diff --git a/.github/workflows/README.md b/.github/workflows/README.md
index a8438871c0..35f6f00bc9 100644
--- a/.github/workflows/README.md
+++ b/.github/workflows/README.md
@@ -23,7 +23,7 @@ Details on some of these jobs are given below.
 
 ## conda
 
-When opening or modifying a pull request to `master`, a single variant is built and tested. This variant is for linux with `python=3.9` and `numpy=1.22`.
+When opening or modifying a pull request to `master`, a single variant is built and tested. This variant is for linux with `python=3.11` and `numpy=1.25`.
 
 > [!NOTE]
 > The action does not publish to conda, instead this is done by jenkins. We will eventually move from jenkins to conda-forge instead.
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index df11e75fef..e915fab70e 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -40,8 +40,8 @@ jobs:
     runs-on: [self-hosted, python, cuda]
     strategy:
       matrix:
-        python-version: [3.9]
-        numpy-version: [1.22]
+        python-version: [3.11]
+        numpy-version: [1.25]
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
@@ -80,15 +80,15 @@ jobs:
     strategy:
       matrix:
         include:
-        - python-version: 3.8
-          numpy-version: 1.21
         - python-version: '3.10'
-          numpy-version: 1.24
+          numpy-version: 1.23
+        - python-version: 3.12
+          numpy-version: 1.26
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
     - name: set requirements
-      run: sed -ri -e '/ python=/d' -e 's/(.* numpy=).*/\1${{ matrix.numpy-version }}/' scripts/requirements-test.yml
+      run: sed -ri -e '/ python=/d' -e 's/(.* numpy=).*/\1${{ matrix.numpy-version }}/' -e 's/=cuda*//' -e '/tigre/d' scripts/requirements-test.yml
     - uses: conda-incubator/setup-miniconda@v3
       with:
         python-version: ${{ matrix.python-version }}
@@ -105,8 +105,8 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.9]
-        numpy-version: [1.22]
+        python-version: [3.11]
+        numpy-version: [1.25]
     steps:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
@@ -132,12 +132,12 @@ jobs:
     - uses: actions/checkout@v4
       with: {fetch-depth: 0, submodules: recursive}
     - uses: conda-incubator/setup-miniconda@v3
-      with: {python-version: '3.10'}
+      with: {python-version: 3.11}
     - name: install dependencies
       run: |
         cd docs
         conda install -c conda-forge -yq conda-merge
-        conda-merge ../scripts/requirements-test.yml docs_environment.yml > environment.yml
+        conda-merge ../scripts/requirements-test.yml docs_environment.yml | sed '/tigre/d' > environment.yml
         conda env update -n test
         conda list
     - name: build cil
diff --git a/CHANGELOG.md b/CHANGELOG.md
index e2397fb503..8b46e03c46 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,4 +1,11 @@
-* x.x.x
+* 24.0.0
+  - Update to new CCPi-Regularisation toolkit v24.0.0. This is a backward incompatible release of the toolkit.
+  - CIL plugin support for TIGRE version v2.6
+  - CIL plugin support for ASTRA-TOOLBOX version v2.1
+  - Dropped support for python 3.8 and 3.9
+  - Added support for python 3.11 and 3.12
+  - Dropped support for numpy 1.21 and 1.22
+  - Added support for numpy 1.25 and 1.26
   - Set CMake Policy CMP0148 to OLD to avoid warnings in CMake 3.27
   - AcquisitionGeometry prints the first and last 10 angles, or all if there are 30 or less, rather than the first 20
   - Added a weight argument to the L1Norm function
@@ -48,7 +55,6 @@
     - `TXRMDataReader`
   - Added the ApproximateGradientSumFunction and SGFunction to allow for stochastic gradient algorithms to be created using functions with an approximate gradient and deterministic algorithms
 
-
 * 23.1.0
   - Fix bug in IndicatorBox proximal_conjugate
   - Allow CCPi Regulariser functions for non CIL object
diff --git a/Dockerfile b/Dockerfile
index 899e8799a7..d59575f7e8 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,7 +11,7 @@ LABEL org.opencontainers.image.source=https://github.com/TomographicImaging/CIL
 LABEL org.opencontainers.image.licenses="Apache-2.0 AND BSD-3-Clause AND GPL-3.0"
 
 # CUDA-specific packages
-ARG CIL_EXTRA_PACKAGES="tigre=2.4 astra-toolbox<2.1"
+ARG CIL_EXTRA_PACKAGES="tigre=2.6 astra-toolbox=2.1.0=cuda*"
 # build & runtime dependencies
 # TODO: sync scripts/create_local_env_for_cil_development.sh, scripts/requirements-test.yml, recipe/meta.yaml (e.g. missing libstdcxx-ng _openmp_mutex pip)?
 # vis. https://github.com/TomographicImaging/CIL/pull/1590
@@ -20,7 +20,7 @@ COPY --chown="${NB_USER}" scripts/requirements-test.yml environment.yml
 RUN sed -ri '/tigre|astra-toolbox/d' environment.yml \
   && for pkg in jupyter-server-proxy $CIL_EXTRA_PACKAGES; do echo "  - $pkg" >> environment.yml; done \
   && conda config --env --set channel_priority strict \
-  && for ch in defaults ccpi intel conda-forge; do conda config --env --add channels $ch; done \
+  && for ch in defaults nvidia ccpi intel conda-forge; do conda config --env --add channels $ch; done \
   && mamba env update -n base \
   && mamba clean -a -y -f \
   && rm environment.yml \
diff --git a/README.md b/README.md
index b8c14d52ee..2689ff4a7f 100644
--- a/README.md
+++ b/README.md
@@ -29,15 +29,16 @@ conda create --name cil -c conda-forge -c intel -c ccpi cil=23.1.0
 To install CIL and the additional packages and plugins needed to run the [CIL demos](https://github.com/TomographicImaging/CIL-Demos) install the environment with:
 
 ```sh
-conda create --name cil -c conda-forge -c intel -c ccpi cil=23.1.0 astra-toolbox tigre ccpi-regulariser tomophantom ipywidgets
+conda create --name cil -c conda-forge -c intel -c ccpi cil=23.1.0 astra-toolbox=*=cuda* tigre ccpi-regulariser tomophantom ipywidgets
 ```
 
 where:
-
-- `astra-toolbox` (requires an NVIDIA GPU) enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) projectors (GPLv3 license)
+- `astra-toolbox` enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) CPU projector (2D Parallel beam only) (GPLv3 license)
+- `astra-toolbox=*=cuda*` (requires an NVIDIA GPU) enables CIL support for [ASTRA toolbox](http://www.astra-toolbox.com) GPU projectors (GPLv3 license)
 - `tigre` (requires an NVIDIA GPU) enables support for [TIGRE](https://github.com/CERN/TIGRE) toolbox projectors (BSD license)
-- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/vais-ral/CCPi-Regularisation-Toolkit)
+- `ccpi-regulariser` is the [CCPi Regularisation Toolkit](https://github.com/TomographicImaging/CCPi-Regularisation-Toolkit)
 - `tomophantom` can generate phantoms to use as test data [Tomophantom](https://github.com/dkazanc/TomoPhantom)
+- `ipywidgets` enables visulisation tools within jupyter noteboooks
 
 ### Dependencies
 
diff --git a/Wrappers/Python/cil/plugins/ccpi_regularisation/functions/regularisers.py b/Wrappers/Python/cil/plugins/ccpi_regularisation/functions/regularisers.py
index 9b81f2c1c6..04a4f9c642 100644
--- a/Wrappers/Python/cil/plugins/ccpi_regularisation/functions/regularisers.py
+++ b/Wrappers/Python/cil/plugins/ccpi_regularisation/functions/regularisers.py
@@ -18,9 +18,9 @@
 
 try:
     from ccpi.filters import regularisers
-    from ccpi.filters.cpu_regularisers import TV_ENERGY
+    from ccpi.filters.TV import TV_ENERGY
 except ImportError as exc:
-    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=20.04"`') from exc
+    raise ImportError('Please `conda install "ccpi::ccpi-regulariser>=24"`') from exc
 
 
 from cil.framework import DataOrder
@@ -229,14 +229,17 @@ def _fista_on_dual_rof(self, in_arr, tau):
 
         """
 
-        res , info = regularisers.FGP_TV(\
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_TV(\
               in_arr,\
               self.alpha * tau,\
               self.max_iteration,\
               self.tolerance,\
               self.methodTV,\
               self.nonnegativity,\
-              self.device)
+              infovector = info,
+              device = self.device)
 
         return res, info
 
@@ -315,14 +318,18 @@ def alpha1(self):
         return 1.
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.TGV(in_arr,
+
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.TGV(in_arr,
               self.alpha * tau,
               self.alpha1,
               self.alpha2,
               self.max_iteration,
               self.LipshitzConstant,
               self.tolerance,
-              self.device)
+              infovector = info,
+              device = self.device)
 
         # info: return number of iteration and reached tolerance
         # https://github.com/vais-ral/CCPi-Regularisation-Toolkit/blob/master/src/Core/regularisers_CPU/TGV_core.c#L168
@@ -400,7 +407,10 @@ def __call__(self,x):
         return np.nan
 
     def proximal_numpy(self, in_arr, tau):
-        res , info = regularisers.FGP_dTV(\
+        
+        info = np.zeros((2,), dtype=np.float32)
+
+        res = regularisers.FGP_dTV(\
                 in_arr,\
                 self.reference,\
                 self.alpha * tau,\
@@ -409,7 +419,8 @@ def proximal_numpy(self, in_arr, tau):
                 self.eta,\
                 self.methodTV,\
                 self.nonnegativity,\
-                self.device)
+                infovector = info,
+                device = self.device)
         return res, info
 
     def convex_conjugate(self, x):
@@ -454,7 +465,7 @@ def __call__(self,x):
     def proximal_numpy(self, in_arr, tau):
         # remove any dimension of size 1
         in_arr = np.squeeze(in_arr)
-
+    
         res = regularisers.TNV(in_arr,
               self.alpha * tau,
               self.max_iteration,
diff --git a/Wrappers/Python/cil/plugins/tigre/FBP.py b/Wrappers/Python/cil/plugins/tigre/FBP.py
index 933203e081..07aa33158a 100644
--- a/Wrappers/Python/cil/plugins/tigre/FBP.py
+++ b/Wrappers/Python/cil/plugins/tigre/FBP.py
@@ -97,7 +97,9 @@ def process(self, out=None):
             arr_out = np.squeeze(arr_out, axis=0)
         else:
             if self.acquisition_geometry.geom_type == 'cone':
-                arr_out = fdk(self.get_input().as_array(), self.tigre_geom, self.tigre_angles)
+                # suppress print statements from TIGRE https://github.com/CERN/TIGRE/issues/532
+                with contextlib.redirect_stdout(io.StringIO()):
+                    arr_out = fdk(self.get_input().as_array(), self.tigre_geom, self.tigre_angles)
             else:
                 arr_out = fbp(self.get_input().as_array(), self.tigre_geom, self.tigre_angles)
 
diff --git a/Wrappers/Python/test/test_DataProcessor.py b/Wrappers/Python/test/test_DataProcessor.py
index bea31dcdf5..a7516d8a65 100644
--- a/Wrappers/Python/test/test_DataProcessor.py
+++ b/Wrappers/Python/test/test_DataProcessor.py
@@ -572,21 +572,21 @@ def test_process_acquisition(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -606,21 +606,21 @@ def test_process_image(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Binner failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -1058,10 +1058,8 @@ def test_process_image_geometry(self):
 
                  # slice to single element
                 {'channel':(None,None,4),'vertical':(None,None,28),'horizontal_x':(None, None,8),'horizontal_y':(None,None,16)},
-
         ]
 
-
         offset_x =0.1*(8-1-1*4)/2
         offset_y =0.2*(16-1-3 * 5)/2
         offset_z =0.3*(28-1-3 * 7)/2
@@ -1343,21 +1341,21 @@ def test_process_acquisition(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -1375,21 +1373,21 @@ def test_process_image(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, geometry_gold,
+        self.assertEqual(geometry_out, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, geometry_gold))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold, data_out.array)
-        self.assertEquals(data_out.geometry, geometry_gold,
+        self.assertEqual(data_out.geometry, geometry_gold,
         msg="Slicer failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, geometry_gold))
 
 
@@ -1770,7 +1768,7 @@ def test_process_acquisition_geometry(self):
         proc.set_input(geometry)
         geometry_padded = proc._process_acquisition_geometry()
 
-        self.assertEquals(geometry_padded, self.ag_padded,
+        self.assertEqual(geometry_padded, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, self.ag_padded))
 
 
@@ -1784,7 +1782,7 @@ def test_process_acquisition_geometry(self):
             0.,   90.,  180.,  270.,\
             360., 450.,  540., 630., 720.]
 
-        self.assertEquals(geometry_padded, geometry_gold,
+        self.assertEqual(geometry_padded, geometry_gold,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, geometry_gold))
 
 
@@ -1795,7 +1793,7 @@ def test_process_acquisition_geometry_origin(self):
         proc.set_input(geometry)
         geometry_padded = proc._process_acquisition_geometry()
 
-        self.assertEquals(geometry_padded, self.ag2_padded,
+        self.assertEqual(geometry_padded, self.ag2_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, self.ag2_padded))
 
 
@@ -1807,7 +1805,7 @@ def test_process_image_geometry(self):
         proc.set_input(geometry)
         geometry_padded = proc._process_image_geometry()
 
-        self.assertEquals(geometry_padded, self.ig_padded,
+        self.assertEqual(geometry_padded, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_padded, self.ig_padded))
 
 
@@ -1846,21 +1844,21 @@ def test_process_acquisition(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, self.ag_padded,
+        self.assertEqual(geometry_out, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, self.ag_padded))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ag_padded,
+        self.assertEqual(data_out.geometry, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ag_padded))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ag_padded,
+        self.assertEqual(data_out.geometry, self.ag_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ag_padded))
 
 
@@ -1880,21 +1878,21 @@ def test_process_image(self):
 
         proc.set_input(data_in.geometry)
         geometry_out = proc.process()
-        self.assertEquals(geometry_out, self.ig_padded,
+        self.assertEqual(geometry_out, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(geometry_out, self.ig_padded))
 
         proc.set_input(data_in)
         data_out = proc.process()
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ig_padded,
+        self.assertEqual(data_out.geometry, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ig_padded))
 
         data_out.fill(0)
         proc.process(out=data_out)
 
         numpy.testing.assert_array_equal(data_gold.array, data_out.array)
-        self.assertEquals(data_out.geometry, self.ig_padded,
+        self.assertEqual(data_out.geometry, self.ig_padded,
         msg="Padder failed with geometry mismatch. Got:\n{0}\nExpected:\n{1}".format(data_out.geometry, self.ig_padded))
 
 
diff --git a/Wrappers/Python/test/test_PluginsRegularisation.py b/Wrappers/Python/test/test_PluginsRegularisation.py
index 63be777d49..05370c82f2 100644
--- a/Wrappers/Python/test/test_PluginsRegularisation.py
+++ b/Wrappers/Python/test/test_PluginsRegularisation.py
@@ -105,7 +105,7 @@ def test_functionality_FGP_TV(self):
         fcil = FGP_TV()
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.FGP_TV(datarr, fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0, 1, 'cpu' )
+        outrgl = regularisers.FGP_TV(datarr, fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0, 1, device='cpu' )
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
 
@@ -118,7 +118,7 @@ def test_functionality_TGV(self):
         fcil = TGV()
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.TGV(datarr, fcil.alpha*tau, 1,1, fcil.max_iteration, 12, fcil.tolerance, 'cpu' )
+        outrgl = regularisers.TGV(datarr, fcil.alpha*tau, 1,1, fcil.max_iteration, 12, fcil.tolerance, device='cpu' )
 
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
@@ -133,7 +133,7 @@ def test_functionality_FGP_dTV(self):
         fcil = FGP_dTV(ref)
         outcil = fcil.proximal(data, tau=tau)
         # use CIL defaults
-        outrgl, info = regularisers.FGP_dTV(datarr, ref.as_array(), fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0.01, 0, 1, 'cpu' )
+        outrgl = regularisers.FGP_dTV(datarr, ref.as_array(), fcil.alpha*tau, fcil.max_iteration, fcil.tolerance, 0.01, 0, 1, device='cpu' )
         np.testing.assert_almost_equal(outrgl, outcil.as_array())
 
 
diff --git a/Wrappers/Python/test/test_algorithms.py b/Wrappers/Python/test/test_algorithms.py
index b90ff3485c..7a32375478 100644
--- a/Wrappers/Python/test/test_algorithms.py
+++ b/Wrappers/Python/test/test_algorithms.py
@@ -523,9 +523,9 @@ def test_PDHG_strongly_convex_gamma_g(self):
         pdhg = PDHG(f=f, g=g, operator=operator, sigma = sigma, tau=tau,
                     max_iteration=5, gamma_g=0.5)
         pdhg.run(1, verbose=0)
-        self.assertAlmostEquals(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_g * tau))
-        self.assertAlmostEquals(pdhg.tau, tau * pdhg.theta)
-        self.assertAlmostEquals(pdhg.sigma, sigma / pdhg.theta)
+        self.assertAlmostEqual(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_g * tau))
+        self.assertAlmostEqual(pdhg.tau, tau * pdhg.theta)
+        self.assertAlmostEqual(pdhg.sigma, sigma / pdhg.theta)
         pdhg.run(4, verbose=0)
         self.assertNotEqual(pdhg.sigma, sigma)
         self.assertNotEqual(pdhg.tau, tau)
@@ -557,9 +557,9 @@ def test_PDHG_strongly_convex_gamma_fcong(self):
         pdhg = PDHG(f=f, g=g, operator=operator, sigma = sigma, tau=tau,
                     max_iteration=5, gamma_fconj=0.5)
         pdhg.run(1, verbose=0)
-        self.assertEquals(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_fconj * sigma))
-        self.assertEquals(pdhg.tau, tau / pdhg.theta)
-        self.assertEquals(pdhg.sigma, sigma * pdhg.theta)
+        self.assertEqual(pdhg.theta, 1.0/ np.sqrt(1 + 2 * pdhg.gamma_fconj * sigma))
+        self.assertEqual(pdhg.tau, tau / pdhg.theta)
+        self.assertEqual(pdhg.sigma, sigma * pdhg.theta)
         pdhg.run(4, verbose=0)
         self.assertNotEqual(pdhg.sigma, sigma)
         self.assertNotEqual(pdhg.tau, tau)
diff --git a/Wrappers/Python/test/test_functions.py b/Wrappers/Python/test/test_functions.py
index c94a529f99..1fd925abbe 100644
--- a/Wrappers/Python/test/test_functions.py
+++ b/Wrappers/Python/test/test_functions.py
@@ -1121,16 +1121,16 @@ def setUp(self) -> None:
         self.alpha_arr = self.ig_real.allocate(0.15)
 
     def test_configure_tv_defaults(self):
-        self.assertEquals(self.tv.warm_start, True)
-        self.assertEquals(self.tv.iterations, 10)
-        self.assertEquals(self.tv.correlation, "Space")
-        self.assertEquals(self.tv.backend, "c")
-        self.assertEquals(self.tv.lower, -np.inf)
-        self.assertEquals(self.tv.upper, np.inf)
-        self.assertEquals(self.tv.isotropic, True)
-        self.assertEquals(self.tv.split, False)
-        self.assertEquals(self.tv.strong_convexity_constant, 0)
-        self.assertEquals(self.tv.tolerance, None)
+        self.assertEqual(self.tv.warm_start, True)
+        self.assertEqual(self.tv.iterations, 10)
+        self.assertEqual(self.tv.correlation, "Space")
+        self.assertEqual(self.tv.backend, "c")
+        self.assertEqual(self.tv.lower, -np.inf)
+        self.assertEqual(self.tv.upper, np.inf)
+        self.assertEqual(self.tv.isotropic, True)
+        self.assertEqual(self.tv.split, False)
+        self.assertEqual(self.tv.strong_convexity_constant, 0)
+        self.assertEqual(self.tv.tolerance, None)
 
     def test_configure_tv_not_defaults(self):
         tv=TotalVariation( max_iteration=100,
@@ -1143,16 +1143,16 @@ def test_configure_tv_not_defaults(self):
                  split = True,
                  strong_convexity_constant = 1.,
                  warm_start=False)
-        self.assertEquals(tv.warm_start, False)
-        self.assertEquals(tv.iterations, 100)
-        self.assertEquals(tv.correlation, "SpaceChannels")
-        self.assertEquals(tv.backend, "numpy")
-        self.assertEquals(tv.lower, 0.)
-        self.assertEquals(tv.upper, 1.)
-        self.assertEquals(tv.isotropic, False)
-        self.assertEquals(tv.split, True)
-        self.assertEquals(tv.strong_convexity_constant, 1.)
-        self.assertEquals(tv.tolerance, 1e-5)
+        self.assertEqual(tv.warm_start, False)
+        self.assertEqual(tv.iterations, 100)
+        self.assertEqual(tv.correlation, "SpaceChannels")
+        self.assertEqual(tv.backend, "numpy")
+        self.assertEqual(tv.lower, 0.)
+        self.assertEqual(tv.upper, 1.)
+        self.assertEqual(tv.isotropic, False)
+        self.assertEqual(tv.split, True)
+        self.assertEqual(tv.strong_convexity_constant, 1.)
+        self.assertEqual(tv.tolerance, 1e-5)
 
 
 
@@ -1407,7 +1407,7 @@ def test_non_scalar_tau_cil_tv(self):
     def test_get_p2_with_warm_start(self):
         data = dataexample.SHAPES.get(size=(16, 16))
         tv=TotalVariation(warm_start=True, max_iteration=10)
-        self.assertEquals(tv._p2, None, msg="tv._p2 not initialised to None")
+        self.assertEqual(tv._p2, None, msg="tv._p2 not initialised to None")
         tv(data)
         checkp2=tv.gradient_operator.range_geometry().allocate(0)
         for i, x in enumerate(tv._get_p2()):
@@ -1424,7 +1424,7 @@ def test_get_p2_with_warm_start(self):
     def test_get_p2_without_warm_start(self):
         data = dataexample.SHAPES.get(size=(16, 16))
         tv=TotalVariation(warm_start=False)
-        self.assertEquals(tv._p2, None, msg="tv._p2 not initialised to None")
+        self.assertEqual(tv._p2, None, msg="tv._p2 not initialised to None")
         tv(data)
         checkp2=tv.gradient_operator.range_geometry().allocate(0)
         for i, x in enumerate(tv._get_p2()):
diff --git a/Wrappers/Python/test/test_io.py b/Wrappers/Python/test/test_io.py
index c47a236121..208ca926ce 100644
--- a/Wrappers/Python/test/test_io.py
+++ b/Wrappers/Python/test/test_io.py
@@ -475,7 +475,7 @@ def test_get_dataset_metadata(self):
         dset_dict = HDF5_utilities.get_dataset_metadata(self.path, self.dset_path)
 
         dict_by_hand  ={'ndim': 3, 'shape': (91, 135, 160), 'size': 1965600, 'dtype': np.float32, 'compression': None, 'chunks': None, 'is_virtual': False}
-        self.assertDictContainsSubset(dict_by_hand,dset_dict)
+        self.assertEqual(dset_dict, dict_by_hand | dset_dict)
 
 
     def test_read(self):
diff --git a/Wrappers/Python/test/test_subset.py b/Wrappers/Python/test/test_subset.py
index ed8323c3df..58db8a1807 100644
--- a/Wrappers/Python/test/test_subset.py
+++ b/Wrappers/Python/test/test_subset.py
@@ -33,7 +33,7 @@ def test_DataContainer(self):
         arr = numpy.arange(0,120).reshape(2,3,4,5)
         data = DataContainer(arr, True,dimension_labels=['c','z','y','x'])
         data.reorder(['x','y','z','c'])
-        self.assertEquals(data.shape,(5,4,3,2))
+        self.assertEqual(data.shape,(5,4,3,2))
         numpy.testing.assert_array_equal(data.array, arr.transpose(3,2,1,0))
 
     def test_ImageData(self):
@@ -41,16 +41,16 @@ def test_ImageData(self):
         data = ig.allocate(None)
         new_order = ['horizontal_x', 'horizontal_y','vertical', 'channel']
         data.reorder(new_order)
-        self.assertEquals(data.shape,(5,4,3,2))
-        self.assertEquals(data.geometry.dimension_labels,tuple(new_order))
+        self.assertEqual(data.shape,(5,4,3,2))
+        self.assertEqual(data.geometry.dimension_labels,tuple(new_order))
 
     def test_AcquisitionData(self):
         ag = AcquisitionGeometry.create_Parallel3D().set_panel([5,4]).set_angles([0,1,2]).set_channels(2).set_labels(['channel','angle','vertical','horizontal'])
         data = ag.allocate(None)
         new_order = ['horizontal', 'vertical','angle', 'channel']
         data.reorder(new_order)
-        self.assertEquals(data.shape,(5,4,3,2))
-        self.assertEquals(data.geometry.dimension_labels,tuple(new_order))
+        self.assertEqual(data.shape,(5,4,3,2))
+        self.assertEqual(data.geometry.dimension_labels,tuple(new_order))
 
     def test_AcquisitionData_forastra(self):
         ag = AcquisitionGeometry.create_Parallel3D().set_panel([5,4]).set_angles([0,1,2]).set_channels(2).set_labels(['horizontal','vertical', 'angle', 'channel'])
@@ -154,44 +154,44 @@ def test_DataContainer(self):
         data = DataContainer(arr, True,dimension_labels=['c','z','y','x'])
 
         data_new = data.get_slice(c=1)
-        self.assertEquals(data_new.shape,(3,4,5))
+        self.assertEqual(data_new.shape,(3,4,5))
         numpy.testing.assert_array_equal(data_new.array, arr[1])
 
         data_new = data.get_slice(c=1,y=3)
-        self.assertEquals(data_new.shape,(3,5))
+        self.assertEqual(data_new.shape,(3,5))
         numpy.testing.assert_array_equal(data_new.array, arr[1,:,3,:])
 
         data_new = data.get_slice(c=1,y=3,z=1)
-        self.assertEquals(data_new.shape,(5,))
+        self.assertEqual(data_new.shape,(5,))
         numpy.testing.assert_array_equal(data_new.array, arr[1,1,3,:])
 
     def test_ImageData(self):
         ig = ImageGeometry(voxel_num_x=5, voxel_num_y=4, voxel_num_z=3, channels=2,  dimension_labels=['channel','vertical','horizontal_y','horizontal_x'])
         data = ig.allocate(None)
         data_new = data.get_slice(vertical=1)
-        self.assertEquals(data_new.shape,(2,4,5))
-        self.assertEquals(data_new.geometry.dimension_labels,('channel','horizontal_y','horizontal_x'))
+        self.assertEqual(data_new.shape,(2,4,5))
+        self.assertEqual(data_new.geometry.dimension_labels,('channel','horizontal_y','horizontal_x'))
 
     def test_AcquisitionData(self):
         ag = AcquisitionGeometry.create_Parallel3D().set_panel([5,4]).set_angles([0,1,2]).set_channels(2).set_labels(['channel','angle','vertical','horizontal'])
         data = ag.allocate(None)
         data_new = data.get_slice(angle=2)
-        self.assertEquals(data_new.shape,(2,4,5))
-        self.assertEquals(data_new.geometry.dimension_labels,('channel','vertical','horizontal'))
+        self.assertEqual(data_new.shape,(2,4,5))
+        self.assertEqual(data_new.geometry.dimension_labels,('channel','vertical','horizontal'))
 
         #won't return a geometry for un-reconstructable slice
         ag = AcquisitionGeometry.create_Cone3D([0,-200,0],[0,200,0]).set_panel([5,4]).set_angles([0,1,2]).set_channels(2).set_labels(['channel','angle','vertical','horizontal'])
         data = ag.allocate('random')
         data_new = data.get_slice(vertical=1,force=True)
-        self.assertEquals(data_new.shape,(2,3,5))
+        self.assertEqual(data_new.shape,(2,3,5))
         self.assertTrue(isinstance(data_new,(DataContainer)))
         self.assertIsNone(data_new.geometry)
-        self.assertEquals(data_new.dimension_labels,('channel','angle','horizontal'))
+        self.assertEqual(data_new.dimension_labels,('channel','angle','horizontal'))
 
         #if 'centre' is between pixels interpolates
         data_new = data.get_slice(vertical='centre')
-        self.assertEquals(data_new.shape,(2,3,5))
-        self.assertEquals(data_new.geometry.dimension_labels,('channel','angle','horizontal'))
+        self.assertEqual(data_new.shape,(2,3,5))
+        self.assertEqual(data_new.geometry.dimension_labels,('channel','angle','horizontal'))
         numpy.testing.assert_allclose(data_new.array, (data.array[:,:,1,:] +data.array[:,:,2,:])/2 )
 
 class TestSubset(unittest.TestCase):
diff --git a/Wrappers/Python/test/utils.py b/Wrappers/Python/test/utils.py
index 148bc6abd7..e2e8d825bd 100644
--- a/Wrappers/Python/test/utils.py
+++ b/Wrappers/Python/test/utils.py
@@ -100,12 +100,13 @@ def initialise_tests():
 #ccpi-regularisation toolkit
 module_info = importlib.util.find_spec("ccpi")
 if module_info != None:
-    module_info = importlib.util.find_spec("ccpi.filters.cpu_regularisers")
+    module_info = importlib.util.find_spec("ccpi.filters.regularisers")
 
 if module_info is None:
     has_ccpi_regularisation = False
 else:
     has_ccpi_regularisation = True
+
 system_state['has_ccpi_regularisation']= has_ccpi_regularisation
 
 
diff --git a/recipe/conda_build_config.yaml b/recipe/conda_build_config.yaml
index ca67db8607..28e16d0af2 100644
--- a/recipe/conda_build_config.yaml
+++ b/recipe/conda_build_config.yaml
@@ -19,31 +19,25 @@
 
 #creates pairs of versions using zip_keys, lists must be the same length
 python:
-  - 3.8
-  - 3.9
   - 3.10
-  - 3.8
-  - 3.9
   - 3.10
-  - 3.8
-  - 3.9
   - 3.10
-  - 3.8
-  - 3.9
   - 3.10
+  - 3.11
+  - 3.11
+  - 3.11
+  - 3.11
+  - 3.12
 numpy:
-  - 1.21
-  - 1.21
-  - 1.21
-  - 1.22
-  - 1.22
-  - 1.22
-  - 1.23
-  - 1.23
   - 1.23
   - 1.24
+  - 1.25
+  - 1.26
+  - 1.23
   - 1.24
-  - 1.24
+  - 1.25
+  - 1.26
+  - 1.26
 zip_keys:
   - python
   - numpy
diff --git a/recipe/meta.yaml b/recipe/meta.yaml
index d229dc744c..7094ff9660 100644
--- a/recipe/meta.yaml
+++ b/recipe/meta.yaml
@@ -36,10 +36,10 @@ test:
     - cvxpy                   # [ linux ]
     - scikit-image
     - tomophantom=2.0.0       # [ linux ]
-    - tigre=2.4               # [ not osx ]
+    - tigre=2.6
     - packaging
-    - ccpi-regulariser=22.0.0 # [ not osx ]
-    - astra-toolbox>=1.9.9.dev5,<2.1
+    - ccpi-regulariser=24.0.0 # [ not osx ]
+    - astra-toolbox=2.1=cuda* # [ not osx ]
 
   source_files:
     - ./Wrappers/Python/test # [win]
@@ -91,9 +91,9 @@ requirements:
   #optional packages with version dependancies
   run_constrained:
     - tomophantom=2.0.0
-    - astra-toolbox>=1.9.9.dev5,<2.1
-    - tigre=2.4
-    - ccpi-regulariser=22.0.0
+    - astra-toolbox>=1.9.9.dev5,<=2.1
+    - tigre>=2.4,<=2.6
+    - ccpi-regulariser=24.0.0
     - ipywidgets
 
 about:
diff --git a/scripts/create_local_env_for_cil_development.sh b/scripts/create_local_env_for_cil_development.sh
old mode 100755
new mode 100644
index 16ef0222a5..b193b37bb2
--- a/scripts/create_local_env_for_cil_development.sh
+++ b/scripts/create_local_env_for_cil_development.sh
@@ -74,8 +74,8 @@ if test $test_deps = 0; then
   conda_args+=(-c conda-forge -c intel -c defaults --override-channels)
 else
   conda_args+=(
-    astra-toolbox'>=1.9.9.dev5,<2.1'
-    ccpi-regulariser=22.0.0
+    astra-toolbox=2.1=cuda*
+    ccpi-regulariser=24.0.0
     cil-data
     cvxpy
     ipywidgets
@@ -83,7 +83,7 @@ else
     python-wget
     setuptools
     scikit-image
-    tigre=2.4
+    tigre=2.6
     tomophantom=2.0.0
     -c conda-forge
     -c intel
diff --git a/scripts/requirements-test.yml b/scripts/requirements-test.yml
index eb72894e91..e0c109c56c 100644
--- a/scripts/requirements-test.yml
+++ b/scripts/requirements-test.yml
@@ -14,27 +14,26 @@
 name: cil_dev
 channels:
   - conda-forge
-  - intel
-  - ccpi
+  - nvidia
   - defaults
 dependencies:
   # base (vis. recipe/conda_build_config.yaml)
   - python=3.10
   - numpy=1.24
-  - cil-data
-  - tigre=2.4
-  - ccpi-regulariser=22.0.0
-  - tomophantom=2.0.0
-  - astra-toolbox >=1.9.9.dev5,<2.1
+  - ccpi::cil-data
+  - ccpi::tigre=2.6
+  - ccpi::ccpi-regulariser=24.0.0
+  - ccpi::tomophantom=2.0.0
+  - astra-toolbox=2.1=cuda*
   - cvxpy
   - python-wget
   - scikit-image
   - packaging
   - cmake >=3.16
   - setuptools
-  - ipp-include >=2021.10
-  - ipp-devel >=2021.10
-  - ipp >=2021.10
+  - intel::ipp-include >=2021.10
+  - intel::ipp-devel >=2021.10
+  - intel::ipp >=2021.10
   - ipywidgets
   - scipy
   - matplotlib