Python-docker
tensorflow_macos
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| Python-docker | tensorflow_macos | |
|---|---|---|
| 17 | 33 | |
| 2,461 | 2,887 | |
| 1.6% | - | |
| 8.3 | 3.4 | |
| 20 days ago | almost 3 years ago | |
| Shell | Shell | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Python-docker
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Containers Demystified 🐳🤔
This defines which image to inherit from, In this case we are using a Python image with the Python 3.11 version running on a slim version of the Bookwork version of Debian linux. Image definitions can be viewed from the DockerHub TAG link such as the python:3.11-slim-bookworm and official images like this one typically have pretty complicated definitions in order to get them highly optimized.
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Ask HN: Why is there no major push towards Android for Servers and Desktops?
> You are going to eventually run into the same issue most people trying to use Alpine Linux just because of simplicity and being lightweight run: musl is not a completely ABI-compatible seamless replacement to glibc and might cause issue with statically linked binaries, and other annoying issues you won't foresee.
Well, if all you need is the server to run Docker/Podman, SSH and some other limited amount of packages, it shouldn't be too bad. Of course, there are also horror stories of Alpine resulting in way worse performance in select use cases: https://github.com/docker-library/python/issues/509 and there's the fact that Alpine might be popular inside of containers, but way less so outside.
Also, because of the short EOL cycle, I personally ditched Debian on servers (and Alpine in containers) myself for Ubuntu everywhere: https://blog.kronis.dev/articles/using-ubuntu-as-the-base-fo... A bit of a polarizing move (though RPM distros aren't much better at the moment), but it seems to have worked out for me in the end.
Doesn't mean that someone can't try, though, maybe their use case is suitable for Alpine.
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What is the point of intermediate CMD layers in Docker images?
This is the actual raw Dockerfile: https://github.com/docker-library/python/blob/master/3.9/slim-bullseye/Dockerfile
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Imaging ADO build agent with python dependencies installed
I usually cannibalize Docker Community's examples: Here
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Why does the official Docker image of Python not create a user but the node one does?
Official Docker image of Python 3.10.5-slim
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Installing python inside docker container
If you want Python v3.7, perhaps try FROM python:3.7 (link)
- Latest Python 3.9/3.8 images break encoding
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Installing Python3 in Linux
Navigate into the Python directory and configure and ensure enable-optimization option is added as shown in the command below.
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I can't run pip for the docker build
It looks like you are running into this bug: https://github.com/docker-library/python/issues/674
- We don't have any control over the signing process of Docker images we publish
tensorflow_macos
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Updated Apple Silicon Guide for M2 Pro and M2 Max Chips
https://github.com/apple/tensorflow_macos is no longer needed
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The hunt for the M1’s neural engine
Tensorflow has a CoreML enabled version which run on ANE.
https://github.com/apple/tensorflow_macos
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M1 Mac users
Apple released a guide on how to use the M1's integrated Neural Chip in TensorFlow. Have a look at this Apple documentation page (and maybe also this GitHub that talks about TensorFlow together with Apple's own ML Compute platform).
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MacBook Air or Wait for new potential MacBook Air with M2
Tensorflow does work on Apple Silicon
- Kernels dying when using tensorflow in Jupyter Notebooks.
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Main PyTorch maintainer confirms that work is being done to support Apple Silicon GPU acceleration for the popular machine learning framework.
Apple did some work to optimize tensorflow for M1, can be found here https://github.com/apple/tensorflow_macos It's alpha, but works fine, I tried it
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The M1 Max is the fastest GPU we have ever measured in Affinity Photo benchmark
https://github.com/apple/tensorflow_macos/issues/25
https://forums.macrumors.com/threads/apple-silicon-deep-lear...
It is expected that the M1 Max should have similar performance to a RTX-2080 or Titan X.
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MacBook Pro M1 Pro benchmark
In case anyone is interested, in ran a fairly simple MNIST benchmark (proposed here : https://github.com/apple/tensorflow_macos/issues/25) on my recently acquired M1 Pro MBP (16-core GPU, 16GB RAM).
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Error while installing tensorflow on Mac M1
The only method I know of to download tensorflow on M1 macs is the one documented here: https://github.com/apple/tensorflow_macos
- How exactly does the Neural Engine benefit the consumer?
What are some alternatives?
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flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
simple_to-do_list - A simple to do list application for your desktop.
coremltools - Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.