diff --git a/src/dataset/create_renders/kubruntu_sdf.Dockerfile b/src/dataset/create_renders/kubruntu_sdf.Dockerfile
new file mode 100644
index 0000000..f61d8f3
--- /dev/null
+++ b/src/dataset/create_renders/kubruntu_sdf.Dockerfile
@@ -0,0 +1,8 @@
+# to build this image, run
+# docker build -f kubruntu_sdf.Dockerfile -t kubruntu_sdf:latest .
+
+FROM kubricdockerhub/kubruntu as kubruntu
+
+# --- install fogleman/sdf
+RUN git clone https://github.com/fogleman/sdf.git
+RUN pip install ./sdf
diff --git a/src/dataset/create_renders/requirements.txt b/src/dataset/create_renders/requirements.txt
new file mode 100644
index 0000000..0161068
--- /dev/null
+++ b/src/dataset/create_renders/requirements.txt
@@ -0,0 +1,5 @@
+tqdm
+wandb
+pillow
+pandas
+numpy
diff --git a/src/dataset/create_renders/sem_simulator.py b/src/dataset/create_renders/sem_simulator.py
new file mode 100644
index 0000000..7918179
--- /dev/null
+++ b/src/dataset/create_renders/sem_simulator.py
@@ -0,0 +1,66 @@
+"""Create many images and log them to wandb"""
+
+import subprocess
+from typing import Tuple
+
+import wandb
+from tqdm import tqdm
+
+wandb.login()
+
+
+def gen_table(d):
+    """create a table version of the task"""
+    for k, v in d.items():
+        v.update({"index": k})
+        yield v
+
+
+def get_cmd(task_uid):
+    return f'sudo docker run --rm -i -u $(id -u):$(id -g) -e KUBRIC_USE_GPU=true --cpus=\
+"64.0" --volume "$(pwd):/kubric" kubruntu_sdf /usr/bin/python3 "sem_worker.py" {task_uid}'
+
+
+def create_rendered_dataset(sizes: Tuple[int, int, int], **kwargs):
+    """Creates a simple dataset and logs it to wandb.
+
+    Args:
+        sizes: Sizes of the train val test splits
+    """
+    with wandb.init(
+        project="FiberDiameter", job_type="create-data", mode="disabled"
+    ) as run:
+        raw_data = wandb.Artifact(
+            "rendered-fibers-large",
+            type="dataset",
+            description="Single straight fibers split into train/val/test",
+            metadata={
+                "train_val_test": sizes,
+            },
+        )
+
+        for set_name, set_size in zip(["train", "val", "test"], sizes):
+            for i in tqdm(range(596, set_size), desc=set_name):
+                task_uid = f"{set_name}{i:04d}"
+                cmd = get_cmd(task_uid)
+                succeed = 0
+                while succeed == 0:
+                    try:
+                        subprocess.run(cmd, shell=True, check=True)
+                        succeed = 1
+                    except Exception as e:
+                        print(e)
+                # # --- load the image to log it to WandB
+                # im = Image.open(f'output/{task_uid}.png')
+                # seg = np.load(f'output/{task_uid}_seg.npz')['y']
+
+                raw_data.add_file(f"output/{task_uid}.png", name=f"{task_uid}.png")
+                raw_data.add_file(
+                    f"output/{task_uid}_seg.npz", name=f"{task_uid}_seg.npz"
+                )
+                raw_data.add_file(f"output/{task_uid}.json", name=f"{task_uid}.json")
+        run.log_artifact(raw_data)
+
+
+if __name__ == "__main__":
+    create_rendered_dataset([8192, 512, 512])
diff --git a/src/dataset/create_renders/sem_worker.py b/src/dataset/create_renders/sem_worker.py
new file mode 100644
index 0000000..5e2365d
--- /dev/null
+++ b/src/dataset/create_renders/sem_worker.py
@@ -0,0 +1,175 @@
+r"""This module is intended to run with the following command:
+
+docker run --rm -i -u $(id -u):$(id -g) --volume "$(pwd):/kubric" \
+kubruntu_sdf /usr/bin/python3 'create/sem_worker.py' <task_id>
+
+It creates a single scene and stores 3 files. An rgba, a segmentation with
+the first 10 fibers and a json with the information about those 10 fibers.
+"""
+import json
+import logging
+import os
+import shutil
+import sys
+from itertools import combinations
+from random import choice
+
+import kubric as kb
+import numpy as np
+from kubric.core.color import Color
+from kubric.core.materials import PrincipledBSDFMaterial
+from kubric.renderer.blender import Blender as KubricRenderer
+from sdf import capped_cylinder
+
+logging.basicConfig(level="WARNING", filename="output/output.log")
+scene_uid = sys.argv[1]
+temp_folder = os.path.join("temp", scene_uid)
+os.makedirs(temp_folder, exist_ok=True)
+
+
+def create_fiber(asset_id, p1, p2, diameter=0.1):
+    filepath = os.path.join(temp_folder, f"{asset_id}.obj")
+    f = capped_cylinder(p1, p2, diameter / 2)
+    # f = f.bend_linear(-Y, 1.5*Y, X/2, ease.in_out_quad)
+    f.save(filepath, sparse=True, samples=2**22, verbose=False)
+    obj = kb.FileBasedObject(
+        asset_id=str(asset_id),
+        render_filename=filepath,
+        bounds=((-1, -1, -1), (1, 1, 1)),
+        simulation_filename=None,
+        segmentation_id=asset_id,
+    )
+    return obj
+
+
+def rectangle_perimeter(p1, p2, t):
+    """t is in range [0,1]
+    points are here:
+    ---2
+    |  |
+    1---
+    """
+    p1 = np.array(p1)
+    p2 = np.array(p2)
+    d = p2 - p1
+
+    if 0 <= t < 0.25:
+        return np.array([4 * t * d[0] + p1[0], p1[1]])
+    elif 0.25 <= t < 0.5:
+        return np.array([p2[0], 4 * (t - 0.25) * d[1] + p1[1]])
+    elif 0.5 <= t < 0.75:
+        return np.array([p2[0] - 4 * (t - 0.5) * d[0], p2[1]])
+    elif 0.75 <= t <= 1:
+        return np.array([p1[0], p2[1] - 4 * (t - 0.75) * d[1]])
+
+
+def random_line_in_perimeter(p1, p2):
+    sides = [[0.00, 0.25], [0.25, 0.50], [0.50, 0.75], [0.75, 1.00]]
+    comb = [x for x in combinations(sides, 2)]
+    s1, s2 = choice(comb)
+    t1 = np.random.uniform(*s1)
+    t2 = np.random.uniform(*s2)
+    return rectangle_perimeter(p1, p2, t1), rectangle_perimeter(p1, p2, t2)
+
+
+def random_fiber(asset_id, z, d):
+    p1, p2 = random_line_in_perimeter([-0.6, -0.6], [0.6, 0.6])
+    p1 = [*p1, z]
+    p2 = [*p2, z]
+    return create_fiber(asset_id, p1, p2, d), p1, p2
+
+
+# bg_color
+c = np.random.rand()
+color_bg = Color(c, c, c, 0.5)
+bg_material = PrincipledBSDFMaterial(color=color_bg)
+
+# --- create scene and attach a renderer to it
+scene = kb.Scene(resolution=(1024, 1024), background=Color(c, c, c, 1))
+renderer = KubricRenderer(
+    scene,
+    use_denoising=False,
+)
+
+c = np.random.rand()
+color_fg = Color(c, c, c, 1)
+fg_material = PrincipledBSDFMaterial(color=color_fg, roughness=np.random.rand())
+
+# --- populate the scene with bg, lights, and cameras
+# bg is id 0
+# floor is id 1
+scene += kb.Cube(
+    name="floor",
+    scale=(10, 10, 0.1),
+    position=(0, 0, -1),
+    material=bg_material,
+    background=True,
+    segmentation_id=0,
+)
+scene += kb.DirectionalLight(
+    name="sun",
+    position=np.random.rand(3) * 3,
+    look_at=(0, 0, 0),
+    intensity=np.random.uniform(1.0, 2.0),
+)
+scene.camera = kb.OrthographicCamera(position=(0, 0, 3), orthographic_scale=1)
+
+# --- create random diameters for fibers
+mu = np.random.uniform(0.03, 0.15)
+d = np.random.normal(mu, 0.01, 50)
+
+# --- create random material for fibers
+fg_material = PrincipledBSDFMaterial(
+    color=color_fg,
+    metallic=np.random.rand(),
+    specular=np.random.rand(),
+    specular_tint=np.random.rand(),
+    roughness=np.random.rand(),
+)
+
+# --- initial z position
+last_z = 0.1
+
+# --- debug
+for asset in scene.foreground_assets:
+    print(asset.segmentation_id)
+
+# --- add the first 10 fibers
+task_data = {0: {"name": "bg"}}
+for i in range(40):
+
+    last_z -= d[i] * 0.5
+    if last_z <= -0.8:
+        break
+
+    asset_id = i + 100
+    obj, p1, p2 = random_fiber(asset_id, last_z, d[i])
+    obj.material = fg_material
+    scene += obj
+    task_data[asset_id] = {
+        "name": f"fiber{i:02d}",
+        "d": d[i],
+        "p1": p1,
+        "p2": p2,
+    }  # the id of the background is 0, so fibers only go from 1 onwards
+
+frame = renderer.render_still(return_layers=["rgba", "segmentation"])
+frame["segmentation"] = kb.adjust_segmentation_idxs(
+    frame["segmentation"], scene.assets, scene.assets
+).astype(np.uint8)
+
+# --- debug
+last_fiber = max(task_data.keys())
+max_seg_id = np.max(frame["segmentation"])
+assert last_fiber == max_seg_id, f"last_fiber {last_fiber}, max_seg_id {max_seg_id}"
+# /--- debug
+
+kb.write_png(frame["rgba"], f"output/{scene_uid}.png")
+kb.write_palette_png(frame["segmentation"], f"output/{scene_uid}_seg.png")
+np.savez(f"output/{scene_uid}_seg.npz", y=frame["segmentation"])
+with open(f"output/{scene_uid}.json", "w") as file:
+    json.dump(task_data, file)
+
+# --- save the output as pngs
+
+shutil.rmtree(temp_folder)