{"push_id":2218911781,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"edeaeb655a96818f7b883697b25bff892f792cec","before":"6d8249df42242dc2ec8c5298f5e00aac118005f7","commits":[{"sha":"edeaeb655a96818f7b883697b25bff892f792cec","author":{"name":"akshay bhat","email":"96040e3f4f7f87a6ba5f58b4a64d3bfbd4ceccfa@gmail.com"},"message":"Further simplify docker entrypoint  \"start_container.py\" by removing\nun-necessary worker/server argument. These are now inferred directly\nby checking environment variables for LAUNCH_SERVER or LAUNCH_NGINX_SERVER\n[skip ci]\n\n- Create a first class TrainingSet model for representing training tasks.\n\n- Factorize LOPQ retriever into a \"TrainedModel\", \"Retriever\" and Indexer / \"IndexEntry\".\n\n- Start single machine YOLO training on AWS GPU instances.\n\n- Above factorization will make trained LOPQ retrievers portable across datasets.\n\n- Incorporate recently released YT8M trained indexer / retriever.","distinct":true,"url":"https://api.github.com/repos/AKSHAYUBHAT/DeepVideoAnalytics/commits/edeaeb655a96818f7b883697b25bff892f792cec"}]}
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