how to save/load a trained model in H2o?

问题

The user tutorial says

Navigate to Data > View All
Choose to filter by the model key
Hit Save Model
Input for path: /data/h2o-training/...
Hit Submit

The problem is that I do not have this menu (H2o, 3.0.0.26, web interface)

回答1:

I am, unfortunately, not familiar with the web interface but I can offer a workaround involving H2O in R. The functions

h2o.saveModel(object, dir = "", name = "", filename = "", force = FALSE)

and

h2o.loadModel(path, conn = h2o.getConnection())

Should offer what you need. I will try to have a look at H2O Flow.

Update

I cannot find the possibility to explicitly save a model either. What you can do instead is save the 'Flow'. You ergo could upload/import your file, build the model and then save / load the status :-)

回答2:

When viewing the model in H2O Flow, you will see an 'Export' button as an action that can be taken against a model

From there, you will be prompted to specify a path in 'Export Model' dialog. Specify the path and hit the 'Export' button. That will save you model to disk.

I'm referring to H2O version 3.2.0.3

回答3:

A working example that I've used recently while building a deep learning model in version 2.8.6 in h2o.The model was saved in hdfs.For latest version you probably have to remove the classification=T switch and have to replace data with training_frame

library(h2o)
h = h2o.init(ip="xx.xxx.xxx.xxx", port=54321, startH2O = F)

cTrain.h2o <- as.h2o(h,cTrain,key="c1")
cTest.h2o <- as.h2o(h,cTest,key="c2")

nh2oD<-h2o.deeplearning(x =c(1:12),y="tgt",data=cTrain.h2o,classification=F,activation="Tanh",
                        rate=0.001,rho=0.99,momentum_start=0.5,momentum_stable=0.99,input_dropout_ratio=0.2,                        
                        hidden=c(12,25,11,11),hidden_dropout_ratios=c(0.4,0.4,0.4,0.4),
                        epochs=150,variable_importances=T,seed=1234,reproducible = T,l1=1e-5,
                        key="dn")

hdfsdir<-"hdfs://xxxxxxxxxx/user/xxxxxx/xxxxx/models"

h2o.saveModel(nh2oD,hdfsdir,name="DLModel1",save_cv=T,force=T)

test=h2o.loadModel(h,path=paste0(hdfsdir,"/","DLModel1"))

回答4:

This should be what you need:

library(h2o)
h2o.init()
path = system.file("extdata", "prostate.csv", package = "h2o")
h2o_df = h2o.importFile(path)
h2o_df$CAPSULE = as.factor(h2o_df$CAPSULE)
model = h2o.glm(y = "CAPSULE",
              x = c("AGE", "RACE", "PSA", "GLEASON"),
              training_frame = h2o_df,
              family = "binomial")
h2o.download_pojo(model)

http://h2o-release.s3.amazonaws.com/h2o/rel-slater/5/docs-website/h2o-docs/index.html#POJO%20Quick%20Start

回答5:

How to save models in H2O Flow:

go to "List All Models"
in the model details, you will find an "Export" option
enter the model name you want to save it as
import it back again

How to save a model trained in h2o-py:

# say "rf" is your H2ORandomForestEstimator object. To export it
>>> path = h2o.save_model(rf, force=True) # save_model() returns the path
>>> path
u'/home/user/rf'

#to import it back again(as a new object)
>>> rafo = h2o.load_model(path)
>>> rafo   # prints model details
Model Details
=============
H2ORandomForestEstimator :  Distributed Random Forest
Model Key:  drf1
Model Summary:
######Prints model details...................

来源：https://stackoverflow.com/questions/31522341/how-to-save-load-a-trained-model-in-h2o

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h2o