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Community Blog How to Use Caffe Deep Learning Framework for Image Classification

How to Use Caffe Deep Learning Framework for Image Classification

In this article, you will get some information on how to use Caffe deep learning framework for image classification.

Caffe is a deep learning framework, with which you can complete image classification model training for deep learning by editing configuration files. In this blog, we will introduce how to process image classification with Caffe in Alibaba Cloud Machine Learning Platform for AI.

Users of Alibaba Cloud Machine Learning Platform for AI can directly enter the following paths in the Data Source Path field of deep learning components:

Testing data: oss://dl-images.oss-cn-shanghai-internal.aliyuncs.com/cifar10/caffe/images/cifar10_test_image_list.txt

Training data: oss://dl-images.oss-cn-shanghai-internal.aliyuncs.com/cifar10/caffe/images/cifar10_train_image_list.txt

Then you can follow the steps below:

  1. Convert the jpg format as the Caffe framework of deep learning currently only supports certain formats.
  2. Set up the Caffe configuration files.
  3. Upload the Solver and Net files to OSS, drag and drop the Caffe component to the canvas, and connect the component to the data source.
  4. Set the Solver OSS Path to the OSS path of the uploaded Solver file and then click Run.
  5. Image classification model files are generated in the model storage path on OSS.

For details, please go to Alibaba Cloud Machine Learning Platform for AI: Image Classification by Caffe.

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NGC can help developers access deep learning containers for free. Deep learning frameworks, including Caffe, Caffe2, CNTK, MXNet, TensorFlow, Theano, and Torch, are pre-integrated, tested, and optimized for NVIDIA GPU, removing the need for manual integration.

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Because Caffe provides relatively primitive distributed support and is not modular, support for Caffe is relatively more difficult compared with the three other frameworks. The support for the three preceding frameworks does not require any changes to the framework code, except few modifications to MXNet. However, we need to make many modifications to the Caffe framework, which mainly include the following:

  1. Change the single-process and multi-GPU model to the single-process and single-GPU model and launch training on multiple machines and GPUs by using MPI.
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  3. It is required to construct a callback mechanism to allow the Ali-Perseus communication framework to notice the Caffe framework that all the gradients in the entire batch have completed communication.

Ali-Perseus also needs to add proper implementations of Caffe. Finally, after the integration, Ali-Perseus can support multiple machines and machines in Caffe.

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At present, NGC has been fully deployed in the gn5 instances. The NGC website provides images of different versions of the current mainstream deep learning frameworks (such as Caffe, Caffe2, CNTK, MxNet, TensorFlow, Theano, and Torch). You can select the desired image to build the environment. By taking the TensorFlow deep learning framework for example, this article describes how to build an NGC environment on gn5 instances.

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Dikky Ryan Pratama June 26, 2023 at 12:44 am

Awesome!

Alibaba Clouder

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