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')
from verta import Client
import cloudpickle
client = Client('https://dev.verta.ai')
proj = client.set_project('Demo - Jenkins+Prometheus')
run = proj.expt_runs[0]
model = run.get_model()
with open('model.pkl', 'wb') as f:
cloudpickle.dump(model, f)
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
from verta.utils import ModelAPI
HOST = "http://localhost:3009"
PROJECT_NAME = "readmission_shared_data_preprocess_v0"
EXPERIMENT_NAME = "readmission_shared_data_preprocess_v0_first_run"
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Template Main Function')
parser.add_argument('--input', type=str, help='input folder')
parser.add_argument('--output', type=str, help='output folder')
parser.add_argument('--vis', type=str, help='vis folder')
args = parser.parse_args()
client = Client(HOST)
proj = client.set_project(PROJECT_NAME)
expt = client.set_experiment(EXPERIMENT_NAME)
run = client.set_experiment_run()
cmd = "readmission_shared_data_preprocess_trainvalidationtest.sh" # modify
lib_param = {}
lib_param["--input"] = args.input
lib_param["--output"] = args.output
lib_param["--vis"] = args.vis
for k, v in lib_param.items():
cmd = cmd + " " + str(k) + " " + str(v)
cmd = "bash " + cmd
print("executing cmd: \n", cmd)
os.system(cmd) # modify
from nntoolbox.metrics import Accuracy, Loss
from nntoolbox.losses import SmoothedCrossEntropy
from verta import Client
from verta.client import ExperimentRun
from experii.verta import ModelDBCB
from experii.ax import AxTuner
torch.backends.cudnn.benchmark = True
EXPERIMENT_NAME = "Hyperparameter Tuning"
# Set up ModelDB:
client = Client(
CLIENT_PARA
) # supply your own ModelDB'S client parameters here (see VertaAI's notebooks)
proj = client.set_project("My second ModelDB project")
exp = client.set_experiment(EXPERIMENT_NAME)
# Define model generating function:
def model_fn(parameterization: Dict[str, Any]) -> nn.Module:
model = Sequential(
ConvolutionalLayer(in_channels=3,
out_channels=16,
kernel_size=3,
activation=nn.ReLU),
ResidualBlockPreActivation(in_channels=16, activation=nn.ReLU),
ConvolutionalLayer(in_channels=16,
out_channels=32,
kernel_size=3,
activation=nn.ReLU),