Launch v0.1.0

Source code for modulus.launch.logging.wandb

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

"""Weights and Biases Routines and Utilities"""

import logging
import os
import wandb
from typing import Literal
from pathlib import Path
from datetime import datetime
from wandb import AlertLevel
from modulus.distributed import DistributedManager
from .utils import create_ddp_group_tag

logger = logging.getLogger(__name__)


[docs]def initialize_wandb( project: str, entity: str, name: str = "train", group: str = None, sync_tensorboard: bool = False, save_code: bool = False, resume: str = None, config=None, mode: Literal["offline", "online", "disabled"] = "offline", results_dir: str = None, ): """Function to initialize wandb client with the weights and biases server. Parameters ---------- project : str Name of the project to sync data with entity : str, Name of the wanbd entity sync_tensorboard : bool, optional sync tensorboard summary writer with wandb, by default False save_code : bool, optional Whether to push a copy of the code to wandb dashboard, by default False name : str, optional Name of the task running, by default "train" group : str, optional Group name of the task running. Good to set for ddp runs, by default None resume: str, optional Sets the resuming behavior. Options: "allow", "must", "never", "auto" or None, by default None. config : optional a dictionary-like object for saving inputs , like hyperparameters. If dict, argparse or absl.flags, it will load the key value pairs into the wandb.config object. If str, it will look for a yaml file by that name, by default None. mode: str, optional Can be "offline", "online" or "disabled", by default "offline" results_dir : str, optional Output directory of the experiment, by default "/<run directory>/wandb" """ # Set default value here for Hydra if results_dir is None: results_dir = str(Path("./wandb").absolute()) if check_wandb_logged_in(): wandb_dir = results_dir if DistributedManager.is_initialized() and DistributedManager().distributed: if group is None: group = create_ddp_group_tag() start_time = time_string = start_time.strftime("%m/%d/%y_%H:%M:%S") wandb_name = f"{name}_Process_{DistributedManager().rank}_{time_string}" else: start_time = time_string = start_time.strftime("%m/%d/%y_%H:%M:%S") wandb_name = f"{name}_{time_string}" if not os.path.exists(wandb_dir): os.makedirs(wandb_dir) wandb.init( project=project, entity=entity, sync_tensorboard=sync_tensorboard, name=wandb_name, resume=resume, config=config, mode=mode, dir=wandb_dir, group=group, save_code=save_code, ) else: raise ConnectionError( "WandB client wasn't logged in. Please make sure to set " "the WANDB_API_KEY env variable or run `wandb login` in " "over the CLI and copy the ~/.netrc file to the container." )
[docs]def alert(title, text, duration=300, level=0, is_master=True): """Send alert.""" alert_levels = {0: AlertLevel.INFO, 1: AlertLevel.WARN, 2: AlertLevel.ERROR} if is_wandb_initialized() and is_master: wandb.alert( title=title, text=text, level=alert_levels[level], wait_duration=duration )
[docs]def is_wandb_initialized(): """Check if wandb has been initialized.""" global _WANDB_INITIALIZED return _WANDB_INITIALIZED
[docs]def check_wandb_logged_in(): """Check if weights and biases have been logged in.""" wandb_logged_in = False try: wandb_api_key = os.getenv("WANDB_API_KEY", None) if wandb_api_key is not None or os.path.exists( os.path.expanduser(DEFAULT_WANDB_CONFIG) ): wandb_logged_in = wandb.login(key=wandb_api_key) return wandb_logged_in except wandb.errors.UsageError: logger.warning("WandB wasn't logged in.") return False
[docs]def to_pixel(arr): """Converts an array to pixel data with type int and values between 0-255""" arr_min = arr.min() arr_max = arr.max() arr = 255 * (arr - arr_min) / (arr_max - arr_min) return arr.astype(int)
© Copyright 2023, NVIDIA Modulus Team. Last updated on Aug 8, 2023.