diff --git a/dacapo/blockwise/predict_worker.py b/dacapo/blockwise/predict_worker.py
index 05c08ae3c..59a5fbc75 100644
--- a/dacapo/blockwise/predict_worker.py
+++ b/dacapo/blockwise/predict_worker.py
@@ -46,7 +46,7 @@ def cli(log_level):
 @click.option(
     "-i",
     "--iteration",
-    type=Optional[int],
+    type=int,
     help="The training iteration of the model to use for prediction.",
     default=None,
 )
diff --git a/dacapo/examples/distance_task/cosem_finetune_example.ipynb b/dacapo/examples/distance_task/cosem_finetune_example.ipynb
index 3517d96e4..d50dfcf90 100644
--- a/dacapo/examples/distance_task/cosem_finetune_example.ipynb
+++ b/dacapo/examples/distance_task/cosem_finetune_example.ipynb
@@ -10,8 +10,7 @@
     "from dacapo.store.create_store import create_config_store\n",
     "\n",
     "config_store = create_config_store()\n",
-    "\n",
-    ""
+    "\n"
    ]
   },
   {
@@ -43,8 +42,7 @@
     "\n",
     "datasplit = datasplit_config.datasplit_type(datasplit_config)\n",
     "viewer = datasplit._neuroglancer()\n",
-    "config_store.store_datasplit_config(datasplit_config)\n",
-    ""
+    "config_store.store_datasplit_config(datasplit_config)\n"
    ]
   },
   {
@@ -73,8 +71,7 @@
     "    tol_distance=40.0,\n",
     "    scale_factor=80.0,\n",
     ")\n",
-    "config_store.store_task_config(task_config)\n",
-    ""
+    "config_store.store_task_config(task_config)\n"
    ]
   },
   {
@@ -106,8 +103,7 @@
     "    constant_upsample=True,\n",
     "    upsample_factors=[(2, 2, 2)],\n",
     ")\n",
-    "config_store.store_architecture_config(architecture_config)\n",
-    ""
+    "config_store.store_architecture_config(architecture_config)\n"
    ]
   },
   {
@@ -154,8 +150,7 @@
     "    min_masked=0.05,\n",
     "    clip_raw=True,\n",
     ")\n",
-    "config_store.store_trainer_config(trainer_config)\n",
-    ""
+    "config_store.store_trainer_config(trainer_config)\n"
    ]
   },
   {
@@ -173,7 +168,6 @@
    "outputs": [],
    "source": [
     "from dacapo.experiments import RunConfig\n",
-    "from dacapo.experiments.run import Run\n",
     "\n",
     "from dacapo.experiments.starts import CosemStartConfig\n",
     "\n",
@@ -199,8 +193,7 @@
     "    )\n",
     "\n",
     "    print(run_config.name)\n",
-    "    config_store.store_run_config(run_config)\n",
-    ""
+    "    config_store.store_run_config(run_config)\n"
    ]
   },
   {
@@ -225,8 +218,7 @@
     "config_store = create_config_store()\n",
     "\n",
     "run = Run(config_store.retrieve_run_config(\"cosem_distance_run_4nm_finetune\"))\n",
-    "train_run(run)\n",
-    ""
+    "train_run(run)\n"
    ]
   },
   {
@@ -237,8 +229,6 @@
    ]
   }
  ],
- "nbformat": 4,
- "nbformat_minor": 2,
  "metadata": {
   "language_info": {
    "codemirror_mode": {
@@ -252,5 +242,7 @@
    "pygments_lexer": "ipython3",
    "version": 3
   }
- }
-}
\ No newline at end of file
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dacapo/examples/distance_task/synthetic_example.ipynb b/dacapo/examples/distance_task/synthetic_example.ipynb
index 3c8d0cfe5..986cc68f1 100644
--- a/dacapo/examples/distance_task/synthetic_example.ipynb
+++ b/dacapo/examples/distance_task/synthetic_example.ipynb
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
        "        <iframe\n",
        "            width=\"1500\"\n",
        "            height=\"600\"\n",
-       "            src=\"http://rhoadesj-ws1.hhmi.org:32941/v/340f4dcc0a81c63c9dadd81778d98c03bd254f4f/\"\n",
+       "            src=\"http://rhoadesj-ws1.hhmi.org:35653/v/04b2f2f543a844f8203aa947f18941c8d38f1df4/\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "            \n",
@@ -110,10 +110,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f9254924550>"
+       "<IPython.lib.display.IFrame at 0x7f0ee1e1e650>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +170,7 @@
        "        <iframe\n",
        "            width=\"1500\"\n",
        "            height=\"600\"\n",
-       "            src=\"http://rhoadesj-ws1.hhmi.org:32941/v/23d4241d1b7fe361c243ca9cf1aeb3b7721b7318/\"\n",
+       "            src=\"http://rhoadesj-ws1.hhmi.org:35653/v/90acf71e45c12fa5e734f81f71c38efbf02fc914/\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "            \n",
@@ -178,10 +178,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f925490e950>"
+       "<IPython.lib.display.IFrame at 0x7f0ee1e1d600>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -221,7 +221,7 @@
        "        <iframe\n",
        "            width=\"1500\"\n",
        "            height=\"600\"\n",
-       "            src=\"http://rhoadesj-ws1.hhmi.org:32941/v/dd6004bdcb0f04434df9c3f5e0fb9c921c9af1e2/\"\n",
+       "            src=\"http://rhoadesj-ws1.hhmi.org:35653/v/a5775eb9eed3776f4f900e07a7f71500b500b958/\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "            \n",
@@ -229,10 +229,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f9470298640>"
+       "<IPython.lib.display.IFrame at 0x7f0ee1fde1a0>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +287,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Neuroglancer link: http://rhoadesj-ws1.hhmi.org:32941/v/d893eb501d1df982c9931ec02a68ee97a11888a5/\n"
+      "Neuroglancer link: http://rhoadesj-ws1.hhmi.org:35653/v/4f2cdd3e9616cf79e45570c9ef61a29d7a799efb/\n"
      ]
     }
    ],
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -462,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -508,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -532,7 +532,7 @@
     "# )\n",
     "\n",
     "iterations = 2000\n",
-    "validation_interval = 200 # iterations // 2\n",
+    "validation_interval = iterations // 2\n",
     "repetitions = 1\n",
     "for i in range(repetitions):\n",
     "    run_config = RunConfig(\n",
@@ -579,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -587,31 +587,142 @@
      "output_type": "stream",
      "text": [
       "Creating FileConfigStore:\n",
-      "\tpath: /nrs/cellmap/rhoadesj/dacapo_runs/configs\n",
-      "<funlib.persistence.arrays.array.Array object at 0x7f9458b47d00>\n"
+      "\tpath: /nrs/cellmap/rhoadesj/dacapo_runs/configs\n"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"1800\"\n",
-       "            height=\"900\"\n",
-       "            src=\"http://rhoadesj-ws1.hhmi.org:32941/v/e47d8bf96e913b730fd365d1e7703fc4705a5740/\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "            \n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x7f9458b468f0>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR:dacapo.train:Found weights for iteration 2000, but run example_synthetic_distance_run was only trained until 1600. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting/resuming training for run <dacapo.experiments.run.Run object at 0x7f0e995455d0>...\n",
+      "Creating FileStatsStore:\n",
+      "\tpath    : /nrs/cellmap/rhoadesj/dacapo_runs/stats\n",
+      "Current state: trained until 1600/2000\n",
+      "Creating local weights store in directory /nrs/cellmap/rhoadesj/dacapo_runs\n",
+      "Retrieving weights for run <dacapo.experiments.run.Run object at 0x7f0e995455d0>, iteration 2000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 1700:  80%|████████  | 1600/2000 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting iteration!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 1700:  85%|████████▌ | 1700/2000 [00:34<01:44,  2.87it/s, loss=0.303] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updating training stats of run example_synthetic_distance_run after iteration 1600\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 1800:  85%|████████▌ | 1700/2000 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting iteration!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 1800:  90%|█████████ | 1800/2000 [00:31<01:02,  3.22it/s, loss=0.151]  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updating training stats of run example_synthetic_distance_run after iteration 1600\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 1900:  90%|█████████ | 1800/2000 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting iteration!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 1900:  95%|█████████▌| 1900/2000 [00:32<00:32,  3.06it/s, loss=0.291] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updating training stats of run example_synthetic_distance_run after iteration 1600\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 2000:  95%|█████████▌| 1901/2000 [00:00<00:11,  8.78it/s, loss=0.0643]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting iteration!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "training until 2000: 100%|█████████▉| 1999/2000 [00:30<00:00,  3.23it/s, loss=0.118] \n",
+      "WARNING:dacapo.store.local_weights_store:Storing weights for run <dacapo.experiments.run.Run object at 0x7f0e995455d0>, iteration 2000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Updating training stats of run example_synthetic_distance_run after iteration 1600\n",
+      "Validating run example_synthetic_distance_run on dataset example_validate_[labels]_['labels']_8nm\n",
+      "Trained until 2000. Finished.\n"
+     ]
     }
    ],
    "source": [
@@ -644,9 +755,325 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validating run example_synthetic_distance_run at iteration 2000...\n",
+      "Creating FileConfigStore:\n",
+      "\tpath: /nrs/cellmap/rhoadesj/dacapo_runs/configs\n",
+      "Creating FileStatsStore:\n",
+      "\tpath    : /nrs/cellmap/rhoadesj/dacapo_runs/stats\n",
+      "Validating run example_synthetic_distance_run on dataset example_validate_[labels]_['labels']_8nm\n",
+      "validation inputs already copied!\n",
+      "Predicting with input size (1952, 1952, 1952), output size (1216, 1216, 1216)\n",
+      "Total input ROI: [-368:4016, -368:4016, -368:4016] (4384, 4384, 4384), output ROI: [0:3648, 0:3648, 0:3648] (3648, 3648, 3648)\n",
+      "Running blockwise prediction with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/predict_worker.py\n",
+      "Defining worker with command:  ['/home/rhoadesj@hhmi.org/micromamba/envs/dacapo/bin/python', '/nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/predict_worker.py', 'start-worker', '--run-name', 'example_synthetic_distance_run', '--input_container', '/nrs/cellmap/rhoadesj/dacapo_runs/example_synthetic_distance_run/validation.zarr', '--input_dataset', \"inputs/example_validate_[labels]_['labels']_8nm/raw\", '--output_container', '/nrs/cellmap/rhoadesj/dacapo_runs/example_synthetic_distance_run/validation.zarr', '--output_dataset', \"2000/example_validate_[labels]_['labels']_8nm/prediction\", '--iteration', '2000']\n",
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/predict_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "validation inputs already copied!\n",
+      "Predicting with input size (1952, 1952, 1952), output size (1216, 1216, 1216)\n",
+      "Total input ROI: [-368:4016, -368:4016, -368:4016] (4384, 4384, 4384), output ROI: [0:3648, 0:3648, 0:3648] (3648, 3648, 3648)\n",
+      "Running blockwise prediction with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/predict_worker.py\n",
+      "Defining worker with command:  ['/home/rhoadesj@hhmi.org/micromamba/envs/dacapo/bin/python', '/nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/predict_worker.py', 'start-worker', '--run-name', 'example_synthetic_distance_run', '--input_container', '/nrs/cellmap/rhoadesj/dacapo_runs/example_synthetic_distance_run/validation.zarr', '--input_dataset', \"inputs/example_validate_[labels]_['labels']_8nm/raw\", '--output_container', '/nrs/cellmap/rhoadesj/dacapo_runs/example_synthetic_distance_run/validation.zarr', '--output_dataset', \"2000/example_validate_[labels]_['labels']_8nm/prediction\", '--iteration', '2000']\n",
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/predict_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n",
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task predict_worker2024-03-20_17-23-01:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Done predicting.\n",
+      "Predicted on dataset example_validate_[labels]_['labels']_8nm\n",
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/threshold_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n",
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task threshold_worker2024-03-20_17-23-49:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Evaluating binary segmentations on evaluation_data of shape: (456, 456, 456)\n",
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/threshold_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n",
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task threshold_worker2024-03-20_17-24-44:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Evaluating binary segmentations on evaluation_data of shape: (456, 456, 456)\n",
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/threshold_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n",
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task threshold_worker2024-03-20_17-25-38:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Evaluating binary segmentations on evaluation_data of shape: (456, 456, 456)\n",
+      "Creating FileStatsStore:\n",
+      "\tpath    : /nrs/cellmap/rhoadesj/dacapo_runs/stats\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "88d8e80ddf214ec4a1db5d8c48c08951",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "predict_worker2024-03-20_17-23-01 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9328578934ff445cbe18ce3674144e8f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "predict_worker2024-03-20_17-23-04 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task predict_worker2024-03-20_17-23-04:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Done predicting.\n",
+      "Predicted on dataset example_validate_[labels]_['labels']_8nm\n",
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/threshold_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b263516ec4184e8ca290441dabb56c7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "threshold_worker2024-03-20_17-23-49 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a35573f7217840898fe6094b279367fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "threshold_worker2024-03-20_17-23-54 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task threshold_worker2024-03-20_17-23-54:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Evaluating binary segmentations on evaluation_data of shape: (456, 456, 456)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7665520192c74064858fffc4bda1efc4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "threshold_worker2024-03-20_17-24-44 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/threshold_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93370a88ab46491cac404dfea3256405",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "threshold_worker2024-03-20_17-25-09 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task threshold_worker2024-03-20_17-25-09:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Evaluating binary segmentations on evaluation_data of shape: (456, 456, 456)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7c3ece3a51d460fa056c2be0e4406b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "threshold_worker2024-03-20_17-25-38 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running blockwise with worker_file:  /nrs/cellmap/rhoadesj/dacapo/dacapo/blockwise/threshold_worker.py\n",
+      "Using compute context: LocalTorch(_device=None, oom_limit=5)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6b1cb85e44c4ab7ada3e11cdf6517c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "threshold_worker2024-03-20_17-26-09 ▶:   0%|          | 0/27 [00:00<?, ?blocks/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Execution Summary\n",
+      "-----------------\n",
+      "\n",
+      "  Task threshold_worker2024-03-20_17-26-09:\n",
+      "\n",
+      "    num blocks : 27\n",
+      "    completed ✔: 27 (skipped 0)\n",
+      "    failed    ✗: 0\n",
+      "    orphaned  ∅: 0\n",
+      "\n",
+      "    all blocks processed successfully\n",
+      "Evaluating binary segmentations on evaluation_data of shape: (456, 456, 456)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:dacapo.store.file_stats_store:Overwriting previous validation scores for run example_synthetic_distance_run\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating FileStatsStore:\n",
+      "\tpath    : /nrs/cellmap/rhoadesj/dacapo_runs/stats\n"
+     ]
+    }
+   ],
    "source": [
     "from dacapo.validate import validate\n",
     "\n",
@@ -663,9 +1090,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"1500\"\n",
+       "            height=\"600\"\n",
+       "            src=\"http://rhoadesj-ws1.hhmi.org:35653/v/b388488fccbad76088318e8cc66a2492b497dbe6/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7f10d8391b40>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from dacapo.predict import predict\n",
     "\n",
@@ -684,7 +1134,7 @@
     ")\n",
     "\n",
     "raw_array = open_ds(str(test_data_path), \"raw\")\n",
-    "pred_array = open_ds(str(test_data_path), \"predictions\")\n",
+    "pred_array = open_ds(str(test_data_path), f\"prediction_{run_config.name}_{iterations}\")\n",
     "gt_array = open_ds(str(test_data_path), \"labels\")\n",
     "\n",
     "arrays = {\n",