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Apps & integrations

Work with all your favourite apps and tools or create your own custom integrations using the Ghost API.

Work with your existing tools

It's possible to connect your Ghost site to hundreds of the most popular apps and tools using integrations that take no more than a few minutes to setup.

Whether you need to automate workflows, connect your email list, build a community or embed products from your ecommerce store, our integrations library has got it all covered with hundreds of tutorials.

Zapier

On top of this, you can connect your Ghost site to more than 1,000 external services using the official integration with Zapier.

Zapier sets up automations with Triggers and Actions, which allows you to create and customise a wide range of connected applications.

Example: When someone new subscribes to a newsletter on a Ghost site (Trigger) then the contact information is automatically pushed into MailChimp (Action).

Here are the most popular Ghost<>Zapier automation templates:

Custom integrations

At the heart of Ghost sits a robust JSON API – designed to create, manage and retrieve content with ease.

It's possible to create custom Ghost integrations with dedicated API keys and webhooks from the Integrations page within Ghost Admin.

Screenshot of custom integrations with webhooks in Ghost Admin

Beyond that, the API allows you to build entirely custom publishing apps. You can send content from your favourite desktop editor, build a custom interface for handling editorial workflow or use Ghost as a full headless CMS with a custom front-end.

The Ghost API is thoroughly documented and straightforward to work with for developers of almost any level.

Final step: Themes

Alright, on to the last post in our welcome-series! If you're curious about creating your own Ghost theme from scratch, find out how that works.

We’re releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet 1 classification has been decreasing by a factor of 2 every 16 months. Compared to 2012, it now takes 44 times less compute to train a neural network to the level of AlexNet 2 (by contrast, Moore’s Law 3 would yield an 11x cost improvement over this period). Our results suggest that for AI tasks with high levels of recent investment, algorithmic progress has yielded more gains than classical hardware efficiency.

Read Paper

Algorithmic improvement is a key factor driving the advance of AI. It’s important to search for measures that shed light on overall algorithmic progress, even though it’s harder than measuring such trends in compute. 4

44x less compute required to get to AlexNet performance 7 years later
201220132014201520162017201820190.050.10.51510Teraflop/s-daysGoogLeNetResnet-18Resnet-34Resnet-50Squeezenet_v1_1DenseNet121MobileNet_v1ShuffleNet_v1_1xShuffleNet_v2_1xMobileNet_v2EfficientNet-b0VGG-11AlexNetResNext_50ShuffleNet_v2_1_5xWide_ResNet_50
Total amount of compute in teraflops/s-days used to train to AlexNet level performance. Lowest compute points at any given time shown in blue, all points measured shown in gray.25678910111213141516

Download charts

Measuring efficiency

Algorithmic efficiency can be defined as reducing the compute needed to train a specific capability. Efficiency is the primary way we measure algorithmic progress on classic computer science problems like sorting. Efficiency gains on traditional problems like sorting are more straightforward to measure than in ML because they have a clearer measure of task difficulty. [1]

However, we can apply the efficiency lens to machine learning by holding performance constant. Efficiency trends can be compared across domains like DNA sequencing17 (10-month doubling), solar energy18 (6-year doubling), and transistor density3 (2-year doubling).

We are standardizing OpenAI’s deep learning framework on PyTorch. In the past, we implemented projects in many frameworks depending on their relative strengths. We’ve now chosen to standardize to make it easier for our team to create and share optimized implementations of our models.

Browse Microscope
$ pip install procgen # install
$ python -m procgen.interactive --env-name starpilot # human
$ python <<EOF # random AI agent
import gym
env = gym.make('procgen:procgen-coinrun-v0')
obs = env.reset()
while True:
    obs, rew, done, info = env.step(env.action_space.sample())
    env.render()
    if done:
        break
EOF

Design principles

We’ve designed all Procgen environments to satisfy the following criteria:

Paper Environment Code Training Code

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benchmarking, and experimenting with AI.