s434742/ium_434742
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

added log scalar to mongo observer
All checks were successful
s434742-training/pipeline/head This commit looks good
Details
s434742-evaluation/pipeline/head This commit looks good
Details

Browse Source
This commit is contained in:
patrycjalazna 2021-05-15 15:33:14 +02:00
parent 60ec48f83c
commit 3261254835
7 changed files with 25 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
mlruns/0/f9a79167828b4ff9a26906623384047a/meta.yaml Normal file
View File

@ -0,0 +1,15 @@
artifact_uri: file:///Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/mlruns/0/f9a79167828b4ff9a26906623384047a/artifacts
end_time: 1621081158822
entry_point_name: ''
experiment_id: '0'
lifecycle_stage: active
name: ''
run_id: f9a79167828b4ff9a26906623384047a
run_uuid: f9a79167828b4ff9a26906623384047a
source_name: ''
source_type: 4
source_version: ''
start_time: 1621081158804
status: 4
tags: []
user_id: patrycjalazna

1
mlruns/0/f9a79167828b4ff9a26906623384047a/tags/mlflow.source.git.commit Normal file
View File

@ -0,0 +1 @@
60ec48f83ca7c4ecae6c609e8db82c91fc4b80ab

1
mlruns/0/f9a79167828b4ff9a26906623384047a/tags/mlflow.source.name Normal file
View File

@ -0,0 +1 @@
/Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/avocado-mlflow.py

1
mlruns/0/f9a79167828b4ff9a26906623384047a/tags/mlflow.source.type Normal file
View File

@ -0,0 +1 @@
LOCAL

1
mlruns/0/f9a79167828b4ff9a26906623384047a/tags/mlflow.user Normal file
View File

@ -0,0 +1 @@
patrycjalazna

4
mlruns/0/meta.yaml Normal file
View File

@ -0,0 +1,4 @@
artifact_location: file:///Users/patrycjalazna/Desktop/inzynieria-uczenia-maszynowego/zadanka/mlruns/0
experiment_id: '0'
lifecycle_stage: active
name: Default

3
sacred-mongoobserver.py
View File

@ -23,7 +23,7 @@ def my_config():
batch_size = 16
@ex.capture
def prepare_model(epochs, batch_size):
def prepare_model(epochs, batch_size, _run):
# odczytanie danych z plików
avocado_train = pd.read_csv('avocado_train.csv')
avocado_test = pd.read_csv('avocado_test.csv')
@ -55,6 +55,7 @@ def prepare_model(epochs, batch_size):
# ewaluacja
rmse = mean_squared_error(y_test, prediction)
_run.log_scalar("rmse", rmse)
# zapisanie modelu
model.save('avocado_model.h5')