mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 23:47:46 +00:00
you-n-g
76 lines
3.3 KiB
Python
76 lines
3.3 KiB
Python
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, QlibDockerConf, 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)
|
|
|
|
def test_docker_mem(self):
|
|
cmd = 'python -c \'print("start"); import numpy as np; size_mb = 500; size = size_mb * 1024 * 1024 // 8; array = np.random.randn(size).astype(np.float64); print("success")\''
|
|
|
|
qtde = QTDockerEnv(QlibDockerConf(mem_limit="10m"))
|
|
qtde.prepare()
|
|
result = qtde.run(local_path=str(DIRNAME / "env_tpl"), entry=cmd)
|
|
self.assertTrue(not result.strip().endswith("success"))
|
|
|
|
qtde = QTDockerEnv(QlibDockerConf(mem_limit="1g"))
|
|
qtde.prepare()
|
|
result = qtde.run(local_path=str(DIRNAME / "env_tpl"), entry=cmd)
|
|
self.assertTrue(result.strip().endswith("success"))
|
|
|
|
# The above command equals to the follow commands with dockr cli.sh
|
|
# docker run --memory=10m -it --rm local_qlib:latest python -c 'import numpy as np; print(123); size_mb = 1; size = size_mb * 1024 * 1024 // 8; array = np.random.randn(size).astype(np.float64); array[0], array[-1] = 1.0, 1.0; print(321)'
|
|
# docker run --memory=10g -it --rm local_qlib:latest python -c 'import numpy as np; print(123); size_mb = 1; size = size_mb * 1024 * 1024 // 8; array = np.random.randn(size).astype(np.float64); array[0], array[-1] = 1.0, 1.0; print(321)'
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|