AI News, Introduction to the Deep Learning Virtual Machine
- On Wednesday, June 6, 2018
- By Read More
Introduction to the Deep Learning Virtual Machine
Increasingly, deep learning algorithms / deep neural networks are becoming one of the popular methods employed in many machine learning problems.
They are especially good at machine cognition tasks like image, text, audio/video understanding often approaching human cognitive levels in some specific domains with advanced deep neural network architectures and access to large set of data to train models.
With the cloud and availability of Graphical Processing Units (GPUs), it is becoming possible to build sophisticated deep neural architectures and train them on a large data set on powerful computing infrastructure on the cloud.
The deep learning virtual machine also tries to make the rich set of tools and samples on the DSVM more easily discoverable by surfacing a catalog of the tools and samples on the virtual machine.
Deep Learning Virtual Machine
The Deep Learning Virtual Machine (DLVM) is a specially configured variant of the Data Science Virtual Machine(DSVM) to make it easier to use GPU-based VM instances for training deep learning models.
tools to acquire and pre-process image, textual data, tools for data science modeling and development activities such as Microsoft R Server Developer Edition, Anaconda Python, Jupyter notebooks for Python and R, IDEs for Python and R , SQL database and many other data science and ML tools.
Provision a Deep Learning Virtual Machine on Azure
The Deep Learning Virtual Machine (DLVM) is a specially configured variant of the popular Data Science Virtual Machine (DSVM) to make it easier to use GPU-based VM instances for rapidly training deep learning models.
tools to acquire and pre-process image, textual data, tools for data science modeling and development activities such as Microsoft R Server Developer Edition, Anaconda Python, Jupyter notebooks for Python and R, IDEs for Python and R, SQL databases and many other data science and ML tools.
To connect to the Linux VM graphical desktop, complete the following procedure on your client: After you sign in to the VM by using either the SSH client or XFCE graphical desktop through the X2Go client, you are ready to start using the tools that are installed and configured on the VM.
Get started with deep learning on AWS
The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale.
You can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks such as Apache MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2, Theano, Torch, PyTorch, Chainer, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques.
Introduction to Azure Data Science Virtual Machine for Linux and Windows
The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science.
This topic discusses what you can do with the Data Science VM, outlines some of the key scenarios for using the VM, itemizes the key features available on the Windows and Linux versions, and provides instructions on how to get started using them.
The goal of the Data Science Virtual Machine (DSVM) is to provide data professionals at all skill levels and in all roles with a friction-free, pre-configured, and fully-integrated data science environment.
It also lowers costs by reducing the sysadmin burden and saving on the time needed to evaluate, install, and maintain the various software packages needed to do advanced analytics.
Enterprise trainers and educators that teach data science classes usually provide a virtual machine image to ensure that their students have a consistent setup and that the samples work predictably.
The Data Science VM can help replicate the data science environment quickly on demand, on scaled out servers that allow experiments requiring high-powered computing resources to be run.
The Data Science VM can be used to evaluate or learn tools such as Microsoft ML Server, SQL Server, Visual Studio tools, Jupyter, deep learning / ML toolkits, and new tools popular in the community with minimal setup effort.
Since the Data Science VM can be set up quickly, it can be applied in other short-term usage scenarios such as replicating published experiments, executing demos, following walkthroughs in online sessions or conference tutorials.
- On Friday, January 18, 2019
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