nvidia-gpu-scheduler
NVIDIA GPU compute task scheduling utility (by jigangkim)
Sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA. (by IDSIA)
| nvidia-gpu-scheduler | Sacred | |
|---|---|---|
| 1 | 6 | |
| 7 | 4,157 | |
| - | 0.1% | |
| 0.0 | 3.5 | |
| over 1 year ago | 2 months ago | |
| Python | Python | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of nvidia-gpu-scheduler.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-25.
-
[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.
Sacred
Posts with mentions or reviews of Sacred.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-05.
-
Sacred VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
-
✨ 7 Best Machine Learning Experiment Logging Tools in 2022 🚀
🔗 https://github.com/IDSIA/sacred
-
https://np.reddit.com/r/MachineLearning/comments/pvs8r5/d_facebook_visdom_vs_google_tensorboard_for/hefg131/
I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it.
-
[D] Facebook Visdom vs Google Tensorboard for Pytorch
I'm using Omniboard (https://github.com/vivekratnavel/omniboard) with Sacred (https://github.com/IDSIA/sacred) for tracking experiments. You can specify custom Observers in Sacred so the model metrics and logs will be saved to a local directory or to a remote DB (e.g., MongoDB). I use a MongoDB database hosted on Atlas. Unlike other suggested options, Sacred and Omniboard are free. Atlas free tier comes with 512MB of free storage which is a huge amount if you're uploading only log files to it. ex = Experiment() ex.observers.append(FileStorageObserver(EXPERIMENTS_ROOT)) ex.observers.append(MongoObserver(url=MONGODB_URL, db_name='sacred'))
-
Can someone tell me good libraries you use on a day to day basis that increases your research productivity in ML/AI?
sacred helped me log my experiments. I did setup my environment only once 4 years ago, and since then I have a list of all my training runs with the hyperparameters and results.
-
[D] How to be more productive while doing Deep Learning experiments?
For 1, setup an experiment tracking framework. I found Sacred to be helpful https://github.com/IDSIA/sacred.
What are some alternatives?
When comparing nvidia-gpu-scheduler and Sacred you can also consider the following projects:
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
MLflow - Open source platform for the machine learning lifecycle
fastapi-cloud-tasks - GCP's Cloud Tasks + Cloud Scheduler + FastAPI = Partial replacement for celery.
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]
stable-diffusion-nvidia-docker - GPU-ready Dockerfile to run Stability.AI stable-diffusion model v2 with a simple web interface. Includes multi-GPUs support.
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
tmux - tmux source code
scikit-learn - scikit-learn: machine learning in Python
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement
nvidia-gpu-scheduler vs detectron2
Sacred vs MLflow
nvidia-gpu-scheduler vs fastapi-cloud-tasks
Sacred vs pytorch-lightning
nvidia-gpu-scheduler vs stable-diffusion-nvidia-docker
Sacred vs tensorflow
nvidia-gpu-scheduler vs pytorch-lightning
Sacred vs Keras
nvidia-gpu-scheduler vs tmux
Sacred vs scikit-learn
nvidia-gpu-scheduler vs aim
Sacred vs Clairvoyant