From 4f77321eb7f0393c4b9dde08cf7cd4ac66446507 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 20:13:57 -0500 Subject: [PATCH 01/79] adding seldon inference graph that uses input transformer for spam classification example --- examples/input_tranformer/README.md | 1 + examples/input_tranformer/SpamClassifier | 1 + .../input_tranformer/Translator/.DS_Store | Bin 0 -> 6148 bytes .../Translator/.s2i/environment | 4 ++ .../input_tranformer/Translator/README.md | 0 .../input_tranformer/Translator/Translator.py | 23 +++++++++ examples/input_tranformer/deploy.yaml | 45 ++++++++++++++++++ examples/input_transformers/.DS_Store | Bin 0 -> 10244 bytes examples/input_transformers/README.md | 1 + examples/input_transformers/SpamClassifier | 1 + .../input_transformers/Translator/.DS_Store | Bin 0 -> 6148 bytes .../Translator/.s2i/environment | 4 ++ .../input_transformers/Translator/README.md | 0 .../Translator/Translator.py | 23 +++++++++ examples/input_transformers/deploy.yaml | 45 ++++++++++++++++++ 15 files changed, 148 insertions(+) create mode 100644 examples/input_tranformer/README.md create mode 160000 examples/input_tranformer/SpamClassifier create mode 100644 examples/input_tranformer/Translator/.DS_Store create mode 100644 examples/input_tranformer/Translator/.s2i/environment create mode 100644 examples/input_tranformer/Translator/README.md create mode 100644 examples/input_tranformer/Translator/Translator.py create mode 100644 examples/input_tranformer/deploy.yaml create mode 100644 examples/input_transformers/.DS_Store create mode 100644 examples/input_transformers/README.md create mode 160000 examples/input_transformers/SpamClassifier create mode 100644 examples/input_transformers/Translator/.DS_Store create mode 100644 examples/input_transformers/Translator/.s2i/environment create mode 100644 examples/input_transformers/Translator/README.md create mode 100644 examples/input_transformers/Translator/Translator.py create mode 100644 examples/input_transformers/deploy.yaml diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md new file mode 100644 index 0000000000..b43bf86b50 --- /dev/null +++ b/examples/input_tranformer/README.md @@ -0,0 +1 @@ +README.md diff --git a/examples/input_tranformer/SpamClassifier b/examples/input_tranformer/SpamClassifier new file mode 160000 index 0000000000..c1a439f5a4 --- /dev/null +++ b/examples/input_tranformer/SpamClassifier @@ -0,0 +1 @@ +Subproject commit c1a439f5a4a57332804844bdaf64260863c62c6c diff --git a/examples/input_tranformer/Translator/.DS_Store b/examples/input_tranformer/Translator/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..79b4f745e59d483826bb2559c85d55222ce3c110 GIT binary patch literal 6148 zcmeHKJ5Iwu5S;-RMl>lY_XJ4c1ST@#0J#7PkpL-?BSrM?xB#c1;tCvvoABlXvav!* z0dJ()=bf2ndtb4$DAkNM;WPrYVX+G&6l^`b~mX!958Mr z=dzazdHBY^9_F@P+pad<3bFe2{iM7+zg>>=>mT#G`{BR&dO@dA0V+TRr~noCcM70q zo7Ja*9H{^mpaNeC*#1!9hD~B0=vxN{zXAa581IH>KU)Kf1%ORrABYG{g9;3)<`qMO zj(EwsCb16;x~P8{@5wJ~-cU48_FBALvdyl2vxVL-U-t|GL zsiH;`#1~3@C5nCn}fO;M%ZG{@$&*f5CySap3&dGxU#y`Q&}}o>glhnsI4va)GS## zFdz!~#g!|XJ0gAi`wtA<{WKpZ*s+34$r$%DR~mHK3h6=YHmx@*I=a=}#L>y=OQLfj ziH;~uaIIb=HMh%CyK&H)qH)bMtZiz{LfbA+u@I={kZEj=skFQ`Wa^2CVYSP7p@1Gr z=z+M}pzD5ZziPF`)!sP0+F_ZocoTDMm*s?|Zc))s>sEc}ZMA9mIO-;Yczoh?cVW@| zvZ~tphVzpqPm%McO41I*(}-((H8r-iM+?Whwg;?`YJ@eTv$b2Zw4koG7=ehIm$Ok6 z&&!jiOmgMB6hST$+7dx+UqXxbv1iRJa7nwu*yIK?OrI8(PYEI`@f5gRJ84N%OvN-T z2rT=yVwcon(Vhit#|0&l)N&WTaM{A9Xa^USORlYYAgJncSmf0fS4*zeZjP9|-p*WF zC%M`}W+Z|`vGSnQ&R((7<=R7Az9V48H$_zgd-VY}&qh(;@(IFrTG|i}W3P(5^EoNl zaK$2T1;0fZQph3{owZ~a(mzTLl0)P$d4xPmUM45WDe^XXpL|R{C1=TJKz1fU+IfQQM3}dw7ykUGw@`b(G^RE0EihHIsD@pH6 z+HXY$s=WfGkvyJzd)#vbWR5JbTdCMBib!u>R_rbj1UZM7l)1}Ajjm8xGm1c^^hq2h zP<^8c)VJhE@(cNu{6>C9ff8Uk3lIv-fkjXb6&My)f)^T~5v6G}Y++$xHwu#qd!ZAe z(95Lh0Nf9UF%UeCq2M_7aRoP^im4LA+&!WsAk&cQ`SQIe9W4g5Zt!lhBDdnI{V zbAzZmM4tGkRO-EEyvcM*jjBjPbc{>GzmX?*v3s6?@|4u15;qFjWlbuVD$_NoT3V8> zNp;flbWK_*dBr@Oa5CWZmrOAukAO; eE#~0ypZ^(lY_XJ4c1ST@#0J#7PkpL-?BSrM?xB#c1;tCvvoABlXvav!* z0dJ()=bf2ndtb4$DAkNM;WPrYVX+G&6l^`b~mX!958Mr z=dzazdHBY^9_F@P+pad<3bFe2{iM7+zg>>=>mT#G`{BR&dO@dA0V+TRr~noCcM70q zo7Ja*9H{^mpaNeC*#1!9hD~B0=vxN{zXAa581IH>KU)Kf1%ORrABYG{g9;3)<`qMO zj(EwsCb16;x~P8{@5wJ~-cU48_FBALv Date: Sat, 2 Nov 2019 20:22:44 -0500 Subject: [PATCH 02/79] adding seldon inference graph that uses input transformer for spam classification example --- examples/input_transformers/.DS_Store | Bin 10244 -> 0 bytes examples/input_transformers/README.md | 1 - examples/input_transformers/SpamClassifier | 1 - .../input_transformers/Translator/.DS_Store | Bin 6148 -> 0 bytes .../Translator/.s2i/environment | 4 -- .../input_transformers/Translator/README.md | 0 .../Translator/Translator.py | 23 --------- examples/input_transformers/deploy.yaml | 45 ------------------ 8 files changed, 74 deletions(-) delete mode 100644 examples/input_transformers/.DS_Store delete mode 100644 examples/input_transformers/README.md delete mode 160000 examples/input_transformers/SpamClassifier delete mode 100644 examples/input_transformers/Translator/.DS_Store delete mode 100644 examples/input_transformers/Translator/.s2i/environment delete mode 100644 examples/input_transformers/Translator/README.md delete mode 100644 examples/input_transformers/Translator/Translator.py delete mode 100644 examples/input_transformers/deploy.yaml diff --git a/examples/input_transformers/.DS_Store b/examples/input_transformers/.DS_Store deleted file mode 100644 index 8ab174d641c4212c4961af59d11fc366c612a4d4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10244 zcmeHMYitxn9G`zFu)Dmr2v_zf^rV(YY3YNOQYd(Bk4Hfsa)p*>dyl2vxVL-U-t|GL zsiH;`#1~3@C5nCn}fO;M%ZG{@$&*f5CySap3&dGxU#y`Q&}}o>glhnsI4va)GS## zFdz!~#g!|XJ0gAi`wtA<{WKpZ*s+34$r$%DR~mHK3h6=YHmx@*I=a=}#L>y=OQLfj ziH;~uaIIb=HMh%CyK&H)qH)bMtZiz{LfbA+u@I={kZEj=skFQ`Wa^2CVYSP7p@1Gr z=z+M}pzD5ZziPF`)!sP0+F_ZocoTDMm*s?|Zc))s>sEc}ZMA9mIO-;Yczoh?cVW@| zvZ~tphVzpqPm%McO41I*(}-((H8r-iM+?Whwg;?`YJ@eTv$b2Zw4koG7=ehIm$Ok6 z&&!jiOmgMB6hST$+7dx+UqXxbv1iRJa7nwu*yIK?OrI8(PYEI`@f5gRJ84N%OvN-T z2rT=yVwcon(Vhit#|0&l)N&WTaM{A9Xa^USORlYYAgJncSmf0fS4*zeZjP9|-p*WF zC%M`}W+Z|`vGSnQ&R((7<=R7Az9V48H$_zgd-VY}&qh(;@(IFrTG|i}W3P(5^EoNl zaK$2T1;0fZQph3{owZ~a(mzTLl0)P$d4xPmUM45WDe^XXpL|R{C1=TJKz1fU+IfQQM3}dw7ykUGw@`b(G^RE0EihHIsD@pH6 z+HXY$s=WfGkvyJzd)#vbWR5JbTdCMBib!u>R_rbj1UZM7l)1}Ajjm8xGm1c^^hq2h zP<^8c)VJhE@(cNu{6>C9ff8Uk3lIv-fkjXb6&My)f)^T~5v6G}Y++$xHwu#qd!ZAe z(95Lh0Nf9UF%UeCq2M_7aRoP^im4LA+&!WsAk&cQ`SQIe9W4g5Zt!lhBDdnI{V zbAzZmM4tGkRO-EEyvcM*jjBjPbc{>GzmX?*v3s6?@|4u15;qFjWlbuVD$_NoT3V8> zNp;flbWK_*dBr@Oa5CWZmrOAukAO; eE#~0ypZ^(lY_XJ4c1ST@#0J#7PkpL-?BSrM?xB#c1;tCvvoABlXvav!* z0dJ()=bf2ndtb4$DAkNM;WPrYVX+G&6l^`b~mX!958Mr z=dzazdHBY^9_F@P+pad<3bFe2{iM7+zg>>=>mT#G`{BR&dO@dA0V+TRr~noCcM70q zo7Ja*9H{^mpaNeC*#1!9hD~B0=vxN{zXAa581IH>KU)Kf1%ORrABYG{g9;3)<`qMO zj(EwsCb16;x~P8{@5wJ~-cU48_FBALv Date: Sat, 2 Nov 2019 20:27:38 -0500 Subject: [PATCH 03/79] commiting contents of classifier --- .../Spam-Classifier/.s2i/environment | 4 +++ .../Spam-Classifier/README.md | 2 ++ .../Spam-Classifier/SpamClassifier.py | 28 +++++++++++++++++++ .../Spam-Classifier/model/model.pkl | 0 .../Spam-Classifier/model/vectorizer.pkl | 0 5 files changed, 34 insertions(+) create mode 100644 examples/input_tranformer/Spam-Classifier/.s2i/environment create mode 100644 examples/input_tranformer/Spam-Classifier/README.md create mode 100644 examples/input_tranformer/Spam-Classifier/SpamClassifier.py create mode 100644 examples/input_tranformer/Spam-Classifier/model/model.pkl create mode 100644 examples/input_tranformer/Spam-Classifier/model/vectorizer.pkl diff --git a/examples/input_tranformer/Spam-Classifier/.s2i/environment b/examples/input_tranformer/Spam-Classifier/.s2i/environment new file mode 100644 index 0000000000..5f81849ec2 --- /dev/null +++ b/examples/input_tranformer/Spam-Classifier/.s2i/environment @@ -0,0 +1,4 @@ +MODEL_NAME=SpamClassifier +API_TYPE=REST +SERVICE_TYPE=MODEL +PERSISTENCE=0 diff --git a/examples/input_tranformer/Spam-Classifier/README.md b/examples/input_tranformer/Spam-Classifier/README.md new file mode 100644 index 0000000000..c1b5ad501c --- /dev/null +++ b/examples/input_tranformer/Spam-Classifier/README.md @@ -0,0 +1,2 @@ +# Spam Classifier + diff --git a/examples/input_tranformer/Spam-Classifier/SpamClassifier.py b/examples/input_tranformer/Spam-Classifier/SpamClassifier.py new file mode 100644 index 0000000000..6da5c4c354 --- /dev/null +++ b/examples/input_tranformer/Spam-Classifier/SpamClassifier.py @@ -0,0 +1,28 @@ +from pathlib import Path +import numpy as np +import pickle +from sklearn.externals import joblib + + +class SpamClassifier(): + + def __init__(self, model_path = Path('./model')): + + self.models_path = model_path + self.clf = joblib.load(model_path.joinpath('model.pkl')) + self.vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl')) + + + def predict(self, text, feature_names): #List[Tuple[float, float]]: + """ + Predict on a english text you got from translator service. The output returns the probability of text being spam + """ + + clean_text = self.preprocess(text) + data = self.vectorizer.transform([clean_text]).todense() + probas = self.clf.predict_proba(data) + prob = probas[0][1] + + return np.array([prob, "spam"]) + + diff --git a/examples/input_tranformer/Spam-Classifier/model/model.pkl b/examples/input_tranformer/Spam-Classifier/model/model.pkl new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/input_tranformer/Spam-Classifier/model/vectorizer.pkl b/examples/input_tranformer/Spam-Classifier/model/vectorizer.pkl new file mode 100644 index 0000000000..e69de29bb2 From c728c71dd544260fa0859bbd1a098ae6d71a9a14 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 20:54:22 -0500 Subject: [PATCH 04/79] updated readme --- .../Spam-Classifier/README.md | 37 ++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/examples/input_tranformer/Spam-Classifier/README.md b/examples/input_tranformer/Spam-Classifier/README.md index c1b5ad501c..4e7e80b23c 100644 --- a/examples/input_tranformer/Spam-Classifier/README.md +++ b/examples/input_tranformer/Spam-Classifier/README.md @@ -1,2 +1,37 @@ -# Spam Classifier +# **Spam Classifier** + + + +#### Activate Virtual Env + +Create a Python virtual environment: + +``` + $ python3 -m venv venv + $ source venv/bin/activate +``` + +#### Install s2i + + + linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/ + mac: brew install source-to-image + + +#### Build + +``` +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/spam-classifier:1.0.0.1 +$ docker push sandhya1594/spam-classifier:1.0.0.1 +``` + +#### Test + +``` +$ docker run --name "spam-classifier" --rm sandhya1594/spam-classifier:1.0.0.1 + +``` + + + From 1ab253cf6efeeb0a8031e97db448cfd4a9de852f Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 20:58:43 -0500 Subject: [PATCH 05/79] deleted --- examples/input_tranformer/SpamClassifier | 1 - 1 file changed, 1 deletion(-) delete mode 160000 examples/input_tranformer/SpamClassifier diff --git a/examples/input_tranformer/SpamClassifier b/examples/input_tranformer/SpamClassifier deleted file mode 160000 index c1a439f5a4..0000000000 --- a/examples/input_tranformer/SpamClassifier +++ /dev/null @@ -1 +0,0 @@ -Subproject commit c1a439f5a4a57332804844bdaf64260863c62c6c From e7735f08998c436ee3e8b01ffd52ef8228a7514a Mon Sep 17 00:00:00 2001 From: Sandhya Gopchandani Date: Sat, 2 Nov 2019 20:59:12 -0500 Subject: [PATCH 06/79] Delete .DS_Store --- examples/input_tranformer/Translator/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/input_tranformer/Translator/.DS_Store diff --git a/examples/input_tranformer/Translator/.DS_Store b/examples/input_tranformer/Translator/.DS_Store deleted file mode 100644 index 79b4f745e59d483826bb2559c85d55222ce3c110..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKJ5Iwu5S;-RMl>lY_XJ4c1ST@#0J#7PkpL-?BSrM?xB#c1;tCvvoABlXvav!* z0dJ()=bf2ndtb4$DAkNM;WPrYVX+G&6l^`b~mX!958Mr z=dzazdHBY^9_F@P+pad<3bFe2{iM7+zg>>=>mT#G`{BR&dO@dA0V+TRr~noCcM70q zo7Ja*9H{^mpaNeC*#1!9hD~B0=vxN{zXAa581IH>KU)Kf1%ORrABYG{g9;3)<`qMO zj(EwsCb16;x~P8{@5wJ~-cU48_FBALv Date: Sat, 2 Nov 2019 22:26:58 -0500 Subject: [PATCH 07/79] updated readme --- .../input_tranformer/Translator/README.md | 33 +++++++++++++++++++ 1 file changed, 33 insertions(+) diff --git a/examples/input_tranformer/Translator/README.md b/examples/input_tranformer/Translator/README.md index e69de29bb2..d6ea376ea4 100644 --- a/examples/input_tranformer/Translator/README.md +++ b/examples/input_tranformer/Translator/README.md @@ -0,0 +1,33 @@ +# **Translator** + + + +#### Activate Virtual Env + +Create a Python virtual environment: + +``` + $ python3 -m venv venv + $ source venv/bin/activate +``` + +#### Install s2i + + + linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/ + mac: brew install source-to-image + + +#### Build + +``` +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/translator:1.0.0.1 +$ docker push sandhya1594/translator:1.0.0.1 +``` + +#### Test + +``` +$ docker run --name "translator" --rm sandhya1594/translator:1.0.0.1 + +``` \ No newline at end of file From 5029dce489dd4aac7272452665cd1fad1fb23052 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 22:28:53 -0500 Subject: [PATCH 08/79] updated readme --- examples/input_tranformer/README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index b43bf86b50..e570026d49 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -1 +1,5 @@ -README.md + +#### Deploy to Seldon Core + + + kubectl apply -f deploy.yaml From 587ef22bc7fee060770df520ca5e102c061ecdaa Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 22:34:13 -0500 Subject: [PATCH 09/79] example for Seldon combiner component --- .../spam_clf_combiner/Combiner/.DS_Store | Bin 0 -> 6148 bytes .../Combiner/.s2i/environment | 4 ++ .../spam_clf_combiner/Combiner/Combiner.py | 22 +++++++ .../spam_clf_combiner/Combiner/deploy.yaml | 45 +++++++++++++ .../KerasSpamClassifier/.s2i/environment | 4 ++ .../KerasSpamClassifier.py | 36 ++++++++++ .../KerasSpamClassifier/README.md | 2 + .../spam_clf_combiner/SpamClassifier | 1 + .../combiners/spam_clf_combiner/deploy.yaml | 62 ++++++++++++++++++ 9 files changed, 176 insertions(+) create mode 100644 examples/combiners/spam_clf_combiner/Combiner/.DS_Store create mode 100644 examples/combiners/spam_clf_combiner/Combiner/.s2i/environment create mode 100644 examples/combiners/spam_clf_combiner/Combiner/Combiner.py create mode 100644 examples/combiners/spam_clf_combiner/Combiner/deploy.yaml create mode 100644 examples/combiners/spam_clf_combiner/KerasSpamClassifier/.s2i/environment create mode 100644 examples/combiners/spam_clf_combiner/KerasSpamClassifier/KerasSpamClassifier.py create mode 100644 examples/combiners/spam_clf_combiner/KerasSpamClassifier/README.md create mode 160000 examples/combiners/spam_clf_combiner/SpamClassifier create mode 100644 examples/combiners/spam_clf_combiner/deploy.yaml diff --git a/examples/combiners/spam_clf_combiner/Combiner/.DS_Store b/examples/combiners/spam_clf_combiner/Combiner/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..79b4f745e59d483826bb2559c85d55222ce3c110 GIT binary patch literal 6148 zcmeHKJ5Iwu5S;-RMl>lY_XJ4c1ST@#0J#7PkpL-?BSrM?xB#c1;tCvvoABlXvav!* z0dJ()=bf2ndtb4$DAkNM;WPrYVX+G&6l^`b~mX!958Mr z=dzazdHBY^9_F@P+pad<3bFe2{iM7+zg>>=>mT#G`{BR&dO@dA0V+TRr~noCcM70q zo7Ja*9H{^mpaNeC*#1!9hD~B0=vxN{zXAa581IH>KU)Kf1%ORrABYG{g9;3)<`qMO zj(EwsCb16;x~P8{@5wJ~-cU48_FBALv Date: Sat, 2 Nov 2019 22:35:21 -0500 Subject: [PATCH 10/79] Delete .DS_Store --- .../spam_clf_combiner/Combiner/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/combiners/spam_clf_combiner/Combiner/.DS_Store diff --git a/examples/combiners/spam_clf_combiner/Combiner/.DS_Store b/examples/combiners/spam_clf_combiner/Combiner/.DS_Store deleted file mode 100644 index 79b4f745e59d483826bb2559c85d55222ce3c110..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKJ5Iwu5S;-RMl>lY_XJ4c1ST@#0J#7PkpL-?BSrM?xB#c1;tCvvoABlXvav!* z0dJ()=bf2ndtb4$DAkNM;WPrYVX+G&6l^`b~mX!958Mr z=dzazdHBY^9_F@P+pad<3bFe2{iM7+zg>>=>mT#G`{BR&dO@dA0V+TRr~noCcM70q zo7Ja*9H{^mpaNeC*#1!9hD~B0=vxN{zXAa581IH>KU)Kf1%ORrABYG{g9;3)<`qMO zj(EwsCb16;x~P8{@5wJ~-cU48_FBALv Date: Sat, 2 Nov 2019 22:43:33 -0500 Subject: [PATCH 11/79] more descriptive names for classifiers --- .../spam_clf_combiner/SpamClassifier | 1 - .../.s2i/environment | 0 .../KerasSpamClassifier.py | 0 .../README.md | 0 .../skl-spam-classifier/.s2i/environment | 4 +++ .../skl-spam-classifier/README.md | 2 ++ .../skl-spam-classifier/SpamClassifier.py | 28 +++++++++++++++++++ 7 files changed, 34 insertions(+), 1 deletion(-) delete mode 160000 examples/combiners/spam_clf_combiner/SpamClassifier rename examples/combiners/spam_clf_combiner/{KerasSpamClassifier => keras-spam-classifier}/.s2i/environment (100%) rename examples/combiners/spam_clf_combiner/{KerasSpamClassifier => keras-spam-classifier}/KerasSpamClassifier.py (100%) rename examples/combiners/spam_clf_combiner/{KerasSpamClassifier => keras-spam-classifier}/README.md (100%) create mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/.s2i/environment create mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md create mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py diff --git a/examples/combiners/spam_clf_combiner/SpamClassifier b/examples/combiners/spam_clf_combiner/SpamClassifier deleted file mode 160000 index c1a439f5a4..0000000000 --- a/examples/combiners/spam_clf_combiner/SpamClassifier +++ /dev/null @@ -1 +0,0 @@ -Subproject commit c1a439f5a4a57332804844bdaf64260863c62c6c diff --git a/examples/combiners/spam_clf_combiner/KerasSpamClassifier/.s2i/environment b/examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment similarity index 100% rename from examples/combiners/spam_clf_combiner/KerasSpamClassifier/.s2i/environment rename to examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment diff --git a/examples/combiners/spam_clf_combiner/KerasSpamClassifier/KerasSpamClassifier.py b/examples/combiners/spam_clf_combiner/keras-spam-classifier/KerasSpamClassifier.py similarity index 100% rename from examples/combiners/spam_clf_combiner/KerasSpamClassifier/KerasSpamClassifier.py rename to examples/combiners/spam_clf_combiner/keras-spam-classifier/KerasSpamClassifier.py diff --git a/examples/combiners/spam_clf_combiner/KerasSpamClassifier/README.md b/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md similarity index 100% rename from examples/combiners/spam_clf_combiner/KerasSpamClassifier/README.md rename to examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/.s2i/environment b/examples/combiners/spam_clf_combiner/skl-spam-classifier/.s2i/environment new file mode 100644 index 0000000000..5f81849ec2 --- /dev/null +++ b/examples/combiners/spam_clf_combiner/skl-spam-classifier/.s2i/environment @@ -0,0 +1,4 @@ +MODEL_NAME=SpamClassifier +API_TYPE=REST +SERVICE_TYPE=MODEL +PERSISTENCE=0 diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md b/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md new file mode 100644 index 0000000000..43d4ec5cde --- /dev/null +++ b/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md @@ -0,0 +1,2 @@ +# Span-Classifier + diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py b/examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py new file mode 100644 index 0000000000..6da5c4c354 --- /dev/null +++ b/examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py @@ -0,0 +1,28 @@ +from pathlib import Path +import numpy as np +import pickle +from sklearn.externals import joblib + + +class SpamClassifier(): + + def __init__(self, model_path = Path('./model')): + + self.models_path = model_path + self.clf = joblib.load(model_path.joinpath('model.pkl')) + self.vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl')) + + + def predict(self, text, feature_names): #List[Tuple[float, float]]: + """ + Predict on a english text you got from translator service. The output returns the probability of text being spam + """ + + clean_text = self.preprocess(text) + data = self.vectorizer.transform([clean_text]).todense() + probas = self.clf.predict_proba(data) + prob = probas[0][1] + + return np.array([prob, "spam"]) + + From 4aea4ebdc3c2902cfb0f07f75ac03de74c5d6faf Mon Sep 17 00:00:00 2001 From: Sandhya Gopchandani Date: Sat, 2 Nov 2019 22:46:12 -0500 Subject: [PATCH 12/79] Delete deploy.yaml --- .../spam_clf_combiner/Combiner/deploy.yaml | 45 ------------------- 1 file changed, 45 deletions(-) delete mode 100644 examples/combiners/spam_clf_combiner/Combiner/deploy.yaml diff --git a/examples/combiners/spam_clf_combiner/Combiner/deploy.yaml b/examples/combiners/spam_clf_combiner/Combiner/deploy.yaml deleted file mode 100644 index b58592734e..0000000000 --- a/examples/combiners/spam_clf_combiner/Combiner/deploy.yaml +++ /dev/null @@ -1,45 +0,0 @@ ---- -apiVersion: machinelearning.seldon.io/v1alpha2 -kind: SeldonDeployment -metadata: - labels: - app: seldon - name: seldon-spam-classifier - namespace: default -spec: - annotations: - project_name: seldon-spam-classifier - deployment_version: v1 - seldon.io/rest-read-timeout: '100000' - seldon.io/rest-connection-timeout: '100000' - seldon.io/grpc-read-timeout: '100000' - name: seldon-spam-classifier - - predictors: - - componentSpecs: - - spec: - containers: - - name: translator - image: translator:1.0.0.1 - imagePullPolicy: Always - - name: spam-classifier - image: spam-classifier:1.0.0.1 - imagePullPolicy: IfNotPresent - # imagePullSecrets: - # - name: regcred - terminationGracePeriodSeconds: 20 - graph: - children: - - name: spam-classifier - endpoint: - type: REST - type: MODEL - children: [] - name: translator - endpoint: - type: REST - type: TRANSFORMER - name: seldon-transformer - replicas: 1 - annotations: - predictor_version: v1 \ No newline at end of file From 826281dafc3689fa82684d4e9a593dca940df8a4 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 22:55:22 -0500 Subject: [PATCH 13/79] adding Translator --- .../Translator/.s2i/environment | 4 ++++ .../Translator/Translator.py | 23 +++++++++++++++++++ 2 files changed, 27 insertions(+) create mode 100644 examples/combiners/spam_clf_combiner/Translator/.s2i/environment create mode 100644 examples/combiners/spam_clf_combiner/Translator/Translator.py diff --git a/examples/combiners/spam_clf_combiner/Translator/.s2i/environment b/examples/combiners/spam_clf_combiner/Translator/.s2i/environment new file mode 100644 index 0000000000..de0283d032 --- /dev/null +++ b/examples/combiners/spam_clf_combiner/Translator/.s2i/environment @@ -0,0 +1,4 @@ +MODEL_NAME=Translator +API_TYPE=REST +SERVICE_TYPE=TRANSFORMER +PERSISTENCE=0 diff --git a/examples/combiners/spam_clf_combiner/Translator/Translator.py b/examples/combiners/spam_clf_combiner/Translator/Translator.py new file mode 100644 index 0000000000..26f7bcb689 --- /dev/null +++ b/examples/combiners/spam_clf_combiner/Translator/Translator.py @@ -0,0 +1,23 @@ +import goslate + +class Translator(): + def __init__(self): + self.gs = goslate.Goslate() + + + + def transform_input(self, text_msg, feature_names=None): + + """the translator logic will go here. This shows a use of simple library. But translator service can be a Machine Learning model itself""" + return self.gs.translate(text_msg, 'en') + + + +# if __name__== "__main__": +# t = Translator() +# translated = t.transform_input('Wie läuft dein Tag') +# print(translated) + + + + From 88b8e730d58aefc2e3ef9d988a19438c109ba937 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 23:27:12 -0500 Subject: [PATCH 14/79] adding readme files --- examples/combiners/spam_clf_combiner/Combiner/README.md | 0 examples/combiners/spam_clf_combiner/Translator/README.md | 0 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 examples/combiners/spam_clf_combiner/Combiner/README.md create mode 100644 examples/combiners/spam_clf_combiner/Translator/README.md diff --git a/examples/combiners/spam_clf_combiner/Combiner/README.md b/examples/combiners/spam_clf_combiner/Combiner/README.md new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/combiners/spam_clf_combiner/Translator/README.md b/examples/combiners/spam_clf_combiner/Translator/README.md new file mode 100644 index 0000000000..e69de29bb2 From eceab69e5bbdbd90ef637014d0efaae3060e6edb Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 23:39:51 -0500 Subject: [PATCH 15/79] updating readme files --- .../spam_clf_combiner/Combiner/README.md | 33 +++++++++++++++++ .../spam_clf_combiner/Translator/README.md | 33 +++++++++++++++++ .../keras-spam-classifier/README.md | 37 ++++++++++++++++++- .../skl-spam-classifier/README.md | 33 ++++++++++++++++- 4 files changed, 134 insertions(+), 2 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/Combiner/README.md b/examples/combiners/spam_clf_combiner/Combiner/README.md index e69de29bb2..9110504cb5 100644 --- a/examples/combiners/spam_clf_combiner/Combiner/README.md +++ b/examples/combiners/spam_clf_combiner/Combiner/README.md @@ -0,0 +1,33 @@ +# **Combiner** + + + +#### Activate Virtual Env + +Create a Python virtual environment: + +``` + $ python3 -m venv venv + $ source venv/bin/activate +``` + +#### Install s2i + + + linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/ + mac: brew install source-to-image + + +#### Build + +``` +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/Combiner:1.0.0.1 +$ docker push sandhya1594/Combiner:1.0.0.1 +``` + +#### Test + +``` +$ docker run --name "Combiner" --rm sandhya1594/Combiner:1.0.0.1 + +``` \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/Translator/README.md b/examples/combiners/spam_clf_combiner/Translator/README.md index e69de29bb2..d6ea376ea4 100644 --- a/examples/combiners/spam_clf_combiner/Translator/README.md +++ b/examples/combiners/spam_clf_combiner/Translator/README.md @@ -0,0 +1,33 @@ +# **Translator** + + + +#### Activate Virtual Env + +Create a Python virtual environment: + +``` + $ python3 -m venv venv + $ source venv/bin/activate +``` + +#### Install s2i + + + linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/ + mac: brew install source-to-image + + +#### Build + +``` +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/translator:1.0.0.1 +$ docker push sandhya1594/translator:1.0.0.1 +``` + +#### Test + +``` +$ docker run --name "translator" --rm sandhya1594/translator:1.0.0.1 + +``` \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md b/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md index 43d4ec5cde..09da154c6d 100644 --- a/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md +++ b/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md @@ -1,2 +1,37 @@ -# Span-Classifier +# **Spam Classifier** + + + +#### Activate Virtual Env + +Create a Python virtual environment: + +``` + $ python3 -m venv venv + $ source venv/bin/activate +``` + +#### Install s2i + + + linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/ + mac: brew install source-to-image + + +#### Build + +``` +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/keras-spam-classifier:1.0.0.1 +$ docker push sandhya1594/keras-spam-classifier:1.0.0.1 +``` + +#### Test + +``` +$ docker run --name "spam-classifier" --rm sandhya1594/keras-spam-classifier:1.0.0.1 + +``` + + + diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md b/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md index 43d4ec5cde..71b67673f1 100644 --- a/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md +++ b/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md @@ -1,2 +1,33 @@ -# Span-Classifier +# **Span-Classifier** + + +#### Activate Virtual Env + +Create a Python virtual environment: + +``` + $ python3 -m venv venv + $ source venv/bin/activate +``` + +#### Install s2i + + + linux: https://computingforgeeks.com/install-source-to-image-toolkit-on-linux/ + mac: brew install source-to-image + + +#### Build + +``` +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/spam-classifier:1.0.0.1 +$ docker push sandhya1594/spam-classifier:1.0.0.1 +``` + +#### Test + +``` +$ docker run --name "spam-classifier" --rm sandhya1594/spam-classifier:1.0.0.1 + +``` \ No newline at end of file From f2091cba6cb35a3d8d5ebfb1287f97714f111cfe Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sat, 2 Nov 2019 23:45:34 -0500 Subject: [PATCH 16/79] upating readme --- examples/combiners/spam_clf_combiner/README.md | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 examples/combiners/spam_clf_combiner/README.md diff --git a/examples/combiners/spam_clf_combiner/README.md b/examples/combiners/spam_clf_combiner/README.md new file mode 100644 index 0000000000..e570026d49 --- /dev/null +++ b/examples/combiners/spam_clf_combiner/README.md @@ -0,0 +1,5 @@ + +#### Deploy to 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Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/seldon_inference_graph.png) + + + kubectl apply -f deploy.yaml From 2be50df262688309a2345779012408f6e18e66e0 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 17:03:23 -0600 Subject: [PATCH 19/79] adding model pipeline image and updating readme" --- examples/input_tranformer/README.md | 2 ++ .../seldon_inference_graph.png | Bin 0 -> 135402 bytes 2 files changed, 2 insertions(+) create mode 100644 examples/input_tranformer/seldon_inference_graph.png diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index e570026d49..92a4117e0f 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -2,4 +2,6 @@ #### Deploy to Seldon Core +![Model Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_transformer/seldon_inference_graph.png) + kubectl apply -f deploy.yaml diff --git a/examples/input_tranformer/seldon_inference_graph.png b/examples/input_tranformer/seldon_inference_graph.png new file mode 100644 index 0000000000000000000000000000000000000000..78612902ffc4087672ab922c8dc63afd11c19d53 GIT binary patch literal 135402 zcmZU31ymGm`#vEdNO!k%hxF1RjUWwDBGTO~-3?@j5ON z-iB4!9d!z$?EE?_`zdWe5f-c-&ch^>B`U~UpuC618ipPshgB`zWj>senu@9$a|C2P znL#b-u<|hU43Pq?lyw-h&Qayon8rv4Z*K{Yu~7IoHS94^2t$gOUcL-Bh-X)HW{iBO zz+<@PEXrrNrYTyjKgY`E&bWku^0t3{s19|)H9YV+6Nu*Q)pFFV7Bis+$^?@lZ6C$y z1431KX(14}jaX4?CHEFX=L#e)94isfabW$qTA6J_pw|4|K;9*|X&tN)T4=)&BSR~a z&ch{dB787Ey0AO>R5P;0D1(Xyt5&w}xDV}(Odp3~S=`g-^QA9?C9tj@2{8GRob;41 ziA9PlWKhtQPCC4dXK5kqC5s@B$8AxkUjD8S;9n9`|6ELl$ramSNZdKujYa1}LRAB( za!~7~(~Vm~)B+P?=6gw*fzKFaqi#MiZmn3KW`L_r!yn9gUUCncSZBM02lj|2LMSd< 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image and updating readme" --- examples/input_tranformer/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index 92a4117e0f..e6966d1e37 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -2,6 +2,6 @@ #### Deploy to Seldon Core -![Model Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_transformer/seldon_inference_graph.png) +![Model Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_tranformer/seldon_inference_graph.png) kubectl apply -f deploy.yaml From 6079e242c6ae4f752bda91353c9e22a28827a958 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 17:08:24 -0600 Subject: [PATCH 21/79] README updated" --- examples/input_tranformer/README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index e6966d1e37..51eebbeedd 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -1,7 +1,17 @@ #### Deploy to Seldon Core +Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. + ![Model Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_tranformer/seldon_inference_graph.png) + + +Each component in Seldon Inference graph is a microservice – specifically a docker container. The Seldon graph links those docker containers together to result in an end output. The repository contains the code for each docker image needed for seldon graph to work. 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z<{tt2o6>P7I)A%@cb_Tn1*Hr=n-1q@Q3HKj$^AN$xqVySvDD!`_A0r@O5UiZRlFOB z>$k!j*1igTfrgJBkXepAzUCeHqF~yJ+A&_cLd=-sLxX2`c)>`s?1tD_`nz7#f~%5ym}TZRYw=jL8=i z0bvKH(zRJG|6xUER508< z5P39k{&KPJ_Vq{|^?cc24ii!v$`oNM9U}}?>&Gh&nu{6Yi4dyKyyG5o#Iql=%V%n! zomX$~wR?U|u!{;c>yFxpHnk?75Pc8GesG<*3zdXK(b-?~Y5e<&+);zW=;W0Dc$l7* z@9sQYX>5>3LDz>h4CtXX|G1U&+W>7|H<%45@}l7bh&c(elJfQXN}bemVPYw67zR*4z8+C{{WFUdq3VS>pmJ{yDZtu9x4+l7@#A*_UmJ)7`C@-m`eKt2jj2e zEpS1I(YB7!o>4lTL@EIC2R)n;M%+Gb6WVSWb#mMeY(Qtv(VBX~7EZ$Xic0g>f7$8^ z2zDFP)K~lWqYzS#KS!9w@*k?U)FJlHa@+5!%3)7>;E!+v10#^_ilG)GCowq=9wt^e zx_nrU+}}U$g2>weRPAm^+8>+G|6jl2$Bcyl+kdo@Ws%WB2cW)Ct=La06)eL453{lg ASpWb4 literal 0 HcmV?d00001 From ab50759399711dcb3f99839e2e71b11dbb4cc308 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 17:21:45 -0600 Subject: [PATCH 23/79] updated readme --- examples/input_tranformer/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index 51eebbeedd..8bf8a84925 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -1,5 +1,5 @@ -#### Deploy to Seldon Core +#### Model Deployment using Seldon Transformer Component Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. @@ -12,6 +12,8 @@ Each component in Seldon Inference graph is a microservice – specifically a do +![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_tranformer/graph_explained.png) + kubectl apply -f deploy.yaml From 61c2c87e6c2a8a47bfabe50114bceb87ea9bc21f Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 17:25:34 -0600 Subject: [PATCH 24/79] updated readme --- examples/input_tranformer/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index 8bf8a84925..0ac7008814 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -8,12 +8,13 @@ Consider, we want to predict whether a text message is spam or not. The text dat -Each component in Seldon Inference graph is a microservice – specifically a docker container. The Seldon graph links those docker containers together to result in an end output. The repository contains the code for each docker image needed for seldon graph to work. And deploy.yaml files refer those docker images and make a seldon graph out of them. - +Each component in Seldon Inference graph is a microservice – specifically a docker container. The Seldon graph links those docker containers (in deploy.yaml file) to result in an end output. ![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_tranformer/graph_explained.png) +The repository contains the folder for for each docker image needed for seldon graph to work. Please note that the docker images would not work as is. You will want to add correct logic and model files for it to wotk. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using + kubectl apply -f deploy.yaml From a1b8a7acc17cdbca711035949505e39747ec1b76 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 17:29:07 -0600 Subject: [PATCH 25/79] graph explanation pic --- examples/input_tranformer/graph_explained.png | Bin 800335 -> 355828 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/examples/input_tranformer/graph_explained.png b/examples/input_tranformer/graph_explained.png index bca79ac2507ed95810bf50532f93d286602e8563..6c2285a31284990b4d3447bb528d0757cc830ef9 100644 GIT binary patch delta 355040 zcmV)VK(D{g>@f7(6(NaGPDc$28VUda01Zh?^&i9!B!CTHbd+ojU+N z;h*nId@AMld97UXtGuOB_N5eMeoLjQyr;x-74E63DxR&5>L^!6VuZG;s(7$c$|pv+ z-)1T_;=59X=Yekzeo^ol0j*U<6)?)9@EK&nFCPWJChw_=q0&f>;g=0RF)=#+UnKrt z?)a<0MRgV9A9?BVOAXc4k@Hy|dGUYwf4T6-#$Q##6Of(+Ch3g~1AzBl7cYsMpMm~< zR`@?F{oB>*s_GaV8O~nXzoY5@-x~f)t-qIoUsKImV9ixYd#sC>GygSA{>TWc)TR6q 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0ac7008814..f07608e926 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -14,7 +14,7 @@ Each component in Seldon Inference graph is a microservice – specifically a do ![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_tranformer/graph_explained.png) -The repository contains the folder for for each docker image needed for seldon graph to work. Please note that the docker images would not work as is. You will want to add correct logic and model files for it to wotk. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using +The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to wotk. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: kubectl apply -f deploy.yaml From 32c049cada15d3d60dba74102ede85a58fe817b3 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 17:32:03 -0600 Subject: [PATCH 27/79] added dockerhub path for docker images --- examples/input_tranformer/deploy.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/input_tranformer/deploy.yaml b/examples/input_tranformer/deploy.yaml index b58592734e..7b724bac53 100644 --- a/examples/input_tranformer/deploy.yaml +++ b/examples/input_tranformer/deploy.yaml @@ -14,19 +14,16 @@ spec: seldon.io/rest-connection-timeout: '100000' seldon.io/grpc-read-timeout: '100000' name: seldon-spam-classifier - predictors: - componentSpecs: - spec: containers: - name: translator - image: translator:1.0.0.1 + image: sandhya1594/translator:1.0.0.1 imagePullPolicy: Always - name: spam-classifier - image: spam-classifier:1.0.0.1 + image: sandhya1594/spam-classifier:1.0.0.1 imagePullPolicy: IfNotPresent - # imagePullSecrets: - # - name: regcred terminationGracePeriodSeconds: 20 graph: children: @@ -42,4 +39,7 @@ spec: name: seldon-transformer replicas: 1 annotations: - predictor_version: v1 \ No newline at end of file + predictor_version: v1 + + + \ No newline at end of file From e42f63009100f6691a96e20c01cf72076c78960c Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 17:34:13 -0600 Subject: [PATCH 28/79] updated deploy.yaml --- examples/combiners/spam_clf_combiner/deploy.yaml | 4 ---- 1 file changed, 4 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/deploy.yaml b/examples/combiners/spam_clf_combiner/deploy.yaml index da19714653..e5e0f10933 100644 --- a/examples/combiners/spam_clf_combiner/deploy.yaml +++ b/examples/combiners/spam_clf_combiner/deploy.yaml @@ -14,8 +14,6 @@ spec: seldon.io/rest-connection-timeout: '100000' seldon.io/grpc-read-timeout: '100000' name: seldon-spam-classifier - oauth_key: oauth-key - oauth_secret: oauth-secret predictors: - componentSpecs: - spec: @@ -32,8 +30,6 @@ spec: - name: translator image: translator:1.0.0.1 imagePullPolicy: IfNotPresent - # imagePullSecrets: - # - name: regcred terminationGracePeriodSeconds: 20 graph: children: From f368c1b53bbb384e707c3ca245f2e3da8f3a94d7 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 22:50:08 -0600 Subject: [PATCH 29/79] updated README --- examples/combiners/spam_clf_combiner/README.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/examples/combiners/spam_clf_combiner/README.md b/examples/combiners/spam_clf_combiner/README.md index 3c52fa17ae..f60a0e6e34 100644 --- a/examples/combiners/spam_clf_combiner/README.md +++ b/examples/combiners/spam_clf_combiner/README.md @@ -1,5 +1,13 @@ -#### Deploy to Seldon Core +#### Model Deployment using Seldon Transformer + Combiner Component + +Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. Moreover, we want to build two classifiers to solve the problem: one using Neural network architecture using keras library and other that uses traditional machine learning classifier from scikit learn. At runtime, we want to run both classifiers on given input and output with the average of the both predictions: + +Example: +If classifier 1 outputs the probability of 0.7 for the message to be spam +Classifier 2 outputs the probability of 0.8 for the message to be spam +We will go with the average of both classifier: 0.75 + ![Model Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/seldon_inference_graph.png) From 8131877b4213d0cdef203a37e5afccc057e016dd Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 22:50:37 -0600 Subject: [PATCH 30/79] updated Combiner.py --- .../spam_clf_combiner/Combiner/Combiner.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py index 3c735880b1..19163793a5 100644 --- a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py +++ b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py @@ -3,19 +3,19 @@ class Combiner(object): def aggregate(self, Xs, features_names=None): + + return np.mean([float(x[0]) for x in Xs]) - return max(Xs, key=lambda item: float(item[0])) +# if __name__== "__main__": +# clf1_res = np.array(['0.80', 'Spam']) +# clf2_res = np.array(['0.9959868467126312e-04', 'Spam']) -if __name__== "__main__": - clf1_res = np.array(['0.80', 'Spam']) - clf2_res = np.array(['0.9959868467126312e-04', 'Spam']) - - example = np.array([clf1_res, clf2_res]) - dc = Combiner() - res = dc.aggregate(example, features_names=None) - print(res) +# example = np.array([clf1_res, clf2_res]) +# dc = Combiner() +# res = dc.aggregate(example, features_names=None) +# print(res) From 005c6117e4190735e5360a42cc8f81bad7191f37 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 22:52:18 -0600 Subject: [PATCH 31/79] updated Combiner.py --- examples/combiners/spam_clf_combiner/Combiner/Combiner.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py index 19163793a5..760cf408b6 100644 --- a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py +++ b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py @@ -3,7 +3,7 @@ class Combiner(object): def aggregate(self, Xs, features_names=None): - + return np.mean([float(x[0]) for x in Xs]) @@ -13,8 +13,8 @@ def aggregate(self, Xs, features_names=None): # clf2_res = np.array(['0.9959868467126312e-04', 'Spam']) # example = np.array([clf1_res, clf2_res]) -# dc = Combiner() -# res = dc.aggregate(example, features_names=None) +# combine = Combiner() +# res = combine.aggregate(example, features_names=None) # print(res) From 9724d338c6426bb6eaadb1921c4714cf479e6abd Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 22:53:40 -0600 Subject: [PATCH 32/79] updated Combiner.py --- examples/combiners/spam_clf_combiner/Combiner/Combiner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py index 760cf408b6..8143dd5717 100644 --- a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py +++ b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py @@ -3,7 +3,7 @@ class Combiner(object): def aggregate(self, Xs, features_names=None): - + """average out the probabilities from multiple classifier and return that as a result""" return np.mean([float(x[0]) for x in Xs]) From 71e3204c6e5238938b56a4532ab1c857fb7675eb Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:07:18 -0600 Subject: [PATCH 33/79] adding graph explanation --- .../spam_clf_combiner/graph_explained.png | Bin 0 -> 500106 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 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The text dat Example: If classifier 1 outputs the probability of 0.7 for the message to be spam Classifier 2 outputs the probability of 0.8 for the message to be spam -We will go with the average of both classifier: 0.75 +We will go with the average probability from both classifiers: 0.75 ![Model Pipeline](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/seldon_inference_graph.png) +Each component in Seldon Inference graph is a microservice – specifically a docker container. The Seldon graph links those docker containers (in deploy.yaml file) to result in an end output. + + +![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/graph_explained.png) + +The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to wotk. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: kubectl apply -f deploy.yaml From 9039d8ce65f93ddd8159da35539cbae9dd7d3675 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:08:53 -0600 Subject: [PATCH 35/79] updated README --- examples/combiners/spam_clf_combiner/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/combiners/spam_clf_combiner/README.md b/examples/combiners/spam_clf_combiner/README.md index fa178f8a37..041fa3c9f7 100644 --- a/examples/combiners/spam_clf_combiner/README.md +++ b/examples/combiners/spam_clf_combiner/README.md @@ -18,6 +18,6 @@ Each component in Seldon Inference graph is a microservice – specifically a do ![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/graph_explained.png) -The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to wotk. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: +The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to work. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: kubectl apply -f deploy.yaml From 3ec7f10cff71bfde151fb8b41475471ebab2d5c4 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:09:20 -0600 Subject: [PATCH 36/79] updated README --- examples/input_tranformer/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index f07608e926..11bebb5e6a 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -14,7 +14,7 @@ Each component in Seldon Inference graph is a microservice – specifically a do ![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_tranformer/graph_explained.png) -The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to wotk. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: +The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to work. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: kubectl apply -f deploy.yaml From 24c2a7f51a09251c9d521dd2ee0609efa0e5443a Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:11:52 -0600 Subject: [PATCH 37/79] updated README --- examples/combiners/spam_clf_combiner/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/README.md b/examples/combiners/spam_clf_combiner/README.md index 041fa3c9f7..03f37592b6 100644 --- a/examples/combiners/spam_clf_combiner/README.md +++ b/examples/combiners/spam_clf_combiner/README.md @@ -1,11 +1,11 @@ #### Model Deployment using Seldon Transformer + Combiner Component -Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. Moreover, we want to build two classifiers to solve the problem: one using Neural network architecture using keras library and other that uses traditional machine learning classifier from scikit learn. At runtime, we want to run both classifiers on given input and output with the average of the both predictions: +Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. Moreover, we want to build two classifiers to solve the problem: one using Neural network architecture using keras library and other that uses traditional machine learning classifier from scikit learn. At runtime, we want to run both classifiers on given input and output with the average of the both predictions. -Example: -If classifier 1 outputs the probability of 0.7 for the message to be spam -Classifier 2 outputs the probability of 0.8 for the message to be spam +Example:
    +If classifier 1 outputs the probability of 0.7 for the message to be spam
    +Classifier 2 outputs the probability of 0.8 for the message to be spam
    We will go with the average probability from both classifiers: 0.75 From 1ba5b1e51fd79096ff012e615dc5b4f7ad0f909e Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:14:36 -0600 Subject: [PATCH 38/79] updated README --- examples/combiners/spam_clf_combiner/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/README.md b/examples/combiners/spam_clf_combiner/README.md index 03f37592b6..29ada953b4 100644 --- a/examples/combiners/spam_clf_combiner/README.md +++ b/examples/combiners/spam_clf_combiner/README.md @@ -1,12 +1,12 @@ #### Model Deployment using Seldon Transformer + Combiner Component -Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. Moreover, we want to build two classifiers to solve the problem: one using Neural network architecture using keras library and other that uses traditional machine learning classifier from scikit learn. At runtime, we want to run both classifiers on given input and output with the average of the both predictions. +Consider, we want to predict whether a text message is spam or not. The text data may contain multiple languages as well as each individual text may also have words from different languages. As per your needs, you are only interested in building a model on English language. So, you want to first translate the text message in English and then pass it to model to classify whether it is a spam or not. Moreover, we want to train two models: one that uses Neural network architecture using keras library and other that uses traditional machine learning classifier from scikit-learn. At runtime, we want to run both mdoels on a given input and output the average of the both predictions: Example:
    If classifier 1 outputs the probability of 0.7 for the message to be spam
    Classifier 2 outputs the probability of 0.8 for the message to be spam
    -We will go with the average probability from both classifiers: 0.75 +The final model using seldon-core graph would output the average probability of both classifiers: 0.75 From a9c7b04f205f4c23b44c9d9a3a41901f41822f7d Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:19:03 -0600 Subject: [PATCH 39/79] updated README files --- examples/combiners/spam_clf_combiner/Combiner/README.md | 6 +++--- examples/combiners/spam_clf_combiner/Translator/README.md | 7 ++++--- .../spam_clf_combiner/skl-spam-classifier/README.md | 6 +++--- 3 files changed, 10 insertions(+), 9 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/Combiner/README.md b/examples/combiners/spam_clf_combiner/Combiner/README.md index 9110504cb5..7523b256dc 100644 --- a/examples/combiners/spam_clf_combiner/Combiner/README.md +++ b/examples/combiners/spam_clf_combiner/Combiner/README.md @@ -21,13 +21,13 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/Combiner:1.0.0.1 -$ docker push sandhya1594/Combiner:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /Combiner:1.0.0.1 +$ docker push /Combiner:1.0.0.1 ``` #### Test ``` -$ docker run --name "Combiner" --rm sandhya1594/Combiner:1.0.0.1 +$ docker run --name "Combiner" --rm /Combiner:1.0.0.1 ``` \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/Translator/README.md b/examples/combiners/spam_clf_combiner/Translator/README.md index d6ea376ea4..fb7a3487b5 100644 --- a/examples/combiners/spam_clf_combiner/Translator/README.md +++ b/examples/combiners/spam_clf_combiner/Translator/README.md @@ -21,13 +21,14 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/translator:1.0.0.1 -$ docker push sandhya1594/translator:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1 +$ docker tag /translator:1.0.0.1 +$ docker push /translator:1.0.0.1 ``` #### Test ``` -$ docker run --name "translator" --rm sandhya1594/translator:1.0.0.1 +$ docker run --name "translator" --rm /translator:1.0.0.1 ``` \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md b/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md index 71b67673f1..740cfcb5a0 100644 --- a/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md +++ b/examples/combiners/spam_clf_combiner/skl-spam-classifier/README.md @@ -21,13 +21,13 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/spam-classifier:1.0.0.1 -$ docker push sandhya1594/spam-classifier:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /spam-classifier:1.0.0.1 +$ docker push /spam-classifier:1.0.0.1 ``` #### Test ``` -$ docker run --name "spam-classifier" --rm sandhya1594/spam-classifier:1.0.0.1 +$ docker run --name "spam-classifier" --rm /spam-classifier:1.0.0.1 ``` \ No newline at end of file From 0521475a390fc8eb8f7a632cdd0d42d85387eaab Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:19:57 -0600 Subject: [PATCH 40/79] updated README files --- .../spam_clf_combiner/keras-spam-classifier/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md b/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md index 09da154c6d..560d582e2d 100644 --- a/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md +++ b/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md @@ -21,14 +21,14 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/keras-spam-classifier:1.0.0.1 -$ docker push sandhya1594/keras-spam-classifier:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /keras-spam-classifier:1.0.0.1 +$ docker push /keras-spam-classifier:1.0.0.1 ``` #### Test ``` -$ docker run --name "spam-classifier" --rm sandhya1594/keras-spam-classifier:1.0.0.1 +$ docker run --name "spam-classifier" --rm /keras-spam-classifier:1.0.0.1 ``` From 38842db1d7f1d1e0beafa8b899ce45dd92ca0b7d Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Sun, 3 Nov 2019 23:20:47 -0600 Subject: [PATCH 41/79] updated README files --- examples/input_tranformer/Spam-Classifier/README.md | 6 +++--- examples/input_tranformer/Translator/README.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/input_tranformer/Spam-Classifier/README.md b/examples/input_tranformer/Spam-Classifier/README.md index 4e7e80b23c..692d537b4a 100644 --- a/examples/input_tranformer/Spam-Classifier/README.md +++ b/examples/input_tranformer/Spam-Classifier/README.md @@ -21,14 +21,14 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/spam-classifier:1.0.0.1 -$ docker push sandhya1594/spam-classifier:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /spam-classifier:1.0.0.1 +$ docker push /spam-classifier:1.0.0.1 ``` #### Test ``` -$ docker run --name "spam-classifier" --rm sandhya1594/spam-classifier:1.0.0.1 +$ docker run --name "spam-classifier" --rm /spam-classifier:1.0.0.1 ``` diff --git a/examples/input_tranformer/Translator/README.md b/examples/input_tranformer/Translator/README.md index d6ea376ea4..4472f4bad3 100644 --- a/examples/input_tranformer/Translator/README.md +++ b/examples/input_tranformer/Translator/README.md @@ -21,13 +21,13 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 sandhya1594/translator:1.0.0.1 -$ docker push sandhya1594/translator:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /translator:1.0.0.1 +$ docker push /translator:1.0.0.1 ``` #### Test ``` -$ docker run --name "translator" --rm sandhya1594/translator:1.0.0.1 +$ docker run --name "translator" --rm /translator:1.0.0.1 ``` \ No newline at end of file From 52aa5b39740a2daf173015437d02ccbb50b0fa51 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 12:28:26 -0600 Subject: [PATCH 42/79] working example with correct model file and vectorizer --- .../Spam-Classifier/.DS_Store | Bin 0 -> 8196 bytes .../Spam-Classifier/SpamClassifier.py | 15 ++- .../Spam-Classifier/Untitled.ipynb | 113 ++++++++++++++++++ .../Spam-Classifier/model/.DS_Store | Bin 0 -> 6148 bytes .../Spam-Classifier/model/model.pkl | Bin 0 -> 137219 bytes .../Spam-Classifier/model/vectorizer.pkl | Bin 0 -> 508071 bytes .../Spam-Classifier/requirements.txt | 3 + 7 files changed, 127 insertions(+), 4 deletions(-) create mode 100644 examples/input_tranformer/Spam-Classifier/.DS_Store create mode 100644 examples/input_tranformer/Spam-Classifier/Untitled.ipynb create mode 100644 examples/input_tranformer/Spam-Classifier/model/.DS_Store create 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pickle -from sklearn.externals import joblib +from sklearn.externals import joblib +import sklearn class SpamClassifier(): @@ -17,12 +18,18 @@ def predict(self, text, feature_names): #List[Tuple[float, float]]: """ Predict on a english text you got from translator service. The output returns the probability of text being spam """ - - clean_text = self.preprocess(text) - data = self.vectorizer.transform([clean_text]).todense() + data = self.vectorizer.transform([text]).todense() probas = self.clf.predict_proba(data) prob = probas[0][1] return np.array([prob, "spam"]) +# if __name__== "__main__": +# print('The scikit-learn version is {}.'.format(sklearn.__version__)) + +# text = 'click here to win the price' +# #vect_path=Path('/Users/sandhya.sandhya/Desktop/data/doc/data'), +# sc = SpamClassifier(model_path=Path('./model')) +# res = sc.predict(text, feature_names=None) +# print(res) \ No newline at end of file diff --git a/examples/input_tranformer/Spam-Classifier/Untitled.ipynb b/examples/input_tranformer/Spam-Classifier/Untitled.ipynb new file mode 100644 index 0000000000..7a029a3b7f --- /dev/null +++ b/examples/input_tranformer/Spam-Classifier/Untitled.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import numpy as np\n", + "import pickle\n", + "import sklearn\n", + "from sklearn.externals import joblib " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "model_path = Path('./model')\n", + "clf = joblib.load(model_path.joinpath('model.pkl'))\n", + "vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl'))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "text = 'click here to win the price'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data = vectorizer.transform([text]).todense()\n", + "probas =clf.predict_proba(data)\n", + "prob = probas[0][1]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9785519250237193" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prob" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The scikit-learn version is 0.21.2.\n" + ] + } + ], + "source": [ + "print('The scikit-learn version is {}.'.format(sklearn.__version__))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git 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.../input_tranformer/Spam-Classifier/.DS_Store | Bin 8196 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/input_tranformer/Spam-Classifier/.DS_Store diff --git a/examples/input_tranformer/Spam-Classifier/.DS_Store b/examples/input_tranformer/Spam-Classifier/.DS_Store deleted file mode 100644 index 29f6714386bb4fd78bc5cf945b41374adad02f92..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8196 zcmeHMO>Epm6n<}$w6>cPCr#V9u+7pmC{5FLlco(pNxRtusz6FZH@{8W#Je_$!E1-L zcQ-$zMB;)_`8)8V7A}B5D4akbae~4H@hd?Q0n`Hr4shX!Kp^qPs z=Kajfd-Ls#o)-Wxp4FNGY5+i>%BZ}Fnrjp$XLU^p`J6JMNcI3`GVe?$O*^?pH(&%2 z2qF+fAc#N^fgl3^j|kA4&5E$jyU(RT8$=+8zzrDzc0WX@G8*&gBB$QfL5-IJ5G^IP zSEx^UfM*l&Y0RgKoO-4-C(jK^sIMh``ziu-v^2(qMxIdQtxVNpjjW95)*M7DFYaWg9jMf+R?rr89{sJD+e> zH|xaJ%orQ>tL{|N?+@6vxzbmYLx!Fom#e#N%T*0a&v?d$rjyF)VZ+kweB91jn&Zur z-~vGq1#w(f=I2`uw>L$a54A5eMdq8Mhg+H=ZEdX!3!<>SzPYn^B0W1d|M0@O3*0UA 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deletions(-) delete mode 100644 examples/input_tranformer/Spam-Classifier/Untitled.ipynb diff --git a/examples/input_tranformer/Spam-Classifier/Untitled.ipynb b/examples/input_tranformer/Spam-Classifier/Untitled.ipynb deleted file mode 100644 index 7a029a3b7f..0000000000 --- a/examples/input_tranformer/Spam-Classifier/Untitled.ipynb +++ /dev/null @@ -1,113 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import numpy as np\n", - "import pickle\n", - "import sklearn\n", - "from sklearn.externals import joblib " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "model_path = Path('./model')\n", - "clf = joblib.load(model_path.joinpath('model.pkl'))\n", - "vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl'))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "text = 'click here to win the price'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "data = vectorizer.transform([text]).todense()\n", - "probas =clf.predict_proba(data)\n", - "prob = probas[0][1]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9785519250237193" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prob" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The scikit-learn version is 0.21.2.\n" - ] - } - ], - "source": [ - "print('The scikit-learn version is {}.'.format(sklearn.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From ea9f71092609516d8c6a8bacd63968b454274eaf Mon Sep 17 00:00:00 2001 From: Sandhya Gopchandani Date: Mon, 4 Nov 2019 12:30:26 -0600 Subject: [PATCH 45/79] Delete .DS_Store --- .../Spam-Classifier/model/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/input_tranformer/Spam-Classifier/model/.DS_Store diff --git a/examples/input_tranformer/Spam-Classifier/model/.DS_Store b/examples/input_tranformer/Spam-Classifier/model/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 Date: Mon, 4 Nov 2019 12:55:41 -0600 Subject: [PATCH 46/79] added example of how to test the image --- .../Spam-Classifier/README.md | 12 +- .../Spam-Classifier/SpamClassifier.py | 2 +- .../Spam-Classifier/Untitled.ipynb | 113 ------------------ .../Spam-Classifier/requirements.txt | 3 +- 4 files changed, 11 insertions(+), 119 deletions(-) delete mode 100644 examples/input_tranformer/Spam-Classifier/Untitled.ipynb diff --git a/examples/input_tranformer/Spam-Classifier/README.md b/examples/input_tranformer/Spam-Classifier/README.md index 692d537b4a..636483b860 100644 --- a/examples/input_tranformer/Spam-Classifier/README.md +++ b/examples/input_tranformer/Spam-Classifier/README.md @@ -21,14 +21,20 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /spam-classifier:1.0.0.1 -$ docker push /spam-classifier:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1 +$ docker push spam-classifier:1.0.0.1 ``` #### Test ``` -$ docker run --name "spam-classifier" --rm /spam-classifier:1.0.0.1 +$ docker run --name "spam-classifier" --rm -d -p 5000:5000 spam-classifier:1.0.0.1 + +curl -g http://localhost:5000/predict --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["click here to win the price"]}}' + +Result:
    + +{"data":{"ndarray":["0.9785519250237192","spam"]},"meta":{}} ``` diff --git a/examples/input_tranformer/Spam-Classifier/SpamClassifier.py b/examples/input_tranformer/Spam-Classifier/SpamClassifier.py index 71f3a46075..8d089698c6 100644 --- a/examples/input_tranformer/Spam-Classifier/SpamClassifier.py +++ b/examples/input_tranformer/Spam-Classifier/SpamClassifier.py @@ -18,7 +18,7 @@ def predict(self, text, feature_names): #List[Tuple[float, float]]: """ Predict on a english text you got from translator service. The output returns the probability of text being spam """ - data = self.vectorizer.transform([text]).todense() + data = self.vectorizer.transform(text).todense() probas = self.clf.predict_proba(data) prob = probas[0][1] diff --git a/examples/input_tranformer/Spam-Classifier/Untitled.ipynb b/examples/input_tranformer/Spam-Classifier/Untitled.ipynb deleted file mode 100644 index 7a029a3b7f..0000000000 --- a/examples/input_tranformer/Spam-Classifier/Untitled.ipynb +++ /dev/null @@ -1,113 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import numpy as np\n", - "import pickle\n", - "import sklearn\n", - "from sklearn.externals import joblib " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "model_path = Path('./model')\n", - "clf = joblib.load(model_path.joinpath('model.pkl'))\n", - "vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl'))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "text = 'click here to win the price'" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "data = vectorizer.transform([text]).todense()\n", - "probas =clf.predict_proba(data)\n", - "prob = probas[0][1]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9785519250237193" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prob" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The scikit-learn version is 0.21.2.\n" - ] - } - ], - "source": [ - "print('The scikit-learn version is {}.'.format(sklearn.__version__))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/input_tranformer/Spam-Classifier/requirements.txt b/examples/input_tranformer/Spam-Classifier/requirements.txt index 7b4dd789bc..79993db472 100644 --- a/examples/input_tranformer/Spam-Classifier/requirements.txt +++ b/examples/input_tranformer/Spam-Classifier/requirements.txt @@ -1,3 +1,2 @@ scikit-learn==0.21.2 -pickle -numpy \ No newline at end of file +numpy>=1.9.2 \ No newline at end of file From fc8149e6e3e627837ba17f98d8b47a5fa20f1f24 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 12:57:55 -0600 Subject: [PATCH 47/79] added example of how to test the image --- examples/input_tranformer/Spam-Classifier/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/input_tranformer/Spam-Classifier/README.md b/examples/input_tranformer/Spam-Classifier/README.md index 636483b860..e191f813ad 100644 --- a/examples/input_tranformer/Spam-Classifier/README.md +++ b/examples/input_tranformer/Spam-Classifier/README.md @@ -22,7 +22,7 @@ Create a Python virtual environment: ``` $ s2i build . seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1 -$ docker push spam-classifier:1.0.0.1 +$ docker push /spam-classifier:1.0.0.1 ``` #### Test @@ -32,7 +32,7 @@ $ docker run --name "spam-classifier" --rm -d -p 5000:5000 spam-classifier:1.0. curl -g http://localhost:5000/predict --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["click here to win the price"]}}' -Result:
    +Result: {"data":{"ndarray":["0.9785519250237192","spam"]},"meta":{}} From 4ec47fcdebc0068b880d532988ac4578f5b83fa8 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 14:32:02 -0600 Subject: [PATCH 48/79] updated code and README for Translator image --- examples/input_tranformer/Translator/README.md | 5 ++++- examples/input_tranformer/Translator/Translator.py | 7 +++++-- examples/input_tranformer/Translator/requirements.txt | 2 ++ 3 files changed, 11 insertions(+), 3 deletions(-) create mode 100644 examples/input_tranformer/Translator/requirements.txt diff --git a/examples/input_tranformer/Translator/README.md b/examples/input_tranformer/Translator/README.md index 4472f4bad3..c96a6ae526 100644 --- a/examples/input_tranformer/Translator/README.md +++ b/examples/input_tranformer/Translator/README.md @@ -21,7 +21,7 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /translator:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1 $ docker push /translator:1.0.0.1 ``` @@ -30,4 +30,7 @@ $ docker push /translator:1.0.0.1 ``` $ docker run --name "translator" --rm /translator:1.0.0.1 +curl -g http://localhost:5000/transform_input --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["Wie läuft dein Tag"]}}' + + ``` \ No newline at end of file diff --git a/examples/input_tranformer/Translator/Translator.py b/examples/input_tranformer/Translator/Translator.py index 26f7bcb689..29f25201f0 100644 --- a/examples/input_tranformer/Translator/Translator.py +++ b/examples/input_tranformer/Translator/Translator.py @@ -1,4 +1,5 @@ import goslate +import numpy as np class Translator(): def __init__(self): @@ -9,13 +10,15 @@ def __init__(self): def transform_input(self, text_msg, feature_names=None): """the translator logic will go here. This shows a use of simple library. But translator service can be a Machine Learning model itself""" - return self.gs.translate(text_msg, 'en') + translated = self.gs.translate(text_msg[0], 'en') + return np.array([translated]) # if __name__== "__main__": # t = Translator() -# translated = t.transform_input('Wie läuft dein Tag') +# example = np.array(['Wie läuft dein Tag']) +# translated = t.transform_input(example) # print(translated) diff --git a/examples/input_tranformer/Translator/requirements.txt b/examples/input_tranformer/Translator/requirements.txt new file mode 100644 index 0000000000..88f2f9e5ec --- /dev/null +++ b/examples/input_tranformer/Translator/requirements.txt @@ -0,0 +1,2 @@ +goslate +numpy \ No newline at end of file From 2a663a5dd4b5df06afe3eb5a387c2269335df7d6 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 15:02:15 -0600 Subject: [PATCH 49/79] updated skl-spam-classifier --- .../combiners/spam_clf_combiner/.DS_Store | Bin 0 -> 6148 bytes .../skl-spam-classifier/.DS_Store | Bin 0 -> 6148 bytes .../skl-spam-classifier/README.md | 18 ++++++++++++++---- .../skl-spam-classifier/SpamClassifier.py | 15 +++++++++++---- .../skl-spam-classifier/model/model.pkl | Bin 0 -> 137219 bytes .../skl-spam-classifier/model/vectorizer.pkl | Bin 0 -> 508071 bytes .../skl-spam-classifier/requirements.txt | 2 ++ 7 files changed, 27 insertions(+), 8 deletions(-) create mode 100644 examples/combiners/spam_clf_combiner/.DS_Store create mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/.DS_Store create mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/model/model.pkl create mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/model/vectorizer.pkl create mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/requirements.txt diff --git a/examples/combiners/spam_clf_combiner/.DS_Store b/examples/combiners/spam_clf_combiner/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..8a7a9ea683124b476cd52dfd2d7ea29bfd593b32 GIT binary patch literal 6148 zcmeHKJ5Iw;5S)b+k!T{(P`(RLa04p}C&&eW1SEt}P%L^^oQtDn_9H~(AkomES!;LR zTkkx}Q+T}qZ27*u0Tuw}bVq!6n3~^rpV(Ptj7aAh1Kx0tS6pK=sXm=>?j?CI=*jxd z?|IU|>lsh(!F4RK0cba^7nj{f_Q6 tpL92_gTfH)m>BJt8*j%~QIvJf*Sy~qj)_5MKIlaK47e^bDe%_{8~`Z-7_I;S literal 0 HcmV?d00001 diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/.DS_Store b/examples/combiners/spam_clf_combiner/skl-spam-classifier/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..ee7d3e968e9f5a7afa98a3efe366bfe982a93221 GIT binary patch literal 6148 zcmeH~O^O0R422VaKyc&IWox|-TTqg^zI56yp8n_^%Z?V<%6m{u1C5+H#IfqCpVcK;vY@8/spam-classifier:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1 $ docker push /spam-classifier:1.0.0.1 ``` #### Test ``` -$ docker run --name "spam-classifier" --rm /spam-classifier:1.0.0.1 +$ docker run --name "spam-classifier" --rm -d -p 5000:5000 spam-classifier:1.0.0.1 + +curl -g http://localhost:5000/predict --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["click here to win the price"]}}' + +Result: + +{"data":{"ndarray":["0.9785519250237192","spam"]},"meta":{}} + +``` + + + -``` \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py b/examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py index 6da5c4c354..8d089698c6 100644 --- a/examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py +++ b/examples/combiners/spam_clf_combiner/skl-spam-classifier/SpamClassifier.py @@ -1,7 +1,8 @@ from pathlib import Path import numpy as np import pickle -from sklearn.externals import joblib +from sklearn.externals import joblib +import sklearn class SpamClassifier(): @@ -17,12 +18,18 @@ def predict(self, text, feature_names): #List[Tuple[float, float]]: """ Predict on a english text you got from translator service. The output returns the probability of text being spam """ - - clean_text = self.preprocess(text) - data = self.vectorizer.transform([clean_text]).todense() + data = self.vectorizer.transform(text).todense() probas = self.clf.predict_proba(data) prob = probas[0][1] return np.array([prob, "spam"]) +# if __name__== "__main__": +# print('The scikit-learn version is {}.'.format(sklearn.__version__)) + +# text = 'click here to win the price' +# #vect_path=Path('/Users/sandhya.sandhya/Desktop/data/doc/data'), +# sc = SpamClassifier(model_path=Path('./model')) +# res = sc.predict(text, feature_names=None) +# print(res) \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/model/model.pkl b/examples/combiners/spam_clf_combiner/skl-spam-classifier/model/model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..36b2d6043fd638ac8b816074d29b70f3f3423ddb GIT binary patch literal 137219 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Sandhya Date: Mon, 4 Nov 2019 16:33:47 -0600 Subject: [PATCH 50/79] added keras model architecture files along with working python code --- .../keras-spam-classifier/.DS_Store | Bin 0 -> 6148 bytes .../KerasSpamClassifier.py | 20 ++++++++++++------ .../model/architecture.json | 1 + .../keras-spam-classifier/model/tokenizer.pkl | Bin 0 -> 376242 bytes .../keras-spam-classifier/model/weights.h5 | Bin 0 -> 406504 bytes .../keras-spam-classifier/requirements.txt | 4 ++++ 6 files changed, 19 insertions(+), 6 deletions(-) create mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/.DS_Store create mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/model/architecture.json create mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/model/tokenizer.pkl create mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/model/weights.h5 create mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/requirements.txt 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a/examples/combiners/spam_clf_combiner/.DS_Store b/examples/combiners/spam_clf_combiner/.DS_Store deleted file mode 100644 index 8a7a9ea683124b476cd52dfd2d7ea29bfd593b32..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKJ5Iw;5S)b+k!T{(P`(RLa04p}C&&eW1SEt}P%L^^oQtDn_9H~(AkomES!;LR zTkkx}Q+T}qZ27*u0Tuw}bVq!6n3~^rpV(Ptj7aAh1Kx0tS6pK=sXm=>?j?CI=*jxd z?|IU|>lsh(!F4RK0cba^7nj{f_Q6 tpL92_gTfH)m>BJt8*j%~QIvJf*Sy~qj)_5MKIlaK47e^bDe%_{8~`Z-7_I;S From cebfc845fec445e0cb395682789fafa24a0f2e71 Mon Sep 17 00:00:00 2001 From: Sandhya Gopchandani Date: Mon, 4 Nov 2019 16:34:34 -0600 Subject: [PATCH 52/79] Delete .DS_Store --- .../skl-spam-classifier/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/.DS_Store diff --git a/examples/combiners/spam_clf_combiner/skl-spam-classifier/.DS_Store b/examples/combiners/spam_clf_combiner/skl-spam-classifier/.DS_Store deleted file mode 100644 index ee7d3e968e9f5a7afa98a3efe366bfe982a93221..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~O^O0R422VaKyc&IWox|-TTqg^zI56yp8n_^%Z?V<%6m{u1C5+H#IfqCpVcK;vY@8 Date: Mon, 4 Nov 2019 16:35:04 -0600 Subject: [PATCH 53/79] Delete .DS_Store --- .../keras-spam-classifier/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/.DS_Store diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/.DS_Store b/examples/combiners/spam_clf_combiner/keras-spam-classifier/.DS_Store deleted file mode 100644 index deeaad7a344c7ab08973fa6718bdef80232095bc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKJ5B^K47FjAMxxo0GFR9egw=3@?WoW)h%F|{HuO0bEf?V|Jbyrph=fFe7Gz7w zb7DK6H<~0SBHlht8=^H44Jd*vN=Kx-YdUl16_9m|d4AmN?rS-AhB?hHS^EVQsx;99 z_16E%TIx9N=ekGCaGPHb<@x1$8DHx3jmPou#~3=z?-r{H27-ZLAQ<>L1GuwAhD*b! zgMnZm82Dg7&WD5|m>rv;9v!GO1pvx3S_Qh~5)zXfvtu)a1;Q2zv{3dGgDo8W$^Eip zGqiAGPd?aZeotOlXUF(S-HEed)WJY7uw>xenrpfLZ}7`>ll)~!w1RzidzL+Jth4A|ifG6bSUiBLEFKN3PUp_9Qy|vSTxpQKVnPf$ Date: Mon, 4 Nov 2019 16:36:55 -0600 Subject: [PATCH 54/79] updated code for Translator plus added requirements --- examples/combiners/spam_clf_combiner/Translator/README.md | 8 +++++--- .../combiners/spam_clf_combiner/Translator/Translator.py | 7 +++++-- .../spam_clf_combiner/Translator/requirements.txt | 2 ++ 3 files changed, 12 insertions(+), 5 deletions(-) create mode 100644 examples/combiners/spam_clf_combiner/Translator/requirements.txt diff --git a/examples/combiners/spam_clf_combiner/Translator/README.md b/examples/combiners/spam_clf_combiner/Translator/README.md index fb7a3487b5..af4947b02d 100644 --- a/examples/combiners/spam_clf_combiner/Translator/README.md +++ b/examples/combiners/spam_clf_combiner/Translator/README.md @@ -7,8 +7,8 @@ Create a Python virtual environment: ``` - $ python3 -m venv venv - $ source venv/bin/activate +$ python3 -m venv venv +$ source venv/bin/activate ``` #### Install s2i @@ -22,7 +22,6 @@ Create a Python virtual environment: ``` $ s2i build . seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1 -$ docker tag /translator:1.0.0.1 $ docker push /translator:1.0.0.1 ``` @@ -31,4 +30,7 @@ $ docker push /translator:1.0.0.1 ``` $ docker run --name "translator" --rm /translator:1.0.0.1 +curl -g http://localhost:5000/transform_input --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["Wie läuft dein Tag"]}}' + + ``` \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/Translator/Translator.py b/examples/combiners/spam_clf_combiner/Translator/Translator.py index 26f7bcb689..29f25201f0 100644 --- a/examples/combiners/spam_clf_combiner/Translator/Translator.py +++ b/examples/combiners/spam_clf_combiner/Translator/Translator.py @@ -1,4 +1,5 @@ import goslate +import numpy as np class Translator(): def __init__(self): @@ -9,13 +10,15 @@ def __init__(self): def transform_input(self, text_msg, feature_names=None): """the translator logic will go here. This shows a use of simple library. But translator service can be a Machine Learning model itself""" - return self.gs.translate(text_msg, 'en') + translated = self.gs.translate(text_msg[0], 'en') + return np.array([translated]) # if __name__== "__main__": # t = Translator() -# translated = t.transform_input('Wie läuft dein Tag') +# example = np.array(['Wie läuft dein Tag']) +# translated = t.transform_input(example) # print(translated) diff --git a/examples/combiners/spam_clf_combiner/Translator/requirements.txt b/examples/combiners/spam_clf_combiner/Translator/requirements.txt new file mode 100644 index 0000000000..88f2f9e5ec --- /dev/null +++ b/examples/combiners/spam_clf_combiner/Translator/requirements.txt @@ -0,0 +1,2 @@ +goslate +numpy \ No newline at end of file From 992fc5d3e0b7c2492b4d5eeef5525e676883de37 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 16:38:25 -0600 Subject: [PATCH 55/79] updated code for Translator plus added requirements --- .../combiners/spam_clf_combiner/.DS_Store | Bin 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/keras-spam-classifier:1.0.0.1 +$ docker run --name "spam-classifier" --rm -d -p 5000:5000 keras-spam-classifier:1.0.0.1 + +curl -g http://localhost:5000/predict --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["please click this link to win the price"]}}' + +Result: + +{"data":{"ndarray":["0.91942614","spam"]},"meta":{}} + ``` + From 2a7683fa94911cd23c5b83c31a8c39738798767a Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 16:47:22 -0600 Subject: [PATCH 57/79] updated readme --- .../combiners/spam_clf_combiner/keras-spam-classifier/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md b/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md index a38b912e67..51721ab6b3 100644 --- a/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md +++ b/examples/combiners/spam_clf_combiner/keras-spam-classifier/README.md @@ -21,7 +21,7 @@ Create a Python virtual environment: #### Build ``` -$ s2i build . seldonio/seldon-core-s2i-python3:0.7 /keras-spam-classifier:1.0.0.1 +$ s2i build . seldonio/seldon-core-s2i-python3:0.7 keras-spam-classifier:1.0.0.1 $ docker push /keras-spam-classifier:1.0.0.1 ``` From 7d5ce26e04b9798669eb0e9238c89257d70fc61f Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 16:48:36 -0600 Subject: [PATCH 58/79] added .gitignore --- examples/input_tranformer/.DS_Store | Bin 0 -> 6148 bytes examples/input_tranformer/.gitignore.exclude | 1 + 2 files changed, 1 insertion(+) create mode 100644 examples/input_tranformer/.DS_Store create mode 100644 examples/input_tranformer/.gitignore.exclude diff --git a/examples/input_tranformer/.DS_Store b/examples/input_tranformer/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..9e0a680e694ae989f0d31217e2e742a3ee5c67f0 GIT binary patch literal 6148 zcmeHKyH3ME5S)b+5h+MXc|SnGADoi#1<42S2uP6(MtSt^`0dO-K(Q>OR1~ml?Tv4} 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similarity index 100% rename from examples/input_tranformer/.gitignore.exclude rename to examples/input_tranformer/.gitignore From 65ea8afd9f78d0e2c5a85b72881a2e501b91275c Mon Sep 17 00:00:00 2001 From: Sandhya Gopchandani Date: Mon, 4 Nov 2019 16:49:57 -0600 Subject: [PATCH 60/79] Delete .DS_Store --- examples/input_tranformer/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/input_tranformer/.DS_Store diff --git a/examples/input_tranformer/.DS_Store b/examples/input_tranformer/.DS_Store deleted file mode 100644 index 9e0a680e694ae989f0d31217e2e742a3ee5c67f0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKyH3ME5S)b+5h+MXc|SnGADoi#1<42S2uP6(MtSt^`0dO-K(Q>OR1~ml?Tv4} zb1P5b^#YLDVSNWo0gUO2_|#=+`docr7ZFhuJL48}G1yX@jAQeaj{;vY;*=pm7W3H({Dv%0% zE1=(pLRYMTouhp^xY!6l95HOhdz&SQMFYed*f}yn6Q>fLDluY+(-|+3R|7jor$ekV z&xy(s6N*@M#*3vxs$;IHKq}Bz;L^8K?f>`mALjo)DR-$rD)6TikkMkfnDLdOw+>!T wdu^dV(7%kimd?dmG0|EvH`7ca5nP-do&W#< From 7b5a6a71135e41b618425ba8bb3c9306b27f969d Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 16:57:22 -0600 Subject: [PATCH 61/79] tested combiner image --- examples/combiners/spam_clf_combiner/Combiner/Combiner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py index 8143dd5717..277954e351 100644 --- a/examples/combiners/spam_clf_combiner/Combiner/Combiner.py +++ b/examples/combiners/spam_clf_combiner/Combiner/Combiner.py @@ -4,7 +4,7 @@ class Combiner(object): def aggregate(self, Xs, features_names=None): """average out the probabilities from multiple classifier and return that as a result""" - return np.mean([float(x[0]) for x in Xs]) + return np.mean([float(x[0]) for x in Xs]) From b3ec36e431a3e733ee89f54dd645a4f51f6923ed Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 21:08:35 -0600 Subject: [PATCH 62/79] updated requirements --- .../keras-spam-classifier/.s2i/{environment => environment} | 2 +- .../spam_clf_combiner/keras-spam-classifier/requirements.txt | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) rename examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/{environment => environment} (82%) diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment b/examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment similarity index 82% rename from examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment rename to examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment index 417a1b038c..526ab17558 100644 --- a/examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment +++ b/examples/combiners/spam_clf_combiner/keras-spam-classifier/.s2i/environment @@ -1,4 +1,4 @@ MODEL_NAME=KerasSpamClassifier API_TYPE=REST SERVICE_TYPE=MODEL -PERSISTENCE=0 \ No newline at end of file +PERSISTENCE=0 diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/requirements.txt b/examples/combiners/spam_clf_combiner/keras-spam-classifier/requirements.txt index e7fe560f5b..0a233c77af 100644 --- a/examples/combiners/spam_clf_combiner/keras-spam-classifier/requirements.txt +++ b/examples/combiners/spam_clf_combiner/keras-spam-classifier/requirements.txt @@ -1,4 +1,3 @@ scikit-learn==0.21.2 numpy>=1.9.2 -pickle keras From 9b7798bbdb75379a2165c0eb167a61da3f2960d4 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 21:53:17 -0600 Subject: [PATCH 63/79] updated src code --- .../KerasSpamClassifier.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/KerasSpamClassifier.py b/examples/combiners/spam_clf_combiner/keras-spam-classifier/KerasSpamClassifier.py index e84f9f732c..8c1f22f665 100644 --- a/examples/combiners/spam_clf_combiner/keras-spam-classifier/KerasSpamClassifier.py +++ b/examples/combiners/spam_clf_combiner/keras-spam-classifier/KerasSpamClassifier.py @@ -24,21 +24,21 @@ def __init__(self, model_path = Path('./model')): - def predict(self, text, feature_names): #List[Tuple[float, float]]: + def predict(self, text, feature_names=None): #List[Tuple[float, float]]: """ Predict on a english text you got from translator service. The output returns the probability of text being spam """ seq= self.tokenizer.texts_to_sequences([text]) padded = sequence.pad_sequences(seq, maxlen=self.max_len) prob = self.model.predict(padded) + prob = prob[0][0] + return np.array([prob, "spam"]) - return np.array([prob[0][0], "spam"]) +# if __name__== "__main__": -if __name__== "__main__": - - text = 'please click this link to win the price' - #vect_path=Path('/Users/sandhya.sandhya/Desktop/data/doc/data'), - sc = KerasSpamClassifier(model_path=Path('./model')) - res = sc.predict(text, feature_names=None) - print(res) \ No newline at end of file +# text = 'please click this link to win the price' +# #vect_path=Path('/Users/sandhya.sandhya/Desktop/data/doc/data'), +# sc = KerasSpamClassifier(model_path=Path('./model')) +# res = sc.predict(text, feature_names=None) +# print(res) \ No newline at end of file From 48b5c7351e2b45305cbaeed336f7d73f1d772705 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 22:14:34 -0600 Subject: [PATCH 64/79] deployment files to work as in on k8s cluster assuming seldon-core is installed --- examples/combiners/spam_clf_combiner/deploy.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/combiners/spam_clf_combiner/deploy.yaml b/examples/combiners/spam_clf_combiner/deploy.yaml index e5e0f10933..0a74bc41e6 100644 --- a/examples/combiners/spam_clf_combiner/deploy.yaml +++ b/examples/combiners/spam_clf_combiner/deploy.yaml @@ -19,16 +19,16 @@ spec: - spec: containers: - name: combiner - image: combiner:1.0.0.1 + image: sandhya1594/combiner:1.0.0.1 imagePullPolicy: IfNotPresent - name: spam-classifier - image: spam-classifier:1.0.0.1 + image: sandhya1594/spam-classifier:1.0.0.1 imagePullPolicy: IfNotPresent - name: keras-spam-classifier - image: keras-spam-classifier:1.0.0.1 + image: sandhya1594/keras-spam-classifier:1.0.0.1 imagePullPolicy: IfNotPresent - name: translator - image: translator:1.0.0.1 + image: sandhya1594/translator:1.0.0.1 imagePullPolicy: IfNotPresent terminationGracePeriodSeconds: 20 graph: @@ -51,7 +51,7 @@ spec: name: translator endpoint: type: REST - type: TRANSLATOR + type: TRANSFORMER name: seldon-combiner replicas: 1 annotations: From 027e2be84111b4cce99bcb459af808fe05d5dba4 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 22:17:54 -0600 Subject: [PATCH 65/79] updated Readme --- examples/combiners/spam_clf_combiner/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/combiners/spam_clf_combiner/README.md b/examples/combiners/spam_clf_combiner/README.md index 29ada953b4..c15663c357 100644 --- a/examples/combiners/spam_clf_combiner/README.md +++ b/examples/combiners/spam_clf_combiner/README.md @@ -18,6 +18,6 @@ Each component in Seldon Inference graph is a microservice – specifically a do ![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/combiners/spam_clf_combiner/graph_explained.png) -The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to work. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: +The repository contains the folders for for each docker image needed for seldon graph to work. The images used for this example are already published in public dockerhub. So, deploy the model on k8s cluster using: kubectl apply -f deploy.yaml From 678fbadc9f7748cb0aa0dc60891ee84007b91ef8 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 22:18:31 -0600 Subject: [PATCH 66/79] updated Readme --- examples/.DS_Store | Bin 0 -> 10244 bytes examples/combiners/.DS_Store | Bin 0 -> 8196 bytes examples/input_tranformer/README.md | 2 +- .../Spam-Classifier/SpamClassifier.py | 2 +- 4 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 examples/.DS_Store create mode 100644 examples/combiners/.DS_Store diff --git a/examples/.DS_Store b/examples/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..4abf0534a83b4b464020488b454b106677b2ba85 GIT binary patch literal 10244 zcmeHMYiu0V6+WNiI5R_Nu0u$6H*vCwV@R9_YsZNlngqW>a2_PYPU5_9_Aw+w*SltC z*N)>FjNk`CKw7mVC;^F=KoAfT2qfAcJgXuIZxDfsN>v3_+aK+}imFjL_ud(^-q?hy zs!}O4(%dumo_qGrJ@ei3oqLyv&{{~<5k-l};9E*Dm5XsMsh9UTSDDvhu>{)_Ig}xr za^xlk&gmUA1Re-H5O^T)K;VJE1OE>mV9u78afy~e8F(P@K;VH<56JisTPmXA* zLI)RW3&66Ro7Kh7ybn+|QBO8|azsmI%D<_15AaOkZ84zDSs#_%SvGreL`!W>(B=eh zWOzFisL|Ojs=5=JwG7I@1Azz5^?=;Fo0Oz1^-`jE|L*oB(%A%S-%j+U9XB6~eTK40 z<&!I>7=~q7msboka2dV26e1WhZM?#c1znsSrr1i!5`8^;xCx4%uhqHNH2 z#Tnlfrgcf^+6~uPJz{!4&v0vB-pOX%op#P;*(0X8?3IpWHnS~fOZ~27HeKk=xILmW z8BZq*>3H65NvAuVBevU}w-4v#(;hdQ%eSeH-45HzwjLo0uD#u61E=3^XERs8JT>+5 z$h_)B%jz4~w0v^ukEV&r>6W#JotMcwH#v50$AFW{AKD#vlXfQMWDf4?cU&itw!1R% zUc0jF5z|ywGiQZD-Gzj6OTozxifY49t+VHZtbHjSX-hUGe=Q|mG<5&yoKR@5)U@Sn zE>oVt!kUoP^j=VYU=C}6Q8Gfs`yy08!h5`kMYk zf2XtbZx8?rGcgMx%*Pd2gvF@E3N+zr{1|Jn9-FWQ*JCTXaR3PC`=O>KXaAdYs<%7Bu>^m5D_F+SU zM@-SZqmcy$<3$*%YSlzclR>R@HIZ7w5M^4kEOM2}C>E2nuHI@e7_VZo);3zJO-8gR z*V;AKI)i~LDzvu6YBS~967-)V@KHKNzhd-#OlRo#jJ&@x>b_ybO+lqX+-%IjTwKYh ztHly5!zwglH6yPL?Tow)*oGU>i7xC!kB2@R8Dwz_a>!#Cw_*gh;||<|d+{^;oYDA8 zJcLJassw)>KJv80Gvnj0zcZIfXEO&`E0HS{fAmp-^3=n3j^cbCd=qm^j@gCJg%bm# ztn!YJ*9*lek)ffw*h;3A`q(N(E3s8vX>4pbhgKwQ@HHW=JjlxPPTeON`Ag^RTtqp? zeLodbPPk*EwYi8J{#%+Ehh4@cwC`dlGf&lmE7Y=XS8QQTw`NSV5{nqgtio}vEc!A3m?Z+G4D3`emCZl0YH@Ae_WK1cEX)+WDeya_JQN&4O8OaoK(-Woz)Ie)! z8||hP4KNkmPsd0Gf*0szdV}6$BKk-Lgg?-q>2LH6{geJhXHkJ^5SWh35LFbkghN3y zT2v_Lz;+G;J2(vNW%{vk5QjJn4B)ULq3@Re2|R|!@dTb^5_$!%;x)X3)A$r$;CB+I zyuI@YX^XD=CBEdR9|Gg=rKc)pr`8J!<1s9y^Qh6!Tk=JV!S%hwG!~scOKCPL;wT6ESoMe~aaza!-MbFb~^e*G^41LLI(O>8voDfxDDkns9 z8Hd$ah#D+M4E3B6HDE2SVMMkvCO30RB*|$zBXYkYr#Nm#T9H#9Cr3kyoNmKixSOfz zJ{-jZIDwORkm>1(A4E^*MPmtB`A`mI;DL+O1C#W;qw@TJJ;(D2{{O#7y%S73@W934 z0hG6Qwzu+J{cNvv+VZSD%J%`jZEI*#XxJdRh#FWlxGyeNIM*Kb5i dWzKfWoTWa^|DXREpm*xQ{y*6NU$p!GZviea4$J@m literal 0 HcmV?d00001 diff --git a/examples/combiners/.DS_Store b/examples/combiners/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..09a52a45bd35bb400c17b0eb7a5c6d152260c51c GIT binary patch literal 8196 zcmeHMOKjXk82bKD2o>ZFse?c{M3b?6uQacXBx? znfd>JJpT5xN52;Uuq&q}0BQh0qRLS&qvke++j(76LjFt%AyPcR7^K032@lt}2NMAU z0RsU80RsU80Rwjd19WEdBCT`o3v18@3l>%y;lb{UJ5{1Ow(SW zKIH*kOw`Auj|&35P+C)D59o)YpJJd0Cwr8;6DECJ5GcX{ML3{;W%M%?^sY{EQO_MP zDKKaQ1_B1wGQe~93UFWw6v(W|-)$PJV1NZWWYuYBN;PejysxWMrs23vO*b)AR9v!Y zvm}*DrCUp%OHJC-DOYiGPM4A$<)f2|JE{8pe%m&e`-(bf=+k3zRkv-qiec$lFWAs@ zaydO@SeiZEW#=r-@zzNRgCsGDjmfohb1fYQ;?czZw)uE;F45dhgZB2O`FSQ)HzW@C zjb~NMZtUJQCvB>UE8>WNc~6_8q%pjr*G04t3txx~*Jh zm8@zgZ#af(>I2h;=1!hcoV0FfhIMv?IJaD5!qBsnwQ}l)ZAHV00%LC=!d+@cjSi`rrTWEnVxnxXQae+aOG^FKKMJ?Y)k&EKV& B2wngH literal 0 HcmV?d00001 diff --git a/examples/input_tranformer/README.md b/examples/input_tranformer/README.md index 11bebb5e6a..d409ef2e87 100644 --- a/examples/input_tranformer/README.md +++ b/examples/input_tranformer/README.md @@ -14,7 +14,7 @@ Each component in Seldon Inference graph is a microservice – specifically a do ![Graph Explanation](https://github.com/SandhyaaGopchandani/seldon-core/blob/seldon_component_example/examples/input_tranformer/graph_explained.png) -The repository contains the folders for for each docker image needed for seldon graph to work. Please note that the docker image would not work as is. You will want to add correct logic and model files and recreate the docker images for it to work. Update deploy.yaml file to add correct reference to the docker images and deploy it to seldon core using: +The repository contains the folders for for each docker image needed for seldon graph to work. The images used for this example are already published in public dockerhub. So, deploy the model on k8s cluster using: kubectl apply -f deploy.yaml diff --git a/examples/input_tranformer/Spam-Classifier/SpamClassifier.py b/examples/input_tranformer/Spam-Classifier/SpamClassifier.py index 8d089698c6..ad9c99d327 100644 --- a/examples/input_tranformer/Spam-Classifier/SpamClassifier.py +++ b/examples/input_tranformer/Spam-Classifier/SpamClassifier.py @@ -21,7 +21,7 @@ def predict(self, text, feature_names): #List[Tuple[float, float]]: data = self.vectorizer.transform(text).todense() probas = self.clf.predict_proba(data) prob = probas[0][1] - + return np.array([prob, "spam"]) From b5b7a8d4eb3b4ba56bdbdae7abfbf097496bdfd7 Mon Sep 17 00:00:00 2001 From: Sandhya Gopchandani Date: Mon, 4 Nov 2019 22:19:09 -0600 Subject: [PATCH 67/79] Delete .DS_Store --- examples/combiners/.DS_Store | Bin 8196 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 examples/combiners/.DS_Store diff --git a/examples/combiners/.DS_Store b/examples/combiners/.DS_Store deleted file mode 100644 index 09a52a45bd35bb400c17b0eb7a5c6d152260c51c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8196 zcmeHMOKjXk82bKD2o>ZFse?c{M3b?6uQacXBx? znfd>JJpT5xN52;Uuq&q}0BQh0qRLS&qvke++j(76LjFt%AyPcR7^K032@lt}2NMAU z0RsU80RsU80Rwjd19WEdBCT`o3v18@3l>%y;lb{UJ5{1Ow(SW zKIH*kOw`Auj|&35P+C)D59o)YpJJd0Cwr8;6DECJ5GcX{ML3{;W%M%?^sY{EQO_MP zDKKaQ1_B1wGQe~93UFWw6v(W|-)$PJV1NZWWYuYBN;PejysxWMrs23vO*b)AR9v!Y zvm}*DrCUp%OHJC-DOYiGPM4A$<)f2|JE{8pe%m&e`-(bf=+k3zRkv-qiec$lFWAs@ zaydO@SeiZEW#=r-@zzNRgCsGDjmfohb1fYQ;?czZw)uE;F45dhgZB2O`FSQ)HzW@C zjb~NMZtUJQCvB>UE8>WNc~6_8q%pjr*G04t3txx~*Jh zm8@zgZ#af(>I2h;=1!hcoV0FfhIMv?IJaD5!qBsnwQ}l)ZAHV00%LC=!d+@cjSi`rrTWEnVxnxXQae+aOG^FKKMJ?Y)k&EKV& B2wngH From 3dd1d4dabbcb49a547acfe9fc39cb2bc62b586f4 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 22:21:53 -0600 Subject: [PATCH 68/79] notebook file to generate the model binaries --- .../Spam Classification.ipynb | 651 ++++++++++++++++++ 1 file changed, 651 insertions(+) create mode 100644 examples/input_tranformer/Spam Classification.ipynb diff --git a/examples/input_tranformer/Spam Classification.ipynb b/examples/input_tranformer/Spam Classification.ipynb new file mode 100644 index 0000000000..1fc647c4af --- /dev/null +++ b/examples/input_tranformer/Spam Classification.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SCIKIT-LEARN" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "from sklearn.externals import joblib\n", + "from pathlib import Path\n", + "import string\n", + "#from nltk.stem import SnowballStemmer\n", + "from nltk.corpus import stopwords\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "import pickle\n", + "from sklearn.svm import SVC\n", + "model_path: Path=Path('./')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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    3hamU dun say so early hor... U c already then say...NaNNaNNaN
    4hamNah I don't think he goes to usf, he lives aro...NaNNaNNaN
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    " + ], + "text/plain": [ + " v1 v2 Unnamed: 2 \\\n", + "0 ham Go until jurong point, crazy.. Available only ... NaN \n", + "1 ham Ok lar... Joking wif u oni... NaN \n", + "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", + "3 ham U dun say so early hor... U c already then say... NaN \n", + "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", + "\n", + " Unnamed: 3 Unnamed: 4 \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " class text\n", + "0 ham Go until jurong point, crazy.. Available only ...\n", + "1 ham Ok lar... Joking wif u oni...\n", + "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", + "3 ham U dun say so early hor... U c already then say...\n", + "4 ham Nah I don't think he goes to usf, he lives aro..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def pre_process(text):\n", + " text = text.translate(str.maketrans('', '', string.punctuation))\n", + " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", + " words = \"\"\n", + " for i in text:\n", + " stemmer = SnowballStemmer(\"english\")\n", + " words += (stemmer.stem(i))+\" \"\n", + " return words" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "features = data['text'].copy()\n", + "features = features.apply(pre_process)\n", + "\n", + "vectorizer = TfidfVectorizer(\"english\")\n", + "_features = vectorizer.fit_transform(features)\n", + "with open('vectorizer.pkl', 'wb') as vect:\n", + " pickle.dump(vectorizer, vect)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl'))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=1.0, kernel='sigmoid',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", + "svc.fit(train_x,train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# save the model to disk\n", + "filename = 'sk_model.pkl'\n", + "pickle.dump(svc, open(filename, 'wb'))\n", + "clf = joblib.load(model_path.joinpath(filename))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9730861244019139" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prediction = clf.predict(test_x)\n", + "accuracy_score(test_y,prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "message = np.array(['click here to win the price'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "data = vectorizer.transform(message).todense()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "probas = clf.predict_proba(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['ham', 'spam'], dtype=object)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.classes_" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.02511554, 0.97488446]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## KERAS" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from keras.models import Model\n", + "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n", + "from keras.optimizers import RMSprop\n", + "from keras.preprocessing.text import Tokenizer\n", + "from keras.preprocessing import sequence\n", + "from keras.utils import to_categorical\n", + "from keras.callbacks import EarlyStopping\n", + "import pickle\n", + "from sklearn.externals import joblib\n", + "\n", + "from keras.engine.saving import model_from_json\n", + "from keras.layers import (\n", + " Bidirectional,\n", + " concatenate,\n", + " Dense,\n", + " Embedding,\n", + " LSTM,\n", + " Masking,\n", + " Reshape,\n", + " SpatialDropout1D,\n", + " TimeDistributed,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    1hamOk lar... Joking wif u oni...
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U c already then say...\n", + "4 ham Nah I don't think he goes to usf, he lives aro..." + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", + "data.head()\n", + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "X = data.text\n", + "Y = data['class']\n", + "le = LabelEncoder()\n", + "Y = le.fit_transform(Y)\n", + "Y = Y.reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n", + "max_words = 1000\n", + "max_len = 150\n", + "tokenizer = Tokenizer(num_words=max_words)\n", + "tokenizer.fit_on_texts(X_train)\n", + "\n", + "with open('tokenizer.pkl', 'wb') as tok:\n", + " pickle.dump(tokenizer, tok)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sequences = tokenizer.texts_to_sequences(X_train)\n", + "sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def RNN():\n", + " inputs = Input(name='inputs',shape=[max_len])\n", + " layer = Embedding(max_words,50,input_length=max_len)(inputs)\n", + " layer = LSTM(64)(layer)\n", + " layer = Dense(256,name='FC1')(layer)\n", + " layer = Activation('relu')(layer)\n", + " layer = Dropout(0.5)(layer)\n", + " layer = Dense(1,name='out_layer')(layer)\n", + " layer = Activation('sigmoid')(layer)\n", + " model = Model(inputs=inputs,outputs=layer)\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = RNN()\n", + "model.summary()\n", + "model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n", + "\n", + "model.fit(sequences_matrix,Y_train,batch_size=128,epochs=10,\n", + "validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#serialize architecture to JSON\n", + "model_json = model.to_json()\n", + "with open(\"architecture.json\", \"w\") as json_file:\n", + " json_file.write(model_json)\n", + "# serialize weights to HDF5\n", + "model.save_weights(\"weights.h5\")\n", + "print(\"Saved model to disk\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = joblib.load('tokenizer.pkl')\n", + "\n", + "# load json and create model\n", + "json_file = open('architecture.json', 'r')\n", + "loaded_model_json = json_file.read()\n", + "json_file.close()\n", + "\n", + "model = model_from_json(loaded_model_json)\n", + "# load weights into new model\n", + "model.load_weights(\"weights.h5\")\n", + "\n", + "txt= 'please click this link to win the price'\n", + "seq= tokenizer.texts_to_sequences([txt])\n", + "padded = sequence.pad_sequences(seq, maxlen=max_len)\n", + "pred = model.predict(padded)\n", + "print(pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From d7ca6d33fd8c079c3ad0f2ba53e99d4145e6f6f1 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Mon, 4 Nov 2019 22:24:06 -0600 Subject: [PATCH 69/79] notebook file to generate the model binaries --- .../Spam Classification.ipynb | 651 ++++++++++++++++++ 1 file changed, 651 insertions(+) create mode 100644 examples/combiners/spam_clf_combiner/Spam Classification.ipynb diff --git a/examples/combiners/spam_clf_combiner/Spam Classification.ipynb b/examples/combiners/spam_clf_combiner/Spam Classification.ipynb new file mode 100644 index 0000000000..1fc647c4af --- /dev/null +++ b/examples/combiners/spam_clf_combiner/Spam Classification.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SCIKIT-LEARN" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "from sklearn.externals import joblib\n", + "from pathlib import Path\n", + "import string\n", + "#from nltk.stem import SnowballStemmer\n", + "from nltk.corpus import stopwords\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "import pickle\n", + "from sklearn.svm import SVC\n", + "model_path: Path=Path('./')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " class text\n", + "0 ham Go until jurong point, crazy.. Available only ...\n", + "1 ham Ok lar... Joking wif u oni...\n", + "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", + "3 ham U dun say so early hor... U c already then say...\n", + "4 ham Nah I don't think he goes to usf, he lives aro..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def pre_process(text):\n", + " text = text.translate(str.maketrans('', '', string.punctuation))\n", + " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", + " words = \"\"\n", + " for i in text:\n", + " stemmer = SnowballStemmer(\"english\")\n", + " words += (stemmer.stem(i))+\" \"\n", + " return words" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "features = data['text'].copy()\n", + "features = features.apply(pre_process)\n", + "\n", + "vectorizer = TfidfVectorizer(\"english\")\n", + "_features = vectorizer.fit_transform(features)\n", + "with open('vectorizer.pkl', 'wb') as vect:\n", + " pickle.dump(vectorizer, vect)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl'))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=1.0, kernel='sigmoid',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", + "svc.fit(train_x,train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# save the model to disk\n", + "filename = 'sk_model.pkl'\n", + "pickle.dump(svc, open(filename, 'wb'))\n", + "clf = joblib.load(model_path.joinpath(filename))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9730861244019139" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prediction = clf.predict(test_x)\n", + "accuracy_score(test_y,prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "message = np.array(['click here to win the price'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "data = vectorizer.transform(message).todense()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "probas = clf.predict_proba(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['ham', 'spam'], dtype=object)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.classes_" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.02511554, 0.97488446]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## KERAS" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from keras.models import Model\n", + "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n", + "from keras.optimizers import RMSprop\n", + "from keras.preprocessing.text import Tokenizer\n", + "from keras.preprocessing import sequence\n", + "from keras.utils import to_categorical\n", + "from keras.callbacks import EarlyStopping\n", + "import pickle\n", + "from sklearn.externals import joblib\n", + "\n", + "from keras.engine.saving import model_from_json\n", + "from keras.layers import (\n", + " Bidirectional,\n", + " concatenate,\n", + " Dense,\n", + " Embedding,\n", + " LSTM,\n", + " Masking,\n", + " Reshape,\n", + " SpatialDropout1D,\n", + " TimeDistributed,\n", + ")" + ] + 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    " + ], + "text/plain": [ + " class text\n", + "0 ham Go until jurong point, crazy.. Available only ...\n", + "1 ham Ok lar... Joking wif u oni...\n", + "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", + "3 ham U dun say so early hor... U c already then say...\n", + "4 ham Nah I don't think he goes to usf, he lives aro..." + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", + "data.head()\n", + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "X = data.text\n", + "Y = data['class']\n", + "le = LabelEncoder()\n", + "Y = le.fit_transform(Y)\n", + "Y = Y.reshape(-1,1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n", + "max_words = 1000\n", + "max_len = 150\n", + "tokenizer = Tokenizer(num_words=max_words)\n", + "tokenizer.fit_on_texts(X_train)\n", + "\n", + "with open('tokenizer.pkl', 'wb') as tok:\n", + " pickle.dump(tokenizer, tok)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sequences = tokenizer.texts_to_sequences(X_train)\n", + "sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def RNN():\n", + " inputs = Input(name='inputs',shape=[max_len])\n", + " layer = Embedding(max_words,50,input_length=max_len)(inputs)\n", + " layer = LSTM(64)(layer)\n", + " layer = Dense(256,name='FC1')(layer)\n", + " layer = Activation('relu')(layer)\n", + " layer = Dropout(0.5)(layer)\n", + " layer = Dense(1,name='out_layer')(layer)\n", + " layer = Activation('sigmoid')(layer)\n", + " model = Model(inputs=inputs,outputs=layer)\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = RNN()\n", + "model.summary()\n", + "model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n", + "\n", + "model.fit(sequences_matrix,Y_train,batch_size=128,epochs=10,\n", + "validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#serialize architecture to JSON\n", + "model_json = model.to_json()\n", + "with open(\"architecture.json\", \"w\") as json_file:\n", + " json_file.write(model_json)\n", + "# serialize weights to HDF5\n", + "model.save_weights(\"weights.h5\")\n", + "print(\"Saved model to disk\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = joblib.load('tokenizer.pkl')\n", + "\n", + "# load json and create model\n", + "json_file = open('architecture.json', 'r')\n", + "loaded_model_json = json_file.read()\n", + "json_file.close()\n", + "\n", + "model = model_from_json(loaded_model_json)\n", + "# load weights into new model\n", + "model.load_weights(\"weights.h5\")\n", + "\n", + "txt= 'please click this link to win the price'\n", + "seq= tokenizer.texts_to_sequences([txt])\n", + "padded = sequence.pad_sequences(seq, maxlen=max_len)\n", + "pred = model.predict(padded)\n", + "print(pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 3f580bf8c082ae8fc9ea7f2c7108af34d540d9f6 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Thu, 7 Nov 2019 00:09:41 -0600 Subject: [PATCH 70/79] notebook files to train the model locally and build docker images using s2i --- examples/.DS_Store | Bin 10244 -> 10244 bytes examples/combiners/.DS_Store | Bin 0 -> 8196 bytes .../spam_clf_combiner/Combiner/README.md | 6 +- .../Spam Classification.ipynb | 801 +++++++++--------- .../keras-spam-classifier/model/tokenizer.pkl | Bin 376242 -> 376442 bytes .../skl-spam-classifier/model/model.pkl | Bin 137219 -> 139571 bytes .../skl-spam-classifier/model/vectorizer.pkl | Bin 508071 -> 508071 bytes .../Spam Classification.ipynb | 609 ++++--------- .../Spam-Classifier/model/model.pkl | Bin 137219 -> 139571 bytes .../Spam-Classifier/model/vectorizer.pkl | Bin 508071 -> 508071 bytes .../input_tranformer/Translator/README.md | 4 +- 11 files changed, 576 insertions(+), 844 deletions(-) create mode 100644 examples/combiners/.DS_Store diff --git 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zh6x/Combiner:1.0.0.1 -$ docker push /Combiner:1.0.0.1 +$ s2i build Combiner/ seldonio/seldon-core-s2i-python3:0.7 combiner:1.0.0.1 +$ docker push /combiner:1.0.0.1 ``` #### Test ``` -$ docker run --name "Combiner" --rm /Combiner:1.0.0.1 +$ docker run --name "Combiner" --rm /combiner:1.0.0.1 ``` \ No newline at end of file diff --git a/examples/combiners/spam_clf_combiner/Spam Classification.ipynb b/examples/combiners/spam_clf_combiner/Spam Classification.ipynb index 1fc647c4af..46ccb17f59 100644 --- a/examples/combiners/spam_clf_combiner/Spam Classification.ipynb +++ b/examples/combiners/spam_clf_combiner/Spam Classification.ipynb @@ -4,12 +4,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## SCIKIT-LEARN" + "## Spam Classification Model (Sklearn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Wrap a ML model for use as a prediction microservice in seldon-core\n", + "- Run locally on Docker to test\n", + "- Deploy on seldon-core running on k8s cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Locally" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -18,246 +34,135 @@ "from sklearn.externals import joblib\n", "from pathlib import Path\n", "import string\n", - "#from nltk.stem import SnowballStemmer\n", + "from nltk.stem import SnowballStemmer\n", "from nltk.corpus import stopwords\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.model_selection import train_test_split\n", "import pickle\n", "from sklearn.svm import SVC\n", + "from sklearn.metrics import accuracy_score\n", "model_path: Path=Path('./')" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    v1v2Unnamed: 2Unnamed: 3Unnamed: 4
    0hamGo until jurong point, crazy.. Available only ...NaNNaNNaN
    1hamOk lar... Joking wif u oni...NaNNaNNaN
    2spamFree entry in 2 a wkly comp to win FA Cup fina...NaNNaNNaN
    3hamU dun say so early hor... U c already then say...NaNNaNNaN
    4hamNah I don't think he goes to usf, he lives aro...NaNNaNNaN
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    " - ], "text/plain": [ - " v1 v2 Unnamed: 2 \\\n", - "0 ham Go until jurong point, crazy.. Available only ... NaN \n", - "1 ham Ok lar... Joking wif u oni... NaN \n", - "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", - "3 ham U dun say so early hor... U c already then say... NaN \n", - "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", - "\n", - " Unnamed: 3 Unnamed: 4 \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN " + "0.9730861244019139" ] }, - "execution_count": 5, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", - "data.head()" + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "data.head()\n", + "\n", + "def pre_process(text):\n", + " text = text.translate(str.maketrans('', '', string.punctuation))\n", + " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", + " words = \"\"\n", + " for i in text:\n", + " stemmer = SnowballStemmer(\"english\")\n", + " words += (stemmer.stem(i))+\" \"\n", + " return words\n", + "\n", + "features = data['text'].copy()\n", + "features = features.apply(pre_process)\n", + "\n", + "vectorizer = TfidfVectorizer(\"english\")\n", + "_features = vectorizer.fit_transform(features)\n", + "with open('skl-spam-classifier/model/vectorizer.pkl', 'wb') as vect:\n", + " pickle.dump(vectorizer, vect)\n", + " \n", + "vectorizer = joblib.load(model_path.joinpath('skl-spam-classifier/model/vectorizer.pkl'))\n", + "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)\n", + "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", + "svc.fit(train_x,train_y)\n", + "# save the model to disk\n", + "filename = 'skl-spam-classifier/model/model.pkl'\n", + "pickle.dump(svc, open(filename, 'wb'))\n", + "\n", + "clf = joblib.load(model_path.joinpath(filename))\n", + "\n", + "prediction = clf.predict(test_x)\n", + "accuracy_score(test_y,prediction)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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    classtext
    0hamGo until jurong point, crazy.. Available only ...
    1hamOk lar... Joking wif u oni...
    2spamFree entry in 2 a wkly comp to win FA Cup fina...
    3hamU dun say so early hor... U c already then say...
    4hamNah I don't think he goes to usf, he lives aro...
    \n", - "
    " - ], "text/plain": [ - " class text\n", - "0 ham Go until jurong point, crazy.. Available only ...\n", - "1 ham Ok lar... Joking wif u oni...\n", - "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", - "3 ham U dun say so early hor... U c already then say...\n", - "4 ham Nah I don't think he goes to usf, he lives aro..." + "array([[0.02762687, 0.97237313]])" ] }, - "execution_count": 6, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", - "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", - "data.head()" + "message = np.array(['click here to win the price'])\n", + "data = vectorizer.transform(message).todense()\n", + "probas = clf.predict_proba(data)\n", + "probas" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['ham', 'spam'], dtype=object)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "def pre_process(text):\n", - " text = text.translate(str.maketrans('', '', string.punctuation))\n", - " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", - " words = \"\"\n", - " for i in text:\n", - " stemmer = SnowballStemmer(\"english\")\n", - " words += (stemmer.stem(i))+\" \"\n", - " return words" + "clf.classes_" ] }, { - "cell_type": "code", - "execution_count": 11, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "features = data['text'].copy()\n", - "features = features.apply(pre_process)\n", + "## Spam Classification Model (keras)\n", "\n", - "vectorizer = TfidfVectorizer(\"english\")\n", - "_features = vectorizer.fit_transform(features)\n", - "with open('vectorizer.pkl', 'wb') as vect:\n", - " pickle.dump(vectorizer, vect)\n" + "- Wrap a ML model for use as a prediction microservice in seldon-core\n", + "- Run locally on Docker to test\n", + "- Deploy on seldon-core running on k8s cluster" ] }, { - "cell_type": "code", - "execution_count": 12, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl'))" + "you can find data here: https://www.kaggle.com/benvozza/spam-classification/data" ] }, { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)" + "### Train Locally" ] }, { @@ -266,357 +171,431 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", - " decision_function_shape='ovr', degree=3, gamma=1.0, kernel='sigmoid',\n", - " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", - " verbose=False)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] } ], "source": [ - "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", - "svc.fit(train_x,train_y)" + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from keras.models import Model\n", + "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n", + "from keras.optimizers import RMSprop\n", + "from keras.preprocessing.text import Tokenizer\n", + "from keras.preprocessing import sequence\n", + "from keras.utils import to_categorical\n", + "from keras.callbacks import EarlyStopping\n", + "import pickle\n", + "from sklearn.externals import joblib\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()\n", + "from keras.engine.saving import model_from_json\n", + "from keras.layers import (\n", + " Bidirectional,\n", + " concatenate,\n", + " Dense,\n", + " Embedding,\n", + " LSTM,\n", + " Masking,\n", + " Reshape,\n", + " SpatialDropout1D,\n", + " TimeDistributed,\n", + ")" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "# save the model to disk\n", - "filename = 'sk_model.pkl'\n", - "pickle.dump(svc, open(filename, 'wb'))\n", - "clf = joblib.load(model_path.joinpath(filename))" + "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", + "\n", + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "\n", + "X = data.text\n", + "Y = data['class']\n", + "le = LabelEncoder()\n", + "Y = le.fit_transform(Y)\n", + "Y = Y.reshape(-1,1)\n", + "\n", + "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n", + "max_words = 1000\n", + "max_len = 150\n", + "tokenizer = Tokenizer(num_words=max_words)\n", + "tokenizer.fit_on_texts(X_train)\n", + "\n", + "with open('keras-spam-classifier/model/tokenizer.pkl', 'wb') as tok:\n", + " pickle.dump(tokenizer, tok)\n", + "\n", + " \n", + "sequences = tokenizer.texts_to_sequences(X_train)\n", + "sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, + "outputs": [], + "source": [ + "def RNN():\n", + " inputs = Input(name='inputs',shape=[max_len])\n", + " layer = Embedding(max_words,50,input_length=max_len)(inputs)\n", + " layer = LSTM(64)(layer)\n", + " layer = Dense(256,name='FC1')(layer)\n", + " layer = Activation('relu')(layer)\n", + " layer = Dropout(0.5)(layer)\n", + " layer = Dense(1,name='out_layer')(layer)\n", + " layer = Activation('sigmoid')(layer)\n", + " model = Model(inputs=inputs,outputs=layer)\n", + " return model\n", + "\n", + "model = RNN()\n", + "model.summary()\n", + "model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n", + "\n", + "model.fit(sequences_matrix,Y_train,batch_size=128,epochs=10,\n", + "validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])\n", + "\n", + "#save model\n", + "model_json = model.to_json()\n", + "with open(\"keras-spam-classifier/model/architecture.json\", \"w\") as json_file:\n", + " json_file.write(model_json)\n", + "model.save_weights(\"keras-spam-classifier/model/weights.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### wrap each model component using s2i" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, "outputs": [ { - 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It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] } ], "source": [ - "prediction = clf.predict(test_x)\n", - "accuracy_score(test_y,prediction)" + "!s2i build keras-spam-classifier/ seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "59be46468915231d3915161343486d083d547fde192baa7367ff411efe34c52f\r\n" + ] + } + ], "source": [ - "message = np.array(['click here to win the price'])\n" + "!docker run --name \"spam-classifier\" -d --rm -p 5000:5000 spam-classifier:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"ndarray\":[\"0.9779371008528993\",\"spam\"]},\"meta\":{}}\r\n" + ] + } + ], "source": [ - "data = vectorizer.transform(message).todense()" + "!curl -g http://localhost:5000/predict --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"click here to win the price\"]}}'\n" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spam-classifier\r\n" + ] + } + ], "source": [ - "probas = clf.predict_proba(data)" + "!docker rm spam-classifier --force" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 47, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array(['ham', 'spam'], dtype=object)" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting scikit-learn==0.21.2 (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/85/04/49633f490f726da6e454fddc8e938bbb5bfed2001681118d3814c219b723/scikit_learn-0.21.2-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n", + "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting keras (from -r requirements.txt (line 3))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl (377kB)\n", + "Collecting scipy>=0.17.0 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. 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It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/8f/42/155696f85f344c066e17af287359c9786b436b1bf86029bb3411283274f3/joblib-0.14.0-py2.py3-none-any.whl (294kB)\n", + "Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (2.9.0)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (5.1)\n", + "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.12.0)\n", + "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.7)\n", + "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.9)\n", + "Installing collected packages: scipy, joblib, scikit-learn, keras\n", + "Successfully installed joblib-0.14.0 keras-2.3.1 scikit-learn-0.21.2 scipy-1.3.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] } ], "source": [ - "clf.classes_" + "!s2i build keras-spam-classifier/ seldonio/seldon-core-s2i-python3:0.7 keras-spam-classifier:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 49, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array([[0.02511554, 0.97488446]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "cf8ebd9bff95cb81ca0cd39393e5c3f2707d7b6394ddc0ace15e41a47248c3e8\r\n" + ] } ], "source": [ - "probas" + "!docker run --name \"keras-spam-classifier\" --rm -d -p 5000:5000 keras-spam-classifier:1.0.0.1" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 50, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting goslate (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/39/0b/50af938a1c3d4f4c595b6a22d37af11ebe666246b05a1a97573e8c8944e5/goslate-1.5.1.tar.gz\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting futures (from goslate->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/05/80/f41cca0ea1ff69bce7e7a7d76182b47bb4e1a494380a532af3e8ee70b9ec/futures-3.1.1-py3-none-any.whl\n", + "Building wheels for collected packages: goslate\n", + "Running setup.py bdist_wheel for goslate: started\n", + "Running setup.py bdist_wheel for goslate: finished with status 'done'\n", + "Stored in directory: /root/.cache/pip/wheels/4f/7f/28/6f52271012a7649b54b1a7adaae329b4246bbbf9d1e4f6e51a\n", + "Successfully built goslate\n", + "Installing collected packages: futures, goslate\n", + "Successfully installed futures-3.1.1 goslate-1.5.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], "source": [ - "## KERAS" + "!s2i build Translator/ seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "26d5f31364f5095680bd89aabae468f0c4cec0f8cc7da5de4ea77434e4836692\r\n" + ] + } + ], "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from keras.models import Model\n", - "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n", - "from keras.optimizers import RMSprop\n", - "from keras.preprocessing.text import Tokenizer\n", - "from keras.preprocessing import sequence\n", - "from keras.utils import to_categorical\n", - "from keras.callbacks import EarlyStopping\n", - "import pickle\n", - "from sklearn.externals import joblib\n", - "\n", - "from keras.engine.saving import model_from_json\n", - "from keras.layers import (\n", - " Bidirectional,\n", - " concatenate,\n", - " Dense,\n", - " Embedding,\n", - " LSTM,\n", - " Masking,\n", - " Reshape,\n", - " SpatialDropout1D,\n", - " TimeDistributed,\n", - ")" + "!docker run --name \"translator\" -d --rm -p 5000:5000 translator:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 54, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
    classtext
    0hamGo until jurong point, crazy.. Available only ...
    1hamOk lar... Joking wif u oni...
    2spamFree entry in 2 a wkly comp to win FA Cup fina...
    3hamU dun say so early hor... U c already then say...
    4hamNah I don't think he goes to usf, he lives aro...
    \n", - "
    " - ], - "text/plain": [ - " class text\n", - "0 ham Go until jurong point, crazy.. Available only ...\n", - "1 ham Ok lar... Joking wif u oni...\n", - "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", - "3 ham U dun say so early hor... U c already then say...\n", - "4 ham Nah I don't think he goes to usf, he lives aro..." - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"names\":[\"message\"],\"ndarray\":[\"How is your day\"]},\"meta\":{}}\r\n" + ] } ], "source": [ - "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", - "data.head()\n", - "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", - "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", - "data.head()" + "!curl -g http://localhost:5000/transform-input --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"Wie läuft dein Tag\"]}}'" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "translator\r\n" + ] + } + ], "source": [ - "X = data.text\n", - "Y = data['class']\n", - "le = LabelEncoder()\n", - "Y = le.fit_transform(Y)\n", - "Y = Y.reshape(-1,1)" + "!docker rm translator --force" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "Build completed successfully\n" + ] + } + ], "source": [ - "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n", - "max_words = 1000\n", - "max_len = 150\n", - "tokenizer = Tokenizer(num_words=max_words)\n", - "tokenizer.fit_on_texts(X_train)\n", - "\n", - "with open('tokenizer.pkl', 'wb') as tok:\n", - " pickle.dump(tokenizer, tok)" + "!s2i build Combiner/ seldonio/seldon-core-s2i-python3:0.7 combiner:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19fbbdfb073c7da1056611aa18d8c7d3da9a533010667638d8c0cb0abfe6e257\r\n" + ] + } + ], "source": [ - "sequences = tokenizer.texts_to_sequences(X_train)\n", - "sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)" + "!docker run --name \"model-combiner\" -d --rm -p 5000:5000 combiner:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ - "def RNN():\n", - " inputs = Input(name='inputs',shape=[max_len])\n", - " layer = Embedding(max_words,50,input_length=max_len)(inputs)\n", - " layer = LSTM(64)(layer)\n", - " layer = Dense(256,name='FC1')(layer)\n", - " layer = Activation('relu')(layer)\n", - " layer = Dropout(0.5)(layer)\n", - " layer = Dense(1,name='out_layer')(layer)\n", - " layer = Activation('sigmoid')(layer)\n", - " model = Model(inputs=inputs,outputs=layer)\n", - " return model" + "#!curl -g http://localhost:5000/aggregate --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [[\"0.7\",\"Spam\"], [\"0.80\", \"Spam\"]]}}'" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model-combiner\r\n" + ] + } + ], "source": [ - "model = RNN()\n", - "model.summary()\n", - "model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n", - "\n", - "model.fit(sequences_matrix,Y_train,batch_size=128,epochs=10,\n", - "validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])" + "!docker rm model-combiner --force" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#serialize architecture to JSON\n", - "model_json = model.to_json()\n", - "with open(\"architecture.json\", \"w\") as json_file:\n", - " json_file.write(model_json)\n", - "# serialize weights to HDF5\n", - "model.save_weights(\"weights.h5\")\n", - "print(\"Saved model to disk\")" + "#### Assuming you have kubernetes cluster running and seldon-core installed, you can deploy your Machine Learning model using:" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "tokenizer = joblib.load('tokenizer.pkl')\n", - "\n", - "# load json and create model\n", - "json_file = open('architecture.json', 'r')\n", - "loaded_model_json = json_file.read()\n", - "json_file.close()\n", - "\n", - "model = model_from_json(loaded_model_json)\n", - "# load weights into new model\n", - "model.load_weights(\"weights.h5\")\n", - "\n", - "txt= 'please click this link to win the price'\n", - "seq= tokenizer.texts_to_sequences([txt])\n", - "padded = sequence.pad_sequences(seq, maxlen=max_len)\n", - "pred = model.predict(padded)\n", - "print(pred)" + "kubectl apply -f deploy.yaml" ] }, { diff --git 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      v1v2Unnamed: 2Unnamed: 3Unnamed: 4
      0hamGo until jurong point, crazy.. Available only ...NaNNaNNaN
      1hamOk lar... Joking wif u oni...NaNNaNNaN
      2spamFree entry in 2 a wkly comp to win FA Cup fina...NaNNaNNaN
      3hamU dun say so early hor... U c already then say...NaNNaNNaN
      4hamNah I don't think he goes to usf, he lives aro...NaNNaNNaN
      \n", - "
      " - ], - "text/plain": [ - " v1 v2 Unnamed: 2 \\\n", - "0 ham Go until jurong point, crazy.. Available only ... NaN \n", - "1 ham Ok lar... Joking wif u oni... NaN \n", - "2 spam Free entry in 2 a wkly comp to win FA Cup fina... NaN \n", - "3 ham U dun say so early hor... U c already then say... NaN \n", - "4 ham Nah I don't think he goes to usf, he lives aro... NaN \n", - "\n", - " Unnamed: 3 Unnamed: 4 \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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      " - ], - "text/plain": [ - " class text\n", - "0 ham Go until jurong point, crazy.. Available only ...\n", - "1 ham Ok lar... Joking wif u oni...\n", - "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", - "3 ham U dun say so early hor... U c already then say...\n", - "4 ham Nah I don't think he goes to usf, he lives aro..." - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ + "data.head()\n", + "\n", "def pre_process(text):\n", " text = text.translate(str.maketrans('', '', string.punctuation))\n", " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", @@ -224,45 +62,20 @@ " for i in text:\n", " stemmer = SnowballStemmer(\"english\")\n", " words += (stemmer.stem(i))+\" \"\n", - " return words" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ + " return words\n", + "\n", "features = data['text'].copy()\n", "features = features.apply(pre_process)\n", "\n", "vectorizer = TfidfVectorizer(\"english\")\n", "_features = vectorizer.fit_transform(features)\n", - "with open('vectorizer.pkl', 'wb') as vect:\n", - " pickle.dump(vectorizer, vect)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "vectorizer = joblib.load(model_path.joinpath('vectorizer.pkl'))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)" + "with open('Spam-Classifier/model/vectorizer.pkl', 'wb') as vect:\n", + " pickle.dump(vectorizer, vect)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -274,31 +87,33 @@ " verbose=False)" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "vectorizer = joblib.load(model_path.joinpath('Spam-Classifier/model/vectorizer.pkl'))\n", + "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)\n", "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", - "svc.fit(train_x,train_y)" + "svc.fit(train_x,train_y)\n", + "# save the model to disk\n", + "filename = 'Spam-Classifier/model/model.pkl'\n", + "pickle.dump(svc, open(filename, 'wb'))" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "# save the model to disk\n", - "filename = 'sk_model.pkl'\n", - "pickle.dump(svc, open(filename, 'wb'))\n", "clf = joblib.load(model_path.joinpath(filename))" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -307,7 +122,7 @@ "0.9730861244019139" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -317,33 +132,6 @@ "accuracy_score(test_y,prediction)" ] }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "message = np.array(['click here to win the price'])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "data = vectorizer.transform(message).todense()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "probas = clf.predict_proba(data)" - ] - }, { "cell_type": "code", "execution_count": 22, @@ -352,7 +140,7 @@ { "data": { "text/plain": [ - "array(['ham', 'spam'], dtype=object)" + "array([[0.0220629, 0.9779371]])" ] }, "execution_count": 22, @@ -361,262 +149,225 @@ } ], "source": [ - "clf.classes_" + "message = np.array(['click here to win the price'])\n", + "data = vectorizer.transform(message).todense()\n", + "probas = clf.predict_proba(data)\n", + "probas" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.02511554, 0.97488446]])" + "array(['ham', 'spam'], dtype=object)" ] }, - "execution_count": 23, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "probas" + "clf.classes_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## KERAS" + "### wrap each model component using s2i" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting scikit-learn==0.21.2 (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/85/04/49633f490f726da6e454fddc8e938bbb5bfed2001681118d3814c219b723/scikit_learn-0.21.2-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n", + "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting scipy>=0.17.0 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/29/50/a552a5aff252ae915f522e44642bb49a7b7b31677f9580cfd11bcc869976/scipy-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (25.2MB)\n", + "Collecting joblib>=0.11 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/8f/42/155696f85f344c066e17af287359c9786b436b1bf86029bb3411283274f3/joblib-0.14.0-py2.py3-none-any.whl (294kB)\n", + "Installing collected packages: scipy, joblib, scikit-learn\n", + "Successfully installed joblib-0.14.0 scikit-learn-0.21.2 scipy-1.3.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from keras.models import Model\n", - "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n", - "from keras.optimizers import RMSprop\n", - "from keras.preprocessing.text import Tokenizer\n", - "from keras.preprocessing import sequence\n", - "from keras.utils import to_categorical\n", - "from keras.callbacks import EarlyStopping\n", - "import pickle\n", - "from sklearn.externals import joblib\n", - "\n", - "from keras.engine.saving import model_from_json\n", - "from keras.layers import (\n", - " Bidirectional,\n", - " concatenate,\n", - " Dense,\n", - " Embedding,\n", - " LSTM,\n", - " Masking,\n", - " Reshape,\n", - " SpatialDropout1D,\n", - " TimeDistributed,\n", - ")" + "!s2i build Spam-Classifier/ seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 49, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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      0hamGo until jurong point, crazy.. Available only ...
      1hamOk lar... Joking wif u oni...
      2spamFree entry in 2 a wkly comp to win FA Cup fina...
      3hamU dun say so early hor... U c already then say...
      4hamNah I don't think he goes to usf, he lives aro...
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      " - ], - "text/plain": [ - " class text\n", - "0 ham Go until jurong point, crazy.. Available only ...\n", - "1 ham Ok lar... Joking wif u oni...\n", - "2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n", - "3 ham U dun say so early hor... U c already then say...\n", - "4 ham Nah I don't think he goes to usf, he lives aro..." - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "1b8159f67b7ddbd2de26833411303ebee8e08331097e28754f04688c1fb86d3c\r\n" + ] } ], "source": [ - "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", - "data.head()\n", - "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", - "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", - "data.head()" + "!docker run --name \"spam-classifier\" -d --rm -p 5000:5000 spam-classifier:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 51, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"ndarray\":[\"0.9779371008528993\",\"spam\"]},\"meta\":{}}\r\n" + ] + } + ], "source": [ - "X = data.text\n", - "Y = data['class']\n", - "le = LabelEncoder()\n", - "Y = le.fit_transform(Y)\n", - "Y = Y.reshape(-1,1)" + "!curl -g http://localhost:5000/predict --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"click here to win the price\"]}}'\n" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spam-classifier\r\n" + ] + } + ], "source": [ - "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n", - "max_words = 1000\n", - "max_len = 150\n", - "tokenizer = Tokenizer(num_words=max_words)\n", - "tokenizer.fit_on_texts(X_train)\n", - "\n", - "with open('tokenizer.pkl', 'wb') as tok:\n", - " pickle.dump(tokenizer, tok)" + "!docker rm spam-classifier --force" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting goslate (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/39/0b/50af938a1c3d4f4c595b6a22d37af11ebe666246b05a1a97573e8c8944e5/goslate-1.5.1.tar.gz\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting futures (from goslate->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/05/80/f41cca0ea1ff69bce7e7a7d76182b47bb4e1a494380a532af3e8ee70b9ec/futures-3.1.1-py3-none-any.whl\n", + "Building wheels for collected packages: goslate\n", + "Running setup.py bdist_wheel for goslate: started\n", + "Running setup.py bdist_wheel for goslate: finished with status 'done'\n", + "Stored in directory: /root/.cache/pip/wheels/4f/7f/28/6f52271012a7649b54b1a7adaae329b4246bbbf9d1e4f6e51a\n", + "Successfully built goslate\n", + "Installing collected packages: futures, goslate\n", + "Successfully installed futures-3.1.1 goslate-1.5.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], "source": [ - "sequences = tokenizer.texts_to_sequences(X_train)\n", - "sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)" + "!s2i build Translator/ seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ca18617eed4ee5b12c1ce835d94a677007e3c095166b8e4e5d0f9fd164757814\r\n" + ] + } + ], "source": [ - "def RNN():\n", - " inputs = Input(name='inputs',shape=[max_len])\n", - " layer = Embedding(max_words,50,input_length=max_len)(inputs)\n", - " layer = LSTM(64)(layer)\n", - " layer = Dense(256,name='FC1')(layer)\n", - " layer = Activation('relu')(layer)\n", - " layer = Dropout(0.5)(layer)\n", - " layer = Dense(1,name='out_layer')(layer)\n", - " layer = Activation('sigmoid')(layer)\n", - " model = Model(inputs=inputs,outputs=layer)\n", - " return model" + "!docker run --name \"eng-translator\" -d --rm -p 5000:5000 translator:1.0.0.1" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"names\":[\"message\"],\"ndarray\":[\"How is your day\"]},\"meta\":{}}\r\n" + ] + } + ], "source": [ - "model = RNN()\n", - "model.summary()\n", - "model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n", - "\n", - "model.fit(sequences_matrix,Y_train,batch_size=128,epochs=10,\n", - "validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])" + "!curl -g http://localhost:5000/transform-input --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"Wie läuft dein Tag\"]}}'" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eng-translator\r\n" + ] + } + ], "source": [ - "#serialize architecture to JSON\n", - "model_json = model.to_json()\n", - "with open(\"architecture.json\", \"w\") as json_file:\n", - " json_file.write(model_json)\n", - "# serialize weights to HDF5\n", - "model.save_weights(\"weights.h5\")\n", - "print(\"Saved model to disk\")" + "!docker rm eng-translator --force" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "tokenizer = joblib.load('tokenizer.pkl')\n", - "\n", - "# load json and create model\n", - "json_file = open('architecture.json', 'r')\n", - "loaded_model_json = json_file.read()\n", - "json_file.close()\n", - "\n", - "model = model_from_json(loaded_model_json)\n", - "# load weights into new model\n", - "model.load_weights(\"weights.h5\")\n", - "\n", - "txt= 'please click this link to win the price'\n", - "seq= tokenizer.texts_to_sequences([txt])\n", - "padded = sequence.pad_sequences(seq, maxlen=max_len)\n", - "pred = model.predict(padded)\n", - "print(pred)" + "Assuming you have kubernetes cluster running and seldon-core installed, you can deploy your Machine Learning model using:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "kubectl apply -f deploy.yaml" ] }, { diff --git a/examples/input_tranformer/Spam-Classifier/model/model.pkl b/examples/input_tranformer/Spam-Classifier/model/model.pkl index 36b2d6043fd638ac8b816074d29b70f3f3423ddb..c9fbf565d0190fb5e61a9449f69615a37f52f9eb 100644 GIT binary patch delta 68381 zcmbTe1z1+g+wTu{w}^n)2qvOpp@WL8h_&tR+IGidcP|xn+ug0(?nFgez#{BzZQE`A zKVP@_{?7ZJb6w|uxULV+^Q<*<&pr3d+>>if+LLDg*))4zCbK_fy-HTb_9c_uk3W0< 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b/examples/input_tranformer/Spam-Classifier/model/vectorizer.pkl index 0bb36fb0ead4a3ea1ed6ef37f055524131ccd7cc..0363730a0ed57d5a2e32895bb2f76336fc7cf9d6 100644 GIT binary patch delta 48 ycmZ49D8IZ>zF`Yv$}dLa>8ZaMO_@28_}l0HVgzC)AZ7+)79eKbKKBzF`Yv$}dKv>8ZaMO_?KH+1uy-VgzC)AZ7+)79eKbKKBY diff --git a/examples/input_tranformer/Translator/README.md b/examples/input_tranformer/Translator/README.md index c96a6ae526..6713084629 100644 --- a/examples/input_tranformer/Translator/README.md +++ b/examples/input_tranformer/Translator/README.md @@ -30,7 +30,9 @@ $ docker push /translator:1.0.0.1 ``` $ docker run --name "translator" --rm /translator:1.0.0.1 -curl -g http://localhost:5000/transform_input --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["Wie läuft dein Tag"]}}' +curl -g http://localhost:5000/transform-input --data-urlencode 'json={"data": {"names": ["message"], "ndarray": ["Wie läuft dein Tag"]}}' +result: +{"data":{"names":["message"],"ndarray":["How is your day"]},"meta":{}} ``` \ No newline at end of file From 0b877a927a6d8593998154a83b6fdd9568075e27 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Thu, 7 Nov 2019 00:14:49 -0600 Subject: [PATCH 71/79] renamed notebook files" --- doc/source/examples/input_transformer_spam.nblink | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 doc/source/examples/input_transformer_spam.nblink diff --git a/doc/source/examples/input_transformer_spam.nblink b/doc/source/examples/input_transformer_spam.nblink new file mode 100644 index 0000000000..d34f7be16c --- /dev/null +++ b/doc/source/examples/input_transformer_spam.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../examples/input_tranformer/spam-classification.ipynb" +} From f8e6dd84b846b8d6c6b0c0ae6650e5a983fd4f73 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Thu, 7 Nov 2019 00:18:30 -0600 Subject: [PATCH 72/79] renamed notebook file --- .../spam-classification.ipynb | 402 ++++++++++++++++++ 1 file changed, 402 insertions(+) create mode 100644 examples/input_tranformer/spam-classification.ipynb diff --git a/examples/input_tranformer/spam-classification.ipynb b/examples/input_tranformer/spam-classification.ipynb new file mode 100644 index 0000000000..083ba419b9 --- /dev/null +++ b/examples/input_tranformer/spam-classification.ipynb @@ -0,0 +1,402 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spam Classification Model (Sklearn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Wrap a ML model for use as a prediction microservice in seldon-core\n", + "- Run locally on Docker to test\n", + "- Deploy on seldon-core running on k8s cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Locally" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "from sklearn.externals import joblib\n", + "from pathlib import Path\n", + "import string\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk.corpus import stopwords\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "import pickle\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import accuracy_score\n", + "model_path: Path=Path('./')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "data.head()\n", + "\n", + "def pre_process(text):\n", + " text = text.translate(str.maketrans('', '', string.punctuation))\n", + " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", + " words = \"\"\n", + " for i in text:\n", + " stemmer = SnowballStemmer(\"english\")\n", + " words += (stemmer.stem(i))+\" \"\n", + " return words\n", + "\n", + "features = data['text'].copy()\n", + "features = features.apply(pre_process)\n", + "\n", + "vectorizer = TfidfVectorizer(\"english\")\n", + "_features = vectorizer.fit_transform(features)\n", + "with open('Spam-Classifier/model/vectorizer.pkl', 'wb') as vect:\n", + " pickle.dump(vectorizer, vect)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=1.0, kernel='sigmoid',\n", + " max_iter=-1, probability=True, random_state=None, shrinking=True, tol=0.001,\n", + " verbose=False)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vectorizer = joblib.load(model_path.joinpath('Spam-Classifier/model/vectorizer.pkl'))\n", + "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)\n", + "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", + "svc.fit(train_x,train_y)\n", + "# save the model to disk\n", + "filename = 'Spam-Classifier/model/model.pkl'\n", + "pickle.dump(svc, open(filename, 'wb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "clf = joblib.load(model_path.joinpath(filename))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9730861244019139" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prediction = clf.predict(test_x)\n", + "accuracy_score(test_y,prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.0220629, 0.9779371]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "message = np.array(['click here to win the price'])\n", + "data = vectorizer.transform(message).todense()\n", + "probas = clf.predict_proba(data)\n", + "probas" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['ham', 'spam'], dtype=object)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.classes_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### wrap each model component using s2i" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting scikit-learn==0.21.2 (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/85/04/49633f490f726da6e454fddc8e938bbb5bfed2001681118d3814c219b723/scikit_learn-0.21.2-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n", + "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting scipy>=0.17.0 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/29/50/a552a5aff252ae915f522e44642bb49a7b7b31677f9580cfd11bcc869976/scipy-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (25.2MB)\n", + "Collecting joblib>=0.11 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/8f/42/155696f85f344c066e17af287359c9786b436b1bf86029bb3411283274f3/joblib-0.14.0-py2.py3-none-any.whl (294kB)\n", + "Installing collected packages: scipy, joblib, scikit-learn\n", + "Successfully installed joblib-0.14.0 scikit-learn-0.21.2 scipy-1.3.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], + "source": [ + "!s2i build Spam-Classifier/ seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1b8159f67b7ddbd2de26833411303ebee8e08331097e28754f04688c1fb86d3c\r\n" + ] + } + ], + "source": [ + "!docker run --name \"spam-classifier\" -d --rm -p 5000:5000 spam-classifier:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"ndarray\":[\"0.9779371008528993\",\"spam\"]},\"meta\":{}}\r\n" + ] + } + ], + "source": [ + "!curl -g http://localhost:5000/predict --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"click here to win the price\"]}}'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spam-classifier\r\n" + ] + } + ], + "source": [ + "!docker rm spam-classifier --force" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting goslate (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/39/0b/50af938a1c3d4f4c595b6a22d37af11ebe666246b05a1a97573e8c8944e5/goslate-1.5.1.tar.gz\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting futures (from goslate->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/05/80/f41cca0ea1ff69bce7e7a7d76182b47bb4e1a494380a532af3e8ee70b9ec/futures-3.1.1-py3-none-any.whl\n", + "Building wheels for collected packages: goslate\n", + "Running setup.py bdist_wheel for goslate: started\n", + "Running setup.py bdist_wheel for goslate: finished with status 'done'\n", + "Stored in directory: /root/.cache/pip/wheels/4f/7f/28/6f52271012a7649b54b1a7adaae329b4246bbbf9d1e4f6e51a\n", + "Successfully built goslate\n", + "Installing collected packages: futures, goslate\n", + "Successfully installed futures-3.1.1 goslate-1.5.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], + "source": [ + "!s2i build Translator/ seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ca18617eed4ee5b12c1ce835d94a677007e3c095166b8e4e5d0f9fd164757814\r\n" + ] + } + ], + "source": [ + "!docker run --name \"eng-translator\" -d --rm -p 5000:5000 translator:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"names\":[\"message\"],\"ndarray\":[\"How is your day\"]},\"meta\":{}}\r\n" + ] + } + ], + "source": [ + "!curl -g http://localhost:5000/transform-input --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"Wie läuft dein Tag\"]}}'" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "eng-translator\r\n" + ] + } + ], + "source": [ + "!docker rm eng-translator --force" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuming you have kubernetes cluster running and seldon-core installed, you can deploy your Machine Learning model using:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "kubectl apply -f deploy.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 0572205d91d769a0ce69bed9cc946c397a434f3b Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Thu, 7 Nov 2019 00:18:58 -0600 Subject: [PATCH 73/79] renamed notebook file --- .../spam-classification.ipynb | 630 ++++++++++++++++++ 1 file changed, 630 insertions(+) create mode 100644 examples/combiners/spam_clf_combiner/spam-classification.ipynb diff --git a/examples/combiners/spam_clf_combiner/spam-classification.ipynb b/examples/combiners/spam_clf_combiner/spam-classification.ipynb new file mode 100644 index 0000000000..46ccb17f59 --- /dev/null +++ b/examples/combiners/spam_clf_combiner/spam-classification.ipynb @@ -0,0 +1,630 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spam Classification Model (Sklearn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Wrap a ML model for use as a prediction microservice in seldon-core\n", + "- Run locally on Docker to test\n", + "- Deploy on seldon-core running on k8s cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Locally" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "from sklearn.externals import joblib\n", + "from pathlib import Path\n", + "import string\n", + "from nltk.stem import SnowballStemmer\n", + "from nltk.corpus import stopwords\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "import pickle\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import accuracy_score\n", + "model_path: Path=Path('./')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9730861244019139" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "data.head()\n", + "\n", + "def pre_process(text):\n", + " text = text.translate(str.maketrans('', '', string.punctuation))\n", + " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", + " words = \"\"\n", + " for i in text:\n", + " stemmer = SnowballStemmer(\"english\")\n", + " words += (stemmer.stem(i))+\" \"\n", + " return words\n", + "\n", + "features = data['text'].copy()\n", + "features = features.apply(pre_process)\n", + "\n", + "vectorizer = TfidfVectorizer(\"english\")\n", + "_features = vectorizer.fit_transform(features)\n", + "with open('skl-spam-classifier/model/vectorizer.pkl', 'wb') as vect:\n", + " pickle.dump(vectorizer, vect)\n", + " \n", + "vectorizer = joblib.load(model_path.joinpath('skl-spam-classifier/model/vectorizer.pkl'))\n", + "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)\n", + "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", + "svc.fit(train_x,train_y)\n", + "# save the model to disk\n", + "filename = 'skl-spam-classifier/model/model.pkl'\n", + "pickle.dump(svc, open(filename, 'wb'))\n", + "\n", + "clf = joblib.load(model_path.joinpath(filename))\n", + "\n", + "prediction = clf.predict(test_x)\n", + "accuracy_score(test_y,prediction)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.02762687, 0.97237313]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "message = np.array(['click here to win the price'])\n", + "data = vectorizer.transform(message).todense()\n", + "probas = clf.predict_proba(data)\n", + "probas" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['ham', 'spam'], dtype=object)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf.classes_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Spam Classification Model (keras)\n", + "\n", + "- Wrap a ML model for use as a prediction microservice in seldon-core\n", + "- Run locally on Docker to test\n", + "- Deploy on seldon-core running on k8s cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "you can find data here: https://www.kaggle.com/benvozza/spam-classification/data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Locally" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from keras.models import Model\n", + "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n", + "from keras.optimizers import RMSprop\n", + "from keras.preprocessing.text import Tokenizer\n", + "from keras.preprocessing import sequence\n", + "from keras.utils import to_categorical\n", + "from keras.callbacks import EarlyStopping\n", + "import pickle\n", + "from sklearn.externals import joblib\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()\n", + "from keras.engine.saving import model_from_json\n", + "from keras.layers import (\n", + " Bidirectional,\n", + " concatenate,\n", + " Dense,\n", + " Embedding,\n", + " LSTM,\n", + " Masking,\n", + " Reshape,\n", + " SpatialDropout1D,\n", + " TimeDistributed,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", + "\n", + "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", + "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", + "\n", + "X = data.text\n", + "Y = data['class']\n", + "le = LabelEncoder()\n", + "Y = le.fit_transform(Y)\n", + "Y = Y.reshape(-1,1)\n", + "\n", + "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n", + "max_words = 1000\n", + "max_len = 150\n", + "tokenizer = Tokenizer(num_words=max_words)\n", + "tokenizer.fit_on_texts(X_train)\n", + "\n", + "with open('keras-spam-classifier/model/tokenizer.pkl', 'wb') as tok:\n", + " pickle.dump(tokenizer, tok)\n", + "\n", + " \n", + "sequences = tokenizer.texts_to_sequences(X_train)\n", + "sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def RNN():\n", + " inputs = Input(name='inputs',shape=[max_len])\n", + " layer = Embedding(max_words,50,input_length=max_len)(inputs)\n", + " layer = LSTM(64)(layer)\n", + " layer = Dense(256,name='FC1')(layer)\n", + " layer = Activation('relu')(layer)\n", + " layer = Dropout(0.5)(layer)\n", + " layer = Dense(1,name='out_layer')(layer)\n", + " layer = Activation('sigmoid')(layer)\n", + " model = Model(inputs=inputs,outputs=layer)\n", + " return model\n", + "\n", + "model = RNN()\n", + "model.summary()\n", + "model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n", + "\n", + "model.fit(sequences_matrix,Y_train,batch_size=128,epochs=10,\n", + "validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])\n", + "\n", + "#save model\n", + "model_json = model.to_json()\n", + "with open(\"keras-spam-classifier/model/architecture.json\", \"w\") as json_file:\n", + " json_file.write(model_json)\n", + "model.save_weights(\"keras-spam-classifier/model/weights.h5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### wrap each model component using s2i" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting scikit-learn==0.21.2 (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/85/04/49633f490f726da6e454fddc8e938bbb5bfed2001681118d3814c219b723/scikit_learn-0.21.2-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n", + "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting keras (from -r requirements.txt (line 3))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl (377kB)\n", + "Collecting joblib>=0.11 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/8f/42/155696f85f344c066e17af287359c9786b436b1bf86029bb3411283274f3/joblib-0.14.0-py2.py3-none-any.whl (294kB)\n", + "Collecting scipy>=0.17.0 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/29/50/a552a5aff252ae915f522e44642bb49a7b7b31677f9580cfd11bcc869976/scipy-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (25.2MB)\n", + "Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (2.9.0)\n", + "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.9)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (5.1)\n", + "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.7)\n", + "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.12.0)\n", + "Installing collected packages: joblib, scipy, scikit-learn, keras\n", + "Successfully installed joblib-0.14.0 keras-2.3.1 scikit-learn-0.21.2 scipy-1.3.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], + "source": [ + "!s2i build keras-spam-classifier/ seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "59be46468915231d3915161343486d083d547fde192baa7367ff411efe34c52f\r\n" + ] + } + ], + "source": [ + "!docker run --name \"spam-classifier\" -d --rm -p 5000:5000 spam-classifier:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"ndarray\":[\"0.9779371008528993\",\"spam\"]},\"meta\":{}}\r\n" + ] + } + ], + "source": [ + "!curl -g http://localhost:5000/predict --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"click here to win the price\"]}}'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spam-classifier\r\n" + ] + } + ], + "source": [ + "!docker rm spam-classifier --force" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting scikit-learn==0.21.2 (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/85/04/49633f490f726da6e454fddc8e938bbb5bfed2001681118d3814c219b723/scikit_learn-0.21.2-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n", + "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting keras (from -r requirements.txt (line 3))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl (377kB)\n", + "Collecting scipy>=0.17.0 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/29/50/a552a5aff252ae915f522e44642bb49a7b7b31677f9580cfd11bcc869976/scipy-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (25.2MB)\n", + "Collecting joblib>=0.11 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/8f/42/155696f85f344c066e17af287359c9786b436b1bf86029bb3411283274f3/joblib-0.14.0-py2.py3-none-any.whl (294kB)\n", + "Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (2.9.0)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (5.1)\n", + "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.12.0)\n", + "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.7)\n", + "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.9)\n", + "Installing collected packages: scipy, joblib, scikit-learn, keras\n", + "Successfully installed joblib-0.14.0 keras-2.3.1 scikit-learn-0.21.2 scipy-1.3.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], + "source": [ + "!s2i build keras-spam-classifier/ seldonio/seldon-core-s2i-python3:0.7 keras-spam-classifier:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cf8ebd9bff95cb81ca0cd39393e5c3f2707d7b6394ddc0ace15e41a47248c3e8\r\n" + ] + } + ], + "source": [ + "!docker run --name \"keras-spam-classifier\" --rm -d -p 5000:5000 keras-spam-classifier:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "---> Installing dependencies ...\n", + "Looking in links: /whl\n", + "Collecting goslate (from -r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/39/0b/50af938a1c3d4f4c595b6a22d37af11ebe666246b05a1a97573e8c8944e5/goslate-1.5.1.tar.gz\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", + "Collecting futures (from goslate->-r requirements.txt (line 1))\n", + " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "Downloading https://files.pythonhosted.org/packages/05/80/f41cca0ea1ff69bce7e7a7d76182b47bb4e1a494380a532af3e8ee70b9ec/futures-3.1.1-py3-none-any.whl\n", + "Building wheels for collected packages: goslate\n", + "Running setup.py bdist_wheel for goslate: started\n", + "Running setup.py bdist_wheel for goslate: finished with status 'done'\n", + "Stored in directory: /root/.cache/pip/wheels/4f/7f/28/6f52271012a7649b54b1a7adaae329b4246bbbf9d1e4f6e51a\n", + "Successfully built goslate\n", + "Installing collected packages: futures, goslate\n", + "Successfully installed futures-3.1.1 goslate-1.5.1\n", + "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", + "You are using pip version 18.1, however version 19.3.1 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\n", + "Build completed successfully\n" + ] + } + ], + "source": [ + "!s2i build Translator/ seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "26d5f31364f5095680bd89aabae468f0c4cec0f8cc7da5de4ea77434e4836692\r\n" + ] + } + ], + "source": [ + "!docker run --name \"translator\" -d --rm -p 5000:5000 translator:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"data\":{\"names\":[\"message\"],\"ndarray\":[\"How is your day\"]},\"meta\":{}}\r\n" + ] + } + ], + "source": [ + "!curl -g http://localhost:5000/transform-input --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"Wie läuft dein Tag\"]}}'" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "translator\r\n" + ] + } + ], + "source": [ + "!docker rm translator --force" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---> Installing application source...\n", + "Build completed successfully\n" + ] + } + ], + "source": [ + "!s2i build Combiner/ seldonio/seldon-core-s2i-python3:0.7 combiner:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19fbbdfb073c7da1056611aa18d8c7d3da9a533010667638d8c0cb0abfe6e257\r\n" + ] + } + ], + "source": [ + "!docker run --name \"model-combiner\" -d --rm -p 5000:5000 combiner:1.0.0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "#!curl -g http://localhost:5000/aggregate --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [[\"0.7\",\"Spam\"], [\"0.80\", \"Spam\"]]}}'" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model-combiner\r\n" + ] + } + ], + "source": [ + "!docker rm model-combiner --force" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Assuming you have kubernetes cluster running and seldon-core installed, you can deploy your Machine Learning model using:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "kubectl apply -f deploy.yaml" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2cd00329e94622a324886c63a3469aab27aec693 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Thu, 7 Nov 2019 00:20:31 -0600 Subject: [PATCH 74/79] nblink for combiner example --- .../examples/.input_transformer_spam.nblink.swp | Bin 0 -> 12288 bytes doc/source/examples/combiner_spam_model.nblink | 3 +++ 2 files changed, 3 insertions(+) create mode 100644 doc/source/examples/.input_transformer_spam.nblink.swp create mode 100644 doc/source/examples/combiner_spam_model.nblink diff --git a/doc/source/examples/.input_transformer_spam.nblink.swp 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Date: Thu, 7 Nov 2019 00:25:13 -0600 Subject: [PATCH 75/79] nblink for transformer example --- doc/source/examples/transformer_spam_model.nblink | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 doc/source/examples/transformer_spam_model.nblink diff --git a/doc/source/examples/transformer_spam_model.nblink b/doc/source/examples/transformer_spam_model.nblink new file mode 100644 index 0000000000..d34f7be16c --- /dev/null +++ b/doc/source/examples/transformer_spam_model.nblink @@ -0,0 +1,3 @@ +{ + "path": "../../../examples/input_tranformer/spam-classification.ipynb" +} From d5d401f9a27520a2d3aa6bdfa973110682e9fc03 Mon Sep 17 00:00:00 2001 From: Sandhya Gopchandani Date: Thu, 7 Nov 2019 00:26:19 -0600 Subject: [PATCH 76/79] Delete input_transformer_spam.nblink --- doc/source/examples/input_transformer_spam.nblink | 3 --- 1 file changed, 3 deletions(-) delete mode 100644 doc/source/examples/input_transformer_spam.nblink diff --git a/doc/source/examples/input_transformer_spam.nblink b/doc/source/examples/input_transformer_spam.nblink deleted file mode 100644 index d34f7be16c..0000000000 --- a/doc/source/examples/input_transformer_spam.nblink +++ /dev/null @@ -1,3 +0,0 @@ -{ - "path": "../../../examples/input_tranformer/spam-classification.ipynb" -} From ddf00bd1b0e2be2be1b934194024c94c36a595d4 Mon Sep 17 00:00:00 2001 From: Sandhya Sandhya Date: Thu, 7 Nov 2019 00:27:16 -0600 Subject: [PATCH 77/79] added path to new nblink files --- doc/source/examples/notebooks.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/source/examples/notebooks.rst b/doc/source/examples/notebooks.rst index e213d206c4..b71c03ee63 100644 --- a/doc/source/examples/notebooks.rst +++ b/doc/source/examples/notebooks.rst @@ -47,4 +47,6 @@ Notebooks Sklearn, Xgboost, Tensorflow Server Examples Tensorflow MNIST TFserving MNIST + Spam Classification TRANSFORMER component + Spam Classification TRANSFORMER + COMBINER Component From 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zE-@I}4qrGt+B!L_1+Njj*5Cr9tgz1E9#)U87ra66mj+`YMn%}@@Ip&in*?tb{1ss) z3#_4vY8TfQ7rbkA*H#I>mS7tN>;~4Iz?6;Aqnz-KizeVFUg2Abwo9~wB7JPU1_pc_ z{=z0YO^EbZ Date: Thu, 7 Nov 2019 00:31:51 -0600 Subject: [PATCH 79/79] deleted model pkl files --- .../Spam Classification.ipynb | 630 ------------------ .../model/architecture.json | 1 - .../keras-spam-classifier/model/tokenizer.pkl | Bin 376442 -> 0 bytes .../keras-spam-classifier/model/weights.h5 | Bin 406504 -> 0 bytes .../skl-spam-classifier/model/model.pkl | Bin 139571 -> 0 bytes .../skl-spam-classifier/model/vectorizer.pkl | Bin 508071 -> 0 bytes 6 files changed, 631 deletions(-) delete mode 100644 examples/combiners/spam_clf_combiner/Spam Classification.ipynb delete mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/model/architecture.json delete mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/model/tokenizer.pkl delete mode 100644 examples/combiners/spam_clf_combiner/keras-spam-classifier/model/weights.h5 delete mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/model/model.pkl delete mode 100644 examples/combiners/spam_clf_combiner/skl-spam-classifier/model/vectorizer.pkl diff --git a/examples/combiners/spam_clf_combiner/Spam Classification.ipynb b/examples/combiners/spam_clf_combiner/Spam Classification.ipynb deleted file mode 100644 index 46ccb17f59..0000000000 --- a/examples/combiners/spam_clf_combiner/Spam Classification.ipynb +++ /dev/null @@ -1,630 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Spam Classification Model (Sklearn)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Wrap a ML model for use as a prediction microservice in seldon-core\n", - "- Run locally on Docker to test\n", - "- Deploy on seldon-core running on k8s cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train Locally" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "import pandas as pd\n", - "from sklearn.externals import joblib\n", - "from pathlib import Path\n", - "import string\n", - "from nltk.stem import SnowballStemmer\n", - "from nltk.corpus import stopwords\n", - "from sklearn.feature_extraction.text import TfidfVectorizer\n", - "from sklearn.model_selection import train_test_split\n", - "import pickle\n", - "from sklearn.svm import SVC\n", - "from sklearn.metrics import accuracy_score\n", - "model_path: Path=Path('./')" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9730861244019139" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", - "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", - "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", - "data.head()\n", - "\n", - "def pre_process(text):\n", - " text = text.translate(str.maketrans('', '', string.punctuation))\n", - " text = [word for word in text.split() if word.lower() not in stopwords.words('english')]\n", - " words = \"\"\n", - " for i in text:\n", - " stemmer = SnowballStemmer(\"english\")\n", - " words += (stemmer.stem(i))+\" \"\n", - " return words\n", - "\n", - "features = data['text'].copy()\n", - "features = features.apply(pre_process)\n", - "\n", - "vectorizer = TfidfVectorizer(\"english\")\n", - "_features = vectorizer.fit_transform(features)\n", - "with open('skl-spam-classifier/model/vectorizer.pkl', 'wb') as vect:\n", - " pickle.dump(vectorizer, vect)\n", - " \n", - "vectorizer = joblib.load(model_path.joinpath('skl-spam-classifier/model/vectorizer.pkl'))\n", - "train_x, test_x, train_y, test_y = train_test_split(_features, data['class'], test_size=0.3, random_state=0)\n", - "svc = SVC(kernel='sigmoid', gamma=1.0, probability=True)\n", - "svc.fit(train_x,train_y)\n", - "# save the model to disk\n", - "filename = 'skl-spam-classifier/model/model.pkl'\n", - "pickle.dump(svc, open(filename, 'wb'))\n", - "\n", - "clf = joblib.load(model_path.joinpath(filename))\n", - "\n", - "prediction = clf.predict(test_x)\n", - "accuracy_score(test_y,prediction)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.02762687, 0.97237313]])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "message = np.array(['click here to win the price'])\n", - "data = vectorizer.transform(message).todense()\n", - "probas = clf.predict_proba(data)\n", - "probas" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['ham', 'spam'], dtype=object)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf.classes_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Spam Classification Model (keras)\n", - "\n", - "- Wrap a ML model for use as a prediction microservice in seldon-core\n", - "- Run locally on Docker to test\n", - "- Deploy on seldon-core running on k8s cluster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "you can find data here: https://www.kaggle.com/benvozza/spam-classification/data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train Locally" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /opt/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/compat/v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "non-resource variables are not supported in the long term\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from keras.models import Model\n", - "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n", - "from keras.optimizers import RMSprop\n", - "from keras.preprocessing.text import Tokenizer\n", - "from keras.preprocessing import sequence\n", - "from keras.utils import to_categorical\n", - "from keras.callbacks import EarlyStopping\n", - "import pickle\n", - "from sklearn.externals import joblib\n", - "import tensorflow.compat.v1 as tf\n", - "tf.disable_v2_behavior()\n", - "from keras.engine.saving import model_from_json\n", - "from keras.layers import (\n", - " Bidirectional,\n", - " concatenate,\n", - " Dense,\n", - " Embedding,\n", - " LSTM,\n", - " Masking,\n", - " Reshape,\n", - " SpatialDropout1D,\n", - " TimeDistributed,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "data = pd.read_csv(\"spam.csv\",encoding='latin-1')\n", - "\n", - "data = data.drop([\"Unnamed: 2\", \"Unnamed: 3\", \"Unnamed: 4\"], axis=1)\n", - "data = data.rename(columns={\"v1\":\"class\", \"v2\":\"text\"})\n", - "\n", - "X = data.text\n", - "Y = data['class']\n", - "le = LabelEncoder()\n", - "Y = le.fit_transform(Y)\n", - "Y = Y.reshape(-1,1)\n", - "\n", - "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.15)\n", - "max_words = 1000\n", - "max_len = 150\n", - "tokenizer = Tokenizer(num_words=max_words)\n", - "tokenizer.fit_on_texts(X_train)\n", - "\n", - "with open('keras-spam-classifier/model/tokenizer.pkl', 'wb') as tok:\n", - " pickle.dump(tokenizer, tok)\n", - "\n", - " \n", - "sequences = tokenizer.texts_to_sequences(X_train)\n", - "sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "def RNN():\n", - " inputs = Input(name='inputs',shape=[max_len])\n", - " layer = Embedding(max_words,50,input_length=max_len)(inputs)\n", - " layer = LSTM(64)(layer)\n", - " layer = Dense(256,name='FC1')(layer)\n", - " layer = Activation('relu')(layer)\n", - " layer = Dropout(0.5)(layer)\n", - " layer = Dense(1,name='out_layer')(layer)\n", - " layer = Activation('sigmoid')(layer)\n", - " model = Model(inputs=inputs,outputs=layer)\n", - " return model\n", - "\n", - "model = RNN()\n", - "model.summary()\n", - "model.compile(loss='binary_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n", - "\n", - "model.fit(sequences_matrix,Y_train,batch_size=128,epochs=10,\n", - "validation_split=0.2,callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)])\n", - "\n", - "#save model\n", - "model_json = model.to_json()\n", - "with open(\"keras-spam-classifier/model/architecture.json\", \"w\") as json_file:\n", - " json_file.write(model_json)\n", - "model.save_weights(\"keras-spam-classifier/model/weights.h5\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### wrap each model component using s2i" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---> Installing application source...\n", - "---> Installing dependencies ...\n", - "Looking in links: /whl\n", - "Collecting scikit-learn==0.21.2 (from -r requirements.txt (line 1))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/85/04/49633f490f726da6e454fddc8e938bbb5bfed2001681118d3814c219b723/scikit_learn-0.21.2-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n", - "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", - "Collecting keras (from -r requirements.txt (line 3))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl (377kB)\n", - "Collecting joblib>=0.11 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", - " Url '/whl' is ignored. 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It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/29/50/a552a5aff252ae915f522e44642bb49a7b7b31677f9580cfd11bcc869976/scipy-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (25.2MB)\n", - "Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (2.9.0)\n", - "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.9)\n", - "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (5.1)\n", - "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.7)\n", - "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.12.0)\n", - "Installing collected packages: joblib, scipy, scikit-learn, keras\n", - "Successfully installed joblib-0.14.0 keras-2.3.1 scikit-learn-0.21.2 scipy-1.3.1\n", - "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "You are using pip version 18.1, however version 19.3.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\n", - "Build completed successfully\n" - ] - } - ], - "source": [ - "!s2i build keras-spam-classifier/ seldonio/seldon-core-s2i-python3:0.7 spam-classifier:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "59be46468915231d3915161343486d083d547fde192baa7367ff411efe34c52f\r\n" - ] - } - ], - "source": [ - "!docker run --name \"spam-classifier\" -d --rm -p 5000:5000 spam-classifier:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"data\":{\"ndarray\":[\"0.9779371008528993\",\"spam\"]},\"meta\":{}}\r\n" - ] - } - ], - "source": [ - "!curl -g http://localhost:5000/predict --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"click here to win the price\"]}}'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "spam-classifier\r\n" - ] - } - ], - "source": [ - "!docker rm spam-classifier --force" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---> Installing application source...\n", - "---> Installing dependencies ...\n", - "Looking in links: /whl\n", - "Collecting scikit-learn==0.21.2 (from -r requirements.txt (line 1))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/85/04/49633f490f726da6e454fddc8e938bbb5bfed2001681118d3814c219b723/scikit_learn-0.21.2-cp36-cp36m-manylinux1_x86_64.whl (6.7MB)\n", - "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", - "Collecting keras (from -r requirements.txt (line 3))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl (377kB)\n", - "Collecting scipy>=0.17.0 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/29/50/a552a5aff252ae915f522e44642bb49a7b7b31677f9580cfd11bcc869976/scipy-1.3.1-cp36-cp36m-manylinux1_x86_64.whl (25.2MB)\n", - "Collecting joblib>=0.11 (from scikit-learn==0.21.2->-r requirements.txt (line 1))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/8f/42/155696f85f344c066e17af287359c9786b436b1bf86029bb3411283274f3/joblib-0.14.0-py2.py3-none-any.whl (294kB)\n", - "Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (2.9.0)\n", - "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (5.1)\n", - "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.12.0)\n", - "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.7)\n", - "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/site-packages (from keras->-r requirements.txt (line 3)) (1.0.9)\n", - "Installing collected packages: scipy, joblib, scikit-learn, keras\n", - "Successfully installed joblib-0.14.0 keras-2.3.1 scikit-learn-0.21.2 scipy-1.3.1\n", - "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "You are using pip version 18.1, however version 19.3.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\n", - "Build completed successfully\n" - ] - } - ], - "source": [ - "!s2i build keras-spam-classifier/ seldonio/seldon-core-s2i-python3:0.7 keras-spam-classifier:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cf8ebd9bff95cb81ca0cd39393e5c3f2707d7b6394ddc0ace15e41a47248c3e8\r\n" - ] - } - ], - "source": [ - "!docker run --name \"keras-spam-classifier\" --rm -d -p 5000:5000 keras-spam-classifier:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---> Installing application source...\n", - "---> Installing dependencies ...\n", - "Looking in links: /whl\n", - "Collecting goslate (from -r requirements.txt (line 1))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/39/0b/50af938a1c3d4f4c595b6a22d37af11ebe666246b05a1a97573e8c8944e5/goslate-1.5.1.tar.gz\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 2)) (1.16.3)\n", - "Collecting futures (from goslate->-r requirements.txt (line 1))\n", - " Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "Downloading https://files.pythonhosted.org/packages/05/80/f41cca0ea1ff69bce7e7a7d76182b47bb4e1a494380a532af3e8ee70b9ec/futures-3.1.1-py3-none-any.whl\n", - "Building wheels for collected packages: goslate\n", - "Running setup.py bdist_wheel for goslate: started\n", - "Running setup.py bdist_wheel for goslate: finished with status 'done'\n", - "Stored in directory: /root/.cache/pip/wheels/4f/7f/28/6f52271012a7649b54b1a7adaae329b4246bbbf9d1e4f6e51a\n", - "Successfully built goslate\n", - "Installing collected packages: futures, goslate\n", - "Successfully installed futures-3.1.1 goslate-1.5.1\n", - "Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.\n", - "You are using pip version 18.1, however version 19.3.1 is available.\n", - "You should consider upgrading via the 'pip install --upgrade pip' command.\n", - "Build completed successfully\n" - ] - } - ], - "source": [ - "!s2i build Translator/ seldonio/seldon-core-s2i-python3:0.7 translator:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "26d5f31364f5095680bd89aabae468f0c4cec0f8cc7da5de4ea77434e4836692\r\n" - ] - } - ], - "source": [ - "!docker run --name \"translator\" -d --rm -p 5000:5000 translator:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"data\":{\"names\":[\"message\"],\"ndarray\":[\"How is your day\"]},\"meta\":{}}\r\n" - ] - } - ], - "source": [ - "!curl -g http://localhost:5000/transform-input --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [\"Wie läuft dein Tag\"]}}'" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "translator\r\n" - ] - } - ], - "source": [ - "!docker rm translator --force" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---> Installing application source...\n", - "Build completed successfully\n" - ] - } - ], - "source": [ - "!s2i build Combiner/ seldonio/seldon-core-s2i-python3:0.7 combiner:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "19fbbdfb073c7da1056611aa18d8c7d3da9a533010667638d8c0cb0abfe6e257\r\n" - ] - } - ], - "source": [ - "!docker run --name \"model-combiner\" -d --rm -p 5000:5000 combiner:1.0.0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "#!curl -g http://localhost:5000/aggregate --data-urlencode 'json={\"data\": {\"names\": [\"message\"], \"ndarray\": [[\"0.7\",\"Spam\"], [\"0.80\", \"Spam\"]]}}'" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "model-combiner\r\n" - ] - } - ], - "source": [ - "!docker rm model-combiner --force" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Assuming you have kubernetes cluster running and seldon-core installed, you can deploy your Machine Learning model using:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "kubectl apply -f deploy.yaml" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/combiners/spam_clf_combiner/keras-spam-classifier/model/architecture.json b/examples/combiners/spam_clf_combiner/keras-spam-classifier/model/architecture.json deleted file mode 100644 index 7cd2d722e9..0000000000 --- a/examples/combiners/spam_clf_combiner/keras-spam-classifier/model/architecture.json +++ /dev/null @@ -1 +0,0 @@ -{"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "inputs", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 150], "dtype": "float32", "sparse": false, "name": "inputs"}, 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