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Initial framework for docker env (#40)
* Initial framework for docker env * Update test name * add features * Download Qlib data with extra_volume * fix pytest error * Fix the parameters --------- Co-authored-by: Young <[email protected]>
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# Environments | ||
.env | ||
.venv | ||
env/ | ||
^env/ | ||
venv/ | ||
ENV/ | ||
env.bak/ | ||
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""" | ||
The motiviation of the utils is for environment management | ||
Tries to create uniform environment for the agent to run; | ||
- All the code and data is expected included in one folder | ||
""" | ||
import os | ||
import docker | ||
from abc import abstractmethod | ||
from pydantic import BaseModel | ||
from typing import Generic, TypeVar | ||
from pathlib import Path | ||
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ASpecificBaseModel = TypeVar("ASpecificBaseModel", bound=BaseModel) | ||
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class Env(Generic[ASpecificBaseModel]): | ||
""" | ||
We use BaseModel as the setting due to the featurs it provides | ||
- It provides base typing and checking featurs. | ||
- loading and dumping the information will be easier: for example, we can use package like `pydantic-yaml` | ||
""" | ||
conf: ASpecificBaseModel # different env have different conf. | ||
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def __init__(self, conf: ASpecificBaseModel): | ||
self.conf = conf | ||
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@abstractmethod | ||
def prepare(self): | ||
""" | ||
Prepare for the environment based on it's configure | ||
""" | ||
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@abstractmethod | ||
def run(self, | ||
entry: str | None, | ||
local_path: str | None = None, | ||
env: dict | None = None) -> str: | ||
""" | ||
Run the folder under the environment. | ||
Parameters | ||
---------- | ||
entry : str | None | ||
We may we the entry point when we run it. | ||
For example, we may have different entries when we run and summarize the project. | ||
local_path : str | None | ||
the local path (to project, mainly for code) will be mounted into the docker | ||
Here are some examples for a None local path | ||
- for example, run docker for updating the data in the extra_volumes. | ||
- simply run the image. The results are produced by output or network | ||
env : dict | None | ||
Run the code with your specific environment. | ||
Returns | ||
------- | ||
the stdout | ||
""" | ||
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## Local Environment ----- | ||
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class LocalConf(BaseModel): | ||
py_entry: str # where you can find your python path | ||
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class LocalEnv(Env[LocalConf]): | ||
""" | ||
Sometimes local environment may be more convinient for testing | ||
""" | ||
conf: LocalConf | ||
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## Docker Environment ----- | ||
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class DockerConf(BaseModel): | ||
image: str # the image you want to run | ||
mount_path: str # the path in the docker image to mount the folder | ||
default_entry: str # the entry point of the image | ||
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extra_volumes: dict | None = {} | ||
# Sometime, we need maintain some extra data for the workspace. | ||
# And the extra data may be shared and the downloading can be time consuming. | ||
# So we just want to download it once. | ||
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QLIB_TORCH_IMAGE = DockerConf(image="linlanglv/qlib_image_nightly_pytorch:nightly", | ||
mount_path="/workspace", | ||
default_entry="qrun conf.yaml", | ||
extra_volumes={Path("~/.qlib/").expanduser().resolve(): "/root/.qlib/"}) | ||
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class DockerEnv(Env[DockerConf]): | ||
# TODO: Save the output into a specific file | ||
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def prepare(self): | ||
""" | ||
Download image if it doesn't exist | ||
""" | ||
client = docker.from_env() | ||
try: | ||
client.images.get(self.conf.image) | ||
except docker.errors.ImageNotFound: | ||
client.images.pull(self.conf.image) | ||
except docker.errors.APIError as e: | ||
raise RuntimeError(f"Error while pulling the image: {e}") | ||
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def run(self, entry: str | None = None, local_path: str | None = None, env: dict | None = None): | ||
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if env is None: | ||
env = {} | ||
client = docker.from_env() | ||
if entry is None: | ||
entry = self.conf.default_entry | ||
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volumns = {} | ||
if local_path is not None: | ||
local_path = os.path.abspath(local_path) | ||
volumns[local_path] = {'bind': self.conf.mount_path, 'mode': 'rw'} | ||
if self.conf.extra_volumes is not None: | ||
for lp, rp in self.conf.extra_volumes.items(): | ||
volumns[lp] = {'bind': rp, 'mode': 'rw'} | ||
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log_output = "" | ||
try: | ||
container = client.containers.run( | ||
image=self.conf.image, | ||
command=entry, | ||
volumes=volumns, | ||
environment=env, | ||
detach=True, | ||
working_dir=self.conf.mount_path, | ||
auto_remove=True, | ||
) | ||
logs = container.logs(stream=True) | ||
for log in logs: | ||
decoded_log = log.strip().decode() | ||
print(decoded_log) | ||
log_output += decoded_log + "\n" | ||
container.wait() | ||
return log_output | ||
except docker.errors.ContainerError as e: | ||
raise RuntimeError(f"Error while running the container: {e}") | ||
except docker.errors.ImageNotFound: | ||
raise RuntimeError("Docker image not found.") | ||
except docker.errors.APIError as e: | ||
raise RuntimeError(f"Error while running the container: {e}") | ||
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class QTDockerEnv(DockerEnv): | ||
"""Qlib Torch Docker""" | ||
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def __init__(self, conf: DockerConf = QLIB_TORCH_IMAGE): | ||
super().__init__(conf) | ||
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def prepare(self): | ||
""" | ||
Download image & data if it doesn't exist | ||
""" | ||
super().prepare() | ||
qlib_data_path = next(iter(self.conf.extra_volumes.keys())) | ||
if not (Path(qlib_data_path) / "qlib_data" / "cn_data").exists(): | ||
cmd = "python -m qlib.run.get_data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn --interval 1d --delete_old False" | ||
self.run(entry=cmd) | ||
else: | ||
print("Data already exists. Download skipped.") |
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jupyter | ||
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python-dotenv | ||
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# infrastructure related. | ||
docker |
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# Introduction | ||
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It is a template for testing. |
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qlib_init: | ||
provider_uri: "~/.qlib/qlib_data/cn_data" | ||
region: cn | ||
market: &market csi300 | ||
benchmark: &benchmark SH000300 | ||
data_handler_config: &data_handler_config | ||
start_time: 2008-01-01 | ||
end_time: 2020-08-01 | ||
fit_start_time: 2008-01-01 | ||
fit_end_time: 2014-12-31 | ||
instruments: *market | ||
port_analysis_config: &port_analysis_config | ||
strategy: | ||
class: TopkDropoutStrategy | ||
module_path: qlib.contrib.strategy | ||
kwargs: | ||
signal: <PRED> | ||
topk: 50 | ||
n_drop: 5 | ||
backtest: | ||
start_time: 2017-01-01 | ||
end_time: 2020-08-01 | ||
account: 100000000 | ||
benchmark: *benchmark | ||
exchange_kwargs: | ||
limit_threshold: 0.095 | ||
deal_price: close | ||
open_cost: 0.0005 | ||
close_cost: 0.0015 | ||
min_cost: 5 | ||
task: | ||
model: | ||
class: LGBModel | ||
module_path: qlib.contrib.model.gbdt | ||
kwargs: | ||
loss: mse | ||
colsample_bytree: 0.8879 | ||
learning_rate: 0.2 | ||
subsample: 0.8789 | ||
lambda_l1: 205.6999 | ||
lambda_l2: 580.9768 | ||
max_depth: 8 | ||
num_leaves: 210 | ||
num_threads: 20 | ||
dataset: | ||
class: DatasetH | ||
module_path: qlib.data.dataset | ||
kwargs: | ||
handler: | ||
class: Alpha158 | ||
module_path: qlib.contrib.data.handler | ||
kwargs: *data_handler_config | ||
segments: | ||
train: [2008-01-01, 2014-12-31] | ||
valid: [2015-01-01, 2016-12-31] | ||
test: [2017-01-01, 2020-08-01] | ||
record: | ||
- class: SignalRecord | ||
module_path: qlib.workflow.record_temp | ||
kwargs: | ||
model: <MODEL> | ||
dataset: <DATASET> | ||
- class: SigAnaRecord | ||
module_path: qlib.workflow.record_temp | ||
kwargs: | ||
ana_long_short: False | ||
ann_scaler: 252 | ||
- class: PortAnaRecord | ||
module_path: qlib.workflow.record_temp | ||
kwargs: | ||
config: *port_analysis_config |
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import os | ||
import sys | ||
import subprocess | ||
import unittest | ||
from pathlib import Path | ||
sys.path.append(str(Path(__file__).resolve().parent.parent)) | ||
from rdagent.utils.env import QTDockerEnv | ||
import shutil | ||
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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): | ||
pass | ||
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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) | ||
... | ||
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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() | ||
qtde.prepare() # you can prepare for multiple times. It is expected to handle it correctly | ||
# the stdout are returned as result | ||
result = qtde.run(local_path=str(DIRNAME / "env_tpl"), entry="qrun conf.yaml") | ||
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mlrun_p = DIRNAME / "env_tpl" / "mlruns" | ||
self.assertTrue(mlrun_p.exists(), f"Expected output file {mlrun_p} not found") | ||
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if __name__ == "__main__": | ||
unittest.main() |