def downloadArtifact(proj, exp_name, exp_run, serialization):
client = Client("http://localhost:3000")
proj = client.set_project(proj)
expt = client.set_experiment(exp_name)
run = client.set_experiment_run(exp_run)
if serialization.lower() == 'pickle':
run.download_model('model.pkl')
def get_modeldb_client(
experiment_name: str
) -> "verta._tracking.experimentrun.ModelDBExperiment":
modeldb_job = f"{get_project_name()}-modeldb-frontend"
my_user = get_neuro_user()
cluster = get_neuro_cluster()
uri = f"http://{modeldb_job}--{my_user}.platform-jobs:3000"
print(f"Connecting to ModelDB client {uri}")
client = ModelDBlient(uri)
exp = client.set_experiment(experiment_name)
return client
import os
import random
import multiprocessing
from verta import Client
from verta.utils import ModelAPI
os.environ['VERTA_EMAIL'] = '*****@*****.**'
os.environ['VERTA_DEV_KEY'] = '3e078522-e479-4cd2-b78c-04ffcacae3f4'
HOST = "dev.verta.ai"
EXPERIMENT_NAME = "Scaling"
client = Client(HOST)
proj = client.set_project('Scaling Test 100 jobs of 500k models')
expt = client.set_experiment(EXPERIMENT_NAME)
# Hyperparam random choice of values
c_list = [0.0001, 0.0002, 0.0004]
solver_list = ['lgfgs', 'grad']
max_iter_list = [7, 15, 28]
# results into 30 metric or hyp keys
paramKeyLimit = 10
def getMetrics(key_limit):
metric_obj = {}
for i in range(key_limit):
metric_obj['val_acc' + str(i)] = random.uniform(0.5, 0.9)
metric_obj['loss' + str(i)] = random.uniform(0.6, 0.8)
from verta import Client
client = Client('https://dev.verta.ai')
client.set_project('Demo - Jenkins+Prometheus')
client.set_experiment('Demo')
run = client.set_experiment_run()
class Predictor(object):
def __init__(self):
pass
def predict(self, X):
return X
run.log_model(Predictor())