""" The motivation 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 """ # TODO: move the scenario specific docker env into other folders. import contextlib import json import os import pickle import re import select import shutil import subprocess import time import uuid import zipfile from abc import abstractmethod from collections import deque from dataclasses import dataclass from datetime import datetime from pathlib import Path from types import MappingProxyType from typing import ( Any, Callable, Deque, Dict, Generator, Generic, Iterable, Mapping, Optional, TypeVar, cast, ) import docker # type: ignore[import-untyped] import docker.models # type: ignore[import-untyped] import docker.models.containers # type: ignore[import-untyped] import docker.types # type: ignore[import-untyped] from pydantic import BaseModel, model_validator from pydantic_settings import SettingsConfigDict from rich import print from rich.console import Console from rich.live import Live from rich.progress import Progress, SpinnerColumn, TextColumn from rich.rule import Rule from rich.table import Table from rich.text import Text from tqdm import tqdm from rdagent.core.conf import ExtendedBaseSettings from rdagent.core.experiment import RD_AGENT_SETTINGS from rdagent.core.utils import cache_with_pickle from rdagent.log import rdagent_logger as logger from rdagent.oai.llm_utils import md5_hash from rdagent.utils import filter_redundant_text from rdagent.utils.agent.tpl import T from rdagent.utils.fmt import shrink_text from rdagent.utils.workflow import wait_retry CacheKeyFunc = Callable[[str | Path], list[list[str]]] def extract_dir_name_from_path_config(path_str: str) -> str: """ Extract the first directory component from a relative path string. This is used to get the basename from path configurations like "./workspace_input/" to use in chmod exclusion patterns. Args: path_str: A path string, typically from T() template configuration Returns: The first directory component, or empty string if not a relative path Examples: "./workspace_input/" -> "workspace_input" "./assets/" -> "assets" "/absolute/path" -> "" """ p = Path(path_str) if not p.is_absolute() and p.parts: return p.parts[0] return "" def cleanup_container(container: docker.models.containers.Container | None, context: str = "") -> None: # type: ignore[no-any-unimported] """ Shared helper function to clean up a Docker container. Always stops the container before removing it. Parameters ---------- container : docker container object or None The container to clean up, or None if no container to clean up context : str Additional context for logging (e.g., "health check", "GPU test") """ if container is not None: try: # Always stop first - stop() doesn't raise error if already stopped container.stop() container.remove() except Exception as cleanup_error: # Log cleanup error but don't mask the original exception context_str = f" {context}" if context else "" logger.warning(f"Failed to cleanup{context_str} container {container.id}: {cleanup_error}") # Normalize all bind paths in volumes to absolute paths using the workspace (working_dir). def normalize_volumes(vols: dict[str, str | dict[str, str]], working_dir: str) -> dict: abs_vols: dict[str, str | dict[str, str]] = {} def to_abs(path: str) -> str: # Converts a relative path to an absolute path using the workspace (working_dir). return os.path.abspath(os.path.join(working_dir, path)) if not os.path.isabs(path) else path for lp, vinfo in vols.items(): # Support both: # 1. {'host_path': {'bind': 'container_path', ...}} # 2. {'host_path': 'container_path'} if isinstance(vinfo, dict): # abs_vols = cast(dict[str, dict[str, str]], abs_vols) vinfo = vinfo.copy() vinfo["bind"] = to_abs(vinfo["bind"]) abs_vols[lp] = vinfo else: # abs_vols = cast(dict[str, str], abs_vols) abs_vols[lp] = to_abs(vinfo) return abs_vols def pull_image_with_progress(image: str) -> None: client = docker.APIClient(base_url="unix://var/run/docker.sock") pull_logs = client.pull(image, stream=True, decode=True) progress_bars = {} for log in pull_logs: if "id" in log and log.get("progressDetail"): layer_id = log["id"] progress_detail = log["progressDetail"] current = progress_detail.get("current", 0) total = progress_detail.get("total", 0) if total: if layer_id not in progress_bars: progress_bars[layer_id] = tqdm(total=total, desc=f"Layer {layer_id}", unit="B", unit_scale=True) progress_bars[layer_id].n = current progress_bars[layer_id].refresh() elif "status" in log: print(log["status"]) for pb in progress_bars.values(): pb.close() class EnvConf(ExtendedBaseSettings): default_entry: str env_dict: dict = {} extra_volumes: dict = {} running_timeout_period: int | None = 3600 # 10 minutes """it is a function to calculating hash keys""" def get_workspace_content_for_hash(self, local_path: str | Path) -> list[list[str]]: """Get content of key files in workspace for cache hash calculation. Scans .py, .csv, and .yaml files. """ # we must add the information of data (beyond code) into the key. # Otherwise, all commands operating on data will become invalid (e.g. rm -r submission.csv) # So we recursively walk in the folder and add the sorted relative filename list as part of the key. # data_key = [] # for path in Path(local_path).rglob("*"): # p = str(path.relative_to(Path(local_path))) # if p.startswith("__pycache__"): # continue # data_key.append(p) # data_key = sorted(data_key) local_path = Path(local_path) return [ [str(path.relative_to(local_path)), path.read_text()] for path in sorted( list(local_path.rglob("*.py")) + list(local_path.rglob("*.csv")) + list(local_path.rglob("*.yaml")) ) ] redirect_stdout_to_file: bool = False # helper settings to support transparent; enable_cache: bool = True retry_count: int = 5 # retry count for the docker run retry_wait_seconds: int = 10 # retry wait seconds for the docker run exclude_chmod_paths: list[str] = [] # List of directory names to exclude from chmod operation model_config = SettingsConfigDict( # TODO: add prefix .... env_parse_none_str="None", # Nthis is the key to accept `RUNNING_TIMEOUT_PERIOD=None` ) ASpecificEnvConf = TypeVar("ASpecificEnvConf", bound=EnvConf) @dataclass class EnvResult: """ The result of running the environment. It contains the stdout, the exit code, and the running time in seconds. """ full_stdout: str exit_code: int running_time: float stored_full_stdout_to_truncated_stdout: Dict[str, str] def __init__(self, stdout: str, exit_code: int, running_time: float): self.full_stdout = stdout self.exit_code = exit_code self.running_time = running_time self.stored_full_stdout_to_truncated_stdout = {} def update_stdout(self, stdout: str) -> None: self.full_stdout = stdout @property def stdout(self) -> str: if self.full_stdout not in self.stored_full_stdout_to_truncated_stdout: truncated: str = self._get_truncated_stdout(self.full_stdout) self.stored_full_stdout_to_truncated_stdout[self.full_stdout] = truncated return self.stored_full_stdout_to_truncated_stdout[self.full_stdout] def hash_full_stdout(self, full_stdout: str) -> str: return md5_hash(full_stdout) @cache_with_pickle(hash_full_stdout) def _get_truncated_stdout(self, full_stdout: str) -> str: return shrink_text( filter_redundant_text(full_stdout), context_lines=RD_AGENT_SETTINGS.stdout_context_len, line_len=RD_AGENT_SETTINGS.stdout_line_len, ) class Env(Generic[ASpecificEnvConf]): """ We use BaseModel as the setting due to the features it provides - It provides base typing and checking features. - loading and dumping the information will be easier: for example, we can use package like `pydantic-yaml` """ conf: ASpecificEnvConf # different env have different conf. def __init__(self, conf: ASpecificEnvConf): self.conf = conf def zip_a_folder_into_a_file(self, folder_path: str, zip_file_path: str) -> None: """ Zip a folder into a file, use zipfile instead of subprocess """ with zipfile.ZipFile(zip_file_path, "w") as z: for root, _, files in os.walk(folder_path): for file in files: z.write( os.path.join(root, file), os.path.relpath(os.path.join(root, file), folder_path), ) def unzip_a_file_into_a_folder( self, zip_file_path: str, folder_path: str, files_to_extract: list[str] | None = None ) -> None: """ Unzip a file into a folder, use zipfile instead of subprocess """ if files_to_extract is None: # Clear folder_path before extracting if os.path.exists(folder_path): shutil.rmtree(folder_path) os.makedirs(folder_path) with zipfile.ZipFile(zip_file_path, "r") as z: if files_to_extract is not None: for file_name in files_to_extract: try: z.extract(file_name, folder_path) except KeyError: logger.warning(f"File {file_name} not found in cache zip.") else: z.extractall(folder_path) @abstractmethod def prepare(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def] """ Prepare for the environment based on it's configure """ def check_output( self, entry: str | None = None, local_path: str = ".", env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), cache_key_extra_func: CacheKeyFunc | None = None, cache_files_to_extract: list[str] | None = None, ) -> str: result = self.run( entry=entry, local_path=local_path, env=env, running_extra_volume=running_extra_volume, cache_key_extra_func=cache_key_extra_func, cache_files_to_extract=cache_files_to_extract, ) return result.stdout def __run_with_retry( self, entry: str | None = None, local_path: str = ".", env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), ) -> EnvResult: for retry_index in range(self.conf.retry_count + 1): try: start = time.time() log_output, return_code = self._run( entry, local_path, env, running_extra_volume=running_extra_volume, ) end = time.time() logger.info(f"Running time: {end - start} seconds") if self.conf.running_timeout_period is not None and end - start + 1 >= self.conf.running_timeout_period: logger.warning( f"The running time exceeds {self.conf.running_timeout_period} seconds, so the process is killed." ) log_output += f"\n\nThe running time exceeds {self.conf.running_timeout_period} seconds, so the process is killed." return EnvResult(log_output, return_code, end - start) except Exception as e: if retry_index == self.conf.retry_count: raise logger.warning( f"Error while running the container: {e}, current try index: {retry_index + 1}, {self.conf.retry_count - retry_index - 1} retries left." ) time.sleep(self.conf.retry_wait_seconds) raise RuntimeError # for passing CI def run( self, entry: str | None = None, local_path: str = ".", env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), cache_key_extra_func: CacheKeyFunc | None = None, cache_files_to_extract: list[str] | None = None, ) -> EnvResult: """ Run the folder under the environment and return the stdout, exit code, and running time. 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. running_extra_volume : Mapping Extra volumes to mount during execution. cache_key_extra_func : CacheKeyFunc | None Optional function to calculate extra information for cache key calculation cache_files_to_extract : list[str] | None Optional list of files to extract from cache zip. If None, extract all. Returns ------- EnvResult: An object containing the stdout, the exit code, and the running time in seconds. """ _env = self.conf.env_dict.copy() if env: _env.update(env) env = _env if entry is None: entry = self.conf.default_entry if "|" in entry: logger.warning( "You are using a command with a shell pipeline (i.e., '|'). " "The exit code ($exit_code) will reflect the result of " "the last command in the pipeline.", ) # Exclude configured directories from chmod operation to prevent modifying # read-only or specially configured directories that may produce warnings. def _get_chmod_cmd(workspace_path: str) -> str: find_cmd = f"find {workspace_path} -mindepth 1 -maxdepth 1" # Use configurable exclude paths from DockerConf for name in self.conf.exclude_chmod_paths: if name: # Skip empty names find_cmd += f" ! -name {name}" chmod_cmd = f"{find_cmd} -exec chmod -R 777 {{}} +" return chmod_cmd if self.conf.redirect_stdout_to_file: log_file_name = md5_hash(entry)[:8] + ".log" log_file = Path(local_path) / f"{log_file_name}" log_file_relative_path = log_file.relative_to(Path(local_path)) entry = f"{entry} > {log_file_relative_path} 2>&1" if self.conf.running_timeout_period is None: timeout_cmd = entry else: timeout_cmd = f"timeout --kill-after=10 {self.conf.running_timeout_period} {entry}" entry_add_timeout = ( f"/bin/sh -c '" # start of the sh command + f"{timeout_cmd}; entry_exit_code=$?; " + ( f"{_get_chmod_cmd(self.conf.mount_path)}; " # We don't have to change the permission of the cache and input folder to remove it # + f"if [ -d {self.conf.mount_path}/cache ]; then chmod 777 {self.conf.mount_path}/cache; fi; " + # f"if [ -d {self.conf.mount_path}/input ]; then chmod 777 {self.conf.mount_path}/input; fi; " if isinstance(self.conf, DockerConf) else "" ) + "exit $entry_exit_code" + "'" # end of the sh command ) if self.conf.enable_cache: result = self.cached_run( entry_add_timeout, local_path, env, running_extra_volume, cache_key_extra_func, cache_files_to_extract, ) else: result = self.__run_with_retry( entry_add_timeout, local_path, env, running_extra_volume, ) if self.conf.redirect_stdout_to_file: stdout = log_file.read_text(errors="replace") log_file.unlink(missing_ok=True) result.update_stdout(stdout) if str(Path(local_path).resolve()) in result.stdout: result.update_stdout(result.stdout.replace(str(Path(local_path).resolve()), "")) return result def cached_run( self, entry: str | None = None, local_path: str = ".", env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), cache_key_extra_func: CacheKeyFunc | None = None, cache_files_to_extract: list[str] | None = None, ) -> EnvResult: """ Run the folder under the environment. Will cache the output and the folder diff for next round of running. Use the python codes and the parameters(entry, running_extra_volume) as key to hash the input. """ target_folder = Path(RD_AGENT_SETTINGS.pickle_cache_folder_path_str) / f"utils.env.run" target_folder.mkdir(parents=True, exist_ok=True) if cache_key_extra_func is not None: cache_key_extra = cache_key_extra_func(local_path) else: cache_key_extra = self.conf.get_workspace_content_for_hash(local_path) key = md5_hash( json.dumps(cache_key_extra) + json.dumps({"entry": entry, "running_extra_volume": dict(running_extra_volume)}) + json.dumps({"extra_volumes": self.conf.extra_volumes}) # + json.dumps(data_key) ) if Path(target_folder / f"{key}.pkl").exists() and Path(target_folder / f"{key}.zip").exists(): with open(target_folder / f"{key}.pkl", "rb") as f: ret = pickle.load(f) self.unzip_a_file_into_a_folder(str(target_folder / f"{key}.zip"), local_path, cache_files_to_extract) else: ret = self.__run_with_retry(entry, local_path, env, running_extra_volume) with open(target_folder / f"{key}.pkl", "wb") as f: pickle.dump(ret, f) self.zip_a_folder_into_a_file(local_path, str(target_folder / f"{key}.zip")) return cast(EnvResult, ret) @abstractmethod def _run( self, entry: str | None, local_path: str = ".", env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), **kwargs: Any, ) -> tuple[str, int]: """ Execute the specified entry point within the given environment and local path. Parameters ---------- entry : str | None The entry point to execute. If None, defaults to the configured entry. local_path : str The local directory path where the execution should occur. env : dict | None Environment variables to set during execution. kwargs : dict Additional keyword arguments for execution customization. Returns ------- tuple[str, int] A tuple containing the standard output and the exit code. """ pass def dump_python_code_run_and_get_results( self, code: str, dump_file_names: list[str], local_path: str, env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), code_dump_file_py_name: Optional[str] = None, ) -> tuple[str, list]: """ Dump the code into the local path and run the code. """ random_file_name = f"{uuid.uuid4()}.py" if code_dump_file_py_name is None else f"{code_dump_file_py_name}.py" with open(os.path.join(local_path, random_file_name), "w") as f: f.write(code) entry = f"python {random_file_name}" log_output = self.check_output(entry, local_path, env, running_extra_volume=dict(running_extra_volume)) results = [] os.remove(os.path.join(local_path, random_file_name)) for name in dump_file_names: if os.path.exists(os.path.join(local_path, f"{name}")): results.append(pickle.load(open(os.path.join(local_path, f"{name}"), "rb"))) os.remove(os.path.join(local_path, f"{name}")) else: return log_output, [] return log_output, results def refresh_env(self) -> None: """Refresh the environment, e.g., pull the latest docker image. rebuild the conda env.""" pass # class EnvWithCache # ## Local Environment ----- class LocalConf(EnvConf): bin_path: str = "" """path like ::, which will be prepend to bin path.""" retry_count: int = 0 # retry count for; run `retry_count + 1` times live_output: bool = True ASpecificLocalConf = TypeVar("ASpecificLocalConf", bound=LocalConf) class LocalEnv(Env[ASpecificLocalConf]): """ Sometimes local environment may be more convenient for testing """ def prepare(self) -> None: ... def _run( self, entry: str | None = None, local_path: str | None = None, env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), **kwargs: dict, ) -> tuple[str, int]: # Handle volume links volumes = {} if self.conf.extra_volumes is not None: for lp, rp in self.conf.extra_volumes.items(): volumes[lp] = rp["bind"] if isinstance(rp, dict) else rp cache_path = "/tmp/sample" if "/sample/" in "".join(self.conf.extra_volumes.keys()) else "/tmp/full" Path(cache_path).mkdir(parents=True, exist_ok=True) volumes[cache_path] = T("scenarios.data_science.share:scen.cache_path").r() for lp, rp in running_extra_volume.items(): volumes[lp] = rp assert local_path is not None, "local_path should not be None" volumes = normalize_volumes(volumes, local_path) @contextlib.contextmanager def _symlink_ctx(vol_map: Mapping[str, str]) -> Generator[None, None, None]: created_links: list[Path] = [] try: for real, link in vol_map.items(): link_path = Path(link) real_path = Path(real) if not link_path.parent.exists(): link_path.parent.mkdir(parents=True, exist_ok=True) if link_path.exists() or link_path.is_symlink(): link_path.unlink() link_path.symlink_to(real_path) created_links.append(link_path) yield finally: for p in created_links: try: if p.is_symlink() or p.exists(): p.unlink() except FileNotFoundError: pass with _symlink_ctx(volumes): # Setup environment if env is None: env = {} # Auto-propagate CUDA_VISIBLE_DEVICES for proper GPU isolation if "CUDA_VISIBLE_DEVICES" in os.environ and "CUDA_VISIBLE_DEVICES" not in env: env["CUDA_VISIBLE_DEVICES"] = os.environ["CUDA_VISIBLE_DEVICES"] path = [ *self.conf.bin_path.split(":"), "/bin/", "/usr/bin/", *env.get("PATH", "").split(":"), ] env["PATH"] = ":".join(path) if entry is None: entry = self.conf.default_entry print(Rule("[bold green]LocalEnv Logs Begin[/bold green]", style="dark_orange")) table = Table(title="Run Info", show_header=False) table.add_column("Key", style="bold cyan") table.add_column("Value", style="bold magenta") table.add_row("Entry", entry) table.add_row("Local Path", local_path or "") table.add_row("Env", "\n".join(f"{k}:{v}" for k, v in env.items())) table.add_row("Volumes", "\n".join(f"{k}:\n {v}" for k, v in volumes.items())) print(table) cwd = Path(local_path).resolve() if local_path else None env = {k: str(v) if isinstance(v, int) else v for k, v in env.items()} process = subprocess.Popen( entry, cwd=cwd, env={**os.environ, **env}, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, shell=True, bufsize=1, universal_newlines=True, ) # Setup polling if process.stdout is None or process.stderr is None: raise RuntimeError("The subprocess did not correctly create stdout/stderr pipes") if self.conf.live_output: stdout_fd = process.stdout.fileno() stderr_fd = process.stderr.fileno() poller = select.poll() poller.register(stdout_fd, select.POLLIN) poller.register(stderr_fd, select.POLLIN) combined_output = "" while True: if process.poll() is not None: break events = poller.poll(100) for fd, event in events: if event & select.POLLIN: if fd == stdout_fd: while True: output = process.stdout.readline() if output == "": break Console().print(output.strip(), markup=False) combined_output += output elif fd == stderr_fd: while True: error = process.stderr.readline() if error == "": break Console().print(error.strip(), markup=False) combined_output += error # Capture any final output remaining_output, remaining_error = process.communicate() if remaining_output: Console().print(remaining_output.strip(), markup=False) combined_output += remaining_output if remaining_error: Console().print(remaining_error.strip(), markup=False) combined_output += remaining_error else: # Sacrifice real-time output to avoid possible standard I/O hangs out, err = process.communicate() Console().print(out, end="", markup=False) Console().print(err, end="", markup=False) combined_output = out + err return_code = process.returncode print(Rule("[bold green]LocalEnv Logs End[/bold green]", style="dark_orange")) return combined_output, return_code class CondaConf(LocalConf): conda_env_name: str default_entry: str = "python main.py" @model_validator(mode="after") def change_bin_path(self, **data: Any) -> "CondaConf": self._update_bin_path() return self def _update_bin_path(self) -> None: """Update bin_path by querying the conda environment's PATH. This is called during initialization and can be called again after prepare() to ensure bin_path is set correctly even if the conda env was just created. """ conda_path_result = subprocess.run( f"conda run -n {self.conda_env_name} --no-capture-output env | grep '^PATH='", capture_output=True, text=True, shell=True, ) self.bin_path = conda_path_result.stdout.strip().split("=")[1] if conda_path_result.returncode == 0 else "" class MLECondaConf(CondaConf): enable_cache: bool = False # aligning with the docker settings. ## Docker Environment ----- class DockerConf(EnvConf): build_from_dockerfile: bool = False dockerfile_folder_path: Optional[Path] = ( None # the path to the dockerfile optional path provided when build_from_dockerfile is False ) image: str # the image you want to build mount_path: str # the path in the docker image to mount the folder default_entry: str # the entry point of the image extra_volumes: dict = {} """It accept a dict of volumes, which can be either {: } or {: {"bind": , "mode": }} """ extra_volume_mode: str = "ro" # by default. only the mount_path should be writable, others are changed to read-only exclude_chmod_paths: list[str] = [] """List of directory names to exclude from chmod -R 777 operation. This prevents modifying permissions of read-only or specially configured directories.""" # Declarative configuration for auto-populating exclude_chmod_paths from share.yaml # Subclasses can override these to specify which config keys to read _scenario_name: str | None = None # e.g., "data_science", "finetune" _exclude_path_keys: list[str] = [] # e.g., ["input_path", "cache_path"] # 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. network: str | None = "bridge" # the network mode for the docker shm_size: str | None = None enable_gpu: bool = True # because we will automatically disable GPU if not available. So we enable it by default. mem_limit: str | None = "48g" # Add memory limit attribute cpu_count: int | None = None # Add CPU limit attribute running_timeout_period: int | None = 3600 # 1 hour enable_cache: bool = True # enable the cache mechanism retry_count: int = 5 # retry count for the docker run retry_wait_seconds: int = 10 # retry wait seconds for the docker run save_logs_to_file: bool = True terminal_tail_lines: int = 20 @model_validator(mode="after") def populate_exclude_chmod_paths(self) -> "DockerConf": """ Automatically populate exclude_chmod_paths from share.yaml configuration. This method reads path configurations from scenarios//share.yaml based on _scenario_name and _exclude_path_keys class attributes. """ if not self.exclude_chmod_paths and self._scenario_name and self._exclude_path_keys: # Extract directory names from scenario configuration self.exclude_chmod_paths = [ name for key in self._exclude_path_keys if ( name := extract_dir_name_from_path_config( T(f"scenarios.{self._scenario_name}.share:scen.{key}").r() ) ) ] return self class QlibCondaConf(CondaConf): conda_env_name: str = "rdagent4qlib" enable_cache: bool = False default_entry: str = "qrun conf.yaml" # extra_volumes: dict = {str(Path("~/.qlib/").expanduser().resolve().absolute()): "/root/.qlib/"} class QlibCondaEnv(LocalEnv[QlibCondaConf]): def prepare(self) -> None: """Prepare the conda environment if not already created.""" try: envs = subprocess.run("conda env list", capture_output=True, text=True, shell=True) if self.conf.conda_env_name not in envs.stdout: print(f"[yellow]Conda env '{self.conf.conda_env_name}' not found, creating...[/yellow]") subprocess.check_call( f"conda create -y -n {self.conf.conda_env_name} python=3.10", shell=True, ) subprocess.check_call( f"conda run -n {self.conf.conda_env_name} pip install --upgrade pip cython", shell=True, ) subprocess.check_call( f"conda run -n {self.conf.conda_env_name} pip install git+https://github.com/microsoft/qlib.git@2fb9380b342556ddb50a4b24e4fe8655d548b2b8", shell=True, ) subprocess.check_call( f"conda run -n {self.conf.conda_env_name} pip install catboost xgboost tables torch", shell=True, ) except Exception as e: print(f"[red]Failed to prepare conda env: {e}[/red]") # ========== Conda Environment Configuration Loader ========== # Config files location: rdagent/scenarios/finetune/env/conda/ FT_CONDA_CONFIG_DIR = Path(__file__).parent.parent / "scenarios" / "finetune" / "env" / "conda" # Track which conda environments have been prepared in this process # This avoids redundant pip install checks that produce verbose output _CONDA_ENV_PREPARED: set[str] = set() def _sync_conda_cache_with_real_envs() -> None: """Ensure the prepared cache includes environments that already exist on disk.""" try: result = subprocess.run( "conda env list", capture_output=True, text=True, shell=True, check=False, ) except Exception as exc: # pragma: no cover - best-effort helper logger.warning(f"Failed to inspect conda env list: {exc}") return env_names: set[str] = set() for line in result.stdout.splitlines(): line = line.strip() if not line or line.startswith("#"): continue # Lines look like: "base * /opt/conda" first_column = line.split()[0] name = first_column.replace("*", "").strip() if name: env_names.add(name) _CONDA_ENV_PREPARED.update(env_names) def _prepare_conda_env(env_name: str, requirements_file: Path, python_version: str = "3.10") -> None: """Prepare conda environment with dependencies from requirements.txt. Creates the env if it doesn't exist, then installs dependencies. Uses a process-level cache to avoid redundant preparation in the same run. Args: env_name: Conda environment name requirements_file: Path to requirements.txt file python_version: Python version for the environment """ # 1. Create conda environment if not exists result = subprocess.run(f"conda env list | grep -q '^{env_name} '", shell=True) if result.returncode != 0: print(f"[yellow]Creating conda env '{env_name}' (Python {python_version})...[/yellow]") subprocess.check_call(f"conda create -y -n {env_name} python={python_version}", shell=True) subprocess.check_call(f"conda run -n {env_name} pip install --upgrade pip", shell=True) print(f"[yellow]Installing dependencies from {requirements_file.name}...[/yellow]") subprocess.check_call(f"conda run -n {env_name} pip install -r {requirements_file}", shell=True) print(f"[green]Conda env '{env_name}' ready[/green]") _CONDA_ENV_PREPARED.add(env_name) # ========== FT (LLaMA Factory) Conda Environment ========== class FTCondaConf(CondaConf): """Conda configuration for LLM fine-tuning environment.""" model_config = SettingsConfigDict(env_prefix="FT_CONDA_") conda_env_name: str = "llm_finetune" default_entry: str = "llamafactory-cli version" enable_cache: bool = False class FTCondaEnv(LocalEnv[FTCondaConf]): """LLaMA Factory Conda Environment with auto-dependency installation. Requirements: rdagent/scenarios/finetune/conda/llm_finetune_requirements.txt Docker equivalent: rdagent/scenarios/finetune/docker/llm_finetune_docker/Dockerfile """ def prepare(self) -> None: try: # Skip if already prepared _sync_conda_cache_with_real_envs() if self.conf.conda_env_name in _CONDA_ENV_PREPARED: return # Step 1: Install base dependencies (torch, llamafactory, etc.) req_file = FT_CONDA_CONFIG_DIR / "llm_finetune_requirements.txt" _prepare_conda_env(self.conf.conda_env_name, req_file) # Step 2: Install flash-attn (requires torch first, uses --no-build-isolation) # --no-cache-dir: avoid cross-filesystem hardlink error when /tmp and ~/.cache/pip are on different mounts # Note: flash-attn>=2.8 is required for B200 (sm_100) support print("[yellow]Installing flash-attn (compiling, may take a few minutes)...[/yellow]") subprocess.check_call( f"conda run -n {self.conf.conda_env_name} pip install 'flash-attn>=2.8' --no-build-isolation --no-cache-dir", shell=True, ) # Re-update bin_path after prepare() in case the conda env was just created if not self.conf.bin_path: self.conf._update_bin_path() except Exception as e: print(f"[red]Failed to prepare LLaMA Factory conda env: {e}[/red]") # ========== Benchmark (OpenCompass) Conda Environment ========== class BenchmarkCondaConf(CondaConf): """Conda configuration for OpenCompass benchmark evaluation.""" model_config = SettingsConfigDict(env_prefix="BENCHMARK_CONDA_") conda_env_name: str = "opencompass" default_entry: str = "opencompass --help" enable_cache: bool = False env_dict: dict = {"COMPASS_DATA_CACHE": "/benchmarks/opencompass_data"} class BenchmarkCondaEnv(LocalEnv[BenchmarkCondaConf]): """OpenCompass Conda Environment with auto-dependency installation. Requirements: rdagent/scenarios/finetune/conda/opencompass_requirements.txt Docker equivalent: rdagent/scenarios/finetune/docker/opencompass/Dockerfile """ def prepare(self) -> None: try: # Skip if already prepared _sync_conda_cache_with_real_envs() if self.conf.conda_env_name in _CONDA_ENV_PREPARED: return req_file = FT_CONDA_CONFIG_DIR / "opencompass_requirements.txt" _prepare_conda_env(self.conf.conda_env_name, req_file) # Re-update bin_path after prepare() in case the conda env was just created if not self.conf.bin_path: self.conf._update_bin_path() except Exception as e: print(f"[red]Failed to prepare OpenCompass conda env: {e}[/red]") class QlibDockerConf(DockerConf): model_config = SettingsConfigDict( env_prefix="QLIB_DOCKER_", env_parse_none_str="None", # Nthis is the key to accept `RUNNING_TIMEOUT_PERIOD=None` ) build_from_dockerfile: bool = True dockerfile_folder_path: Path = Path(__file__).parent.parent / "scenarios" / "qlib" / "docker" image: str = "local_qlib:latest" mount_path: str = "/workspace/qlib_workspace/" default_entry: str = "qrun conf.yaml" extra_volumes: dict = { str(Path("~/.qlib/").expanduser().resolve().absolute()): { "bind": "/root/.qlib/", "mode": "rw", } } shm_size: str | None = "16g" enable_gpu: bool = True enable_cache: bool = False save_logs_to_file: bool = True # Explicitly inherit from DockerConf for compatibility class KGDockerConf(DockerConf): model_config = SettingsConfigDict(env_prefix="KG_DOCKER_") build_from_dockerfile: bool = True dockerfile_folder_path: Path = Path(__file__).parent.parent / "scenarios" / "kaggle" / "docker" / "kaggle_docker" image: str = "local_kg:latest" # image: str = "gcr.io/kaggle-gpu-images/python:latest" mount_path: str = "/workspace/kg_workspace/" default_entry: str = "python train.py" # extra_volumes: dict = { # # TODO connect to the place where the data is stored # Path("git_ignore_folder/data").resolve(): "/root/.data/" # } running_timeout_period: int | None = 600 mem_limit: str | None = ( "48g" # Add memory limit attribute # new-york-city-taxi-fare-prediction may need more memory ) class DSDockerConf(DockerConf): model_config = SettingsConfigDict(env_prefix="DS_DOCKER_") build_from_dockerfile: bool = True dockerfile_folder_path: Path = Path(__file__).parent.parent / "scenarios" / "kaggle" / "docker" / "DS_docker" image: str = "local_ds:latest" mount_path: str = "/kaggle/workspace" default_entry: str = "python main.py" running_timeout_period: int | None = 600 mem_limit: str | None = ( "48g" # Add memory limit attribute # new-york-city-taxi-fare-prediction may need more memory ) # Declarative configuration: automatically loads from scenarios/data_science/share.yaml _scenario_name: str = "data_science" _exclude_path_keys: list[str] = ["input_path", "cache_path"] class MLEBDockerConf(DockerConf): model_config = SettingsConfigDict(env_prefix="MLEB_DOCKER_") build_from_dockerfile: bool = True dockerfile_folder_path: Path = Path(__file__).parent.parent / "scenarios" / "kaggle" / "docker" / "mle_bench_docker" image: str = "local_mle:latest" # image: str = "gcr.io/kaggle-gpu-images/python:latest" mount_path: str = "/workspace/data_folder/" default_entry: str = "mlebench prepare --all" # extra_volumes: dict = { # # TODO connect to the place where the data is stored # Path("git_ignore_folder/data").resolve(): "/root/.data/" # } mem_limit: str | None = ( "48g" # Add memory limit attribute # new-york-city-taxi-fare-prediction may need more memory ) enable_cache: bool = False class FTDockerConf(DockerConf): model_config = SettingsConfigDict(env_prefix="FT_DOCKER_") build_from_dockerfile: bool = True dockerfile_folder_path: Path = ( Path(__file__).parent.parent / "scenarios" / "finetune" / "env" / "docker" / "llm_finetune" ) image: str = "local_llm_finetune:latest" mount_path: str = "/workspace/" default_entry: str = "llamafactory-cli version" running_timeout_period: int | None = 36000 # 10 hours for training mem_limit: str | None = "48g" # Large memory for LLM training shm_size: str | None = "16g" # Shared memory for multi-GPU training enable_gpu: bool = True # Enable GPU for LLM training enable_cache: bool = False # Disable cache to avoid conflicts during training, True for debug # Override log output control for FT training save_logs_to_file: bool = True terminal_tail_lines: int = 20 # Declarative configuration: automatically loads from scenarios/finetune/share.yaml _scenario_name: str = "finetune" _exclude_path_keys: list[str] = ["assets_path"] network: str | None = "host" # Use host network for finetune access to litellm proxy def get_workspace_content_for_hash(self, local_path: str | Path) -> list[list[str]]: """Include dataset_info.json in cache key calculation.""" content = super().get_workspace_content_for_hash(local_path) local_path = Path(local_path) # Add dataset_info.json if it exists # NOTE: data.json is excluded because it is a generated file for path in local_path.rglob("dataset_info.json"): content.append([str(path.relative_to(local_path)), path.read_text()]) # Sort again to ensure deterministic order (though super is sorted, appended one might not be) content.sort(key=lambda x: x[0]) return content class BenchmarkDockerConf(DockerConf): """Docker configuration for OpenCompass benchmark evaluation.""" model_config = SettingsConfigDict(env_prefix="BENCHMARK_DOCKER_") build_from_dockerfile: bool = True dockerfile_folder_path: Path = ( Path(__file__).parent.parent / "scenarios" / "finetune" / "env" / "docker" / "opencompass" ) image: str = "rdagent-opencompass:latest" mount_path: str = "/workspace/" default_entry: str = "opencompass --help" running_timeout_period: int | None = 3600 # 1 hour default for benchmarks mem_limit: str | None = "32g" # Moderate memory for inference shm_size: str | None = "8g" # Shared memory for model loading enable_gpu: bool = True # Enable GPU for fast inference enable_cache: bool = False # Disable cache for reproducibility # Benchmark-specific log settings save_logs_to_file: bool = True terminal_tail_lines: int = 50 # Show more lines for benchmark progress network: str | None = "host" # Use host network for benchmark access to litellm proxy env_dict: dict = {"COMPASS_DATA_CACHE": "/benchmarks/opencompass_data"} # physionet.org/files/mimic-eicu-fiddle-feature/1.0.0/FIDDLE_mimic3 class DockerEnv(Env[DockerConf]): # TODO: Save the output into a specific file def prepare(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def] """ Download image if it doesn't exist """ client = docker.from_env() if ( self.conf.build_from_dockerfile and self.conf.dockerfile_folder_path is not None and self.conf.dockerfile_folder_path.exists() ): logger.info(f"Building the image from dockerfile: {self.conf.dockerfile_folder_path}") resp_stream = client.api.build( path=str(self.conf.dockerfile_folder_path), tag=self.conf.image, network_mode=self.conf.network, ) if isinstance(resp_stream, str): logger.info(resp_stream) with Progress(SpinnerColumn(), TextColumn("{task.description}")) as p: task = p.add_task("[cyan]Building image...") for part in resp_stream: lines = part.decode("utf-8").split("\r\n") for line in lines: if line.strip(): status_dict = json.loads(line) if "error" in status_dict: p.update( task, description=f"[red]error: {status_dict['error']}", ) raise docker.errors.BuildError(status_dict["error"], "") if "stream" in status_dict: p.update(task, description=status_dict["stream"]) logger.info(f"Finished building the image from dockerfile: {self.conf.dockerfile_folder_path}") try: client.images.get(self.conf.image) except docker.errors.ImageNotFound: image_pull = client.api.pull(self.conf.image, stream=True, decode=True) current_status = "" layer_set = set() completed_layers = 0 with Progress(TextColumn("{task.description}"), TextColumn("{task.fields[progress]}")) as sp: main_task = sp.add_task("[cyan]Pulling image...", progress="") status_task = sp.add_task("[bright_magenta]layer status", progress="") for line in image_pull: if "error" in line: sp.update( status_task, description=f"[red]error", progress=line["error"], ) raise docker.errors.APIError(line["error"]) layer_id = line["id"] status = line["status"] p_text = line.get("progress", None) if layer_id not in layer_set: layer_set.add(layer_id) if p_text: current_status = p_text if status == "Pull complete" or status == "Already exists": completed_layers += 1 sp.update( main_task, progress=f"[green]{completed_layers}[white]/{len(layer_set)} layers completed", ) sp.update( status_task, description=f"[bright_magenta]layer {layer_id} [yellow]{status}", progress=current_status, ) except docker.errors.APIError as e: raise RuntimeError(f"Error while pulling the image: {e}") def _gpu_kwargs(self, client: docker.DockerClient) -> dict: # type: ignore[no-any-unimported] """get gpu kwargs based on its availability. Supports GPU selection via CUDA_VISIBLE_DEVICES environment variable. If set, only the specified GPUs will be available in the container. Example: CUDA_VISIBLE_DEVICES=0,1 will only expose GPU 0 and 1. """ if not self.conf.enable_gpu: return {} # Check if specific GPUs are requested via CUDA_VISIBLE_DEVICES cuda_visible = os.environ.get("CUDA_VISIBLE_DEVICES") if cuda_visible: # Use device_ids to specify exact GPUs (cannot use count with device_ids) device_ids = [gpu.strip() for gpu in cuda_visible.split(",") if gpu.strip()] gpu_kwargs = { "device_requests": [docker.types.DeviceRequest(device_ids=device_ids, capabilities=[["gpu"]])], } logger.info(f"GPU selection: using specific GPUs {device_ids}") else: # Default: use all available GPUs gpu_kwargs = { "device_requests": [docker.types.DeviceRequest(count=-1, capabilities=[["gpu"]])], } def get_image(image_name: str) -> None: try: client.images.get(image_name) except docker.errors.ImageNotFound: pull_image_with_progress(image_name) @wait_retry(5, 10) def _f() -> dict: container = None try: get_image(self.conf.image) container = client.containers.run(self.conf.image, "nvidia-smi", detach=True, **gpu_kwargs) # Wait for container to complete container.wait() logger.info("GPU Devices are available.") except docker.errors.APIError: return {} finally: cleanup_container(container, context="GPU test") return gpu_kwargs return _f() def _generate_log_header(self, entry: str | None = None) -> str: """ Generate a header for log files with execution info. Args: entry: Command entry that was executed Returns: Formatted header string """ timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") header = "=" * 80 + "\n" header += f"Docker Execution Log\n" header += f"Timestamp: {timestamp}\n" header += f"Image: {self.conf.image}\n" if entry: header += f"Command: {entry}\n" header += "=" * 80 + "\n\n" return header def _process_container_logs(self, logs: Iterable[bytes], local_path: str = ".", entry: str | None = None) -> str: """ Process Docker container logs with optional tail mode. This method can be controlled via configuration: - save_logs_to_file: Save full logs to timestamped files in logs/ subdirectory - terminal_tail_lines: Show only last N lines in terminal (0 = show all) Args: logs: Docker container log stream local_path: Path to workspace for saving log files entry: Command entry that was executed (for logging header) Returns: Complete log output as string """ log_output = "" # Determine if we should use tail mode use_tail_mode = self.conf.terminal_tail_lines > 0 save_to_file = self.conf.save_logs_to_file # Set up log file with timestamp if needed log_file_path = None if save_to_file and local_path: workspace = Path(local_path) # Create logs subdirectory logs_dir = workspace / "logs" logs_dir.mkdir(parents=True, exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") log_file_path = logs_dir / f"docker_execution_{timestamp}.log" # Write header with execution info header = self._generate_log_header(entry) with open(log_file_path, "w", encoding="utf-8") as f: f.write(header) # Also create/update a symlink to the latest log for convenience latest_link = logs_dir / "docker_execution_latest.log" print(f"[cyan]Full logs will be saved to: {log_file_path.absolute()}[/cyan]") # Process logs with tail mode if use_tail_mode: log_buffer: Deque[str] = deque(maxlen=self.conf.terminal_tail_lines) def format_tail_display() -> Text: text = Text() text.append( f"[Showing last {len(log_buffer)}/{self.conf.terminal_tail_lines} lines", style="dim", ) if log_file_path: text.append(f" | Full log: {log_file_path.name}]\n", style="dim cyan") else: text.append("]\n", style="dim") text.append("-" * 80 + "\n", style="dim") for line in log_buffer: text.append(line + "\n") return text with Live(format_tail_display(), refresh_per_second=2, console=Console()) as live: for log in logs: decoded_log = log.strip().decode() log_output += decoded_log + "\n" log_buffer.append(decoded_log) if log_file_path: with open(log_file_path, "a", encoding="utf-8") as f: f.write(decoded_log + "\n") live.update(format_tail_display()) else: # Default behavior: show all logs for log in logs: decoded_log = log.strip().decode() Console().print(decoded_log, markup=False) log_output += decoded_log + "\n" if log_file_path: with open(log_file_path, "a", encoding="utf-8") as f: f.write(decoded_log + "\n") # Show log file location and create latest symlink if log_file_path and log_file_path.exists(): print(f"[green]Full execution log saved to: {log_file_path.absolute()}[/green]") # Create or update symlink to latest log latest_link = log_file_path.parent / "docker_execution_latest.log" if latest_link.exists() or latest_link.is_symlink(): latest_link.unlink() try: latest_link.symlink_to(log_file_path.name) print(f"[dim]Latest log symlink: logs/{latest_link.name} -> {log_file_path.name}[/dim]") except Exception: # Symlinks might not work on all systems (e.g., Windows without admin) pass return log_output def _run( self, entry: str | None = None, local_path: str = ".", env: dict | None = None, running_extra_volume: Mapping = MappingProxyType({}), **kwargs: Any, ) -> tuple[str, int]: if env is None: env = {} env["PYTHONWARNINGS"] = "ignore" env["TF_CPP_MIN_LOG_LEVEL"] = "2" env["PYTHONUNBUFFERED"] = "1" env["TOKENIZERS_PARALLELISM"] = "false" # Avoid tokenizer fork warning in multi-process training client = docker.from_env() volumes = {} if local_path is not None: local_path = os.path.abspath(local_path) volumes[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(): volumes[lp] = rp if isinstance(rp, dict) else {"bind": rp, "mode": self.conf.extra_volume_mode} cache_path = "/tmp/sample" if "/sample/" in "".join(self.conf.extra_volumes.keys()) else "/tmp/full" Path(cache_path).mkdir(parents=True, exist_ok=True) volumes[cache_path] = { "bind": T("scenarios.data_science.share:scen.cache_path").r(), "mode": "rw", } for lp, rp in running_extra_volume.items(): volumes[lp] = rp if isinstance(rp, dict) else {"bind": rp, "mode": self.conf.extra_volume_mode} volumes = normalize_volumes(cast(dict[str, str | dict[str, str]], volumes), self.conf.mount_path) log_output = "" container: docker.models.containers.Container | None = None # type: ignore[no-any-unimported] try: container = client.containers.run( image=self.conf.image, command=entry, volumes=volumes, environment=env, detach=True, working_dir=self.conf.mount_path, # auto_remove=True, # remove too fast might cause the logs not to be get network=self.conf.network, shm_size=self.conf.shm_size, mem_limit=self.conf.mem_limit, # Set memory limit cpu_count=self.conf.cpu_count, # Set CPU limit **self._gpu_kwargs(client), ) assert container is not None # Ensure container was created successfully logs = container.logs(stream=True) print(Rule("[bold green]Docker Logs Begin[/bold green]", style="dark_orange")) table = Table(title="Run Info", show_header=False) table.add_column("Key", style="bold cyan") table.add_column("Value", style="bold magenta") table.add_row("Image", self.conf.image) table.add_row("Container ID", container.id) table.add_row("Container Name", container.name) table.add_row("Entry", entry) table.add_row("Env", "\n".join(f"{k}:{v}" for k, v in env.items())) table.add_row("Volumes", "\n".join(f"{k}:\n {v}" for k, v in volumes.items())) print(table) # Process logs (supports tail mode if configured) log_output = self._process_container_logs(logs, local_path, entry=entry) exit_status = container.wait()["StatusCode"] print(Rule("[bold green]Docker Logs End[/bold green]", style="dark_orange")) return log_output, exit_status 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}") finally: cleanup_container(container) def refresh_env(self) -> None: """Remove the Docker image associated with this environment.""" client = docker.from_env() try: # Remove the specific image client.images.remove(image=self.conf.image, force=True) logger.info(f"Removed Docker image: {self.conf.image}") client.images.prune() client.api.prune_builds() logger.info(f"Successfully removed Docker image: {self.conf.image}") except docker.errors.ImageNotFound: logger.warning(f"Docker image not found, cannot remove: {self.conf.image}") except docker.errors.APIError as e: logger.error(f"Error while removing Docker image: {e}") self.prepare() class QTDockerEnv(DockerEnv): """Qlib Torch Docker""" def __init__(self, conf: DockerConf = QlibDockerConf()): super().__init__(conf) def prepare(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def] """ 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(): logger.info("We are downloading!") cmd = "python -m qlib.run.get_data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn --interval 1d --delete_old False" self.check_output(entry=cmd) else: logger.info("Data already exists. Download skipped.") class KGDockerEnv(DockerEnv): """Kaggle Competition Docker""" def __init__(self, competition: str | None = None, conf: DockerConf = KGDockerConf()): super().__init__(conf) class MLEBDockerEnv(DockerEnv): """MLEBench Docker""" def __init__(self, conf: DockerConf = MLEBDockerConf()): super().__init__(conf) class FTDockerEnv(DockerEnv): """ LLM Fine-tuning Docker Environment with improved log output control. FTDockerConf enables: - save_logs_to_file: True (saves full logs to workspace/docker_execution.log) - terminal_tail_lines: 20 (only shows last 20 lines in terminal) To customize, set environment variables: export FT_DOCKER_terminal_tail_lines=50 # show last 50 lines export FT_DOCKER_save_logs_to_file=false # disable log file """ def __init__(self, conf: DockerConf = FTDockerConf()): super().__init__(conf) class BenchmarkDockerEnv(DockerEnv): """ OpenCompass Benchmark Docker Environment. Uses BenchmarkDockerConf for evaluation-specific settings: - Moderate memory/GPU allocation for inference - Longer terminal output (50 lines) to track benchmark progress - Automatic Dockerfile building from scenarios/finetune/docker/opencompass To customize, set environment variables: export BENCHMARK_DOCKER_running_timeout_period=7200 # 2 hours export BENCHMARK_DOCKER_terminal_tail_lines=100 # show last 100 lines """ def __init__(self, conf: DockerConf = BenchmarkDockerConf()): super().__init__(conf)