&phoeenniixx [BUG] change progress bar default to simple and decouple rich core dependency to avoid default failure

pyproject.toml

@@ -54,8 +54,8 @@ dependencies = [
     "matplotlib>3.1",
     "ipywidgets",
     "einops>=0.6.0,<0.8.0",
-    "rich>=11.0.0",
     "fsspec>=2022.5.0,<2024.4.0; python_version == '3.8'",
+    "rich",
 ]
 
 

src/pytorch_tabular/config/config.py

@@ -540,7 +540,7 @@ class TrainerConfig:
         },
     )
     progress_bar: str = field(
-        default="rich",
+        default="simple",
         metadata={"help": "Progress bar type. Can be one of: `none`, `simple`, `rich`. Defaults to `rich`."},
     )
     precision: str = field(

src/pytorch_tabular/tabular_model.py

@@ -12,7 +12,7 @@
 from collections import defaultdict
 from functools import partial
 from pathlib import Path
-from pprint import pformat
+from pprint import pformat, pprint
 from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
 
 import joblib
@@ -32,8 +32,6 @@
 from pytorch_lightning.tuner.tuning import Tuner
 from pytorch_lightning.utilities.model_summary import summarize
 from pytorch_lightning.utilities.rank_zero import rank_zero_only
-from rich import print as rich_print
-from rich.pretty import pprint
 from sklearn.base import TransformerMixin
 from sklearn.model_selection import BaseCrossValidator, KFold, StratifiedKFold
 from torch import nn
@@ -1675,14 +1673,14 @@ def summary(self, model=None, max_depth: int = -1) -> None:
         elif self.has_model:
             print(summarize(self.model, max_depth=max_depth))
         else:
-            rich_print(f"[bold green]{self.__class__.__name__}[/bold green]")
-            rich_print("-" * 100)
-            rich_print("[bold yellow]Config[/bold yellow]")
-            rich_print("-" * 100)
+            print(self.__class__.__name__)
+            print("-" * 100)
+            print("Config")
+            print("-" * 100)
             pprint(self.config.__dict__["_content"])
-            rich_print(
-                ":triangular_flag:[bold red]Full Model Summary once model has "
-                "been initialized or passed in as an argument[/bold red]"
+            print(
+                "⚠ Full Model Summary once model has been initialized "
+                "or passed in as an argument"
             )
 
     def ret_summary(self, model=None, max_depth: int = -1) -> str:

src/pytorch_tabular/utils/logger.py

@@ -1,18 +1,22 @@
 import logging
 import os
-
-from rich.logging import RichHandler
+import sys
 
 
 def get_logger(name):
     logger = logging.getLogger(name)
     # ch = logging.StreamHandler()
     logger.setLevel(level=os.environ.get("PT_LOGLEVEL", "INFO"))
-    formatter = logging.Formatter("%(asctime)s - {%(name)s:%(lineno)d} - %(levelname)s - %(message)s")
-    if not logger.hasHandlers():
-        ch = RichHandler(show_level=False, show_time=False, show_path=False, rich_tracebacks=True)
-        ch.setLevel(logging.DEBUG)
-        ch.setFormatter(formatter)
-        logger.addHandler(ch)
+
+    if not logger.handlers:
+        handler = logging.StreamHandler(sys.stderr)
+        handler.setLevel(logging.DEBUG)
+
+        fmt = "%(asctime)s - {%(name)s:%(lineno)d} - %(levelname)s - %(message)s"
+        formatter = logging.Formatter(fmt)
+        handler.setFormatter(formatter)
+
+        logger.addHandler(handler)
         logger.propagate = False
+
     return logger