maestral
darts
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maestral | darts | |
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15 | 47 | |
2,976 | 7,130 | |
- | 3.1% | |
9.0 | 9.1 | |
1 day ago | 7 days ago | |
Python | Python | |
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.
maestral
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New Dropbox client moves files from –/ to –/Library
Worth mention the open source project Maestral: https://github.com/SamSchott/maestral
(an unofficial Dropbox client)
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If you're archiving stuff to a cloud-synced folder, you NEED a strategy for ensuring ongoing integrity.
Sometime late last fall, my NAS rebooted and my ZFS pools didn't come back online automatically, but Docker did automatically restart the Maestral client. In so doing, it detected an empty folder...and...promptly deleted EVERYTHING in my Dropbox, assuming I'd just deleted everything locally. It's an issue that has had at least a few bug reports and which as of yet still hasn't been addressed - basically, the client needs but does not have a way to say "a bunch of stuff is changing, are you sure?" before doing it - especially on a restart of the client.
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Things I can’t do on macOS which I can do on Ubuntu
>- Debug apps which don't opt into debugging, using gdb/lldb, without disabling SIP. It's my computer, I should be root, I should be able to introspect how processes execute on it. Not being able to do so prevented me from debugging https://github.com/samschott/maestral/issues/597, since I had to disable SIP, which required rebooting, which stopped the bug from happening.
Another option is to re-sign the application with the entitlements necessary for debugging.
e.g:
https://gist.github.com/talaviram/1f21e141a137744c89e81b58f7...
- Debug apps which don't opt into debugging, using gdb/lldb, without disabling SIP. It's my computer, I should be root, I should be able to introspect how processes execute on it. Not being able to do so prevented me from debugging https://github.com/samschott/maestral/issues/597, since I had to disable SIP, which required rebooting, which stopped the bug from happening.
- Edit $PATH for IDEs launched from the Mac GUI (to add MacPorts/Homebrew-installed Ninja to Qt Creator's binary search $PATH). ~/.profile isn't evaluated at login time (only in terminals), /etc/paths doesn't work (forgot if it affected terminals, definitely doesn't affect GUI apps), and `launchctl setenv PATH` didn't work in my testing.
- Install libraries like SDL systemwide in paths searched by default by build systems and runtimes, like on Linux. MacPorts installs to /opt/local, Homebrew on M1 installs to /opt/homebrew, neither of which is searched by build systems. I might try setting up developer environments using Nix at some point, but I haven't learned how to use Nix/nix-darwin beyond editing the set of global apps.
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Dropbox Sync does not natively support Apple Silicon
3) No support for extended file attributes (so you cannot sync .app bundles)
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Nine Raspberry Pis power this entire office
If you still haven't found a use for them: they make for good low-power, always-on, headless networked servers for processing-light tasks.
Other commentators mentioned pihole/DNS, WireGuard, and music streaming, but you can also use them for a (slow) NFS server, persistent Syncthing node, Maestral host[1] (third-party Dropbox client written in Python (that can actually run on the Raspberry Pi, unlike the official Dropbox client)), or device that maintains a connection to a distributed network (e.g. Hyporborea/cjdns).
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Hacker News top posts: Aug 12, 2021
Open-source Dropbox client, with multi-account, no-device-limit and M1 support\ (147 comments)
- Open-source Dropbox client, with multi-account, no-device-limit and M1 support
darts
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[D] Doubts on the implementation of LSTMs for timeseries prediction (like including weather forecasts)
Don't use an LSTM. Get up to date with SoTA methods and read the papers in the field. LSTMs are not the way forward. Read the papers I suggested. It would be very useful to come to grips with both the Time Series Repository (https://github.com/thuml/Time-Series-Library) and Darts (https://github.com/unit8co/darts) as these are widely used for research and in industry.
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Facebook Prophet: library for generating forecasts from any time series data
As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.
The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).
That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
3. darts
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Aeon: A unified framework for machine learning with time series
Looking forward to checking this out! How does this compare with darts[1]?
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
active support
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Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
Darts gives you a lot of options, including newer deep learning approaches like NBEATS and NHiTS.
I would recommend Darts in Python [1]. It's easy to use (think fit()/predict()) and includes
* Statistical models (ETS, (V)ARIMA(X), etc)
* ML models (sklearn models, LGBM, etc)
* Many recent deep learning models (N-BEATS, TFT, etc)
* Seamlessly works on multi-dimensional series
* Models can be trained on multiple series
* Many models offer rich support for probabilistic forecasts
* Model evaluation is easy: Darts has many metrics, offers backtest etc
* Deep learning scales to large datasets, using GPUs, TPUs, etc
* There's even now an explainability module for some of the models - showing you what matters for computing the forecasts
* (coming soon): an anomaly detection module :)
* (also, it even include FB Prophet if you really want to use it)
Warning: I'm probably biased because I'm Darts creator.
To be fair, Darts looks pretty good relative to forecast: https://github.com/unit8co/darts
I would generally prefer R for this kind of stuff as the experts generally write the code, but Darts seems OK and is well-tested, at the very least (haven't had a chance to use it in anger yet).
- [D] Time Series Question
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[D] Fool me once, shame on you; fool me twice, shame on me: Exponential Smoothing vs. Facebook's Neural-Prophet.
There is also a version of N-BEATS in Darts (https://github.com/unit8co/darts) that extends the original N-BEATS by * Accepting exogenous covariate time series * Being able to produce probabilistic forecasts * Working on multivariate time series (all of this out of the box, fit() / predict() style) :D
What are some alternatives?
sktime - A unified framework for machine learning with time series
pytorch-forecasting - Time series forecasting with PyTorch
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
nixtla - Python SDK for TimeGPT, a foundational time series model
neural_prophet - NeuralProphet: A simple forecasting package
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
onedrive - OneDrive Client for Linux
greykite - A flexible, intuitive and fast forecasting library
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).