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