def work(self):
project = ProjectProvider(self.session).by_id(self.project)
self.info(f'Task = {self.train_task} child_task: {self.child_task}')
model = Model(created=now(),
name=self.name,
project=self.project,
equations='',
fold=self.fold)
provider = ModelProvider(self.session)
if self.train_task:
task_provider = TaskProvider(self.session)
task = task_provider.by_id(self.train_task)
model.score_local = task.score
task_dir = join(TASK_FOLDER, str(self.child_task or task.id))
src_log = f'{task_dir}/log'
models_dir = join(MODEL_FOLDER, project.name)
os.makedirs(models_dir, exist_ok=True)
model_path_tmp = f'{src_log}/traced.pth'
traced = trace_model_from_checkpoint(src_log, self, file=self.file)
model_path = f'{models_dir}/{model.name}.pth'
model_weight_path = f'{models_dir}/{model.name}_weight.pth'
torch.jit.save(traced, model_path_tmp)
shutil.copy(model_path_tmp, model_path)
file = self.file = 'best_full'
shutil.copy(f'{src_log}/checkpoints/{file}.pth', model_weight_path)
provider.add(model)
def work(self):
project = ProjectProvider(self.session).by_id(self.project)
self.info(f'Task = {self.train_task} child_task: {self.child_task}')
model = Model(
created=now(),
name=self.name,
project=self.project,
equations='',
fold=self.fold
)
provider = ModelProvider(self.session)
if self.train_task:
task_provider = TaskProvider(self.session)
dag_provider = DagProvider(self.session)
task = task_provider.by_id(self.train_task)
dag = dag_provider.by_id(task.dag)
task_dir = join(TASK_FOLDER, str(self.child_task or task.id))
# get log directory
config = yaml_load(dag.config)
executor_config = config['executors'][task.executor]
catalyst_config_file = executor_config['args']['config']
catalyst_config_file = join(task_dir, catalyst_config_file)
catalyst_config = yaml_load(file=catalyst_config_file)
catalyst_logdir = catalyst_config['args']['logdir']
model.score_local = task.score
src_log = f'{task_dir}/{catalyst_logdir}'
models_dir = join(MODEL_FOLDER, project.name)
os.makedirs(models_dir, exist_ok=True)
model_path_tmp = f'{src_log}/traced.pth'
traced = trace_model_from_checkpoint(src_log, self, file=self.file)
model_path = f'{models_dir}/{model.name}.pth'
model_weight_path = f'{models_dir}/{model.name}_weight.pth'
torch.jit.save(traced, model_path_tmp)
shutil.copy(model_path_tmp, model_path)
file = self.file = 'best_full'
shutil.copy(f'{src_log}/checkpoints/{file}.pth',
model_weight_path)
provider.add(model)
def work(self):
task_provider = TaskProvider(self.session)
task = task_provider.by_id(self.train_task)
dag = DagProvider(self.session).by_id(self.dag_pipe,
joined_load=[Dag.project_rel])
task_dir = join(TASK_FOLDER, str(self.child_task or task.id))
src_log = f'{task_dir}/log'
models_dir = join(MODEL_FOLDER, dag.project_rel.name)
os.makedirs(models_dir, exist_ok=True)
self.info(f'Task = {self.task} child_task: {self.child_task}')
model_path_tmp = f'{src_log}/traced.pth'
traced = trace_model_from_checkpoint(src_log, self)
model = Model(dag=self.dag_pipe,
interface=self.interface,
slot=self.slot,
score_local=task.score,
created=now(),
name=self.name,
project=dag.project,
interface_params=yaml_dump(self.interface_params))
provider = ModelProvider(self.session)
provider.add(model, commit=False)
try:
model_path = f'{models_dir}/{model.name}.pth'
model_weight_path = f'{models_dir}/{model.name}_weight.pth'
torch.jit.save(traced, model_path_tmp)
shutil.copy(model_path_tmp, model_path)
shutil.copy(f'{src_log}/checkpoints/best.pth', model_weight_path)
interface_params = yaml_load(model.interface_params)
interface_params['file'] = join('models', model.name + '.pth')
model.interface_params = yaml_dump(interface_params)
provider.update()
except Exception as e:
provider.rollback()
raise e