import os import sys import unittest from pathlib import Path sys.path.append(str(Path(__file__).resolve().parent.parent)) import shutil from rdagent.utils.env import LocalConf, LocalEnv, QTDockerEnv DIRNAME = Path(__file__).absolute().resolve().parent class EnvUtils(unittest.TestCase): def setUp(self): pass def tearDown(self): # NOTE: For a docker file, the output are generated with root permission. # mlrun_p = DIRNAME / "env_tpl" / "mlruns" # if mlrun_p.exists(): # shutil.rmtree(mlrun_p) ... # NOTE: Since I don't know the exact environment in which it will be used, here's just an example. # NOTE: Because you need to download the data during the prepare process. So you need to have pyqlib in your environment. def test_local(self): local_conf = LocalConf( py_bin="/home/v-linlanglv/miniconda3/envs/RD-Agent-310/bin", default_entry="qrun conf.yaml", ) qle = LocalEnv(conf=local_conf) qle.prepare() conf_path = str(DIRNAME / "env_tpl" / "conf.yaml") qle.run(entry="qrun " + conf_path) mlrun_p = DIRNAME / "env_tpl" / "mlruns" self.assertTrue(mlrun_p.exists(), f"Expected output file {mlrun_p} not found") def test_docker(self): """ We will mount `env_tpl` into the docker image. And run the docker image with `qrun conf.yaml` """ qtde = QTDockerEnv() qtde.prepare() # you can prepare for multiple times. It is expected to handle it correctly # qtde.run("nvidia-smi") # NOTE: you can check your GPU with this command # the stdout are returned as result result = qtde.run(local_path=str(DIRNAME / "env_tpl"), entry="qrun conf.yaml") mlrun_p = DIRNAME / "env_tpl" / "mlruns" self.assertTrue(mlrun_p.exists(), f"Expected output file {mlrun_p} not found") # read experiment result = qtde.run(local_path=str(DIRNAME / "env_tpl"), entry="python read_exp_res.py") print(result) if __name__ == "__main__": unittest.main()