.NET Runtime VS Poetry

These can also be passed as arguments to `dotnet publish` if necessary.

Reference:

- https://learn.microsoft.com/en-us/dotnet/core/deploying/nati...

- https://github.com/dotnet/runtime/blob/main/src/coreclr/nati...

- https://github.com/dotnet/runtime/blob/5b4e770daa190ce69f402... (full list of recognized keys for IlcInstructionSet)

Yes, that is true. I'm not sure about JVM implementation details but the reason the comment says "virtual and interface" calls is to outline the difference. Virtual calls in .NET are sufficiently close[0] to virtual calls in C++. Interface calls, however, are coded differently[1].

Also you are correct - virtual calls are not terribly expensive, but they encroach on ever limited* CPU resources like indirect jump and load predictors and, as noted in parent comments, block inlining, which is highly undesirable for small and frequently called methods, particularly when they are in a loop.

* through great effort of our industry to take back whatever performance wins each generation brings with even more abstractions that fail to improve our productivity

[0] https://github.com/dotnet/coreclr/blob/4895a06c/src/vm/amd64...

[1] https://github.com/dotnet/runtime/blob/main/docs/design/core... (mind you, the text was initially written 18 ago, wow)

If you care about portable SIMD and performance, you may want to save yourself trouble and skip to C# instead, it also has an extensive guide to using it: https://github.com/dotnet/runtime/blob/69110bfdcf5590db1d32c...

CoreLib and many new libraries are using it heavily to match performance of manually intensified C++ code.

DEBUG: packageFiles with updates (repository=local) "config": { "nuget": [ { "deps": [ { "datasource": "nuget", "depType": "nuget", "depName": "Microsoft.Extensions.Hosting", "currentValue": "7.0.0", "updates": [ { "bucket": "non-major", "newVersion": "7.0.1", "newValue": "7.0.1", "releaseTimestamp": "2023-02-14T13:21:52.713Z", "newMajor": 7, "newMinor": 0, "updateType": "patch", "branchName": "renovate/dotnet-monorepo" }, { "bucket": "major", "newVersion": "8.0.0", "newValue": "8.0.0", "releaseTimestamp": "2023-11-14T13:23:17.653Z", "newMajor": 8, "newMinor": 0, "updateType": "major", "branchName": "renovate/major-dotnet-monorepo" } ], "packageName": "Microsoft.Extensions.Hosting", "versioning": "nuget", "warnings": [], "sourceUrl": "https://github.com/dotnet/runtime", "registryUrl": "https://api.nuget.org/v3/index.json", "homepage": "https://dot.net/", "currentVersion": "7.0.0", "isSingleVersion": true, "fixedVersion": "7.0.0" } ], "packageFile": "RenovateDemo.csproj" } ] }

https://github.com/dotnet/runtime/issues/59591

Support zstd Content-Encoding:

We might not be that far away already. There is this issue[1] on Github, where Microsoft and the community discuss some significant changes.

There is still a lot of questions unanswered, but initial tests look promising.

Ref: https://github.com/dotnet/runtime/issues/94620

https://github.com/dotnet/dotnet exists for source build that stitches together SDK, Roslyn, runtime and other dependencies. A lot of them can be built and used individually, which is what contributors usually do. For example, you can clone and build https://github.com/dotnet/runtime and use the produced artifacts to execute .NET assemblies or build .NET binaries.

Yeah, it kind of is. There are quite a few of experiments that are conducted to see if they show promise in the prototype form and then are taken further for proper integration if they do.

Unfortunately, object stack allocation was not one of them even though DOTNET_JitObjectStackAllocation configuration knob exists today, enabling it makes zero impact as it almost never kicks in. By the end of the experiment[0], it was concluded that before investing effort in this kind of feature becomes profitable given how a lot of C# code is written, there are many other lower hanging fruits.

To contrast this, in continuation to green threads experiment, a runtime handled tasks experiment[1] which moves async state machine handling from IL emitted by Roslyn to special-cased methods and then handling purely in runtime code has been a massive success and is now being worked on to be integrated in one of the future version of .NET (hopefully 10?)

[0] https://github.com/dotnet/runtime/issues/11192

[1] https://github.com/dotnet/runtimelab/blob/feature/async2-exp...

Orderby Linq Code Reference

You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.

Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:

Poetry

The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:

Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry?

https://github.com/python-poetry/poetry/issues/6409

Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.

Not sure where I would stand if I fully investigated it tho.

[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...

We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.

Here are the two main packaging issues I run into, specifically when using Poetry:

1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.

2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.

I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.

Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.

[0] https://github.com/python-poetry/poetry/issues/2740

[1] https://github.com/python-poetry/poetry/issues/2184

[2] https://pypi.org/project/pypiserver/

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github