Apache MXNet
A deep learning software framework, used to train, and deploy deep neural networks
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| + | Multi-Language Support | Supports multiple programming languages including Python, R, Julia, and Go. |
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| + | Hybrid Programming | Combines declarative symbolic expression with imperative tensor computation |
| + | Auto Differentiation | Offers automatic differentiation to derive gradients |
| + | Efficient Execution | Runs efficiently on various systems, from mobile devices to GPU clusters |
| + | Optimization | Provides global computation graph for performance and memory optimization |
| + | KVStore | Facilitates data synchronization over devices with a distributed key-value store |
| + | Graph Optimization | Employs strategies like memory allocation and operation grouping for efficiency |
| + | Scalable | Designed for scalability across distributed systems for large-scale deep neural network applications |
| - | Community Size | Smaller open-source community compared to TensorFlow. |
| - | Delayed Improvements | Longer time for improvements and bug fixes due to limited community support. |
| - | Data-Parallelism | Supports data-parallelism only (single GPU model size limitation) |
| - | Sparse Model Incompatibility | Not friendly to sparse models; BytePS focuses on dense DNN models. |
| - | Scala Conversion Overhead | Overhead in converting collections when using Scala |
| - | Increased JAR File Size | Larger JAR files due to Scala inclusion |
System Requirements
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Developer
Written in
C++, Python, Perl, Scala, CUDA, R, JavaScript, Julia, Golang
Initial Release
8 April 2014
License
Categories
Alternatives
Machine Learning
Deep Learning
Deep Learning