for hp in self.hpg.enumerate()
]
return expts
'''
Config Instances
'''
B1 = Build(image_url='efnet:latest',
build_steps=['docker build -t efnet:latest .'])
Co1 = Container(build=B1)
S1 = StorageDirs(working_dir='.', data_dir='/data/imagenette-160')
L = LoggerConfig(mlflow=MLFlowConfig())
Re1 = Resources(storage=S1, ctr=Co1, loggers=L)
H1 = HP(batch_size=32, epochs=1, gpu_id=0, model_name='efficientnet-b0')
HPG1 = HPG(batch_size=16,
epochs=1,
gpu_id=0,
model_name=['efficientnet-b0', 'efficientnet-b1'])
# docker engine
Ru1 = Run(
cmd="python main.py {{run.data_dir}} --output-dir {{run.output_dir}}\
-a {{hp.model_name}} --pretrained -b {{hp.batch_size}} \
-j 0 --epochs {{hp.epochs}} --gpu {{hp.gpu_id}}", # --experiment_name {{er.run.experiment_name}}
experiment_name="efnet0")
return expts
'''
Config Instances
'''
B1 = Build(image_url='pytorch_expt',
build_steps=['docker build -t pytorch_expt .'])
Co1 = Container(build=B1)
S1 = StorageDirs(data_dir='./data')
#L = LoggerConfig(names=['mlflow', 'trains'])
Lm = MLFlowConfig(port=5000)
L = LoggerConfig(mlflow=Lm)
R1 = Resources(storage=S1, ctr=Co1, loggers=L) #or ctr=None
# for k8 engine
# replace multiple attributes, recursively
R2 = rreplace(R1, {
'loggers.mlflow.client_in_cluster': True,
'loggers.mlflow.port': 30005,
})
H1 = HPGroup(alpha=0.05, l1_ratio=[0.01, 0.015, 0.0015])
Ru1 = Run(
cmd=
"python train.py --data-dir {{run.data_dir}} --output-dir {{run.output_dir}} --alpha {{hp.alpha}} \
--l1_ratio {{ hp.l1_ratio }}", #template refers to fields of Experiment instance
return expts[:1]
'''
Config Instances
'''
B1 = Build(image_url='tensorflow/tensorflow:latest-devel-gpu-py3',
build_steps=[]) #'docker build -t xx .'])
Ho1 = HostStore(working_dir='.', data_dir='./data')
S1 = Storage(host=Ho1)
Co1 = ContainerDirs()
Lm = MLFlowConfig(client_in_cluster=False, port=5000)
L = LoggerConfig(mlflow=Lm)
from lightex.mulogger.trains_logger import TrainsConfig
L.register_logger('trains', TrainsConfig())
R1 = Resources(build=B1, storage=S1, ctr=Co1, loggers=L)
H1 = HP()
Ru1 = Run(
cmd="python tensorflow_mnist_with_summaries.py --data_dir {{run.data_dir}}\
--max_steps {{hp.max_steps}}\
--log_dir {{run.output_dir}}/logs\
--save_path {{run.output_dir}}/models/model.ckpt",
experiment_name="tf_mnist_summ",
)
C1 = Config(er=R1, hp=H1, run=Ru1)
def get_experiments(self):
hps = self.hpg.flatten()
expts = [Experiment(er=self.er, hp=hp, run=self.run) for hp in hps]
return expts[:1]
'''
Config Instances
'''
B1 = Build(image_url='sklearn', build_steps=['docker build -t sklearn .'])
#from lightex.mulogger.trains_logger import TrainsConfig
# trconf = TrainsConfig(config_file='./trains.conf')
L = LoggerConfig(loggers=['mlflow']) #trains=trconf
#L = LoggerConfig(mlflow=MLFlowConfig()) #trains=trconf
Co1 = Container(build=B1)
S1 = StorageDirs(working_dir='.')
R1 = Resources(storage=S1, ctr=Co1, loggers=L)
H1 = HPGroup(alpha=0.05, l1_ratio=[0.01, 0.015, 0.0015])
Ru1 = Run(
cmd=
"python train.py --data-dir {{run.data_dir}} --output-dir {{run.output_dir}} --alpha {{hp.alpha}} \
--l1_ratio {{ hp.l1_ratio }}",
experiment_name="m2test",
)