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Deep Learning Containers (DLC) has entered beta stage, according to Google's Cloud Platform team.
The service lets users you run up an instant machine learning environment - local or remote - pre-configured for popular frameworks like TensorFlow and PyTorch.
DLC - Google - Deep - Learning - VMs
DLC are sold alongside Google’s existing Deep Learning VMs, a set of Debian-based disk images, complete with access to NVIDIA GPUs, for running ML frameworks. Currently there are 14 such VM images, including TensorFlow, PyTorch (an ML library for Python), R 3.5, and Intel MKL (Math Kernel Library) with CUDA, though it should be noted that half of the images on offer are marked Experimental.
At the time of writing there are 20 Deep Learning Container images including TensorFlow, PyTorch and R, with both CPU and GPU options. Others are to follow.
Parity - Deep - Learning - VM - Types
“We are working to reach parity with all Deep Learning VM types,” states the Google blurb.
A nice thing about these images is that you do not have to spend any immediate money to run them locally. Each image is set up with a Python3 environment, the selected ML libraries, and a Jupyter server which runs automatically. There are both CPU and GPU options. All you need is a working docker setup, with nvidia-docker and a CUDA 10 GPU, if you want to run with acceleration via NVIDIA’s CUDA parallel computing platform. You install the gcloud SDK, pull the container image you want and run. The images are relatively large, with the TensorFlow image nearly 6GB, and PyTorch around 8GB.
Jupyter - Source - Tool - Code - Equations
Jupyter is an open source interactive tool for working with and sharing code, equations, visualisations and text, which has become a standard in the data science community.
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