adaptdl
polyaxon
| adaptdl | polyaxon | |
|---|---|---|
| 4 | 9 | |
| 395 | 3,486 | |
| 0.0% | 0.5% | |
| 0.0 | 8.7 | |
| about 1 year ago | 14 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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adaptdl
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Introduction to PyTorch
AdaptDL
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[Discussion] Open source scheduler and queuing system for model training/inferencing tasks?
check out https://github.com/petuum/adaptdl. It natively supports AWS/EKS (for the autoscaling feature), otherwise it can run anywhere on Kubernetes.
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Reduce cost by 3x in the cloud and improve GPU usage in shared clusters with AdaptDL for PyTorch
AdaptDL monitors training job performance in real-time, and elastically re-scales resources (GPUs, compute instances) while jobs are running. For each training job, AdaptDL automatically tunes the batch size, learning rate, and gradient accumulation. In the cloud (e.g. AWS), AdaptDL can auto-scale the number of provisioned Spot Instances. We’ve seen shared-cluster training jobs at Petuum and our partners complete 2–3x faster on average, with 3x cheaper cost in AWS using Spot Instances!
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How we were able to achieve hyper-parameter tuning (HPT) for deep learning workflows at 1.5x faster in our clusters and 3x cheaper on AWS
To tackle the problem of long and expensive HPT workflows, our team at Petuum collaborated with Microsoft to integrate AdaptDL with Neural Network Intelligence (NNI). AdaptDL is an open-source tool in the CASL (Composable, Automatic, and Scalable Learning) ecosystem. AdaptDL offers adaptive resource management for distributed clusters, and reduces the cost of deep learning workloads ranging from a few training/tuning trials to thousands. NNI from the Microsoft open-source community, is a toolkit for automatic machine learning (AutoML) and hyper-parameter tuning.
polyaxon
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Any MLOps platform you use?
If you're not concerned about self-hosting, WandB is one of the more fully featured training monitoring tools (I've used it in the past without any issues but the lack of data and training privacy and lack of self-hosting possibilities makes it a hard no for anything that isn't scholastic). Polyaxon is an alternative but rewriting all your variable logging to conform to their requirements makes it very difficult to switch to it in the middle of a project so you have to commit to it from the get-go.
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[D] Kubernetes for ML - how are y'all doing it?
We use Polyaxon and it’s pretty good
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[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Polyaxon.
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Does anyone have experience with polyaxon?
I just came across https://github.com/polyaxon/polyaxon because mlflow gives me a hard time and costs my company money by the day because it is not working as expected.
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[D] Productionalizing machine learning pipelines for small teams
For running experiments, http://polyaxon.com/ is a really good free open-source package that has lots of nice integrations so you can quickly run experiments in k8s but it might be overkill in some cases.
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Top 5 tools to get started with MLOps !
Polyaxon : https://polyaxon.com
- Open source alternative to AWS Sagemaker, Google AI Platform, and Azure ML
What are some alternatives?
HandyRL - HandyRL is a handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.
MLflow - Open source platform for the machine learning lifecycle
alpa - Training and serving large-scale neural networks with auto parallelization.
kubeflow - Machine Learning Toolkit for Kubernetes
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.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
dvc - 🦉 ML Experiments and Data Management with Git
tutorials - PyTorch tutorials.
onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]