Change Details

Similar to T309623, but this task is for Outlinks topic model (non-revscoring-based model). We should test if we can reduce the latency by using the [[ https://github.com/kserve/kserve/blob/9fa4940c6c10e96a86d5cb1bc855f7405b1276c4/python/kserve/kserve/kfmodel.py#L67 | async http client ]] provided by Tornado in Kserve to get outlinks from the article and their associated Wikidata IDs for predicting article topics. Currently the outlink_transformer calls the MW API with a [[ https://github.com/wikimedia/machinelearning-liftwing-inference-services/blob/391f80b8fc06004fbc217b04e640b6bb475ca6ef/outlink-topic-model/transformer/outlink_transformer/outlink_transformer.py#L12 | blocking code ]].

Similar to T309623, but this task is for Outlinks topic model (non-revscoring-based model). We should test if we can reduce the latency by using non-blocking HTTP calls in Kserve to get outlinks from an article and their associated Wikidata IDs for predicting article topics. Currently the outlink_transformer calls the MW API with a [[ https://github.com/wikimedia/machinelearning-liftwing-inference-services/blob/391f80b8fc06004fbc217b04e640b6bb475ca6ef/outlink-topic-model/transformer/outlink_transformer/outlink_transformer.py#L12 | blocking code ]].

Similar to T309623, but this task is for Outlinks topic model (non-revscoring-based model). We should test if we can reduce the latency by using the [[ https://github.com/kserve/kserve/blob/9fa4940c6c10e96a86d5cb1bc855f7405b1276c4/python/kserve/kserve/kfmodel.py#L67 | async http client ]] provided by Tornadonon-blocking HTTP calls in Kserve to get outlinks from thean article and their associated Wikidata IDs for predicting article topics. Currently the outlink_transformer calls the MW API with a [[ https://github.com/wikimedia/machinelearning-liftwing-inference-services/blob/391f80b8fc06004fbc217b04e640b6bb475ca6ef/outlink-topic-model/transformer/outlink_transformer/outlink_transformer.py#L12 | blocking code ]].