orchest VS cli

You can check out our OSS https://github.com/orchest/orchest

The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/

First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!

Disclaimer: we're building something very similar and I'm curious about a couple of things.

One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.

Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.

How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.

Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!

For those who are curious, I'm one of the authors of https://github.com/orchest/orchest

Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).

If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest

You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation

We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest

For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.

Disclaimer: author of Orchest https://github.com/orchest/orchest

You need an access token from ipinfo to access their API. Go to ipinfo.io and sign up for an account. After signing up, you'll get an access token. Keep it handy as we'll need it in the next step.

stories: - name: get-ip-info steps: - name: get-ip url: https://api.ipify.org/?format=json http_method: GET - name: get-location url: https://ipinfo.io/${{steps.get-ip.response.body.ip}}/geo http_method: GET expectations: - field: Body operation: Contains value: "Australia" schedule: initial_delay: 10 interval: 60 alerts: - url: https://my.site/notify

3- IPinfo

Mullvad VPN location set to Frankfurt germany

Using the us east test server:

Proxy Score: 10/100 - Very likely not a Proxy

VPN Score: 0/55 - Very likely not a VPN

Using german test server:

Proxy Score: 10/100 - Very likely not a Proxy

VPN Score: 30/55 - Very likely a VPN

The thing that changed between both tests was the flow latencies vs ping latencies check. Clicking on more info it said: "flow variance too large in relation to avg flow: 0.6937484181219945"

I guess it works kinda, but not very consistent.

But e.g. https://ipinfo.io/ reveals immediately that this IP is a VPN IP address.

If it's a work computer it may be connecting to it's own VPN automatically. If you go to a site like https://ipinfo.io/ do you recognize the external IP address, region, and org?

Then just check IP of the server when ping is high for you, put IP in https://ipinfo.io/, and block range for all other servers in same datacenter.

3. Check the IP address information by ipinfo.io, and confirm the IP location information is right with okeyproxy's we pasted into ProxyHelper.

IPinfo.io | Various Roles | Remote (Anywhere) | Fulltime / Contract | https://ipinfo.io

IPinfo is a leading provider of IP address data. Our API handles almost 100 billion requests a month, and we also license our data for use in many products and services you might have used. We started as a side project back in 2013, offering a free geolocation API, and we've since bootstrapped ourselves to a profitable business with a global team of over 20, and grown our data offerings to include geolocation, IP to company, carrier detection, and VPN detection. Our customers include T-Mobile, Nike, DataDog, DemandBase, Clearbit and many more.

We're looking for exceptional and ambitious people to join our team. The main role we're hiring for currently is a Product Manager - ideally with SaaS/DaaS experience, and a technical understanding of our products / services. Apply here: https://ipinfo.bamboohr.com/careers/45?source=aWQ9Mjg%3D

We're always on the look out to strong software or data engineers with an internet measuremenet / analysis background to join our team too. Any questions about the roles or IPinfo feel free to reach out to me directly at [email protected]