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One internal project of Techainer discover a bug that when using python3 server.py with a content like this:
python3 server.py
from mlchain.base import ServeModel from model import Model from mlchain import mlconfig # mlconfig.load_config('mlconfig.yaml') model = Model(weight_path=mlconfig.weight, debug=mlconfig.debug) model = ServeModel(model) if __name__ == "__main__": from mlchain.rpc.server.flask_server import FlaskServer FlaskServer(model).run(bind=['127.0.0.1:8004'], gunicorn=True)
Have cause the self.sess.run inside the model class to hang forever. While using mlchain run CLI doesn't.
self.sess.run
mlchain run
Noted that this model use Tensorflow 1.14, 1.15 suffer the same problem
This DOES NOT effect production usage since we only use mlchain run but this bug is worth more examination.
The text was updated successfully, but these errors were encountered:
lamhoangtung
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One internal project of Techainer discover a bug that when using
python3 server.py
with a content like this:Have cause the
self.sess.run
inside the model class to hang forever. While usingmlchain run
CLI doesn't.Noted that this model use Tensorflow 1.14, 1.15 suffer the same problem
This DOES NOT effect production usage since we only use
mlchain run
but this bug is worth more examination.The text was updated successfully, but these errors were encountered: