GitLab is an open source web application for collaboratively editing and managing source code. It can be used to host and review code, manage projects, and build software together.
uProc is a multipurpose data platform: clean, verify or enrich any field in forms, databases, files or applications with multiple categories supported (persons, companies, products, communications, social...).uProc Integrations
GitLab + Google SheetsCreate rows on Google Sheets for new GitLab commits Read More...
GitLab + Microsoft TeamsPost every GitLab commit that your team makes to a Microsoft Teams channel Read More...
GitLab + SlackPost every GitLab commit that your team makes to a Slack channel Read More...
Gmail + GitLabCreate issues in GitLab on new emails in Gmail [REQUIRED : Business Gmail Account] Read More...
Gmail is one of the most popular email services today. It is used extensively in many corporate offices across the globe. If you’re using your Gmail account as a point of communication for receiving customer feedback or any technical issues from team members, then this integration is great for you. After you set it up, whenever a new email is received on Gmail, Appy Pie Connect will automatically create a new issue on GitLab from the details so that your team can take it up right away.
Note: To use this integration you must have a Business Gmail account.
It's easy to connect GitLab + uProc without coding knowledge. Start creating your own business flow.
Trigger when a commit is made on the specified project.
Triggers on issue events, e.g. when an issue is opened, updated, or closed.
Triggers when a new job occurred.
Triggers on an open, merge, or close merge request event.
Select a tool to perform verification or enrichment
GitLab is a cloud-based, open source software development platform that enables teams to cplaborate and work from a single location. The software development lifecycle of GitLab includes the fplowing phases. planning, development, testing, shipping, and operations. When operational, it can be used to deploy software to a live production environment quickly. In addition to the main GitLab open source product, the company offers paid products and services including an enterprise edition called GitLab Enterprise Edition (EE. and application monitoring software called GitLab Pipeline. The software is written in Ruby using the Sinatra framework and integrates closely with VMware, Ansible, Docker, Kubernetes, Amazon Web Services, Google Cloud Platform, Microsoft Azure, Red Hat OpenShift, and others. The company is led by co-founder and CEO Sid Sijbrandij, who previously worked at MySQL and Cisco.
“Software that belongs together works better together.”
uProc is a framework for designing and implementing data analysis pipelines. It utilizes the power of Python and takes advantage of the extensive library of scientific computing tops available in Python. It includes graphical user interfaces for interacting with various components in the pipeline and utilizes Pandas for data manipulation. uProc has been developed by researchers at the University of Washington since 2015 and is released under the BSD license. For more information on uProc see http://uproc.github.io/.
GitLab provides a central repository for all code and documentation as well as a continuous integration/continuous deployment (CI/CD. top. To create a project in GitLab you first need to install GitLab on your local machine or your server and then create a new project in GitLab. This project can contain multiple repositories but the same one can be used for different projects. Each repository contains different branches, tags, branches, commits, and files. A branch is a line of development that diverges from another branch or from the tip of a branch. All changes made to the code are committed to a branch so that the changes can be later merged into other branches or into the master branch. Branch names are unique within a repository and are often named using “issue-IDs” or “feature-IDs” so that they can easily be associated to specific changes in issues or features. A tag is a label on a commit object which allows us to refer to the commit in the future even if it changes its name or its history changes due to rebasing. Tags are normally used to mark release points such as versions even though they have been renamed multiple times during their lifetime. A commit contains all changes made during one step of development. A file contains all changes made to a particular file over time. Different types of files can be contained in a repository including text files such as .txt, .md, .rspec, .yml, etc., binary files such as .pdf or .mp4, databases such as MySQL or PostgreSQL, etc. Epics are used to organize workflows so that users can break up their work into tasks called stories which are tracked by different GitHub repositories called epics. Epics can be tagged with labels that allow you to filter epics within your projects according to your needs so that you can focus on certain tasks rather than others in your workflow. Epics can also be assigned to people within your organization so that they can be tracked by teams rather than individuals. Epics can be moved between different stages in your workflow depending on its status at any given time so that you can focus on the ones that need more attention rather than having them all fill up your backlog. Epics can also be merged with other epics in order to provide release notes on an epic’s merge request page so that users can easily see what has changed between releases rather than having to read through all of these changes on GitHub. Projects within GitLab also have milestones that represent key dates during your project’s life cycle such as feature freeze where no new features will be added to the release before its release date as well as cut-off date when new features will not be added after this date either. Milestones are important because they provide users with an indication of how far along their project is in its release cycle so that they know when they should start thinking about marketing strategies for their next release as well as when they should begin testing their product so as to ensure it is ready for its final release date. Users can also set up CI/CD environments for testing new versions of their product through GitLab CI/CD before releasing them publicly through staging environments so that testing is done before users get their hands on their product which helps reduce errors during later stages of production which can lead to costly fixes down the road.
Pipelines are used in uProc for modeling data analyses by describing the steps required for completing an analysis task from beginning to end. Each stage within a pipeline represents a different step in the analysis process such as preprocessing raw data into a standard form prior to being analyzed or modeling data for analysis using statistical software such as R or Python. Stages within pipelines may be reordered based on user preference so that users can choose which steps they want to do first or last based on what they think will take longer or require more work on their part respectively even though uProc does not force you to specify a particular order to your stages like some other pipelines such as RStudio’s Knitr do. Because uProc pipelines are implemented using Python they can be used both inside and outside of IPython notebooks which makes it easy for users to incorporate code from external libraries without having to worry about whether or not they will run outside of an IPython notebook environment because they should run just fine regardless of where they are running from! Pipelines created using uProc are saved as plain text files so that they can be easily shared among team members for cplaboration purposes without having to worry about source contrp management since uProc’s versioning system uses git! Because uProc pipelines use Pandas DataFrames, a DataFrame is created each time a pipeline is executed which means that data analytics outputs are saved every time you run your pipeline which makes it easy for you to compare results between runs so that you can determine which runs gave you better results and why this was the case! Reinitialization lets users define custom initialization commands which can be executed before pipeline execution starts so that users can define functions which perform tasks such as setting environment variables, loading packages into memory, reading configuration files, etc., before executing the rest of the pipeline! Demo mode let’s users play around with their pipeline before actually running it so that they don’t have to worry about breaking their environment if something goes wrong! If something does go wrong with your code while running it you will get an error message indicating what went wrong so that you know what you need to fix instead of guessing where in your code something might have gone wrong! Because uProc is implemented using Python it can utilize all of Python’s built-in libraries such as NumPy for mathematical operations, Python’s built-in plotting library Plotly for making plots, etc., across multiple platforms including Windows 7+, OS X 10.9+, and Linux distributions! uProc supports both Python 2 and 3 out of the box so that users don’t have to worry about porting their code from Python 2 over to Python 3! One drawback of uProc however is that while there are plenty of guides out there explaining how to set up uProc pipelines there isn’t much documentation explaining how to write custom functions and classes using Python which could make it difficult for users who are unfamiliar with Python or who don’t have much experience with writing their own functions or classes in Python! uProc also supports parallel processing out of the box using IPython clusters which lets users make use of multiple cores/processors within their computer when performing calculations using NumPy’s multithreaded functions! This makes it ideal for data analysts who want to maximize performance when doing computationally intensive numerical calculations!
The process to integrate GitLab and uProc may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.