incubator-mxnet | Flexible Distributed/Mobile Deep Learning | Machine Learning library
kandi X-RAY | incubator-mxnet Summary
kandi X-RAY | incubator-mxnet Summary
[Twitter Follow] Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix [symbolic and imperative programming] to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scalable to many GPUs and machines. MXNet is more than a deep learning project. It is a [community] on a mission of democratizing AI. It is a collection of [blue prints and guidelines] for building deep learning systems, and interesting insights of DL systems for hackers. Licensed under an [Apache-2.0] license. | Branch | Build Status | |:-------:|:-------------:| | [master] | [Clang Build Status] [Sanity Build Status] [Website Build Status] [Documentation Status] | | [v1.x] | [Clang Build Status] [Sanity Build Status] [Website Build Status] [Documentation Status] |.
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incubator-mxnet Key Features
incubator-mxnet Examples and Code Snippets
Lines of Code : 45License : Strong Copyleft (CC BY-SA 4.0)
Copyimport mxnet as mx
import numpy as np
import tensorly as tl
import matplotlib.pyplot as plt
import tensorly.decomposition
# Load data
mnist = mx.test_utils.get_mnist()
train_data = mnist['train_data'][:,0]
err = np.zeros([28,28]) # herel.asnumpy().sum()
!sudo ln -sfT /usr/local/cuda/cuda-10.0/ /usr/local/cuda
!pip install mxnet-cu100mkl
import mxnet
mxnet.__version__
!sudo ln -sfT /usr/local/cuda/cuda-11.0/ /usr/local/cuda
!pip install mxnet-cu110
outputs = (P_u * Q_i).sum(axis=1) + b_u.squeeze() + b_i.squeeze()
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)
nn.init.normal_(self.P.weight, std=0.01)
nn.init.nXGBoostError: [12:32:18] /opt/conda/envs/rapids/conda-bld/xgboost_1603491651651/work/src/c_api/../data/../common/device_helpers.cuh:400: Memory allocation error on worker 0: std::bad_alloc: CUDA error at: ../include/rmm/mr/device/cuda_memoC= cp.random.random([10000,10000], dtype=cp.float32)
D = cp.random.random([10000,10000], dtype=cp.float32)
from mxnet import nd
def mxnet_convolve(x):
B, C, H, W = x.shape
weight = nd.ones((C, C, 1, 1))
return nd.Convolution(x, weight, no_bias=True, kernel=(1,1), num_filter=C)
x = nd.ones((16, 3, 32, 32))
mxnet_convolve(x)
"C:\Program Files\Python37\Scripts\pip" install torch==1.5.1 torchvision -f https://download.pytorch.org/whl/torch_stable.html
"C:\Program Files\Python37\Scripts\pip" install -U d2l
criterion = gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False)
from PyInstaller.utils.hooks import get_package_paths
datas = [(get_package_paths('theano')[1],"theano"),]
Lib\site-packages\PyInstaller\hooks
pyinstaller myApp.py -p \Lib\site-packages
Community Discussions
Trending Discussions on incubator-mxnet
QUESTION
I'm trying to install mxnet with gpu on colab.
I guess current colab has cuda 11.1 installed by default as
ANSWER
Answered 2021-Sep-25 at 19:06The following approach works for cuda-10.0 and cuda-11.0:
QUESTION
How do I call a custom mxnet operator from DJL? E.g. the my_gemm operator from the examples.
ANSWER
Answered 2021-Apr-11 at 15:09It is possible by manually calling the JnaUtils in the same way as the built-in mxnet engine does, just with your custom lib. For the my_gemm example, this looks like this:
QUESTION
I am afraid that my Neural Network in MXNet, written in Python, has a memory leak. I have tried the MXNet profiler and the tracemalloc module to get an understanding of memory profiling, but I want to get information on any potential memory leaks, just like I'd do with valgrind in C.
I found Detecting Memory Leaks and Buffer Overflows in MXNet, and after managing to build like described in section "Using ASAN builds with MXNet", by replacing the "ubuntu_cpu" part in docker/Dockerfile.build.ubuntu_cpu -t mxnetci/build.ubuntu_cpu with "ubuntu_cpu_python", I tried executing in an AWS Sagemaker Notebook like this:
ANSWER
Answered 2020-Aug-30 at 20:51In MXNet, we automatically test for this through examining the garbage collection records. You can find how it's implemented here: https://github.com/apache/incubator-mxnet/blob/c3aff732371d6177e5d522c052fb7258978d8ce4/tests/python/conftest.py#L26-L79
QUESTION
While parsing the payload received from a GitHub WebHook, facing this issue JSONDecodeError: Expecting value: line 1 column 1 (char 0)
payload looks like
...ANSWER
Answered 2020-Feb-29 at 03:08After closely looking at the GitHub WebHook configuration and the payload output, there was a mismatch.
GitHub WebHook was configured to pass request in content type : x-www-form-urlencoded.
Further the payload print also looks like urlencoded and not json.
But my helper function in AWS Lambda that parses the webhook was expecting a json.
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