devops-exercises
ivy
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| devops-exercises | ivy | |
|---|---|---|
| 37 | 17 | |
| 63,339 | 14,022 | |
| - | 0.5% | |
| 7.3 | 10.0 | |
| 12 days ago | 5 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
devops-exercises
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DevOps Exercises
https://github.com/bregman-arie/devops-exercises this collection might help. (Also first result for „DevOps exercises“. Get good at using a search engine, it‘s a must have skill)
- DevOps/SRE Exercises Repository
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What will be asked for devOps engineer interview?
Here's a huge collection of devops related technical interview questions: https://github.com/bregman-arie/devops-exercises.
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5 GitHub Projects to Help You Become a Better DevOps Engineer ⚡
3. DevOps Exercises
- When am I ready for a junior role?
- algun libro para aprender devops?
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Programming Languages/Certs for Networking?
https://github.com/bregman-arie/devops-exercises -> lots of jumping-off points
- GitHub - bregman-arie/devops-exercises: Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
ivy
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Keras 3.0
See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks
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Show HN: Carton – Run any ML model from any programming language
is this ancillary to what [these guys](https://github.com/unifyai/ivy) are trying to do?
- Ivy: All in one machine learning framework
- Ivy ML Transpiler and Framework
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[D] Keras 3.0 Announcement: Keras for TensorFlow, JAX, and PyTorch
https://unify.ai/ They are trying to do what Ivy is doing already.
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Ask for help: what is the best way to have code both support torch and numpy?
Check Ivy.
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CoreML Stable Diffusion
ROCm's great for data centers, but good luck finding anything about desktop GPUs on their site apart from this lone blog post: https://community.amd.com/t5/instinct-accelerators/exploring...
There's a good explanation of AMD's ROCm targets here: https://news.ycombinator.com/item?id=28200477
It's currently a PITA to get common Python libs like Numba to even talk to AMD cards (admittedly Numba won't talk to older Nvidia cards either and they deprecate ruthlessly; I had to downgrade 8 versions to get it working with a 5yo mobile workstation). YC-backed Ivy claims to be working on unifying ML frameworks in a hardware-agnostic way but I don't have enough experience to assess how well they're succeeding yet: https://lets-unify.ai
I was happy to see DiffusionBee does talk the GPU in my late-model intel Mac, though for some reason it only uses 50% of its power right now. I'm sure the situation will improve as Metal 3.0 and Vulkan get more established.
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DL Frameworks in a nutshell
Won't it all come together with https://lets-unify.ai/ ?
- Unified Machine Learning
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[Discussion] Opinions on unify AI
What do you think about unify AI https://lets-unify.ai.
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