diff --git a/environment.yml b/environment.yml
new file mode 100644
index 00000000..3b2ae478
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,50 @@
+name: ncsn
+channels:
+  - anaconda
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - ca-certificates=2023.08.22=h06a4308_0
+  - certifi=2021.5.30=py36h06a4308_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=9.1.0=hdf63c60_0
+  - libstdcxx-ng=9.1.0=hdf63c60_0
+  - ncurses=6.3=h7f8727e_2
+  - openssl=1.1.1w=h7f8727e_0
+  - pip=21.2.2=py36h06a4308_0
+  - python=3.6.13=h12debd9_1
+  - pyyaml=5.4.1=py36h27cfd23_1
+  - readline=8.1.2=h7f8727e_1
+  - setuptools=58.0.4=py36h06a4308_0
+  - sqlite=3.38.5=hc218d9a_0
+  - tk=8.6.12=h1ccaba5_0
+  - wheel=0.37.1=pyhd3eb1b0_0
+  - xz=5.2.5=h7f8727e_1
+  - yaml=0.2.5=h7b6447c_0
+  - zlib=1.2.12=h7f8727e_2
+  - pip:
+    - cycler==0.11.0
+    - dataclasses==0.8
+    - importlib-resources==5.4.0
+    - kiwisolver==1.3.1
+    - matplotlib==3.3.4
+    - numpy==1.19.5
+    - packaging==21.3
+    - pandas==1.1.5
+    - pillow==8.4.0
+    - protobuf==4.21.0
+    - pyparsing==3.1.1
+    - python-dateutil==2.8.2
+    - pytz==2024.1
+    - scipy==1.5.4
+    - seaborn==0.11.2
+    - six==1.16.0
+    - tensorboardx==2.6.2.2
+    - torch==1.10.1
+    - torchvision==0.11.2
+    - tqdm==4.64.1
+    - typing-extensions==4.1.1
+    - zipp==3.6.0
+prefix: /opt/conda/envs/ncsn
+
diff --git a/main.py b/main.py
index 8a04f9b7..9b46151c 100644
--- a/main.py
+++ b/main.py
@@ -37,8 +37,10 @@ def parse_args_and_config():
             config = yaml.load(f)
         new_config = dict2namespace(config)
     else:
-        with open(os.path.join(args.log, 'config.yml'), 'r') as f:
-            config = yaml.load(f)
+        # Register the constructor for the `!!python/object:argparse.Namespace` tag
+        yaml.SafeLoader.add_constructor('tag:yaml.org,2002:python/object:argparse.Namespace', construct_namespace)
+        config_path = os.path.join(args.log, 'config.yml')
+        config = load_config(config_path)
         new_config = config
 
     if not args.test:
@@ -103,6 +105,20 @@ def dict2namespace(config):
         setattr(namespace, key, new_value)
     return namespace
 
+# Define a constructor for handling `argparse.Namespace`
+def construct_namespace(loader, node):
+    # Construct a mapping from the node
+    value = loader.construct_mapping(node)
+    # Return an argparse.Namespace object constructed from the mapping
+    return argparse.Namespace(**value)
+
+# Function to load the YAML file
+def load_config(file_path):
+    with open(file_path, 'r') as f:
+        # Load the YAML using the SafeLoader
+        config = yaml.load(f, Loader=yaml.SafeLoader)
+    return config
+
 
 def main():
     args, config = parse_args_and_config()
diff --git a/runners/anneal_runner.py b/runners/anneal_runner.py
index 14b58245..015cc329 100644
--- a/runners/anneal_runner.py
+++ b/runners/anneal_runner.py
@@ -149,7 +149,8 @@ def train(self):
                 optimizer.step()
 
                 tb_logger.add_scalar('loss', loss, global_step=step)
-                logging.info("step: {}, loss: {}".format(step, loss.item()))
+                if step % 200 == 0:
+                    logging.info("step: {}, loss: {}".format(step, loss.item()))
 
                 if step >= self.config.training.n_iters:
                     return 0
@@ -180,8 +181,16 @@ def train(self):
                         score.state_dict(),
                         optimizer.state_dict(),
                     ]
+                    logging.info("step: {}, checkpoint_: {}".format(step, os.path.join(self.args.log, 'checkpoint.pth')))
                     torch.save(states, os.path.join(self.args.log, 'checkpoint_{}.pth'.format(step)))
                     torch.save(states, os.path.join(self.args.log, 'checkpoint.pth'))
+            states = [
+                score.state_dict(),
+                optimizer.state_dict(),
+            ]
+            logging.info("step: {}, checkpoint_: {}".format(step, os.path.join(self.args.log, 'checkpoint.pth')))
+            torch.save(states, os.path.join(self.args.log, 'checkpoint_{}.pth'.format(step)))
+            torch.save(states, os.path.join(self.args.log, 'checkpoint.pth'))
 
     def Langevin_dynamics(self, x_mod, scorenet, n_steps=200, step_lr=0.00005):
         images = []