- amazon-sagemaker-examples VS aws-lambda-docker-serverless-inference
- amazon-sagemaker-examples VS Popular-RL-Algorithms
- amazon-sagemaker-examples VS sp-api-sdk
- amazon-sagemaker-examples VS catam-julia
- amazon-sagemaker-examples VS Hello-AWS-Data-Services
- amazon-sagemaker-examples VS AnnA_Anki_neuronal_Appendix
Amazon-sagemaker-examples Alternatives
Similar projects and alternatives to amazon-sagemaker-examples
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aws-lambda-docker-serverless-inference
Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
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Popular-RL-Algorithms
PyTorch implementation of Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), Actor-Critic (AC/A2C), Proximal Policy Optimization (PPO), QT-Opt, PointNet..
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SonarLint
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socksort
Sort socks into pairs using deep learning image classification and Kafka stream processing
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Hello-AWS-Data-Services
Sample code for AWS data service and ML courses on LinkedIn Learning
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Scout APM
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hugging-face-workshop
A 90-minute hands on workshop about Hugging Face on SageMaker.
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AnnA_Anki_neuronal_Appendix
using machine learning on anki collection for: enhanced scheduling, semantic plotting, semantic clustering, searching by semantic similarity
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better amazon-sagemaker-examples alternative or higher similarity.
amazon-sagemaker-examples reviews and mentions
Posts with mentions or reviews of amazon-sagemaker-examples.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-04.
- amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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[email protected] Newsletter 04/2022
Train EleutherAI GPT-J using SageMaker EleutherAI released GPT-J 6B as an open-source alternative to OpenAI's GPT-3. EleutherAI’s goal was to train a model that is equivalent in size to GPT-3 and make it available to the public under an open license and has since gained a lot of interest from Researchers, Data Scientists, and even Software Developers. This notebook shows you how to easily train and tune GPT-J using Amazon SageMaker Distributed Training and Hugging Face on NVIDIA GPU instances.
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AWS - NLP newsletter November 2021
Amazon SageMaker Asynchronous Inference with Hugging Face Model Amazon SageMaker Asynchronous Inference is a new capability in SageMaker that queues incoming requests and processes them asynchronously. SageMaker currently offers two inference options for customers to deploy machine learning models: 1) a real-time option for low-latency workloads 2) Batch transform, an offline option to process inference requests on batches of data available upfront. Real-time inference is suited for workloads with payload sizes of less than 6 MB and require inference requests to be processed within 60 seconds. Batch transform is suitable for offline inference on batches of data. This notebook provides an introduction on how to use the SageMaker Asynchronous inference capability with Hugging Face models. This notebook will cover the steps required to create an Asynchronous inference endpoint and test it with some sample requests.
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[D] Pretraining/transfer learning with SageMaker BlazingText (word2vec)?
I have a training set consisting of a description and a binary label. From reading previous work, I know that using pretrained fasttext embeddings should work well for my use case. I need to be able to make predictions on unseen words (OOV). My company is already using aws/sagemaker, so using SageMaker Blazing text with subword embedding seems like a good approach.
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Train an ML Model using Apache Spark in EMR and deploy in SageMaker
Reference
- Is this a good architecture for a data heavy web-app?
- I can't find a way to use pytorch for machine learning
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Sorting my socks with deep learning — Part 1
A more extensive explanation here
Stats
Basic amazon-sagemaker-examples repo stats
8
6,865
9.2
4 days ago
awslabs/amazon-sagemaker-examples is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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