Adding upstream version 18.2.2.upstream/18.2.2

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

85 files changed, 875 insertions, 0 deletions

diff --git a/src/pybind/mgr/diskprediction_local/__init__.py b/src/pybind/mgr/diskprediction_local/__init__.py
new file mode 100644
index 000000000..ee85dc9d3
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/__init__.py
@@ -0,0 +1,2 @@
+# flake8: noqa
+from .module import Module
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/config.json b/src/pybind/mgr/diskprediction_local/models/prophetstor/config.json
new file mode 100644
index 000000000..9a1485ca3
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/config.json
@@ -0,0 +1,77 @@
+{
+"svm_123.pkl": ["smart_197_raw", "smart_183_raw", "smart_200_raw", "smart_194_raw", "smart_254_raw", "smart_252_raw", "smart_4_raw", "smart_222_raw", "smart_187_raw", "smart_184_raw"],
+"svm_105.pkl": ["smart_197_raw", "smart_4_raw", "smart_5_raw", "smart_252_raw", "smart_184_raw", "smart_223_raw", "smart_198_raw", "smart_10_raw", "smart_189_raw", "smart_222_raw"],
+"svm_82.pkl":["smart_184_raw", "smart_2_raw", "smart_187_raw", "smart_225_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_13_raw", "smart_188_raw", "smart_251_raw"],
+"svm_186.pkl":["smart_3_raw", "smart_11_raw", "smart_198_raw", "smart_250_raw", "smart_13_raw", "smart_200_raw", "smart_224_raw", "smart_187_raw", "smart_22_raw", "smart_4_raw", "smart_220_raw"],
+"svm_14.pkl":["smart_12_raw", "smart_226_raw", "smart_187_raw", "smart_196_raw", "smart_5_raw", "smart_183_raw", "smart_255_raw", "smart_250_raw", "smart_201_raw", "smart_8_raw"],
+"svm_10.pkl":["smart_251_raw", "smart_4_raw", "smart_223_raw", "smart_13_raw", "smart_255_raw", "smart_188_raw", "smart_197_raw", "smart_201_raw", "smart_250_raw", "smart_15_raw"],
+"svm_235.pkl":["smart_15_raw", "smart_255_raw", "smart_252_raw", "smart_197_raw", "smart_250_raw", "smart_254_raw", "smart_13_raw", "smart_251_raw", "smart_198_raw", "smart_189_raw", "smart_191_raw"],
+"svm_234.pkl":["smart_187_raw", "smart_183_raw", "smart_3_raw", "smart_4_raw", "smart_222_raw", "smart_184_raw", "smart_5_raw", "smart_198_raw", "smart_200_raw", "smart_8_raw", "smart_10_raw"],
+"svm_119.pkl":["smart_254_raw", "smart_8_raw", "smart_183_raw", "smart_184_raw", "smart_195_raw", "smart_252_raw", "smart_191_raw", "smart_10_raw", "smart_200_raw", "smart_197_raw"],
+"svm_227.pkl":["smart_254_raw", "smart_189_raw", "smart_225_raw", "smart_224_raw", "smart_197_raw", "smart_223_raw", "smart_4_raw", "smart_183_raw", "smart_11_raw", "smart_184_raw", "smart_13_raw"],
+"svm_18.pkl":["smart_197_raw", "smart_3_raw", "smart_220_raw", "smart_193_raw", "smart_10_raw", "smart_187_raw", "smart_188_raw", "smart_225_raw", "smart_194_raw", "smart_13_raw"],
+"svm_78.pkl":["smart_10_raw", "smart_183_raw", "smart_191_raw", "smart_13_raw", "smart_198_raw", "smart_22_raw", "smart_195_raw", "smart_12_raw", "smart_224_raw", "smart_200_raw"],
+"svm_239.pkl":["smart_3_raw", "smart_254_raw", "smart_199_raw", "smart_225_raw", "smart_187_raw", "smart_195_raw", "smart_197_raw", "smart_2_raw", "smart_193_raw", "smart_220_raw", "smart_183_raw"],
+"svm_174.pkl":["smart_183_raw", "smart_196_raw", "smart_225_raw", "smart_189_raw", "smart_4_raw", "smart_3_raw", "smart_9_raw", "smart_198_raw", "smart_15_raw", "smart_5_raw", "smart_194_raw"],
+"svm_104.pkl":["smart_12_raw", "smart_198_raw", "smart_197_raw", "smart_4_raw", "smart_240_raw", "smart_187_raw", "smart_225_raw", "smart_8_raw", "smart_3_raw", "smart_2_raw"],
+"svm_12.pkl":["smart_222_raw", "smart_251_raw", "smart_194_raw", "smart_9_raw", "smart_184_raw", "smart_191_raw", "smart_187_raw", "smart_255_raw", "smart_4_raw", "smart_11_raw"],
+"svm_97.pkl":["smart_15_raw", "smart_197_raw", "smart_190_raw", "smart_199_raw", "smart_200_raw", "smart_12_raw", "smart_191_raw", "smart_254_raw", "smart_194_raw", "smart_201_raw"],
+"svm_118.pkl":["smart_11_raw", "smart_225_raw", "smart_196_raw", "smart_197_raw", "smart_198_raw", "smart_200_raw", "smart_3_raw", "smart_10_raw", "smart_191_raw", "smart_22_raw"],
+"svm_185.pkl":["smart_191_raw", "smart_254_raw", "smart_3_raw", "smart_190_raw", "smart_15_raw", "smart_22_raw", "smart_2_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_225_raw"],
+"svm_206.pkl":["smart_183_raw", "smart_192_raw", "smart_197_raw", "smart_255_raw", "smart_187_raw", "smart_254_raw", "smart_198_raw", "smart_13_raw", "smart_226_raw", "smart_240_raw", "smart_8_raw"],
+"svm_225.pkl":["smart_224_raw", "smart_11_raw", "smart_5_raw", "smart_4_raw", "smart_225_raw", "smart_197_raw", "smart_15_raw", "smart_183_raw", "smart_193_raw", "smart_190_raw", "smart_187_raw"],
+"svm_169.pkl":["smart_252_raw", "smart_183_raw", "smart_254_raw", "smart_11_raw", "smart_193_raw", "smart_22_raw", "smart_226_raw", "smart_189_raw", "smart_225_raw", "smart_198_raw", "smart_200_raw"],
+"svm_79.pkl":["smart_184_raw", "smart_196_raw", "smart_4_raw", "smart_226_raw", "smart_199_raw", "smart_187_raw", "smart_193_raw", "smart_188_raw", "smart_12_raw", "smart_250_raw"],
+"svm_69.pkl":["smart_187_raw", "smart_9_raw", "smart_200_raw", "smart_11_raw", "smart_252_raw", "smart_189_raw", "smart_4_raw", "smart_188_raw", "smart_255_raw", "smart_201_raw"],
+"svm_201.pkl":["smart_224_raw", "smart_8_raw", "smart_250_raw", "smart_2_raw", "smart_198_raw", "smart_15_raw", "smart_193_raw", "smart_223_raw", "smart_3_raw", "smart_11_raw", "smart_191_raw"],
+"svm_114.pkl":["smart_226_raw", "smart_188_raw", "smart_2_raw", "smart_11_raw", "smart_4_raw", "smart_193_raw", "smart_184_raw", "smart_194_raw", "smart_198_raw", "smart_13_raw"],
+"svm_219.pkl":["smart_12_raw", "smart_22_raw", "smart_8_raw", "smart_191_raw", "smart_197_raw", "smart_254_raw", "smart_15_raw", "smart_193_raw", "smart_199_raw", "smart_225_raw", "smart_192_raw"],
+"svm_168.pkl":["smart_255_raw", "smart_191_raw", "smart_193_raw", "smart_220_raw", "smart_5_raw", "smart_3_raw", "smart_222_raw", "smart_223_raw", "smart_197_raw", "smart_196_raw", "smart_22_raw"],
+"svm_243.pkl":["smart_11_raw", "smart_255_raw", "smart_10_raw", "smart_189_raw", "smart_225_raw", "smart_240_raw", "smart_222_raw", "smart_197_raw", "smart_183_raw", "smart_198_raw", "smart_12_raw"],
+"svm_195.pkl":["smart_183_raw", "smart_5_raw", "smart_11_raw", "smart_197_raw", "smart_15_raw", "smart_9_raw", "smart_4_raw", "smart_220_raw", "smart_12_raw", "smart_192_raw", "smart_240_raw"],
+"svm_222.pkl":["smart_10_raw", "smart_13_raw", "smart_188_raw", "smart_15_raw", "smart_192_raw", "smart_224_raw", "smart_225_raw", "smart_187_raw", "smart_222_raw", "smart_220_raw", "smart_252_raw"],
+"svm_62.pkl":["smart_196_raw", "smart_251_raw", "smart_187_raw", "smart_224_raw", "smart_11_raw", "smart_12_raw", "smart_8_raw", "smart_199_raw", "smart_220_raw", "smart_195_raw"],
+"svm_151.pkl":["smart_187_raw", "smart_223_raw", "smart_200_raw", "smart_189_raw", "smart_251_raw", "smart_255_raw", "smart_222_raw", "smart_192_raw", "smart_12_raw", "smart_183_raw", "smart_22_raw"],
+"svm_125.pkl":["smart_9_raw", "smart_252_raw", "smart_197_raw", "smart_251_raw", "smart_11_raw", "smart_12_raw", "smart_188_raw", "smart_240_raw", "smart_10_raw", "smart_223_raw"],
+"svm_124.pkl":["smart_193_raw", "smart_187_raw", "smart_183_raw", "smart_11_raw", "smart_10_raw", "smart_8_raw", "smart_194_raw", "smart_189_raw", "smart_222_raw", "smart_191_raw"],
+"svm_67.pkl":["smart_2_raw", "smart_8_raw", "smart_225_raw", "smart_240_raw", "smart_13_raw", "smart_5_raw", "smart_187_raw", "smart_198_raw", "smart_199_raw", "smart_3_raw"],
+"svm_115.pkl":["smart_222_raw", "smart_193_raw", "smart_223_raw", "smart_195_raw", "smart_252_raw", "smart_189_raw", "smart_199_raw", "smart_187_raw", "smart_15_raw", "smart_184_raw"],
+"svm_1.pkl":["smart_201_raw", "smart_8_raw", "smart_200_raw", "smart_252_raw", "smart_251_raw", "smart_187_raw", "smart_9_raw", "smart_188_raw", "smart_15_raw", "smart_184_raw"],
+"svm_112.pkl":["smart_220_raw", "smart_197_raw", "smart_10_raw", "smart_188_raw", "smart_12_raw", "smart_4_raw", "smart_196_raw", "smart_3_raw", "smart_240_raw", "smart_225_raw"],
+"svm_138.pkl":["smart_183_raw", "smart_10_raw", "smart_191_raw", "smart_195_raw", "smart_223_raw", "smart_189_raw", "smart_187_raw", "smart_255_raw", "smart_226_raw", "smart_8_raw"],
+"svm_229.pkl":["smart_224_raw", "smart_8_raw", "smart_192_raw", "smart_220_raw", "smart_195_raw", "smart_183_raw", "smart_250_raw", "smart_187_raw", "smart_225_raw", "smart_4_raw", "smart_252_raw"],
+"svm_145.pkl":["smart_190_raw", "smart_8_raw", "smart_226_raw", "smart_184_raw", "smart_225_raw", "smart_220_raw", "smart_193_raw", "smart_183_raw", "smart_201_raw", "smart_187_raw", "smart_2_raw"],
+"svm_59.pkl":["smart_188_raw", "smart_11_raw", "smart_184_raw", "smart_2_raw", "smart_220_raw", "smart_198_raw", "smart_225_raw", "smart_240_raw", "smart_197_raw", "smart_251_raw"],
+"svm_204.pkl":["smart_15_raw", "smart_240_raw", "smart_225_raw", "smart_223_raw", "smart_252_raw", "smart_22_raw", "smart_200_raw", "smart_13_raw", "smart_220_raw", "smart_198_raw", "smart_191_raw"],
+"svm_88.pkl":["smart_198_raw", "smart_3_raw", "smart_8_raw", "smart_225_raw", "smart_251_raw", "smart_222_raw", "smart_188_raw", "smart_10_raw", "smart_240_raw", "smart_189_raw"],
+"svm_182.pkl":["smart_10_raw", "smart_190_raw", "smart_250_raw", "smart_15_raw", "smart_193_raw", "smart_22_raw", "smart_200_raw", "smart_8_raw", "smart_4_raw", "smart_187_raw", "smart_9_raw"],
+"svm_61.pkl":["smart_5_raw", "smart_12_raw", "smart_9_raw", "smart_198_raw", "smart_195_raw", "smart_252_raw", "smart_15_raw", "smart_240_raw", "smart_255_raw", "smart_224_raw"],
+"svm_50.pkl":["smart_220_raw", "smart_5_raw", "smart_194_raw", "smart_250_raw", "smart_15_raw", "smart_240_raw", "smart_8_raw", "smart_198_raw", "smart_224_raw", "smart_191_raw"],
+"svm_210.pkl":["smart_8_raw", "smart_15_raw", "smart_195_raw", "smart_224_raw", "smart_5_raw", "smart_191_raw", "smart_198_raw", "smart_225_raw", "smart_200_raw", "smart_251_raw", "smart_240_raw"],
+"svm_16.pkl":["smart_222_raw", "smart_10_raw", "smart_250_raw", "smart_189_raw", "smart_191_raw", "smart_2_raw", "smart_5_raw", "smart_193_raw", "smart_9_raw", "smart_187_raw"],
+"svm_85.pkl":["smart_252_raw", "smart_184_raw", "smart_9_raw", "smart_5_raw", "smart_254_raw", "smart_3_raw", "smart_195_raw", "smart_10_raw", "smart_12_raw", "smart_222_raw"],
+"svm_36.pkl":["smart_201_raw", "smart_251_raw", "smart_184_raw", "smart_3_raw", "smart_5_raw", "smart_183_raw", "smart_194_raw", "smart_195_raw", "smart_224_raw", "smart_2_raw"],
+"svm_33.pkl":["smart_223_raw", "smart_254_raw", "smart_225_raw", "smart_9_raw", "smart_199_raw", "smart_5_raw", "smart_189_raw", "smart_194_raw", "smart_240_raw", "smart_4_raw"],
+"svm_3.pkl":["smart_225_raw", "smart_194_raw", "smart_3_raw", "smart_189_raw", "smart_9_raw", "smart_254_raw", "smart_240_raw", "smart_5_raw", "smart_255_raw", "smart_223_raw"],
+"svm_93.pkl":["smart_8_raw", "smart_188_raw", "smart_5_raw", "smart_10_raw", "smart_222_raw", "smart_2_raw", "smart_254_raw", "smart_12_raw", "smart_193_raw", "smart_224_raw"],
+"svm_120.pkl":["smart_189_raw", "smart_224_raw", "smart_222_raw", "smart_193_raw", "smart_5_raw", "smart_201_raw", "smart_8_raw", "smart_254_raw", "smart_194_raw", "smart_22_raw"],
+"svm_128.pkl":["smart_195_raw", "smart_184_raw", "smart_251_raw", "smart_8_raw", "smart_5_raw", "smart_196_raw", "smart_10_raw", "smart_4_raw", "smart_225_raw", "smart_191_raw"],
+"svm_212.pkl":["smart_225_raw", "smart_192_raw", "smart_10_raw", "smart_12_raw", "smart_222_raw", "smart_184_raw", "smart_13_raw", "smart_226_raw", "smart_5_raw", "smart_201_raw", "smart_22_raw"],
+"svm_221.pkl":["smart_255_raw", "smart_2_raw", "smart_224_raw", "smart_192_raw", "smart_252_raw", "smart_13_raw", "smart_183_raw", "smart_193_raw", "smart_15_raw", "smart_199_raw", "smart_200_raw"],
+"svm_223.pkl":["smart_4_raw", "smart_194_raw", "smart_9_raw", "smart_255_raw", "smart_188_raw", "smart_201_raw", "smart_3_raw", "smart_226_raw", "smart_192_raw", "smart_251_raw", "smart_191_raw"],
+"svm_44.pkl":["smart_255_raw", "smart_11_raw", "smart_200_raw", "smart_3_raw", "smart_195_raw", "smart_201_raw", "smart_4_raw", "smart_5_raw", "smart_10_raw", "smart_191_raw"],
+"svm_213.pkl":["smart_22_raw", "smart_191_raw", "smart_183_raw", "smart_4_raw", "smart_194_raw", "smart_255_raw", "smart_254_raw", "smart_193_raw", "smart_11_raw", "smart_10_raw", "smart_220_raw"],
+"svm_131.pkl":["smart_22_raw", "smart_194_raw", "smart_184_raw", "smart_250_raw", "smart_10_raw", "smart_189_raw", "smart_183_raw", "smart_240_raw", "smart_12_raw", "smart_252_raw"],
+"svm_6.pkl":["smart_194_raw", "smart_250_raw", "smart_223_raw", "smart_224_raw", "smart_184_raw", "smart_191_raw", "smart_201_raw", "smart_9_raw", "smart_252_raw", "smart_3_raw"],
+"svm_161.pkl":["smart_255_raw", "smart_222_raw", "smart_226_raw", "smart_254_raw", "smart_183_raw", "smart_22_raw", "smart_12_raw", "smart_190_raw", "smart_11_raw", "smart_192_raw", "smart_251_raw"],
+"svm_72.pkl":["smart_13_raw", "smart_184_raw", "smart_223_raw", "smart_240_raw", "smart_250_raw", "smart_251_raw", "smart_201_raw", "smart_196_raw", "smart_5_raw", "smart_4_raw"],
+"svm_27.pkl":["smart_189_raw", "smart_188_raw", "smart_255_raw", "smart_251_raw", "smart_240_raw", "smart_15_raw", "smart_9_raw", "smart_191_raw", "smart_226_raw", "smart_10_raw"],
+"svm_141.pkl":["smart_9_raw", "smart_191_raw", "smart_2_raw", "smart_226_raw", "smart_13_raw", "smart_22_raw", "smart_193_raw", "smart_222_raw", "smart_220_raw", "smart_225_raw", "smart_3_raw"],
+"svm_57.pkl":["smart_12_raw", "smart_252_raw", "smart_190_raw", "smart_226_raw", "smart_10_raw", "smart_189_raw", "smart_193_raw", "smart_2_raw", "smart_9_raw", "smart_223_raw"],
+"svm_236.pkl":["smart_200_raw", "smart_189_raw", "smart_226_raw", "smart_252_raw", "smart_250_raw", "smart_193_raw", "smart_13_raw", "smart_2_raw", "smart_254_raw", "smart_22_raw", "smart_9_raww"],
+"svm_208.pkl":["smart_223_raw", "smart_15_raw", "smart_251_raw", "smart_5_raw", "smart_198_raw", "smart_252_raw", "smart_4_raw", "smart_8_raw", "smart_220_raw", "smart_254_raw", "smart_193_raw"],
+"svm_230.pkl":["smart_184_raw", "smart_5_raw", "smart_191_raw", "smart_198_raw", "smart_11_raw", "smart_255_raw", "smart_189_raw", "smart_254_raw", "smart_196_raw", "smart_199_raw", "smart_223_raw"],
+"svm_134.pkl":["smart_8_raw", "smart_194_raw", "smart_4_raw", "smart_189_raw", "smart_223_raw", "smart_5_raw", "smart_187_raw", "smart_9_raw", "smart_192_raw", "smart_220_raw"],
+"svm_71.pkl":["smart_220_raw", "smart_13_raw", "smart_194_raw", "smart_197_raw", "smart_192_raw", "smart_22_raw", "smart_184_raw", "smart_199_raw", "smart_222_raw", "smart_183_raw"],
+"svm_109.pkl":["smart_224_raw", "smart_252_raw", "smart_2_raw", "smart_200_raw", "smart_5_raw", "smart_194_raw", "smart_222_raw", "smart_198_raw", "smart_4_raw", "smart_13_raw"]
+}
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_1.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_1.pkl
new file mode 100644
index 000000000..5eb30f300
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_1.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_10.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_10.pkl
new file mode 100644
index 000000000..9259c1e74
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_10.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_104.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_104.pkl
new file mode 100644
index 000000000..d5d5cf5b7
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_104.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_105.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_105.pkl
new file mode 100644
index 000000000..4aadc3cfb
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_105.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_109.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_109.pkl
new file mode 100644
index 000000000..c99c353be
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_109.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_112.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_112.pkl
new file mode 100644
index 000000000..367a3304a
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_112.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_114.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_114.pkl
new file mode 100644
index 000000000..946d5cef1
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_114.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_115.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_115.pkl
new file mode 100644
index 000000000..ff834929e
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_115.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_118.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_118.pkl
new file mode 100644
index 000000000..eec8689ea
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_118.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_119.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_119.pkl
new file mode 100644
index 000000000..6a26c0502
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_119.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_12.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_12.pkl
new file mode 100644
index 000000000..5cbe9775a
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_12.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_120.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_120.pkl
new file mode 100644
index 000000000..d2041c267
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_120.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_123.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_123.pkl
new file mode 100644
index 000000000..0ab6187e9
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_123.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_124.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_124.pkl
new file mode 100644
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_125.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_125.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_128.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_128.pkl
new file mode 100644
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_131.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_131.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_134.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_134.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_138.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_138.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_14.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_14.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_141.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_141.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_145.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_145.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_151.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_151.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_16.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_16.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_161.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_161.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_168.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_168.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_169.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_169.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_174.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_174.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_18.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_18.pkl
new file mode 100644
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_182.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_182.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_185.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_185.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_186.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_186.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_195.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_195.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_201.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_201.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_227.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_227.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_243.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_243.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_27.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_27.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_3.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_3.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_33.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_33.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_36.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_36.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_50.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_50.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_57.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_57.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_59.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_59.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_6.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_6.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_62.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_62.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_69.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_69.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_79.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_79.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_82.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_82.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_85.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_85.pkl
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diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_88.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_88.pkl
new file mode 100644
index 000000000..715633982
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_88.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_93.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_93.pkl
new file mode 100644
index 000000000..703429fe3
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_93.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_97.pkl b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_97.pkl
new file mode 100644
index 000000000..9653d20f3
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/prophetstor/svm_97.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/redhat/config.json b/src/pybind/mgr/diskprediction_local/models/redhat/config.json
new file mode 100644
index 000000000..62a0d8282
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/redhat/config.json
@@ -0,0 +1,4 @@
+{
+"seagate": ["user_capacity", "smart_1_raw", "smart_5_raw", "smart_7_raw", "smart_10_raw", "smart_187_raw", "smart_188_raw", "smart_190_raw", "smart_193_raw", "smart_197_raw", "smart_198_raw", "smart_241_raw", "smart_1_normalized", "smart_5_normalized", "smart_7_normalized", "smart_10_normalized", "smart_187_normalized", "smart_188_normalized", "smart_190_normalized", "smart_193_normalized", "smart_197_normalized", "smart_198_normalized", "smart_241_normalized"],
+"hgst": ["user_capacity", "smart_1_normalized", "smart_1_raw", "smart_2_normalized", "smart_2_raw", "smart_3_normalized", "smart_3_raw", "smart_4_raw", "smart_5_normalized", "smart_5_raw", "smart_7_normalized", "smart_7_raw", "smart_8_normalized", "smart_8_raw", "smart_9_normalized", "smart_9_raw", "smart_10_normalized", "smart_10_raw", "smart_12_raw", "smart_192_normalized", "smart_192_raw", "smart_193_normalized", "smart_193_raw", "smart_194_normalized", "smart_194_raw", "smart_196_normalized", "smart_196_raw", "smart_197_normalized", "smart_197_raw", "smart_198_raw", "smart_199_raw"]
+}
diff --git a/src/pybind/mgr/diskprediction_local/models/redhat/hgst_predictor.pkl b/src/pybind/mgr/diskprediction_local/models/redhat/hgst_predictor.pkl
new file mode 100644
index 000000000..9894d9f55
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/redhat/hgst_predictor.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/redhat/hgst_scaler.pkl b/src/pybind/mgr/diskprediction_local/models/redhat/hgst_scaler.pkl
new file mode 100644
index 000000000..6f77b85cc
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/redhat/hgst_scaler.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/redhat/seagate_predictor.pkl b/src/pybind/mgr/diskprediction_local/models/redhat/seagate_predictor.pkl
new file mode 100644
index 000000000..280d59a08
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/redhat/seagate_predictor.pkl
diff --git a/src/pybind/mgr/diskprediction_local/models/redhat/seagate_scaler.pkl b/src/pybind/mgr/diskprediction_local/models/redhat/seagate_scaler.pkl
new file mode 100644
index 000000000..691bb03c5
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/models/redhat/seagate_scaler.pkl
diff --git a/src/pybind/mgr/diskprediction_local/module.py b/src/pybind/mgr/diskprediction_local/module.py
new file mode 100644
index 000000000..450dc9c83
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/module.py
@@ -0,0 +1,305 @@
+"""
+diskprediction with local predictor
+"""
+import json
+import datetime
+from threading import Event
+import time
+from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
+from mgr_module import CommandResult, MgrModule, Option
+# Importing scipy early appears to avoid a future deadlock when
+# we try to do
+#
+#  from .predictor import get_diskfailurepredictor_path
+#
+# in a command thread.  See https://tracker.ceph.com/issues/42764
+import scipy  # noqa: ignore=F401
+from .predictor import DevSmartT, Predictor, get_diskfailurepredictor_path
+
+
+TIME_FORMAT = '%Y%m%d-%H%M%S'
+TIME_DAYS = 24 * 60 * 60
+TIME_WEEK = TIME_DAYS * 7
+
+
+class Module(MgrModule):
+    MODULE_OPTIONS = [
+        Option(name='sleep_interval',
+               default=600),
+        Option(name='predict_interval',
+               default=86400),
+        Option(name='predictor_model',
+               default='prophetstor')
+    ]
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super(Module, self).__init__(*args, **kwargs)
+        # options
+        for opt in self.MODULE_OPTIONS:
+            setattr(self, opt['name'], opt['default'])
+        # other
+        self._run = True
+        self._event = Event()
+        # for mypy which does not run the code
+        if TYPE_CHECKING:
+            self.sleep_interval = 0
+            self.predict_interval = 0
+            self.predictor_model = ''
+
+    def config_notify(self) -> None:
+        for opt in self.MODULE_OPTIONS:
+            setattr(self,
+                    opt['name'],
+                    self.get_module_option(opt['name']))
+            self.log.debug(' %s = %s', opt['name'], getattr(self, opt['name']))
+        if self.get_ceph_option('device_failure_prediction_mode') == 'local':
+            self._event.set()
+
+    def refresh_config(self) -> None:
+        for opt in self.MODULE_OPTIONS:
+            setattr(self,
+                    opt['name'],
+                    self.get_module_option(opt['name']))
+            self.log.debug(' %s = %s', opt['name'], getattr(self, opt['name']))
+
+    def self_test(self) -> None:
+        self.log.debug('self_test enter')
+        ret, out, err = self.predict_all_devices()
+        assert ret == 0
+
+    def serve(self) -> None:
+        self.log.info('Starting diskprediction local module')
+        self.config_notify()
+        last_predicted = None
+        ls = self.get_store('last_predicted')
+        if ls:
+            try:
+                last_predicted = datetime.datetime.strptime(ls, TIME_FORMAT)
+            except ValueError:
+                pass
+        self.log.debug('Last predicted %s', last_predicted)
+
+        while self._run:
+            self.refresh_config()
+            mode = self.get_ceph_option('device_failure_prediction_mode')
+            if mode == 'local':
+                now = datetime.datetime.utcnow()
+                if not last_predicted:
+                    next_predicted = now
+                else:
+                    predicted_frequency = self.predict_interval or 86400
+                    seconds = (last_predicted - datetime.datetime.utcfromtimestamp(0)).total_seconds()
+                    seconds -= seconds % predicted_frequency
+                    seconds += predicted_frequency
+                    next_predicted = datetime.datetime.utcfromtimestamp(seconds)
+                    self.log.debug('Last scrape %s, next scrape due %s',
+                                   last_predicted.strftime(TIME_FORMAT),
+                                   next_predicted.strftime(TIME_FORMAT))
+                if now >= next_predicted:
+                    self.predict_all_devices()
+                    last_predicted = now
+                    self.set_store('last_predicted', last_predicted.strftime(TIME_FORMAT))
+
+            sleep_interval = self.sleep_interval or 60
+            self.log.debug('Sleeping for %d seconds', sleep_interval)
+            self._event.wait(sleep_interval)
+            self._event.clear()
+
+    def shutdown(self) -> None:
+        self.log.info('Stopping')
+        self._run = False
+        self._event.set()
+
+    @staticmethod
+    def _convert_timestamp(predicted_timestamp: int, life_expectancy_day: int) -> str:
+        """
+        :param predicted_timestamp: unit is nanoseconds
+        :param life_expectancy_day: unit is seconds
+        :return:
+            date format '%Y-%m-%d' ex. 2018-01-01
+        """
+        return datetime.datetime.fromtimestamp(
+            predicted_timestamp / (1000 ** 3) + life_expectancy_day).strftime('%Y-%m-%d')
+
+    def _predict_life_expectancy(self, devid: str) -> str:
+        predicted_result = ''
+        health_data: Dict[str, Dict[str, Any]] = {}
+        predict_datas: List[DevSmartT] = []
+        try:
+            r, outb, outs = self.remote(
+                'devicehealth', 'show_device_metrics', devid=devid, sample='')
+            if r != 0:
+                self.log.error('failed to get device %s health', devid)
+                health_data = {}
+            else:
+                health_data = json.loads(outb)
+        except Exception as e:
+            self.log.error('failed to get device %s health data due to %s', devid, str(e))
+
+        # initialize appropriate disk failure predictor model
+        obj_predictor = Predictor.create(self.predictor_model)
+        if obj_predictor is None:
+            self.log.error('invalid value received for MODULE_OPTIONS.predictor_model')
+            return predicted_result
+        try:
+            obj_predictor.initialize(
+                "{}/models/{}".format(get_diskfailurepredictor_path(), self.predictor_model))
+        except Exception as e:
+            self.log.error('Error initializing predictor: %s', e)
+            return predicted_result
+
+        if len(health_data) >= 6:
+            o_keys = sorted(health_data.keys(), reverse=True)
+            for o_key in o_keys:
+                # get values for current day (?)
+                dev_smart = {}
+                s_val = health_data[o_key]
+
+                # add all smart attributes
+                ata_smart = s_val.get('ata_smart_attributes', {})
+                for attr in ata_smart.get('table', []):
+                    # get raw smart values
+                    if attr.get('raw', {}).get('string') is not None:
+                        if str(attr.get('raw', {}).get('string', '0')).isdigit():
+                            dev_smart['smart_%s_raw' % attr.get('id')] = \
+                                int(attr.get('raw', {}).get('string', '0'))
+                        else:
+                            if str(attr.get('raw', {}).get('string', '0')).split(' ')[0].isdigit():
+                                dev_smart['smart_%s_raw' % attr.get('id')] = \
+                                    int(attr.get('raw', {}).get('string',
+                                                                '0').split(' ')[0])
+                            else:
+                                dev_smart['smart_%s_raw' % attr.get('id')] = \
+                                    attr.get('raw', {}).get('value', 0)
+                    # get normalized smart values
+                    if attr.get('value') is not None:
+                        dev_smart['smart_%s_normalized' % attr.get('id')] = \
+                            attr.get('value')
+                # add power on hours manually if not available in smart attributes
+                power_on_time = s_val.get('power_on_time', {}).get('hours')
+                if power_on_time is not None:
+                    dev_smart['smart_9_raw'] = int(power_on_time)
+                # add device capacity
+                user_capacity = s_val.get('user_capacity', {}).get('bytes')
+                if user_capacity is not None:
+                    dev_smart['user_capacity'] = user_capacity
+                else:
+                    self.log.debug('user_capacity not found in smart attributes list')
+                # add device model
+                model_name = s_val.get('model_name')
+                if model_name is not None:
+                    dev_smart['model_name'] = model_name
+                # add vendor
+                vendor = s_val.get('vendor')
+                if vendor is not None:
+                    dev_smart['vendor'] = vendor
+                # if smart data was found, then add that to list
+                if dev_smart:
+                    predict_datas.append(dev_smart)
+                if len(predict_datas) >= 12:
+                    break
+        else:
+            self.log.error('unable to predict device due to health data records less than 6 days')
+
+        if len(predict_datas) >= 6:
+            predicted_result = obj_predictor.predict(predict_datas)
+        return predicted_result
+
+    def predict_life_expectancy(self, devid: str) -> Tuple[int, str, str]:
+        result = self._predict_life_expectancy(devid)
+        if result.lower() == 'good':
+            return 0, '>6w', ''
+        elif result.lower() == 'warning':
+            return 0, '>=2w and <=6w', ''
+        elif result.lower() == 'bad':
+            return 0, '<2w', ''
+        else:
+            return 0, 'unknown', ''
+
+    def _reset_device_life_expectancy(self, device_id: str) -> int:
+        result = CommandResult('')
+        self.send_command(result, 'mon', '', json.dumps({
+            'prefix': 'device rm-life-expectancy',
+            'devid': device_id
+        }), '')
+        ret, _, outs = result.wait()
+        if ret != 0:
+            self.log.error(
+                'failed to reset device life expectancy, %s' % outs)
+        return ret
+
+    def _set_device_life_expectancy(self,
+                                    device_id: str,
+                                    from_date: str,
+                                    to_date: Optional[str] = None) -> int:
+        result = CommandResult('')
+
+        if to_date is None:
+            self.send_command(result, 'mon', '', json.dumps({
+                'prefix': 'device set-life-expectancy',
+                'devid': device_id,
+                'from': from_date
+            }), '')
+        else:
+            self.send_command(result, 'mon', '', json.dumps({
+                'prefix': 'device set-life-expectancy',
+                'devid': device_id,
+                'from': from_date,
+                'to': to_date
+            }), '')
+        ret, _, outs = result.wait()
+        if ret != 0:
+            self.log.error(
+                'failed to set device life expectancy, %s' % outs)
+        return ret
+
+    def predict_all_devices(self) -> Tuple[int, str, str]:
+        self.log.debug('predict_all_devices')
+        devices = self.get('devices').get('devices', [])
+        for devInfo in devices:
+            if not devInfo.get('daemons'):
+                continue
+            if not devInfo.get('devid'):
+                continue
+            self.log.debug('%s' % devInfo)
+            result = self._predict_life_expectancy(devInfo['devid'])
+            if result == 'unknown':
+                self._reset_device_life_expectancy(devInfo['devid'])
+                continue
+            predicted = int(time.time() * (1000 ** 3))
+
+            if result.lower() == 'good':
+                life_expectancy_day_min = (TIME_WEEK * 6) + TIME_DAYS
+                life_expectancy_day_max = 0
+            elif result.lower() == 'warning':
+                life_expectancy_day_min = (TIME_WEEK * 2)
+                life_expectancy_day_max = (TIME_WEEK * 6)
+            elif result.lower() == 'bad':
+                life_expectancy_day_min = 0
+                life_expectancy_day_max = (TIME_WEEK * 2) - TIME_DAYS
+            else:
+                predicted = 0
+                life_expectancy_day_min = 0
+                life_expectancy_day_max = 0
+
+            if predicted and devInfo['devid'] and life_expectancy_day_min:
+                from_date = None
+                to_date = None
+                try:
+                    assert life_expectancy_day_min
+                    from_date = self._convert_timestamp(predicted, life_expectancy_day_min)
+
+                    if life_expectancy_day_max:
+                        to_date = self._convert_timestamp(predicted, life_expectancy_day_max)
+
+                    self._set_device_life_expectancy(devInfo['devid'], from_date, to_date)
+                    self._logger.info(
+                        'succeed to set device {} life expectancy from: {}, to: {}'.format(
+                            devInfo['devid'], from_date, to_date))
+                except Exception as e:
+                    self._logger.error(
+                        'failed to set device {} life expectancy from: {}, to: {}, {}'.format(
+                            devInfo['devid'], from_date, to_date, str(e)))
+            else:
+                self._reset_device_life_expectancy(devInfo['devid'])
+        return 0, 'succeed to predicted all devices', ''
diff --git a/src/pybind/mgr/diskprediction_local/predictor.py b/src/pybind/mgr/diskprediction_local/predictor.py
new file mode 100644
index 000000000..3bbe7a4b7
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/predictor.py
@@ -0,0 +1,484 @@
+"""Machine learning model for disk failure prediction.
+
+This classes defined here provide the disk failure prediction module.
+RHDiskFailurePredictor uses the models developed at the AICoE in the
+Office of the CTO at Red Hat. These models were built using the open
+source Backblaze SMART metrics dataset.
+PSDiskFailurePredictor uses the models developed by ProphetStor as an
+example.
+
+An instance of the predictor is initialized by providing the path to trained
+models. Then, to predict hard drive health and deduce time to failure, the
+predict function is called with 6 days worth of SMART data from the hard drive.
+It will return a string to indicate disk failure status: "Good", "Warning",
+"Bad", or "Unknown".
+
+An example code is as follows:
+
+>>> model = RHDiskFailurePredictor()
+>>> model.initialize(get_diskfailurepredictor_path() + "/models/redhat")
+>>> vendor = list(RHDiskFailurePredictor.MANUFACTURER_MODELNAME_PREFIXES.keys())[0]
+>>> disk_days = [{'vendor': vendor}]
+>>> model.predict(disk_days)
+'Unknown'
+"""
+import os
+import json
+import pickle
+import logging
+from typing import Any, Dict, List, Optional, Sequence, Tuple
+
+import numpy as np
+
+
+def get_diskfailurepredictor_path() -> str:
+    path = os.path.abspath(__file__)
+    dir_path = os.path.dirname(path)
+    return dir_path
+
+
+DevSmartT = Dict[str, Any]
+AttrNamesT = List[str]
+AttrDiffsT = List[Dict[str, int]]
+
+
+class Predictor:
+    @classmethod
+    def create(cls, name: str) -> Optional['Predictor']:
+        if name == 'prophetstor':
+            return PSDiskFailurePredictor()
+        elif name == 'redhat':
+            return RHDiskFailurePredictor()
+        else:
+            return None
+
+    def initialize(self, model_dir: str) -> None:
+        raise NotImplementedError()
+
+    def predict(self, dataset: Sequence[DevSmartT]) -> str:
+        raise NotImplementedError()
+
+
+class RHDiskFailurePredictor(Predictor):
+    """Disk failure prediction module developed at Red Hat
+
+    This class implements a disk failure prediction module.
+    """
+
+    # json with manufacturer names as keys
+    # and features used for prediction as values
+    CONFIG_FILE = "config.json"
+    PREDICTION_CLASSES = {-1: "Unknown", 0: "Good", 1: "Warning", 2: "Bad"}
+
+    # model name prefixes to identify vendor
+    MANUFACTURER_MODELNAME_PREFIXES = {
+        "WDC": "WDC",
+        "Toshiba": "Toshiba",  # for cases like "Toshiba xxx"
+        "TOSHIBA": "Toshiba",  # for cases like "TOSHIBA xxx"
+        "toshiba": "Toshiba",  # for cases like "toshiba xxx"
+        "S": "Seagate",        # for cases like "STxxxx" and "Seagate BarraCuda ZAxxx"
+        "ZA": "Seagate",       # for cases like "ZAxxxx"
+        "Hitachi": "Hitachi",
+        "HGST": "HGST",
+    }
+
+    LOGGER = logging.getLogger()
+
+    def __init__(self) -> None:
+        """
+        This function may throw exception due to wrong file operation.
+        """
+        self.model_dirpath = ""
+        self.model_context: Dict[str, List[str]] = {}
+
+    def initialize(self, model_dirpath: str) -> None:
+        """Initialize all models. Save paths of all trained model files to list
+
+        Arguments:
+            model_dirpath {str} -- path to directory of trained models
+
+        Returns:
+            str -- Error message. If all goes well, return None
+        """
+        # read config file as json, if it exists
+        config_path = os.path.join(model_dirpath, self.CONFIG_FILE)
+        if not os.path.isfile(config_path):
+            raise Exception("Missing config file: " + config_path)
+        with open(config_path) as f_conf:
+            self.model_context = json.load(f_conf)
+
+        # ensure all manufacturers whose context is defined in config file
+        # have models and scalers saved inside model_dirpath
+        for manufacturer in self.model_context:
+            scaler_path = os.path.join(model_dirpath, manufacturer + "_scaler.pkl")
+            if not os.path.isfile(scaler_path):
+                raise Exception(f"Missing scaler file: {scaler_path}")
+            model_path = os.path.join(model_dirpath, manufacturer + "_predictor.pkl")
+            if not os.path.isfile(model_path):
+                raise Exception(f"Missing model file: {model_path}")
+
+        self.model_dirpath = model_dirpath
+
+    def __preprocess(self, disk_days: Sequence[DevSmartT], manufacturer: str) -> Optional[np.ndarray]:
+        """Scales and transforms input dataframe to feed it to prediction model
+
+        Arguments:
+            disk_days {list} -- list in which each element is a dictionary with key,val
+                                as feature name,value respectively.
+                                e.g.[{'smart_1_raw': 0, 'user_capacity': 512 ...}, ...]
+            manufacturer {str} -- manufacturer of the hard drive
+
+        Returns:
+            numpy.ndarray -- (n, d) shaped array of n days worth of data and d
+                                features, scaled
+        """
+        # get the attributes that were used to train model for current manufacturer
+        try:
+            model_smart_attr = self.model_context[manufacturer]
+        except KeyError:
+            RHDiskFailurePredictor.LOGGER.debug(
+                "No context (SMART attributes on which model has been trained) found for manufacturer: {}".format(
+                    manufacturer
+                )
+            )
+            return None
+
+        # convert to structured array, keeping only the required features
+        # assumes all data is in float64 dtype
+        try:
+            struc_dtypes = [(attr, np.float64) for attr in model_smart_attr]
+            values = [tuple(day[attr] for attr in model_smart_attr) for day in disk_days]
+            disk_days_sa = np.array(values, dtype=struc_dtypes)
+        except KeyError:
+            RHDiskFailurePredictor.LOGGER.debug(
+                "Mismatch in SMART attributes used to train model and SMART attributes available"
+            )
+            return None
+
+        # view structured array as 2d array for applying rolling window transforms
+        # do not include capacity_bytes in this. only use smart_attrs
+        disk_days_attrs = disk_days_sa[[attr for attr in model_smart_attr if 'smart_' in attr]]\
+            .view(np.float64).reshape(disk_days_sa.shape + (-1,))
+
+        # featurize n (6 to 12) days data - mean,std,coefficient of variation
+        # current model is trained on 6 days of data because that is what will be
+        # available at runtime
+
+        # rolling time window interval size in days
+        roll_window_size = 6
+
+        # rolling means generator
+        dataset_size = disk_days_attrs.shape[0] - roll_window_size + 1
+        gen = (disk_days_attrs[i: i + roll_window_size, ...].mean(axis=0)
+               for i in range(dataset_size))
+        means = np.vstack(gen)  # type: ignore
+
+        # rolling stds generator
+        gen = (disk_days_attrs[i: i + roll_window_size, ...].std(axis=0, ddof=1)
+               for i in range(dataset_size))
+        stds = np.vstack(gen)  # type: ignore
+
+        # coefficient of variation
+        cvs = stds / means
+        cvs[np.isnan(cvs)] = 0
+        featurized = np.hstack((means,
+                                stds,
+                                cvs,
+                                disk_days_sa['user_capacity'][: dataset_size].reshape(-1, 1)))
+
+        # scale features
+        scaler_path = os.path.join(self.model_dirpath, manufacturer + "_scaler.pkl")
+        with open(scaler_path, 'rb') as f:
+            scaler = pickle.load(f)
+        featurized = scaler.transform(featurized)
+        return featurized
+
+    @staticmethod
+    def __get_manufacturer(model_name: str) -> Optional[str]:
+        """Returns the manufacturer name for a given hard drive model name
+
+        Arguments:
+            model_name {str} -- hard drive model name
+
+        Returns:
+            str -- manufacturer name
+        """
+        for prefix, manufacturer in RHDiskFailurePredictor.MANUFACTURER_MODELNAME_PREFIXES.items():
+            if model_name.startswith(prefix):
+                return manufacturer.lower()
+        # print error message
+        RHDiskFailurePredictor.LOGGER.debug(
+            f"Could not infer manufacturer from model name {model_name}")
+        return None
+
+    def predict(self, disk_days: Sequence[DevSmartT]) -> str:
+        # get manufacturer preferably as a smartctl attribute
+        # if not available then infer using model name
+        manufacturer = disk_days[0].get("vendor")
+        if manufacturer is None:
+            RHDiskFailurePredictor.LOGGER.debug(
+                '"vendor" field not found in smartctl output. Will try to infer manufacturer from model name.'
+            )
+            manufacturer = RHDiskFailurePredictor.__get_manufacturer(
+                disk_days[0].get("model_name", ""))
+
+        # print error message, return Unknown, and continue execution
+        if manufacturer is None:
+            RHDiskFailurePredictor.LOGGER.debug(
+                "Manufacturer could not be determiend. This may be because \
+                DiskPredictor has never encountered this manufacturer before, \
+                    or the model name is not according to the manufacturer's \
+                        naming conventions known to DiskPredictor"
+            )
+            return RHDiskFailurePredictor.PREDICTION_CLASSES[-1]
+
+        # preprocess for feeding to model
+        preprocessed_data = self.__preprocess(disk_days, manufacturer)
+        if preprocessed_data is None:
+            return RHDiskFailurePredictor.PREDICTION_CLASSES[-1]
+
+        # get model for current manufacturer
+        model_path = os.path.join(
+            self.model_dirpath, manufacturer + "_predictor.pkl"
+        )
+        with open(model_path, 'rb') as f:
+            model = pickle.load(f)
+
+        # use prediction for most recent day
+        # TODO: ensure that most recent day is last element and most previous day
+        # is first element in input disk_days
+        pred_class_id = model.predict(preprocessed_data)[-1]
+        return RHDiskFailurePredictor.PREDICTION_CLASSES[pred_class_id]
+
+
+class PSDiskFailurePredictor(Predictor):
+    """Disk failure prediction developed at ProphetStor
+
+    This class implements a disk failure prediction module.
+    """
+
+    CONFIG_FILE = "config.json"
+    EXCLUDED_ATTRS = ["smart_9_raw", "smart_241_raw", "smart_242_raw"]
+
+    def __init__(self) -> None:
+        """
+        This function may throw exception due to wrong file operation.
+        """
+
+        self.model_dirpath = ""
+        self.model_context: Dict[str, List[str]] = {}
+
+    def initialize(self, model_dirpath: str) -> None:
+        """
+        Initialize all models.
+
+        Args: None
+
+        Returns:
+            Error message. If all goes well, return an empty string.
+
+        Raises:
+        """
+
+        config_path = os.path.join(model_dirpath, self.CONFIG_FILE)
+        if not os.path.isfile(config_path):
+            raise Exception(f"Missing config file: {config_path}")
+        with open(config_path) as f_conf:
+            self.model_context = json.load(f_conf)
+
+        for model_name in self.model_context:
+            model_path = os.path.join(model_dirpath, model_name)
+
+            if not os.path.isfile(model_path):
+                raise Exception(f"Missing model file: {model_path}")
+
+        self.model_dirpath = model_dirpath
+
+    def __preprocess(self, disk_days: Sequence[DevSmartT]) -> Sequence[DevSmartT]:
+        """
+        Preprocess disk attributes.
+
+        Args:
+            disk_days: Refer to function predict(...).
+
+        Returns:
+            new_disk_days: Processed disk days.
+        """
+
+        req_attrs = []
+        new_disk_days = []
+
+        attr_list = set.intersection(*[set(disk_day.keys()) for disk_day in disk_days])
+        for attr in attr_list:
+            if (
+                attr.startswith("smart_") and attr.endswith("_raw")
+            ) and attr not in self.EXCLUDED_ATTRS:
+                req_attrs.append(attr)
+
+        for disk_day in disk_days:
+            new_disk_day = {}
+            for attr in req_attrs:
+                if float(disk_day[attr]) >= 0.0:
+                    new_disk_day[attr] = disk_day[attr]
+
+            new_disk_days.append(new_disk_day)
+
+        return new_disk_days
+
+    @staticmethod
+    def __get_diff_attrs(disk_days: Sequence[DevSmartT]) -> Tuple[AttrNamesT, AttrDiffsT]:
+        """
+        Get 5 days differential attributes.
+
+        Args:
+            disk_days: Refer to function predict(...).
+
+        Returns:
+            attr_list: All S.M.A.R.T. attributes used in given disk. Here we
+                       use intersection set of all disk days.
+
+            diff_disk_days: A list struct comprises 5 dictionaries, each
+                            dictionary contains differential attributes.
+
+        Raises:
+            Exceptions of wrong list/dict operations.
+        """
+
+        all_attrs = [set(disk_day.keys()) for disk_day in disk_days]
+        attr_list = list(set.intersection(*all_attrs))
+        prev_days = disk_days[:-1]
+        curr_days = disk_days[1:]
+        diff_disk_days = []
+        # TODO: ensure that this ordering is correct
+        for prev, cur in zip(prev_days, curr_days):
+            diff_disk_days.append(
+                {attr: (int(cur[attr]) - int(prev[attr])) for attr in attr_list}
+            )
+
+        return attr_list, diff_disk_days
+
+    def __get_best_models(self, attr_list: AttrNamesT) -> Optional[Dict[str, List[str]]]:
+        """
+        Find the best model from model list according to given attribute list.
+
+        Args:
+            attr_list: All S.M.A.R.T. attributes used in given disk.
+
+        Returns:
+            modelpath: The best model for the given attribute list.
+            model_attrlist: 'Ordered' attribute list of the returned model.
+                            Must be aware that SMART attributes is in order.
+
+        Raises:
+        """
+
+        models = self.model_context.keys()
+
+        scores = []
+        for model_name in models:
+            scores.append(
+                sum(attr in attr_list for attr in self.model_context[model_name])
+            )
+        max_score = max(scores)
+
+        # Skip if too few matched attributes.
+        if max_score < 3:
+            print("Too few matched attributes")
+            return None
+
+        best_models: Dict[str, List[str]] = {}
+        best_model_indices = [
+            idx for idx, score in enumerate(scores) if score > max_score - 2
+        ]
+        for model_idx in best_model_indices:
+            model_name = list(models)[model_idx]
+            model_path = os.path.join(self.model_dirpath, model_name)
+            model_attrlist = self.model_context[model_name]
+            best_models[model_path] = model_attrlist
+
+        return best_models
+        # return os.path.join(self.model_dirpath, model_name), model_attrlist
+
+    @staticmethod
+    def __get_ordered_attrs(disk_days: Sequence[DevSmartT], model_attrlist: List[str]) -> List[List[float]]:
+        """
+        Return ordered attributes of given disk days.
+
+        Args:
+            disk_days: Unordered disk days.
+            model_attrlist: Model's ordered attribute list.
+
+        Returns:
+            ordered_attrs: Ordered disk days.
+
+        Raises: None
+        """
+
+        ordered_attrs = []
+
+        for one_day in disk_days:
+            one_day_attrs = []
+
+            for attr in model_attrlist:
+                if attr in one_day:
+                    one_day_attrs.append(one_day[attr])
+                else:
+                    one_day_attrs.append(0)
+
+            ordered_attrs.append(one_day_attrs)
+
+        return ordered_attrs
+
+    def predict(self, disk_days: Sequence[DevSmartT]) -> str:
+        """
+        Predict using given 6-days disk S.M.A.R.T. attributes.
+
+        Args:
+            disk_days: A list struct comprises 6 dictionaries. These
+                       dictionaries store 'consecutive' days of disk SMART
+                       attributes.
+        Returns:
+            A string indicates prediction result. One of following four strings
+            will be returned according to disk failure status:
+            (1) Good : Disk is health
+            (2) Warning : Disk has some symptoms but may not fail immediately
+            (3) Bad : Disk is in danger and data backup is highly recommended
+            (4) Unknown : Not enough data for prediction.
+
+        Raises:
+            Pickle exceptions
+        """
+
+        all_pred = []
+
+        proc_disk_days = self.__preprocess(disk_days)
+        attr_list, diff_data = PSDiskFailurePredictor.__get_diff_attrs(proc_disk_days)
+        modellist = self.__get_best_models(attr_list)
+        if modellist is None:
+            return "Unknown"
+
+        for modelpath in modellist:
+            model_attrlist = modellist[modelpath]
+            ordered_data = PSDiskFailurePredictor.__get_ordered_attrs(
+                diff_data, model_attrlist
+            )
+
+            try:
+                with open(modelpath, "rb") as f_model:
+                    clf = pickle.load(f_model)
+
+            except UnicodeDecodeError:
+                # Compatibility for python3
+                with open(modelpath, "rb") as f_model:
+                    clf = pickle.load(f_model, encoding="latin1")
+
+            pred = clf.predict(ordered_data)
+
+            all_pred.append(1 if any(pred) else 0)
+
+        score = 2 ** sum(all_pred) - len(modellist)
+        if score > 10:
+            return "Bad"
+        if score > 4:
+            return "Warning"
+        return "Good"
diff --git a/src/pybind/mgr/diskprediction_local/requirements.txt b/src/pybind/mgr/diskprediction_local/requirements.txt
new file mode 100644
index 000000000..d9c3157fd
--- /dev/null
+++ b/src/pybind/mgr/diskprediction_local/requirements.txt
@@ -0,0 +1,3 @@
+numpy==1.15.1
+scipy==1.1.0
+scikit-learn==0.19.2