diff --git a/.flake8 b/.flake8
deleted file mode 100644
index b4f0315..0000000
--- a/.flake8
+++ /dev/null
@@ -1,10 +0,0 @@
-[flake8]
-show-source = True
-max-line-length = 100
-extend-exclude =
-    venv
-    .venv
-    build
-extend-ignore =
-    E203  # No whitespace before ':' in [x : y]
-    E731  # No lambdas — too strict
diff --git a/MANIFEST.in b/MANIFEST.in
index 24a7794..8039ffc 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -5,6 +5,5 @@
 
 # Include individual files
 include LICENSE.txt
-include requirements.txt
 prune tests
 prune examples
diff --git a/README.md b/README.md
index d4941ee..2380aff 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,10 @@ pip install -e DEHB  # -e stands for editable, lets you modify the code and reru
 To run PyTorch example: (*note additional requirements*) 
 ```bash
 python examples/03_pytorch_mnist_hpo.py \
-     --min_budget 1 --max_budget 3 --verbose --runtime 60
+    --min_budget 1 \
+    --max_budget 3 \
+    --runtime 60 \
+    --verbose
 ```
 
 ### Running DEHB in a parallel setting
@@ -58,8 +61,13 @@ to it by that DEHB run.
 
 To run the PyTorch MNIST example on a single node using 2 workers:  
 ```bash
-python examples/03_pytorch_mnist_hpo.py --min_budget 1 --max_budget 3 \
-  --verbose --runtime 60 --n_workers 2 --single_node_with_gpus
+python examples/03_pytorch_mnist_hpo.py \
+    --min_budget 1 \
+    --max_budget 3 \
+    --runtime 60 \
+    --n_workers 2 \
+    --single_node_with_gpus \
+    --verbose
 ```
 
 #### Multi-node runs
@@ -80,10 +88,19 @@ manner on clusters managed by SLURM. (*not expected to work off-the-shelf*)
 
 To run the PyTorch MNIST example on a multi-node setup using 4 workers:
 ```bash
-bash utils/run_dask_setup.sh -f dask_dump/scheduler.json -e env_name -n 4
+bash utils/run_dask_setup.sh \
+    -f dask_dump/scheduler.json \  # This is how the workers will be discovered by DEHB
+    -e env_name \
+    -n 4
+
+# Make sure to sleep to allow the workers to setup properly
 sleep 5
-python examples/03_pytorch_mnist_hpo.py --min_budget 1 --max_budget 3 \
-  --verbose --runtime 60 --scheduler_file dask_dump/scheduler.json 
+python examples/03_pytorch_mnist_hpo.py \
+    --min_budget 1 \
+    --max_budget 3 \
+    --runtime 60 \
+    --scheduler_file dask_dump/scheduler.json \
+    --verbose
 ```
 
 ### DEHB Hyperparameters
@@ -127,8 +144,8 @@ represents the *mutation* strategy while `bin` represents the *binomial crossove
 }
 
 @online{Awad-arXiv-2023,
-title = {MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization},
-author = {Noor Awad and Ayushi Sharma and Frank Hutter},
-year = {2023},
-keywords = {}
+title       = {MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization},
+author      = {Noor Awad and Ayushi Sharma and Frank Hutter},
+year        = {2023},
+keywords    = {}
 }
diff --git a/__init__.py b/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/docs/index.md b/docs/index.md
index bea8d2e..297e931 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -13,12 +13,17 @@ To start using the `dehb` package, you can install it via pip. You can either in
 ```bash
 # Install from pypi
 pip install dehb
-
-# Install as editable from github
-git clone https://github.com/automl/DEHB.git
-pip install -e DEHB  # -e stands for editable, lets you modify the code and rerun things
 ```
 
+!!! note "From Source"
+
+    To install directly from from source
+
+    ```bash
+    git clone https://github.com/automl/DEHB.git
+    pip install -e DEHB  # -e stands for editable, lets you modify the code and rerun things
+    ```
+
 ## Getting Started
 
 In the following sections we provide some basic examplatory setup for running DEHB with a single worker or in a multi-worker setup.
@@ -26,47 +31,54 @@ In the following sections we provide some basic examplatory setup for running DE
 ### Basic single worker setup
 A basic setup for optimizing can be done as follows. Please note, that this is example should solely show a simple setup of `dehb`. More in-depth examples can be found in the [examples folder](../examples/). First we need to setup a `ConfigurationSpace`, from which Configurations will be sampled:
 
-```python
-import ConfigSpace
+```python exec="true" source="material-block" result="python" title="Configuration Space" session="someid"
+from ConfigSpace import ConfigurationSpace, Configuration
 
-cs = ConfigSpace.ConfigurationSpace()
-cs.add_hyperparameter(ConfigSpace.UniformFloatHyperparameter("x0", lower=3, upper=10, log=False))
+cs = ConfigurationSpace({"x0": (3.0, 10.0), "x1": ["red", "green"]})
+print(cs)
 ```
 
 Next, we need an `object_function`, which we are aiming to optimize:
-```python
+```python exec="true" source="material-block" result="python" title="Configuration Space" session="someid"
 import numpy as np
-def objective_function(x, budget, **kwargs):
-    """Toy objective function.
-
-    Args:
-        x (ConfigSpace.Configuration): Configuration to evaluate
-        budget (float): Budget to evaluate x on
-
-    Returns:
-        dict: Result dictionary
-    """
-    # This obviously does not make sense in a real world example. Replace this with your actual objective value (y) and cost.
-    y = np.random.uniform()
-    cost = 5
-    result = {
-        "fitness": y,
-        "cost": cost
-    }
-    return result
+
+def objective_function(x: Configuration, budget: float, **kwargs):
+    # Replace this with your actual objective value (y) and cost.
+    cost = (10 if x["x1"] == "red" else 100) + budget
+    y = x["x0"] + np.random.uniform()
+    return {"fitness": y, "cost": x["x0"]}
+
+sample_config = cs.sample_configuration()
+print(sample_config)
+
+result = objective_function(sample_config, budget=10)
+print(result)
 ```
 
 Finally, we can setup our optimizer and run DEHB:
 
-```python
+```python exec="true" source="material-block" result="python" title="Configuration Space" session="someid"
 from dehb import DEHB
 
 dim = len(cs.get_hyperparameters())
-optimizer = DEHB(f=objective_function, cs=cs, dimensions=dim, min_budget=3, output_path="./logs",
-                max_budget=27, eta=3, n_workers=1)
-
-# Run optimization for 10 brackets. Output files will be save to ./logs
-traj, runtime, history = opt.run(brackets=10, verbose=True)
+optimizer = DEHB(
+    f=objective_function,
+    cs=cs,
+    dimensions=dim,
+    min_budget=3,
+    max_budget=27,
+    eta=3,
+    n_workers=1,
+    output_path="./logs",
+)
+
+# Run optimization for 1 bracket. Output files will be saved to ./logs
+traj, runtime, history = optimizer.run(brackets=1, verbose=True)
+config, fitness, runtime, budget, _ = history[0]
+print("config", config)
+print("fitness", fitness)
+print("runtime", runtime)
+print("budget", budget)
 ```
 
 ### Running DEHB in a parallel setting
@@ -99,8 +111,13 @@ to it by that DEHB run.
 
 To run the PyTorch MNIST example on a single node using 2 workers:  
 ```bash
-python examples/03_pytorch_mnist_hpo.py --min_budget 1 --max_budget 3 \
-  --verbose --runtime 60 --n_workers 2 --single_node_with_gpus
+python examples/03_pytorch_mnist_hpo.py \
+    --min_budget 1 \
+    --max_budget 3 \
+    --runtime 60 \
+    --n_workers 2 \
+    --single_node_with_gpus \
+    --verbose
 ```
 
 #### Multi-node runs
@@ -121,10 +138,20 @@ manner on clusters managed by SLURM. (*not expected to work off-the-shelf*)
 
 To run the PyTorch MNIST example on a multi-node setup using 4 workers:
 ```bash
-bash utils/run_dask_setup.sh -f dask_dump/scheduler.json -e env_name -n 4
+bash utils/run_dask_setup.sh \
+    -n 4 \
+    -f dask_dump/scheduler.json \   # This is how the workers will be discovered by DEHB
+    -e env_name
+
+# Make sure to sleep to allow the workers to setup properly
 sleep 5
-python examples/03_pytorch_mnist_hpo.py --min_budget 1 --max_budget 3 \
-  --verbose --runtime 60 --scheduler_file dask_dump/scheduler.json 
+
+python examples/03_pytorch_mnist_hpo.py \
+    --min_budget 1 \
+    --max_budget 3 \
+    --runtime 60 \
+    --scheduler_file dask_dump/scheduler.json \
+    --verbose
 ```
 
 ## To cite the paper or code
@@ -142,9 +169,9 @@ If you use DEHB in one of your research projects, please cite our paper(s):
 }
 
 @online{Awad-arXiv-2023,
-title = {MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization},
-author = {Noor Awad and Ayushi Sharma and Frank Hutter},
-year = {2023},
-keywords = {}
+    title       = {MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization},
+    author      = {Noor Awad and Ayushi Sharma and Frank Hutter},
+    year        = {2023},
+    keywords    = {}
 }
 ```
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index f2afacb..e9ed3a5 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -45,8 +45,39 @@ theme:
         icon: material/eye
         name: Switch to dark mode
 
+markdown_extensions:
+  - admonition
+  - tables
+  - attr_list
+  - md_in_html
+  - toc:
+      permalink: "#"
+  - pymdownx.highlight:
+      anchor_linenums: true
+  - pymdownx.magiclink:
+      hide_protocol: true
+      repo_url_shortener: true
+      repo_url_shorthand: true
+      user: automl
+      repo: DEHB
+  - pymdownx.highlight
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.details
+  - pymdownx.tabbed:
+      alternate_style: true
+  - pymdownx.superfences:
+      custom_fences:
+      - name: mermaid
+        class: mermaid
+        format: !!python/name:pymdownx.superfences.fence_code_format
+  - pymdownx.emoji:
+      emoji_index: !!python/name:materialx.emoji.twemoji
+      emoji_generator: !!python/name:materialx.emoji.to_svg
+
 plugins:
   - search
+  - markdown-exec
   - mkdocstrings:
       default_handler: python
       enable_inventory: true
diff --git a/pyproject.toml b/pyproject.toml
index e598cb7..8aeaa3e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,33 +1,83 @@
 # For TOML reference
 # https://learnxinyminutes.com/docs/toml/
+[project]
+urls = { Documentation = "https://automl.github.io/DEHB/", Github = "https://github.com/automl/DEHB" }
+
+name = "DEHB"
+version = "0.0.7"
+dependencies = [
+  "numpy>=1.18.2",
+  "loguru>=0.5.3",
+  "dask>=2.27.0",
+  "distributed>=2.27.0",
+  "ConfigSpace>=0.4.16",
+]
+classifiers = [
+  "Programming Language :: Python :: 3.8",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Natural Language :: English",
+  "Intended Audience :: Developers",
+  "Intended Audience :: Education",
+  "Intended Audience :: Science/Research",
+  "Topic :: Scientific/Engineering",
+  "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+license = { file = "LICENSE.txt" }
+readme = "README.md"
+description = "Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization"
+authors = [
+  { name = "Neeratyoy Mallik", email = "mallik@cs.uni-freiburg.de" },
+  { name = "Noor Awad" },
+  { name = "Frank Hutter" },
+  { name = "Janis Fix", email = "fixj@cs.uni-freiburg.de" },
+]
+requires-python = ">=3.8"
+[project.optional-dependencies]
+dev = [
+  # Test
+  "pytest>=4.6",
+  "pytest-cov",
+  "pytest-xdist",
+  "pytest-timeout",
+  # Docs
+  "mkdocs",
+  "mkdocs-material",
+  "mkdocstrings[python]",
+  "markdown-exec[ansi]",
+  # Others
+  "ruff",
+  "black",
+  "pre-commit",
+]
 
 [tool.pytest.ini_options]
-testpaths = ["tests"]             # path to the test directory
+testpaths = ["tests"]                   # path to the test directory
 minversion = "3.8"
 addopts = "--cov=src --cov-report=lcov" # Should be package name
-pythonpath = [
-  "."
-]
+pythonpath = ["."]
 
 [tool.coverage.run]
 branch = true
 context = "dehb" # Should be package name
 omit = [
-    "dehb/__init__.py", # Has variables only needed for setup.py
+  "dehb/__init__.py", # Has variables only needed for setup.py
 ]
 
 [tool.coverage.report]
 show_missing = true
 skip_covered = true
 exclude_lines = [
-    "pragma: no cover",
-    '\.\.\.',
-    "raise NotImplementedError",
-    "if TYPE_CHECKING",
+  "pragma: no cover",
+  '\.\.\.',
+  "raise NotImplementedError",
+  "if TYPE_CHECKING",
 ] # These are lines to exclude from coverage
 
 [tool.black]
 target-version = ['py38']
+line-length = 100
 
 # https://github.com/charliermarsh/ruff
 [tool.ruff]
@@ -183,4 +233,4 @@ warn_return_any = true
 module = ["tests.*"]
 disallow_untyped_defs = false       # Sometimes we just want to ignore verbose types
 disallow_untyped_decorators = false # Test decorators are not properly typed
-disallow_incomplete_defs = false    # Sometimes we just want to ignore verbose types
\ No newline at end of file
+disallow_incomplete_defs = false    # Sometimes we just want to ignore verbose types
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index b7d3788..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-numpy>=1.18.2
-loguru>=0.5.3
-dask>=2.27.0
-distributed>=2.27.0
-ConfigSpace>=0.4.16
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 8dd7673..0000000
--- a/setup.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import os
-
-import setuptools
-
-import datetime
-
-
-name = "DEHB"
-package_name = "dehb"
-author = "Neeratyoy, Noor, Janis, Frank"
-author_email = "mallik@cs.uni-freiburg.de"
-description = "Evolutionary Hyperband for Scalable, Robust and Efficient Hyperparameter Optimization"
-url = "https://github.com/automl/DEHB"
-project_urls = {
-    "Documentation": "https://automl.github.io/DEHB/",
-    "Source Code": "https://github.com/automl/DEHB",
-}
-copyright = f"Copyright {datetime.date.today().strftime('%Y')}, Neeratyoy, Noor, Frank"
-version = "0.0.7"
-
-HERE = os.path.dirname(os.path.realpath(__file__))
-
-
-def read_file(filepath: str) -> str:
-    """
-    Read in a files contents
-
-    Parameters
-    ----------
-    filepath : str
-        The name of the file.
-
-    Returns
-    -------
-    str
-        The contents of the file.
-    """
-
-    with open(filepath, "r", encoding="utf-8") as fh:
-        return fh.read()
-
-
-extras_require = {
-    "dev": [
-        # Test
-        "pytest>=4.6",
-        "pytest-cov",
-        "pytest-xdist",
-        "pytest-timeout",
-        # Docs
-        "mkdocs-material",
-        "mkdocstrings",
-        # Others
-        "ruff",
-        "black",
-        "pre-commit",
-    ]
-}
-
-setuptools.setup(
-    name=package_name,
-    author=author,
-    author_email=author_email,
-    description=description,
-    long_description=read_file(os.path.join(HERE, "README.md")),
-    long_description_content_type="text/markdown",
-    license="Apache-2.0",
-    url=url,
-    project_urls=project_urls,
-    version=version,
-    packages=setuptools.find_packages("src", exclude=["tests"]),
-    package_dir={"": "src"},
-    python_requires=">=3.8",
-    install_requires=read_file(os.path.join(HERE, "requirements.txt")).split("\n"),
-    extras_require=extras_require,
-    test_suite="pytest",
-    platforms=["Linux"],
-    classifiers=[
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.10",
-        "Programming Language :: Python :: 3.11",
-        "Natural Language :: English",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Education",
-        "Intended Audience :: Science/Research",
-        "Topic :: Scientific/Engineering",
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
-    ],
-)
diff --git a/src/dehb/README.md b/src/dehb/README.md
deleted file mode 100644
index e69de29..0000000
diff --git a/utils/README.md b/utils/README.md
index b791438..65f4058 100644
--- a/utils/README.md
+++ b/utils/README.md
@@ -20,7 +20,8 @@ sbatch temp/workers.sh
 
 Alternatively, to enable GPU usage by the workers,
 ```bash
-python utils/generate_slurm_jobs.py --worker_p [cpu_node] --scheduler_p [cpu_node] --nworkers 10 \
+python utils/generate_slurm_jobs.py \
+    --worker_p [cpu_node] --scheduler_p [cpu_node] --nworkers 10 \
     --scheduler_path ./scheduler --scheduler_file scheduler_gpu.json --output_path temp \
     --setup_file ./setup.sh --gpu
 # generates 2 shell scripts
@@ -34,8 +35,13 @@ One or more DEHB processes can share this pool of 10 workers.
 For example, running a DEHB optimization by specifiying `scheduler_file` makes that DEHB process, 
 connect to the Dask cluster runnning.
 ```bash
-python examples/03_pytorch_mnist_hpo.py --min_budget 1 --max_budget 9 --verbose \
-    --scheduler_file scheduler/scheduler_gpu.json --runtime 200 --seed 123
+python examples/03_pytorch_mnist_hpo.py \
+    --min_budget 1 \
+    --max_budget 9 \
+    --runtime 200 \
+    --seed 123 \
+    --scheduler_file scheduler/scheduler_gpu.json \
+    --verbose
 ```
 The decoupled Dask cluster remains alive even after the DEHB optimization is over. 
 It can be reused by other DEHB runs or processes. 
diff --git a/utils/dask_scheduler.sh b/utils/dask_scheduler.sh
index 297f3be..f556e1d 100644
--- a/utils/dask_scheduler.sh
+++ b/utils/dask_scheduler.sh
@@ -1,5 +1,4 @@
-#! /bin/bash
-
+#!/bin/bash
 #SBATCH -p cluster-name
 #SBATCH --gres=gpu:0
 #SBATCH --mem 0
@@ -12,13 +11,17 @@ do
     case "${flag}" in
         f) filename=${OPTARG};;  # specified as -f
         e) envname=${OPTARG};;   # specified as -e
+        *) echo "usage: $0 [-f] [-e]"
+           echo "  -f: filename of scheduler file"
+           echo "  -e: name of conda environment"
+           exit 1 ;;
     esac
 done
 
 # setting up environment
-source $HOME/anaconda3/bin/activate $envname
+source "$HOME/anaconda3/bin/activate" "$envname"
 
 # Creating a Dask scheduler
-PYTHONPATH=$PWD dask-scheduler --scheduler-file $filename
+PYTHONPATH=$PWD dask-scheduler --scheduler-file "$filename"
 
 # for more options: https://docs.dask.org/en/latest/setup/cli.html#dask-scheduler
diff --git a/utils/dask_workers.sh b/utils/dask_workers.sh
index c19a412..c01e4d0 100644
--- a/utils/dask_workers.sh
+++ b/utils/dask_workers.sh
@@ -1,5 +1,4 @@
-#! /bin/bash
-
+#!/bin/bash
 #SBATCH -p cluster-name
 #SBATCH --gres=gpu:1
 #SBATCH --mem 0
@@ -12,13 +11,22 @@ do
         f) filename=${OPTARG};;    # specified as -f
         e) envname=${OPTARG};;     # specified as -e
         w) workername=${OPTARG};;  # specified as -w
+        *) echo "usage: $0 [-f] [-e] [-w]"
+           echo "  -f: filename of scheduler file"
+           echo "  -e: name of conda environment"
+           echo "  -w: name of worker"
+           exit 1 ;;
     esac
 done
 
 # setting up environment
-source $HOME/anaconda3/bin/activate $envname
+source "$HOME/anaconda3/bin/activate" "$envname"
 
 # creating a Dask worker
-PYTHONPATH=$PWD dask-worker --scheduler-file $filename --name $workername --resources "GPU=1" --no-nanny
+PYTHONPATH=$PWD dask-worker \
+  --scheduler-file "$filename" \
+  --name "$workername" \
+  --resources "GPU=1" \
+  --no-nanny
 
 # for more options: https://docs.dask.org/en/latest/setup/cli.html#dask-worker
diff --git a/utils/generate_slurm_jobs.py b/utils/generate_slurm_jobs.py
index aeaa36b..a286966 100644
--- a/utils/generate_slurm_jobs.py
+++ b/utils/generate_slurm_jobs.py
@@ -27,11 +27,11 @@ def scheduler_command(scheduler_file):
 
 
 def worker_command(scheduler_file, worker_name, gpu=False, gpu_per_worker=1):
-    cmd = "dask-worker --scheduler-file {} --name \"{}_\"$SLURM_ARRAY_TASK_ID --no-nanny"
+    cmd = 'dask-worker --scheduler-file {} --name "{}_"$SLURM_ARRAY_TASK_ID --no-nanny'
     extra_args = " --reconnect --nprocs 1 --nthreads 1"
     cmd = cmd.format(scheduler_file, worker_name)
     if gpu:
-        cmd += " --resources \"GPU={}\"".format(gpu_per_worker)
+        cmd += ' --resources "GPU={}"'.format(gpu_per_worker)
     cmd += extra_args
     cmd += "\n"
     return cmd
@@ -72,25 +72,31 @@ def input_arguments():
         "--scheduler_file",
         default="scheduler.json",
         type=str,
-        help="The file name storing the Dask cluster connections"
+        help="The file name storing the Dask cluster connections",
     )
     parser.add_argument(
         "--scheduler_path",
         default="./scheduler",
         type=str,
-        help="The path to keep the scheduler.json like files for Dask"
+        help="The path to keep the scheduler.json like files for Dask",
     )
     parser.add_argument(
         "--setup_file",
         default=None,
         type=str,
-        help="The path to file that will be sourced to load environment and set path variables"
+        help="The path to file that will be sourced to load environment and set path variables",
     )
     parser.add_argument(
-        "--output_path", default="./", type=str, help="The path to dump the generated script"
+        "--output_path",
+        default="./",
+        type=str,
+        help="The path to dump the generated script",
     )
     parser.add_argument(
-        "--slurm_dump_path", default="./slurm-logs", type=str, help="Path to dump the slurm logs"
+        "--slurm_dump_path",
+        default="./slurm-logs",
+        type=str,
+        help="Path to dump the slurm logs",
     )
     parser.add_argument(
         "--nworkers", default=10, type=int, help="Number of workers to run"
@@ -98,24 +104,31 @@ def input_arguments():
     parser.add_argument(
         "--worker_name", default="w", type=str, help="Dask worker name prefix"
     )
+    parser.add_argument("-c", default=2, type=int, help="CPUs per task requested")
     parser.add_argument(
-        "-c", default=2, type=int, help="CPUs per task requested"
-    )
-    parser.add_argument(
-        "--gpu", default=False, action="store_true", help="If set, the workers request GPUs"
+        "--gpu",
+        default=False,
+        action="store_true",
+        help="If set, the workers request GPUs",
     )
     parser.add_argument(
         "--gpu_per_worker", default=1, type=int, help="Number of GPUs per worker"
     )
     parser.add_argument(
-        "--scheduler_p", default=None, required=True, type=str, help="The node to submit schedulers"
-    )
-    parser.add_argument(
-        "--worker_p", default=None, required=True, type=str, help="The node to submit workers"
+        "--scheduler_p",
+        default=None,
+        required=True,
+        type=str,
+        help="The node to submit schedulers",
     )
     parser.add_argument(
-        "-t", default="1:00:00", type=str, help="TIMELIMIT"
+        "--worker_p",
+        default=None,
+        required=True,
+        type=str,
+        help="The node to submit workers",
     )
+    parser.add_argument("-t", default="1:00:00", type=str, help="TIMELIMIT")
     parser.add_argument(
         "-J", default="dehb", type=str, help="Prefix to scheduler and worker job names"
     )
@@ -151,7 +164,7 @@ def input_arguments():
         scheduler_file=scheduler,
         worker_name=args.worker_name,
         gpu=args.gpu,
-        gpu_per_worker=args.gpu_per_worker
+        gpu_per_worker=args.gpu_per_worker,
     )
     with open(worker_file, "w") as f:
         f.writelines(cmd)