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Notebook Containers

Base Containers

When you launch a Notebook, it runs inside a container preloaded with the notebook files and dependencies. The following is a list of containers that Paperspace maintains:

Name

Description

Container Tag

URL

Fast.ai

Paperspace's Fast.ai template is built for getting up and running with the enormously popular Fast.ai online MOOC.

paperspace/fastai:1.0-CUDA9.2-base-3.0-v1.0.6

GitHub

All-in-one

All ML/DL frameworks in a single template with CUDA/cuDNN and other libraries. Python 36.

ufoym/deepo:all-py36-jupyter

GitHub

TensorFlow 2.0

Preview of TensorFlow 2.0 with GPU support. Python 36.

tensorflow/tensorflow:2.0.0a0-gpu-py3-jupyter

DockerHub

NVIDIA RAPIDS

NVIDIA's open source libraries to execute end-to-end data science and analytics pipelines. v0.8.

cuda10.0-devel-ubuntu18.04

NVIDIA

PyTorch

Latest PyTorch release (1.2) with GPU support. Python 36.

paperspace/dl-containers:pytorch-py36-cu100-jupyter

DockerHub

TensorFlow

Latest stable release (1.14.0) with GPU support. Python 36.

paperspace/dl-containers:tensorflow1140-py36-cu100-cdnn7-jupyter

DockerHub

Other Containers

Name

Description

Container Tag

URL

TensorFlow (1.5.0 GPU Py3)

Official docker images for deep learning framework TensorFlow (http://www.tensorflow.org)

tensorflow/tensorflow:1.5.0-gpu-py3

DockerHub

TensorFlow (1.5.0 CPU Py3)

Official docker images for deep learning framework TensorFlow (http://www.tensorflow.org)

tensorflow/tensorflow:1.5.0-py3

DockerHub

Deepo (Python 2.7)

A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment. (https://hub.docker.com/r/ufoym/deepo)

ufoym/deepo:all-py27-jupyter

GitHub

JupyterLab Data Science Stack

Jupyter Notebook Data Science Stack

jupyter/datascience-notebook

DockerHub

JupyterLab Data R Stack

Jupyter Notebook R Stack

jupyter/r-notebook

DockerHub

Custom Containers

Custom containers feature lets you pull your own image from a container registry such as Docker Hub. This article will help you prepare a custom Docker container to use with Gradient, show you how to bring that Container into Gradient, and create a notebook with your custom container. We recommend using Docker to get the container image from your system to Gradient.

Require field:

  • Container Name = Path and tags of image eg ufoym/deepo:all-jupyter-py36

Optional Parameters:

  • Username = your Docker Hub username, can be left blank for public images

  • Password = your Docker Hub password, can be left blank for public images

  • Default Entrypoint = must be Jupyter compatible, defaults to 'jupyter notebook' if left blank

  • Container user = optional user, defaults to 'root' if left blank

See a tutorial on using custom containers here.