determined
alpa
determined | alpa | |
---|---|---|
10 | 4 | |
2,861 | 2,986 | |
2.3% | 0.7% | |
9.9 | 5.1 | |
6 days ago | 5 months ago | |
Go | Python | |
Apache License 2.0 | 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.
determined
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Open Source Advent Fun Wraps Up!
17. Determined AI | Github | tutorial
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ML Experiments Management with Git
Use Determined if you want a nice UI https://github.com/determined-ai/determined#readme
- Determined: Deep Learning Training Platform
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Queueing/Resource Management Solutions for Self Hosted Workstation?
I looked up and found [Determined Platform](determined.ai), tho it looks a very young project that I don't know if it's reliable enough.
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Ask HN: Who is hiring? (June 2022)
- Developer Support Engineer (~1/3 client facing, triaging feature requests and bug reports, etc; 2/3 debugging/troubleshooting)
We are developing enterprise grade artificial intelligence products/services for AI engineering teams and fortune 500 companies and need more software devs to fill the increasing demand.
Find out more at https://determined.ai/. If AI piques your curiosity or you want to interface with highly skilled engineers in the community, apply within (search "determined ai" at careers.hpe.com and drop me a message at asnell AT hpe PERIOD com).
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How to train large deep learning models as a startup
Check out Determined https://github.com/determined-ai/determined to help manage this kind of work at scale: Determined leverages Horovod under the hood, automatically manages cloud resources and can get you up on spot instances, T4's, etc. and will work on your local cluster as well. Gives you additional features like experiment management, scheduling, profiling, model registry, advanced hyperparameter tuning, etc.
Full disclosure: I'm a founder of the project.
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[D] managing compute for long running ML training jobs
These are some of the problems we are trying to solve with the Determined training platform. Determined can be run with or without k8s - the k8s version inherits some of the scheduling problems of k8s, but the non-k8s version uses a custom gang scheduler designed for large scale ML training. Determined offers a priority scheduler that allows smaller jobs to run while being able to schedule a large distributed job whenever you need, by setting a higher priority.
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Cerebras’ New Monster AI Chip Adds 1.4T Transistors
Ah I see - I think we're pretty much on the same page in terms of timetables. Although if you include TPU, I think it's fair to say that custom accelerators are already a moderate success.
Updated my profile. I've been working on DL training platforms and distributed training benchmarking for a bit so I've gotten a nice view into the GPU/TPU battle.
Shameless plug: you should check out the open-source training platform we are building, Determined[1]. One of the goals is to take our hard-earned expertise on training infrastructure and build a tool where people don't need to have that infrastructure expertise. We don't support TPUs, partially because a lack of demand/TPU availability, and partially because our PyTorch TPU experiments were so unimpressive.
[1] GH: https://github.com/determined-ai/determined, Slack: https://join.slack.com/t/determined-community/shared_invite/...
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[D] Software stack to replicate Azure ML / Google Auto ML on premise
Take a look at Determined https://github.com/determined-ai/determined
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AWS open source news and updates No.41
determined is an open-source deep learning training platform that makes building models fast and easy. This project provides a CloudFormation template to bootstrap you into AWS and then has a number of tutorials covering how to manage your data, train and then deploy inference endpoints. If you are looking to explore more open source machine learning projects, then check this one out.
alpa
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How to Train Large Models on Many GPUs?
- Alpa does training and serving with 175B parameter models https://github.com/alpa-projects/alpa
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how much does it actually cost in terms of computer power for open AI to respond
alpa.ai states "You will need at least 350GB GPU memory on your entire cluster to serve the OPT-175B model. For example, you can use 4 x AWS p3.16xlarge instances, which provide 4 (instance) x 8 (GPU/instance) x 16 (GB/GPU) = 512 GB memory."
- Alpa: Auto-parallelizing large model training and inference (by UC Berkeley)
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Alpa: Automated Model-Parallel Deep Learning
GitHub code: https://github.com/alpa-projects/alpa
What are some alternatives?
ColossalAI - Making large AI models cheaper, faster and more accessible
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Dagger.jl - A framework for out-of-core and parallel execution
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
aws-virtual-gpu-device-plugin - AWS virtual gpu device plugin provides capability to use smaller virtual gpus for your machine learning inference workloads
FedML - FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale.
cfn-diagram - CLI tool to visualise CloudFormation/SAM/CDK stacks as visjs networks, draw.io or ascii-art diagrams.
awesome-tensor-compilers - A list of awesome compiler projects and papers for tensor computation and deep learning.
goofys - a high-performance, POSIX-ish Amazon S3 file system written in Go
adaptdl - Resource-adaptive cluster scheduler for deep learning training.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)