[BUG] Basic client: 'Syncer' has no attribute 'instance'

[ThanhTran81]

i was able to setup the server and successfully ran all the code in https://github.com/mitdbg/modeldb/tree/master/client/python/samples/sklearn.
However when i tried the basic client, all the samples code in here https://github.com/mitdbg/modeldb/tree/master/client/python/samples/basic yields this error Syncer.instance.add_to_buffer(fit_event)
AttributeError: type object 'Syncer' has no attribute 'instance'

[AndrewDotson]

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()

[AndrewDotson]

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.

[justinanderson]

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.

[justinanderson]

Resolved by updating the PyPi distribution. New version is 0.0.1a29.