Traceback (most recent call last):


 "/private/tmp/venv/lib/python3.8/site-packages/napari/_vispy/vispy_base_layer.py", line 264, in get_max_texture_sizes

MAX_TEXTURE_SIZE_2D = gl.glGetParameter(gl.GL_MAX_TEXTURE_SIZE)


 "/private/tmp/venv/lib/python3.8/site-packages/vispy/gloo/gl/_gl2.py", line 792, in glGetParameter

return _glGetIntegerv(pname)


 "/private/tmp/venv/lib/python3.8/site-packages/vispy/gloo/gl/_gl2.py", line 684, in _glGetIntegerv

nativefunc = _glGetIntegerv._native = _get_gl_func("glGetIntegerv", None, (ctypes.c_uint, ctypes.POINTER(ctypes.c_int),))


 "/private/tmp/venv/lib/python3.8/site-packages/vispy/gloo/gl/gl2.py", line 69, in _get_gl_func

raise RuntimeError('Could not load OpenGL library, gl cannot be used')
RuntimeError: Could not load OpenGL library, gl cannot be used

Traceback (most recent call last):


 "train.py", line 327, in <module>

main(params)


 "train.py", line 280, in main

trainer.mt_step(lang, lang, params.lambda_ae)


 "/mounts/Users/cisintern/achron/XLM-no-changes/src/trainer.py", line 851, in mt_step

enc1 = self.encoder('fwd', x=x1, lengths=len1, langs=langs1, causal=False)


 "/opt/softw/Miniconda3/envs/nmt/lib/python3.6/site-packages/torch/nn/modules/module.py", line 547, in __call__

result = self.forward(*input, **kwargs)


 "/opt/softw/Miniconda3/envs/nmt/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 459, in forward

self.reducer.prepare_for_backward([])
RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by (1) passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`; (2) making sure all `forward` function outputs participate in calculating loss. If you already have done the above two steps, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). (prepare_for_backward at /opt/conda/conda-bld/pytorch_1565272279342/work/torch/csrc/distributed/c10d/reducer.cpp:518)
