nvidia-gpu-scheduler
aim
| nvidia-gpu-scheduler | aim | |
|---|---|---|
| 1 | 70 | |
| 7 | 4,797 | |
| - | 1.5% | |
| 0.0 | 8.0 | |
| over 1 year ago | about 15 hours 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.
nvidia-gpu-scheduler
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[D] How to be more productive while doing Deep Learning experiments?
Sure. No, a simple bash script is not enough. In my case, we have several machines shared in the department, some with GPUs, some without. What I have is a python script that gets a list of jobs and then it schedule them in the first available machine (according to memory/CPU/GPU availability). Unfortunately, what I have is really entangled with our computing platform (Docker-based with a shared filesystem) and not really easy to have it as standalone project (that's why I said "know you infrastructure"). The most similar thing that I could find online is this project. I believe there are then some HPC tools that could be useful (e.g. Slurm), but that's way too much for what we need.
aim
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aim VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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End-to-end observability for LlamaIndex environment
LlamaIndex Observer is one of the logging apps built in AimOS (aimstack.io).
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Data Registry suggestions for ML projects
I've been working with Aim for a while, and it's been solid. What stands out for me is its open-source nature. https://aimstack.io/
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Building and debugging LLMs with Aim: self-hosted and open-source AI metadata tracking tool
If you haven't yet, drop a star to support open-source project! ⭐️ https://github.com/aimhubio/aim
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Any tools that offer In-depth tracking of model runtime performance?
Here is the GitHub repository: https://github.com/aimhubio/aim
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Using MLflow(Machine Learning experimentation tracking tool) in Kaggle notebooks with the help of DagsHub
You can also check out Aim, which has an integration with MLflow, called aimlflow.
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Visualize metadata with Aim on Hugging Face Spaces and seamlessly share training results with anyone
Hope you enjoyed reading and thanks for your time! Feel free to share your thoughts, would love to read them. Support Aim by dropping a star on GitHub: https://github.com/aimhubio/aim
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Effortless image tracking and analysis for 3D segmentation task with Aim
Aim: An easy-to-use & supercharged open-source AI metadata tracker aimstack.io
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Evaluate Different Vector Databases
Seems useful: https://github.com/aimhubio/aim
- Metadata visualization via Aim Explorers
What are some alternatives?
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
tensorboard - TensorFlow's Visualization Toolkit
fastapi-cloud-tasks - GCP's Cloud Tasks + Cloud Scheduler + FastAPI = Partial replacement for celery.
dvc - 🦉 ML Experiments and Data Management with Git
stable-diffusion-nvidia-docker - GPU-ready Dockerfile to run Stability.AI stable-diffusion model v2 with a simple web interface. Includes multi-GPUs support.
guildai - Experiment tracking, ML developer tools
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
tmux - tmux source code
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.