Download models to tmp folders instead of working directory (#1012)

Author: vishalbollu

examples/keras/document-denoiser/cortex.yaml

@@ -5,7 +5,7 @@
     type: python
     path: predictor.py
     config:
-      model: s3://cortex-examples/keras/document-denoiser
+      model: s3://cortex-examples/keras/document-denoiser/model.h5
       resize_shape: [540, 260]
   compute:
     cpu: 1

examples/keras/document-denoiser/predictor.py

@@ -39,11 +39,12 @@ def __init__(self, config):
         # download the model
         bucket, key = re.match("s3://(.+?)/(.+)", config["model"]).groups()
         s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED))
-        model_name = "model.h5"
-        s3.download_file(bucket, os.path.join(key, model_name), model_name)
+
+        model_path = os.path.join("/tmp/model.h5")
+        s3.download_file(bucket, key, model_path)
 
         # load the model
-        self.model = load_model(model_name)
+        self.model = load_model(model_path)
 
         # resize shape (width, height)
         self.resize_shape = tuple(config["resize_shape"])

examples/tensorflow/license-plate-reader/cortex_lite.yaml

@@ -5,7 +5,7 @@
     type: python
     path: predictor_lite.py
     config:
-      yolov3: s3://cortex-examples/tensorflow/license-plate-reader/yolov3_keras
+      yolov3: s3://cortex-examples/tensorflow/license-plate-reader/yolov3_keras/model.h5
       yolov3_model_config: config.json
   compute:
     cpu: 1

examples/tensorflow/license-plate-reader/predictor_lite.py

@@ -16,11 +16,11 @@ def __init__(self, config):
         # download yolov3 model
         bucket, key = re.match("s3://(.+?)/(.+)", config["yolov3"]).groups()
         s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED))
-        model_name = "model.h5"
-        s3.download_file(bucket, os.path.join(key, model_name), model_name)
+        model_path = "/tmp/model.h5"
+        s3.download_file(bucket, key, model_path)
 
         # load yolov3 model
-        self.yolov3_model = load_model(model_name)
+        self.yolov3_model = load_model(model_path)
 
         # get configuration for yolov3 model
         with open(config["yolov3_model_config"]) as json_file: