This pull request makes it possible to run your model inside a Docker environment, which makes it easier for other people to run it. We're using an open source tool called Cog to make this process easier.
This also means we can make a web page where other people can try out your model! The models are pre-loaded so very easy switch among them for fast inference, view it here: https://replicate.com/google-research/pix2seq. You can find the docker file under the tab ‘run model with docker’.
Do claim the page so you can own the page, customise the Example gallery as you like, push any future update to the web demo, and we'll feature it on our website and tweet about it too. You can find the 'Claim this model' button on the top of the page. Any member of the google-research organization on GitHub can claim the model ~ When the page is claimed, it will be automatically linked to the arXiv website as well (under “Demos”).
In case you're wondering who I am, I'm from Replicate, where we're trying to make machine learning reproducible. We got frustrated that we couldn't run all the really interesting ML work being done. So, we're going round implementing models we like. 😊
This pull request makes it possible to run your model inside a Docker environment, which makes it easier for other people to run it. We're using an open source tool called Cog to make this process easier.
This also means we can make a web page where other people can try out your model! The models are pre-loaded so very easy switch among them for fast inference, view it here: https://replicate.com/google-research/pix2seq. You can find the docker file under the tab ‘run model with docker’.
Do claim the page so you can own the page, customise the Example gallery as you like, push any future update to the web demo, and we'll feature it on our website and tweet about it too. You can find the 'Claim this model' button on the top of the page. Any member of the google-research organization on GitHub can claim the model ~ When the page is claimed, it will be automatically linked to the arXiv website as well (under “Demos”).
In case you're wondering who I am, I'm from Replicate, where we're trying to make machine learning reproducible. We got frustrated that we couldn't run all the really interesting ML work being done. So, we're going round implementing models we like. 😊