This is my code:
from sagemaker.sklearn import SKLearnModel
role = sagemaker.get_execution_role()
model = SKLearnModel(
model_data= f"s3://{default_bucket}/{prefix}/model.tar.gz",
role=role,
entry_point="inference.py",
framework_version="1.2-1",
py_version="py3",
)
predictor = model.deploy(
instance_type="ml.c5.large",
initial_instance_count=1,
container_startup_health_check_timeout=180
)
s3://{default_bucket}/{prefix}/model.tar.gz contains:
Contents of the tarball: (I also tried putting requirements.txt in code/ as advised on documentation for pytorch models on tarball structure)
?rw-r--r-- sagemaker-user/users 4349839 2024-11-29 19:22:21 model.pkl
?rw-r--r-- sagemaker-user/users 24 2024-12-02 14:43:26 inference.py
?rw-r--r-- sagemaker-user/users 44212 2024-11-29 19:23:17 explainer
?rw-r--r-- sagemaker-user/users 24 2024-12-02 14:43:26 requirements.txt
requirements.txt contains:
dill
pandas
joblib
(and I even know pandas is installed by default, checking the aws code)
When I try to deploy I get an error because inference.py has on the first line import dill saying the module is not found and in Cloudwatch I see only inference 1.0.0 is installed (which I assume is my script).
I know I could probably create a subprocess within inference.py and call pip there, but I want to do this properly.
You are including the requirements.txt and inference.py in the model.tar.gz file, but please try to put those files under "scripts" (or whatever you want to use), and specify the path as the "source_dir" argument of the SKLearnModel. Dependencies in requirements.txt file should be automatically installed.
For an example how to use source_dir and requirements.txt, please refer to this sample.
model = SKLearnModel(
role=role,
model_data=model_data,
framework_version="1.2-1",
py_version="py3",
source_dir="code",
entry_point="inference.py",
)
Under the "code/" directory, you can find requirements.txt.