bambi: Unexpected Error when testing Bambi package
I’m working on a laptop with Windows 10 and using Anaconda 64-bit. I have created an environment for working with Pymc3 and bambi. I have been able to test Pymc3 and it is working for an Hierarchical Linear Regression model. For bambi I was testing the example described at this link (growth curves of Pigs example) [https://bambinos.github.io/bambi/master/notebooks/multi-level_regression.html]. I run into an error I had not seen with bambi before (actually from the output this looks like it is occurring at Theano, but I’m not a programmer or a developer).
Here is my environment and the complete traceback of the error
# packages in environment at C:\ProgramData\Anaconda3\envs\pm3env:
#
# Name Version Build Channel
argon2-cffi 20.1.0 py37hcc03f2d_2 conda-forge
arviz 0.11.1 pypi_0 pypi
async_generator 1.10 py_0 conda-forge
attrs 21.2.0 pyhd8ed1ab_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
bambi 0.5.0 pypi_0 pypi
blas 1.0 mkl
bleach 4.0.0 pyhd8ed1ab_0 conda-forge
ca-certificates 2021.5.30 h5b45459_0 conda-forge
cached-property 1.5.2 pypi_0 pypi
cachetools 4.2.2 pypi_0 pypi
cairo 1.16.0 hb19e0ff_1008 conda-forge
certifi 2021.5.30 py37h03978a9_0 conda-forge
cffi 1.14.6 py37hd8e9650_0 conda-forge
cftime 1.5.0 pypi_0 pypi
colorama 0.4.4 pyh9f0ad1d_0 conda-forge
cycler 0.10.0 py_2 conda-forge
decorator 5.0.9 pyhd8ed1ab_0 conda-forge
defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge
dill 0.3.4 pypi_0 pypi
entrypoints 0.3 pyhd8ed1ab_1003 conda-forge
et-xmlfile 1.1.0 pypi_0 pypi
expat 2.4.1 h39d44d4_0 conda-forge
fastprogress 1.0.0 pypi_0 pypi
filelock 3.0.12 pypi_0 pypi
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.13.1 h1989441_1005 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
formulae 0.1.3 pypi_0 pypi
freetype 2.10.4 h546665d_1 conda-forge
fribidi 1.0.10 h8d14728_0 conda-forge
getopt-win32 0.1 h8ffe710_0 conda-forge
gettext 0.19.8.1 h1a89ca6_1005 conda-forge
graphite2 1.3.13 1000 conda-forge
graphviz 2.48.0 hefbd956_0 conda-forge
gts 0.7.6 h7c369d9_2 conda-forge
h5py 3.1.0 pypi_0 pypi
harfbuzz 2.8.2 hc601d6f_0 conda-forge
icc_rt 2019.0.0 h0cc432a_1
icu 68.1 h0e60522_0 conda-forge
importlib-metadata 2.1.1 pypi_0 pypi
intel-openmp 2021.3.0 h57928b3_3372 conda-forge
ipykernel 5.5.5 py37h7813e69_0 conda-forge
ipython 7.26.0 py37h4038f58_0 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
ipywidgets 7.6.3 pyhd3eb1b0_1
jbig 2.1 h8d14728_2003 conda-forge
jedi 0.18.0 py37h03978a9_2 conda-forge
jinja2 3.0.1 pyhd8ed1ab_0 conda-forge
jpeg 9d h8ffe710_0 conda-forge
jsonschema 3.2.0 pyhd8ed1ab_3 conda-forge
jupyter 1.0.0 py37_7
jupyter_client 6.1.12 pyhd8ed1ab_0 conda-forge
jupyter_console 6.4.0 pyhd8ed1ab_0 conda-forge
jupyter_core 4.7.1 py37h03978a9_0 conda-forge
jupyterlab_pygments 0.1.2 pyh9f0ad1d_0 conda-forge
jupyterlab_widgets 1.0.0 pyhd8ed1ab_1 conda-forge
kiwisolver 1.3.1 py37h8c56517_1 conda-forge
lcms2 2.12 h2a16943_0 conda-forge
lerc 2.2.1 h0e60522_0 conda-forge
libclang 11.1.0 default_h5c34c98_1 conda-forge
libdeflate 1.7 h8ffe710_5 conda-forge
libffi 3.3 h0e60522_2 conda-forge
libgd 2.3.2 h138e682_0 conda-forge
libglib 2.68.3 h1e62bf3_0 conda-forge
libiconv 1.16 he774522_0 conda-forge
libpng 1.6.37 h1d00b33_2 conda-forge
libpython 2.1 py37_0
libsodium 1.0.18 h8d14728_1 conda-forge
libtiff 4.3.0 h0c97f57_1 conda-forge
libwebp 1.2.0 h57928b3_0 conda-forge
libwebp-base 1.2.0 h8ffe710_2 conda-forge
libxcb 1.13 hcd874cb_1003 conda-forge
libxml2 2.9.12 hf5bbc77_0 conda-forge
llvmlite 0.36.0 py37habb0c8c_0 conda-forge
lz4-c 1.9.3 h8ffe710_1 conda-forge
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
markupsafe 2.0.1 py37hcc03f2d_0 conda-forge
matplotlib 3.3.2 haa95532_0
matplotlib-base 3.3.2 py37h3379fd5_1 conda-forge
matplotlib-inline 0.1.2 pyhd8ed1ab_2 conda-forge
mistune 0.8.4 py37hcc03f2d_1004 conda-forge
mkl 2020.4 hb70f87d_311 conda-forge
mkl-service 2.3.0 py37h196d8e1_0
mkl_fft 1.3.0 py37hda49f71_1 conda-forge
mkl_random 1.2.0 py37h414f9d2_1 conda-forge
mpmath 1.2.1 pyhd8ed1ab_0 conda-forge
msys2-conda-epoch 20160418 1 conda-forge
nbclient 0.5.3 pyhd8ed1ab_0 conda-forge
nbconvert 6.1.0 py37h03978a9_0 conda-forge
nbformat 5.1.3 pyhd8ed1ab_0 conda-forge
nest-asyncio 1.5.1 pyhd8ed1ab_0 conda-forge
netcdf4 1.5.7 pypi_0 pypi
notebook 6.4.0 pyha770c72_0 conda-forge
numba 0.53.1 py37h4e635f9_0 conda-forge
numpy 1.19.2 py37hadc3359_0
numpy-base 1.19.2 py37ha3acd2a_0
olefile 0.46 pyh9f0ad1d_1 conda-forge
openjpeg 2.4.0 hb211442_1 conda-forge
openpyxl 3.0.7 pypi_0 pypi
openssl 1.1.1k h8ffe710_0 conda-forge
packaging 21.0 pyhd8ed1ab_0 conda-forge
pandas 1.2.1 py37hf11a4ad_0
pandoc 2.14.1 h8ffe710_0 conda-forge
pandocfilters 1.4.2 py_1 conda-forge
pango 1.48.7 hd84fcdd_0 conda-forge
parso 0.8.2 pyhd8ed1ab_0 conda-forge
patsy 0.5.1 pypi_0 pypi
pcre 8.45 h0e60522_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 8.3.1 py37hd7d9ad0_0 conda-forge
pip 20.3.3 py37haa95532_0
pixman 0.40.0 h8ffe710_0 conda-forge
prometheus_client 0.11.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.19 pyha770c72_0 conda-forge
prompt_toolkit 3.0.19 hd8ed1ab_0 conda-forge
pthread-stubs 0.4 hcd874cb_1001 conda-forge
pycparser 2.20 pyh9f0ad1d_2 conda-forge
pygments 2.9.0 pyhd8ed1ab_0 conda-forge
pymc3 3.11.2 pypi_0 pypi
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
pyqt 5.12.3 py37h03978a9_7 conda-forge
pyqt-impl 5.12.3 py37hf2a7229_7 conda-forge
pyqt5-sip 4.19.18 py37hf2a7229_7 conda-forge
pyqtchart 5.12 py37hf2a7229_7 conda-forge
pyqtwebengine 5.12.1 py37hf2a7229_7 conda-forge
pyrsistent 0.17.3 py37hcc03f2d_2 conda-forge
python 3.7.9 h60c2a47_0
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-graphviz 0.16 pyh243d235_2 conda-forge
python_abi 3.7 2_cp37m conda-forge
pytz 2021.1 pyhd8ed1ab_0 conda-forge
pywin32 300 py37hcc03f2d_0 conda-forge
pywinpty 1.1.3 py37h7f67f24_0 conda-forge
pyzmq 22.2.0 py37hcce574b_0 conda-forge
qt 5.12.9 h5909a2a_4 conda-forge
qtconsole 5.1.1 pyhd8ed1ab_0 conda-forge
qtpy 1.9.0 py_0 conda-forge
scipy 1.7.1 pypi_0 pypi
semver 2.13.0 pypi_0 pypi
send2trash 1.7.1 pyhd8ed1ab_0 conda-forge
setuptools 49.6.0 py37h03978a9_3 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
sqlite 3.36.0 h8ffe710_0 conda-forge
statsmodels 0.12.2 pypi_0 pypi
sympy 1.7.1 py37h03978a9_1 conda-forge
terminado 0.10.1 py37h03978a9_0 conda-forge
testpath 0.5.0 pyhd8ed1ab_0 conda-forge
theano-pymc 1.1.2 pypi_0 pypi
tk 8.6.10 h8ffe710_1 conda-forge
tornado 6.1 py37hcc03f2d_1 conda-forge
traitlets 5.0.5 py_0 conda-forge
typing 3.7.4.3 pypi_0 pypi
typing_extensions 3.10.0.0 pyha770c72_0 conda-forge
ucrt 10.0.20348.0 h57928b3_0 conda-forge
vc 14.2 hb210afc_5 conda-forge
vs2015_runtime 14.29.30037 h902a5da_5 conda-forge
watermark 2.2.0 pypi_0 pypi
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
wheel 0.36.2 pyhd3deb0d_0 conda-forge
widgetsnbextension 3.5.1 py37h03978a9_4 conda-forge
wincertstore 0.2 py37h03978a9_1006 conda-forge
winpty 0.4.3 4 conda-forge
xarray 0.16.2 pypi_0 pypi
xorg-kbproto 1.0.7 hcd874cb_1002 conda-forge
xorg-libice 1.0.10 hcd874cb_0 conda-forge
xorg-libsm 1.2.3 hcd874cb_1000 conda-forge
xorg-libx11 1.7.2 hcd874cb_0 conda-forge
xorg-libxau 1.0.9 hcd874cb_0 conda-forge
xorg-libxdmcp 1.1.3 hcd874cb_0 conda-forge
xorg-libxext 1.3.4 hcd874cb_1 conda-forge
xorg-libxpm 3.5.13 hcd874cb_0 conda-forge
xorg-libxt 1.2.1 hcd874cb_2 conda-forge
xorg-xextproto 7.3.0 hcd874cb_1002 conda-forge
xorg-xproto 7.0.31 hcd874cb_1007 conda-forge
xz 5.2.5 h62dcd97_1 conda-forge
zeromq 4.3.4 h0e60522_0 conda-forge
zipp 3.5.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h62dcd97_1010 conda-forge
zstd 1.5.0 h6255e5f_0 conda-forge
Model I tried to run is:
model = bmb.Model("Weight ~ Time + (Time|Pig)", data)
results = model.fit()
And the error I get is:
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-11-99071b9bde96> in <module>
1 model = bmb.Model("Weight ~ Time + (Time|Pig)", data)
----> 2 results = model.fit()
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\bambi\models.py in fit(self, omit_offsets, backend, **kwargs)
213 )
214
--> 215 return self.backend.run(omit_offsets=omit_offsets, **kwargs)
216
217 def build(self, backend="pymc"):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\bambi\backends\pymc.py in run(self, start, method, init, n_init, omit_offsets, **kwargs)
139 n_init=n_init,
140 return_inferencedata=True,
--> 141 **kwargs,
142 )
143
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs)
502 progressbar=progressbar,
503 jitter_max_retries=jitter_max_retries,
--> 504 **kwargs,
505 )
506 if start is None:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\sampling.py in init_nuts(init, chains, n_init, model, random_seed, progressbar, jitter_max_retries, **kwargs)
2185 raise ValueError(f"Unknown initializer: {init}.")
2186
-> 2187 step = pm.NUTS(potential=potential, model=model, **kwargs)
2188
2189 return start, step
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\step_methods\hmc\nuts.py in __init__(self, vars, max_treedepth, early_max_treedepth, **kwargs)
166 `pm.sample` to the desired number of tuning steps.
167 """
--> 168 super().__init__(vars, **kwargs)
169
170 self.max_treedepth = max_treedepth
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\step_methods\hmc\base_hmc.py in __init__(self, vars, scaling, step_scale, is_cov, model, blocked, potential, dtype, Emax, target_accept, gamma, k, t0, adapt_step_size, step_rand, **theano_kwargs)
86 vars = inputvars(vars)
87
---> 88 super().__init__(vars, blocked=blocked, model=model, dtype=dtype, **theano_kwargs)
89
90 self.adapt_step_size = adapt_step_size
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\step_methods\arraystep.py in __init__(self, vars, model, blocked, dtype, logp_dlogp_func, **theano_kwargs)
252
253 if logp_dlogp_func is None:
--> 254 func = model.logp_dlogp_function(vars, dtype=dtype, **theano_kwargs)
255 else:
256 func = logp_dlogp_func
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\model.py in logp_dlogp_function(self, grad_vars, tempered, **kwargs)
1002 varnames = [var.name for var in grad_vars]
1003 extra_vars = [var for var in self.free_RVs if var.name not in varnames]
-> 1004 return ValueGradFunction(costs, grad_vars, extra_vars, **kwargs)
1005
1006 @property
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\pymc3\model.py in __init__(self, costs, grad_vars, extra_vars, dtype, casting, compute_grads, **kwargs)
689
690 if compute_grads:
--> 691 grad = tt.grad(self._cost_joined, self._vars_joined)
692 grad.name = "__grad"
693 outputs = [self._cost_joined, grad]
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in grad(cost, wrt, consider_constant, disconnected_inputs, add_names, known_grads, return_disconnected, null_gradients)
637 assert g.type.dtype in theano.tensor.float_dtypes
638
--> 639 rval = _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name)
640
641 for i in range(len(rval)):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name)
1438 return grad_dict[var]
1439
-> 1440 rval = [access_grad_cache(elem) for elem in wrt]
1441
1442 return rval
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1438 return grad_dict[var]
1439
-> 1440 rval = [access_grad_cache(elem) for elem in wrt]
1441
1442 return rval
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in <listcomp>(.0)
1059 inputs = node.inputs
1060
-> 1061 output_grads = [access_grad_cache(var) for var in node.outputs]
1062
1063 # list of bools indicating if each output is connected to the cost
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_grad_cache(var)
1391 for idx in node_to_idx[node]:
1392
-> 1393 term = access_term_cache(node)[idx]
1394
1395 if not isinstance(term, Variable):
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\gradient.py in access_term_cache(node)
1218 )
1219
-> 1220 input_grads = node.op.L_op(inputs, node.outputs, new_output_grads)
1221
1222 if input_grads is None:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in L_op(self, inputs, outs, ograds)
562
563 # compute grad with respect to broadcasted input
--> 564 rval = self._bgrad(inputs, outs, ograds)
565
566 # TODO: make sure that zeros are clearly identifiable
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in _bgrad(self, inputs, outputs, ograds)
666 ret.append(None)
667 continue
--> 668 ret.append(transform(scalar_igrad))
669
670 return ret
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in transform(r)
657 return DimShuffle((), ["x"] * nd)(res)
658
--> 659 new_r = Elemwise(node.op, {})(*[transform(ipt) for ipt in node.inputs])
660 return new_r
661
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in <listcomp>(.0)
657 return DimShuffle((), ["x"] * nd)(res)
658
--> 659 new_r = Elemwise(node.op, {})(*[transform(ipt) for ipt in node.inputs])
660 return new_r
661
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\tensor\elemwise.py in transform(r)
657 return DimShuffle((), ["x"] * nd)(res)
658
--> 659 new_r = Elemwise(node.op, {})(*[transform(ipt) for ipt in node.inputs])
660 return new_r
661
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in __call__(self, *inputs, **kwargs)
251
252 if config.compute_test_value != "off":
--> 253 compute_test_value(node)
254
255 if self.default_output is not None:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in compute_test_value(node)
124
125 # Create a thunk that performs the computation
--> 126 thunk = node.op.make_thunk(node, storage_map, compute_map, no_recycling=[])
127 thunk.inputs = [storage_map[v] for v in node.inputs]
128 thunk.outputs = [storage_map[v] for v in node.outputs]
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in make_thunk(self, node, storage_map, compute_map, no_recycling, impl)
632 )
633 try:
--> 634 return self.make_c_thunk(node, storage_map, compute_map, no_recycling)
635 except (NotImplementedError, MethodNotDefined):
636 # We requested the c code, so don't catch the error.
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\graph\op.py in make_c_thunk(self, node, storage_map, compute_map, no_recycling)
599 raise NotImplementedError("float16")
600 outputs = cl.make_thunk(
--> 601 input_storage=node_input_storage, output_storage=node_output_storage
602 )
603 thunk, node_input_filters, node_output_filters = outputs
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in make_thunk(self, input_storage, output_storage, storage_map)
1202 init_tasks, tasks = self.get_init_tasks()
1203 cthunk, module, in_storage, out_storage, error_storage = self.__compile__(
-> 1204 input_storage, output_storage, storage_map
1205 )
1206
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in __compile__(self, input_storage, output_storage, storage_map)
1140 input_storage,
1141 output_storage,
-> 1142 storage_map,
1143 )
1144 return (
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in cthunk_factory(self, error_storage, in_storage, out_storage, storage_map)
1632 for node in self.node_order:
1633 node.op.prepare_node(node, storage_map, None, "c")
-> 1634 module = get_module_cache().module_from_key(key=key, lnk=self)
1635
1636 vars = self.inputs + self.outputs + self.orphans
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\cmodule.py in module_from_key(self, key, lnk)
1189 try:
1190 location = dlimport_workdir(self.dirname)
-> 1191 module = lnk.compile_cmodule(location)
1192 name = module.__file__
1193 assert name.startswith(location)
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\basic.py in compile_cmodule(self, location)
1548 lib_dirs=self.lib_dirs(),
1549 libs=libs,
-> 1550 preargs=preargs,
1551 )
1552 except Exception as e:
C:\ProgramData\Anaconda3\envs\pm3env\lib\site-packages\theano\link\c\cmodule.py in compile_str(module_name, src_code, location, include_dirs, lib_dirs, libs, preargs, py_module, hide_symbols)
2545 compile_stderr = compile_stderr.replace("\n", ". ")
2546 raise Exception(
-> 2547 f"Compilation failed (return status={status}): {compile_stderr}"
2548 )
2549 elif config.cmodule__compilation_warning and compile_stderr:
Exception: ("Compilation failed (return status=1): C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp: In member function 'int {anonymous}::__struct_compiled_op_m67599e776bb0a5edbe20464e4ef6902fada5652e9f038845aa3f408620203691::run()':. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:506:39: warning: narrowing conversion of 'V5_n0' from 'npy_intp' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. int init_totals[2] = {V5_n0, V1_n1};. ^. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:506:39: warning: narrowing conversion of 'V1_n1' from 'npy_intp' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:521:5: warning: narrowing conversion of 'V5_stride0' from 'ssize_t' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. };. ^. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:521:5: warning: narrowing conversion of 'V1_stride0' from 'ssize_t' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. C:\\Users\\sreedatta\\AppData\\Local\\Theano\\compiledir_Windows-10-10.0.19041-SP0-Intel64_Family_6_Model_140_Stepping_1_GenuineIntel-3.7.9-64\\tmp81eb_5sq\\mod.cpp:521:5: warning: narrowing conversion of 'V1_stride1' from 'ssize_t' {aka 'long long int'} to 'int' inside { } [-Wnarrowing]. At global scope:. cc1plus.exe: warning: unrecognized command line option '-Wno-c++11-narrowing'. C:\\Users\\SREEDATTA\\AppData\\Local\\Temp\\ccjNNew1.s: Assembler messages:\r. C:\\Users\\SREEDATTA\\AppData\\Local\\Temp\\ccjNNew1.s:4410: Error: invalid register for .seh_savexmm\r. ", 'FunctionGraph(Elemwise{mul}(<TensorType(float64, col)>, <TensorType(int8, (True, True))>))')
Can one of you help?
Sree
About this issue
- Original URL
- State: closed
- Created 3 years ago
- Comments: 20
@tomicapretto thanks for the additional information regarding the version with Aesara. As one last attempt to get it to work on Windows 10, I have created an
environment.ymlfile from the Windows 8.1 install where it is working and will test with Windows 10. I have found that when installingpymc3=3.11.2orbambi=0.6.0,matplotlib=3.4.3is unable to be compiled. It is breaking the entire installation process. I manually forced on Windows 8.1 to install the previousmatplotlib=3.4.2. This compiles well. The issue is cropping on Windows 10 as well where '``matplotlib=3.4.3``` fails compilation.I now have a good working environment for
pymc3=3.11.2on WIndows 10 and Windows 8.1; I have a good workingpymc3=3.11.2andbambi=0.5.0together on Windows 8.1. I will see if the approach with the environment file will do the trick for bambi on Windows 10. If I get them to work, I will post those environment files for others to try and use.Thanks again - Sree
@tomicapretto thank you for a detailed step by step process description. I will get familiar with this process in a couple of days. Beginning this Friday, I will start sharing content. I will reach out if I get stuck or have a question.
Sree