In this post you will:
- Be introduced to the main Deploit web app functionalities
- Learn how to run the Google AI DeepVariant pipeline on the Deploit platform as shown on our latest webinar
- Delve into Deploit’s interactive graphics for job monitoring analytics (there will be gifs!)
This tutorial is an animated, step-by-step guide on how to run your first pipeline on the Deploit bioinformatics analysis and deployment platform. With Deploit, running an analysis is easy as 1-2-3.
- Select a pipeline
- Select input data and parameters, and
- Run analysis 🚀
Deploit takes care of everything else in between, from job scheduling all the way to results. You can, well .. deploy 🙂 your analysis on the Deploit app either by using the
First things first: The Deploit platform on lifebit.ai
You can find a link to try the Deploit platform as soon as you land on our website. You can register for a free account by 👆clicking on the blue button
Complete your registration form and welcome aboard 🚀 ! We provide you with a Lifebit cloud account with pre-loaded credits to start your analyses. You can always switch to your cloud provider later from the ⚙️
on the Deploit platform
After registration, we are redirected to the
Exploring the Deploit platform: Navigation bar icons
You can browse the Deploit app menu from the navigation bar on the left anytime to access the following:
Homepage with an overview of your jobs<code><code><strong>Home </strong>:
Executed projects<code><code><strong>Jobs </strong>:
Pipelines with configured parameters.<code><code><strong>Projects </strong>:
The collection of the pipelines available on Deploit<code><code><strong>Pipelines</strong>:
All the datasets you have uploaded<code><strong>Data </strong>:
Documentation on how to use Deploit at our GitBook.io<code><code><strong>Docs </strong>:
Profile settings (user profile details, linked accounts eg. Cloud, GitHub, Docker, Lifebit API key)<code><code><strong>Settings </strong>:
from the Deploit pipelines catalogue
As mentioned above, Deploit is already populated with community curated, containerized pipelines. You can always access and browse the available Deploit pipelines, by clicking on the pipelines icon in the navigation bar on the left.
Parameters and input data
After selecting the
For now, let’s go with the example parameters and input data that are already available on Deploit. Just 👆click on
After loading the example, you will notice that you can also preview the respective code snippet at the
Cloud configuration and job scheduling
By now you have successfully selected a pipeline and loaded the example data and parameters. Now it’s time for deploying the analysis! After clicking ‘Run’ on the top righthand of your screen, you will be prompted to select your preferred execution platform (eg. AWS Cloud or Azure Cloud) and choose configuration which boils down to how quick you want your job to be finished.
This means that you can go low (cost) and slow(er), or give your analysis an extra boost if you have increased demands for a specific project and/or need your results as soon as possible.
As shown above, once you click ‘Run job’ you will be notified that your job has been successfully scheduled ✅. You are now free to go have a coffee and return to find your analysis results ready!
Job completion, abort mission and troubleshooting
where you can monitor your job status, a real-time update of the analysis cost (charged by your Cloud provider), and you can also have a look at the 🎉 results button, which is initially grey . You will know your results are ready when this has turned green and a checkmark ✅ has appeared. Check it out below:
Having second thoughts about running the analysis 🤔 ? Well, you can always just abort mission by clicking on the far right black icon:
And you know, bad things happen even to the best pipelines out there and jobs sometimes fail.
But, fear not! You can always raise an issue on the pipeline repo on our GitHub and we’ll have a look. Everything is saved on the log file, which facilitates troubleshooting and debugging. You can also go detective mode yourself as well, and access the log file from the
Raising GitHub issues is actually something we encourage a lot as a team, since it helps us keep track of everything and improve the pipeline as we go. As an added bonus, the issues themselves serve like Q&A answers, useful for other users so GitHub has a special place in our heart. Of course, you can also always reach out in real time while on the Deploit platform by accessing the blue icon 💬 on the bottom right corner.
Explore and Download Results
After your job has successfully finished, you can access your results by clicking on the green 🎉button. This time, we tried the
You can view the
Job monitoring analytics with interactive graphics
After job completion, you can access job monitor analytics, ie
An overview of the job monitoring analytics is shown below, which includes information about the
Now you have an overview of the Deploit platform and you are ready to run your first analysis. You can experiment with the available examples that include a comprehensive bundle of example input data, pret-set parameters and curated pipelines. This means that you don’t have to bring anything on the platform (eg. code, data, cloud) on your first time trying it, and you can check how running a pipeline on Deploit 🚀 actually looks and feels like straightaway.
Already have analyzed data (+ code) sitting somewhere from an old project? Wondering how the experience of running the analysis yourself compares to the Deploit way? You can then try with your own data and compare with your previous implementations ( for example ease of configuration with cloud, programmer time Vs machine time in each case).
Something missing from Deploit? Bring on your questions or suggestions for new features you would like to see included in the platform and we are happy to get coding.
As mentioned, we particularly encourage raising GitHub issues or contacting us via Twitter, email or in real-time when you run your pipelines from the conversation menu on the Deploit app, so that we can help.
Happy Deploit-ing! 🚀
We are still only beginning and we have big plans for the future! Don’t miss a thing and let us keep you updated on our upcoming posts by signing up to our Newsletter or by visiting us on Twitter, LinkedIn, and Facebook.
We’re also actively looking for great engineers and bioinformaticians who want to help us shape Lifebit – if this is something you’d be interested in, we’d love to hear from you: email@example.com.