cortx
99-ML-Learning-Projects
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| cortx | 99-ML-Learning-Projects | |
|---|---|---|
| 2 | 1 | |
| 632 | 557 | |
| - | - | |
| 0.0 | 0.0 | |
| 3 months ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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.
cortx
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How do Commercial Open Source Startups manage GitHub insights > 14 days? Is everyone using a workaround? How are "unique" cloners and viewers kept track of?
This is what we did: https://github.com/Seagate/cortx/tree/main/metrics
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The Wiretrustee SATA Pi Board Is a True SATA NAS
I keep hoping some day the drives will have their own networking built in. Kioxia, a Toshiba spin off, announced a network-attached NVMe-oF drive last September[1], and I seem to recall one of the major drive players had similar intents a bit back... ah yes, the Seagate Kinetic drives with dual 1Gbit[2] & an object storage OS built in to the drive. These days Seagate seems to be pushing a software platform CORTX[3], which I hope some day perhaps has hardware products too (but right now seems to be for classic linux-based network appliances)
Ideally we start using 5 or 10Gbit ethernet for these cases. We could continue to treat these drives like they are direct attached, even though they are network attached, and either have one computer running RAID, or have Ceph and a bunch of computers running it's distributed system to tap the drives.
Ideally though, we need new clustered file-systems, where any computer can read the drives. That is, I'd guess, a long way off. Legacy devices (home media players) would need to go through some kind of legacy gateway.
[1] https://business.kioxia.com/en-us/news/2020/ssd-20200922-2.h...
[2] https://www.snia.org/sites/default/files/MayurShelty_Seagate...
[3] https://github.com/Seagate/cortx
99-ML-Learning-Projects
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I created a way to learn machine learning through Jupyter
Looks cool. Also sounds like it would fit will with the 99 ML Projects repo. Maybe you could contribute here https://github.com/gimseng/99-ML-Learning-Projects
What are some alternatives?
contributor_covenant - Pledge your respect and appreciation for contributors of all kinds to your open source project.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Seaweed File System - SeaweedFS is a fast distributed storage system for blobs, objects, files, and data lake, for billions of files! Blob store has O(1) disk seek, cloud tiering. Filer supports Cloud Drive, cross-DC active-active replication, Kubernetes, POSIX FUSE mount, S3 API, S3 Gateway, Hadoop, WebDAV, encryption, Erasure Coding. [Moved to: https://github.com/seaweedfs/seaweedfs]
PySyft - Perform data science on data that remains in someone else's server
QuantumKatas - Tutorials and programming exercises for learning Q# and quantum computing
Python Cheatsheet - All-inclusive Python cheatsheet
codeduel.org - I wish I had heard of this site! - Alexander Hamilton
FinMind - Open Data, more than 50 financial data. 提供超過 50 個金融資料(台股為主)，每天更新 https://finmind.github.io/
MooseFS - MooseFS – Open Source, Petabyte, Fault-Tolerant, Highly Performing, Scalable Network Distributed File System (Software-Defined Storage)
Practical_RL - A course in reinforcement learning in the wild
seaweedfs - SeaweedFS is a fast distributed storage system for blobs, objects, files, and data lake, for billions of files! Blob store has O(1) disk seek, cloud tiering. Filer supports Cloud Drive, cross-DC active-active replication, Kubernetes, POSIX FUSE mount, S3 API, S3 Gateway, Hadoop, WebDAV, encryption, Erasure Coding.
100DaysofMLCode - My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1: