The Baidu AI Cloud AI development platform BML is an end-to-end AI development and deployment platform. Based on the BML, users can accomplish the one-stop data pre-processing, model training and evaluation, service deployment, and other works. The platform provides a high-performance cluster training environment, massive algorithm frameworks and model cases, as well as easy-to-operate prediction service tools. Thus, it allows users to focus on the model and algorithm and obtain excellent model and prediction results.
Working area The fully hosted interactive programming environment realizes the data processing and code debugging. |
Have a click-to-run interactive operating environment JupyterThe fully hosted interactive programming environment realizes the data processing and code debugging. Have a click-to-run interactive operating environment Jupyter The fully hosted Jupyter environment has several built-in algorithms framework and software library. You can click it to use without configuration. Meanwhile, the CPU instance supports users to install a third-party software library and customize the environment, ensuring flexibility in your use. Provide GPU resourcesThe Jupyter operating environment in the working area provides users with GPU computing resources. The Jupyter accomplishes your light-weight data processing and training requirements quickly and efficiently. Also, it allows you always to get ready for massive training tasks. Automatically synchronize the BOS dataIt can upload the training data stored in the Baidu Object Storage (BOS) automatically and synchronizes the data in the container to the BOS. |
Training Several in-depth/machine learning frameworks enable you to initiate massive training jobs by a one-click operation. |
Support several in-depth/machine learning frameworksSupport several in-depth learning frameworks, including Tensorflow/Pytorch/PaddlePaddle, and Rapids cuML machine learning framework. With the code, you can initiate a job by a one-click operation. AutoDL/AutoMLSupport auto image classification and logic regression hyperparameter optimization. To accomplish model training and continuous optimization, you need to provide training data and parameters only. Thus, it maximizes training efficiency and effectiveness. Massively distributed trainingProvide several kinds of CPU and GPU packages and support multiple-machine and multiple-card scenarios. You can use up to 8 Nvidia Tesla V100 GPU cards in a single machine. |
Prediction The prediction model is launched for the Beta test and provides high-efficiency and low-latency prediction service. |
Support several frameworksSupport several prediction service frameworks, including TensorRT, PaddlePaddle, Anakin (a prediction service framework deeply optimized based on the PaddlePaddle). Prediction model libraryMatch the model data and model operation environment (Container Image), and manage (adds/deletes/modifies) deployable prediction models and their versions. Resource managementConfigure cluster resources for service endpoints, monitor services in the production environment, and change the service resources online while ensuring the service availability. A/B TestThe endpoint service supports the launch of different versions of the model. Thus, it enables customers to evaluate the effectiveness of various versions of the model. Load managementControl the data flow to different endpoints, and provide an effective mechanism for Beta test of the new model, load balance, and service quality control. |
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The click-to-run Jupyter environment has several kinds of common built-in frameworks, without having to configure the environment. Meanwhile, it supports several kinds of Auto algorithms, eliminating such onerous works as programming and hyperparameter optimization.
By merely clicking or using the API Calling in the console, you can initiate the training task in a one-stop manner, obtain the training model, and start the prediction service. It covers the whole process of AI development & deployment.
By merely clicking or using the API Calling in the console, you can initiate the training task in a one-stop manner, obtain the training model, and start the prediction service. It covers the whole process of AI development & deployment.
The product resources adopt container technology to achieve fast startup and release. The multiple-machine and multiple-card distributed training, and enterprise-level very-large-scale data support can shorten the development time significantly.
An elastic and high-availability container cluster management platform
A stable, secure, high-efficiency and highly-scalable cloud storage service