MXNet: Difference between revisions
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(Complete refactor of MXNet. Added instructions to install per our wheels.) |
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[https://mxnet.incubator.apache.org/ 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. | [https://mxnet.incubator.apache.org/ 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. | ||
= Available wheels = | = Available wheels = <!--T:9--> | ||
You can list available wheels using the <tt>avail_wheels</tt> command. | You can list available wheels using the <tt>avail_wheels</tt> command. | ||
{{Command | {{Command | ||
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}} | }} | ||
= Installing in a Python virtual environment = | = Installing in a Python virtual environment = <!--T:10--> | ||
1. Create and activate a Python virtual environment. | 1. Create and activate a Python virtual environment. | ||
{{Commands | {{Commands | ||
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}} | }} | ||
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2. Install MXNet and its Python dependencies. | 2. Install MXNet and its Python dependencies. | ||
{{Command | {{Command | ||
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}} | }} | ||
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3. Validate it, by importing or listing its runtime features. | 3. Validate it, by importing or listing its runtime features. | ||
{{Command | {{Command | ||
Revision as of 14:46, 13 July 2022
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.
Available wheels[edit]
You can list available wheels using the avail_wheels command.
[name@server ~]$ avail_wheels mxnet
name version python arch
------ --------- -------- ------
mxnet 1.9.1 cp39 avx2
mxnet 1.9.1 cp38 avx2
mxnet 1.9.1 cp310 avx2
Installing in a Python virtual environment[edit]
1. Create and activate a Python virtual environment.
[name@server ~]$ module load python/3.10
[name@server ~]$ virtualenv --no-download ~/env
[name@server ~]$ source ~/env/bin/activate
2. Install MXNet and its Python dependencies.
(env) [name@server ~] pip install --no-index mxnet
3. Validate it, by importing or listing its runtime features.
(env) [name@server ~] python -c "import mxnet as mx"
(env) [name@server ~] python -c "from mxnet import runtime; print(runtime.feature_list())"
[✔ CUDA, ✔ CUDNN, ✔ NCCL, ✔ CUDA_RTC, ✖ TENSORRT, ✔ CPU_SSE, ✔ CPU_SSE2, ✔ CPU_SSE3, ✔ CPU_SSE4_1, ✔ CPU_SSE4_2, ✖ CPU_SSE4A, ✔ CPU_AVX, ✖ CPU_AVX2, ✔ OPENMP, ✖ SSE, ✔ F16C, ✖ JEMALLOC, ✖ BLAS_OPEN, ✖ BLAS_ATLAS, ✖ BLAS_MKL, ✖ BLAS_APPLE, ✔ LAPACK, ✔ MKLDNN, ✔ OPENCV, ✖ CAFFE, ✖ PROFILER, ✔ DIST_KVSTORE, ✖ CXX14, ✖ INT64_TENSOR_SIZE, ✔ SIGNAL_HANDLER, ✖ DEBUG, ✖ TVM_OP]