diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index fee343308..f1a4fa9b6 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -44,6 +44,12 @@ jobs:
     - name: check if readme yaml works
       run: rm -r zoutput && python main_out.py -c ./examples/conf/vlcs_diva_mldg_dial.yaml
     - name: test if examples in markdown works
-      run: bash -x -v ci_run_examples.sh
+      run: bash -x -v scripts/ci_run_examples.sh
     - name: test if benchmark works
-      run: pip install snakemake==7.32.0 && pip install pulp==2.7.0 && sed -i '1s/^/#!\/bin\/bash -x -v\n/' run_benchmark_standalone.sh && bash -x -v run_benchmark_standalone.sh examples/benchmark/demo_shared_hyper_grid.yaml && cat zoutput/benchmarks/mnist_benchmark_grid/hyperparameters.csv && cat zoutput/benchmarks/mnist_benchmark_grid/results.csv
+      run: | 
+          pip install snakemake==7.32.0 && pip install pulp==2.7.0
+          echo "insert a shebang line (#!/bin/bash -x -v) at the beginning of the bash script"
+          sed -i '1s/^/#!\/bin\/bash -x -v\n/' run_benchmark_standalone.sh
+          bash -x -v run_benchmark_standalone.sh examples/benchmark/demo_shared_hyper_grid.yaml
+          cat zoutput/benchmarks/mnist_benchmark_grid*/hyperparameters.csv
+          cat zoutput/benchmarks/mnist_benchmark_grid*/results.csv
diff --git a/.gitignore b/.gitignore
index 2e1aa7a74..a351b31fe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,13 @@
 .ropeproject
-./zdpath
-./zoutput
+/zdpath
+/zoutput
 tests/__pycache__/
 *.pyc
 .vscode/
 domainlab/zdata/pacs
+/data/
+/.snakemake/
+/dist 
+/domainlab.egg-info
+/runs
+/slurm_errors.txt
\ No newline at end of file
diff --git a/docs/doc_benchmark.md b/docs/doc_benchmark.md
index 41b19bffb..ec73fb36e 100644
--- a/docs/doc_benchmark.md
+++ b/docs/doc_benchmark.md
@@ -74,10 +74,10 @@ hyperparameter sampling and pytorch.
 The following script will help to find out which job has failed and the error message, so that you could direct to the
 specific log file
 ```cluster
-bash ./sh_list_error.sh ./zoutput/slurm_logs
+bash ./sh_list_error.sh ./zoutput/benchmarks/[output folder of the sepcifed benchmark in the yaml file]/slurm_logs
 ```
 #### Map between slurm job id and sampled hyperparameter index
-suppose the slurm job id is 14144163, one could the corresponding log file in `./zoutput/slurm_logs` folder via
+suppose the slurm job id is 14144163, one could the corresponding log file in `./zoutput/[output folder of the sepcifed benchmark in the yaml file]/slurm_logs` folder via
 `find . | grep -i "14144163"`
 
 the results can be
diff --git a/docs/doc_usage_cmd.md b/docs/doc_usage_cmd.md
index 7230fec25..6ee916f06 100644
--- a/docs/doc_usage_cmd.md
+++ b/docs/doc_usage_cmd.md
@@ -21,7 +21,34 @@ To run DomainLab, the minimum necessary parameters are:
 ### Advanced Configuration
 
 - **Learning Rate (`--lr`):** Set the training learning rate.
-- **Regularization (`--gamma_reg`):** Weight of regularization loss.
+- **Regularization (`--gamma_reg`):** Sets the weight of the regularization
+ loss. This parameter can be configured either as
+ a single value applied to individual classes,
+ or using a dictionary to specify different
+ weights for different models and trainers.
+
+  - **Command Line Usage:**
+    - For a single value: `python script.py --gamma_reg=0.1`
+    - For multiple values: `python script.py --gamma_reg='default=0.1,dann=0.05,jigen=0.2'`
+
+  - **YAML Configuration:**
+    - For a single value:
+
+      ```yaml
+      gamma_reg: 0.1
+      ```
+
+    - For different values:
+
+      ```yaml
+      gamma_reg:
+        dann: 0.05
+        dial: 0.2
+        default: 0.1 # value for every other instance
+      ```  
+Gamma reg is available for the trainers, as well as the 
+dann and jigen model. 
+
 - **Early Stopping (`--es`):** Steps for early stopping.
 - **Random Seed (`--seed`):** Seed for reproducibility.
 - **CUDA Options (`--nocu`, `--device`):** Configure CUDA usage and device settings.
diff --git a/domainlab/algos/builder_dann.py b/domainlab/algos/builder_dann.py
index 65b26a62a..73b373c15 100644
--- a/domainlab/algos/builder_dann.py
+++ b/domainlab/algos/builder_dann.py
@@ -13,6 +13,7 @@
 from domainlab.compos.zoo_nn import FeatExtractNNBuilderChainNodeGetter
 from domainlab.models.model_dann import mk_dann
 from domainlab.utils.utils_cuda import get_device
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class NodeAlgoBuilderDANN(NodeAlgoBuilder):
@@ -55,7 +56,7 @@ def init_business(self, exp):
         model = mk_dann(list_str_y=task.list_str_y,
                         net_classifier=net_classifier)(
             list_d_tr=task.list_domain_tr,
-            alpha=args.gamma_reg,
+            alpha=get_gamma_reg(args, 'dann'),
             net_encoder=net_encoder,
             net_discriminator=net_discriminator,
             builder=self)
diff --git a/domainlab/algos/builder_jigen1.py b/domainlab/algos/builder_jigen1.py
index e899e32f2..de671affe 100644
--- a/domainlab/algos/builder_jigen1.py
+++ b/domainlab/algos/builder_jigen1.py
@@ -15,6 +15,7 @@
 from domainlab.dsets.utils_wrapdset_patches import WrapDsetPatches
 from domainlab.models.model_jigen import mk_jigen
 from domainlab.utils.utils_cuda import get_device
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class NodeAlgoBuilderJiGen(NodeAlgoBuilder):
@@ -56,7 +57,7 @@ def init_business(self, exp):
         model = mk_jigen(
             list_str_y=task.list_str_y,
             net_classifier=net_classifier)(
-            coeff_reg=args.gamma_reg,
+            coeff_reg=get_gamma_reg(args, 'jigen'),
             net_encoder=net_encoder,
             net_classifier_permutation=net_classifier_perm,
             n_perm=args.nperm,
diff --git a/domainlab/algos/trainers/a_trainer.py b/domainlab/algos/trainers/a_trainer.py
index d9bda1513..f5fce7c16 100644
--- a/domainlab/algos/trainers/a_trainer.py
+++ b/domainlab/algos/trainers/a_trainer.py
@@ -258,3 +258,11 @@ def dset_decoration_args_algo(self, args, ddset):
         if self._decoratee is not None:
             return self._decoratee.dset_decoration_args_algo(args, ddset)
         return ddset
+
+    def print_parameters(self):
+        """
+        Function to print all parameters of the object. 
+        Can be used to print the parameters of any child class
+        """
+        params = vars(self)
+        print(f"Parameters of {type(self).__name__}: {params}")
diff --git a/domainlab/algos/trainers/train_dial.py b/domainlab/algos/trainers/train_dial.py
index 75a5e34f0..4fe700f45 100644
--- a/domainlab/algos/trainers/train_dial.py
+++ b/domainlab/algos/trainers/train_dial.py
@@ -5,6 +5,7 @@
 from torch.autograd import Variable
 
 from domainlab.algos.trainers.train_basic import TrainerBasic
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerDIAL(TrainerBasic):
@@ -49,4 +50,4 @@ def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):
         tensor_x_adv = self.gen_adversarial(self.device, tensor_x, tensor_y)
         tensor_x_batch_adv_no_grad = Variable(tensor_x_adv, requires_grad=False)
         loss_dial = self.model.cal_task_loss(tensor_x_batch_adv_no_grad, tensor_y)
-        return [loss_dial], [self.aconf.gamma_reg]
+        return [loss_dial], [get_gamma_reg(self.aconf, self.name)]
diff --git a/domainlab/algos/trainers/train_fishr.py b/domainlab/algos/trainers/train_fishr.py
index 1a11e3780..3580a0721 100644
--- a/domainlab/algos/trainers/train_fishr.py
+++ b/domainlab/algos/trainers/train_fishr.py
@@ -13,6 +13,7 @@
     backpack = None
 
 from domainlab.algos.trainers.train_basic import TrainerBasic
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerFishr(TrainerBasic):
@@ -39,7 +40,7 @@ def tr_epoch(self, epoch):
             dict_layerwise_var_var_grads_sum = \
                 {key: val.sum() for key, val in dict_layerwise_var_var_grads.items()}
             loss_fishr = sum(dict_layerwise_var_var_grads_sum.values())
-            loss = sum(list_loss_erm) + self.aconf.gamma_reg * loss_fishr
+            loss = sum(list_loss_erm) + get_gamma_reg(self.aconf, self.name) * loss_fishr
             loss.backward()
             self.optimizer.step()
             self.epo_loss_tr += loss.detach().item()
diff --git a/domainlab/algos/trainers/train_irm.py b/domainlab/algos/trainers/train_irm.py
new file mode 100644
index 000000000..0da2d0bde
--- /dev/null
+++ b/domainlab/algos/trainers/train_irm.py
@@ -0,0 +1,70 @@
+"""
+use random start to generate adversarial images
+"""
+import torch
+from torch import autograd
+from torch.nn import functional as F
+from domainlab.algos.trainers.train_basic import TrainerBasic
+
+
+class TrainerIRM(TrainerBasic):
+    """
+    IRMv1 split a minibatch into half, and use an unbiased estimate of the
+    squared gradient norm via inner product
+    $$\\delta_{w|w=1} \\ell(w\\dot \\Phi(X^{e, i}), Y^{e, i})$$
+    of dimension dim(Grad)
+    with
+    $$\\delta_{w|w=1} \\ell(w\\dot \\Phi(X^{e, j}), Y^{e, j})$$
+    of dimension dim(Grad)
+    For more details, see section 3.2 and Appendix D of :
+    Arjovsky et al., “Invariant Risk Minimization.”
+    """
+    def tr_epoch(self, epoch):
+        list_loaders = list(self.dict_loader_tr.values())
+        loaders_zip = zip(*list_loaders)
+        self.model.train()
+        self.epo_loss_tr = 0
+
+        for ind_batch, tuple_data_domains_batch in enumerate(loaders_zip):
+            self.optimizer.zero_grad()
+            list_domain_loss_erm = []
+            list_domain_reg = []
+            for batch_domain_e in tuple_data_domains_batch:
+                tensor_x, tensor_y, tensor_d, *others = batch_domain_e
+                tensor_x, tensor_y, tensor_d = \
+                    tensor_x.to(self.device), tensor_y.to(self.device), \
+                    tensor_d.to(self.device)
+                list_domain_loss_erm.append(
+                    self.model.cal_task_loss(tensor_x, tensor_y))
+                list_1ele_loss_irm, _ = \
+                    self._cal_reg_loss(tensor_x, tensor_y, tensor_d, others)
+                list_domain_reg += list_1ele_loss_irm
+            loss = torch.sum(torch.stack(list_domain_loss_erm)) + \
+                self.aconf.gamma_reg * torch.sum(torch.stack(list_domain_reg))
+            loss.backward()
+            self.optimizer.step()
+            self.epo_loss_tr += loss.detach().item()
+            self.after_batch(epoch, ind_batch)
+
+        flag_stop = self.observer.update(epoch)  # notify observer
+        return flag_stop
+
+    def _cal_phi(self, tensor_x):
+        logits = self.model.cal_logit_y(tensor_x)
+        return logits
+
+    def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):
+        """
+        Let trainer behave like a model, so that other trainer could use it
+        """
+        _ = tensor_d
+        _ = others
+        y = tensor_y
+        phi = self._cal_phi(tensor_x)
+        dummy_w_scale = torch.tensor(1.).to(tensor_x.device).requires_grad_()
+        loss_1 = F.cross_entropy(phi[::2] * dummy_w_scale, y[::2])
+        loss_2 = F.cross_entropy(phi[1::2] * dummy_w_scale, y[1::2])
+        grad_1 = autograd.grad(loss_1, [dummy_w_scale], create_graph=True)[0]
+        grad_2 = autograd.grad(loss_2, [dummy_w_scale], create_graph=True)[0]
+        loss_irm = torch.sum(grad_1 * grad_2)
+        return [loss_irm], [self.aconf.gamma_reg]
diff --git a/domainlab/algos/trainers/train_matchdg.py b/domainlab/algos/trainers/train_matchdg.py
index 6a3edd996..72c14ab83 100644
--- a/domainlab/algos/trainers/train_matchdg.py
+++ b/domainlab/algos/trainers/train_matchdg.py
@@ -13,6 +13,7 @@
 )
 from domainlab.tasks.utils_task_dset import DsetIndDecorator4XYD
 from domainlab.utils.logger import Logger
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerMatchDG(AbstractTrainer):
@@ -36,7 +37,7 @@ def init_business(
         self.base_domain_size = get_base_domain_size4match_dg(self.task)
         self.epo_loss_tr = 0
         self.flag_erm = flag_erm
-        self.lambda_ctr = self.aconf.gamma_reg
+        self.lambda_ctr = get_gamma_reg(aconf, self.name)
         self.mk_match_tensor(epoch=0)
         self.flag_match_tensor_sweep_over = False
         self.tuple_tensor_ref_domain2each_y = None
diff --git a/domainlab/algos/trainers/train_mldg.py b/domainlab/algos/trainers/train_mldg.py
index 90318286c..e91310adf 100644
--- a/domainlab/algos/trainers/train_mldg.py
+++ b/domainlab/algos/trainers/train_mldg.py
@@ -10,6 +10,7 @@
 from domainlab.algos.trainers.train_basic import TrainerBasic
 from domainlab.tasks.utils_task import mk_loader
 from domainlab.tasks.utils_task_dset import DsetZip
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerMLDG(AbstractTrainer):
@@ -108,7 +109,7 @@ def tr_epoch(self, epoch):
             loss = (
                 loss_source_task.sum()
                 + source_reg_tr.sum()
-                + self.aconf.gamma_reg * loss_look_forward.sum()
+                + get_gamma_reg(self.aconf, self.name) * loss_look_forward.sum()
             )
             #
             loss.backward()
diff --git a/domainlab/algos/trainers/zoo_trainer.py b/domainlab/algos/trainers/zoo_trainer.py
index 1cfa15a7f..1800e307c 100644
--- a/domainlab/algos/trainers/zoo_trainer.py
+++ b/domainlab/algos/trainers/zoo_trainer.py
@@ -7,6 +7,7 @@
 from domainlab.algos.trainers.train_matchdg import TrainerMatchDG
 from domainlab.algos.trainers.train_mldg import TrainerMLDG
 from domainlab.algos.trainers.train_fishr import TrainerFishr
+from domainlab.algos.trainers.train_irm import TrainerIRM
 
 
 class TrainerChainNodeGetter(object):
@@ -49,6 +50,7 @@ def __call__(self, lst_candidates=None, default=None, lst_excludes=None):
         chain = TrainerMatchDG(chain)
         chain = TrainerMLDG(chain)
         chain = TrainerFishr(chain)
+        chain = TrainerIRM(chain)
         chain = TrainerHyperScheduler(chain)
         node = chain.handle(self.request)
         head = node
diff --git a/domainlab/arg_parser.py b/domainlab/arg_parser.py
index 7c7e004e7..1b8593e8f 100644
--- a/domainlab/arg_parser.py
+++ b/domainlab/arg_parser.py
@@ -12,6 +12,37 @@
 from domainlab.models.args_vae import add_args2parser_vae
 from domainlab.utils.logger import Logger
 
+class ParseValuesOrKeyValuePairs(argparse.Action):
+    """Class used for arg parsing where values are provided in a key value format"""
+
+    def __call__(self, parser: argparse.ArgumentParser,
+    namespace: argparse.Namespace, values: str, option_string: str = None):
+        """
+            Handle parsing of key value pairs, or a single value instead
+
+            Args:
+                parser (argparse.ArgumentParser): The ArgumentParser object.
+                namespace (argparse.Namespace): The namespace object to store parsed values.
+                values (str): The string containing key=value pairs or a single float value.
+                option_string (str, optional): The option string that triggered this action (unused).
+
+            Raises:
+                ValueError: If the values cannot be parsed to float.
+        """
+        if "=" in values:
+            my_dict = {}
+            for kv in values.split(","):
+                k, v = kv.split("=")
+                try:
+                    my_dict[k.strip()] = float(v.strip())
+                except ValueError:
+                    raise ValueError(f"Invalid value in key-value pair: '{kv}', must be float")
+            setattr(namespace, self.dest, my_dict)
+        else:
+            try:
+                setattr(namespace, self.dest, float(values))
+            except ValueError:
+                raise ValueError(f"Invalid value for {self.dest}: '{values}', must be float")
 
 def mk_parser_main():
     """
@@ -31,7 +62,13 @@ def mk_parser_main():
     parser.add_argument("--lr", type=float, default=1e-4, help="learning rate")
 
     parser.add_argument(
-        "--gamma_reg", type=float, default=0.1, help="weight of regularization loss"
+        "--gamma_reg",
+        default=0.1,
+        help="weight of regularization loss in the form of $$\ell(\cdot) + \mu \times R(\cdot)$$ \
+        can specify per model as 'default=3.0, dann=1.0,jigen=2.0', where default refer to gamma for trainer \
+        note diva is implemented $$\ell(\cdot) + \mu \times R(\cdot)$$ \
+        so diva does not have gamma_reg",
+        action=ParseValuesOrKeyValuePairs
     )
 
     parser.add_argument("--es", type=int, default=1, help="early stop steps")
diff --git a/domainlab/exp_protocol/aggregate_results.py b/domainlab/exp_protocol/aggregate_results.py
index d67cf5a0a..2d72316bb 100644
--- a/domainlab/exp_protocol/aggregate_results.py
+++ b/domainlab/exp_protocol/aggregate_results.py
@@ -43,6 +43,7 @@ def agg_from_directory(input_dir: str, output_file: str):
 
 def agg_main(bm_dir: str, skip_plotting: bool = False):
     """Aggregates partial results and generate plots."""
+    bm_dir.rstrip("/")
     agg_output = f"{bm_dir}/results.csv"
     agg_input = f"{bm_dir}/rule_results"
     agg_from_directory(agg_input, agg_output)
diff --git a/domainlab/exp_protocol/benchmark.smk b/domainlab/exp_protocol/benchmark.smk
index fcc9eb4f3..84661ee61 100644
--- a/domainlab/exp_protocol/benchmark.smk
+++ b/domainlab/exp_protocol/benchmark.smk
@@ -72,6 +72,8 @@ rule parameter_sampling:
         expand("{path}", path=config_path)
     output:
         dest=expand("{output_dir}/hyperparameters.csv", output_dir=config["output_dir"])
+    # resources:
+    #    log_dir="slurm_logs_test"
     params:
         sampling_seed=os.environ["DOMAINLAB_CUDA_HYPERPARAM_SEED"]
     run:
@@ -159,6 +161,8 @@ rule agg_results:
     # put different csv file in a big csv file
     input:
         exp_results=experiment_result_files
+    # resources:
+    #    log_dir="slurm_logs_test"
     output:
         out_file=expand("{output_dir}/results.csv", output_dir=config["output_dir"])
     run:
diff --git a/domainlab/models/a_model.py b/domainlab/models/a_model.py
index beb867167..71b0db334 100644
--- a/domainlab/models/a_model.py
+++ b/domainlab/models/a_model.py
@@ -178,3 +178,35 @@ def dset_decoration_args_algo(self, args, ddset):
         if self._decoratee is not None:
             return self._decoratee.dset_decoration_args_algo(args, ddset)
         return ddset
+
+    @property
+    def p_na_prefix(self):
+        """
+        common prefix for Models
+        """
+        return "Model"
+
+    @property
+    def name(self):
+        """
+        get the name of the algorithm
+        """
+        na_prefix = self.p_na_prefix
+        len_prefix = len(na_prefix)
+        na_class = type(self).__name__
+        if na_class[:len_prefix] != na_prefix:
+            raise RuntimeError(
+                "Model builder node class must start with ",
+                na_prefix,
+                "the current class is named: ",
+                na_class,
+            )
+        return type(self).__name__[len_prefix:].lower()
+
+    def print_parameters(self):
+        """
+        Function to print all parameters of the object. 
+        Can be used to print the parameters of every child class.
+        """
+        params = vars(self)
+        print(f"Parameters of {type(self).__name__}: {params}")
diff --git a/domainlab/models/model_dann.py b/domainlab/models/model_dann.py
index 2abd3feda..59a619e7e 100644
--- a/domainlab/models/model_dann.py
+++ b/domainlab/models/model_dann.py
@@ -85,13 +85,15 @@ def hyper_update(self, epoch, fun_scheduler):
             dict_rst = fun_scheduler(
                 epoch
             )  # the __call__ method of hyperparameter scheduler
-            self.alpha = dict_rst["alpha"]
+            self.alpha = dict_rst[self.name + "_alpha"]
 
         def hyper_init(self, functor_scheduler):
             """hyper_init.
             :param functor_scheduler:
             """
-            return functor_scheduler(trainer=None, alpha=self.alpha)
+            parameters = {}
+            parameters[self.name + "_alpha"] = self.alpha
+            return functor_scheduler(trainer=None, **parameters)
 
         def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others):
             _ = others
diff --git a/domainlab/models/model_diva.py b/domainlab/models/model_diva.py
index b67272784..362c14518 100644
--- a/domainlab/models/model_diva.py
+++ b/domainlab/models/model_diva.py
@@ -98,9 +98,9 @@ def hyper_update(self, epoch, fun_scheduler):
             :param fun_scheduler:
             """
             dict_rst = fun_scheduler(epoch)
-            self.beta_d = dict_rst["beta_d"]
-            self.beta_y = dict_rst["beta_y"]
-            self.beta_x = dict_rst["beta_x"]
+            self.beta_d = dict_rst[self.name + "_beta_d"]
+            self.beta_y = dict_rst[self.name + "_beta_x"]
+            self.beta_x = dict_rst[self.name + "_beta_y"]
 
         def hyper_init(self, functor_scheduler):
             """
@@ -108,8 +108,12 @@ def hyper_init(self, functor_scheduler):
 
             :param functor_scheduler: the class name of the scheduler
             """
+            parameters = {}
+            parameters[self.name + "_beta_d"] = self.beta_d
+            parameters[self.name + "_beta_y"] = self.beta_y
+            parameters[self.name + "_beta_x"] = self.beta_x
             return functor_scheduler(
-                trainer=None, beta_d=self.beta_d, beta_y=self.beta_y, beta_x=self.beta_x
+                trainer=None, **parameters
             )
 
         def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):
diff --git a/domainlab/models/model_hduva.py b/domainlab/models/model_hduva.py
index 61411982e..5c7bb290d 100644
--- a/domainlab/models/model_hduva.py
+++ b/domainlab/models/model_hduva.py
@@ -66,10 +66,10 @@ def hyper_update(self, epoch, fun_scheduler):
             dict_rst = fun_scheduler(
                 epoch
             )  # the __call__ function of hyper-para-scheduler object
-            self.beta_d = dict_rst["beta_d"]
-            self.beta_y = dict_rst["beta_y"]
-            self.beta_x = dict_rst["beta_x"]
-            self.beta_t = dict_rst["beta_t"]
+            self.beta_d = dict_rst[self.name + "_beta_d"]
+            self.beta_y = dict_rst[self.name + "_beta_y"]
+            self.beta_x = dict_rst[self.name + "_beta_x"]
+            self.beta_t = dict_rst[self.name + "_beta_t"]
 
         def hyper_init(self, functor_scheduler):
             """hyper_init.
@@ -78,12 +78,13 @@ def hyper_init(self, functor_scheduler):
             # calling the constructor of the hyper-parameter-scheduler class, so that this scheduler
             # class build a dictionary {"beta_d":self.beta_d, "beta_y":self.beta_y}
             # constructor signature is def __init__(self, **kwargs):
+            parameters = {}
+            parameters[self.name + "_beta_d"] = self.beta_d
+            parameters[self.name + "_beta_y"] = self.beta_y
+            parameters[self.name + "_beta_x"] = self.beta_x
+            parameters[self.name + "_beta_t"] = self.beta_t
             return functor_scheduler(
-                trainer=None,
-                beta_d=self.beta_d,
-                beta_y=self.beta_y,
-                beta_x=self.beta_x,
-                beta_t=self.beta_t,
+                trainer=None, **parameters
             )
 
         @store_args
diff --git a/domainlab/utils/generate_benchmark_plots.py b/domainlab/utils/generate_benchmark_plots.py
index 420bcd76c..9c0be5fae 100644
--- a/domainlab/utils/generate_benchmark_plots.py
+++ b/domainlab/utils/generate_benchmark_plots.py
@@ -90,7 +90,8 @@ def gen_plots(dataframe: pd.DataFrame, output_dir: str, use_param_index: bool):
     ['param_index','task',' algo',' epos',' te_d',' seed',' params',' acc','precision',...]
     """
     os.makedirs(output_dir, exist_ok=True)
-    obj = dataframe.columns[G_DF_PLOT_COL_METRIC_START:G_DF_PLOT_COL_METRIC_END]
+    pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+    obj = dataframe.columns[G_DF_PLOT_COL_METRIC_START:pos_numeric_end]
     # boxplots
     for objective in obj:
         boxplot(
@@ -267,7 +268,8 @@ def scatterplot_matrix(
         but also between the parameter setups
     """
     dataframe = dataframe_in.copy()
-    index = list(range(G_DF_PLOT_COL_METRIC_START, G_DF_PLOT_COL_METRIC_END))
+    pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+    index = list(range(G_DF_PLOT_COL_METRIC_START, pos_numeric_end))
     if distinguish_param_setups:
         dataframe_ = dataframe.iloc[:, index]
         dataframe_.insert(
@@ -280,7 +282,8 @@ def scatterplot_matrix(
 
         g_p = sns.pairplot(data=dataframe_, hue="label", corner=True, kind=kind)
     else:
-        index_ = list(range(G_DF_PLOT_COL_METRIC_START, G_DF_PLOT_COL_METRIC_END))
+        pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+        index_ = list(range(G_DF_PLOT_COL_METRIC_START, pos_numeric_end))
         index_.insert(0, G_DF_TASK_COL)
         dataframe_ = dataframe.iloc[:, index_]
 
@@ -417,7 +420,8 @@ def radar_plot(dataframe_in, file=None, distinguish_hyperparam=True):
     else:
         dataframe.insert(0, "label", dataframe[COLNAME_METHOD])
     # we need "G_DF_PLOT_COL_METRIC_START + 1" as we did insert the columns 'label' at index 0
-    index = list(range(G_DF_PLOT_COL_METRIC_START + 1, G_DF_PLOT_COL_METRIC_END))
+    pos_numeric_end = min(G_DF_PLOT_COL_METRIC_END, dataframe.shape[1])
+    index = list(range(G_DF_PLOT_COL_METRIC_START + 1, pos_numeric_end))
     num_lines = len(dataframe["label"].unique())
     _, axis = plt.subplots(
         figsize=(9, 9 + (0.28 * num_lines)), subplot_kw=dict(polar=True)
diff --git a/domainlab/utils/hyperparameter_retrieval.py b/domainlab/utils/hyperparameter_retrieval.py
new file mode 100644
index 000000000..5a1960e2e
--- /dev/null
+++ b/domainlab/utils/hyperparameter_retrieval.py
@@ -0,0 +1,18 @@
+"""
+retrieval for hyperparameters
+"""
+
+def get_gamma_reg(args, component_name):
+    """
+    Retrieves either a shared gamma regularization, or individual ones for each specified object
+    """
+    gamma_reg = args.gamma_reg
+    if isinstance(gamma_reg, dict):
+        if component_name in gamma_reg:
+            return gamma_reg[component_name]
+        if 'default' in gamma_reg:
+            return gamma_reg['default']
+        raise ValueError("""If a gamma_reg dict is specified,
+                              but no value set for every model and trainer,
+                              a default value must be specified.""")
+    return gamma_reg  # Return the single value if it's not a dictionary
diff --git a/examples/benchmark/benchmark_pacs_resnet_model.yaml b/examples/benchmark/benchmark_pacs_resnet_model.yaml
index 7c9b9dbd7..c3bfde043 100644
--- a/examples/benchmark/benchmark_pacs_resnet_model.yaml
+++ b/examples/benchmark/benchmark_pacs_resnet_model.yaml
@@ -33,13 +33,13 @@ domainlab_args:
 Shared params:
   num_shared_param_samples: 9
   gamma_y:
-    min: 1e4
+    min: 1
     max: 1e8
     step: 1000
     distribution: loguniform
 
   gamma_d:
-    min: 1e4
+    min: 1
     max: 1e8
     step: 1000
     distribution: loguniform
diff --git a/examples/conf/vlcs_diva_mldg_dial.yaml b/examples/conf/vlcs_diva_mldg_dial.yaml
index 5ed10d0c5..e24cfd22e 100644
--- a/examples/conf/vlcs_diva_mldg_dial.yaml
+++ b/examples/conf/vlcs_diva_mldg_dial.yaml
@@ -5,7 +5,10 @@ val_threshold: 0.8                  # threashold before which training does not
 model: dann_diva                    # combine model DANN with DIVA
 epos: 1                             # number of epochs
 trainer: mldg_dial                  # combine trainer MLDG and DIAL
-gamma_reg: 1.0                      # hyperparameter of DANN
+gamma_reg: 
+  default: 1.0
+  dann: 1.5
+# in this case, mldg and dial get the default gamma_reg value 1.0
 gamma_y: 700000.0                   # hyperparameter of diva
 gamma_d: 100000.0                   # hyperparameter of diva
 npath: examples/nets/resnet.py      # neural network for class classification
diff --git a/examples/yaml/slurm/config.yaml b/examples/yaml/slurm/config.yaml
index e180df74d..642367adc 100644
--- a/examples/yaml/slurm/config.yaml
+++ b/examples/yaml/slurm/config.yaml
@@ -1,6 +1,6 @@
 # This yaml file has been adapted from https://github.com/jdblischak/smk-simple-slurm
 cluster:
-  mkdir -p zoutput/slurm_logs/{rule} &&
+  mkdir -p $logdir/{rule} &&
   sbatch
     --partition=gpu_p
     --qos=gpu_normal
@@ -10,8 +10,8 @@ cluster:
     -c 2
     --mem=160G
     --job-name=smk-{rule}-{wildcards}
-    --output=zoutput/slurm_logs/{rule}/{rule}-{wildcards}-%j.out
-    --error=zoutput/slurm_logs/{rule}/{rule}-{wildcards}-%j.err
+    --output=$logdir/{rule}/{rule}-{wildcards}-%j.out
+    --error=$logdir/{rule}/{rule}-{wildcards}-%j.err
 default-resources:
   - partition=gpu_p
   - qos=gpu_normal
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 6e8b0c48e..000000000
--- a/poetry.lock
+++ /dev/null
@@ -1,1561 +0,0 @@
-[[package]]
-name = "atomicwrites"
-version = "1.4.1"
-description = "Atomic file writes."
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-
-[[package]]
-name = "attrs"
-version = "23.2.0"
-description = "Classes Without Boilerplate"
-category = "dev"
-optional = false
-python-versions = ">=3.7"
-
-[package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
-
-[[package]]
-name = "backpack-for-pytorch"
-version = "1.6.0"
-description = "BackPACK: Packing more into backprop"
-category = "main"
-optional = false
-python-versions = ">=3.8"
-
-[package.dependencies]
-einops = ">=0.3.0,<1.0.0"
-torch = ">=1.9.0"
-torchvision = ">=0.7.0"
-unfoldNd = ">=0.2.0,<1.0.0"
-
-[package.extras]
-docs = ["matplotlib", "memory-profiler", "sphinx-gallery", "sphinx-rtd-theme", "tabulate"]
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-
-[[package]]
-name = "beautifulsoup4"
-version = "4.12.3"
-description = "Screen-scraping library"
-category = "main"
-optional = false
-python-versions = ">=3.6.0"
-
-[package.dependencies]
-soupsieve = ">1.2"
-
-[package.extras]
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-chardet = ["chardet"]
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-html5lib = ["html5lib"]
-lxml = ["lxml"]
-
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-name = "certifi"
-version = "2023.11.17"
-description = "Python package for providing Mozilla's CA Bundle."
-category = "main"
-optional = false
-python-versions = ">=3.6"
-
-[[package]]
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-version = "3.3.2"
-description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-category = "main"
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-python-versions = ">=3.7.0"
-
-[[package]]
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-optional = false
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-
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-category = "main"
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-python-versions = ">=3.9"
-
-[package.dependencies]
-numpy = ">=1.20,<2.0"
-
-[package.extras]
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-docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
-mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.6.1)", "types-Pillow"]
-test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
-test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
-
-[[package]]
-name = "coverage"
-version = "7.4.1"
-description = "Code coverage measurement for Python"
-category = "dev"
-optional = false
-python-versions = ">=3.8"
-
-[package.dependencies]
-tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""}
-
-[package.extras]
-toml = ["tomli"]
-
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-category = "main"
-optional = false
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-
-[package.extras]
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-
-[[package]]
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-[package.extras]
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-
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-
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-    {file = "torchvision-0.13.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:099874088df104d54d8008f2a28539ca0117b512daed8bf3c2bbfa2b7ccb187a"},
-    {file = "torchvision-0.13.1-cp310-cp310-win_amd64.whl", hash = "sha256:8e4d02e4d8a203e0c09c10dfb478214c224d080d31efc0dbf36d9c4051f7f3c6"},
-    {file = "torchvision-0.13.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:5e631241bee3661de64f83616656224af2e3512eb2580da7c08e08b8c965a8ac"},
-    {file = "torchvision-0.13.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:899eec0b9f3b99b96d6f85b9aa58c002db41c672437677b553015b9135b3be7e"},
-    {file = "torchvision-0.13.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:83e9e2457f23110fd53b0177e1bc621518d6ea2108f570e853b768ce36b7c679"},
-    {file = "torchvision-0.13.1-cp37-cp37m-win_amd64.whl", hash = "sha256:7552e80fa222252b8b217a951c85e172a710ea4cad0ae0c06fbb67addece7871"},
-    {file = "torchvision-0.13.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:f230a1a40ed70d51e463ce43df243ec520902f8725de2502e485efc5eea9d864"},
-    {file = "torchvision-0.13.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e9a563894f9fa40692e24d1aa58c3ef040450017cfed3598ff9637f404f3fe3b"},
-    {file = "torchvision-0.13.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:7cb789ceefe6dcd0dc8eeda37bfc45efb7cf34770eac9533861d51ca508eb5b3"},
-    {file = "torchvision-0.13.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:87c137f343197769a51333076e66bfcd576301d2cd8614b06657187c71b06c4f"},
-    {file = "torchvision-0.13.1-cp38-cp38-win_amd64.whl", hash = "sha256:4d8bf321c4380854ef04613935fdd415dce29d1088a7ff99e06e113f0efe9203"},
-    {file = "torchvision-0.13.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0298bae3b09ac361866088434008d82b99d6458fe8888c8df90720ef4b347d44"},
-    {file = "torchvision-0.13.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c5ed609c8bc88c575226400b2232e0309094477c82af38952e0373edef0003fd"},
-    {file = "torchvision-0.13.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:3567fb3def829229ec217c1e38f08c5128ff7fb65854cac17ebac358ff7aa309"},
-    {file = "torchvision-0.13.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b167934a5943242da7b1e59318f911d2d253feeca0d13ad5d832b58eed943401"},
-    {file = "torchvision-0.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:0e77706cc90462653620e336bb90daf03d7bf1b88c3a9a3037df8d111823a56e"},
-]
-tqdm = [
-    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
-    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
-]
-typing-extensions = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
-]
-unfoldNd = [
-    {file = "unfoldNd-0.2.1-py3-none-any.whl", hash = "sha256:5d9a7f23e9004127486eb8c58f18025529b106ad407e6271ab57f13d910a7458"},
-    {file = "unfoldNd-0.2.1.tar.gz", hash = "sha256:3d91e0e6006d1ab45702db4b36f28e6150aef11c96d46331a273efe963e5efef"},
-]
-urllib3 = [
-    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
-    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
-]
-where = [
-    {file = "where-1.0.2-py2.py3-none-any.whl", hash = "sha256:66abc8edf95be7516e949d08a771f25acacff708ef481618562ab484fe5bc63e"},
-    {file = "where-1.0.2.zip", hash = "sha256:325ef3a492a26189a47819f7375bc146887d39edd36fce132e86514334803fb1"},
-]
-zipp = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
-]
diff --git a/pyproject.toml b/pyproject.toml
index 629aa6fac..07416486a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "domainlab"
-version = "0.6.3"
+version = "0.6.4"
 readme = "README.md"
 repository = "https://github.com/marrlab/DomainLab"
 homepage = "https://marrlab.github.io/DomainLab/"
@@ -9,9 +9,10 @@ authors = ["Xudong Sun <smilesun.east@gmail.com>", "Carla Feistner", "Alexej Gos
 
 [tool.poetry.dependencies]
 python = "^3.9"
+setuptools = "^69.5.1"
 numpy = "^1.23.4"
-matplotlib = "^3.6.1"
-seaborn = "0.12.2"
+matplotlib = "^3.9.0"
+seaborn = "^0.13.2"
 torchmetrics = "^0.10.0"
 torch = "^1.12.0"
 torchvision = "^0.13.0"
diff --git a/requirements.txt b/requirements.txt
index 88c294a6e..fc2fc3fd3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,45 +1,46 @@
 backpack-for-pytorch==1.6.0 ; python_version >= "3.9" and python_version < "4.0"
 beautifulsoup4==4.12.3 ; python_version >= "3.9" and python_version < "4.0"
-certifi==2023.11.17 ; python_version >= "3.9" and python_version < "4.0"
+certifi==2024.6.2 ; python_version >= "3.9" and python_version < "4.0"
 charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "4.0"
 colorama==0.4.6 ; python_version >= "3.9" and python_version < "4.0" and platform_system == "Windows"
-contourpy==1.2.0 ; python_version >= "3.9" and python_version < "4.0"
+contourpy==1.2.1 ; python_version >= "3.9" and python_version < "4.0"
 cycler==0.12.1 ; python_version >= "3.9" and python_version < "4.0"
-einops==0.7.0 ; python_version >= "3.9" and python_version < "4.0"
-filelock==3.13.1 ; python_version >= "3.9" and python_version < "4.0"
-fonttools==4.47.2 ; python_version >= "3.9" and python_version < "4.0"
+einops==0.8.0 ; python_version >= "3.9" and python_version < "4.0"
+filelock==3.15.4 ; python_version >= "3.9" and python_version < "4.0"
+fonttools==4.53.0 ; python_version >= "3.9" and python_version < "4.0"
 gdown==4.7.3 ; python_version >= "3.9" and python_version < "4.0"
-idna==3.6 ; python_version >= "3.9" and python_version < "4.0"
-importlib-resources==6.1.1 ; python_version >= "3.9" and python_version < "3.10"
-joblib==1.3.2 ; python_version >= "3.9" and python_version < "4.0"
+idna==3.7 ; python_version >= "3.9" and python_version < "4.0"
+importlib-resources==6.4.0 ; python_version >= "3.9" and python_version < "3.10"
+joblib==1.4.2 ; python_version >= "3.9" and python_version < "4.0"
 kiwisolver==1.4.5 ; python_version >= "3.9" and python_version < "4.0"
 markdown-it-py==3.0.0 ; python_version >= "3.9" and python_version < "4.0"
-matplotlib==3.8.2 ; python_version >= "3.9" and python_version < "4.0"
+matplotlib==3.9.0 ; python_version >= "3.9" and python_version < "4.0"
 mdurl==0.1.2 ; python_version >= "3.9" and python_version < "4.0"
-numpy==1.26.3 ; python_version < "4.0" and python_version >= "3.9"
-packaging==23.2 ; python_version >= "3.9" and python_version < "4.0"
+numpy==1.26.4 ; python_version < "4.0" and python_version >= "3.9"
+packaging==24.1 ; python_version >= "3.9" and python_version < "4.0"
 pandas==1.5.3 ; python_version >= "3.9" and python_version < "4.0"
 pillow==9.5.0 ; python_version >= "3.9" and python_version < "4.0"
-pygments==2.17.2 ; python_version >= "3.9" and python_version < "4.0"
-pyparsing==3.1.1 ; python_version >= "3.9" and python_version < "4.0"
+pygments==2.18.0 ; python_version >= "3.9" and python_version < "4.0"
+pyparsing==3.1.2 ; python_version >= "3.9" and python_version < "4.0"
 pysocks==1.7.1 ; python_version >= "3.9" and python_version < "4.0"
-python-dateutil==2.8.2 ; python_version >= "3.9" and python_version < "4.0"
-pytz==2023.4 ; python_version >= "3.9" and python_version < "4.0"
+python-dateutil==2.9.0.post0 ; python_version >= "3.9" and python_version < "4.0"
+pytz==2024.1 ; python_version >= "3.9" and python_version < "4.0"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "4.0"
-requests==2.31.0 ; python_version >= "3.9" and python_version < "4.0"
-requests[socks]==2.31.0 ; python_version >= "3.9" and python_version < "4.0"
-rich==13.7.0 ; python_version >= "3.9" and python_version < "4.0"
-scikit-learn==1.4.0 ; python_version >= "3.9" and python_version < "4.0"
-scipy==1.12.0 ; python_version >= "3.9" and python_version < "4.0"
+requests==2.32.3 ; python_version >= "3.9" and python_version < "4.0"
+requests[socks]==2.32.3 ; python_version >= "3.9" and python_version < "4.0"
+rich==13.7.1 ; python_version >= "3.9" and python_version < "4.0"
+scikit-learn==1.5.0 ; python_version >= "3.9" and python_version < "4.0"
+scipy==1.13.1 ; python_version >= "3.9" and python_version < "4.0"
 seaborn==0.12.2 ; python_version >= "3.9" and python_version < "4.0"
+setuptools==69.5.1 ; python_version >= "3.9" and python_version < "4.0"
 six==1.16.0 ; python_version >= "3.9" and python_version < "4.0"
 soupsieve==2.5 ; python_version >= "3.9" and python_version < "4.0"
-threadpoolctl==3.2.0 ; python_version >= "3.9" and python_version < "4.0"
+threadpoolctl==3.5.0 ; python_version >= "3.9" and python_version < "4.0"
 torch==1.12.1 ; python_version >= "3.9" and python_version < "4.0"
 torchmetrics==0.10.3 ; python_version >= "3.9" and python_version < "4.0"
 torchvision==0.13.1 ; python_version >= "3.9" and python_version < "4.0"
-tqdm==4.66.1 ; python_version >= "3.9" and python_version < "4.0"
-typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "4.0"
-unfoldnd==0.2.1 ; python_version >= "3.9" and python_version < "4.0"
-urllib3==2.1.0 ; python_version >= "3.9" and python_version < "4.0"
-zipp==3.17.0 ; python_version >= "3.9" and python_version < "3.10"
+tqdm==4.66.4 ; python_version >= "3.9" and python_version < "4.0"
+typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "4.0"
+unfoldnd==0.2.2 ; python_version >= "3.9" and python_version < "4.0"
+urllib3==2.2.2 ; python_version >= "3.9" and python_version < "4.0"
+zipp==3.19.2 ; python_version >= "3.9" and python_version < "3.10"
diff --git a/run_benchmark_slurm.sh b/run_benchmark_slurm.sh
index 61615198a..5ad2b94ff 100755
--- a/run_benchmark_slurm.sh
+++ b/run_benchmark_slurm.sh
@@ -1,35 +1,38 @@
 #!/bin/bash
 
-timestamp() {
-#  date +"%T" # current time
-  date +"%Y-%m-%d_%H-%M-%S"
-}
+# Source the common functions script
+source scripts/sh_benchmark_utils.sh
 
 # CONFIGFILE="examples/yaml/test_helm_benchmark.yaml"
 CONFIGFILE=$1
-export DOMAINLAB_CUDA_START_SEED=$2
-
-if [ -z "$2" ]
-then
-      echo "argument 2: DOMAINLAB_CUDA_START_SEED empty, will set to 0"
-      export DOMAINLAB_CUDA_START_SEED=0
-      # in fact, the smk code will hash empty string to zero, see standalone script,
-      # but here we just want to ensure the seed is 0 without worrying a different
-      # behavior of the hash function
-else
-      export DOMAINLAB_CUDA_START_SEED=$2
-fi
+logfile=$(create_log_file)
+echo "Configuration file: $CONFIGFILE"
+echo "verbose log: $logfile"
 
+# Check if the second argument is empty and provide feedback
+if [ -z "$2" ]; then
+    echo "argument 2: DOMAINLAB_CUDA_START_SEED empty, will set to 0"
+fi
+# Set DOMAINLAB_CUDA_START_SEED to the second command-line argument if provided,
+# otherwise, default to 0.
+DOMAINLAB_CUDA_START_SEED=${2:-0}
+export DOMAINLAB_CUDA_START_SEED
+echo "argument 2: DOMAINLAB_CUDA_START_SEED=$DOMAINLAB_CUDA_START_SEED"
 
 # ensure all runs sample the same hyperparameters
 export DOMAINLAB_CUDA_HYPERPARAM_SEED=0
 
 export NUMBER_GPUS=1
-logdir="zoutput/logs"
-mkdir -p $logdir
-logfile="$logdir/$(timestamp).out"
-echo "Configuration file: $CONFIGFILE"
-echo "starting seed is: $DOMAINLAB_CUDA_START_SEED"
-echo "verbose log: $logfile"
+
+results_dir=$(extract_output_dir "$CONFIGFILE")
+
+echo "Starting seed is: $DOMAINLAB_CUDA_START_SEED"
+echo "Hyperparameter seed is: $DOMAINLAB_CUDA_HYPERPARAM_SEED"
+echo "Number of GPUs: $NUMBER_GPUS"
+echo "Results will be stored in: $results_dir"
+
 # Helmholtz
-snakemake --profile "examples/yaml/slurm" --config yaml_file=$CONFIGFILE --keep-going --keep-incomplete --notemp --cores 3 -s "domainlab/exp_protocol/benchmark.smk" --configfile "$CONFIGFILE" 2>&1 | tee "$logfile"
+export logdir="${results_dir}/slurm_logs/"
+echo "slurm logs going into ${logdir}"
+# snakemake --config logdir="zoutput/benchmark/logs" does not seem to work
+snakemake --profile "examples/yaml/slurm" --config yaml_file="$CONFIGFILE" --keep-going --keep-incomplete --notemp --cores 3 -s "domainlab/exp_protocol/benchmark.smk" --configfile "$CONFIGFILE" --config output_dir="$results_dir" 2>&1 | tee "$logfile"
diff --git a/run_benchmark_standalone.sh b/run_benchmark_standalone.sh
index 8f712d1d1..72214b340 100755
--- a/run_benchmark_standalone.sh
+++ b/run_benchmark_standalone.sh
@@ -1,52 +1,41 @@
-set -e
-
-timestamp() {
-#  date +"%T" # current time
-  date +"%Y-%m-%d_%H-%M-%S"
-}
-
+#!/bin/bash
 
-logdir="zoutput/logs"
-mkdir -p $logdir
-logfile="$logdir/$(timestamp).out"
-echo "verbose log: $logfile"
+set -e
 
+# Source the common functions script
+source scripts/sh_benchmark_utils.sh
 
 CONFIGFILE=$1
+logfile=$(create_log_file)
+echo "Configuration file: $CONFIGFILE"
+echo "verbose log: $logfile"
 
-
-
-echo "argument 2=$2"
-if [ -z "$2" ]
-then
-      echo "argument 2: DOMAINLAB_CUDA_START_SEED empty"
-      echo "empty string will be hashed into 0"
-fi
-
-export DOMAINLAB_CUDA_START_SEED=$2
-
-
-echo "argument 3: $3"
-
-if [ -z "$3" ]
-then
-      echo "argument 3: DOMAINLAB_CUDA_HYPERPARAM_SEED empty, will set to 0"
-      export DOMAINLAB_CUDA_HYPERPARAM_SEED=0
-else
-      export DOMAINLAB_CUDA_HYPERPARAM_SEED=$3
+# Check if the second argument is empty and provide feedback
+if [ -z "$2" ]; then
+    echo "argument 2: DOMAINLAB_CUDA_START_SEED empty, will set to 0"
 fi
+# Set DOMAINLAB_CUDA_START_SEED to the second command-line argument if provided,
+# otherwise, default to 0.
+DOMAINLAB_CUDA_START_SEED=${2:-0}
+export DOMAINLAB_CUDA_START_SEED
+echo "argument 2: DOMAINLAB_CUDA_START_SEED=$DOMAINLAB_CUDA_START_SEED"
 
+# Configuring DOMAINLAB_CUDA_HYPERPARAM_SEED
+DOMAINLAB_CUDA_HYPERPARAM_SEED=${3:-0}
+export DOMAINLAB_CUDA_HYPERPARAM_SEED
+echo "argument 3: DOMAINLAB_CUDA_HYPERPARAM_SEED=$DOMAINLAB_CUDA_HYPERPARAM_SEED"
 
 echo "argument 4: NUMBER_GPUS=$4"
-if [ -z "$4" ]
-then
-      export NUMBER_GPUS=1
-      echo "argument 4: NUMBER_GPUS set to 1"
-      echo "argument 4: NUMBER_GPUS=$NUMBER_GPUS"
-else
-      export NUMBER_GPUS=$4
-fi
+NUMBER_GPUS=${4:-1}
+export NUMBER_GPUS
+echo "argument 4: NUMBER_GPUS set to ${NUMBER_GPUS}"
 
+results_dir=$(extract_output_dir "$CONFIGFILE")
+
+echo "Starting seed is: $DOMAINLAB_CUDA_START_SEED"
+echo "Hyperparameter seed is: $DOMAINLAB_CUDA_HYPERPARAM_SEED"
+echo "Number of GPUs: $NUMBER_GPUS"
+echo "Results will be stored in: $results_dir"
 
 # -n: dry-run  (A dry run is a software testing process where the effects of a possible failure are intentionally mitigated, For example, there is rsync utility for transfer data over some interface, but user can try rsync with dry-run option to check syntax and test communication without data transferring.)
 # -p: print shell commands
@@ -55,14 +44,11 @@ fi
 # -- configfile: configuration yaml file of the benchmark
 
 
-
-
 # first display all tasks
 snakemake --rerun-incomplete --cores 1 -s "domainlab/exp_protocol/benchmark.smk" --configfile "$CONFIGFILE" --keep-going --summary  # this will give us a clue first what jobs will be run
 
 # second submit the jobs, make sure you have more than 4 cores on your laptop, otherwise adjust the cores
-snakemake --config yaml_file=$CONFIGFILE --rerun-incomplete --resources nvidia_gpu=$NUMBER_GPUS --cores 4 -s "domainlab/exp_protocol/benchmark.smk" --configfile "$CONFIGFILE" 2>&1 | tee "$logfile"
-
+snakemake --config yaml_file="$CONFIGFILE" --rerun-incomplete --resources nvidia_gpu="$NUMBER_GPUS" --cores 4 -s "domainlab/exp_protocol/benchmark.smk" --configfile "$CONFIGFILE" --config output_dir="$results_dir" 2>&1 | tee "$logfile"
 
 # snakemake --rerun-incomplete --cores 1 -s "domainlab/exp_protocol/benchmark.smk" --configfile "examples/yaml/demo_benchmark.yaml"
 # snakemake -np -s "domainlab/exp_protocol/benchmark.smk" --configfile "examples/yaml/demo_benchmark.yaml"
diff --git a/ci.sh b/scripts/ci.sh
similarity index 100%
rename from ci.sh
rename to scripts/ci.sh
diff --git a/ci_benchmark.sh b/scripts/ci_benchmark.sh
similarity index 100%
rename from ci_benchmark.sh
rename to scripts/ci_benchmark.sh
diff --git a/ci_pytest_cov.sh b/scripts/ci_pytest_cov.sh
similarity index 100%
rename from ci_pytest_cov.sh
rename to scripts/ci_pytest_cov.sh
diff --git a/ci_run_examples.sh b/scripts/ci_run_examples.sh
similarity index 100%
rename from ci_run_examples.sh
rename to scripts/ci_run_examples.sh
diff --git a/clean.sh b/scripts/clean.sh
similarity index 100%
rename from clean.sh
rename to scripts/clean.sh
diff --git a/gen_doc.sh b/scripts/gen_doc.sh
similarity index 100%
rename from gen_doc.sh
rename to scripts/gen_doc.sh
diff --git a/scripts/sh_benchmark_utils.sh b/scripts/sh_benchmark_utils.sh
new file mode 100644
index 000000000..ef1b05876
--- /dev/null
+++ b/scripts/sh_benchmark_utils.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+
+# This script contains common functions used in multiple scripts
+
+# Function to generate a timestamp
+timestamp() {
+    date +"%Y-%m-%d_%H-%M-%S"
+}
+
+# Function to create a log file with a timestamp in the name
+create_log_file() {
+    local logdir="zoutput/logs"
+    mkdir -p "$logdir"
+    local logfile="$logdir/$(timestamp).out"
+    echo "$logfile"
+}
+
+# Function to extract the output directory from a config file and append a timestamp to it
+extract_output_dir() {
+    local config_file=$1
+    local output_dir=$(awk '/output_dir:/ {print $2}' "$config_file")
+    if [ -z "$output_dir" ]; then
+        echo "Error: output_dir not specified in $config_file"
+        exit 1
+    fi
+    echo "${output_dir}_$(timestamp)"
+}
diff --git a/sh_publish.sh b/scripts/sh_publish.sh
similarity index 100%
rename from sh_publish.sh
rename to scripts/sh_publish.sh
diff --git a/setup.py b/setup.py
index e185f6377..c56a64115 100644
--- a/setup.py
+++ b/setup.py
@@ -38,7 +38,7 @@ def copy_dir(dir_path="zdata"):
     package_data={
         "zdata": [f for f in copy_dir()],
     },
-    version="0.6.3",
+    version="0.6.4",
     description="Library of modular domain generalization for deep learning",
     url="https://github.com/marrlab/DomainLab",
     author="Xudong Sun, et.al.",
diff --git a/tests/test_a_model.py b/tests/test_a_model.py
new file mode 100644
index 000000000..fac060f06
--- /dev/null
+++ b/tests/test_a_model.py
@@ -0,0 +1,69 @@
+"""
+Test a model functionality
+"""
+import pytest
+from domainlab.models.a_model import AModel
+
+class ModelTest(AModel):
+    """
+    A test model class conforming to model naming
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.test_param = 42
+
+    def cal_task_loss(self, tensor_x, tensor_y):
+        return 0
+
+    def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):
+        return 0
+
+    @property
+    def metric4msel(self):
+        return ""
+
+
+class InvalidTest(AModel):
+    """
+    A test model class that does not conform to the "Model" prefix naming convention
+    """
+
+    def cal_task_loss(self, tensor_x, tensor_y):
+        return 0
+
+    def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):
+        return 0
+
+    @property
+    def metric4msel(self):
+        return ""
+
+
+def test_model_name_valid():
+    """
+    Test a valid model name
+    """
+    model = ModelTest()
+    assert model.name == "test", f"Expected 'test' but got '{model.name}'"
+
+
+def test_model_name_invalid():
+    """
+    Test an invalid model name
+    """
+    model = InvalidTest()
+    with pytest.raises(RuntimeError, match="Model builder node class must start with"):
+        _ = model.name
+
+
+def test_print_parameters(capsys):
+    """
+    Test the printing of parameters
+    """
+    model = ModelTest()
+    model.print_parameters()
+    captured = capsys.readouterr()
+    assert "Parameters of ModelTest:" in captured.out
+    assert "'test_param': 42" in captured.out
+  
\ No newline at end of file
diff --git a/tests/test_a_trainer.py b/tests/test_a_trainer.py
new file mode 100644
index 000000000..d4e073ae3
--- /dev/null
+++ b/tests/test_a_trainer.py
@@ -0,0 +1,34 @@
+"""
+Test a trainer functionality
+"""
+
+from domainlab.algos.trainers.a_trainer import AbstractTrainer
+
+class TrainerTest(AbstractTrainer):
+    """
+    A test trainer class conforming to model naming
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.test_param = 42
+
+    def tr_epoch(self, epoch):
+        """
+        :param epoch:
+        """
+
+    def before_tr(self):
+        """
+        before training, probe model performance
+        """
+
+def test_print_parameters(capsys):
+    """
+    Test the printing of parameters
+    """
+    trainer = TrainerTest()
+    trainer.print_parameters()
+    captured = capsys.readouterr()
+    assert "Parameters of TrainerTest:" in captured.out
+    assert "'test_param': 42" in captured.out
diff --git a/tests/test_argparse.py b/tests/test_argparse.py
index 683f66911..2eaaae10b 100644
--- a/tests/test_argparse.py
+++ b/tests/test_argparse.py
@@ -1,14 +1,11 @@
 """
 Test argparser functionality
 """
-
 import os
 import sys
-
 import pytest
-
 from domainlab.arg_parser import apply_dict_to_args, mk_parser_main, parse_cmd_args
-
+from domainlab.arg_parser import ParseValuesOrKeyValuePairs
 
 def test_parse_cmd_args_warning():
     """Call argparser for command line"""
@@ -54,3 +51,31 @@ def test_apply_dict_to_args():
     apply_dict_to_args(args, data, extend=True)
     assert args.a == 1
     assert args.model == "diva"
+
+def test_store_dict_key_value_valid():
+    """Testing to parse valid gamma_reg value"""
+    parser = mk_parser_main()
+    parser.add_argument("--keypair", action=ParseValuesOrKeyValuePairs)
+    namespace = parser.parse_args(["--keypair", "1"])
+    assert namespace.keypair == 1.0
+
+def test_store_dict_key_value_pair_valid():
+    """Testing to parse valid gamma_reg key value paris"""
+    parser = mk_parser_main()
+    parser.add_argument("--keypair", action=ParseValuesOrKeyValuePairs)
+    namespace = parser.parse_args(["--keypair", "value1=1,value2=2"])
+    assert namespace.keypair == {"value1": 1.0, "value2": 2.0}
+
+def test_store_dict_key_value_invalid():
+    """Testing to parse invalid gamma_reg value"""
+    parser = mk_parser_main()
+    parser.add_argument("--keypair", action=ParseValuesOrKeyValuePairs)
+    with pytest.raises(ValueError):
+        parser.parse_args(["--keypair", "invalid"])
+
+def test_store_dict_key_value_pair_invalid():
+    """Testing to parse invalid gamma_reg key value pairs"""
+    parser = mk_parser_main()
+    parser.add_argument("--keypair", action=ParseValuesOrKeyValuePairs)
+    with pytest.raises(ValueError):
+        parser.parse_args(["--keypair", "value1=1,value2=invalid"])
diff --git a/tests/test_hyperparameter_retrieval.py b/tests/test_hyperparameter_retrieval.py
new file mode 100644
index 000000000..a9a141329
--- /dev/null
+++ b/tests/test_hyperparameter_retrieval.py
@@ -0,0 +1,44 @@
+"""
+unit test for hyperparameter parsing
+"""
+import pytest
+from domainlab.arg_parser import mk_parser_main
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
+
+def test_store_dict_key_pair_single_value():
+    """Test to parse a single gamma_reg parameter"""
+    parser = mk_parser_main()
+    args = parser.parse_args(['--gamma_reg', '0.5'])
+    assert args.gamma_reg == 0.5
+
+def test_store_dict_key_pair_dict_value():
+    """Test to parse a dict for the gamma_reg"""
+    parser = mk_parser_main()
+    args = parser.parse_args(['--gamma_reg', 'dann=1.0,jigen=2.0'])
+    assert args.gamma_reg == {'dann': 1.0, 'jigen': 2.0}
+
+def test_get_gamma_reg_single_value():
+    """Test to retrieve a single gamma_reg parameter which is applied to all objects"""
+    parser = mk_parser_main()
+    args = parser.parse_args(['--gamma_reg', '0.5'])
+    assert get_gamma_reg(args, 'dann') == 0.5
+
+def test_get_gamma_reg_dict_value():
+    """Test to retrieve a dict of gamma_reg parameters for different objects"""
+    parser = mk_parser_main()
+    args = parser.parse_args(['--gamma_reg', 'default=5.0,dann=1.0,jigen=2.0'])
+    assert get_gamma_reg(args, 'dann') == 1.0
+    assert get_gamma_reg(args, 'jigen') == 2.0
+    assert get_gamma_reg(args, 'nonexistent') == 5.0  # if we implement other
+    # model/trainers,
+    # since not specified in command line arguments, the new model/trainer
+    # called "nonexistent" should
+    # get the default value 5.0.
+
+def test_exception():
+    """Test to not specify a default value"""
+    parser = mk_parser_main()
+    args = parser.parse_args(['--gamma_reg', 'dann=1.0'])
+
+    with pytest.raises(ValueError, match="If a gamma_reg dict is specified"):
+        get_gamma_reg(args, 'jigen')
diff --git a/tests/test_irm.py b/tests/test_irm.py
new file mode 100644
index 000000000..5ed8b4ceb
--- /dev/null
+++ b/tests/test_irm.py
@@ -0,0 +1,22 @@
+"""
+end-end test
+"""
+from tests.utils_test import utils_test_algo
+
+
+def test_irm():
+    """
+    train with Invariant Risk Minimization
+    """
+    args = "--te_d=caltech --task=mini_vlcs --debug --bs=2 --model=erm \
+        --trainer=irm --nname=alexnet"
+    utils_test_algo(args)
+
+
+def test_irm_mnist():
+    """
+    train with Invariant Risk Minimization
+    """
+    args = "--te_d=0 --task=mnistcolor10 --keep_model --model=erm \
+        --trainer=irm --nname=conv_bn_pool_2"
+    utils_test_algo(args)