| #!/usr/bin/env python | |
| ## This CNN network to identify MNIST dataset | |
| import time | |
| import random | |
| import numpy as np | |
| from pprint import pprint | |
| import os, sys |
A "Hello, world!" Prefect flow running on an ephemeral Dask Cluster on Kubernetes.
The Prefect Core docs suggest running flows on a Dask cluster via Dask Distributed and an article from the MET Office Informatics Lab demonstrates running an adaptive Dask cluster on k8s. This example is inspired by those sources, as well as the respective docs for the technologies used.
| #!/usr/bin/env python3 | |
| import argparse | |
| import os | |
| import shutil | |
| from xattr import xattr | |
| top_folder = "." | |
| CACHE_FOLDERS = {"__pycache__", ".pytest_cache", ".cache"} |
| # Now available here: https://github.com/y0ast/pytorch-snippets/tree/main/minimal_cifar |
Now available here: https://github.com/y0ast/pytorch-snippets/tree/main/fast_mnist
| ''' | |
| trying keras to be random seeded | |
| Switch backends by changin ~/.keras/keras.json: | |
| Theano: https://drive.google.com/open?id=0B-FcStylmYuVU1E5S25QX2JXcGs | |
| TensorFlow: https://drive.google.com/open?id=0B-FcStylmYuVNnpfZERoeUo0QnM | |
| Keras Backend: | |
| *Theano (1 epoch, GPU): | |
| Test loss: 0.111780489241 |
