Thanks for you quick answer Andy! Unfortunately, I cannot share any information about the use cases (company policy) for the moment but, of course, if we publish something in the future, I will be glad to share our feedback with the community.
Looking forward to this 1.0 release. Best regards, Iyán -----Mensaje original----- De: Andy Christianson [mailto:aichr...@protonmail.com] Enviado el: jueves, 05 de abril de 2018 17:08 Para: users@nifi.apache.org Asunto: Re: MiNiFi C++ and Tensorflow future plans? **This Message originated from a Non-ArcelorMittal source** -----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Iyán, > I was wondering what is the roadmap and future plans of MiNiFi C++ > agent regarding Tensorflow processors. With the three that are > mentioned in the article it is possible to classify images on edge but > I would like to know if other processors from TF will be included so > we can train a neural network on edge. > > Also, if you share with me a list of features that will be implemented > in the near futures, it would be very helpful. Glad to hear you are interested in the project. The current plan is to cover the common edge inference use-cases. This includes general ML inference tasks as well as computer vision tasks including object classification, object detection (multiple output FlowFiles with bounding boxes + class), classification/anomaly detection in log files, time series anomaly detection (think temperature sensors). The benefit of using MiNiFi - C++ rather than just a pure TensorFlow model is that all of the usual NiFi techniques like routing on attribute (i.e. object class or other ML-inferred metadata), sending to cloud endpoints (S2S to a NiFi instance, or ingest into a Kafka or MQTT queue, etc.), and arbitrary scriptable actions (ExecuteScript processor) are all fairly simple to do. MiNiFi - C++ is a community-driven Apache project, so it ultimately will include any FlowFile -> tensor or tensor -> FlowFile processor that is developed to satisfy community requirements. What use cases do you have in mind? MiNiFi - C++ is nearing its 1.0 release and as such most common use cases, including deep neural networks or even training on the edge (given sufficient resources, i.e. GPU) should be possible without too much custom programming effort. We'd like to get your input/feedback as a potential community participant so that MiNiFi will become more useful to everyone over time. Regards, Andy I.C. -----BEGIN PGP SIGNATURE----- Version: GnuPG v2.0.22 (GNU/Linux) iQEcBAEBAgAGBQJaxjuRAAoJEG1+mBKNMpID8AIH/3qpZXhOPZOWoXwxn+RUJeBz eoFFFw9QjCUhhgUeyOUAe1YH4EaMBjE0qewrBhZrybBxjYgvaOs7GGjqrTpbZmf6 1vhRUTqzNeI68ke2+tjk0xCtzTQd3Ag92kpBLQn4y6CshUS9YSvXzbPjXatvx2gw w6uKR+Y9uTplf9UIxWXAUdgL1vPLPaXy5OgiUwIflfd4Eb5OvS3Dh8yS6MhfzjcF Zfwn84Op+gtJQr2YJTOH/dgOiQC1SPpR8RYsme5QcQIZ6qfcR2SIfMD70nixQi4W RXlj9Ot3crV0a0hEnFBWfhO/UWTagwM/isMD543RBdmbGcYUoTWnVH/Wau7GMJ0= =VJFY -----END PGP SIGNATURE----- Sent from ProtonMail, Swiss-based encrypted email.
