Comments (4)
You're very certainly running out of memory. You can easily check that this is the case by training a model with a smaller vocabulary, typically:
curl -X POST "http://localhost:8080/train" -d "{\"service\":\"n20\",\"async\":true,\"parameters\":{\"mllib\":{\"gpu\":true,\"solver\":{\"iterations\":2000,\"test_interval\":200,\"base_lr\":0.05},\"net\":{\"batch_size\":300}},\"input\":{\"shuffle\":true,\"test_split\":0.2,\"min_count\":10,\"min_word_length\":3,\"count\":false},\"output\":{\"measure\":[\"mcll\",\"f1\"]}},\"data\":[\"models/n20/news20\"]}"
Note the min_count:10
.
That being said, the way to go independently from available memory is to use a database input. This is is easily done by first telling the service ML lib to use a db
, and at training time the txt
connector to produce a db
. See the steps below.
- Creating the service
curl -X PUT "http://localhost:8080/services/n20" -d "{\"mllib\":\"caffe\",\"description\":\"newsgroup classification service\",\"type\":\"supervised\",\"parameters\":{\"input\":{\"connector\":\"txt\"},\"mllib\":{\"template\":\"mlp\",\"db\":true,\"nclasses\":20,\"layers\":[200,200],\"activation\":\"relu\"}},\"model\":{\"templates\":\"../templates/caffe/\",\"repository\":\"models/n20\"}}"
Note the db:true
added to the mllib
component.
- Training the model:
curl -X POST "http://localhost:8080/train" -d "{\"service\":\"n20\",\"async\":true,\"parameters\":{\"mllib\":{\"gpu\":true,\"solver\":{\"iterations\":2000,\"test_interval\":200,\"base_lr\":0.05},\"net\":{\"batch_size\":300}},\"input\":{\"shuffle\":true,\"test_split\":0.2,\"min_count\":3,\"min_word_length\":3,\"count\":false,\"db\":true},\"output\":{\"measure\":[\"mcll\",\"f1\"]}},\"data\":[\"models/n20/news20\"]}"
Note the db:true
added to the input
connector object here.
Let me know how this goes.
from deepdetect.
Thanks for the quick response, @beniz! I'll let you know.
from deepdetect.
hey @beniz, Im trying to make a train, but, no success.
My response after perform a get is:
{"status":{"code":200,"msg":"OK"},"head":{"method":"/train","job":1,"status":"error"},"body":{}}
My cpu process is low, I have 8gm in ram in my macos, but, Im using docker.
from deepdetect.
Look at the docker logs, and to do this look at the docker readme.
from deepdetect.
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