adaptdl
torchlambda
| adaptdl | torchlambda | |
|---|---|---|
| 4 | 2 | |
| 395 | 123 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| about 1 year ago | over 2 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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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.
torchlambda
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AWS lambda inference taking 3s even after warmup
Ok I see. Have you maximized the ram in the lambdas? Since performance scale with ram. I have been using torchlambda.
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[D] Anyone deploy DL models with AWS Lambda? Trying to estimate costs
I don't think aws lambda has gpu support. We use torchlambda to static build the deployment. You end up with a small binary
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.
python-paho-mqtt-for-aws-iot - Use Python and paho client with AWS IoT for MQTT messaging
alpa - Training and serving large-scale neural networks with auto parallelization.
faiss-server - faiss serving :)
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.
python-lambdarest - Flask like web framework for AWS Lambda
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.
random-dose-of-knowledge - Using the latest Software Engineering practices to create a modern and simple app.
tutorials - PyTorch tutorials.
cmake_min_version - Determine the minimal requirement CMake version of a project
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
jina - ☁️ Build multimodal AI applications with cloud-native stack