77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
#
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# Copyright 2017-2018 IBM Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import boto3
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import botocore
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import os
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import zipfile
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import importlib
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class Serving(object):
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def __init__(self):
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training_id = os.environ.get("TRAINING_ID")
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endpoint_url = os.environ.get("BUCKET_ENDPOINT_URL")
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bucket_name = os.environ.get("BUCKET_NAME")
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bucket_key = os.environ.get("BUCKET_KEY")
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bucket_secret = os.environ.get("BUCKET_SECRET")
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model_file_name = os.environ.get("MODEL_FILE_NAME")
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model_class_name = os.environ.get("MODEL_CLASS_NAME")
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model_class_file = os.environ.get("MODEL_CLASS_FILE")
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# Uncomment the below print statement for debugging purpose.
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# print("Training id:{} endpoint URL:{} key:{} secret:{}".format(training_id,endpoint_url,bucket_key,bucket_secret))
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# Define S3 resource and download the model files
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client = boto3.resource(
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's3',
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endpoint_url=endpoint_url,
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aws_access_key_id=bucket_key,
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aws_secret_access_key=bucket_secret,
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)
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KEY = training_id + '/' + model_file_name
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model_files = training_id + '/_submitted_code/model.zip'
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try:
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client.Bucket(bucket_name).download_file(KEY, 'model.pt')
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client.Bucket(bucket_name).download_file(model_files, 'model.zip')
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except botocore.exceptions.ClientError as e:
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if e.response['Error']['Code'] == "404":
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print("The object does not exist.")
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else:
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raise
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zip_ref = zipfile.ZipFile('model.zip', 'r')
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zip_ref.extractall('model_files')
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zip_ref.close()
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modulename = 'model_files.' + model_class_file.split('.')[0].replace('-', '_')
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'''
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We required users to define where the model class is located or follow
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some naming convention we have provided.
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'''
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model_class = getattr(importlib.import_module(modulename), model_class_name)
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self.model = model_class()
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self.model.load_state_dict(torch.load("model.pt"))
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self.model.eval()
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def predict(self, X, feature_names):
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return self.model(X)
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