使用带有python包bert_embeddings和mxnet的GPU失败

Question

我正在使用下面的代码来启用GPU，它使用bert_embeddings包的mxnet包来提取Bert嵌入：

from bert_embedding import BertEmbedding
import mxnet as mx
ctx = mx.gpu()

bert_embedding = BertEmbedding(ctx=ctx)

由此产生的错误如下：

MXNetError: [13:51:52] src/ndarray/ndarray.cc:1280: GPU is not enabled
Stack trace:
  [bt] (0) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x259c2b) [0x7fbf015d3c2b]
  [bt] (1) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::CopyFromTo(mxnet::NDArray const&, mxnet::NDArray const&, int, bool)+0x6db) [0x7fbf0395234b]
  [bt] (2) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::imperative::PushFComputeEx(std::function<void (nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&)> const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource, std::allocator<mxnet::Resource> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&)::{lambda(mxnet::RunContext)#1}::operator()(mxnet::RunContext) const+0x128) [0x7fbf03807668]
  [bt] (3) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::imperative::PushFComputeEx(std::function<void (nnvm::NodeAttrs const&, mxnet::OpContext const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, std::vector<mxnet::NDArray, std::allocator<mxnet::NDArray> > const&)> const&, nnvm::Op const*, nnvm::NodeAttrs const&, mxnet::Context const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::engine::Var*, std::allocator<mxnet::engine::Var*> > const&, std::vector<mxnet::Resource, std::allocator<mxnet::Resource> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&)+0x4bb) [0x7fbf03813ceb]
  [bt] (4) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::Imperative::InvokeOp(mxnet::Context const&, nnvm::NodeAttrs const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::OpReqType, std::allocator<mxnet::OpReqType> > const&, mxnet::DispatchMode, mxnet::OpStatePtr)+0x961) [0x7fbf03819511]
  [bt] (5) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(mxnet::Imperative::Invoke(mxnet::Context const&, nnvm::NodeAttrs const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&, std::vector<mxnet::NDArray*, std::allocator<mxnet::NDArray*> > const&)+0x25b) [0x7fbf03819c5b]
  [bt] (6) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(+0x23a9879) [0x7fbf03723879]
  [bt] (7) /user/anaconda3/lib/python3.7/site-packages/mxnet/libmxnet.so(MXImperativeInvokeEx+0x6f) [0x7fbf03723e6f]
  [bt] (8) /user/anaconda3/lib/python3.7/lib-dynload/../../libffi.so.6(ffi_call_unix64+0x4c) [0x7fbf9d1d8ec0]

其他详细信息：

操作系统: Ubuntu 18.04 GPU: NVIDIA

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.48                 Driver Version: 410.48                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 1080    Off  | 00000000:65:00.0  On |                  N/A |
| 34%   50C    P2    38W / 180W |    592MiB /  8110MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

Answer 1

您需要安装mxnet的GPU版本

例如：

pip install mxnet-cu92

完整说明可在此处查看：http://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Linux&language=Python&processor=GPU