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[#]: subject: "A Project For An Open Source 3D-Printed VR Headgear From Europe"
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[#]: via: "https://www.opensourceforu.com/2022/09/a-project-for-an-open-source-3d-printed-vr-headgear-from-europe/"
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[#]: author: "Laveesh Kocher https://www.opensourceforu.com/author/laveesh-kocher/"
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[#]: collector: "lkxed"
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[#]: translator: "zjsoftceo"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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A Project For An Open Source 3D-Printed VR Headgear From Europe
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======
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*A 6GHz WiFi 6E wireless open source virtual reality headset has been created by three European businesses.*
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On the Somnium VR ONE headgear, the Czech 3D printing expert Prusa Research is collaborating with the UK’s Somnium Space and simulator creator Vrgineers. This is intended to change the constrained supply in the virtual reality market by being as open as feasible and can be connected or standalone.
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With the Android 11 operating system being an open source operating system with the source code publicly available, it will be sold under an unrestricted commercial licence. The Qualcomm Snapdragon XR2 CPU will be its central processing unit, and it will support microSD memory cards and have 8 GB of LPDDR5 RAM and 512 GB of UFS flash storage.
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Instead of the currently congested 5GHz and 2.4Ghz WiFi frequencies, it will employ the new WiFi 6e high bandwidth wireless protocol in the 6GHz frequency for higher bandwidth and low latency connections. It will include two 3.2-inch 2880RGB*2880 Fast LCD screens with a 120-degree horizontal field of vision (FoV) and a 100-degree vertical FoV.
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It will have two USB-C 10 Gbit/s links for external gadgets and a USB-C USB2.0 battery pack (USB 3.2 Gen2). Somnium Space will sell the electronics and unique lenses online enabling users to 3D print their own headsets in collaboration with Vrgineers, a VR training company in Prague. There will also be fully constructed headsets available.
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The business was established in Prague in 2012, and it already employs over 700 people. The open source Prusa i3 design is the most widely used 3D printer in the world, with direct shipments from Prague to over 160 countries of over 10,000 Original Prusa printers each month.
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Synthetic Training Environments (STE) are provided by Vrgineers, a simulator developer in the Czech Republic, to business and governmental clients. It has created the professional 8K headgear known as XTAL, which is used by NASA, Airbus Defense & Space, and BAE Systems, and it currently employs an international team of 50 specialists in Prague, Brno, and Las Vegas.
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London-based A blockchain-based open, social, and permanent virtual reality platform is called Somnium Space. Users can own, trade, and exchange digital goods without obtaining authorization thanks to its distinct decentralised NFT-based economy. It has already collaborated with Lynx on the Lynx R-1 with Qualcomm and Ultraleap, a standalone augmented reality (AR) headset design.
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/09/a-project-for-an-open-source-3d-printed-vr-headgear-from-europe/
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作者：[Laveesh Kocher][a]
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选题：[lkxed][b]
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译者：[zjoftceo](https://github.com/zjsoftceo)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/laveesh-kocher/
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[b]: https://github.com/lkxed
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@ -2,7 +2,7 @@
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[#]: via: "https://opensource.com/article/21/8/first-programming-language"
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[#]: author: "Jen Wike Huger https://opensource.com/users/jen-wike"
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[#]: collector: "lujun9972"
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[#]: translator: " "
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[#]: translator: "gpchn"
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[#]: reviewer: " "
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||||
[#]: publisher: " "
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||||
[#]: url: " "
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@ -1,221 +0,0 @@
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[#]: subject: "Turn your Python script into a command-line application"
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[#]: via: "https://opensource.com/article/22/7/bootstrap-python-command-line-application"
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[#]: author: "Mark Meyer https://opensource.com/users/ofosos"
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[#]: collector: "lkxed"
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[#]: translator: "MjSeven"
|
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[#]: reviewer: " "
|
||||
[#]: publisher: " "
|
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[#]: url: " "
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Turn your Python script into a command-line application
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======
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With scaffold and click in Python, you can level up even a simple utility into a full-fledged command-line interface tool.
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![Python programming language logo and Tux the Penguin logo for Linux][1]
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Image by: Opensource.com
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I've written, used, and seen a lot of loose scripts in my career. They start with someone that needs to semi-automate some task. After a while, they grow. They can change hands many times in their lifetime. I've often wished for a more command-line *tool-like* feeling in those scripts. But how hard is it really to bump the quality level from a one-off script to a proper tool? It turns out it's not that hard in Python.
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### Scaffolding
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In this article, I start with a little Python snippet. I'll drop it into a `scaffold` module, and extend it with `click` to accept command-line arguments.
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```
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#!/usr/bin/python
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from glob import glob
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from os.path import join, basename
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from shutil import move
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from datetime import datetime
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from os import link, unlink
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LATEST = 'latest.txt'
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ARCHIVE = '/Users/mark/archive'
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INCOMING = '/Users/mark/incoming'
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TPATTERN = '%Y-%m-%d'
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def transmogrify_filename(fname):
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bname = basename(fname)
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ts = datetime.now().strftime(TPATTERN)
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return '-'.join([ts, bname])
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def set_current_latest(file):
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latest = join(ARCHIVE, LATEST)
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try:
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unlink(latest)
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except:
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pass
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link(file, latest)
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def rotate_file(source):
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target = join(ARCHIVE, transmogrify_filename(source))
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move(source, target)
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set_current_latest(target)
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def rotoscope():
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file_no = 0
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folder = join(INCOMING, '*.txt')
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print(f'Looking in {INCOMING}')
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for file in glob(folder):
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rotate_file(file)
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print(f'Rotated: {file}')
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file_no = file_no + 1
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print(f'Total files rotated: {file_no}')
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if __name__ == '__main__':
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print('This is rotoscope 0.4.1. Bleep, bloop.')
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rotoscope()
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```
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All non-inline code samples in this article refer to a specific version of the code you can find at [https://codeberg.org/ofosos/rotoscope][2]. Every commit in that repo describes some meaningful step in the course of this how-to article.
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This snippet does a few things:
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* Check whether there are any text files in the path specified in `INCOMING`
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* If it exists, it creates a new filename with the current timestamp and moves the file to `ARCHIVE`
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* Delete the current `ARCHIVE/latest.txt` link and create a new one pointing to the file just added
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As an example, this is pretty small, but it gives you an idea of the process.
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### Create an application with pyscaffold
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First, you need to install the `scaffold`, `click`, and [tox Python modules][3].
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```
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$ python3 -m pip install scaffold click tox
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```
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After installing `scaffold`, change to the directory where the example `rotoscope` project resides, and then execute the following command:
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|
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```
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$ putup rotoscope -p rotoscope \
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--force --no-skeleton -n rotoscope \
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-d 'Move some files around.' -l GLWT \
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-u http://codeberg.org/ofosos/rotoscope \
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--save-config --pre-commit --markdown
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```
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|
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Pyscaffold overwrote my `README.md`, so restore it from Git:
|
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|
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```
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$ git checkout README.md
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```
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Pyscaffold set up a complete sample project in the docs hierarchy, which I won't cover here but feel free to explore it later. Besides that, Pyscaffold can also provide you with continuous integration (CI) templates in your project.
|
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|
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* packaging: Your project is now PyPi enabled, so you can upload it to a repo and install it from there.
|
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* documentation: Your project now has a complete docs folder hierarchy, based on Sphinx and including a readthedocs.org builder.
|
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* testing: Your project can now be used with the tox test runner, and the tests folder contains all necessary boilerplate to run pytest-based tests.
|
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* dependency management: Both the packaging and test infrastructure need a way to manage dependencies. The `setup.cfg` file solves this and includes dependencies.
|
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* pre-commit hook: This includes the Python source formatter "black" and the "flake8" Python style checker.
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Take a look into the tests folder and run the `tox` command in the project directory. It immediately outputs an error. The packaging infrastructure cannot find your package.
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|
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Now create a Git tag (for instance, `v0.2` ) that the tool recognizes as an installable version. Before committing the changes, take a pass through the auto-generated `setup.cfg` and edit it to suit your use case. For this example, you might adapt the `LICENSE` and project descriptions. Add those changes to Git's staging area, I have to commit them with the pre-commit hook disabled. Otherwise, I'd run into an error because flake8, Python style checker, complains about lousy style.
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```
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$ PRE_COMMIT_ALLOW_NO_CONFIG=1 git commit
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```
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It would also be nice to have an entry point into this script that users can call from the command line. Right now, you can only run it by finding the `.py` file and executing it manually. Fortunately, Python's packaging infrastructure has a nice "canned" way to make this an easy configuration change. Add the following to the `options.entry_points` section of your `setup.cfg` :
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|
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```
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console_scripts =
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roto = rotoscope.rotoscope:rotoscope
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```
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This change creates a shell command called `roto`, which you can use to call the rotoscope script. Once you install rotoscope with `pip`, you can use the `roto` command.
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That's that. You have all the packaging, testing, and documentation setup for free from Pyscaffold. You also got a pre-commit hook to keep you (mostly) honest.
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### CLI tooling
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Right now, there are values hardcoded into the script that would be more convenient as command [arguments][4]. The `INCOMING` constant, for instance, would be better as a command-line parameter.
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|
||||
First, import the [click][5] library. Annotate the `rotoscope()` method with the command annotation provided by Click, and add an argument that Click passes to the `rotoscope` function. Click provides a set of validators, so add a path validator to the argument. Click also conveniently uses the function's here-string as part of the command-line documentation. So you end up with the following method signature:
|
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|
||||
```
|
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@click.command()
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@click.argument('incoming', type=click.Path(exists=True))
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def rotoscope(incoming):
|
||||
"""
|
||||
Rotoscope 0.4 - Bleep, blooop.
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Simple sample that move files.
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"""
|
||||
```
|
||||
|
||||
The main section calls `rotoscope()`, which is now a Click command. It doesn't need to pass any parameters.
|
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|
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Options can get filled in automatically by [environment variables][6], too. For instance, change the `ARCHIVE` constant to an option:
|
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|
||||
```
|
||||
@click.option('archive', '--archive', default='/Users/mark/archive', envvar='ROTO_ARCHIVE', type=click.Path())
|
||||
```
|
||||
|
||||
The same path validator applies again. This time, let Click fill in the environment variable, defaulting to the old constant's value if nothing's provided by the environment.
|
||||
|
||||
Click can do many more things. It has colored console output, prompts, and subcommands that allow you to build complex CLI tools. Browsing through the Click documentation reveals more of its power.
|
||||
|
||||
Now add some tests to the mix.
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|
||||
### Testing
|
||||
|
||||
Click has some advice on [running end-to-end tests][7] using the CLI runner. You can use this to implement a complete test (in the [sample project][8], the tests are in the `tests` folder.)
|
||||
|
||||
The test sits in a method of a testing class. Most of the conventions follow what I'd use in any other Python project very closely, but there are a few specifics because rotoscope uses `click`. In the `test` method, I create a `CliRunner`. The test uses this to run the command in an isolated file system. Then the test creates `incoming` and `archive` directories and a dummy `incoming/test.txt` file within the isolated file system. Then it invokes the CliRunner just like you'd invoke a command-line application. After the run completes, the test examines the isolated filesystem and verifies that `incoming` is empty, and that `archive` contains two files (the latest link and the archived file.)
|
||||
|
||||
```
|
||||
from os import listdir, mkdir
|
||||
from click.testing import CliRunner
|
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from rotoscope.rotoscope import rotoscope
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||||
|
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class TestRotoscope:
|
||||
def test_roto_good(self, tmp_path):
|
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runner = CliRunner()
|
||||
|
||||
with runner.isolated_filesystem(temp_dir=tmp_path) as td:
|
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mkdir("incoming")
|
||||
mkdir("archive")
|
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with open("incoming/test.txt", "w") as f:
|
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f.write("hello")
|
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|
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result = runner.invoke(rotoscope, ["incoming", "--archive", "archive"])
|
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assert result.exit_code == 0
|
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|
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print(td)
|
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incoming_f = listdir("incoming")
|
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archive_f = listdir("archive")
|
||||
assert len(incoming_f) == 0
|
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assert len(archive_f) == 2
|
||||
```
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|
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To execute these tests on my console, run `tox` in the project's root directory.
|
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|
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During implementing the tests, I found a bug in my code. When I did the Click conversion, rotoscope just unlinked the latest file, whether it was present or not. The tests started with a fresh file system (not my home folder) and promptly failed. I can prevent this kind of bug by running in a nicely isolated and automated test environment. That'll avoid a lot of "it works on my machine" problems.
|
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|
||||
### Scaffolding and modules
|
||||
|
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This completes our tour of advanced things you can do with `scaffold` and `click`. There are many possibilities to level up a casual Python script, and make even your simple utilities into full-fledged CLI tools.
|
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|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/22/7/bootstrap-python-command-line-application
|
||||
|
||||
作者：[Mark Meyer][a]
|
||||
选题：[lkxed][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/ofosos
|
||||
[b]: https://github.com/lkxed
|
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[1]: https://opensource.com/sites/default/files/lead-images/python_linux_tux_penguin_programming.png
|
||||
[2]: https://codeberg.org/ofosos/rotoscope
|
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[3]: https://opensource.com/article/19/5/python-tox
|
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[4]: https://opensource.com/article/21/8/linux-terminal#argument
|
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[5]: https://click.palletsprojects.com
|
||||
[6]: https://opensource.com/article/19/8/what-are-environment-variables
|
||||
[7]: https://click.palletsprojects.com/en/8.1.x/testing
|
||||
[8]: https://codeberg.org/ofosos/rotoscope/commit/dfa60c1bfcb1ac720ad168e5e98f02bac1fde17d
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@ -1,140 +0,0 @@
|
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[#]: subject: "Platforms that Help Deploy AI and ML Applications on the Cloud"
|
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[#]: via: "https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/"
|
||||
[#]: author: "Dr Kumar Gaurav https://www.opensourceforu.com/author/dr-gaurav-kumar/"
|
||||
[#]: collector: "lkxed"
|
||||
[#]: translator: " "
|
||||
[#]: reviewer: " "
|
||||
[#]: publisher: " "
|
||||
[#]: url: " "
|
||||
|
||||
Platforms that Help Deploy AI and ML Applications on the Cloud
|
||||
======
|
||||
*Artificial intelligence and machine learning are impacting nearly every industry today. This article underlines the various ways in which these are being used in our everyday lives and how some open source cloud platforms are enabling their deployment.*
|
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|
||||
The goal of artificial intelligence (AI) is to construct machines and automated systems that are able to mimic human cognition. On a global scale, AI is transforming societies, politics, and economies in a variety of ways. Examples of the applications of AI include Google Help, Siri, Alexa, and self-driving cars like Tesla.
|
||||
|
||||
Today, AI is being used to solve difficult problems in an effective manner in a wide range of industries. It is being used in the healthcare industry to make more accurate and faster diagnoses than humans. Doctors can use AI to diagnose a disease, and get an alert when a patient’s condition is deteriorating.
|
||||
|
||||
Data security is critical for every business, and the number of cyberattacks is continually increasing. Using artificial intelligence, the security of data can be improved. An example of this is the integration of intelligent bots to identify software bugs and cyberattacks.
|
||||
|
||||
Twitter, WhatsApp, Facebook and Snapchat are just a few of the social media platforms that store and manage billions of profiles by using AI algorithms. AI can arrange and sift through massive amounts of data to find the latest trends, hashtags, and needs of various people.
|
||||
|
||||
![Figure 1: Key applications of machine learning][1]
|
||||
|
||||
The tourism industry is becoming increasingly reliant on AI, as the latter can help with a variety of travel-related tasks including booking hotels, flights, and the best routes for consumers. For better and faster customer service, chatbots driven by artificial intelligence are being used in the travel industry.
|
||||
|
||||
Table 1: Tools and frameworks for machine learning
|
||||
|
||||
| Tool/Platform | URL |
|
||||
| :- | :- |
|
||||
| Streamlit | https://github.com/streamlit/streamlit |
|
||||
| TensorFlow | https://www.tensorflow.org/ |
|
||||
| PyTorch | https://pytorch.org/ |
|
||||
| scikit-learn | https://scikit-learn.org/ |
|
||||
| Apache Spark | https://spark.apache.org/ |
|
||||
| Torch | http://torch.ch/ |
|
||||
| Hugging Face | https://huggingface.co/ |
|
||||
| Keras | https://keras.io/ |
|
||||
| TensorFlowJS | https://www.tensorflow.org/js |
|
||||
| KNIME | https://www.knime.com/ |
|
||||
| Apache Mahout | https://mahout.apache.org/ |
|
||||
| Accord | http://accord-framework.net/ |
|
||||
| Shogun | http://shogun-toolbox.org/ |
|
||||
| RapidMiner | https://rapidminer.com/ |
|
||||
| Blocks | https://github.com/mila-iqia/blocks |
|
||||
| TuriCreate | https://github.com/apple/turicreate |
|
||||
| Dopamine | https://github.com/google/dopamine |
|
||||
| FlairNLP | https://github.com/flairNLP/flair |
|
||||
|
||||
### Machine learning in different domains
|
||||
|
||||
All techniques and tools that let software applications and gadgets respond and develop on their own are referred to as machine learning (ML). AI can learn without really being explicitly programmed to perform the required action, thanks to machine learning techniques. Rather than relying on predefined computer instructions, the ML algorithm learns a pattern from sample inputs, and then anticipates and executes tasks completely based on the learned pattern. If rigorous algorithms aren’t an option, machine learning can be a life-saver. It will pick up the new procedure by analysing prior ones and then putting it into action. ML has cleared the way for technical advancements and technologies that were previously unimaginable in a variety of industries. It is used in a variety of cutting-edge technologies today — from predictive algorithms to Internet TV live streaming.
|
||||
|
||||
A notable ML and AI technique is image recognition, which is a method for categorising and detecting a feature or an item in a digital image. Classification and face recognition are done using this method.
|
||||
|
||||
![Figure 2: Streamlit cloud for machine learning][2]
|
||||
|
||||
The use of machine learning for recommender systems is among its most widely used and well-known applications. In today’s e-commerce world, product recommendation is a prominent tool that utilises powerful machine learning techniques. Websites use AI and ML to keep track of past purchases, search trends, and shopping cart history, and then generate product recommendations based on that data.
|
||||
|
||||
There is a lot of interest in employing machine learning algorithms in the healthcare industry. Emergency room wait times can be predicted across multiple hospital departments by using an ML algorithm. Details of staff shifts, patient data, and recordings of department discussions and emergency room layouts are all used to help create the algorithm. Machine learning algorithms can be used for detecting a disease, planning treatments, and prognostication.
|
||||
|
||||
**Key features of the cloud platforms used for machine learning**:
|
||||
|
||||
* Algorithms or features extraction
|
||||
* Association rule mining
|
||||
* Big Data based predictive analytics
|
||||
* Classification, regression and clustering
|
||||
* Data loading and transformation
|
||||
* Data preparation, data preprocessing and visualisation
|
||||
* Dimensionality reduction
|
||||
* Distributed linear algebra
|
||||
* Hypothesis tests and kernel methods
|
||||
* Processing of image, audio, signal and vision data sets
|
||||
* Model selection and optimisation module
|
||||
* Preprocessing and dataflow programming
|
||||
* Recommender systems
|
||||
* Support for text mining and image mining through plugins
|
||||
* Visualisation and plotting
|
||||
|
||||
### Cloud based deployment of AI and ML applications
|
||||
|
||||
The applications of AI and ML can be deployed on cloud platforms. A number of cloud service providers nowadays enable programmers to build models for effective decision-making in their domain.
|
||||
|
||||
These cloud based platforms are integrated with pre-trained machine learning and deep learning models on which the applications can be deployed without any coding or with minimum scripting.
|
||||
|
||||
![Figure 3: Categories of ML deployments in Streamlit][3]
|
||||
|
||||
**Streamlit:** Streamlit gives data scientists and ML experts access to assorted machine learning models. It is open source and compatible with cloud deployments. The ML models can be made ready to be used with data sets in a few moments.
|
||||
|
||||
Streamlit provides a range of machine learning models and source code in multiple categories including natural language processing, geography, education, computer vision, etc.
|
||||
|
||||
Streamlit provides a range of machine learning models and source code in multiple categories including natural language processing, geography, education, computer vision, etc.
|
||||
|
||||
![Figure 4: Hugging Face for machine learning][4]
|
||||
|
||||
**Hugging Face:** This is another platform with pre-trained models and architectures for ML and AI in a range of categories. Many corporate giants are using this platform including Facebook AI, Microsoft, Google AI, Amazon Web Services, and Grammarly.
|
||||
|
||||
A number of pre-trained and deployment-ready models are available in Hugging Face for different applications including natural language processing and computer vision.
|
||||
|
||||
The following tasks can be carried out by using the ML models in Hugging Face:
|
||||
|
||||
* Audio-to-audio processing
|
||||
* Automatic speech recognition
|
||||
* Computer vision
|
||||
* Fill-mask
|
||||
* Image classification
|
||||
* Image segmentation
|
||||
* Object detection
|
||||
* Answering of questions
|
||||
* Sentence similarity
|
||||
* Summarisation
|
||||
* Text classification
|
||||
* Text generation
|
||||
* Text-to-speech translation
|
||||
* Token classification
|
||||
* Translation classification
|
||||
|
||||
The problem solvers available in Hugging Face are optimised and effective, helping models to be deployed rapidly (Figure 5).
|
||||
|
||||
![Figure 5: Problem solvers and models in Hugging Face][5]
|
||||
|
||||
These cloud based platforms are useful for researchers, practitioners and data scientists in multiple domains, and simplify the development of real-world applications that perform well.
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/
|
||||
|
||||
作者：[Dr Kumar Gaurav][a]
|
||||
选题：[lkxed][b]
|
||||
译者：[译者ID](https://github.com/译者ID)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.opensourceforu.com/author/dr-gaurav-kumar/
|
||||
[b]: https://github.com/lkxed
|
||||
[1]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-1-Key-applications-of-machine-learning.jpg
|
||||
[2]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-2-Streamlit-cloud-for-machine-learning.png
|
||||
[3]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-3-Categories-of-ML-deployments-in-Streamlit.png
|
||||
[4]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-4-Hugging-Face-for-machine-learning.png
|
||||
[5]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-5-Problem-solvers-and-models-in-Hugging-Face.png
|
||||
@ -0,0 +1,37 @@
|
||||
[#]: subject: "A Project For An Open Source 3D-Printed VR Headgear From Europe"
|
||||
[#]: via: "https://www.opensourceforu.com/2022/09/a-project-for-an-open-source-3d-printed-vr-headgear-from-europe/"
|
||||
[#]: author: "Laveesh Kocher https://www.opensourceforu.com/author/laveesh-kocher/"
|
||||
[#]: collector: "lkxed"
|
||||
[#]: translator: "zjsoftceo"
|
||||
[#]: reviewer: " "
|
||||
[#]: publisher: " "
|
||||
[#]: url: " "
|
||||
|
||||
来自欧洲的一个开源3D打印VR头盔项目
|
||||
======
|
||||
*一个6GHz WiFi 6E无线开源虚拟现实耳机已由三家欧洲企业创建。*
|
||||
|
||||
在索姆尼姆VR ONE头盔上，捷克3D打印专家普鲁萨研究公司正在与英国的索姆空间和模拟器创造者Vrgineers合作。这旨在通过尽可能开放来改变虚拟现实市场中受限的供应，并且可以连接或者独立。
|
||||
由于Android 11操作系统是一个开源的操作系统，其源代码是公开的，因此它将在不受限制的商业许可下可以出售。高通骁龙XR2 CPU将成为其中央处理单元，它将支持微型SD存储卡，并具有8GB的LPDDR5 RAM和512GB的UFS闪存。
|
||||
|
||||
它将采用新的WiFi 6E高带宽无线协议，而不是目前拥挤的5GHz和2.4GHz WiFi频率，以实现更高的带宽和低延迟连接。它将包括两个3.2英寸2880RGB * 2880LCD屏幕，具有120度水平视野（FoV）和100度垂直FoV。
|
||||
|
||||
它将具有两个用于外部小工具的USB-C 10 Gbit/s 链路和一个USB-C USB2.0 电池组（USB3.2 Gen2）。Somnium Space将在线销售电子产品和独特镜头，使用户能够与布拉格的VRgineers合作，3D打印自己的头显,还将提供完全构造耳机。
|
||||
|
||||
该企业于2012年在布拉格成立，已经拥有700多名员工。开源的Prusa i3设计是世界上使用最广泛的开源3D打印机，每个月从布拉格直接运送到160多个国家，超过10，000台原始的Prusa 3D打印机。
|
||||
合成训练环境（STE）是由捷克共和国的模拟器开发商Vrgineers向企业和政府客户提供。它创造了被称为XTAL的专业8K头盔，该头盔被NASA，空中客车防务与航天和BAE系统公司使用，目前在布拉格，布尔诺和拉斯维加斯拥有一支由50名专家组成的国际团队。
|
||||
|
||||
基于伦敦的一个基于区块链的开放、社交和永久虚拟现实平台被称为Somnium Space。由于其独特的分散式NFT经济，用户可以拥有，交易和交换数字商品而无需获得授权。它已经与Lynx合作开发Lynx R-1，高通公司和Ultraleap，一个独立的增强现实（AR）耳机设计。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
via: https://www.opensourceforu.com/2022/09/a-project-for-an-open-source-3d-printed-vr-headgear-from-europe/
|
||||
|
||||
作者：[Laveesh Kocher][a]
|
||||
选题：[lkxed][b]
|
||||
译者：[zjoftceo](https://github.com/zjsoftceo)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.opensourceforu.com/author/laveesh-kocher/
|
||||
[b]: https://github.com/lkxed
|
||||
@ -0,0 +1,225 @@
|
||||
[#]: subject: "Turn your Python script into a command-line application"
|
||||
[#]: via: "https://opensource.com/article/22/7/bootstrap-python-command-line-application"
|
||||
[#]: author: "Mark Meyer https://opensource.com/users/ofosos"
|
||||
[#]: collector: "lkxed"
|
||||
[#]: translator: "MjSeven"
|
||||
[#]: reviewer: " "
|
||||
[#]: publisher: " "
|
||||
[#]: url: " "
|
||||
|
||||
将你的 Python 脚本转换为命令行程序
|
||||
======
|
||||
使用 Python 中的 scaffold 和 click 库，你可以将一个简单的实用程序升级为一个成熟的命令行界面工具。
|
||||
|
||||
![Python 吉祥物和 Linux 的吉祥物企鹅][1]
|
||||
|
||||
Image by: Opensource.com
|
||||
|
||||
在我的职业生涯中，我写过、用过和看到过很多松散的脚本。一些人需要半自动化的任务，于是它们诞生了。一段时间后，它们变得越来越大。它们在一生中可能转手很多次。我常常希望这些脚本提供更多的命令行**类似工具**的感觉。但是，从一次性脚本到合适的工具，真正提高质量水平有多难呢？事实证明这在 Python 中并不难。
|
||||
|
||||
### Scaffolding
|
||||
|
||||
在本文中，我将从一小段 Python 代码开始。我将把它应用到 `scaffold` 模块中，并使用 `click` 库扩展它以接受命令行参数。
|
||||
|
||||
```
|
||||
#!/usr/bin/python
|
||||
|
||||
from glob import glob
|
||||
from os.path import join, basename
|
||||
from shutil import move
|
||||
from datetime import datetime
|
||||
from os import link, unlink
|
||||
|
||||
LATEST = 'latest.txt'
|
||||
ARCHIVE = '/Users/mark/archive'
|
||||
INCOMING = '/Users/mark/incoming'
|
||||
TPATTERN = '%Y-%m-%d'
|
||||
|
||||
def transmogrify_filename(fname):
|
||||
bname = basename(fname)
|
||||
ts = datetime.now().strftime(TPATTERN)
|
||||
return '-'.join([ts, bname])
|
||||
|
||||
def set_current_latest(file):
|
||||
latest = join(ARCHIVE, LATEST)
|
||||
try:
|
||||
unlink(latest)
|
||||
except:
|
||||
pass
|
||||
link(file, latest)
|
||||
|
||||
def rotate_file(source):
|
||||
target = join(ARCHIVE, transmogrify_filename(source))
|
||||
move(source, target)
|
||||
set_current_latest(target)
|
||||
|
||||
def rotoscope():
|
||||
file_no = 0
|
||||
folder = join(INCOMING, '*.txt')
|
||||
print(f'Looking in {INCOMING}')
|
||||
for file in glob(folder):
|
||||
rotate_file(file)
|
||||
print(f'Rotated: {file}')
|
||||
file_no = file_no + 1
|
||||
print(f'Total files rotated: {file_no}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
print('This is rotoscope 0.4.1. Bleep, bloop.')
|
||||
rotoscope()
|
||||
```
|
||||
|
||||
本文的所有非内联代码示例，你都可以在 [https://codeberg.org/ofosos/rotoscope][2] 中找到特定版本的代码。该仓库中的每个提交都描述了本文操作过程中一些有意义的步骤。
|
||||
|
||||
这个片段做了几件事：
|
||||
|
||||
* 检查 `INCOMING` 指定的路径中是否有文本文件
|
||||
* 如果存在，则使用当前时间戳创建一个新文件名，并将其移动到 `ARCHIVE`
|
||||
* 删除当前的 `ARCHIVE/latest.txt` 链接，并创建一个指向刚刚添加文件的新链接
|
||||
|
||||
作为一个示例，它很简单，但它会让你理解这个过程。
|
||||
|
||||
### 使用 pyscaffold 创建应用程序
|
||||
|
||||
首先，你需要安装 `scaffold`、`click` 和 `tox` [Python 库][3]。
|
||||
|
||||
```
|
||||
$ python3 -m pip install scaffold click tox
|
||||
```
|
||||
|
||||
安装 `scaffold` 后，切换到示例 `rotoscope` 项目所在的目录，然后执行以下命令：
|
||||
|
||||
```
|
||||
$ putup rotoscope -p rotoscope \
|
||||
--force --no-skeleton -n rotoscope \
|
||||
-d 'Move some files around.' -l GLWT \
|
||||
-u http://codeberg.org/ofosos/rotoscope \
|
||||
--save-config --pre-commit --markdown
|
||||
```
|
||||
|
||||
Pyscaffold 会重写我的 `README.md`，所以从 Git 恢复它：
|
||||
|
||||
```
|
||||
$ git checkout README.md
|
||||
```
|
||||
|
||||
Pyscaffold 在文档中说明了如何设置一个完整的示例项目，我不会在这里介绍，你之后可以探索。除此之外，Pyscaffold 还可以在项目中为你提供持续集成（CI）模板。
|
||||
|
||||
* 打包: 你的项目现在启用了 PyPi，所以你可以将其上传到一个仓库并从那里安装它。
|
||||
|
||||
* 文档: 你的项目现在有了一个完整的文档文件夹层次结构，它基于 Sphinx，包括一个 readthedocs.org 构建器。
|
||||
|
||||
* 测试: 你的项目现在可以与 tox 一起使用，测试文件夹包含运行基于 pytest 的测试所需的所有样板文件。
|
||||
|
||||
* 依赖管理: 打包和测试基础结构都需要一种管理依赖关系的方法。`setup.cfg` 文件解决了这个问题，它包含所有依赖项。
|
||||
|
||||
* 预提交钩子: 包含 Python 源代码格式工具 "black" 和 Python 风格检查器 "flake8"。
|
||||
|
||||
查看测试文件夹并在项目目录中运行 `tox` 命令，它会立即输出一个错误：打包基础设施无法找到相关库。
|
||||
|
||||
现在创建一个 `Git` 标记（例如 `v0.2`），此工具会将其识别为可安装版本。在提交更改之前，浏览一下自动生成的 `setup.cfg` 并根据需要编辑它。对于此示例，你可以修改 `LICENSE` 和项目描述，将这些更改添加到 Git 的暂存区，我必须禁用预提交钩子，然后提交它们。否则，我会遇到错误，因为 Python 风格检查器 flake8 会抱怨糟糕的格式。
|
||||
|
||||
```
|
||||
$ PRE_COMMIT_ALLOW_NO_CONFIG=1 git commit
|
||||
```
|
||||
|
||||
如果这个脚本有一个入口点，用户可以从命令行调用，那就更好了。现在，你只能通过找 `.py` 文件并手动执行它来运行。幸运的是，Python 的打包基础设施有一个很好的“罐装”方式，可以轻松地进行配置更改。将以下内容添加到 `setup.cfg` 的 `options.entry_points` 部分：
|
||||
|
||||
```
|
||||
console_scripts =
|
||||
roto = rotoscope.rotoscope:rotoscope
|
||||
```
|
||||
|
||||
这个更改会创建一个名为 `roto` 的 shell 命令，你可以使用它来调用 rotoscope 脚本，使用 `pip` 安装 rotoscope 后，可以使用 `roto` 命令。
|
||||
|
||||
就是这样，你可以从 Pyscaffold 免费获得所有打包、测试和文档设置。你还获得了一个预提交钩子来保证（大部分情况下）你按照设定规则提交。
|
||||
|
||||
### CLI 工具
|
||||
|
||||
现在，一些值会硬编码到脚本中，它们作为命令[参数][4]会更方便。例如，将 `INCOMING` 常量作为命令行参数会更好。
|
||||
|
||||
首先，导入 [click][5] 库，使用 click 提供的命令装饰器对 `rotoscope()` 方法进行装饰，并添加一个 Click 传递给 `rotoscope` 函数的参数。Click 提供了一组验证器，因此要向参数添加一个路径验证器。Click 还方便地使用函数的内嵌字符串作为命令行文档的一部分。所以你最终会得到以下方法签名：
|
||||
|
||||
```
|
||||
@click.command()
|
||||
@click.argument('incoming', type=click.Path(exists=True))
|
||||
def rotoscope(incoming):
|
||||
"""
|
||||
Rotoscope 0.4 - Bleep, blooop.
|
||||
Simple sample that move files.
|
||||
"""
|
||||
```
|
||||
|
||||
主函数会调用 `rotoscope()`，它现在是一个 Click 命令，不需要传递任何参数。
|
||||
|
||||
选项也可以使用[环境变量][6]自动填充。例如，将 `ARCHIVE` 常量改为一个选项：
|
||||
|
||||
```
|
||||
@click.option('archive', '--archive', default='/Users/mark/archive', envvar='ROTO_ARCHIVE', type=click.Path())
|
||||
```
|
||||
|
||||
使用相同的路径验证器。这一次，让 Click 填充环境变量，如果环境变量没有提供任何内容，则默认为旧常量的值。
|
||||
|
||||
Click 可以做更多的事情，它有彩色的控制台输出、提示和子命令，可以让你构建复杂的 CLI 工具。浏览 Click 文档会发现它的更多功能。
|
||||
|
||||
现在添加一些测试。
|
||||
|
||||
### 测试
|
||||
|
||||
Click 对使用 CLI 运行器[运行端到端测试][7]提供了一些建议。你可以用它来实现一个完整的测试（在[示例项目][8]中，测试在 `tests` 文件夹中。）
|
||||
|
||||
测试位于测试类的一个方法中。大多数约定与我在任何其他 Python 项目中使用的非常接近，但有一些细节，因为 rotoscope 使用 `click`。在 `test` 方法中，我创建了一个 `CliRunner`。测试使用它在一个隔离的文件系统中运行此命令。然后测试在隔离的文件系统中创建 `incoming` 和 `archive` 目录和一个虚拟的 `incoming/test.txt` 文件，然后它调用 CliRunner，就像你调用命令行应用程序一样。运行完成后，测试会检查隔离的文件系统，并验证 `incoming` 为空，并且 `archive` 包含两个文件（最新链接和存档文件）。
|
||||
|
||||
```
|
||||
from os import listdir, mkdir
|
||||
from click.testing import CliRunner
|
||||
from rotoscope.rotoscope import rotoscope
|
||||
|
||||
class TestRotoscope:
|
||||
def test_roto_good(self, tmp_path):
|
||||
runner = CliRunner()
|
||||
|
||||
with runner.isolated_filesystem(temp_dir=tmp_path) as td:
|
||||
mkdir("incoming")
|
||||
mkdir("archive")
|
||||
with open("incoming/test.txt", "w") as f:
|
||||
f.write("hello")
|
||||
|
||||
result = runner.invoke(rotoscope, ["incoming", "--archive", "archive"])
|
||||
assert result.exit_code == 0
|
||||
|
||||
print(td)
|
||||
incoming_f = listdir("incoming")
|
||||
archive_f = listdir("archive")
|
||||
assert len(incoming_f) == 0
|
||||
assert len(archive_f) == 2
|
||||
```
|
||||
|
||||
要在控制台上执行这些测试，在项目的根目录中运行 `tox`。
|
||||
|
||||
在执行测试期间，我在代码中发现了一个错误。当我进行 Click 转换时，rotoscope 只是取消了最新文件的链接，无论它是否存在。测试从一个新的文件系统（不是我的主文件夹）开始，很快就失败了。我可以通过在一个很好的隔离和自动化测试环境中运行来防止这种错误。这将避免很多“它在我的机器上正常工作”的问题。
|
||||
|
||||
### Scaffolding 和模块
|
||||
|
||||
本文到此结束，我们可以使用 `scaffold` 和 `click` 完成一些高级操作。有很多方法可以升级一个普通的 Python 脚本，甚至可以将你的简单实用程序变成成熟的 CLI 工具。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/22/7/bootstrap-python-command-line-application
|
||||
|
||||
作者：[Mark Meyer][a]
|
||||
选题：[lkxed][b]
|
||||
译者：[MjSeven](https://github.com/MjSeven)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/ofosos
|
||||
[b]: https://github.com/lkxed
|
||||
[1]: https://opensource.com/sites/default/files/lead-images/python_linux_tux_penguin_programming.png
|
||||
[2]: https://codeberg.org/ofosos/rotoscope
|
||||
[3]: https://opensource.com/article/19/5/python-tox
|
||||
[4]: https://opensource.com/article/21/8/linux-terminal#argument
|
||||
[5]: https://click.palletsprojects.com
|
||||
[6]: https://opensource.com/article/19/8/what-are-environment-variables
|
||||
[7]: https://click.palletsprojects.com/en/8.1.x/testing
|
||||
[8]: https://codeberg.org/ofosos/rotoscope/commit/dfa60c1bfcb1ac720ad168e5e98f02bac1fde17d
|
||||
@ -0,0 +1,138 @@
|
||||
[#]: subject: "Platforms that Help Deploy AI and ML Applications on the Cloud"
|
||||
[#]: via: "https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/"
|
||||
[#]: author: "Dr Kumar Gaurav https://www.opensourceforu.com/author/dr-gaurav-kumar/"
|
||||
[#]: collector: "lkxed"
|
||||
[#]: translator: "misitebao"
|
||||
[#]: reviewer: " "
|
||||
[#]: publisher: " "
|
||||
[#]: url: " "
|
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|
||||
# 帮助在云端部署人工智能（AI）和机器学习（ML）应用程序的平台
|
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|
||||
_人工智能和机器学习正在影响当今几乎每个行业。本文重点介绍了这些技术在我们日常生活中的各种使用方式，以及一些开源云平台如何实现其部署。_
|
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|
||||
人工智能 (AI) 的目标是构建能够模仿人类认知的机器和自动化系统。在全球范围内，人工智能正在以多种方式改变着社会、政治和经济。人工智能应用的例子包括谷歌帮助 (Google Help)、Siri、Alexa 和 Tesla (特斯拉) 等自动驾驶汽车。
|
||||
|
||||
如今，人工智能正被广泛使用，以有效的方式解决各行各业的难题。它被用于医疗保健行业，以做出比人类更准确、更快速的诊断。医生可以使用人工智能来诊断疾病，并在患者病情恶化时收到警报。
|
||||
|
||||
数据安全对每个企业都至关重要，网络攻击的数量也在不断增加。使用人工智能，可以提高数据的安全性。这方面的一个例子是集成智能机器人来识别软件错误和网络攻击。
|
||||
|
||||
推特 (Twitter)、WhatsApp、Facebook (脸书) 和 Snapchat 只是使用 AI 算法存储和管理数十亿个人资料的社交媒体平台中的一小部分。人工智能可以整理和筛选大量数据，以找到最新趋势、标签和各种各样人的需求。
|
||||
|
||||
![Figure 1: Key applications of machine learning][1]
|
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|
||||
旅游业越来越依赖人工智能，因为后者可以帮助完成各种与旅行相关的任务，包括为消费者预订酒店、航班和最佳路线。为了提供更好、更快的客户服务，由人工智能驱动的聊天机器人正被用于旅游业。
|
||||
|
||||
表 1: 机器学习的工具和框架
|
||||
|
||||
| 工具/平台 | 链接 |
|
||||
| :------------ | :------------------------------------- |
|
||||
| Streamlit | https://github.com/streamlit/streamlit |
|
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| TensorFlow | https://www.tensorflow.org/ |
|
||||
| PyTorch | https://pytorch.org/ |
|
||||
| scikit-learn | https://scikit-learn.org/ |
|
||||
| Apache Spark | https://spark.apache.org/ |
|
||||
| Torch | http://torch.ch/ |
|
||||
| Hugging Face | https://huggingface.co/ |
|
||||
| Keras | https://keras.io/ |
|
||||
| TensorFlowJS | https://www.tensorflow.org/js |
|
||||
| KNIME | https://www.knime.com/ |
|
||||
| Apache Mahout | https://mahout.apache.org/ |
|
||||
| Accord | http://accord-framework.net/ |
|
||||
| Shogun | http://shogun-toolbox.org/ |
|
||||
| RapidMiner | https://rapidminer.com/ |
|
||||
| Blocks | https://github.com/mila-iqia/blocks |
|
||||
| TuriCreate | https://github.com/apple/turicreate |
|
||||
| Dopamine | https://github.com/google/dopamine |
|
||||
| FlairNLP | https://github.com/flairNLP/flair |
|
||||
|
||||
### 不同领域的机器学习
|
||||
|
||||
让软件应用程序和小工具自行响应和开发的所有技术和工具都称为机器学习 (ML)。多亏了机器学习技术，人工智能可以在没有真正被明确编程来执行所需操作的情况下进行学习。ML 算法不依赖于预定义的计算机指令，而是从样本输入中学习一个模式，然后完全基于学习到的模式来预测和执行任务。如果不能选择严格的算法，机器学习可以成为救命稻草。它将通过分析以前的程序来选择新程序，然后将其付诸实施。ML 为技术进步和以前在各种行业中无法想象的技术扫清了道路。如今，它被用于各种尖端技术 — 从预测算法到互联网电视直播。
|
||||
|
||||
一个值得注意的 ML 和 AI 技术是图像识别，它是一种对数字图像中的特征或项进行分类和检测的方法。分类和人脸识别是使用这种方法完成的。
|
||||
|
||||
![Figure 2: Streamlit cloud for machine learning][2]
|
||||
|
||||
在推荐系统中使用机器学习是其最广泛使用和知名的应用之一。在当今的电子商务世界中，产品推荐是一种利用强大的机器学习技术的突出工具。网站使用人工智能和机器学习来跟踪过去的购买、搜索趋势和购物车历史，然后根据这些数据生成产品推荐。
|
||||
|
||||
在医疗保健行业中使用机器学习算法引起了很多兴趣。通过使用 ML 算法，可以跨多个医院部门预测急诊室等待时间。员工轮班的详细信息、患者数据以及科室讨论和急诊室布局的记录都用于帮助创建算法。机器学习算法可用于检测疾病、计划治疗和预测。
|
||||
|
||||
**用于机器学习的云平台的主要特点**:
|
||||
|
||||
- 算法或特征提取
|
||||
- 关联规则挖掘
|
||||
- 基于大数据的预测分析
|
||||
- 分类、回归和聚类
|
||||
- 数据加载和转换
|
||||
- 数据准备、数据预处理和可视化
|
||||
- 降维
|
||||
- 分布式线性代数
|
||||
- 假设检验和核方法
|
||||
- 处理图像、音频、信号和视觉数据集
|
||||
- 模型选择和优化模块
|
||||
- 预处理和数据流编程
|
||||
- 推荐系统
|
||||
- 通过插件支持文本挖掘和图像挖掘
|
||||
- 可视化和绘图
|
||||
|
||||
### 基于云的 AI 和 ML 应用程序部署
|
||||
|
||||
AI 和 ML 的应用可以部署在云平台上。如今，许多云服务提供商使程序员能够构建模型以在其领域内进行有效的决策。
|
||||
|
||||
这些基于云的平台与预先训练的机器学习和深度学习模型集成在一起，无需任何编码或最少的脚本即可在这些模型上部署应用程序。
|
||||
|
||||
![Figure 3: Categories of ML deployments in Streamlit][3]
|
||||
|
||||
**Streamlit:** Streamlit 让数据科学家和机器学习专家能够访问各种机器学习模型。它是开源的并且与云部署兼容。ML 模型可以在几分钟内准备好与数据集一起使用
|
||||
|
||||
Streamlit 提供一系列机器学习模型和多个类别的源代码，包括自然语言处理、地理、教育、计算机视觉等。
|
||||
|
||||
![Figure 4: Hugging Face for machine learning][4]
|
||||
|
||||
**Hugging Face:** 这是另一个平台，为各种类别的 ML 和 AI 提供预先训练的模型和架 构。许多企业巨头都在使用这个平台，包括 Facebook AI、微软、谷歌 AI、亚马逊网络服务和 Grammarly。
|
||||
|
||||
Hugging Face 中提供了许多预训练和部署就绪的模型，用于不同的应用程序，包括自然语言处理和计算机视觉。
|
||||
|
||||
使用 Hugging Face 中的 ML 模型可以执行以下任务:
|
||||
|
||||
- 音频到音频处理
|
||||
- 自动语音识别
|
||||
- 计算机视觉
|
||||
- 填充蒙版
|
||||
- 图像分类
|
||||
- 图像分割
|
||||
- 物体检测
|
||||
- 问题应答
|
||||
- 句子相似度
|
||||
- 总结
|
||||
- 文本分类
|
||||
- 文本生成
|
||||
- 文本到语音翻译
|
||||
- 令牌分类
|
||||
- 翻译分类
|
||||
|
||||
Hugging Face 中可用的问题解决器经过优化且有效，有助于快速部署模型（图 5）。
|
||||
|
||||
![Figure 5: Problem solvers and models in Hugging Face][5]
|
||||
|
||||
这些基于云的平台对多个领域的研究人员、从业者和数据科学家非常有用，并简化了性能良好的实际应用程序的开发。
|
||||
|
||||
---
|
||||
|
||||
via: https://www.opensourceforu.com/2022/09/platforms-that-help-deploy-ai-and-ml-applications-on-the-cloud/
|
||||
|
||||
作者：[Dr Kumar Gaurav][a]
|
||||
选题：[lkxed][b]
|
||||
译者：[Misite Bao](https://github.com/misitebao)
|
||||
校对：[校对者 ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux 中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://www.opensourceforu.com/author/dr-gaurav-kumar/
|
||||
[b]: https://github.com/lkxed
|
||||
[1]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-1-Key-applications-of-machine-learning.jpg
|
||||
[2]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-2-Streamlit-cloud-for-machine-learning.png
|
||||
[3]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-3-Categories-of-ML-deployments-in-Streamlit.png
|
||||
[4]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-4-Hugging-Face-for-machine-learning.png
|
||||
[5]: https://www.opensourceforu.com/wp-content/uploads/2022/08/Figure-5-Problem-solvers-and-models-in-Hugging-Face.png
|
||||
Loading…
Reference in New Issue
Block a user