# Publishing options

The Ghost editor post settings menu has everything you need to fully optimise and distribute your content effectively.

Access the post settings menu by clicking the settings icon in the top right hand corner of the editor and discover everything you need to get your content ready for publishing. This is where you can edit things like tags, post URL, publish date and custom meta data.

## Feature images, URL & excerpts

Insert your post feature image from the very top of the post settings menu. Consider resizing or optimising your image first to ensure it's an appropriate size. Below this, you can set your post URL, publish date and add a custom excerpt.

## Tags & authors

You can easily add multiple tags and authors to any post to filter and organise the relationships between your content in Ghost.

## Structured data & SEO

There's no need to hard code your meta data. In fact, Ghost will generate default meta data automatically using the content in your post.

Alternatively, you can override this by adding a custom meta title and description, as well as unique information for social media sharing cards on Facebook and Twitter.

It's also possible to set custom canonicals, which is useful for guest posts or curated lists of external links.

Ghost will automatically implement structured data for your publication using JSON-LD to further optimise your content.

{
"@context": "https://schema.org",
"@type": "Article",
"publisher": {
"@type": "Organization",
"name": "Publishing options",
"logo": "https://static.ghost.org/ghost-logo.svg"
},
"author": {
"@type": "Person",
"name": "Ghost",
"url": "http://demo.ghost.io/author/ghost/",
"sameAs": []
},
"url": "http://demo.ghost.io/publishing-options",
"datePublished": "2018-08-08T11:44:00.000Z",
"dateModified": "2018-08-09T12:06:21.000Z",
"keywords": "Getting Started",
"description": "The Ghost editor has everything you need to fully optimise your content. This is where you can add tags and authors, feature a post, or turn a post into a page.",
}
}


You can test that the structured data schema on your site is working as it should using Google’s structured data tool.

## Code injection

This tool allows you to inject code on a per post or page basis, or across your entire site. This means you can modify CSS, add unique tracking codes, or add other scripts to the head or foot of your publication without making edits to your theme files.

To add code site-wide, use the code injection tool in the main admin menu. This is useful for adding a Google Analytics tracking code, or to start tracking with any other analytics tool.

To add code to a post or page, use the code injection tool within the post settings menu. This is useful if you want to add art direction, scripts or styles that are only applicable to one post or page.

Now you understand how to create and optimise content, let's explore some admin settings so you can invite your team and start collaborating.

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.

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.

##### 44x less compute required to get to AlexNet performance 7 years later
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

## 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.

$pip install procgen # install$ 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:

• High Diversity: Environment generation logic is given maximal freedom, subject to basic design constraints. The diversity in the resulting level distributions presents agents with meaningful generalization challenges.

• Fast Evaluation: Environment difficulty is calibrated such that baseline agents make significant progress after training for 200M timesteps. Moreover, the environments are optimized to perform thousands of steps per second on a single CPU core, enabling a fast experimental pipeline.

• Tunable Difficulty: All environments support two well-calibrated difficulty settings: easy and hard. While we report results using the hard difficulty setting, we make the easy difficulty setting available for those with limited access to compute power. Easy environments require approximately an eighth of the resources to train.

• Emphasis on Visual Recognition and Motor Control: In keeping with precedent, environments mimic the style of many Atari and Gym Retro games. Performing well primarily depends on identifying key assets in the observation space and enacting appropriate low level motor responses.

OpenAI builds free software for training,
benchmarking, and experimenting with AI.