I had the same problem but I wanted to handle jpeg requests.
Once you have your `model_data` ready, you can deploy it with the following lines.
```
from sagemaker.tensorflow.model import TensorFlowModel
sagemaker_model = TensorFlowModel(
model_data = 's3://path/to/model/model.tar.gz',
role = role,
framework_version = '1.12',
entry_point = 'train.py',
source_dir='my_src',
env={'SAGEMAKER_REQUIREMENTS': 'requirements.txt'}
)
predictor = sagemaker_model.deploy(
initial_instance_count=1,
instance_type='ml.m4.xlarge',
endpoint_name='resnet-tensorflow-classifier'
)
```
Your notebook should have a `my_src` directory which contains a file `train.py` and a `requirements.txt` file. The `train.py` file should have a function `input_fn` defined. For me, that function handled image/jpeg content:
```
CSV_CONTENT_TYPE = 'text/csv'
# Deserialize the Invoke request body into an object we can perform prediction on
def input_fn(request_body, content_type=CSV_CONTENT_TYPE):
# process an image uploaded to the endpoint
if content_type == CSV_CONTENT_TYPE:
##handle input
return handled_input
else:
raise errors.UnsupportedFormatError(content_type)
```
If your`train.py` code imports some modules, you must supply `requirements.txt` defining those dependencies (that was the part I had trouble finding in the docs).
Hope this helps someone in the future.
I had the same problem and finally figured out how to do it.
Once you have your `model_data` ready, you can deploy it with the following lines.
```
from sagemaker.tensorflow.model import TensorFlowModel
sagemaker_model = TensorFlowModel(
model_data = 's3://path/to/model/model.tar.gz',
role = role,
framework_version = '1.12',
entry_point = 'train.py',
source_dir='my_src',
env={'SAGEMAKER_REQUIREMENTS': 'requirements.txt'}
)
predictor = sagemaker_model.deploy(
initial_instance_count=1,
instance_type='ml.m4.xlarge',
endpoint_name='resnet-tensorflow-classifier'
)
```
Your notebook should have a `my_src` directory which contains a file `train.py` and a `requirements.txt` file. The `train.py` file should have a function `input_fn` defined. For me, that function handled image/jpeg content:
```
import io
import numpy as np
from PIL import Image
from keras.applications.resnet50 import preprocess_input
from keras.preprocessing import image
JPEG_CONTENT_TYPE = 'image/jpeg'
# Deserialize the Invoke request body into an object we can perform prediction on
def input_fn(request_body, content_type=JPEG_CONTENT_TYPE):
# process an image uploaded to the endpoint
if content_type == JPEG_CONTENT_TYPE:
img = Image.open(io.BytesIO(request_body)).resize((300, 300))
img_array = np.array(img)
expanded_img_array = np.expand_dims(img_array, axis=0)
x = preprocess_input(expanded_img_array)
return x
else:
raise errors.UnsupportedFormatError(content_type)
```
Your processing code will depend on the model architecture you used. I was doing transfer learning off resnet50, so I used `preprocess_input` from `keras.applications.resnet50`.
Note that since my `train.py` code imports some modules, I had to supply `requirements.txt` defining those modules (that was the part I had trouble finding in the docs).
Hope this helps someone in the future.
my `requirements.txt`:
```
absl-py==0.7.1
astor==0.8.0
backports.weakref==1.0.post1
enum34==1.1.6
funcsigs==1.0.2
futures==3.2.0
gast==0.2.2
grpcio==1.20.1
h5py==2.9.0
Keras==2.2.4
Keras-Applications==1.0.7
Keras-Preprocessing==1.0.9
Markdown==3.1.1
mock==3.0.5
numpy==1.16.3
Pillow==6.0.0
protobuf==3.7.1
PyYAML==5.1
scipy==1.2.1
six==1.12.0
tensorboard==1.13.1
tensorflow==1.13.1
tensorflow-estimator==1.13.0
termcolor==1.1.0
virtualenv==16.5.0
Werkzeug==0.15.4
```
