I have trained a tensorflow model and saved it in the form of a
.h5 file, about the size of 700mb. What’s the most efficient way to load this into the web app. I have tried storing it on digitalOcean space, but loading is not very efficient, takes about over 45 seconds. Any tips for speeding this up? Below is the code I used to load the model.
def load(): session1 = session.Session() client = session1.client('s3', region_name='nyc3', endpoint_url=DO_SPACES_ENDPOINT, aws_access_key_id=DO_SPACES_ACCESS_KEY, aws_secret_access_key=DO_SPACES_SECRET_KEY) model_object = client.get_object(Bucket=BUCKET_NAME, Key=WEIGHTS_FILE) model_content = model_object['Body'].read() with tempfile.NamedTemporaryFile(suffix='.h5', delete=False) as fp: fp.write(model_content) model_path = fp.name return model_path
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Hi There, can you share where you are running this app relative to the bucket? Are they in the same datacenter?