ceshine
/ bit_models.py
Created Jul 6, 2020
(Customized) Big Transfer (BiT) Models from google-research/big_transfer
View bit_models.py
| # Adapted from: https://github.com/google-research/big_transfer/blob/6c83d6459e009fa89d84c1e904611e9b162e6eff/bit_pytorch/models.py | |
| # Copyright 2020 Google LLC | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software |
ceshine
/ async_test.jl
Created May 24, 2020
A simple script to demonstrate the @async macro in Julia
View async_test.jl
| begin | |
| tmp1 = @async sleep(20) | |
| tmp2 = @async sleep(30) | |
| @time fetch(tmp1) | |
| @time fetch(tmp2) | |
| end |
ceshine
/ ts_handler.py
Created May 4, 2020
A custom handler example for TensorServe (image classification)
View ts_handler.py
| import io | |
| import os | |
| import logging | |
| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from PIL import Image | |
| from torch.autograd import Variable | |
| from torchvision import transforms |
View profiling.ipynb
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ceshine
/ run_tf_glue.ipynb
Last active Feb 23, 2020
Train huggingface/transformers BERT model on Colab CPU with TF 2.1
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ceshine
/ lr_scheduler_example.ipynb
Created Dec 30, 2019
LR Scheduler which works with TF 2.x distributed mode
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ceshine
/ run_tf_glue.ipynb
Created Dec 26, 2019
Train huggingface/transformers BERT model on Cloud CPU with TF 2.1 (nightly build)
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ceshine
/ pytorch-memory-profiling.ipynb
Created Aug 21, 2019
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ceshine
/ spacy_sentencizer.ipynb
Created Aug 14, 2019
Customizing Spacy's Statistical Sentence Segmenter with Custom Rules
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ceshine
/ convert.py
Created Jul 17, 2019
A simple script to convert markdown image specification to hugo shortcode (result is automatically copied to clipboard)
View convert.py
| from subprocess import Popen, PIPE | |
| def image_markdown_conversion(): | |
| try: | |
| while True: | |
| text = input("Input:").strip() | |
| brackets, split_point = 1, 0 | |
| description = "" | |
| assert len(text) > 5, "wrong format!" |
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