Component Translated Unfinished Unfinished words Unfinished characters Untranslated Checks Suggestions Comments
Documentation-1.2 GFDL-1.3 8% 3,428 68,162 435,355 3,388 24 0 0
Documentation-1.4 GFDL-1.3 6% 5,873 120,334 761,656 5,625 35 0 0
Documentation-1.6 GFDL-1.3 6% 5,873 120,334 761,656 5,625 35 0 0

Overview

Project website siril.org
14 hours ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 16,263 316,951 2,010,380
Translated 6% 1,089 2% 8,121 2% 51,713
Needs editing 3% 536 1% 2,412 1% 18,163
Read-only 0% 0 0% 0 0% 0
Failing checks 1% 94 1% 495 1% 3,607
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 90% 14,638 96% 306,418 96% 1,940,504
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Resource updated
The “po/zh_CN.po” file was changed. 2 weeks ago
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Yes, we know it's a shame. In fact we would have liked to use GTK as the toolkit for writing sirilpy scripts, but unfortunately the binary wheel has been broken on Windows for a long time and doesn't show any sign of being fixed. We can't release core functionality that doesn't work on one of the target OSes. And if we could add ``gi`` we would get tickets asking for ``pyqt``, and then other packages and before you know it Siril would be a multi-gigabyte download essentially the same size as an entire OS. So we have settled on TKinter. Yes, it's a bit plain but it's simple to use, it's supported everywhere and since it's a part of Python's standard library the risk of problems with it is very low.
 
2 weeks ago
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**Dependencies** Note that both torch and jax use GPU library pypi packages, so installation of them may pull in very large chains of dependencies - the full set of NVIDIA packages (CUDA, CuDNN etc.) is around 1GB. So when using these in scripts be sure to warn users that they may incur a large and potentially slow download on first use!
 
2 weeks ago
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**jax** - Not a dedicated ML framework as such but a means of offloading computation to the GPU. jax provides a mostly numpy-compatible module called jax.numpy which can greatly accelerate many mathematical array processing operations by offloading them to the GPU and also provides functions for gradient computation that numpy does not. Bearing in mind that the slow part of GPU processing is the data transfer between system memory and GPU memory, jax provides a jit compiler to precompile its offloaded workloads but it still requires careful algorithm design to keep data on the GPU for as long as possible, only returning the final result. Jax has good GPU support on Linux but only CPU support on Apple and Windows, although experimental Metal support and Windows GPU support is in progress. This is therefore not recommended for actual scripts at present, but is highlighted as an interesting framework to develop against in the (hopefully) near future when its support broadens. A ``JaxHelper()`` class is under development that will help with selection of the correct jax package to install and testing to confirm it works properly.
 
2 weeks ago
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**torch** - This is a heavyweight among ML frameworks and where GPU support was once very much CUDA-based it is now broadening its coverage: suupport for NVidia and Apple Silicon GPUs is good, and support for AMD GPUs (using ROCm) and Intel GPUs is in development (ROCm is stable enough to use on Linux, not yet on Windows). Unfortunately torch has some dependency issues (it is excessively strict about the version of CuDNN it requires) which causes problems using it in the same venv as some other modules (including jax - see below) that pull in the nvidia-cudnn package. A ``TorchHelper()`` class is under development that will help with selection of the correct torch package to install and testing to confirm it works properly.
 
2 weeks ago
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**onnxruntime** - used in GraXpert and Riccardo's AberrationRemover.py script, this package is available for all the target OSes. There are different backends to support different GPUs, but in some cases these are fragile. A helper class ``ONNXHelper()`` is available to help with identification and installation of the optimum package as well as ensuring correct runtime fallback to CPU. Note that currently on Windows the ONNXHelper will select the DirectML runtime for all supported GPUs. Even though in theory the CUDA-based onnxruntime-gpu package might be a bit faster, it unfortunately depends heavily on system libraries being installed and configured correctly and the installation is considered too fragile to be reliable in the kind of automated python environment used by Siril. If users wish to use it and are confident they can get it to work on their system they can install it manually using ``sirilpy.ensure_installed("onnxruntime-gpu") but this is not supported and related issues will be closed.
 
2 weeks ago
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A variety of machine learning modules exist which can be used to write "AI" scripts. However the state of support for heterogenous computing (i.e. GPU use) is patchy and often requires system libraries such as CUDA or ROCm to be installed. The following modules are regarded as key for GPU acceleration and either are supported with helper classes or have helper classes in development.
 
2 weeks ago
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Machine Learning Modules
 
2 weeks ago
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In filefilter specifications ``tkfilebrowser`` requires multiple extensions to be provided separated by | whereas filedialog requires space-separated extensions: ``sirilpy.tkfilebrowser`` has been adapted to accept either format.
 
2 weeks ago
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``sirilpy.tkfilebrowser.askdirectory()`` has been added as an alias for ``tkfilebrowser.askopendirname()`` to maintain compatibility with ``tk.filedialog.askdirectory()``.
 
2 weeks ago
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