Need advice about which tool to choose?Ask the StackShare community!
Deepo vs TensorFlow.js: What are the differences?
Developers describe Deepo as "A Docker image containing almost all popular deep learning frameworks". Deepo is a Docker image with a full reproducible deep learning research environment. It contains most popular deep learning frameworks: theano, tensorflow, sonnet, pytorch, keras, lasagne, mxnet, cntk, chainer, caffe, torch. On the other hand, TensorFlow.js is detailed as "Machine Learning in JavaScript". Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.
Deepo and TensorFlow.js can be primarily classified as "Machine Learning" tools.
Deepo and TensorFlow.js are both open source tools. It seems that TensorFlow.js with 11.2K GitHub stars and 816 forks on GitHub has more adoption than Deepo with 4.92K GitHub stars and 578 GitHub forks.
Pros of Deepo
Pros of TensorFlow.js
- Open Source6
- NodeJS Powered5
- Deploy python ML model directly into javascript2
- Cost - no server needed for inference1
- Privacy - no data sent to server1
- Runs Client Side on device1
- Can run TFJS on backend, frontend, react native, + IOT1
- Easy to share and use - get more eyes on your research1