from __future__ import annotations import configparser import json import logging import multiprocessing import os import pathlib import platform import re import shutil import socket import sys import tempfile from datetime import datetime from typing import Any, Literal, Sequence from urllib.parse import quote, unquote, urlencode if sys.version_info >= (3, 11): from typing import Self else: from typing_extensions import Self from google.protobuf.wrappers_pb2 import BoolValue, DoubleValue, Int32Value, StringValue from pydantic import ( BaseModel, ConfigDict, Field, computed_field, field_validator, model_validator, ) from pydantic_core import SchemaValidator, core_schema import wandb from wandb import env, termwarn, util from wandb.apis.internal import Api from wandb.errors import UsageError from wandb.proto import wandb_settings_pb2 from .lib import apikey, credentials, filesystem, ipython from .lib.gitlib import GitRepo from .lib.run_moment import RunMoment from .lib.runid import generate_id def _path_convert(*args: str) -> str: """Join path and apply os.path.expanduser to it.""" return os.path.expanduser(os.path.join(*args)) class Settings(BaseModel, validate_assignment=True): """Settings for the W&B SDK.""" # Pydantic configuration. model_config = ConfigDict( extra="forbid", # throw an error if extra fields are provided # validate_default=True, # validate default values ) # Public settings. # Flag to allow table artifacts to be synced in offline mode. # # To revert to the old behavior, set this to False. allow_offline_artifacts: bool = True allow_val_change: bool = False # Controls anonymous data logging. Possible values are: # - "never": requires you to link your W&B account before # tracking the run, so you don't accidentally create an anonymous # run. # - "allow": lets a logged-in user track runs with their account, but # lets someone who is running the script without a W&B account see # the charts in the UI. # - "must": sends the run to an anonymous account instead of to a # signed-up user account. anonymous: Literal["allow", "must", "never"] | None = None # The W&B API key. api_key: str | None = None azure_account_url_to_access_key: dict[str, str] | None = None # The URL of the W&B backend, used for GraphQL and filestream operations. base_url: str = "https://api.wandb.ai" code_dir: str | None = None config_paths: Sequence[str] | None = None # The type of console capture to be applied. Possible values are: # "auto" - Automatically selects the console capture method based on the # system environment and settings. # # "off" - Disables console capture. # # "redirect" - Redirects low-level file descriptors for capturing output. # # "wrap" - Overrides the write methods of sys.stdout/sys.stderr. Will be # mapped to either "wrap_raw" or "wrap_emu" based on the state of the system. # # "wrap_raw" - Same as "wrap" but captures raw output directly instead of # through an emulator. # # "wrap_emu" - Same as "wrap" but captures output through an emulator. console: Literal["auto", "off", "wrap", "redirect", "wrap_raw", "wrap_emu"] = Field( default="auto", validate_default=True, ) # Whether to produce multipart console log files. console_multipart: bool = False # Path to file for writing temporary access tokens. credentials_file: str = Field( default_factory=lambda: str(credentials.DEFAULT_WANDB_CREDENTIALS_FILE) ) # Whether to disable code saving. disable_code: bool = False # Whether to disable capturing the git state. disable_git: bool = False # Whether to disable the creation of a job artifact for W&B Launch. disable_job_creation: bool = True # The Docker image used to execute the script. docker: str | None = None # The email address of the user. email: str | None = None # The W&B entity, like a user or a team. entity: str | None = None force: bool = False fork_from: RunMoment | None = None git_commit: str | None = None git_remote: str = "origin" git_remote_url: str | None = None git_root: str | None = None heartbeat_seconds: int = 30 host: str | None = None # The custom proxy servers for http requests to W&B. http_proxy: str | None = None # The custom proxy servers for https requests to W&B. https_proxy: str | None = None # Path to file containing an identity token (JWT) for authentication. identity_token_file: str | None = None # Unix glob patterns relative to `files_dir` to not upload. ignore_globs: tuple[str, ...] = () # Time in seconds to wait for the wandb.init call to complete before timing out. init_timeout: float = 90.0 # Whether to insecurely disable SSL verification. insecure_disable_ssl: bool = False job_name: str | None = None job_source: Literal["repo", "artifact", "image"] | None = None label_disable: bool = False launch: bool = False launch_config_path: str | None = None login_timeout: float | None = None mode: Literal["online", "offline", "dryrun", "disabled", "run", "shared"] = Field( default="online", validate_default=True, ) notebook_name: str | None = None # Path to the script that created the run, if available. program: str | None = None # The absolute path from the root repository directory to the script that # created the run. # # Root repository directory is defined as the directory containing the # .git directory, if it exists. Otherwise, it's the current working directory. program_abspath: str | None = None program_relpath: str | None = None # The W&B project ID. project: str | None = None quiet: bool = False reinit: bool = False relogin: bool = False # Specifies the resume behavior for the run. The available options are: # # "must": Resumes from an existing run with the same ID. If no such run exists, # it will result in failure. # # "allow": Attempts to resume from an existing run with the same ID. If none is # found, a new run will be created. # # "never": Always starts a new run. If a run with the same ID already exists, # it will result in failure. # # "auto": Automatically resumes from the most recent failed run on the same # machine. resume: Literal["allow", "must", "never", "auto"] | None = None resume_from: RunMoment | None = None # Indication from the server about the state of the run. # # This is different from resume, a user provided flag. resumed: bool = False # The root directory that will be used to derive other paths, # such as the wandb directory, and the run directory. root_dir: str = Field(default_factory=lambda: os.path.abspath(os.getcwd())) run_group: str | None = None # The ID of the run. run_id: str | None = None run_job_type: str | None = None run_name: str | None = None run_notes: str | None = None run_tags: tuple[str, ...] | None = None sagemaker_disable: bool = False save_code: bool | None = None settings_system: str = Field( default_factory=lambda: _path_convert( os.path.join("~", ".config", "wandb", "settings") ) ) show_colors: bool | None = None show_emoji: bool | None = None show_errors: bool = True show_info: bool = True show_warnings: bool = True silent: bool = False start_method: str | None = None strict: bool | None = None summary_timeout: int = 60 summary_warnings: int = 5 # TODO: kill this with fire sweep_id: str | None = None sweep_param_path: str | None = None symlink: bool = Field( default_factory=lambda: False if platform.system() == "Windows" else True ) sync_tensorboard: bool | None = None table_raise_on_max_row_limit_exceeded: bool = False username: str | None = None # Internal settings. # # These are typically not meant to be set by the user and should not be considered # a part of the public API as they may change or be removed in future versions. # CLI mode. x_cli_only_mode: bool = False # Disable the collection of system metadata. x_disable_meta: bool = False # Pre-wandb-core, this setting was used to disable the (now legacy) wandb service. # # TODO: this is deprecated and will be removed in future versions. x_disable_service: bool = False # Do not use setproctitle for internal process in legacy service. x_disable_setproctitle: bool = False # Disable system metrics collection. x_disable_stats: bool = False # Disable check for latest version of wandb, from PyPI. x_disable_update_check: bool = False # Prevent early viewer query. x_disable_viewer: bool = False # Disable automatic machine info collection. x_disable_machine_info: bool = False # Python executable x_executable: str | None = None # Additional headers to add to all outgoing HTTP requests. x_extra_http_headers: dict[str, str] | None = None # An approximate maximum request size for the filestream API. # # This applies when wandb-core is enabled. Its purpose is to prevent # HTTP requests from failing due to containing too much data. # # This number is approximate: requests will be slightly larger. x_file_stream_max_bytes: int | None = None # Max line length for filestream jsonl files. x_file_stream_max_line_bytes: int | None = None # Interval in seconds between filestream transmissions. x_file_stream_transmit_interval: float | None = None # Filestream retry client configuration. # max number of retries x_file_stream_retry_max: int | None = None # min wait time between retries x_file_stream_retry_wait_min_seconds: float | None = None # max wait time between retries x_file_stream_retry_wait_max_seconds: float | None = None # timeout for individual HTTP requests x_file_stream_timeout_seconds: float | None = None # file transfer retry client configuration x_file_transfer_retry_max: int | None = None x_file_transfer_retry_wait_min_seconds: float | None = None x_file_transfer_retry_wait_max_seconds: float | None = None x_file_transfer_timeout_seconds: float | None = None # override setting for the computed files_dir x_files_dir: str | None = None # flow control configuration for file stream x_flow_control_custom: bool | None = None x_flow_control_disabled: bool | None = None # graphql retry client configuration x_graphql_retry_max: int | None = None x_graphql_retry_wait_min_seconds: float | None = None x_graphql_retry_wait_max_seconds: float | None = None x_graphql_timeout_seconds: float | None = None x_internal_check_process: float = 8.0 x_jupyter_name: str | None = None x_jupyter_path: str | None = None x_jupyter_root: str | None = None # Label to assign to system metrics and console logs collected for the run # to group by on the frontend. Can be used to distinguish data from different # nodes in a distributed training job. x_label: str | None = None x_live_policy_rate_limit: int | None = None x_live_policy_wait_time: int | None = None x_log_level: int = logging.INFO x_network_buffer: int | None = None # Determines whether to save internal wandb files and metadata. # In a distributed setting, this is useful for avoiding file overwrites on secondary nodes # when only system metrics and logs are needed, as the primary node handles the main logging. x_primary_node: bool = True # [deprecated, use http(s)_proxy] custom proxy servers for the requests to W&B # [scheme -> url]. x_proxies: dict[str, str] | None = None x_runqueue_item_id: str | None = None x_require_legacy_service: bool = False x_save_requirements: bool = True x_service_transport: str | None = None x_service_wait: float = 30.0 x_show_operation_stats: bool = True # The start time of the run in seconds since the Unix epoch. x_start_time: float | None = None # PID of the process that started the wandb-core process to collect system stats for. x_stats_pid: int = os.getpid() # Sampling interval for the system monitor in seconds. x_stats_sampling_interval: float = Field(default=10.0) # Path to store the default config file for the neuron-monitor tool # used to monitor AWS Trainium devices. x_stats_neuron_monitor_config_path: str | None = None # Open metrics endpoint names and urls. x_stats_open_metrics_endpoints: dict[str, str] | None = None # Filter to apply to metrics collected from OpenMetrics endpoints. # Supports two formats: # - {"metric regex pattern, including endpoint name as prefix": {"label": "label value regex pattern"}} # - ("metric regex pattern 1", "metric regex pattern 2", ...) x_stats_open_metrics_filters: dict[str, dict[str, str]] | Sequence[str] | None = ( None ) # HTTP headers to add to OpenMetrics requests. x_stats_open_metrics_http_headers: dict[str, str] | None = None # System paths to monitor for disk usage. x_stats_disk_paths: Sequence[str] | None = Field( default_factory=lambda: ("/", "/System/Volumes/Data") if platform.system() == "Darwin" else ("/",) ) # GPU device indices to monitor (e.g. [0, 1, 2]). # If not set, captures metrics for all GPUs. # Assumes 0-based indexing matching CUDA/ROCm device enumeration. x_stats_gpu_device_ids: Sequence[int] | None = None # Number of system metric samples to buffer in memory in the wandb-core process. # Can be accessed via run._system_metrics. x_stats_buffer_size: int = 0 # Flag to indicate whether we are syncing a run from the transaction log. x_sync: bool = False # Controls whether this process can update the run's final state (finished/failed) on the server. # Set to False in distributed training when only the main process should determine the final state. x_update_finish_state: bool = True # Model validator to catch legacy settings. @model_validator(mode="before") @classmethod def catch_private_settings(cls, values): """Check if a private field is provided and assign to the corrsponding public one. This is a compatibility layer to handle previous versions of the settings. """ new_values = {} for key in values: # Internal settings are prefixed with "x_" instead of "_" # as Pydantic does not allow "_" in field names. if key.startswith("_"): new_values["x" + key] = values[key] else: new_values[key] = values[key] return new_values @model_validator(mode="after") def validate_mutual_exclusion_of_branching_args(self) -> Self: if ( sum( o is not None for o in [ self.fork_from, self.resume, self.resume_from, ] ) > 1 ): raise ValueError( "`fork_from`, `resume`, or `resume_from` are mutually exclusive. " "Please specify only one of them." ) return self # Field validators. @field_validator("x_disable_service", mode="after") @classmethod def validate_disable_service(cls, value): if value: termwarn( "Disabling the wandb service is deprecated as of version 0.18.0 " "and will be removed in future versions. ", repeat=False, ) return value @field_validator("api_key", mode="after") @classmethod def validate_api_key(cls, value): if value is not None and (len(value) > len(value.strip())): raise UsageError("API key cannot start or end with whitespace") return value @field_validator("base_url", mode="after") @classmethod def validate_base_url(cls, value): cls.validate_url(value) # wandb.ai-specific checks if re.match(r".*wandb\.ai[^\.]*$", value) and "api." not in value: # user might guess app.wandb.ai or wandb.ai is the default cloud server raise ValueError( f"{value} is not a valid server address, did you mean https://api.wandb.ai?" ) elif re.match(r".*wandb\.ai[^\.]*$", value) and not value.startswith("https"): raise ValueError("http is not secure, please use https://api.wandb.ai") return value.rstrip("/") @field_validator("code_dir", mode="before") @classmethod def validate_code_dir(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("console", mode="after") @classmethod def validate_console(cls, value, info): if value != "auto": return value if ( ipython.in_jupyter() or (info.data.get("start_method") == "thread") or not info.data.get("disable_service") or platform.system() == "Windows" ): value = "wrap" else: value = "redirect" return value @field_validator("x_executable", mode="before") @classmethod def validate_x_executable(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("x_file_stream_max_line_bytes", mode="after") @classmethod def validate_file_stream_max_line_bytes(cls, value): if value is not None and value < 1: raise ValueError("File stream max line bytes must be greater than 0") return value @field_validator("x_files_dir", mode="before") @classmethod def validate_x_files_dir(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("fork_from", mode="before") @classmethod def validate_fork_from(cls, value, info) -> RunMoment | None: run_moment = cls._runmoment_preprocessor(value) if run_moment and info.data.get("run_id") == run_moment.run: raise ValueError( "Provided `run_id` is the same as the run to `fork_from`. " "Please provide a different `run_id` or remove the `run_id` argument. " "If you want to rewind the current run, please use `resume_from` instead." ) return run_moment @field_validator("http_proxy", mode="after") @classmethod def validate_http_proxy(cls, value): if value is None: return None cls.validate_url(value) return value.rstrip("/") @field_validator("https_proxy", mode="after") @classmethod def validate_https_proxy(cls, value): if value is None: return None cls.validate_url(value) return value.rstrip("/") @field_validator("ignore_globs", mode="after") @classmethod def validate_ignore_globs(cls, value): return tuple(value) if not isinstance(value, tuple) else value @field_validator("program", mode="before") @classmethod def validate_program(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("program_abspath", mode="before") @classmethod def validate_program_abspath(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("program_relpath", mode="before") @classmethod def validate_program_relpath(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("project", mode="after") @classmethod def validate_project(cls, value, info): if value is None: return None invalid_chars_list = list("/\\#?%:") if len(value) > 128: raise UsageError(f"Invalid project name {value!r}: exceeded 128 characters") invalid_chars = {char for char in invalid_chars_list if char in value} if invalid_chars: raise UsageError( f"Invalid project name {value!r}: " f"cannot contain characters {','.join(invalid_chars_list)!r}, " f"found {','.join(invalid_chars)!r}" ) return value @field_validator("resume", mode="before") @classmethod def validate_resume(cls, value): if value is False: return None if value is True: return "auto" return value @field_validator("resume_from", mode="before") @classmethod def validate_resume_from(cls, value, info) -> RunMoment | None: run_moment = cls._runmoment_preprocessor(value) if run_moment and info.data.get("run_id") != run_moment.run: raise ValueError( "Both `run_id` and `resume_from` have been specified with different ids." ) return run_moment @field_validator("root_dir", mode="before") @classmethod def validate_root_dir(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("run_id", mode="after") @classmethod def validate_run_id(cls, value, info): if value is None: return None if len(value) == 0: raise UsageError("Run ID cannot be empty") if len(value) > len(value.strip()): raise UsageError("Run ID cannot start or end with whitespace") if not bool(value.strip()): raise UsageError("Run ID cannot contain only whitespace") return value @field_validator("settings_system", mode="after") @classmethod def validate_settings_system(cls, value): if isinstance(value, pathlib.Path): return str(_path_convert(value)) return _path_convert(value) @field_validator("x_service_wait", mode="after") @classmethod def validate_service_wait(cls, value): if value < 0: raise UsageError("Service wait time cannot be negative") return value @field_validator("start_method") @classmethod def validate_start_method(cls, value): if value is None: return value available_methods = ["thread"] if hasattr(multiprocessing, "get_all_start_methods"): available_methods += multiprocessing.get_all_start_methods() if value not in available_methods: raise UsageError( f"Settings field `start_method`: {value!r} not in {available_methods}" ) return value @field_validator("x_stats_gpu_device_ids", mode="before") @classmethod def validate_x_stats_gpu_device_ids(cls, value): if isinstance(value, str): return json.loads(value) return value @field_validator("x_stats_neuron_monitor_config_path", mode="before") @classmethod def validate_x_stats_neuron_monitor_config_path(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value @field_validator("x_stats_open_metrics_endpoints", mode="before") @classmethod def validate_stats_open_metrics_endpoints(cls, value): if isinstance(value, str): return json.loads(value) return value @field_validator("x_stats_open_metrics_filters", mode="before") @classmethod def validate_stats_open_metrics_filters(cls, value): if isinstance(value, str): return json.loads(value) return value @field_validator("x_stats_open_metrics_http_headers", mode="before") @classmethod def validate_stats_open_metrics_http_headers(cls, value): if isinstance(value, str): return json.loads(value) return value @field_validator("x_stats_sampling_interval", mode="after") @classmethod def validate_stats_sampling_interval(cls, value): if value < 0.1: raise UsageError("Stats sampling interval cannot be less than 0.1 seconds") return value @field_validator("sweep_id", mode="after") @classmethod def validate_sweep_id(cls, value): if value is None: return None if len(value) == 0: raise UsageError("Sweep ID cannot be empty") if len(value) > len(value.strip()): raise UsageError("Sweep ID cannot start or end with whitespace") if not bool(value.strip()): raise UsageError("Sweep ID cannot contain only whitespace") return value @field_validator("sweep_param_path", mode="before") @classmethod def validate_sweep_param_path(cls, value): # TODO: add native support for pathlib.Path if isinstance(value, pathlib.Path): return str(value) return value # Computed fields. @computed_field # type: ignore[prop-decorator] @property def _args(self) -> list[str]: if not self._jupyter: return sys.argv[1:] return [] @computed_field # type: ignore[prop-decorator] @property def _aws_lambda(self) -> bool: """Check if we are running in a lambda environment.""" from sentry_sdk.integrations.aws_lambda import ( # type: ignore[import-not-found] get_lambda_bootstrap, ) lambda_bootstrap = get_lambda_bootstrap() if not lambda_bootstrap or not hasattr( lambda_bootstrap, "handle_event_request" ): return False return True @computed_field # type: ignore[prop-decorator] @property def _code_path_local(self) -> str | None: """The relative path from the current working directory to the code path. For example, if the code path is /home/user/project/example.py, and the current working directory is /home/user/project, then the code path local is example.py. If couldn't find the relative path, this will be an empty string. """ return self._get_program_relpath(self.program) if self.program else None @computed_field # type: ignore[prop-decorator] @property def _colab(self) -> bool: return "google.colab" in sys.modules @computed_field # type: ignore[prop-decorator] @property def _ipython(self) -> bool: return ipython.in_ipython() @computed_field # type: ignore[prop-decorator] @property def _jupyter(self) -> bool: return ipython.in_jupyter() @computed_field # type: ignore[prop-decorator] @property def _kaggle(self) -> bool: return util._is_likely_kaggle() @computed_field # type: ignore[prop-decorator] @property def _noop(self) -> bool: return self.mode == "disabled" @computed_field # type: ignore[prop-decorator] @property def _notebook(self) -> bool: return self._ipython or self._jupyter or self._colab or self._kaggle @computed_field # type: ignore[prop-decorator] @property def _offline(self) -> bool: return self.mode in ("offline", "dryrun") @computed_field # type: ignore[prop-decorator] @property def _os(self) -> str: """The operating system of the machine running the script.""" return platform.platform(aliased=True) @computed_field # type: ignore[prop-decorator] @property def _platform(self) -> str: return f"{platform.system()}-{platform.machine()}".lower() @computed_field # type: ignore[prop-decorator] @property def _python(self) -> str: return f"{platform.python_implementation()} {platform.python_version()}" @computed_field # type: ignore[prop-decorator] @property def _shared(self) -> bool: """Whether we are in shared mode. In "shared" mode, multiple processes can write to the same run, for example from different machines. """ return self.mode == "shared" @computed_field # type: ignore[prop-decorator] @property def _start_datetime(self) -> str: if self.x_start_time is None: return "" datetime_now = datetime.fromtimestamp(self.x_start_time) return datetime_now.strftime("%Y%m%d_%H%M%S") @computed_field # type: ignore[prop-decorator] @property def _tmp_code_dir(self) -> str: return _path_convert( self.wandb_dir, f"{self.run_mode}-{self.timespec}-{self.run_id}", "tmp", "code", ) @computed_field # type: ignore[prop-decorator] @property def _windows(self) -> bool: return platform.system() == "Windows" @computed_field # type: ignore[prop-decorator] @property def colab_url(self) -> str | None: """The URL to the Colab notebook, if running in Colab.""" if not self._colab: return None if self.x_jupyter_path and self.x_jupyter_path.startswith("fileId="): unescaped = unquote(self.x_jupyter_path) return "https://colab.research.google.com/notebook#" + unescaped return None @computed_field # type: ignore[prop-decorator] @property def deployment(self) -> Literal["local", "cloud"]: return "local" if self.is_local else "cloud" @computed_field # type: ignore[prop-decorator] @property def files_dir(self) -> str: """Absolute path to the local directory where the run's files are stored.""" return self.x_files_dir or _path_convert( self.wandb_dir, f"{self.run_mode}-{self.timespec}-{self.run_id}", "files", ) @computed_field # type: ignore[prop-decorator] @property def is_local(self) -> bool: return str(self.base_url) != "https://api.wandb.ai" @computed_field # type: ignore[prop-decorator] @property def log_dir(self) -> str: """The directory for storing log files.""" return _path_convert( self.wandb_dir, f"{self.run_mode}-{self.timespec}-{self.run_id}", "logs" ) @computed_field # type: ignore[prop-decorator] @property def log_internal(self) -> str: """The path to the file to use for internal logs.""" return _path_convert(self.log_dir, "debug-internal.log") @computed_field # type: ignore[prop-decorator] @property def log_symlink_internal(self) -> str: """The path to the symlink to the internal log file of the most recent run.""" return _path_convert(self.wandb_dir, "debug-internal.log") @computed_field # type: ignore[prop-decorator] @property def log_symlink_user(self) -> str: """The path to the symlink to the user-process log file of the most recent run.""" return _path_convert(self.wandb_dir, "debug.log") @computed_field # type: ignore[prop-decorator] @property def log_user(self) -> str: """The path to the file to use for user-process logs.""" return _path_convert(self.log_dir, "debug.log") @computed_field # type: ignore[prop-decorator] @property def project_url(self) -> str: """The W&B URL where the project can be viewed.""" project_url = self._project_url_base() if not project_url: return "" query = self._get_url_query_string() return f"{project_url}{query}" @computed_field # type: ignore[prop-decorator] @property def resume_fname(self) -> str: """The path to the resume file.""" return _path_convert(self.wandb_dir, "wandb-resume.json") @computed_field # type: ignore[prop-decorator] @property def run_mode(self) -> Literal["run", "offline-run"]: return "run" if not self._offline else "offline-run" @computed_field # type: ignore[prop-decorator] @property def run_url(self) -> str: """The W&B URL where the run can be viewed.""" project_url = self._project_url_base() if not all([project_url, self.run_id]): return "" query = self._get_url_query_string() return f"{project_url}/runs/{quote(self.run_id or '')}{query}" @computed_field # type: ignore[prop-decorator] @property def settings_workspace(self) -> str: """The path to the workspace settings file.""" return _path_convert(self.wandb_dir, "settings") @computed_field # type: ignore[prop-decorator] @property def sweep_url(self) -> str: """The W&B URL where the sweep can be viewed.""" project_url = self._project_url_base() if not all([project_url, self.sweep_id]): return "" query = self._get_url_query_string() return f"{project_url}/sweeps/{quote(self.sweep_id or '')}{query}" @computed_field # type: ignore[prop-decorator] @property def sync_dir(self) -> str: return _path_convert( self.wandb_dir, f"{self.run_mode}-{self.timespec}-{self.run_id}" ) @computed_field # type: ignore[prop-decorator] @property def sync_file(self) -> str: """Path to the append-only binary transaction log file.""" return _path_convert(self.sync_dir, f"run-{self.run_id}.wandb") @computed_field # type: ignore[prop-decorator] @property def sync_symlink_latest(self) -> str: return _path_convert(self.wandb_dir, "latest-run") @computed_field # type: ignore[prop-decorator] @property def timespec(self) -> str: return self._start_datetime @computed_field # type: ignore[prop-decorator] @property def wandb_dir(self) -> str: """Full path to the wandb directory. The setting exposed to users as `dir=` or `WANDB_DIR` is the `root_dir`. We add the `__stage_dir__` to it to get the full `wandb_dir` """ root_dir = self.root_dir or "" # We use the hidden version if it already exists, otherwise non-hidden. if os.path.exists(os.path.join(root_dir, ".wandb")): __stage_dir__ = ".wandb" + os.sep else: __stage_dir__ = "wandb" + os.sep path = os.path.join(root_dir, __stage_dir__) if not os.access(root_dir or ".", os.W_OK): termwarn( f"Path {path} wasn't writable, using system temp directory.", repeat=False, ) path = os.path.join( tempfile.gettempdir(), __stage_dir__ or ("wandb" + os.sep) ) return os.path.expanduser(path) # Methods to collect and update settings from different sources. # # The Settings class does not track the source of the settings, # so it is up to the developer to ensure that the settings are applied # in the correct order. Most of the updates are done in # wandb/sdk/wandb_setup.py::_WandbSetup__WandbSetup._settings_setup. def update_from_system_config_file(self): """Update settings from the system config file.""" if not self.settings_system or not os.path.exists(self.settings_system): return for key, value in self._load_config_file(self.settings_system).items(): if value is not None: setattr(self, key, value) def update_from_workspace_config_file(self): """Update settings from the workspace config file.""" if not self.settings_workspace or not os.path.exists(self.settings_workspace): return for key, value in self._load_config_file(self.settings_workspace).items(): if value is not None: setattr(self, key, value) def update_from_env_vars(self, environ: dict[str, Any]): """Update settings from environment variables.""" env_prefix: str = "WANDB_" private_env_prefix: str = env_prefix + "_" special_env_var_names = { "WANDB_DISABLE_SERVICE": "x_disable_service", "WANDB_SERVICE_TRANSPORT": "x_service_transport", "WANDB_DIR": "root_dir", "WANDB_NAME": "run_name", "WANDB_NOTES": "run_notes", "WANDB_TAGS": "run_tags", "WANDB_JOB_TYPE": "run_job_type", "WANDB_HTTP_TIMEOUT": "x_graphql_timeout_seconds", "WANDB_FILE_PUSHER_TIMEOUT": "x_file_transfer_timeout_seconds", "WANDB_USER_EMAIL": "email", } env = dict() for setting, value in environ.items(): if not setting.startswith(env_prefix): continue if setting in special_env_var_names: key = special_env_var_names[setting] elif setting.startswith(private_env_prefix): key = "x_" + setting[len(private_env_prefix) :].lower() else: # otherwise, strip the prefix and convert to lowercase key = setting[len(env_prefix) :].lower() if key in self.__dict__: if key in ("ignore_globs", "run_tags"): value = value.split(",") env[key] = value for key, value in env.items(): if value is not None: setattr(self, key, value) def update_from_system_environment(self): """Update settings from the system environment.""" # For code saving, only allow env var override if value from server is true, or # if no preference was specified. if (self.save_code is True or self.save_code is None) and ( os.getenv(env.SAVE_CODE) is not None or os.getenv(env.DISABLE_CODE) is not None ): self.save_code = env.should_save_code() self.disable_git = env.disable_git() # Attempt to get notebook information if not already set by the user if self._jupyter and (self.notebook_name is None or self.notebook_name == ""): meta = wandb.jupyter.notebook_metadata(self.silent) # type: ignore self.x_jupyter_path = meta.get("path") self.x_jupyter_name = meta.get("name") self.x_jupyter_root = meta.get("root") elif ( self._jupyter and self.notebook_name is not None and os.path.exists(self.notebook_name) ): self.x_jupyter_path = self.notebook_name self.x_jupyter_name = self.notebook_name self.x_jupyter_root = os.getcwd() elif self._jupyter: wandb.termwarn( "WANDB_NOTEBOOK_NAME should be a path to a notebook file, " f"couldn't find {self.notebook_name}.", ) # host and username are populated by apply_env_vars if corresponding env # vars exist -- but if they don't, we'll fill them in here if self.host is None: self.host = socket.gethostname() # type: ignore _executable = ( self.x_executable or os.environ.get(env._EXECUTABLE) or sys.executable or shutil.which("python3") or "python3" ) self.x_executable = _executable self.docker = env.get_docker(util.image_id_from_k8s()) # proceed if not in CLI mode if self.x_cli_only_mode: return program = self.program or self._get_program() if program is not None: repo = GitRepo() root = repo.root or os.getcwd() self.program_relpath = self.program_relpath or self._get_program_relpath( program, root ) program_abspath = os.path.abspath( os.path.join(root, os.path.relpath(os.getcwd(), root), program) ) if os.path.exists(program_abspath): self.program_abspath = program_abspath else: program = "" self.program = program def update_from_dict(self, settings: dict[str, Any]) -> None: """Update settings from a dictionary.""" for key, value in dict(settings).items(): if value is not None: setattr(self, key, value) def update_from_settings(self, settings: Settings) -> None: """Update settings from another instance of `Settings`.""" d = {field: getattr(settings, field) for field in settings.model_fields_set} if d: self.update_from_dict(d) # Helper methods. def to_proto(self) -> wandb_settings_pb2.Settings: """Generate a protobuf representation of the settings.""" settings_proto = wandb_settings_pb2.Settings() for k, v in self.model_dump(exclude_none=True).items(): # special case for x_stats_open_metrics_filters if k == "x_stats_open_metrics_filters": if isinstance(v, (list, set, tuple)): setting = getattr(settings_proto, k) setting.sequence.value.extend(v) elif isinstance(v, dict): setting = getattr(settings_proto, k) for key, value in v.items(): for kk, vv in value.items(): setting.mapping.value[key].value[kk] = vv else: raise TypeError(f"Unsupported type {type(v)} for setting {k}") continue if isinstance(v, bool): getattr(settings_proto, k).CopyFrom(BoolValue(value=v)) elif isinstance(v, int): getattr(settings_proto, k).CopyFrom(Int32Value(value=v)) elif isinstance(v, float): getattr(settings_proto, k).CopyFrom(DoubleValue(value=v)) elif isinstance(v, str): getattr(settings_proto, k).CopyFrom(StringValue(value=v)) elif isinstance(v, (list, set, tuple)): # we only support sequences of strings for now sequence = getattr(settings_proto, k) sequence.value.extend(v) elif isinstance(v, dict): mapping = getattr(settings_proto, k) for key, value in v.items(): # we only support dicts with string values for now mapping.value[key] = value elif isinstance(v, RunMoment): getattr(settings_proto, k).CopyFrom( wandb_settings_pb2.RunMoment( run=v.run, value=v.value, metric=v.metric, ) ) elif v is None: # None means that the setting value was not set. pass else: raise TypeError(f"Unsupported type {type(v)} for setting {k}") return settings_proto def handle_resume_logic(self): """Handle logic for resuming runs.""" # handle auto resume logic if self.resume == "auto": if os.path.exists(self.resume_fname): with open(self.resume_fname) as f: resume_run_id = json.load(f)["run_id"] if self.run_id is None: self.run_id = resume_run_id elif self.run_id != resume_run_id: wandb.termwarn( "Tried to auto resume run with " f"id {resume_run_id} but id {self.run_id} is set.", ) if self.run_id is None: self.run_id = generate_id() # persist run_id in case of failure if self.resume == "auto" and self.resume_fname is not None: filesystem.mkdir_exists_ok(self.wandb_dir) with open(self.resume_fname, "w") as f: f.write(json.dumps({"run_id": self.run_id})) def handle_sweep_logic(self): """Update settings based on sweep context. When running a sweep, the project, entity, and run_id are handled externally, and should be ignored if they are set. """ if self.sweep_id is None: return for key in ("project", "entity", "run_id"): value = getattr(self, key) if value is not None: wandb.termwarn(f"Ignoring {key} {value!r} when running a sweep.") setattr(self, key, None) def handle_launch_logic(self): """Update settings based on launch context. When running in a launch context, the project, entity, and run_id are handled externally, and should be ignored if they are set. """ if not self.launch: return for key in ("project", "entity", "run_id"): value = getattr(self, key) if value is not None: wandb.termwarn( f"Ignoring {key} {value!r} when running from wandb launch context." ) setattr(self, key, None) @staticmethod def validate_url(url: str) -> None: """Validate a URL string.""" url_validator = SchemaValidator( core_schema.url_schema( allowed_schemes=["http", "https"], strict=True, ) ) url_validator.validate_python(url) def _get_program(self) -> str | None: """Get the program that started the current process.""" if not self._jupyter: # If not in a notebook, try to get the program from the environment # or the __main__ module for scripts run as `python -m ...`. program = os.getenv(env.PROGRAM) if program is not None: return program try: import __main__ if __main__.__spec__ is None: return __main__.__file__ return f"-m {__main__.__spec__.name}" except (ImportError, AttributeError): return None else: # If in a notebook, try to get the program from the notebook metadata. if self.notebook_name: return self.notebook_name if not self.x_jupyter_path: return self.program if self.x_jupyter_path.startswith("fileId="): return self.x_jupyter_name else: return self.x_jupyter_path @staticmethod def _get_program_relpath(program: str, root: str | None = None) -> str | None: """Get the relative path to the program from the root directory.""" if not program: return None root = root or os.getcwd() if not root: return None full_path_to_program = os.path.join( root, os.path.relpath(os.getcwd(), root), program ) if os.path.exists(full_path_to_program): relative_path = os.path.relpath(full_path_to_program, start=root) if "../" in relative_path: return None return relative_path return None @staticmethod def _load_config_file(file_name: str, section: str = "default") -> dict: """Load a config file and return the settings for a given section.""" parser = configparser.ConfigParser() parser.add_section(section) parser.read(file_name) config: dict[str, Any] = dict() for k in parser[section]: config[k] = parser[section][k] if k == "ignore_globs": config[k] = config[k].split(",") return config def _project_url_base(self) -> str: """Construct the base URL for the project.""" if not all([self.entity, self.project]): return "" app_url = util.app_url(self.base_url) return f"{app_url}/{quote(self.entity or '')}/{quote(self.project or '')}" def _get_url_query_string(self) -> str: """Construct the query string for project, run, and sweep URLs.""" # TODO: remove dependency on Api() if Api().settings().get("anonymous") != "true": return "" api_key = apikey.api_key(settings=self) return f"?{urlencode({'apiKey': api_key})}" @staticmethod def _runmoment_preprocessor(val: RunMoment | str | None) -> RunMoment | None: """Preprocess the setting for forking or resuming a run.""" if isinstance(val, RunMoment) or val is None: return val elif isinstance(val, str): return RunMoment.from_uri(val)