I am learning AWS SageMaker which is supposed to be a serverless compute environment for Machine Learning. In this type of serverless compute environment, who is supposed to ensure the software package consistency and update the versions?
For example, I ran the demo program that came with SageMaker, deepar_synthetic. In this second cell, it executes the following: !conda install -y s3fs
However, I got the following warning message:
Solving environment: done ==> WARNING: A newer version of conda exists. <== current version: 4.4.10 latest version: 4.5.4 Please update conda by running $ conda update -n base conda
Since it is serverless compute, am I still supposed to update the software packages myself?
Another example is as follows. I wrote a few simple lines to find out the package versions in Jupyter notebook:
import platform
import tensorflow as tf
print(platform.python_version())
print (tf.version)
However, I got the following warning messages:
/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
/home/ec2-user/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/h5py/init.py:36: FutureWarning: Conversion of the second argument of issubdtype from float
to np.floating
is deprecated. In future, it will be treated as np.float64 == np.dtype(float).type
.
from ._conv import register_converters as _register_converters
The prints still worked and I got the results shown beolow:
3.6.4 1.4.0
I am wondering what I have to do to get the package consistent so that I don't get the warning messages. Thanks.