Move project to python 3.8

.github/workflows/run_pytest.yml

@@ -10,7 +10,7 @@ jobs:
     runs-on: 'ubuntu-latest'
     strategy:
       matrix:
-        python-version: ['3.7']
+        python-version: ['3.8']
 
     steps:
     - name: 'Checkout repo'

README.rst

@@ -45,8 +45,8 @@ CellBender can be installed via
 
   $ pip install cellbender
 
-(and we recommend installing in its own ``conda`` environment to prevent
-conflicts with other software).
+(and we recommend installing in its own ``conda`` environment, using python 3.8,
+to prevent conflicts with other software).
 
 CellBender is run as a command-line tool, as in
 
@@ -98,7 +98,7 @@ Create a conda environment and activate it:
 
 .. code-block:: console
 
-  $ conda create -n cellbender python=3.7
+  $ conda create -n cellbender python=3.8
   $ conda activate cellbender
 
 Install the `pytables <https://www.pytables.org>`_ module:

cellbender/remove_background/tests/benchmarking/cuda_check_inputs.json

@@ -1,4 +1,4 @@
 {
-  "check_pytorch_cuda_status.run_check_pytorch_cuda_status.docker_image": "us.gcr.io/broad-dsde-methods/cellbender:20230414"
+  "check_pytorch_cuda_status.run_check_pytorch_cuda_status.docker_image": "us.gcr.io/broad-dsde-methods/cellbender:20230424"
 }
 

cellbender/remove_background/tests/benchmarking/docker_image_check_cuda_status.wdl

@@ -20,6 +20,8 @@ task run_check_pytorch_cuda_status {
         import torch
         assert torch.cuda.is_available()
         CODE
+
+        cellbender remove-background -h
     }
     runtime {
         docker: "${docker_image}"

cellbender/remove_background/tests/test_checkpoint.py

@@ -6,7 +6,7 @@
 import torch
 from torch.distributions import constraints
 import pyro
-import pyro.optim as optim
+import pyro.optim
 
 import cellbender
 from cellbender.remove_background.checkpoint import make_tarball, unpack_tarball, \
@@ -261,7 +261,7 @@ def test_save_and_load_pyro_checkpoint(tmpdir_factory, batch_size_n):
     initial_model = PyroModel(dim=dim)
 
     # set up the inference process
-    scheduler = optim.ClippedAdam({'lr': lr, 'clip_norm': 10.})
+    scheduler = pyro.optim.ClippedAdam({'lr': lr, 'clip_norm': 10.})
     svi = pyro.infer.SVI(initial_model.model, initial_model.guide, scheduler, loss=pyro.infer.Trace_ELBO())
     w1 = _get_params(initial_model.encoder)
 
@@ -316,7 +316,7 @@ def test_save_and_load_pyro_checkpoint(tmpdir_factory, batch_size_n):
 
     # one-shot training straight through
     model_one_shot = PyroModel(dim=dim)  # resets random state
-    scheduler = optim.ClippedAdam({'lr': lr, 'clip_norm': 10.})
+    scheduler = pyro.optim.ClippedAdam({'lr': lr, 'clip_norm': 10.})
     train_loader = new_train_loader(data=dataset, batch_size=batch_size_n)
     svi_one_shot = pyro.infer.SVI(model_one_shot.model, model_one_shot.guide, scheduler, loss=pyro.infer.Trace_ELBO())
     model_one_shot.loss.extend(train_pyro(n_epochs=epochs, data_loader=train_loader, svi=svi_one_shot))
@@ -526,13 +526,20 @@ def test_save_and_load_cellbender_checkpoint(tmpdir_factory, cuda, scheduler):
     #             'Guide traces disagree with and without checkpoint restart'
 
 
+# def ExponentialLR(optim_args) -> pyro.optim.PyroLRScheduler:
+#     """
+#     Wraps :class:`pyro.optim.clipped_adam.ClippedAdam` with :class:`~pyro.optim.optim.PyroOptim`.
+#     """
+#     return pyro.optim.PyroLRScheduler(torch.optim.lr_scheduler.ExponentialLR, optim_args)
+
+
 @pytest.mark.parametrize(
     "Optim, config",
     [
-        (optim.ClippedAdam, {"lr": 0.01}),
-        (optim.ExponentialLR, {"optimizer": torch.optim.SGD,
-                               "optim_args": {"lr": 0.01},
-                               "gamma": 0.9}),
+        (pyro.optim.ClippedAdam, {"lr": 0.01}),
+        (pyro.optim.ExponentialLR, {"optimizer": torch.optim.SGD,
+                         "optim_args": {"lr": 0.01},
+                         "gamma": 0.9}),
     ],
 )
 def test_optimizer_checkpoint_restart(Optim, config, tmpdir_factory):
@@ -557,7 +564,7 @@ def guide():
 
     def get_snapshot(optimizer):
         s = {k: v.data.clone() for k, v in store.items()}
-        if type(optimizer) == optim.lr_scheduler.PyroLRScheduler:
+        if type(optimizer) == pyro.optim.lr_scheduler.PyroLRScheduler:
             lr = list(optimizer.optim_objs.values())[0].get_last_lr()[0]
         else:
             lr = list(optimizer.optim_objs.values())[0].param_groups[0]['lr']
@@ -573,7 +580,7 @@ def get_snapshot(optimizer):
     for _ in range(5 + 10):
         svi.step()
         expected.append(get_snapshot(optimizer))
-        if type(optimizer) == optim.lr_scheduler.PyroLRScheduler:
+        if type(optimizer) == pyro.optim.lr_scheduler.PyroLRScheduler:
             svi.optim.step()
     del svi, optimizer
 
@@ -586,7 +593,7 @@ def get_snapshot(optimizer):
     for _ in range(5):
         svi.step()
         actual.append(get_snapshot(optimizer))
-        if type(optimizer) == optim.lr_scheduler.PyroLRScheduler:
+        if type(optimizer) == pyro.optim.lr_scheduler.PyroLRScheduler:
             svi.optim.step()
 
     # checkpoint
@@ -605,7 +612,7 @@ def get_snapshot(optimizer):
     for _ in range(10):
         svi.step()
         actual.append(get_snapshot(optimizer))
-        if type(optimizer) == optim.lr_scheduler.PyroLRScheduler:
+        if type(optimizer) == pyro.optim.lr_scheduler.PyroLRScheduler:
             svi.optim.step()
 
     # display learning rates and actual/expected values for z_loc

docker/Dockerfile

@@ -17,7 +17,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certifi
  && apt-get clean \
  && sudo rm -rf /var/lib/apt/lists/* \
 # get miniconda
- && curl -so $HOME/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py37_23.1.0-1-Linux-x86_64.sh \
+ && curl -so $HOME/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py38_23.1.0-1-Linux-x86_64.sh \
  && chmod +x $HOME/miniconda.sh \
  && $HOME/miniconda.sh -b -p $CONDA_DIR \
  && rm $HOME/miniconda.sh \
@@ -30,6 +30,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certifi
 # get compiled crcmod for gsutil
  && conda install -y -c conda-forge crcmod \
 # install cellbender and its dependencies
- && yes | pip install /software/cellbender/ \
+ && yes | pip install -e /software/cellbender/ \
  && conda clean -yaf \
  && sudo rm -rf ~/.cache/pip

docker/DockerfileGit

@@ -17,7 +17,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certifi
  && apt-get clean \
  && sudo rm -rf /var/lib/apt/lists/* \
 # get miniconda
- && curl -so $HOME/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py37_23.1.0-1-Linux-x86_64.sh \
+ && curl -so $HOME/miniconda.sh https://repo.anaconda.com/miniconda/Miniconda3-py38_23.1.0-1-Linux-x86_64.sh \
  && chmod +x $HOME/miniconda.sh \
  && $HOME/miniconda.sh -b -p $CONDA_DIR \
  && rm $HOME/miniconda.sh \

docs/source/installation/index.rst

@@ -26,7 +26,7 @@ packages you may have installed.
 
 .. code-block:: console
 
-  $ conda create -n cellbender python=3.7
+  $ conda create -n cellbender python=3.8
   $ conda activate cellbender
   (cellbender) $ pip install cellbender
 
@@ -38,7 +38,7 @@ Create a conda environment and activate it:
 
 .. code-block:: console
 
-  $ conda create -n cellbender python=3.7
+  $ conda create -n cellbender python=3.8
   $ conda activate cellbender
 
 Install the `pytables <https://www.pytables.org>`_ module:

setup.py

@@ -46,7 +46,7 @@ def get_version() -> str:
       'Development Status :: 4 - Beta',
       'Intended Audience :: Science/Research',
       'License :: OSI Approved :: BSD License',
-      'Programming Language :: Python :: 3.7',
+      'Programming Language :: Python :: 3.8',
       'Topic :: Scientific/Engineering :: Bio-Informatics',
     ],
     keywords='scRNA-seq bioinformatics',