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 <afe.young@gmail.com>

test/utils/env_tpl/README.md

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+
+# Introduction
+
+It is a template for testing.

test/utils/env_tpl/conf.yaml

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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

test/utils/test_env.py

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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
+
+
+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)
+        ...
+
+    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")
+
+        mlrun_p = DIRNAME / "env_tpl" / "mlruns" 
+        self.assertTrue(mlrun_p.exists(), f"Expected output file {mlrun_p} not found")
+
+
+if __name__ == "__main__":
+    unittest.main()