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@yearofthewhopper
Created March 29, 2019 22:55
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from tensorflow.python.client import device_lib
device_lib.list_local_devices()
!cat /proc/meminfo
!cat /proc/cpuinfo
add question mark (?) see functions
# Which file to send?
file_name = "something.tar"
from googleapiclient.http import MediaFileUpload
from googleapiclient.discovery import build
auth.authenticate_user()
drive_service = build('drive', 'v3')
def save_file_to_drive(name, path):
file_metadata = {'name': name, 'mimeType': 'application/octet-stream'}
media = MediaFileUpload(path, mimetype='application/octet-stream', resumable=True)
created = drive_service.files().create(body=file_metadata, media_body=media, fields='id').execute()
return created
save_file_to_drive(file_name, file_name)
#tensorboard
# You can change the directory name
LOG_DIR = 'tb_logs'
!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
!unzip ngrok-stable-linux-amd64.zip
import os
if not os.path.exists(LOG_DIR):
os.makedirs(LOG_DIR)
get_ipython().system_raw(
'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'
.format(LOG_DIR))
get_ipython().system_raw('./ngrok http 6006 &')
!curl -s http://localhost:4040/api/tunnels | python3 -c \
"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])"
CoreML 2.0
# Supported models for swift 2.0 (CoreML 2.0)
# Supported frameworks
# Neural networks
# Feedforward, convolutional, recurrent
# Caffe v1
# Keras 1.2.2+
# Tree ensembles
# Random forests, boosted trees, decision trees
# scikit-learn 0.18
# XGBoost 0.6
# Support vector machines
# Scalar regression, multiclass classification
# scikit-learn 0.18
# LIBSVM 3.22
# Generalized linear models
# Linear regression, logistic regression
# scikit-learn 0.18
# Feature engineering
# Sparse vectorization, dense vectorization, categorical processing
# scikit-learn 0.18
# Pipeline models
# Sequentially chained models
# scikit-learn 0.18
# converting caffe to coreml
# setting is_bgr = True for caffe.convert() when generating your mlmodel
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