Ray
ara
Ray | ara | |
---|---|---|
43 | 82 | |
31,179 | 1,798 | |
1.8% | 0.7% | |
10.0 | 6.8 | |
1 day ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 only |
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.
Ray
- Ray: Unified framework for scaling AI and Python applications
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Open Source Advent Fun Wraps Up!
22. Ray | Github | tutorial
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Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Custom Models
Training times for GSM8k are mentioned here: https://github.com/ray-project/ray/tree/master/doc/source/te...
- Ray – an open source project for scaling AI workloads
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Methods to keep agents inside grid world.
Here's a reference from RLlib that points to docs and an example, and here's one from one of my projects that includes all my own implementations
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TransformerXL + PPO Baseline + MemoryGym
RLlib
- Is dynamic action masking possible in Rllib?
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AWS re:Invent 2022 Recap | Data & Analytics services
⦿ AWS Glue Data Quality - Automatic data quality rule recommendations based on your data AWS Glue for Ray - Data integration with Ray (ray.io), a popular new open-source compute framework that helps you scale Python workloads
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Think about it for a second
https://ray.io (just dropping the link)
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Elixir Livebook now as a desktop app
I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.
By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.
Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).
[0] https://github.com/ray-project/ray
ara
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With what should I use ansbile?
Look into AWX as an alternative to Tower. If you just want better reporting on runs, check out ARA or callback plugins.
- Show HN: ARA Records Ansible and makes it easier to understand and troubleshoot
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Ansible-Semaphore vs Ansible AWX
Also worth considering is ARA for playbook reporting, and then whatever you want for orchestration (Jenkins, Azure Devops, Rundeck, etc).
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Zabbix to monitor ansible
Why not use ara?
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How would I get a list of IP addresses of failed hosts and details?
For general recording of playbook activity with a web dashboard, ARA works really well.
- Planning on writing a callback plugin - is there a unique variable to identify a particular play being run?
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A way to log which user excuted a playbook
If you don’t want any UI / access control then you can also look at ARA - https://ara.recordsansible.org This works as a callback plugin to capture the job run data.
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Benchmarking ansible-core 2.11 vs 2.14 and python 3.9 vs 3.11 along with ara's database backends
I'm not sure how to interpret running 100 debug messages (https://github.com/ansible-community/ara/blob/master/tests/integration/benchmark_tasks.yaml) into real life performance. Mitogen's Benchmark used either 100 times a "hostname" command on the target machine (https://github.com/mitogen-hq/mitogen/blob/master/tests/ansible/bench/loop-100-items.yml) or running the DebOps project (https://github.com/debops/debops-playbooks/blob/master/playbooks/common.yml) for some sorta real-world module usage.
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The Bullhorn #92 (Ansible Newsletter)
Reach out on Mastodon or see this issue on GitHub.
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Best options for monitoring Ansible deployment times
There has been discussions about a prometheus exporter for monitoring, metrics and eventually fancy graphs in grafana but work on this has not started yet: https://github.com/ansible-community/ara/issues/177
What are some alternatives?
optuna - A hyperparameter optimization framework
awx - AWX provides a web-based user interface, REST API, and task engine built on top of Ansible. It is one of the upstream projects for Red Hat Ansible Automation Platform.
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
semaphore - Modern UI for Ansible
Faust - Python Stream Processing
CIS-Ubuntu-20.04-Ansible - Ansible Role to Automate CIS v1.1.0 Ubuntu Linux 18.04 LTS, 20.04 LTS Remediation
gevent - Coroutine-based concurrency library for Python
elk-ansible - Using ELK to Build a Fact Search Engine and Inventory CMDB for Ansible Tower
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
foreman-ansible-modules - Ansible modules for interacting with the Foreman API and various plugin APIs such as Katello
SCOOP (Scalable COncurrent Operations in Python) - SCOOP (Scalable COncurrent Operations in Python)
django-cms - The easy-to-use and developer-friendly enterprise CMS powered by Django