【推荐】手把手深度学习模型部署指南

                                                                                                                                                                                      

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摘要

 

转自：爱可可-爱生活

Or, I just trained a machine learning model - now what?

This post goes over a quick and dirty way to deploy a trained machine learning model to production.

Read this if: You've successfully trained a ML model using an ML framework such as Tensorflow or Caffe that you would like to put up as a demo, preferably sooner rather than later, and you prefer lighter solutions rather than spinning up an entire tech stack.

Reading time: 10-15 mins

TL;DR check out the repo

• Check your tensorflow installation

• Run online classification from stdin

• Run online classification on localhost

• Put classifiers behind a hardcoded proxy

• Put classifiers behind a proxy with service discovery

• Call classifiers using a pseudo-DNS

ML in production

When we first entered the machine learning space here at Hive, we already had millions of ground truth labeled images, allowing us to train a state-of-the-art deep convolutional image classification model from scratch (i.e. randomized weights) in under a week, specialized for our use case. The more typical ML use case, though, is usually on the order of hundreds of images, for which I would recommend fine-tuning an existing model. For instance, https://www.tensorflow.org/tutorials/image_retraining has a great tutorial on how to fine-tune an Imagenet model (trained on 1.2M images, 1000 classes) to classify a sample dataset of flowers (3647 images, 5 classes).

For a quick tl;dr of the linked Tensorflow tutorial, after installing bazel and tensorflow, you would need to run the following code, which takes around 30 mins to build and 5 minutes to train:

链接::

https://thehive.ai/blog/simple-ml-serving

github链接：

https://github.com/hiveml/simple-ml-serving

原文链接：

https://m.weibo.cn/1402400261/4199226748064166

“完整内容”请点击【阅读原文】

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