Chia-Plot-Status
cudf
Chia-Plot-Status | cudf | |
---|---|---|
8 | 23 | |
186 | 7,291 | |
- | 1.8% | |
0.0 | 9.9 | |
over 2 years ago | 4 days ago | |
C# | C++ | |
MIT License | Apache License 2.0 |
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.
Chia-Plot-Status
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Monitoring when plotting for HPOOL
Not tried this but there are several about. https://github.com/grayfallstown/Chia-Plot-Status. Also check out MrPig91 on GitHub and YouTube. I used some of his code and made my own.
- My plotter was up and running till today morning but suddenly the mechine is not getting sync. Can someone help? Is it possible for someone steel my plots
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New pipelined multi-threaded plotter implementation (work in progress)
Chia-Plot-Status now has basic support for this chia-plotter logs to make comparing performance easier
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Chia Plot Status 0.9.13 (MIT License)
Never trust any download source other than the official github repository https://github.com/grayfallstown/Chia-Plot-Status or the official website https://grayfallstown.github.io/Chia-Plot-Status/
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Safety of Chiabot from joaquimguimaraes on Github
As seen on https://github.com/grayfallstown/Chia-Plot-Status/graphs/contributors there is only one other person who contributed a pull request so far and that wasn't code but a documentation change.
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[Unexpected] Best part of getting into Chia
Data reporting tools like grayfallstown/Chia-Plot-Status all the way up to mtail + prometheus + grafana for some amazing dashboards
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Need some help getting started? I researched a bunch and found some resources for beginners
Chia Plot Status: https://github.com/grayfallstown/Chia-Plot-Status
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Announcing ChiaPlotStatus, a Tool to Analyse Chia Plotting log files, show progress of running plots and estimated time to completion
Available on Github
cudf
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A Polars exploration into Kedro
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
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Why we dropped Docker for Python environments
Perhaps the largest for package size is the NVIDIA developed rapids toolkit https://rapids.ai/ . Even still adding things like pandas and some geospatial tools, you rapidly end up with an image well over a gigabyte, despite following cutting edge best practice with docker and python.
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Introducing TeaScript C++ Library
Yes sure, that is how OpenMP does; but on the other side: you seem to already do some basic type inference, and building an AST, no? Then you know as well the size and type of your vectors, and can execute actions in parallel if there is enough data to be worth parallelizing. Is there anyone who don't want their code to execute faster if it is possible? Those that do work in big data domain do use threads and vectorized instructions without user having to type in any directive; just import different library. Example, numpy or numpy with cuda backend, or similar GPU accelerated libraries like cudf.
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[D] Can we use Ray for distributed training on vertex ai ? Can someone provide me examples for the same ? Also which dataframe libraries you guys used for training machine learning models on huge datasets (100 gb+) (because pandas can't handle huge data).
Not the answer about Ray: you could use rapids.ai. I'm using it for for dataframe manipulation on GPU
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Story of my life
To put Data Analytics on GPU Steroids, Try RAPIDS cudf https://rapids.ai/
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Artificial Intelligence in Python
You can scope out https://rapids.ai/. Nvidia's AI toolkits. They have some handy notebooks to poke at to get you started.
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[D] [R] Large-scale clustering
try https://rapids.ai/
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[P] Looking for state of the art clustering algorithms
As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).
- Integrating multiple point clouds?
- Buka | Sains Data GPU RAPIDS
What are some alternatives?
chia-plotter
Numba - NumPy aware dynamic Python compiler using LLVM
plotng - PlotNG - plotting utility for Chia.Net
chia-blockchain - Chia blockchain python implementation (full node, farmer, harvester, timelord, and wallet)
wif500 - Try to find the WIF key and get a donation 200 btc
ChiaMonitor - An approach for monitoring multiple Chia harvester in one web app based dashboard
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
plotman - Chia plotting manager
rmm - RAPIDS Memory Manager
chia_plot_manager - Python scripts to manage Chia plots and drive space, providing full reports. Also monitors the number of chia coins you have. Auto Drive helps to automate the addition of new hard drives to your system and to the chia config.
CUDA.jl - CUDA programming in Julia.