| import torch | |
| import subprocess | |
| import time | |
| import logging | |
| # Takes about 8GB | |
| ndim = 25_000 | |
| logging.basicConfig(format='[%(asctime)s] %(filename)s [%(levelname).1s] %(message)s', level=logging.DEBUG) | |
| def get_gpu_usage(): |
| # This isn't supposed to run as a bash script, i named it with ".sh" for syntax highlighting. | |
| # https://developer.nvidia.com/nsight-systems | |
| # https://docs.nvidia.com/nsight-systems/profiling/index.html | |
| # My preferred nsys (command line executable used to create profiles) commands | |
| # | |
| # In your script, write | |
| # torch.cuda.nvtx.range_push("region name") | |
| # ... |
| def inference_tfserving(eval_data, batch=BATCH_SIZE, | |
| repeat=1000, signature='predict'): | |
| url = 'http://localhost:8501/v1/models/mnist_classifier:predict' | |
| instances = [[float(i) for i in list(eval_data[img])] for img in range(batch)] | |
| request_data = {'signature_name': signature, | |
| 'instances': instances} | |
| time_start = datetime.utcnow() | |
| for i in range(repeat): |
| import numpy as np | |
| def softmax(x): | |
| """ Compute the softmax for each row of the input x | |
| Arguments: | |
| x -- A N dimensional veector or M X N dimensional numpy matrix. | |
| Return: | |
| x -- modified x in-place | |
| """ | |
Here are the steps to installing and setting up GDB on Mac OS Sierra/High Sierra.
Run brew install gdb.
On starting gdb, you will get the following error:
Unable to find Mach task port for process-id 2133: (os/kern) failure (0x5).
(please check gdb is codesigned - see taskgated(8))To fix this error, follow the following steps:
| """ | |
| Example TensorFlow script for finetuning a VGG model on your own data. | |
| Uses tf.contrib.data module which is in release v1.2 | |
| Based on PyTorch example from Justin Johnson | |
| (https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c) | |
| Required packages: tensorflow (v1.2) | |
| Download the weights trained on ImageNet for VGG: | |
| ``` | |
| wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
Create a new repository, or reuse an existing one.
Generate a new SSH key:
ssh-keygen -t rsa -C "your_email@example.com"
Copy the contents of the file ~/.ssh/id_rsa.pub to your SSH keys in your GitHub account settings (https://github.com/settings/keys).
Test SSH key: