Apache AGE VS Pytorch

What about the AGE extension for Postgres? https://age.apache.org/

Waiting for Postgres to release theirs.

I think they are talking about the AGE extension https://age.apache.org

It's my understanding of the "incubation" period of Apache Software Foundation projects is to determine if they're able to actually execute the ASF process, and a bunch of other "project maturity metrics" (https://community.apache.org/apache-way/apache-project-matur...) of which AGE currently has some self-certification: https://age.apache.org/?l=maturity#

I recognize that's not exactly an answer to the question you asked, but I would be surprised if someone other than a project member knows a more forward-looking one

Postgres has a graph extension: https://age.apache.org. This means you can keep all your data in PG and use both models.

PostgreSQL with graph extension, developed by a team at Apache Software Foundation as Apache AGE. Apache AGE uses Gremlin.

The big thing that graph dbs provide is transitive traversals of join relationships.

The problem with graph dbs is trying to return something that is not a graph. Like a count. Or derived information. And which graph model do you use? There’s more than one. Lots of information is very poorly modeled in graph dbs. Temporal organization, for example.

Ultimately, graphs are a way to use relations. But relations allow you much more flexibility to associate information (subject to the issue of transitive relationship traversal).

Mixed graph-relational is perfectly reasonable. Reasonable start here: [https://age.apache.org/]

their actual landing page is actually better than the Github one. It's a translation layer(s) to allow querying Postgres using openCypher

Depending on how one views "postgres", there are at least two extensions that allegedly do it: https://age.apache.org/ and the AgensGraph from which AGE derives

There's an extension to postgresql (I haven't used it, but I am familiar with node/edge tables in MSSQL) that allows you to do this: https://age.apache.org/

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():