successful running mnist on cometml

.github/workflows/code-quality-main.yaml

@@ -13,10 +13,10 @@ jobs:
 
     steps:
       - name: Checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
 
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v3
 
       - name: Run pre-commits
-        uses: pre-commit/action@v2.0.3
+        uses: pre-commit/action@v3.0.0

.gitignore

@@ -152,3 +152,5 @@ configs/local/default.yaml
 
 # Aim logging
 .aim
+
+.env.example

configs/train.yaml

@@ -7,8 +7,8 @@ defaults:
   - data: mnist
   - model: mnist
   - callbacks: default
-  - logger: null # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
-  - trainer: default
+  - logger: comet # set logger here or use command line (e.g. `python train.py logger=tensorboard`)
+  - trainer: gpu
   - paths: default
   - extras: default
   - hydra: default
@@ -18,7 +18,7 @@ defaults:
   - experiment: null
 
   # config for hyperparameter optimization
-  - hparams_search: null
+  - hparams_search: mnist_optuna
 
   # optional local config for machine/user specific settings
   # it's optional since it doesn't need to exist and is excluded from version control
@@ -46,4 +46,4 @@ test: True
 ckpt_path: null
 
 # seed for random number generators in pytorch, numpy and python.random
-seed: null
+seed: 42

src/train.py

@@ -1,6 +1,7 @@
 from typing import Any, Dict, List, Optional, Tuple
 
 import hydra
+import comet_ml
 import lightning as L
 import rootutils
 import torch
@@ -41,7 +42,8 @@
 
 @task_wrapper
 def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
-    """Trains the model. Can additionally evaluate on a testset, using best weights obtained during
+    """
+    Trains the model. Can additionally evaluate on a testset, using best weights obtained during
     training.
 
     This method is wrapped in optional @task_wrapper decorator, that controls the behavior during
@@ -114,6 +116,7 @@ def main(cfg: DictConfig) -> Optional[float]:
     """
     # apply extra utilities
     # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.)
+
     extras(cfg)
 
     # train the model