public class Train extends BaseSubCommand
Modifier and Type | Field and Description |
---|---|
Properties |
configProps |
String |
configurationFile |
static String |
DEFAULT_INPUT_FORMAT_CLASSNAME |
static String |
EXECUTION_RUNTIME_MODE_DEFAULT |
static String |
EXECUTION_RUNTIME_MODE_KEY |
static String |
INPUT_DATA_FILENAME_KEY |
static String |
INPUT_FORMAT_KEY |
static String |
OUTPUT_FILENAME_KEY |
args
Modifier and Type | Method and Description |
---|---|
org.canova.api.formats.input.InputFormat |
createInputFormat()
Create an input format
|
void |
execLocal()
Execute local training
|
void |
execOnHadoop() |
void |
execOnSpark() |
void |
execute()
TODO:
- lots of things to do here
- runtime: if we're running on a cluster, then we have a different workflow / tracking setup
|
void |
loadConfigFile() |
public static final String EXECUTION_RUNTIME_MODE_KEY
public static final String EXECUTION_RUNTIME_MODE_DEFAULT
public static final String OUTPUT_FILENAME_KEY
public static final String INPUT_DATA_FILENAME_KEY
public static final String INPUT_FORMAT_KEY
public static final String DEFAULT_INPUT_FORMAT_CLASSNAME
public String configurationFile
public Properties configProps
public Train()
public Train(String[] args)
public void execute()
public void execLocal()
public void execOnSpark()
public void execOnHadoop()
public org.canova.api.formats.input.InputFormat createInputFormat()
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