For DRF, GBM, and DL models only: Use model checkpointing to resume training a model. Copy the model_id number from a built model and paste it into the checkpoint field in the buildModel cell.

Note: When you are running H2O on Hadoop, H2O tries to determine the home HDFS directory so it can use that as the download location. If the default home HDFS directory is not found, manually set the download location from the command line using the -flow_dir parameter. For example,

hadoop jar h2odriver.jar <...> -flow_dir hdfs:///user/yourname/yourflowdir).

You can view the default download directory in the logs by clicking Admin > View logs... and looking for the line that begins with Flow dir:.

After a Flow is saved, you can load it by clicking on the Flows tab in the right sidebar. Then in the pop-up confirmation window that appears, select Load Notebook. Refer to Loading Flows for more information.

In Flow, you can also import specific models rather than entire Flows. Refer to Exporting and Importing Models for more information.

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)