Machine Learning 101
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#GeekyJokes
Programming
A key virtue of a programmer is laziness. As an example it is what inspires me to automate my home to the point where I don’t have to lift a finger to switch on the light. Removing friction from a system is a anesthetic joy. The drug of efficiency, feels really good.
I still write code and people get surprised by that sometimes – maybe it’s the quality of the code 🤓.
Tesla REST APIs v3.8.2
It has been a while since I played with the various Tesla endpoint (APIs) – been too busy and haven’t had the time. I de-compiled the Tesla app and noticed a few new things in there – or at least new to me.
The following are the ones which seem new and stand out. How exactly some of these are used, can only be one’s guess, but I can certainly infer a few things from this.
- VEHICLE_DATA_LEGACY – So this seems to be the ‘old’ end point, hence the legacy. The new endpoint is now at ‘VEHICLE_DATA’ which seems to return a combined (some) vehicle information, and, consolidate data state of the vehicle. This seems to be cleaner than the earlier version where it was too isolated and multiple calls.
- NEARBY_CHARGING_SITES – The name says it all – returns a list of Tesla chargers close by (both superchargers, and destination chargers).
- Media – there are a few media controls that are outlined below. I think these were part of earlier updates when a passenger could control the media playback from their phone. Most of the names are self explanatory and I skipped outlining them below.
- MEDIA_NEXT_TRACK and MEDIA_PREVIOUS_TRACK – plays the next and previous track respectively.
- MEDIA_NEXT_FAVORITE and MEDIA_PREVIOUS_FAVORITE – This skips to the next / previous favourite station (different from the track).
- DEACTIVATE_DEVICE_TOKEN – This is new but I am not sure how this is different from REVOKE_AUTH_TOKEN. What kind of devices is this looking to revoke? AFAIK, it doesn’t seem to be related to the Powerwall.
- ROADSIDE_ASSISTANCE_DATA – Intrigued seeing this and not sure what data it is sending (need to spend more time writing code to examine the output of the API). I wonder if this is related to the ETA details that might be pushed out (see Elon’s tweet).
- SET_SENTRY_MODE – As the name suggests, this toggles Sentry mode for the car.
- Software updates (from the phone) – as expected a couple of API’s to start and cancel software updates – SCHEDULE_SOFTWARE_UPDATE, CANCEL_SOFTWARE_UPDATE
- REMOTE_SEAT_HEATER_REQUEST – Switching on the seat heating in the car. I presume there will be parameters on which seat, and the setting for each of the seats.
- REFERRAL_DATA – I would be interested to see what this shows and how it is changing on the backend given that Tesla can’t seem to make up their mind on how to run this and keep changing it adhoc.
- Message Center – there are a bunch of API’s that are around message center and I wonder what that exactly means. Is it messages in the app (you know, the Inbox that you have seen), or is it something new coming out on the screen in the car. (e.g. MESSAGE_CENTER_MESSAGE, MESSAGE_CENTER_MESSAGE_ACTION_UPDATE, etc.
I have the full output pasted below for you to have a look . This is as of v3.8.2 and it includes not just the car, but the powerwall, and the charging sites (both destination and superchargers).
{
"AUTHENTICATE": {
"TYPE": "POST",
"URI": "oauth/token",
"AUTH": false
},
"REVOKE_AUTH_TOKEN": {
"TYPE": "POST",
"URI": "oauth/revoke",
"AUTH": true
},
"PRODUCT_LIST": {
"TYPE": "GET",
"URI": "api/1/products",
"AUTH": true
},
"VEHICLE_LIST": {
"TYPE": "GET",
"URI": "api/1/vehicles",
"AUTH": true
},
"VEHICLE_SUMMARY": {
"TYPE": "GET",
"URI": "api/1/vehicles/{vehicle_id}",
"AUTH": true
},
"VEHICLE_DATA_LEGACY": {
"TYPE": "GET",
"URI": "api/1/vehicles/{vehicle_id}/data",
"AUTH": true
},
"VEHICLE_DATA": {
"TYPE": "GET",
"URI": "api/1/vehicles/{vehicle_id}/vehicle_data",
"AUTH": true
},
"NEARBY_CHARGING_SITES": {
"TYPE": "GET",
"URI": "api/1/vehicles/{vehicle_id}/nearby_charging_sites",
"AUTH": true
},
"WAKE_UP": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/wake_up",
"AUTH": true
},
"UNLOCK": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/door_unlock",
"AUTH": true
},
"LOCK": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/door_lock",
"AUTH": true
},
"HONK_HORN": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/honk_horn",
"AUTH": true
},
"FLASH_LIGHTS": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/flash_lights",
"AUTH": true
},
"CLIMATE_ON": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/auto_conditioning_start",
"AUTH": true
},
"CLIMATE_OFF": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/auto_conditioning_stop",
"AUTH": true
},
"CHANGE_CLIMATE_TEMPERATURE_SETTING": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/set_temps",
"AUTH": true
},
"CHANGE_CHARGE_LIMIT": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/set_charge_limit",
"AUTH": true
},
"CHANGE_SUNROOF_STATE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/sun_roof_control",
"AUTH": true
},
"ACTUATE_TRUNK": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/actuate_trunk",
"AUTH": true
},
"REMOTE_START": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/remote_start_drive",
"AUTH": true
},
"CHARGE_PORT_DOOR_OPEN": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/charge_port_door_open",
"AUTH": true
},
"CHARGE_PORT_DOOR_CLOSE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/charge_port_door_close",
"AUTH": true
},
"START_CHARGE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/charge_start",
"AUTH": true
},
"STOP_CHARGE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/charge_stop",
"AUTH": true
},
"MEDIA_TOGGLE_PLAYBACK": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/media_toggle_playback",
"AUTH": true
},
"MEDIA_NEXT_TRACK": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/media_next_track",
"AUTH": true
},
"MEDIA_PREVIOUS_TRACK": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/media_prev_track",
"AUTH": true
},
"MEDIA_NEXT_FAVORITE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/media_next_fav",
"AUTH": true
},
"MEDIA_PREVIOUS_FAVORITE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/media_prev_fav",
"AUTH": true
},
"MEDIA_VOLUME_UP": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/media_volume_up",
"AUTH": true
},
"MEDIA_VOLUME_DOWN": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/media_volume_down",
"AUTH": true
},
"SEND_LOG": {
"TYPE": "POST",
"URI": "api/1/logs",
"AUTH": true
},
"RETRIEVE_NOTIFICATION_PREFERENCES": {
"TYPE": "GET",
"URI": "api/1/notification_preferences",
"AUTH": true
},
"SEND_NOTIFICATION_PREFERENCES": {
"TYPE": "POST",
"URI": "api/1/notification_preferences",
"AUTH": true
},
"RETRIEVE_NOTIFICATION_SUBSCRIPTION_PREFERENCES": {
"TYPE": "GET",
"URI": "api/1/vehicle_subscriptions",
"AUTH": true
},
"SEND_NOTIFICATION_SUBSCRIPTION_PREFERENCES": {
"TYPE": "POST",
"URI": "api/1/vehicle_subscriptions",
"AUTH": true
},
"DEACTIVATE_DEVICE_TOKEN": {
"TYPE": "POST",
"URI": "api/1/device/{device_token}/deactivate",
"AUTH": true
},
"CALENDAR_SYNC": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/upcoming_calendar_entries",
"AUTH": true
},
"SET_VALET_MODE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/set_valet_mode",
"AUTH": true
},
"RESET_VALET_PIN": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/reset_valet_pin",
"AUTH": true
},
"SPEED_LIMIT_ACTIVATE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/speed_limit_activate",
"AUTH": true
},
"SPEED_LIMIT_DEACTIVATE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/speed_limit_deactivate",
"AUTH": true
},
"SPEED_LIMIT_SET_LIMIT": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/speed_limit_set_limit",
"AUTH": true
},
"SPEED_LIMIT_CLEAR_PIN": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/speed_limit_clear_pin",
"AUTH": true
},
"SCHEDULE_SOFTWARE_UPDATE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/schedule_software_update",
"AUTH": true
},
"CANCEL_SOFTWARE_UPDATE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/cancel_software_update",
"AUTH": true
},
"SET_SENTRY_MODE": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/set_sentry_mode",
"AUTH": true
},
"POWERWALL_ORDER_SESSION_DATA": {
"TYPE": "GET",
"URI": "api/1/users/powerwall_order_entry_data",
"AUTH": true
},
"POWERWALL_ORDER_PAGE": {
"TYPE": "GET",
"URI": "powerwall_order_page",
"AUTH": true,
"CONTENT": "HTML"
},
"ONBOARDING_EXPERIENCE": {
"TYPE": "GET",
"URI": "api/1/users/onboarding_data",
"AUTH": true
},
"ONBOARDING_EXPERIENCE_PAGE": {
"TYPE": "GET",
"URI": "onboarding_page",
"AUTH": true,
"CONTENT": "HTML"
},
"SERVICE_SELF_SCHEDULING_ELIGIBILITY": {
"TYPE": "GET",
"URI": "api/1/users/service_scheduling_data",
"AUTH": true
},
"SERVICE_SELF_SCHEDULING_PAGE": {
"TYPE": "GET",
"URI": "service_scheduling_page",
"AUTH": true,
"CONTENT": "HTML"
},
"REFERRAL_DATA": {
"TYPE": "GET",
"URI": "api/1/users/referral_data",
"AUTH": true
},
"REFERRAL_PAGE": {
"TYPE": "GET",
"URI": "referral_page",
"AUTH": true,
"CONTENT": "HTML"
},
"ROADSIDE_ASSISTANCE_DATA": {
"TYPE": "GET",
"URI": "api/1/users/roadside_assistance_data",
"AUTH": true
},
"ROADSIDE_ASSISTANCE_PAGE": {
"TYPE": "GET",
"URI": "roadside_assistance_page",
"AUTH": true,
"CONTENT": "HTML"
},
"MESSAGE_CENTER_MESSAGE_LIST": {
"TYPE": "GET",
"URI": "api/1/messages",
"AUTH": true
},
"MESSAGE_CENTER_MESSAGE": {
"TYPE": "GET",
"URI": "api/1/messages/{message_id}",
"AUTH": true
},
"MESSAGE_CENTER_COUNTS": {
"TYPE": "GET",
"URI": "api/1/messages/count",
"AUTH": true
},
"MESSAGE_CENTER_MESSAGE_ACTION_UPDATE": {
"TYPE": "POST",
"URI": "api/1/messages/{message_id}/actions",
"AUTH": true
},
"MESSAGE_CENTER_CTA_PAGE": {
"TYPE": "GET",
"URI": "messages_cta_page",
"AUTH": true,
"CONTENT": "HTML"
},
"AUTH_COMMAND_TOKEN": {
"TYPE": "POST",
"URI": "api/1/users/command_token",
"AUTH": true
},
"SEND_DEVICE_KEY": {
"TYPE": "POST",
"URI": "api/1/users/keys",
"AUTH": true
},
"DIAGNOSTICS_ENTITLEMENTS": {
"TYPE": "GET",
"URI": "api/1/diagnostics",
"AUTH": true
},
"SEND_DIAGNOSTICS": {
"TYPE": "POST",
"URI": "api/1/diagnostics",
"AUTH": true
},
"BATTERY_SUMMARY": {
"TYPE": "GET",
"URI": "api/1/powerwalls/{battery_id}/status",
"AUTH": true
},
"BATTERY_DATA": {
"TYPE": "GET",
"URI": "api/1/powerwalls/{battery_id}",
"AUTH": true
},
"BATTERY_POWER_TIMESERIES_DATA": {
"TYPE": "GET",
"URI": "api/1/powerwalls/{battery_id}/powerhistory",
"AUTH": true
},
"BATTERY_ENERGY_TIMESERIES_DATA": {
"TYPE": "GET",
"URI": "api/1/powerwalls/{battery_id}/energyhistory",
"AUTH": true
},
"BATTERY_BACKUP_RESERVE": {
"TYPE": "POST",
"URI": "api/1/powerwalls/{battery_id}/backup",
"AUTH": true
},
"BATTERY_SITE_NAME": {
"TYPE": "POST",
"URI": "api/1/powerwalls/{battery_id}/site_name",
"AUTH": true
},
"BATTERY_OPERATION_MODE": {
"TYPE": "POST",
"URI": "api/1/powerwalls/{battery_id}/operation",
"AUTH": true
},
"SITE_SUMMARY": {
"TYPE": "GET",
"URI": "api/1/energy_sites/{site_id}/status",
"AUTH": true
},
"SITE_DATA": {
"TYPE": "GET",
"URI": "api/1/energy_sites/{site_id}/live_status",
"AUTH": true
},
"SITE_CONFIG": {
"TYPE": "GET",
"URI": "api/1/energy_sites/{site_id}/site_info",
"AUTH": true
},
"HISTORY_DATA": {
"TYPE": "GET",
"URI": "api/1/energy_sites/{site_id}/history",
"AUTH": true
},
"BACKUP_RESERVE": {
"TYPE": "POST",
"URI": "api/1/energy_sites/{site_id}/backup",
"AUTH": true
},
"SITE_NAME": {
"TYPE": "POST",
"URI": "api/1/energy_sites/{site_id}/site_name",
"AUTH": true
},
"OPERATION_MODE": {
"TYPE": "POST",
"URI": "api/1/energy_sites/{site_id}/operation",
"AUTH": true
},
"TIME_OF_USE_SETTINGS": {
"TYPE": "POST",
"URI": "api/1/energy_sites/{site_id}/time_of_use_settings",
"AUTH": true
},
"STORM_MODE_SETTINGS": {
"TYPE": "POST",
"URI": "api/1/energy_sites/{site_id}/storm_mode",
"AUTH": true
},
"SEND_NOTIFICATION_CONFIRMATION": {
"TYPE": "POST",
"URI": "api/1/notification_confirmations",
"AUTH": true
},
"NAVIGATION_REQUEST": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/navigation_request",
"AUTH": true
},
"REMOTE_SEAT_HEATER_REQUEST": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/remote_seat_heater_request",
"AUTH": true
},
"REMOTE_STEERING_WHEEL_HEATER_REQUEST": {
"TYPE": "POST",
"URI": "api/1/vehicles/{vehicle_id}/command/remote_steering_wheel_heater_request",
"AUTH": true
}
}
Python
Getting DonkeyCar working on a Mac
I have been playing with a #selfdriving car for a while, and that is super exciting. From a #AI and #ML perspective it is small scale, but allows one to exploit all aspects of the tech stack and also appreciate the limitations of not only the software, but also the hardware.
With this You run a NN on a raspberry pi that uses TensorFlow, and Keras and runs inference on the edge. The pi doesn’t have enough power to train, so you need to do that on a beefier machine and then deploy the model back to run this.
Now, I didn’t have any issues in getting this running on Windows, but to get it on a Mac was a different story. The documentation is there that outlines all the steps, and even if you follow it to the T, it breaks right in the end.
When I tried to create a car, using a createcar command (this essentially creates the buckets, where you would save the training images, and the model, and the configuration of the car when you connect to it from your machine). The actual file paths would probably be different for you but, essentially it is the same thing.
(donkey) AMAC02XN1T9JGH5:donkeycar amit.bahree$ donkey createcar ~/mycar
Traceback (most recent call last):
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 660, in _build_master
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 968, in require
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 859, in resolve
pkg_resources.ContextualVersionConflict: (imageio 2.4.1 (/anaconda3/envs/donkey/lib/python3.6/site-packages), Requirement.parse('imageio<3.0,>=2.5'), {'moviepy'})
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/anaconda3/envs/donkey/bin/donkey", line 6, in <module>
from pkg_resources import load_entry_point
File "<frozen importlib._bootstrap>", line 961, in _find_and_load
File "<frozen importlib._bootstrap>", line 950, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 646, in _load_unlocked
File "<frozen importlib._bootstrap>", line 616, in _load_backward_compatible
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 2985, in <module>
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 2971, in _call_aside
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 2998, in _initialize_master_working_set
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 662, in _build_master
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 675, in _build_from_requirements
File "/anaconda3/envs/donkey/lib/python3.6/site-packages/setuptools-27.2.0-py3.6.egg/pkg_resources/__init__.py", line 854, in resolve
pkg_resources.DistributionNotFound: The 'imageio<3.0,>=2.5' distribution was not found and is required by moviepy
The key here to focus is on the last lines on both of those blocks of code – the main thing causing the issue is MoviePy (see highlighted lines above).
MoviePy is a Python library for video editing: cutting, concatenations, title insertions, video compositing (a.k.a. non-linear editing), video processing, and creation of custom effects.
It seems like when you go through the steps – clone the repo, setup anaconda, install tensorflow and get the car configured – there is a mismatch in the MoviePy dependencies which it doesn’t like. The way to fix the issue is outlined below.
Skip MoviePy
MoviePy is something you don’t need to use right away but later when trying to make a movie (using the makemovie command – which allows you to create a movie file from the images in a Tub.); this is not essential. To do this, the easiest way is to remove (or my suggestion it to comment) out the moviepy dependency from the setup.py file.
This should be line 33 in the setup.py file that you will find in the same folder where you cloned the git repo. As an example the updated file is below, where the moviepy dependency is commented out (see highlighted). And once you save this and go about creating the car, it should work. Of course you cannot use the makemovie option later.
from setuptools import setup, find_packages
import os
with open("README.md", "r") as fh:
long_description = fh.read()
setup(name='donkeycar',
version='2.5.7',
description='Self driving library for python.',
long_description=long_description,
long_description_content_type="text/markdown",
url='https://github.com/autorope/donkeycar',
download_url='https://github.com/autorope/donkeycar/archive/2.1.5.tar.gz',
author='Will Roscoe',
author_email='wroscoe@gmail.com',
license='MIT',
entry_points={
'console_scripts': [
'donkey=donkeycar.management.base:execute_from_command_line',
],
},
install_requires=['numpy',
'pillow',
'docopt',
'tornado==4.5.3',
'requests',
'h5py',
'python-socketio',
'flask',
'eventlet',
#'moviepy',
'pandas',
],
extras_require={
'tf': ['tensorflow>=1.9.0'],
'tf_gpu': ['tensorflow-gpu>=1.9.0'],
'pi': [
'picamera',
'Adafruit_PCA9685',
],
'dev': [
'pytest',
'pytest-cov',
'responses'
],
'ci': ['codecov']
},
include_package_data=True,
classifiers=[
# How mature is this project? Common values are
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
'Development Status :: 3 - Alpha',
# Indicate who your project is intended for
'Intended Audience :: Developers',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
# Pick your license as you wish (should match "license" above)
'License :: OSI Approved :: MIT License',
# Specify the Python versions you support here. In particular, ensure
# that you indicate whether you support Python 2, Python 3 or both.
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
],
keywords='selfdriving cars donkeycar diyrobocars',
packages=find_packages(exclude=(['tests', 'docs', 'site', 'env'])),
)
Once you have saved the setup.py file, you need to run the installation again with the following command and then run the create car command. Both of these are outlined below.
pip install -e .
donkey createcar ~/mycar
Once you run these, then you should see the successful installation as shown by the output below. Note – your output might be a little different depending on the conda state of packages
(donkey) AMAC02XN1T9JGH5:donkeycar amit.bahree$ pip install -e .
Obtaining file:///Users/amit.bahree/CloudStation/Documents/Code/donkeycar
Requirement already satisfied: numpy in /anaconda3/envs/donkey/lib/python3.6/site-packages (from donkeycar==2.5.7) (1.14.5)
Requirement already satisfied: pillow in /anaconda3/envs/donkey/lib/python3.6/site-packages (from donkeycar==2.5.7) (4.2.1)
Requirement already satisfied: docopt in /anaconda3/envs/donkey/lib/python3.6/site-packages (from donkeycar==2.5.7) (0.6.2)
Collecting tornado==4.5.3 (from donkeycar==2.5.7)
Requirement already satisfied: requests in /anaconda3/envs/donkey/lib/python3.6/site-packages (from donkeycar==2.5.7) (2.18.4)
Requirement already satisfied: h5py in /anaconda3/envs/donkey/lib/python3.6/site-packages (from donkeycar==2.5.7) (2.7.1)
Collecting python-socketio (from donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/a1/71/118e4b7fb453d7095d6863f4b783dbaa57109af4bc2380300649c8942d61/python_socketio-4.0.0-py2.py3-none-any.whl
Collecting flask (from donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/7f/e7/08578774ed4536d3242b14dacb4696386634607af824ea997202cd0edb4b/Flask-1.0.2-py2.py3-none-any.whl
Collecting eventlet (from donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/86/7e/96e1412f96eeb2f2eca9342dcc4d5bc9305880a448b603b0a8e54439b71c/eventlet-0.24.1-py2.py3-none-any.whl
Collecting pandas (from donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/99/12/bf4c58eea94cea4f91ff931f284146337814fb8546e6eb0b52584446fd52/pandas-0.24.1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Requirement already satisfied: olefile in /anaconda3/envs/donkey/lib/python3.6/site-packages (from pillow->donkeycar==2.5.7) (0.44)
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Requirement already satisfied: certifi>=2017.4.17 in /anaconda3/envs/donkey/lib/python3.6/site-packages (from requests->donkeycar==2.5.7) (2017.7.27.1)
Requirement already satisfied: idna<2.7,>=2.5 in /anaconda3/envs/donkey/lib/python3.6/site-packages (from requests->donkeycar==2.5.7) (2.6)
Requirement already satisfied: urllib3<1.23,>=1.21.1 in /anaconda3/envs/donkey/lib/python3.6/site-packages (from requests->donkeycar==2.5.7) (1.22)
Requirement already satisfied: six in /anaconda3/envs/donkey/lib/python3.6/site-packages (from h5py->donkeycar==2.5.7) (1.10.0)
Collecting python-engineio>=3.2.0 (from python-socketio->donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/95/91/d083bd7b5d408af53633377dfbf87bf181236c8916d36213388b12eaa999/python_engineio-3.4.3-py2.py3-none-any.whl
Collecting click>=5.1 (from flask->donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/fa/37/45185cb5abbc30d7257104c434fe0b07e5a195a6847506c074527aa599ec/Click-7.0-py2.py3-none-any.whl
Collecting itsdangerous>=0.24 (from flask->donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/76/ae/44b03b253d6fade317f32c24d100b3b35c2239807046a4c953c7b89fa49e/itsdangerous-1.1.0-py2.py3-none-any.whl
Collecting Werkzeug>=0.14 (from flask->donkeycar==2.5.7)
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Collecting Jinja2>=2.10 (from flask->donkeycar==2.5.7)
Using cached https://files.pythonhosted.org/packages/7f/ff/ae64bacdfc95f27a016a7bed8e8686763ba4d277a78ca76f32659220a731/Jinja2-2.10-py2.py3-none-any.whl
Collecting monotonic>=1.4 (from eventlet->donkeycar==2.5.7)
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Collecting greenlet>=0.3 (from eventlet->donkeycar==2.5.7)
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Collecting pytz>=2011k (from pandas->donkeycar==2.5.7)
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Installing collected packages: tornado, python-engineio, python-socketio, click, itsdangerous, Werkzeug, MarkupSafe, Jinja2, flask, monotonic, greenlet, dnspython, eventlet, pytz, python-dateutil, pandas, donkeycar
Found existing installation: tornado 4.5.1
Uninstalling tornado-4.5.1:
Successfully uninstalled tornado-4.5.1
Found existing installation: Werkzeug 0.12.2
Uninstalling Werkzeug-0.12.2:
Successfully uninstalled Werkzeug-0.12.2
Running setup.py develop for donkeycar
Successfully installed Jinja2-2.10 MarkupSafe-1.1.1 Werkzeug-0.14.1 click-7.0 dnspython-1.16.0 donkeycar eventlet-0.24.1 flask-1.0.2 greenlet-0.4.15 itsdangerous-1.1.0 monotonic-1.5 pandas-0.24.1 python-dateutil-2.8.0 python-engineio-3.4.3 python-socketio-4.0.0 pytz-2018.9 tornado-4.5.3
And when I run the createcar, you can see it worked as expected. In my case creating the ‘mycar’ folder in my home directory. Of course you can choose this wherever you prefer.
(donkey) AMAC02XN1T9JGH5:donkeycar amit.bahree$ donkey createcar ~/mycar
using donkey version: 2.5.7 ...
Creating car folder: /Users/amit.bahree/mycar
making dir /Users/amit.bahree/mycar
Creating data & model folders.
making dir /Users/amit.bahree/mycar/models
making dir /Users/amit.bahree/mycar/data
making dir /Users/amit.bahree/mycar/logs
Copying car application template: donkey2
Copying car config defaults. Adjust these before starting your car.
Donkey setup complete.
It is interesting to see this is more stable on Windows, than on a Mac. Also, one last thing to leave you with – when I first ran the installation, the hint that someone was wrong was in the output, but I didn’t pay too much attention to it. See the red line highlighted in the output below.
Don’t know at this time on what the solution for moviepy is to get this sorted – luckily its not a big deal at the moment.
threads
Some people, when confronted with a problem, think, ‘I know, I’ll use threads’ – and then two they hav erpoblesms.
#GeekyJokes and if you don’t get it, see this. 🙂
VSCode + Python on a mac
As my experimentation continues, I wanted to get Visual Studio Code installed on a mac, and wanted to use python as the language of choice – main reason for the mac is to understand and explore the #ML libraries, runtimes, and their support on a mac (both natively and in containers – docker).
Now, Microsoft has a very nice tutorial to get VSCode setup and running on a mac, including some basic configuration (e.g. touchbar support). But when it comes to getting python setup, and running, that is a different matter. Whilst the tutorial is good, it doesn’t actually work and errors out.
Below is the code that Microsoft outlines in the tutorial for python. It essentially is the HelloWorld using packages and is quite simple; but this will fail and won’t work.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 20, 100) # Create a list of evenly-spaced numbers over the range
plt.plot(x, np.sin(x)) # Plot the sine of each x point
plt.show() # Display the plot
When you run this, you will see an error that is something like the one outlined below.
2019-01-18 14:23:34.648 python[38527:919087] -[NSApplication _setup:]: unrecognized selector sent to instance 0x7fbafa49bf10
2019-01-18 14:23:34.654 python[38527:919087] *** Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: '-[NSApplication _setup:]: unrecognized selector sent to instance 0x7fbafa49bf10'
*** First throw call stack:
(
0 CoreFoundation 0x00007fff521a1ecd __exceptionPreprocess + 256
1 libobjc.A.dylib 0x00007fff7e25d720 objc_exception_throw + 48
2 CoreFoundation 0x00007fff5221f275 -[NSObject(NSObject) __retain_OA] + 0
3 CoreFoundation 0x00007fff52143b40 ___forwarding___ + 1486
4 CoreFoundation 0x00007fff521434e8 _CF_forwarding_prep_0 + 120
5 libtk8.6.dylib 0x000000011523031d TkpInit + 413
6 libtk8.6.dylib 0x000000011518817e Initialize + 2622
7 _tkinter.cpython-37m-darwin.so 0x0000000114fb2a0f _tkinter_create + 1183
8 python 0x0000000101836ba6 _PyMethodDef_RawFastCallKeywords + 230
9 python 0x00000001019772b1 call_function + 257
10 python 0x0000000101974daf _PyEval_EvalFrameDefault + 45215
11 python 0x0000000101968a42 _PyEval_EvalCodeWithName + 418
12 python 0x0000000101835867 _PyFunction_FastCallDict + 231
13 python 0x00000001018b9481 slot_tp_init + 193
14 python 0x00000001018c3441 type_call + 241
15 python 0x0000000101836573 _PyObject_FastCallKeywords + 179
16 python 0x000000010197733f call_function + 399
17 python 0x0000000101975052 _PyEval_EvalFrameDefault + 45890
18 python 0x0000000101836368 function_code_fastcall + 120
19 python 0x0000000101977265 call_function + 181
20 python 0x0000000101974daf _PyEval_EvalFrameDefault + 45215
21 python 0x0000000101968a42 _PyEval_EvalCodeWithName + 418
22 python 0x0000000101835867 _PyFunction_FastCallDict + 231
23 python 0x0000000101839782 method_call + 130
24 python 0x00000001018371e2 PyObject_Call + 130
25 python 0x00000001019751c6 _PyEval_EvalFrameDefault + 46262
26 python 0x0000000101968a42 _PyEval_EvalCodeWithName + 418
27 python 0x0000000101836a73 _PyFunction_FastCallKeywords + 195
28 python 0x0000000101977265 call_function + 181
29 python 0x0000000101974f99 _PyEval_EvalFrameDefault + 45705
30 python 0x0000000101836368 function_code_fastcall + 120
31 python 0x0000000101977265 call_function + 181
32 python 0x0000000101974f99 _PyEval_EvalFrameDefault + 45705
33 python 0x0000000101968a42 _PyEval_EvalCodeWithName + 418
34 python 0x0000000101836a73 _PyFunction_FastCallKeywords + 195
35 python 0x0000000101977265 call_function + 181
36 python 0x0000000101974f99 _PyEval_EvalFrameDefault + 45705
37 python 0x0000000101968a42 _PyEval_EvalCodeWithName + 418
38 python 0x0000000101836a73 _PyFunction_FastCallKeywords + 195
39 python 0x0000000101977265 call_function + 181
40 python 0x0000000101974daf _PyEval_EvalFrameDefault + 45215
41 python 0x0000000101968a42 _PyEval_EvalCodeWithName + 418
42 python 0x00000001019cc9a0 PyRun_FileExFlags + 256
43 python 0x00000001019cc104 PyRun_SimpleFileExFlags + 388
44 python 0x00000001019f7edc pymain_main + 9148
45 python 0x0000000101808ece main + 142
46 libdyld.dylib 0x00007fff7f32bed9 start + 1
47 ??? 0x0000000000000003 0x0 + 3
)
libc++abi.dylib: terminating with uncaught exception of type NSException
[Done] exited with code=null in 1.017 seconds
The main reason this fails is that one has to be a little more explicit with matplot (the library that we are trying to use). Matplot has this concept of backends, which essentially is the runtime dependencies needed to support various execution environments – including both interactive and non-interactive environments.
For matplot to work on a mac, the raster graphics c++ library that it uses is based on something called Anti-Grain Geometry (AGG). And for the library to render, we need to be explicit on which agg to use (there are multiple raster libraries).
In addition on a mac OS X there is a limitation when rendering in OSX windows (presently lacks blocking show() behavior when matplotlib is in non-interactive mode).
To get around this, we explicitly tell matplot to use the specific agg (“TkAgg in our case) and then it will all work. I have a updated code sample below, which adds more points, and also waits for the console input, so one can see what the output looks like.
import matplotlib
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
import numpy as np
def waitforuser():
input("Press enter to continue ...")
return
x = np.linspace(0, 50, 200) # Create a list of evenly-spaced numbers over the range
y = np.sin(x)
print(x)
waitforuser()
print(y)
waitforuser()
plt.plot(x,y)
plt.show()
And incase you are wondering what it looks like, below are a few screenshots showing the output.
To get everything working, make sure you setup the Linting, debugger, and the python environment properly. And of course, you can go nuts with containers! Happy coding!
Azure Cognitive Services in containers is the smart way to go
{Cross posted from my post on Avanade}
Containers just got smarter.
That’s the news from Microsoft, which announced recently that Azure Cognitive Services now supports containers. The marriage of AI and containers is a technology story, of course, but it’s a potentially even bigger business story, one that affects where and how you can do business and gain competitive advantage.
First, the technology story
Containers aren’t new, of course. They’re an increasingly popular technology with a big impact on business. That’s because they boost the agility and flexibility with which a business can roll out new tools to employees and new products and services to customers.
With containers, a business can get software releases and changes out faster and more frequently, increasing its competitive advantage. Because containers abstract applications from their underlying operating systems and other services—like virtual machines abstracted from hardware—those applications can run anywhere: in the cloud, on a laptop, in a kiosk or in an intelligent Internet-of-Things (IoT) edge device in the field.
In many respects this frees up the application’s developer, who can focus on creating the best, most useful software for the business. With Microsoft’s announcement, that software can now more easily include object detection, vision recognition, text and language understanding.
At Avanade, we take containers a step further by including support for them in our modern engineering platform, a key part of our overall approach to intelligent IT. So, you can automate your creation and management of containers—including AI-enabled containers—for a faster, easier, more seamless DevOps process. You can take greater advantage of IoT capabilities and move technologies such as AI closer to the edge, where they can reduce latency and boost performance.
What AI containers do for business
And you can do much more, which is where the business story gets interesting. With the greater agility and adaptability that comes with container-based AI services, you can respond more quickly to new competition, regulatory environments and business models. That contrasts with the more limited responses that have been possible with traditional, cloud-based AI.
For example, data sovereignty laws and GDPR requirements generally restrict the transfer of data to the cloud, where cloud-based cognitive services can interact with it. Now, with containers that support cognitive services, you can avoid those restrictions by running your services locally.
A retail bank might use containerized AI to identify customers, address their needs, process payments and offer additional services, boosting customer satisfaction and bank revenue—all without sending private financial data outside the region (or even outside the bank) in accordance with GDPR.
Similarly, regional medical centers and clinics subject to HIPAA privacy laws in the US can process protected information on site with containerized AI to cut patient wait times and deliver better health outcomes.
Or, think about limited-connectivity or disconnected environments—such as manufacturing shop floors, remote customer sites or oil rigs or tankers—that can’t count on accessing AI that resides in the always-on cloud. Previously, these sites might have had to batch their data to process it during narrow periods of cloud connectivity, with the delays greatly limiting the timeliness and usefulness of AI.
Now, these sites can combine IoT and AI to anticipate and respond to manufacturing disruptions before they occur, increasing safety, productivity and product quality while reducing errors and costs.
If you can’t bring your data to your AI, now you can bring your AI to your data. That’s the message of container-hosted AI and the modern engineering platform. Together, they optimize your ability to bring AI into environments where you can’t count on the cloud. Using AI where you couldn’t before makes innovative solutions possible—and innovative solutions deliver competitive advantage.
Boost ROI and scale
If you’re already using Azure Cognitive Services, you’ve invested time and money to train the models that support your use cases. Because those models are now portable, you can take advantage of them in regulated, limited-connectivity and disconnected environments, increasing your return on that investment.
You can also scale your use of AI with a combination of cloud- and container-based architectures. That enables you to apply the most appropriate architectural form for any given environment or use. At the same time, you’re deploying consistent AI technology across the enterprise, increasing reliability while decreasing your operating cost.
Keep in mind…
Here are three things to keep in mind as you think about taking advantage of this important news:
- Break the barriers between your data scientists and business creatives. Containerized cognitive services is about far more than putting AI where you couldn’t before. It’s about using it in exciting new ways to advance the business. Unless you have heterogeneous teams bringing diverse perspectives to the table, you may miss some of the most important innovation possibilities for your business.
- You need a cloud strategy that’s not just about the cloud. If you don’t yet have a cloud strategy, you’re behind the curve. But if your cloud strategy is limited to the cloud, you may be about to fall behind the next curve. Microsoft’s announcement is further proof that the cloud is crucial to the enterprise—and also part of a larger environment, including both legacy and edge platforms, with which it must integrate.
- Be prepared for the ethics issues. Putting cognitive services in places you couldn’t before could raise new ethics issues. After all, we’re talking about the ability to read people’s expressions and even their emotions. This shouldn’t put you off—but it should put you on alert. Plug your ethics committee into these discussions when appropriate. If you don’t already have an ethics committee, create one. But that’s another post. 🙂
Want to learn more?
Microsoft’s announcement furthers the democratization of AI: the use of AI in more places and in more ways throughout the enterprise and beyond. Whether you turn to us for your AI solutions or look to us to assist you in developing your own, we’re ready to help with the greatest concentration of Microsoft expertise outside of Microsoft itself.
Bugs
It is a known bug with the programming language. 🙂
#GeekyJokes #ProgrammerHumor
Docker container running Ubuntu on Windows
Containers are all the rage right now and rightfully so – not only do they help abstract away some of the complexity and dependencies of your apps and solutions, they also make managing of environments, and, deployments much simpler. And the fact that you can do it in a consistent, and repeatable fashion is just icing on the cake.
As a simple example, with Docker, on Windows (as in my case), I can run a dockerized app, on a different OS than the host, which can also be interactive.
The command below will spawn a container, pull down the image of Ubuntu and then run an interactive terminal, tying the terminal to the standard input. Of course in this example, this requires that you already have Docker installed (the Community Edition would be just fine to play around with).
docker run --interactive --tty ubuntu bash
Now, with Docker if you do get the following error (on Windows): “Error response from daemon: operating system on which parent image was created is not Windows.” as also shown below, the way to fix it is to switch on Experimental features.
To try and fix this, right click on the docker icon in the system tray, choose Settings, and from the setting screen, in the Daemon tab, enable experimental features as shown below.
And after enabling the experimental features, the docker daemon will restart. And post that, if you run the docker command again, it would work as expected:
- It pulls down the image (which is used to run in the container)
- Runs Ubuntu in an interactive session (this is because of the option I choose)
- And all within my PowerShell console on Windows.
This is just the beginning, there of course is a lot more to it. 🙂
Ubuntu on Surface Book
I am writing this on a Microsoft Surface Book, running Ubuntu natively, and there isn’t any Windows option – I blew away, the Windows partition, and there isn’t any other OS on it.
Why, some of you might think? Well, why not. 🙂 For me the motive is two fold: one am a geek and love to hack what works and cannot work – how else will one learn? And two, explore and see which AI frameworks, tools, and runtimes works better on Linux natively
Well I must say, this experiment has been a pleasant surprise and much more successful that I originally thought of. Most of the things are working quite well on Surface with Ubuntu – including touch and pen (both seem like mouse clicks). As the screenshot below shows, Ubuntu is running quite nicely – including most of the features. There are a few things that quite don’t – I have them listed later in the post.
So much so, that Visual Studio code is running natively and whilst I haven’t had a chance to use it much (yet), that fact that it can even so much was something I wasn’t expecting without running some containers or VM’s or the likes.
So, how does one go about doing this? It is quite simple these days to be honest. Below are the steps I had followed. I do think the real magic is the hard work that JakeDay has done to get the kernel and firmware supported.
Disclaimer: My experience outlined here is related to the Surface Book – it can also run and be supported on other Surface devices, and the exact nature of what works or doesn’t work would be a little different.
- Hardware – Have a USB keyboard and mouse handy just in case; and if you are on a Surface Pro or something with only one usb port, then a usb hub. And you of course would need a USB drive to boot Ubuntu off.
- Disable Secure boot – without this getting the bootloader sequence would be challenging. If you aren’t sure how, then check out the instructions here to disable secure boot.
- Delete / Shrink the windows partition – If you don’t care about Windows and have a copy of the license somewhere to get back you might want to just delete this. If you do want to shrink it (say this is your primary machine and you want to get back at some point, then goto Disk Management in Windows and resize the partition – keep this to at least 50 GB.
- Ubuntu USB drive – if you don’t have one already, create a ubuntu bootable usb drive. You can get more instructions here. And if you are on Windows, I would recommend using Rufus.
- Install Ubuntu – Boot off the usb drive you created, and before that make sure you have disabled secure boot. I would pick most of the default options for Ubuntu for now.
- Patched Kernel – Once you have ubuntu running, I would recommend installing the patched kernel and headers that allows for Surface support. Steps for these are outlined below and need to be execute in a terminal.
- Install Dependencies: sudo apt install git curl wget sed
- Clone the repo: git clone https://github.com/jakeday/linux-surface.git ~/linux-surface
- Change working directory: cd ~/linux-surface
- Run setup: sudo sh setup.sh
- Reboot on the patched kernel
Change boot kernel: Finally, after you have rebooted, the odds of Ubuntu booting off the ‘right’ kernel is quite slim and best to manually pick this. You can of course use the grub, or what I find better – install the grub customizer, and then choose the correct option as shown below. Once picked and you had hit save, you also need to run the following in a terminal to make these persist: sudo update-grub
And that is all to it for getting the base install and customization running.
If you are super curious on what that setup script does, the code is below (also listed on github). What is interesting to see the various hardware models supported.
LX_BASE=""
LX_VERSION=""
if [ -r /etc/os-release ]; then
. /etc/os-release
if [ $ID = arch ]; then
LX_BASE=$ID
elif [ $ID = ubuntu ]; then
LX_BASE=$ID
LX_VERSION=$VERSION_ID
elif [ ! -z "$UBUNTU_CODENAME" ] ; then
LX_BASE="ubuntu"
LX_VERSION=$VERSION_ID
else
LX_BASE=$ID
LX_VERSION=$VERSION
fi
else
echo "Could not identify your distro. Please open script and run commands manually."
exit
fi
SUR_MODEL="$(dmidecode | grep "Product Name" -m 1 | xargs | sed -e 's/Product Name: //g')"
SUR_SKU="$(dmidecode | grep "SKU Number" -m 1 | xargs | sed -e 's/SKU Number: //g')"
echo "\nRunning $LX_BASE version $LX_VERSION on a $SUR_MODEL.\n"
read -rp "Press enter if this is correct, or CTRL-C to cancel." cont;echo
echo "\nContinuing setup...\n"
echo "Coping the config files under root to where they belong...\n"
cp -Rb root/* /
echo "Making /lib/systemd/system-sleep/sleep executable...\n"
chmod a+x /lib/systemd/system-sleep/sleep
read -rp "Do you want to replace suspend with hibernate? (type yes or no) " usehibernate;echo
if [ "$usehibernate" = "yes" ]; then
if [ "$LX_BASE" = "ubuntu" ] && [ 1 -eq "$(echo "${LX_VERSION} >= 17.10" | bc)" ]; then
echo "Using Hibernate instead of Suspend...\n"
ln -sfb /lib/systemd/system/hibernate.target /etc/systemd/system/suspend.target && sudo ln -sfb /lib/systemd/system/systemd-hibernate.service /etc/systemd/system/systemd-suspend.service
else
echo "Using Hibernate instead of Suspend...\n"
ln -sfb /usr/lib/systemd/system/hibernate.target /etc/systemd/system/suspend.target && sudo ln -sfb /usr/lib/systemd/system/systemd-hibernate.service /etc/systemd/system/systemd-suspend.service
fi
else
echo "Not touching Suspend\n"
fi
read -rp "Do you want use the patched libwacom packages? (type yes or no) " uselibwacom;echo
if [ "$uselibwacom" = "yes" ]; then
echo "Installing patched libwacom packages..."
dpkg -i packages/libwacom/*.deb
apt-mark hold libwacom
else
echo "Not touching libwacom"
fi
if [ "$SUR_MODEL" = "Surface Pro 3" ]; then
echo "\nInstalling i915 firmware for Surface Pro 3...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_bxt.zip -d /lib/firmware/i915/
fi
if [ "$SUR_MODEL" = "Surface Pro" ]; then
echo "\nInstalling IPTS firmware for Surface Pro 2017...\n"
mkdir -p /lib/firmware/intel/ipts
unzip -o firmware/ipts_firmware_v102.zip -d /lib/firmware/intel/ipts/
echo "\nInstalling i915 firmware for Surface Pro 2017...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_kbl.zip -d /lib/firmware/i915/
fi
if [ "$SUR_MODEL" = "Surface Pro 4" ]; then
echo "\nInstalling IPTS firmware for Surface Pro 4...\n"
mkdir -p /lib/firmware/intel/ipts
unzip -o firmware/ipts_firmware_v78.zip -d /lib/firmware/intel/ipts/
echo "\nInstalling i915 firmware for Surface Pro 4...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_skl.zip -d /lib/firmware/i915/
fi
if [ "$SUR_MODEL" = "Surface Pro 2017" ]; then
echo "\nInstalling IPTS firmware for Surface Pro 2017...\n"
mkdir -p /lib/firmware/intel/ipts
unzip -o firmware/ipts_firmware_v102.zip -d /lib/firmware/intel/ipts/
echo "\nInstalling i915 firmware for Surface Pro 2017...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_kbl.zip -d /lib/firmware/i915/
fi
if [ "$SUR_MODEL" = "Surface Pro 6" ]; then
echo "\nInstalling IPTS firmware for Surface Pro 6...\n"
mkdir -p /lib/firmware/intel/ipts
unzip -o firmware/ipts_firmware_v102.zip -d /lib/firmware/intel/ipts/
echo "\nInstalling i915 firmware for Surface Pro 6...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_kbl.zip -d /lib/firmware/i915/
fi
if [ "$SUR_MODEL" = "Surface Laptop" ]; then
echo "\nInstalling IPTS firmware for Surface Laptop...\n"
mkdir -p /lib/firmware/intel/ipts
unzip -o firmware/ipts_firmware_v79.zip -d /lib/firmware/intel/ipts/
echo "\nInstalling i915 firmware for Surface Laptop...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_skl.zip -d /lib/firmware/i915/
fi
if [ "$SUR_MODEL" = "Surface Book" ]; then
echo "\nInstalling IPTS firmware for Surface Book...\n"
mkdir -p /lib/firmware/intel/ipts
unzip -o firmware/ipts_firmware_v76.zip -d /lib/firmware/intel/ipts/
echo "\nInstalling i915 firmware for Surface Book...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_skl.zip -d /lib/firmware/i915/
fi
if [ "$SUR_MODEL" = "Surface Book 2" ]; then
echo "\nInstalling IPTS firmware for Surface Book 2...\n"
mkdir -p /lib/firmware/intel/ipts
if [ "$SUR_SKU" = "Surface_Book_1793" ]; then
unzip -o firmware/ipts_firmware_v101.zip -d /lib/firmware/intel/ipts/
else
unzip -o firmware/ipts_firmware_v137.zip -d /lib/firmware/intel/ipts/
fi
echo "\nInstalling i915 firmware for Surface Book 2...\n"
mkdir -p /lib/firmware/i915
unzip -o firmware/i915_firmware_kbl.zip -d /lib/firmware/i915/
echo "\nInstalling nvidia firmware for Surface Book 2...\n"
mkdir -p /lib/firmware/nvidia/gp108
unzip -o firmware/nvidia_firmware_gp108.zip -d /lib/firmware/nvidia/gp108/
fi
if [ "$SUR_MODEL" = "Surface Go" ]; then
echo "\nInstalling ath10k firmware for Surface Go...\n"
mkdir -p /lib/firmware/ath10k
unzip -o firmware/ath10k_firmware.zip -d /lib/firmware/ath10k/
fi
echo "Installing marvell firmware...\n"
mkdir -p /lib/firmware/mrvl/
unzip -o firmware/mrvl_firmware.zip -d /lib/firmware/mrvl/
read -rp "Do you want to set your clock to local time instead of UTC? This fixes issues when dual booting with Windows. (type yes or no) " uselocaltime;echo
if [ "$uselocaltime" = "yes" ]; then
echo "Setting clock to local time...\n"
timedatectl set-local-rtc 1
hwclock --systohc --localtime
else
echo "Not setting clock"
fi
read -rp "Do you want this script to download and install the latest kernel for you? (type yes or no) " autoinstallkernel;echo
if [ "$autoinstallkernel" = "yes" ]; then
echo "Downloading latest kernel...\n"
urls=$(curl --silent "https://api.github.com/repos/jakeday/linux-surface/releases/latest" | grep '"browser_download_url":' | sed -E 's/.*"([^"]+)".*/\1/')
resp=$(wget -P tmp $urls)
echo "Installing latest kernel...\n"
dpkg -i tmp/*.deb
rm -rf tmp
else
echo "Not downloading latest kernel"
fi
echo "\nAll done! Please reboot."
Lastly, below are the things not working for me – none of these are deal breakers but something to be aware of.
- Cameras are not supported – either of the two.
- Dedicated GPU (if you have one). This was a little bummed out as I got the dedicated GPU for some of the #MachineLearning experimentation, but then this whole thing is a different type of experimentation, so am OK.
- Can control the volume using the speaker widget thing on the top right corner, but the volume buttons on top aren’t.
- Sleep / Hibernation – It has some issues and for now I have sleep disabled but have hibernation setup.
- Detaching the screen will immediately terminate everything and power off the machine (not a clean poweroff) – I am guessing it cannot transition between the two batteries of the base and the screen. However if already detached then it will work without any issues.
Happy hacking!
Roots of #AI
The naming is unfortunate when talking about #AI. There isn’t anything about intelligence – not as we humans know of it. If we can rewind back to the 50’s we can perhaps rename it to something like Computational Intelligence, which is more accurate. And although I have outlined the difference between some of the elements of AI in the past, I wanted to get back to what the intent was and how this area started.
Can machines think? Some say, the origins of #AI go back to Turing and started with his paper “Computing machinery and intelligence” (PDF) when it was published in 1950.Whilst, Turing might have planed the seed, it was a program called Logic Theorist created Allen Newell, Cliff Shaw, and Herbert Simon which was the first #ArtificialIntelligence program. Of course it wasn’t called #AI then.
That started back in 1956 when a Logic Theorist was presented at a conference in Dartmouth College called “Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI)” (PDF). The term “#AI” was coined at the conference.
Since then, AI has had a roller coaster of a ride over the decades – from colder than hell (I presume) winters, to hotter than lava with it being everywhere. As someone said, time will heal all wounds.
Today, many of us use #AI, #DeepLearning, and, #MachineLearning interchangeably. Over the course of last couple of years, I have learned to ignore that, but fundamentally the distinction is important.
AI, we would say is more computational intelligence – allowing computers to do tasks that would be difficult for humans to do, certainly at scale. And these tasks are accomplished using different mechanisms and techniques, using “intelligent agents”.
Machine learning is a subset of AI, where the program or algorithm can learn from previous outputs, and improve based on that data – hence the “learning” part. It is akin to it learning from experience, but isn’t the same thing as we humans can comprehend and understand. Some of us think, the program is rewriting itself, which technically isn’t an accurate description.
Deep Learning is a set of techniques and algorithms of machine learning that are inspired from how the neurals in our brain connect together and work. These set of techniques are also called Neural Networks, and essentially are nothing but type of machine learning
For any of this AI “magic” to work, the one thing it needs to feed on is data. Without data, none of this would be possible. This data is classified into two categories – features and labels.
- Features – these are aspects of whatever we are interested in. For example if we are interested in vehicles features could be the colour, make, and, model of the vehicle.
- Labels – these are buckets of categories we put the things we are interested in. Using the same vehicles examples, we can have labels such as SUV, Sedan, Sports Car, Trucks, etc. that categorize vehicles.
One key principle to remember when it comes to #AI – all the outcomes that are described are in the terms of probabilities and not absolutes. All it suggests is the likelihood of something to happen, and most things cannot be predicted with total certainty. And this fundamental aspect one should remember when making decisions.
There isn’t a universal definition of AI, which sometimes doesn’t help. Each has their own perception. I have gotten over it to come to their terms and ensure we are talking the same lingo and meaning. It doesn’t help to get academic about it. 🙂
For example taking three leading analysts (Gartner, IDC, and Forrester) definition of AI (outlined below) is a good indicator on how this can get confusing.
- Gartner – At its core, AI is about solving business problems in novel ways. It stretches across any organization from innovation, R&D and IT to data science.
- IDC defines cognitive/Artificial Intelligence (AI) systems as a set of technologies that use deep natural language processing and understanding to answer questions and provide recommendations and direction. IDC’s coverage of cognitive/AI systems examines:
- Digital assistants
- Automated advisors
- Artificial intelligence, deep learning and machine learning
- Automated recommendation systems
- Forrester defines AI as a liberatory technology at its core, and businesses that integrate it will free workers to become more innovative, creative, and adaptive than ever before. But these technologies are still in early stages.
And the field is just exploding now – not just with new research around #DeepLearning or #MachineLearning, but also net new aspects from a business perspectives; things like:
- Digital Ethics
- Conversational AI
- Democratization of AI
- Data Engineering (OK, not new, but certainly key)
- Model Management
- RPA (or #IntelligentAutomation)
- AI Strategy
It is a new and exciting world that spans multiple spectrum. Don’t try and drink from the fire-hose, but take it in slowly, appreciate the nuances and what one brings value and discuss in terms of outcomes.
Computer – a male or female?
So, both these arguments make sense. I can’t decide which one is accurate.
Patent – Systems and methods for organizing and presenting skill progressions
This has been a long time coming – our patent filed a about 4 years ago was finally awarded today by the USPTO. Some details below.
United States Patent 10,102,774
Bahree , et al. October 16, 2018
Systems and methods for organizing and presenting skill progression
In any organization, the skills collectively possessed by individuals of the organization can determine the capabilities of the organization as a whole. Previously, there was no centralized method or system for managing skills which are complex and wide-ranging. There was also no effective way for individuals to review skills they possess and to discover other skills which they can cross-train and leverage—either to enhance their existing roles and responsibilities, or possibly change skills and get involved with another area and thereby grow their career. The limited visualizations of skill sets offered to the individuals were static and non-interactive, which is not ideal.
When organizations grow and begin hiring new technical employees, this tremendous influx of new resources and talent makes the overall skill set of the organization increasingly difficult to comprehend. The challenge gets increasingly difficult over time. Further, to allow such companies to both retain and attract talent such companies want to ensure that they can provide a clear path for employees to manage their careers and talent growth effectively. Such companies are also challenged to be able to efficiently allocate technical resources, and to visualize technical areas in which their current employees are strong, and areas in which their current employees need further training (or new employees need to be recruited) to help the company compete in the marketplace.
This patent represents a subset of our work on cohesive systems, methods, and devices for presenting and managing interrelated sets of skills for a person. We used a map interface to represent a set of interrelated skills to a user, and which allows the user an opportunity to strategize regarding how best the related and advanced skills may be acquired to advance on a career path.
The convergence of Tech Trends – in the past Mobility, Big Data, and Cloud (and today #DataScience, #ModernEngineering, #AI, #ML, and #Cloud) helps the creation of modern skills management systems. The solution at the heart of the patent is help address this and we deem to have wide applicability across industry domains, industry sectors, and vertical industry segments.
Since filing the patent, and awarding today – elements of this we have adopted at Avanade and rolled it out globally to our workforce across 20 countries allowing them to help manage complex skills, advance career and help establish a 3D career path.
What is MVP?
#MVP you ask? #EnoughSaid
Update on Tesla .ssq files
Sometime back, I noticed the car downloaded a large file (5.1 GB) which was a .ssq file. I hadn’t heard of a ssq file, and was curious on what this was.
I researched a little and as it turns out, a .ssq file is a compressed file system which is often used in an embedded Linux system, where storage size might be a area of concern. This file-system is called SquashFS, and is usually used on a read-only mode.
SquashFS is interesting, as it lets one mount the file-system directly and is distributed as a kernel source patch – which makes it easy to daisy chain and use it other regular Linux tools.
SquashFS tools are useful to mount and create a SquashFS file-system. As shown below, I can mount the downloaded file, using unsquashfs.
I think it is known that Tesla uses Valhalla for their maps and this file is the updated maps data. Valhalla, is a open source routing library which is using OpenStreetMap. Valhalla, also incorporates the traditional travelling salesman problem which is a non-deterministic polynomial problem.
When extracted and mounted, we see the following directory structure; each of these folders (and files therein) are in fact the tiles that make up the maps (next time in the car, when you zoom in or out or search of a non-cached location, notice carefully on how it is loading and you can just about make out the tiles – it is quick and easy to miss). And it is these tiles that is used for routing as part of the navigation.
Tiled based routing is supposed to be beneficial – it uses less memory (the graph can be decomposed much easier, with a smaller set of it loaded in memory), cahce-able, easier to manage (update-able), etc. We can see a glimpse on how the routing and calculation happen on a tile basis below.
When, extracted we see there are three levels of hierarchy (0, 1, and, 2). In the file-system these are shown as directories, but there is a method to the madness.
- Level 0 – these contain edges pertaining to roads that are considered highway / freeway / motorway roads. These are stored as 4 degree tiles.
- Level 1 – contains roads that are at a arterial level and are saved in 1 degree tiles.
- Level 2 – these are local roads and are saved as 0.25 degree tiles.
For example, the world at Level 0 would look like what we are seeing in the image below. And Pennsylvania can be seen below that; Level 0 colored in light blue, Level 1 in light green, and finally Level 2 in light red (which might not be obvious with the translucency).
So, to use this, one can use a few helper functions to get the exact tile to load and vice-versa. For example using the GPS coordinate of 41.413203, -73.623787 (which is just outside of Brewster, NY), loading Level 2 (via the get_title_2 function) would give us the structure of /2/000/756/425.gph using which we know which tile to load.
Helper function (in python) that help obtain levels, tile ids, tile lists, lat/long coordinates, etc. from an intersecting box.
valhalla_tiles = [{'level': 2, 'size': 0.25}, {'level': 1, 'size': 1.0}, {'level': 0, 'size': 4.0}]
LEVEL_BITS = 3
TILE_INDEX_BITS = 22
ID_INDEX_BITS = 21
LEVEL_MASK = (2**LEVEL_BITS) - 1
TILE_INDEX_MASK = (2**TILE_INDEX_BITS) - 1
ID_INDEX_MASK = (2**ID_INDEX_BITS) - 1
INVALID_ID = (ID_INDEX_MASK << (TILE_INDEX_BITS + LEVEL_BITS)) | (TILE_INDEX_MASK << LEVEL_BITS) | LEVEL_MASK
def get_tile_level(id):
return id & LEVEL_MASK
def get_tile_index(id):
return (id >> LEVEL_BITS) & TILE_INDEX_MASK
def get_index(id):
return (id >> (LEVEL_BITS + TILE_INDEX_BITS)) & ID_INDEX_MASK
def tiles_for_bounding_box(left, bottom, right, top):
#if this is crossing the anti meridian split it up and combine
if left > right:
east = tiles_for_bounding_box(left, bottom, 180.0, top)
west = tiles_for_bounding_box(-180.0, bottom, right, top)
return east + west
#move these so we can compute percentages
left += 180
right += 180
bottom += 90
top += 90
tiles = []
#for each size of tile
for tile_set in valhalla_tiles:
#for each column
for x in range(int(left/tile_set['size']), int(right/tile_set['size']) + 1):
#for each row
for y in range(int(bottom/tile_set['size']), int(top/tile_set['size']) + 1):
#give back the level and the tile index
tiles.append((tile_set['level'], int(y * (360.0/tile_set['size']) + x)))
return tiles
def get_tile_id(tile_level, lat, lon):
level = filter(lambda x: x['level'] == tile_level, valhalla_tiles)[0]
width = int(360 / level['size'])
return int((lat + 90) / level['size']) * width + int((lon + 180 ) / level['size'])
def get_ll(id):
tile_level = get_tile_level(id)
tile_index = get_tile_index(id)
level = filter(lambda x: x['level'] == tile_level, valhalla_tiles)[0]
width = int(360 / level['size'])
height = int(180 / level['size'])
return int(tile_index / width) * level['size'] - 90, (tile_index % width) * level['size'] - 180Tesla has actually open-sourced their implementation of Valhalla, which is based on C++. This still seems like an active project, but parts of the code haven’t been updated for a while.
Whilst I haven’t tried to set this up myself, it seems quite simple. Below are the instructions to get this going on Ubuntu or Debian (I think Mac is also supported, but needs a little different dependency set).
#below are the dependencies needed
sudo add-apt-repository -y ppa:valhalla-core/valhalla
sudo apt-get update
sudo apt-get install -y autoconf automake make libtool pkg-config g++ gcc jq lcov protobuf-compiler vim-common libboost-all-dev libboost-all-dev libcurl4-openssl-dev libprime-server0.6.3-dev libprotobuf-dev prime-server0.6.3-bin
#if you plan to compile with data building support, see below for more info
sudo apt-get install -y libgeos-dev libgeos++-dev liblua5.2-dev libspatialite-dev libsqlite3-dev lua5.2
if [[ $(grep -cF xenial /etc/lsb-release) > 0 ]]; then sudo apt-get install -y libsqlite3-mod-spatialite; fi
#if you plan to compile with python bindings, see below for more info
sudo apt-get install -y python-all-dev
#install with the following
git submodule update --init --recursive
./autogen.sh
./configure
make test -j$(nproc)
sudo make installThere you have it – we know now what the .ssq files are and how they are used. Just need more time to get it going and play with it – perhaps another project for another time. 🙂
Tesla and Spotify
Something seems to be up, with the car tickling an endpoint for connectivity perhaps? Its only 663 bytes up and 222 bytes down. This is still on v8.1 (36.2)
