Comments (4)
I also seem to be to having this issue. I'm trying to run a slightly modified version of the BasicWorkflow.py. My file dies on the line "syncer_obj.sync_model("train", model_config1, mdb_model1)" with the same error as reported above.
Everything else works just fine; I'm able to sync and view the model.
I'm running ModelDB on an Ubuntu 16 VM. I have attached the file I'm trying to run below.
from modeldb.basic.Structs import (
Model, ModelConfig, ModelMetrics, Dataset)
from modeldb.basic.ModelDbSyncerBase import *
import pyspark
# Create a syncer using a convenience API
# syncer_obj = Syncer.create_syncer("gensim test", "test_user", \
# "using modeldb light logging")
# Example: Create a syncer from a config file
syncer_obj = Syncer.create_syncer_from_config("/home/testuser/modeldb/client/syncer.json")
# Create a syncer using a convenience API
#syncer_obj = Syncer.create_syncer("Sample Project", "test_user", "sample description")
# or
# Create a syncer explicitly
#syncer_obj = Syncer(
# NewOrExistingProject("PySpark_test", "Andrew", "pyspark test using modeldb light logging"),
# DefaultExperiment(),
# NewExperimentRun("", "sha_A1B2C3D4"))
# Example: Create a syncer from an existing experiment run
# experiment_run_id = int(sys.argv[len(sys.argv) - 1])
# syncer_obj = Syncer.create_syncer_for_experiment_run(experiment_run_id)
print "I'm training some model"
# create Datasets by specifying their filepaths and optional metadata
# associate a tag (key) for each Dataset (value)
datasets = {
"train": Dataset("/home/testuser/modeldb/data/titanic_train.csv", {"num_cols": 12,}),
"test": Dataset("/home/testuser/modeldb/data/titanic_test.csv", {"num_cols": 12,})
}
# create the Model, ModelConfig, and ModelMetrics instances
model = "model_obj"
mdb_model1 = Model("LogReg", model, "client/python/samples/sklearn/Titanic-LogisticRegression.py")
model_config1 = ModelConfig("NN", {"l1": 10})
model_metrics1 = ModelMetrics({"accuracy": 0.8})
#mdb_model2 = Model("NN", model, "/path/to/model2")
#model_config2 = ModelConfig("NN", {"l1": 20})
#model_metrics2 = ModelMetrics({"accuracy": 0.9})
syncer_obj.sync_datasets(datasets)
syncer_obj.sync_model("train", model_config1, mdb_model1)
syncer_obj.sync_metrics("test", mdb_model1, model_metrics1)
#syncer_obj.sync_model("train", model_config2, mdb_model2)
#syncer_obj.sync_metrics("test", mdb_model2, model_metrics2)
syncer_obj.sync()
from modeldb.
I was able to fix this problem by modifying the 'sync_model' and 'sync_metrics' methods in /usr/local/lib/python2.7/dist-packages/modeldb/basic/ModelDbSyncerBase.py. I changed Syncer.instance.add_to_buffer()
to self.add_to_buffer()
and the script ran.
I don't know what other issues this fix may cause, but so far I've not noticed any.
from modeldb.
It looks like this is a problem with the pip distribution of modeldb. If you build from source it works without modification.
I'll see about updating the PyPi distribution.
from modeldb.
Resolved by updating the PyPi distribution. New version is 0.0.1a29.
from modeldb.
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from modeldb.