torchlambda
sagemaker-training-toolkit
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| torchlambda | sagemaker-training-toolkit | |
|---|---|---|
| 2 | 1 | |
| 123 | 466 | |
| - | 2.6% | |
| 0.0 | 6.7 | |
| over 2 years ago | 29 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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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
sagemaker-training-toolkit
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Distributed training with Horovod/MPI
I'm using sagemaker-training-toolkit to attempt hyperparameter optimization and trying to take advantage of all the cores on each machine using their MPI options (which uses Horovod with MPI to my understanding). I'm pretty new to this space and can't find anything that describes in somewhat lay-terms how training works in this distributed model. With AllReduce, how often does the reduce happen? I'm trying to figure out if all training threads are training a shared model such that every thread is training on the "latest" version of the model.
What are some alternatives?
python-paho-mqtt-for-aws-iot - Use Python and paho client with AWS IoT for MQTT messaging
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
faiss-server - faiss serving :)
jina - ☁️ Build multimodal AI applications with cloud-native stack
python-lambdarest - Flask like web framework for AWS Lambda
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
random-dose-of-knowledge - Using the latest Software Engineering practices to create a modern and simple app.
spotty - Training deep learning models on AWS and GCP instances
cmake_min_version - Determine the minimal requirement CMake version of a project
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
sagemaker-tensorflow-training-toolkit - Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.