aws-robomaker-sample-application-meirorunner
determined
aws-robomaker-sample-application-meirorunner | determined | |
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1 | 10 | |
4 | 2,861 | |
- | 2.3% | |
3.0 | 9.9 | |
about 2 years ago | 7 days ago | |
Python | Go | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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aws-robomaker-sample-application-meirorunner
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AWS open source news and updates No.41
aws-robomaker-sample-application-meirorunner this is a cool open source sample application that you can run on AWS RoboMaker and demonstrate reinforcement learning machine learning for robotics. The folks have also written a supporting blog post, Escape from the maze by training a Reinforcement Learning model on AWS RoboMaker Takuji Kawata and Tatsuya Arai provide additional information to help you run this yourself.
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.
What are some alternatives?
aws-virtual-gpu-device-plugin - AWS virtual gpu device plugin provides capability to use smaller virtual gpus for your machine learning inference workloads
ColossalAI - Making large AI models cheaper, faster and more accessible
Lambda-Extension-Secrets-Wrapper-Python - Repository with my AWS Lambda Wrapper for reading parameters from SSM
Dagger.jl - A framework for out-of-core and parallel execution
booster - Software development framework specialized in building highly scalable microservices with CQRS and Event-Sourcing. It uses the semantics of the code to build a fully working GraphQL API that supports real-time subscriptions.
aws-auto-cleanup - Programmatically delete AWS resources based on an allowlist and time to live (TTL) settings
cfn-diagram - CLI tool to visualise CloudFormation/SAM/CDK stacks as visjs networks, draw.io or ascii-art diagrams.
aws-sdk-js-v3 - Modularized AWS SDK for JavaScript.
goofys - a high-performance, POSIX-ish Amazon S3 file system written in Go
cdk-appsync-project - Projen managed AppSync Transformer project
alpa - Training and serving large-scale neural networks with auto parallelization.