[ZetaCloud]

kyegomez · Dec 21, 2023 · 7be1d82 · 7be1d82
1 parent f341442
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Showing 3 changed files with 21 additions and 56 deletions.
diff --git a/README.md b/README.md
@@ -353,7 +353,7 @@ out.shape
 
 
 ### ZetaCloud
-Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. 
+Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first `pip install zetascale` then run `zeta -h` in the terminal. [Here is the docs for more](https://zeta.apac.ai/en/latest/zeta/cloud/main/)
 
 - Flexible Pricing with pooling from many clouds
 - Easy Deployment with 1 click

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "zetascale"
-version = "1.2.0"
+version = "1.2.1"
 description = "Transformers at zeta scales"
 authors = ["Zeta Team <kye@apac.ai>"]
 license = "MIT"

diff --git a/zeta/utils/disable_logging.py b/zeta/utils/disable_logging.py
@@ -1,48 +1,3 @@
-# import logging
-# import os
-# import warnings
-
-
-# def disable_warnings_and_logs():
-#     """
-#     Disables various warnings and logs.
-#     """
-#     # disable warnings
-#     warnings.filterwarnings("ignore")
-
-#     # disable tensorflow warnings
-#     os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
-
-#     # disable bnb warnings and others
-#     logging.getLogger().setLevel(logging.WARNING)
-
-#     class CustomFilter(logging.Filter):
-#         def filter(self, record):
-#             unwanted_logs = [
-#                 "Setting ds_accelerator to mps (auto detect)",
-#                 (
-#                     "NOTE: Redirects are currently not supported in Windows or"
-#                     " MacOs."
-#                 ),
-#             ]
-#             return not any(log in record.getMessage() for log in unwanted_logs)
-
-#     # add custom filter to root logger
-#     logger = logging.getLogger()
-#     f = CustomFilter()
-#     logger.addFilter(f)
-
-#     # disable specific loggers
-#     loggers = [
-#         "real_accelerator",
-#         "torch.distributed.elastic.multiprocessing.redirects",
-#     ]
-
-#     for logger_name in loggers:
-#         logger = logging.getLogger(logger_name)
-#         logger.setLevel(logging.CRITICAL)
-
-
 import logging
 import os
 import warnings
@@ -51,15 +6,6 @@ def disable_warnings_and_logs():
     """
     Disables various warnings and logs.
     """
-    # disable warnings
-    warnings.filterwarnings("ignore")
-
-    # disable tensorflow warnings
-    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
-
-    # disable bnb warnings and others
-    logging.getLogger().setLevel(logging.WARNING)
-
     class CustomFilter(logging.Filter):
         def filter(self, record):
             unwanted_logs = [
@@ -71,10 +17,29 @@ def filter(self, record):
             ]
             return not any(log in record.getMessage() for log in unwanted_logs)
 
+    # disable warnings
+    warnings.filterwarnings("ignore")
+
+    # disable tensorflow warnings
+    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+    # disable bnb warnings and others
+    logging.getLogger().setLevel(logging.WARNING)
+
     # add custom filter to root logger
     logger = logging.getLogger()
     f = CustomFilter()
     logger.addFilter(f)
 
+    # disable specific loggers
+    loggers = [
+        "real_accelerator",
+        "torch.distributed.elastic.multiprocessing.redirects",
+    ]
+
+    for logger_name in loggers:
+        logger = logging.getLogger(logger_name)
+        logger.setLevel(logging.CRITICAL)
+
     # disable all loggers
     logging.disable(logging.CRITICAL)