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:
Solver OSS Pathto the OSS path of the uploaded Solver file and then click
For details, please go to Alibaba Cloud Machine Learning Platform for AI: Image Classification by Caffe.
At the 2018 Computing Conference Shenzhen Summit on March 28, Alibaba Cloud announced the cooperation with NVIDIA GPU Cloud (NGC). Now, developers can download the NVIDIA GPU Cloud image from the Alibaba Cloud Marketplace and run NGC containers to use the NVIDIA GPU computing platform on Alibaba Cloud.
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.
In this article, we discuss how Ali-Perseus can help create a highly optimized and unified distributed communication framework for deep learning on Alibaba Cloud.
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:
Ali-Perseus also needs to add proper implementations of Caffe. Finally, after the integration, Ali-Perseus can support multiple machines and machines in Caffe.
As a deep learning ecosystem from NVIDIA, NVIDIA GPU CLOUD (NGC) allows developers to access the deep learning software stack free of charge and is fit for creating a deep learning development environment.
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.
Caffe is a lightweight, scalable, and fast deep learning framework developed by Berkeley AI Research (BAIR) and by community contributors. This is a basic introduction for Caffe in deep learning.
Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine Learning Platform for AI combines all of these services to make AI more accessible than ever.
Alibaba Cloud Elastic Compute Service (ECS) provides fast memory and the latest Intel CPUs to help you to power your cloud applications and achieve faster results with low latency. All ECS instances come with Anti-DDoS protection to safeguard your data and applications from DDoS and Trojan attacks. Now NGC for deep learning has been deployed in the gn5 instances on ECS.
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