diff --git a/.circleci/config.yml b/.circleci/config.yml
index 6e91f8e5..8b899a83 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -135,8 +135,8 @@ jobs:
             . /opt/conda/etc/profile.d/conda.sh
             conda activate tiktorch-server-env
             ./scripts/conda_build.sh conda-recipe-client
-            ./scripts/conda_build.sh pybio-core-recipe
-            ./scripts/conda_build.sh pybio-torch-recipe
+            ./scripts/conda_build.sh bioimageio-core-recipe
+            ./scripts/conda_build.sh bioimageio-torch-recipe
             ./scripts/conda_build.sh conda-recipe
             ./scripts/conda_build.sh conda-recipe-cpu
 
diff --git a/environment.yml b/environment.yml
index fd0e1303..2bcaeaf4 100644
--- a/environment.yml
+++ b/environment.yml
@@ -16,7 +16,7 @@ dependencies:
   - inferno=v0.4.*
   - pillow=6.2.1
   - pyyaml=5.3
-  - marshmallow=3.4.0
+  - marshmallow=3.12.*
   - pytest=4.3.0
   - grpcio>=1.31
   - grpcio-tools
@@ -34,6 +34,9 @@ dependencies:
   - scikit-learn
   - pip>=19
   - pip:
-      - tensorflow
+      - marshmallow_union
       - onnxruntime
       - pytest-grpc==0.7.0
+      - python-stdnum
+      - spdx-license-list
+      - tensorflow
diff --git a/pybio-core-recipe/meta.yaml b/pybio-core-recipe/meta.yaml
index d53d427d..19d69b4c 100644
--- a/pybio-core-recipe/meta.yaml
+++ b/pybio-core-recipe/meta.yaml
@@ -28,14 +28,14 @@ requirements:
 
 test:
   imports:
-    - pybio.spec
+    - bioimageio.spec
 
 about:
   home: https://github.com/bioimage-io/python-bioimage-io
   license: MIT
   license_family: MIT
   license_file: LICENSE
-  summary: 'Tools for parsing pybio model specification'
+  summary: 'Tools for parsing bioimageio model specification'
 
   doc_url: https://github.com/bioimage-io/python-bioimage-io
   dev_url: https://github.com/bioimage-io/python-bioimage-io
diff --git a/pybio-torch-recipe/meta.yaml b/pybio-torch-recipe/meta.yaml
index fbef67f2..6ba9694d 100644
--- a/pybio-torch-recipe/meta.yaml
+++ b/pybio-torch-recipe/meta.yaml
@@ -27,15 +27,15 @@ requirements:
 
 test:
   imports:
-    - pybio.torch
-    - pybio.torch.training
+    - bioimageio.torch
+    - bioimageio.torch.training
 
 about:
   home: https://github.com/bioimage-io/pytorch-bioimage-io
   license: MIT
   license_family: MIT
   license_file: LICENSE
-  summary: 'Pytorch related components of pybio'
+  summary: 'Pytorch related components of bioimageio'
 
   doc_url: https://github.com/bioimage-io/pytorch-bioimage-io
   dev_url: https://github.com/bioimage-io/pytorch-bioimage-io
diff --git a/setup.py b/setup.py
index a0ae41aa..e9044bc4 100644
--- a/setup.py
+++ b/setup.py
@@ -30,8 +30,8 @@
         "numpy",
         "pyyaml",
         "torch",
-        "pybio.core @ git+ssh://git@github.com/bioimage-io/python-bioimage-io#egg=pybio.core",
-        "pybio.torch @ git+ssh://git@github.com/bioimage-io/pytorch-bioimage-io#egg=pybio.torch",
+        "bioimageio.core @ git+ssh://git@github.com/bioimage-io/python-bioimage-io#egg=bioimageio.core",
+        "bioimageio.torch @ git+ssh://git@github.com/bioimage-io/pytorch-bioimage-io#egg=bioimageio.torch",
     ],
     entry_points={"console_scripts": ["tiktorch=tiktorch.server.base:main"]},
     # extras_require={"test": ["pytest"]},
diff --git a/tests/conftest.py b/tests/conftest.py
index c2ee4cfd..d3c46e0c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -15,11 +15,11 @@
 import pytest
 
 TEST_DATA = "data"
-TEST_PYBIO_ZIPFOLDER = "unet2d"
-TEST_PYBIO_ONNX = "unet2d_onnx"
-TEST_PYBIO_DUMMY = "dummy"
-TEST_PYBIO_TENSORFLOW_DUMMY = "dummy_tensorflow"
-TEST_PYBIO_TORCHSCRIPT = "unet2d_torchscript"
+TEST_BIOIMAGEIO_ZIPFOLDER = "unet2d"
+TEST_BIOIMAGEIO_ONNX = "unet2d_onnx"
+TEST_BIOIMAGEIO_DUMMY = "dummy"
+TEST_BIOIMAGEIO_TENSORFLOW_DUMMY = "dummy_tensorflow"
+TEST_BIOIMAGEIO_TORCHSCRIPT = "unet2d_torchscript"
 
 NNModel = namedtuple("NNModel", ["model", "state"])
 
@@ -82,8 +82,8 @@ def assert_threads_cleanup():
 
 
 @pytest.fixture
-def pybio_model_bytes(data_path):
-    zip_folder = data_path / TEST_PYBIO_ZIPFOLDER
+def bioimageio_model_bytes(data_path):
+    zip_folder = data_path / TEST_BIOIMAGEIO_ZIPFOLDER
     data = io.BytesIO()
     with ZipFile(data, mode="w") as zip_model:
         for f_path in zip_folder.iterdir():
@@ -97,18 +97,18 @@ def pybio_model_bytes(data_path):
 
 
 @pytest.fixture
-def pybio_model_zipfile(pybio_model_bytes):
-    with ZipFile(pybio_model_bytes, mode="r") as zf:
+def bioimageio_model_zipfile(bioimageio_model_bytes):
+    with ZipFile(bioimageio_model_bytes, mode="r") as zf:
         yield zf
 
 
 @pytest.fixture
-def pybio_dummy_model_filepath(data_path, tmpdir):
-    pybio_net_dir = Path(data_path) / TEST_PYBIO_DUMMY
+def bioimageio_dummy_model_filepath(data_path, tmpdir):
+    bioimageio_net_dir = Path(data_path) / TEST_BIOIMAGEIO_DUMMY
     path = tmpdir / "dummy_model.zip"
 
     with ZipFile(path, mode="w") as zip_model:
-        for f_path in pybio_net_dir.iterdir():
+        for f_path in bioimageio_net_dir.iterdir():
             if str(f_path.name).startswith("__"):
                 continue
 
@@ -119,11 +119,11 @@ def pybio_dummy_model_filepath(data_path, tmpdir):
 
 
 @pytest.fixture
-def pybio_dummy_model_bytes(data_path):
-    pybio_net_dir = Path(data_path) / TEST_PYBIO_DUMMY
+def bioimageio_dummy_model_bytes(data_path):
+    bioimageio_net_dir = Path(data_path) / TEST_BIOIMAGEIO_DUMMY
     data = io.BytesIO()
     with ZipFile(data, mode="w") as zip_model:
-        for f_path in pybio_net_dir.iterdir():
+        for f_path in bioimageio_net_dir.iterdir():
             if str(f_path.name).startswith("__"):
                 continue
 
@@ -156,22 +156,22 @@ def _archive(path_to_archive):
 
 
 @pytest.fixture
-def pybio_dummy_tensorflow_model_bytes(data_path):
-    pybio_net_dir = Path(data_path) / TEST_PYBIO_TENSORFLOW_DUMMY
-    return archive(pybio_net_dir)
+def bioimageio_dummy_tensorflow_model_bytes(data_path):
+    bioimageio_net_dir = Path(data_path) / TEST_BIOIMAGEIO_TENSORFLOW_DUMMY
+    return archive(bioimageio_net_dir)
 
 
 @pytest.fixture
-def pybio_unet2d_onnx_bytes(data_path):
-    pybio_net_dir = Path(data_path) / TEST_PYBIO_ONNX
-    return archive(pybio_net_dir)
+def bioimageio_unet2d_onnx_bytes(data_path):
+    bioimageio_net_dir = Path(data_path) / TEST_BIOIMAGEIO_ONNX
+    return archive(bioimageio_net_dir)
 
 
 @pytest.fixture
-def pybio_unet2d_onnx_test_data(data_path):
-    pybio_net_dir = Path(data_path) / TEST_PYBIO_ONNX
-    test_input = pybio_net_dir / "test_input.npy"
-    test_output = pybio_net_dir / "test_output.npy"
+def bioimageio_unet2d_onnx_test_data(data_path):
+    bioimageio_net_dir = Path(data_path) / TEST_BIOIMAGEIO_ONNX
+    test_input = bioimageio_net_dir / "test_input.npy"
+    test_output = bioimageio_net_dir / "test_output.npy"
     return {"test_input": test_input, "test_output": test_output}
 
 
@@ -184,19 +184,14 @@ def npy_zeros_file(tmpdir):
 
 
 @pytest.fixture
-def pybio_unet2d_torchscript_bytes(data_path):
-    pybio_net_dir = Path(data_path) / TEST_PYBIO_TORCHSCRIPT
-    return archive(pybio_net_dir)
+def bioimageio_unet2d_torchscript_bytes(data_path):
+    bioimageio_net_dir = Path(data_path) / TEST_BIOIMAGEIO_TORCHSCRIPT
+    return archive(bioimageio_net_dir)
 
 
 @pytest.fixture
-def pybio_unet2d_torchscript_test_data(data_path):
-    pybio_net_dir = Path(data_path) / TEST_PYBIO_TORCHSCRIPT
-    test_input = pybio_net_dir / "test_input.npy"
-    test_output = pybio_net_dir / "test_output.npy"
+def bioimageio_unet2d_torchscript_test_data(data_path):
+    bioimageio_net_dir = Path(data_path) / TEST_BIOIMAGEIO_TORCHSCRIPT
+    test_input = bioimageio_net_dir / "test_input.npy"
+    test_output = bioimageio_net_dir / "test_output.npy"
     return {"test_input": test_input, "test_output": test_output}
-
-
-@pytest.fixture
-def cache_path(tmp_path):
-    return Path(getenv("PYBIO_CACHE_PATH", tmp_path))
diff --git a/tests/data/unet2d_onnx/UNet2DNucleiBroad.model.yaml b/tests/data/unet2d_onnx/UNet2DNucleiBroad.model.yaml
index 39593edc..7225c75d 100644
--- a/tests/data/unet2d_onnx/UNet2DNucleiBroad.model.yaml
+++ b/tests/data/unet2d_onnx/UNet2DNucleiBroad.model.yaml
@@ -57,7 +57,7 @@ outputs:
 
 language: python
 framework: pytorch
-source: pybio.torch.models.unet2d.UNet2d
+source: bioimageio.torch.models.unet2d.UNet2d
 kwargs: {input_channels: 1, output_channels: 1}
 dependencies: conda:../environment.yaml
 
diff --git a/tests/data/unet2d_torchscript/UNet2DNucleiBroad.model.yaml b/tests/data/unet2d_torchscript/UNet2DNucleiBroad.model.yaml
index c8b7a8cd..eeca5837 100644
--- a/tests/data/unet2d_torchscript/UNet2DNucleiBroad.model.yaml
+++ b/tests/data/unet2d_torchscript/UNet2DNucleiBroad.model.yaml
@@ -50,7 +50,7 @@ outputs:
 
 language: python
 framework: pytorch
-source: pybio.torch.models.unet2d.UNet2d
+source: bioimageio.torch.models.unet2d.UNet2d
 kwargs: {input_channels: 1, output_channels: 1}
 dependencies: conda:../environment.yaml
 
diff --git a/tests/data/unet2d_torchscript/weights.pt b/tests/data/unet2d_torchscript/weights.pt
index bfa4c536..abe68864 100644
Binary files a/tests/data/unet2d_torchscript/weights.pt and b/tests/data/unet2d_torchscript/weights.pt differ
diff --git a/tests/test_server/test_grpc/test_inference_servicer.py b/tests/test_server/test_grpc/test_inference_servicer.py
index 80a86e4f..ee353119 100644
--- a/tests/test_server/test_grpc/test_inference_servicer.py
+++ b/tests/test_server/test_grpc/test_inference_servicer.py
@@ -44,13 +44,13 @@ def method_requiring_session(self, request, grpc_stub):
         method_name, req = request.param
         return getattr(grpc_stub, method_name), req
 
-    def test_model_session_creation(self, grpc_stub, pybio_model_bytes):
-        model = grpc_stub.CreateModelSession(valid_model_request(pybio_model_bytes))
+    def test_model_session_creation(self, grpc_stub, bioimageio_model_bytes):
+        model = grpc_stub.CreateModelSession(valid_model_request(bioimageio_model_bytes))
         assert model.id
         grpc_stub.CloseModelSession(model)
 
-    def test_model_session_creation_using_upload_id(self, grpc_stub, data_store, pybio_dummy_model_bytes):
-        id_ = data_store.put(pybio_dummy_model_bytes.getvalue())
+    def test_model_session_creation_using_upload_id(self, grpc_stub, data_store, bioimageio_dummy_model_bytes):
+        id_ = data_store.put(bioimageio_dummy_model_bytes.getvalue())
 
         rq = inference_pb2.CreateModelSessionRequest(model_uri=f"upload://{id_}", deviceIds=["cpu"])
         model = grpc_stub.CreateModelSession(rq)
@@ -103,12 +103,12 @@ def test_if_model_create_fails_devices_are_released(self, grpc_stub):
         if model:
             grpc_stub.CloseModelSession(model)
 
-    def test_use_device(self, grpc_stub, pybio_model_bytes):
+    def test_use_device(self, grpc_stub, bioimageio_model_bytes):
         device_by_id = self._query_devices(grpc_stub)
         assert "cpu" in device_by_id
         assert inference_pb2.Device.Status.AVAILABLE == device_by_id["cpu"].status
 
-        model = grpc_stub.CreateModelSession(valid_model_request(pybio_model_bytes, device_ids=["cpu"]))
+        model = grpc_stub.CreateModelSession(valid_model_request(bioimageio_model_bytes, device_ids=["cpu"]))
 
         device_by_id = self._query_devices(grpc_stub)
         assert "cpu" in device_by_id
@@ -116,15 +116,15 @@ def test_use_device(self, grpc_stub, pybio_model_bytes):
 
         grpc_stub.CloseModelSession(model)
 
-    def test_using_same_device_fails(self, grpc_stub, pybio_model_bytes):
-        model = grpc_stub.CreateModelSession(valid_model_request(pybio_model_bytes, device_ids=["cpu"]))
+    def test_using_same_device_fails(self, grpc_stub, bioimageio_model_bytes):
+        model = grpc_stub.CreateModelSession(valid_model_request(bioimageio_model_bytes, device_ids=["cpu"]))
         with pytest.raises(grpc.RpcError):
-            model = grpc_stub.CreateModelSession(valid_model_request(pybio_model_bytes, device_ids=["cpu"]))
+            model = grpc_stub.CreateModelSession(valid_model_request(bioimageio_model_bytes, device_ids=["cpu"]))
 
         grpc_stub.CloseModelSession(model)
 
-    def test_closing_session_releases_devices(self, grpc_stub, pybio_model_bytes):
-        model = grpc_stub.CreateModelSession(valid_model_request(pybio_model_bytes, device_ids=["cpu"]))
+    def test_closing_session_releases_devices(self, grpc_stub, bioimageio_model_bytes):
+        model = grpc_stub.CreateModelSession(valid_model_request(bioimageio_model_bytes, device_ids=["cpu"]))
 
         device_by_id = self._query_devices(grpc_stub)
         assert "cpu" in device_by_id
@@ -138,8 +138,8 @@ def test_closing_session_releases_devices(self, grpc_stub, pybio_model_bytes):
 
 
 class TestGetLogs:
-    def test_returns_ack_message(self, pybio_model_bytes, grpc_stub):
-        model = grpc_stub.CreateModelSession(valid_model_request(pybio_model_bytes))
+    def test_returns_ack_message(self, bioimageio_model_bytes, grpc_stub):
+        model = grpc_stub.CreateModelSession(valid_model_request(bioimageio_model_bytes))
         resp = grpc_stub.GetLogs(inference_pb2.Empty())
         record = next(resp)
         assert inference_pb2.LogEntry.Level.INFO == record.level
@@ -154,8 +154,8 @@ def test_call_fails_with_unknown_model_session_id(self, grpc_stub):
         assert grpc.StatusCode.FAILED_PRECONDITION == e.value.code()
         assert "model-session with id myid1 doesn't exist" in e.value.details()
 
-    def test_call_predict(self, grpc_stub, pybio_dummy_model_bytes):
-        model = grpc_stub.CreateModelSession(valid_model_request(pybio_dummy_model_bytes))
+    def test_call_predict(self, grpc_stub, bioimageio_dummy_model_bytes):
+        model = grpc_stub.CreateModelSession(valid_model_request(bioimageio_dummy_model_bytes))
 
         arr = xr.DataArray(np.arange(32 * 32).reshape(1, 32, 32), dims=("c", "x", "y"))
         expected = arr + 1
diff --git a/tests/test_server/test_predict.py b/tests/test_server/test_predict.py
index 6762b1a3..cd7a1113 100644
--- a/tests/test_server/test_predict.py
+++ b/tests/test_server/test_predict.py
@@ -19,13 +19,13 @@ def npy_zeros_file(tmpdir):
     return path
 
 
-def test_running_predict_with_valid_arguments(testdir, pybio_dummy_model_filepath, npy_zeros_file, output_path):
+def test_running_predict_with_valid_arguments(testdir, bioimageio_dummy_model_filepath, npy_zeros_file, output_path):
     result = testdir.run(
         "python",
         "-m",
         "tiktorch.server.predict",
         "--model",
-        pybio_dummy_model_filepath,
+        bioimageio_dummy_model_filepath,
         npy_zeros_file,
         "--output",
         output_path,
@@ -41,20 +41,20 @@ def test_running_predict_fails_when_model_unspecified(testdir, npy_zeros_file, o
     assert result.ret != 0
 
 
-def test_running_predict_fails_when_no_images_specified(testdir, pybio_dummy_model_filepath, output_path):
+def test_running_predict_fails_when_no_images_specified(testdir, bioimageio_dummy_model_filepath, output_path):
     result = testdir.run(
-        "python", "-m", "tiktorch.server.predict", "--model", pybio_dummy_model_filepath, "--output", output_path
+        "python", "-m", "tiktorch.server.predict", "--model", bioimageio_dummy_model_filepath, "--output", output_path
     )
     assert result.ret != 0
 
 
-def test_running_predict_fails_when_invalid_image_specified(testdir, pybio_dummy_model_filepath, output_path):
+def test_running_predict_fails_when_invalid_image_specified(testdir, bioimageio_dummy_model_filepath, output_path):
     result = testdir.run(
         "python",
         "-m",
         "tiktorch.server.predict",
         "--model",
-        pybio_dummy_model_filepath,
+        bioimageio_dummy_model_filepath,
         "nonexisting",
         "--output",
         output_path,
@@ -77,9 +77,9 @@ def test_running_predict_fails_without_when_model_file_does_not_exist(testdir, t
 
 
 def test_running_predict_failes_when_output_is_unspecified(
-    testdir, pybio_dummy_model_filepath, npy_zeros_file, output_path
+    testdir, bioimageio_dummy_model_filepath, npy_zeros_file, output_path
 ):
     result = testdir.run(
-        "python", "-m", "tiktorch.server.predict", "--model", pybio_dummy_model_filepath, npy_zeros_file
+        "python", "-m", "tiktorch.server.predict", "--model", bioimageio_dummy_model_filepath, npy_zeros_file
     )
     assert result.ret != 0
diff --git a/tests/test_server/test_prediction_pipeline/test_prediction_pipeline.py b/tests/test_server/test_prediction_pipeline/test_prediction_pipeline.py
index 6a327474..426015f1 100644
--- a/tests/test_server/test_prediction_pipeline/test_prediction_pipeline.py
+++ b/tests/test_server/test_prediction_pipeline/test_prediction_pipeline.py
@@ -7,32 +7,34 @@
 from tiktorch.server.reader import eval_model_zip
 
 
-def test_eval_onnx_model_zip_predict(pybio_unet2d_onnx_bytes, pybio_unet2d_onnx_test_data, cache_path):
-    with ZipFile(pybio_unet2d_onnx_bytes) as zf:
-        adapter = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path, preserve_batch_dim=True)
-        test_input = xarray.DataArray(np.load(pybio_unet2d_onnx_test_data["test_input"]), dims=("b", "c", "x", "y"))
+def test_eval_onnx_model_zip_predict(bioimageio_unet2d_onnx_bytes, bioimageio_unet2d_onnx_test_data):
+    with ZipFile(bioimageio_unet2d_onnx_bytes) as zf:
+        adapter = eval_model_zip(zf, devices=["cpu"], preserve_batch_dim=True)
+        test_input = xarray.DataArray(
+            np.load(bioimageio_unet2d_onnx_test_data["test_input"]), dims=("b", "c", "x", "y")
+        )
         # TODO: Figure out why test output doesn't match result
         adapter.forward(test_input)
 
 
 def test_eval_torchscript_model_zip_predict(
-    pybio_unet2d_torchscript_bytes, pybio_unet2d_torchscript_test_data, cache_path
+    bioimageio_unet2d_torchscript_bytes, bioimageio_unet2d_torchscript_test_data
 ):
-    with ZipFile(pybio_unet2d_torchscript_bytes) as zf:
-        pipeline = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path, preserve_batch_dim=True)
+    with ZipFile(bioimageio_unet2d_torchscript_bytes) as zf:
+        pipeline = eval_model_zip(zf, devices=["cpu"], preserve_batch_dim=True)
         test_input = xarray.DataArray(
-            np.load(pybio_unet2d_torchscript_test_data["test_input"]).astype(np.float32), dims=("b", "c", "x", "y")
+            np.load(bioimageio_unet2d_torchscript_test_data["test_input"]).astype(np.float32), dims=("b", "c", "x", "y")
         )
-        test_output = np.load(pybio_unet2d_torchscript_test_data["test_output"])
+        test_output = np.load(bioimageio_unet2d_torchscript_test_data["test_output"])
         result = pipeline.forward(test_input)
         assert_array_almost_equal(result.data, test_output, decimal=4)
 
 
 def test_eval_model_zip_metadata_no_batch_dim(
-    pybio_unet2d_torchscript_bytes, pybio_unet2d_torchscript_test_data, cache_path
+    bioimageio_unet2d_torchscript_bytes, bioimageio_unet2d_torchscript_test_data
 ):
-    with ZipFile(pybio_unet2d_torchscript_bytes) as zf:
-        pipeline = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path, preserve_batch_dim=False)
+    with ZipFile(bioimageio_unet2d_torchscript_bytes) as zf:
+        pipeline = eval_model_zip(zf, devices=["cpu"], preserve_batch_dim=False)
         assert pipeline.name == "UNet 2D Nuclei Broad"
         assert pipeline.input_axes == "cyx"
         assert pipeline.output_axes == "cyx"
@@ -40,9 +42,9 @@ def test_eval_model_zip_metadata_no_batch_dim(
         assert pipeline.halo == [("c", 0), ("y", 32), ("x", 32)]
 
 
-def test_eval_model_zip(pybio_model_bytes, cache_path):
-    with ZipFile(pybio_model_bytes) as zf:
-        pipeline = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path, preserve_batch_dim=True)
+def test_eval_model_zip(bioimageio_model_bytes):
+    with ZipFile(bioimageio_model_bytes) as zf:
+        pipeline = eval_model_zip(zf, devices=["cpu"], preserve_batch_dim=True)
 
     assert pipeline.input_axes == "bcyx"
     assert pipeline.output_axes == "bcyx"
@@ -51,10 +53,10 @@ def test_eval_model_zip(pybio_model_bytes, cache_path):
 
 
 def test_eval_model_zip_metadata_with_batch_dim(
-    pybio_unet2d_torchscript_bytes, pybio_unet2d_torchscript_test_data, cache_path
+    bioimageio_unet2d_torchscript_bytes, bioimageio_unet2d_torchscript_test_data
 ):
-    with ZipFile(pybio_unet2d_torchscript_bytes) as zf:
-        pipeline = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path, preserve_batch_dim=True)
+    with ZipFile(bioimageio_unet2d_torchscript_bytes) as zf:
+        pipeline = eval_model_zip(zf, devices=["cpu"], preserve_batch_dim=True)
         assert pipeline.input_axes == "bcyx"
         assert pipeline.output_axes == "bcyx"
         assert pipeline.input_shape == [("b", 1), ("c", 1), ("y", 512), ("x", 512)]
diff --git a/tests/test_server/test_prediction_pipeline/test_preprocessing.py b/tests/test_server/test_prediction_pipeline/test_preprocessing.py
index 01a8ca8d..709eed2d 100644
--- a/tests/test_server/test_prediction_pipeline/test_preprocessing.py
+++ b/tests/test_server/test_prediction_pipeline/test_preprocessing.py
@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 import xarray as xr
-from pybio.spec.nodes import Preprocessing
+from bioimageio.spec.nodes import Preprocessing
 
 from tiktorch.server.prediction_pipeline._preprocessing import ADD_BATCH_DIM, make_preprocessing
 
diff --git a/tests/test_server/test_reader.py b/tests/test_server/test_reader.py
index 5dbbad8f..b23154b9 100644
--- a/tests/test_server/test_reader.py
+++ b/tests/test_server/test_reader.py
@@ -19,15 +19,15 @@ def test_guess_model_path_without_model_file(paths):
     assert guess_model_path(paths) is None
 
 
-def test_eval_model_zip(pybio_model_bytes, cache_path):
-    with ZipFile(pybio_model_bytes) as zf:
-        exemplum = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path)
+def test_eval_model_zip(bioimageio_model_bytes):
+    with ZipFile(bioimageio_model_bytes) as zf:
+        exemplum = eval_model_zip(zf, devices=["cpu"])
         assert isinstance(exemplum, PredictionPipeline)
 
 
-def test_eval_tensorflow_model_zip(pybio_dummy_tensorflow_model_bytes, cache_path):
-    with ZipFile(pybio_dummy_tensorflow_model_bytes) as zf:
-        pipeline = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path)
+def test_eval_tensorflow_model_zip(bioimageio_dummy_tensorflow_model_bytes):
+    with ZipFile(bioimageio_dummy_tensorflow_model_bytes) as zf:
+        pipeline = eval_model_zip(zf, devices=["cpu"])
         assert isinstance(pipeline, PredictionPipeline)
 
         test_input = xr.DataArray(np.zeros(shape=(1, 128, 128)), dims=("c", "y", "x"))
@@ -38,13 +38,13 @@ def test_eval_tensorflow_model_zip(pybio_dummy_tensorflow_model_bytes, cache_pat
         xr.testing.assert_equal(result, test_output)
 
 
-def test_eval_torchscript_model_zip(pybio_unet2d_torchscript_bytes, cache_path):
-    with ZipFile(pybio_unet2d_torchscript_bytes) as zf:
-        adapter = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path)
+def test_eval_torchscript_model_zip(bioimageio_unet2d_torchscript_bytes):
+    with ZipFile(bioimageio_unet2d_torchscript_bytes) as zf:
+        adapter = eval_model_zip(zf, devices=["cpu"])
         assert isinstance(adapter, PredictionPipeline)
 
 
-def test_eval_onnx_model_zip(pybio_unet2d_onnx_bytes, cache_path):
-    with ZipFile(pybio_unet2d_onnx_bytes) as zf:
-        adapter = eval_model_zip(zf, devices=["cpu"], cache_path=cache_path)
+def test_eval_onnx_model_zip(bioimageio_unet2d_onnx_bytes):
+    with ZipFile(bioimageio_unet2d_onnx_bytes) as zf:
+        adapter = eval_model_zip(zf, devices=["cpu"])
         assert isinstance(adapter, PredictionPipeline)
diff --git a/tiktorch/server/prediction_pipeline/_model_adapters/_model_adapter.py b/tiktorch/server/prediction_pipeline/_model_adapters/_model_adapter.py
index 1dc8c658..0790e6e5 100644
--- a/tiktorch/server/prediction_pipeline/_model_adapters/_model_adapter.py
+++ b/tiktorch/server/prediction_pipeline/_model_adapters/_model_adapter.py
@@ -2,7 +2,7 @@
 from typing import Callable, List, Optional, Type
 
 import xarray as xr
-from pybio.spec import nodes
+from bioimageio.spec import nodes
 
 #: Known weigh types in order of priority
 #: First match wins
@@ -15,12 +15,7 @@ class ModelAdapter(abc.ABC):
     """
 
     @abc.abstractmethod
-    def __init__(
-        self,
-        *,
-        pybio_model: nodes.Model,
-        devices=List[str],
-    ):
+    def __init__(self, *, bioimageio_model: nodes.Model, devices=List[str]):
         ...
 
     @abc.abstractmethod
@@ -56,15 +51,15 @@ def get_weight_formats() -> List[str]:
 
 
 def create_model_adapter(
-    *, pybio_model: nodes.Model, devices=List[str], weight_format: Optional[str] = None
+    *, bioimageio_model: nodes.Model, devices=List[str], weight_format: Optional[str] = None
 ) -> ModelAdapter:
     """
     Creates model adapter based on the passed spec
     Note: All specific adapters should happen inside this function to prevent different framework
     initializations interfering with each other
     """
-    spec = pybio_model
-    weights = pybio_model.weights
+    spec = bioimageio_model
+    weights = bioimageio_model.weights
     weight_formats = get_weight_formats()
 
     if weight_format is not None:
@@ -75,7 +70,7 @@ def create_model_adapter(
     for weight in weight_formats:
         if weight in weights:
             adapter_cls = _get_model_adapter(weight)
-            return adapter_cls(pybio_model=pybio_model, devices=devices)
+            return adapter_cls(bioimageio_model=bioimageio_model, devices=devices)
 
     raise NotImplementedError(f"No supported weight_formats in {spec.weights.keys()}")
 
diff --git a/tiktorch/server/prediction_pipeline/_model_adapters/_onnx_model_adapter.py b/tiktorch/server/prediction_pipeline/_model_adapters/_onnx_model_adapter.py
index f12aab7a..53c7cc16 100644
--- a/tiktorch/server/prediction_pipeline/_model_adapters/_onnx_model_adapter.py
+++ b/tiktorch/server/prediction_pipeline/_model_adapters/_onnx_model_adapter.py
@@ -3,7 +3,7 @@
 
 import onnxruntime as rt
 import xarray as xr
-from pybio.spec import nodes
+from bioimageio.spec import nodes
 
 from tiktorch.server.prediction_pipeline._model_adapters._model_adapter import ModelAdapter
 
@@ -11,13 +11,8 @@
 
 
 class ONNXModelAdapter(ModelAdapter):
-    def __init__(
-        self,
-        *,
-        pybio_model: nodes.Model,
-        devices=List[str],
-    ):
-        spec = pybio_model
+    def __init__(self, *, bioimageio_model: nodes.Model, devices=List[str]):
+        spec = bioimageio_model
         self.name = spec.name
 
         if len(spec.inputs) != 1 or len(spec.outputs) != 1:
diff --git a/tiktorch/server/prediction_pipeline/_model_adapters/_pytorch_model_adapter.py b/tiktorch/server/prediction_pipeline/_model_adapters/_pytorch_model_adapter.py
index d14ecd59..a170be8b 100644
--- a/tiktorch/server/prediction_pipeline/_model_adapters/_pytorch_model_adapter.py
+++ b/tiktorch/server/prediction_pipeline/_model_adapters/_pytorch_model_adapter.py
@@ -3,8 +3,8 @@
 
 import torch
 import xarray as xr
-from pybio.spec import nodes
-from pybio.spec.utils import get_instance
+from bioimageio.spec import nodes
+from bioimageio.spec.utils import get_instance
 
 from ._model_adapter import ModelAdapter
 
@@ -12,15 +12,10 @@
 
 
 class PytorchModelAdapter(ModelAdapter):
-    def __init__(
-        self,
-        *,
-        pybio_model: nodes.Model,
-        devices=Sequence[str],
-    ):
-        self._internal_output_axes = pybio_model.outputs[0].axes
-        spec = pybio_model
-        self.model = get_instance(pybio_model)
+    def __init__(self, *, bioimageio_model: nodes.Model, devices=Sequence[str]):
+        self._internal_output_axes = bioimageio_model.outputs[0].axes
+        spec = bioimageio_model
+        self.model = get_instance(bioimageio_model)
         self.devices = [torch.device(d) for d in devices]
         self.model.to(self.devices[0])
         assert isinstance(self.model, torch.nn.Module)
diff --git a/tiktorch/server/prediction_pipeline/_model_adapters/_tensorflow_model_adapter.py b/tiktorch/server/prediction_pipeline/_model_adapters/_tensorflow_model_adapter.py
index 5c0e1e73..6b51affc 100644
--- a/tiktorch/server/prediction_pipeline/_model_adapters/_tensorflow_model_adapter.py
+++ b/tiktorch/server/prediction_pipeline/_model_adapters/_tensorflow_model_adapter.py
@@ -3,20 +3,15 @@
 import numpy as np
 import tensorflow as tf
 import xarray as xr
-from pybio.spec import nodes
-from pybio.spec.utils import get_instance
+from bioimageio.spec import nodes
+from bioimageio.spec.utils import get_instance
 
 from ._model_adapter import ModelAdapter
 
 
 class TensorflowModelAdapter(ModelAdapter):
-    def __init__(
-        self,
-        *,
-        pybio_model: nodes.Model,
-        devices=List[str],
-    ):
-        spec = pybio_model
+    def __init__(self, *, bioimageio_model: nodes.Model, devices=List[str]):
+        spec = bioimageio_model
         self.name = spec.name
 
         spec.inputs[0]
@@ -24,7 +19,7 @@ def __init__(
         # FIXME: TF probably uses different axis names
         self._internal_output_axes = _output.axes
 
-        self.model = get_instance(pybio_model)
+        self.model = get_instance(bioimageio_model)
         self.devices = []
         tf_model = tf.keras.models.load_model(spec.weights["tensorflow_saved_model_bundle"].source)
         self.model.set_model(tf_model)
diff --git a/tiktorch/server/prediction_pipeline/_model_adapters/_torchscript_model_adapter.py b/tiktorch/server/prediction_pipeline/_model_adapters/_torchscript_model_adapter.py
index e61f4a0d..5708c0d9 100644
--- a/tiktorch/server/prediction_pipeline/_model_adapters/_torchscript_model_adapter.py
+++ b/tiktorch/server/prediction_pipeline/_model_adapters/_torchscript_model_adapter.py
@@ -3,19 +3,14 @@
 import numpy as np
 import torch
 import xarray as xr
-from pybio.spec import nodes
+from bioimageio.spec import nodes
 
 from ._model_adapter import ModelAdapter
 
 
 class TorchscriptModelAdapter(ModelAdapter):
-    def __init__(
-        self,
-        *,
-        pybio_model: nodes.Model,
-        devices=List[str],
-    ):
-        spec = pybio_model
+    def __init__(self, *, bioimageio_model: nodes.Model, devices=List[str]):
+        spec = bioimageio_model
         self.name = spec.name
 
         _input = spec.inputs[0]
diff --git a/tiktorch/server/prediction_pipeline/_postprocessing.py b/tiktorch/server/prediction_pipeline/_postprocessing.py
index 4bb343e0..d12983b0 100644
--- a/tiktorch/server/prediction_pipeline/_postprocessing.py
+++ b/tiktorch/server/prediction_pipeline/_postprocessing.py
@@ -1,7 +1,7 @@
 from typing import List
 
 import xarray as xr
-from pybio.spec.nodes import Postprocessing
+from bioimageio.spec.nodes import Postprocessing
 
 from ._preprocessing import chain
 from ._types import Transform
@@ -21,10 +21,7 @@ def sigmoid(tensor: xr.DataArray, **kwargs):
     return 1 / (1 + xr.ufuncs.exp(-tensor))
 
 
-KNOWN_POSTPROCESSING = {
-    "__tiktorch_remove_batch_dim": remove_batch_dim,
-    "sigmoid": sigmoid,
-}
+KNOWN_POSTPROCESSING = {"__tiktorch_remove_batch_dim": remove_batch_dim, "sigmoid": sigmoid}
 
 
 def make_postprocessing(spec: List[Postprocessing]) -> Transform:
diff --git a/tiktorch/server/prediction_pipeline/_prediction_pipeline.py b/tiktorch/server/prediction_pipeline/_prediction_pipeline.py
index 7ad78873..5807d002 100644
--- a/tiktorch/server/prediction_pipeline/_prediction_pipeline.py
+++ b/tiktorch/server/prediction_pipeline/_prediction_pipeline.py
@@ -2,7 +2,7 @@
 from typing import Callable, List, Optional, Tuple
 
 import xarray as xr
-from pybio.spec import nodes
+from bioimageio.spec import nodes
 
 from ._model_adapters import ModelAdapter, create_model_adapter
 from ._postprocessing import REMOVE_BATCH_DIM, make_postprocessing
@@ -129,11 +129,7 @@ def set_max_num_iterations(self, val: int) -> None:
 
 
 def create_prediction_pipeline(
-    *,
-    pybio_model: nodes.Model,
-    devices=List[str],
-    preserve_batch_dim=False,
-    weight_format: Optional[str] = None,
+    *, bioimageio_model: nodes.Model, devices=List[str], preserve_batch_dim=False, weight_format: Optional[str] = None
 ) -> PredictionPipeline:
     """
     Creates prediction pipeline which includes:
@@ -141,14 +137,14 @@ def create_prediction_pipeline(
     * model prediction
     * postprocessing
     """
-    if len(pybio_model.inputs) != 1 or len(pybio_model.outputs) != 1:
+    if len(bioimageio_model.inputs) != 1 or len(bioimageio_model.outputs) != 1:
         raise NotImplementedError("Only models with single input and output are supported")
 
     model_adapter: ModelAdapter = create_model_adapter(
-        pybio_model=pybio_model, devices=devices, weight_format=weight_format
+        bioimageio_model=bioimageio_model, devices=devices, weight_format=weight_format
     )
 
-    input = pybio_model.inputs[0]
+    input = bioimageio_model.inputs[0]
     input_shape = input.shape
     input_axes = input.axes
     preprocessing_spec = input.preprocessing.copy()
@@ -161,9 +157,9 @@ def create_prediction_pipeline(
     input_named_shape = list(zip(input_axes, input_shape))
     preprocessing: Transform = make_preprocessing(preprocessing_spec)
 
-    output = pybio_model.outputs[0]
+    output = bioimageio_model.outputs[0]
     halo_shape = output.halo or [0 for _ in output.axes]
-    output_axes = pybio_model.outputs[0].axes
+    output_axes = bioimageio_model.outputs[0].axes
     postprocessing_spec = output.postprocessing.copy()
     if has_batch_dim(output_axes) and not preserve_batch_dim:
         postprocessing_spec.append(REMOVE_BATCH_DIM)
@@ -174,7 +170,7 @@ def create_prediction_pipeline(
     postprocessing: Transform = make_postprocessing(postprocessing_spec)
 
     return _PredictionPipelineImpl(
-        name=pybio_model.name,
+        name=bioimageio_model.name,
         input_axes=input_axes,
         input_shape=input_named_shape,
         output_axes=output_axes,
diff --git a/tiktorch/server/prediction_pipeline/_preprocessing.py b/tiktorch/server/prediction_pipeline/_preprocessing.py
index 4c0b819e..c779da69 100644
--- a/tiktorch/server/prediction_pipeline/_preprocessing.py
+++ b/tiktorch/server/prediction_pipeline/_preprocessing.py
@@ -1,7 +1,7 @@
 from typing import List
 
 import xarray as xr
-from pybio.spec.nodes import Preprocessing
+from bioimageio.spec.nodes import Preprocessing
 
 from ._types import Transform
 
diff --git a/tiktorch/server/reader.py b/tiktorch/server/reader.py
index de816e64..37039583 100644
--- a/tiktorch/server/reader.py
+++ b/tiktorch/server/reader.py
@@ -5,7 +5,7 @@
 from typing import List, Optional, Sequence
 from zipfile import ZipFile
 
-from pybio import spec
+from bioimageio import spec
 
 from tiktorch.server.prediction_pipeline import PredictionPipeline, create_prediction_pipeline
 
@@ -21,12 +21,8 @@ def guess_model_path(file_names: List[str]) -> Optional[str]:
     return None
 
 
-def eval_model_zip(
-    model_zip: ZipFile, devices: Sequence[str], cache_path: Optional[Path] = None, *, preserve_batch_dim=False
-) -> PredictionPipeline:
+def eval_model_zip(model_zip: ZipFile, devices: Sequence[str], *, preserve_batch_dim=False) -> PredictionPipeline:
     temp_path = Path(tempfile.mkdtemp(prefix="tiktorch_"))
-    if cache_path is None:
-        cache_path = temp_path / "cache"
 
     model_zip.extractall(temp_path)
 
@@ -36,8 +32,10 @@ def eval_model_zip(
             "Model config file not found, make sure that .model.yaml file in the root of your model archive"
         )
 
-    pybio_model = spec.load_and_resolve_spec(spec_file_str)
-    ret = create_prediction_pipeline(pybio_model=pybio_model, devices=devices, preserve_batch_dim=preserve_batch_dim)
+    bioimageio_model = spec.load_and_resolve_spec(Path(spec_file_str))
+    ret = create_prediction_pipeline(
+        bioimageio_model=bioimageio_model, devices=devices, preserve_batch_dim=preserve_batch_dim
+    )
 
     def _on_error(function, path, exc_info):
         logger.warning("Failed to delete temp directory %s", path)
diff --git a/tiktorch/server/session/process.py b/tiktorch/server/session/process.py
index ed4e2c03..9157bb72 100644
--- a/tiktorch/server/session/process.py
+++ b/tiktorch/server/session/process.py
@@ -1,12 +1,10 @@
 import dataclasses
 import io
 import multiprocessing as _mp
-import os
 import uuid
 import zipfile
 from concurrent.futures import Future
 from multiprocessing.connection import Connection
-from pathlib import Path
 from typing import List, Optional, Tuple
 
 import numpy
@@ -33,12 +31,8 @@ class ModelInfo:
 
 class ModelSessionProcess(IRPCModelSession):
     def __init__(self, model_zip: bytes, devices: List[str]) -> None:
-        cache_path = os.getenv("PYBIO_CACHE_PATH", None)
-        if cache_path is not None:
-            cache_path = Path(cache_path)
-
         with zipfile.ZipFile(io.BytesIO(model_zip)) as model_file:
-            self._model = eval_model_zip(model_file, devices, cache_path=cache_path)
+            self._model = eval_model_zip(model_file, devices)
 
         self._datasets = {}
         self._worker = base.SessionBackend(self._model)
diff --git a/tiktorch/server/test_model.py b/tiktorch/server/test_model.py
index b9fc5a68..6ab37c7d 100644
--- a/tiktorch/server/test_model.py
+++ b/tiktorch/server/test_model.py
@@ -10,8 +10,8 @@
 
 import numpy as np
 import xarray as xr
+from bioimageio import spec
 from numpy.testing import assert_array_almost_equal
-from pybio import spec
 
 from .prediction_pipeline import create_prediction_pipeline, get_weight_formats
 from .reader import guess_model_path
@@ -47,20 +47,22 @@ def main():
     # try opening model from model.zip
     try:
         with ZipFile(args.model, "r") as model_zip:
-            pybio_model, cache_path = _load_from_zip(model_zip)
+            bioimageio_model, cache_path = _load_from_zip(model_zip)
     # otherwise open from model.yaml
     except BadZipFile:
         spec_path = os.path.abspath(args.model)
-        pybio_model = spec.load_and_resolve_spec(spec_path)
+        bioimageio_model = spec.load_and_resolve_spec(spec_path)
         cache_path = None
 
     model = create_prediction_pipeline(
-        pybio_model=pybio_model, devices=["cpu"], weight_format=args.weight_format, preserve_batch_dim=True
+        bioimageio_model=bioimageio_model, devices=["cpu"], weight_format=args.weight_format, preserve_batch_dim=True
     )
 
-    input_args = [load_data(inp, inp_spec) for inp, inp_spec in zip(pybio_model.test_inputs, pybio_model.inputs)]
+    input_args = [
+        load_data(inp, inp_spec) for inp, inp_spec in zip(bioimageio_model.test_inputs, bioimageio_model.inputs)
+    ]
     expected_outputs = [
-        load_data(out, out_spec) for out, out_spec in zip(pybio_model.test_outputs, pybio_model.outputs)
+        load_data(out, out_spec) for out, out_spec in zip(bioimageio_model.test_outputs, bioimageio_model.outputs)
     ]
 
     results = [model.forward(*input_args)]
diff --git a/vendor/python-bioimage-io b/vendor/python-bioimage-io
index 9716ad46..7d47a8cd 160000
--- a/vendor/python-bioimage-io
+++ b/vendor/python-bioimage-io
@@ -1 +1 @@
-Subproject commit 9716ad46d8b357dbb76647d857191d135a118ba1
+Subproject commit 7d47a8cdf311348d9ab2ceca13fd925f708c4218
diff --git a/vendor/pytorch-bioimage-io b/vendor/pytorch-bioimage-io
index 53373c45..25f6bac5 160000
--- a/vendor/pytorch-bioimage-io
+++ b/vendor/pytorch-bioimage-io
@@ -1 +1 @@
-Subproject commit 53373c45d3833b35657914953aaac544ec794a7e
+Subproject commit 25f6bac5a22d8a76553bd4484a515f634bcb9ee2