Accessing Red Hat OpenShift Streams for Apache Kafka from Python

Recently Red Hat launched a way how to get managed Kafka instance and you can get one for 2 days for free. There is a limit for 1 MB per second. So far I was only using Kafka without any auth and without any encription, so here is what I had to do to make it work - typing here so I do not need to reinvent once I forgot it :-) I'm using python-kafka.

I have created a cluster and under it's "Connection" menu item I got bootstrap server jhutar--c-jc--gksg-rukm-fu-a.bf2.kafka-stage.rhcloud.com:443. It also advised me to create a service account, so I created one and it generated "Client ID" like srvc-acct-00000000-0000-0000-0000-000000000000 and "Client secret" like 00000000-0000-0000-0000-000000000000. Although "SASL/OAUTHBEARER" authentication method is recommended, as of now it is too complicated for my poor head, so I used "SASL/PLAIN" where you just use "Client ID" as username and "Client secret" as password. To create a topic, there is UI as well

To create producer and consumer:

producer = KafkaProducer(
    bootstrap_servers='jhutar--c-jc--gksg-rukm-fu-a.bf2.kafka-stage.rhcloud.com:443',
    sasl_plain_username='srvc-acct-00000000-0000-0000-0000-000000000000',
    sasl_plain_password='00000000-0000-0000-0000-000000000000',
    security_protocol='SASL_SSL',
    sasl_mechanism='PLAIN',
)

And consumer needs same parameters:

consumer = KafkaConsumer(
    '<topic>',
    bootstrap_servers='jhutar--c-jc--gksg-rukm-fu-a.bf2.kafka-stage.rhcloud.com:443',
    sasl_plain_username='srvc-acct-00000000-0000-0000-0000-000000000000',
    sasl_plain_password='00000000-0000-0000-0000-000000000000',
    security_protocol='SASL_SSL',
    sasl_mechanism='PLAIN',
)

Different Numpy results on different systems

Recently mine wife in her compute intensive project noticed strange issue when same input date and same code produced different output on different hosts in the cloud she is using. She tracked it down to this simple python & numpy test case:

#!/usr/bin/env python

import numpy

a = [[0.67115835, -0.74131401], [0.74131401, 0.67115835]]
b = [[-4.95494273, -1.77170756, ...], [1.87737564, 4.99951546, ...]]
c = numpy.matmul(a, b)
print(c)

On one host it was returning (correct result):

[[-4.71727605 -4.89530718 -4.71727605 -4.89530718 -4.71727605 -4.89530718
...

On different host it was returning (wrong result):

[[ 0.34761728  0.12429531  0.34761728  0.12429531 -0.13170853 -0.35074431
...

We have been googling a bit and found some tips:

• Same Python code, same data, different results on different machines: There is a suggestion to use export MKL_CBWR=AUTO - this did not helped
• Different results on different computers when using Scipy stack compiled with MKL: Some more tips are here: export MKL_CBWR=AVX; export OMP_NUM_THREADS=1 or with export MKL_CBWR=SSE4_2 - this did not helped again (anyway, our Numpy does not seem to be build with this Intel Math Kernel Library - see ldd output below)
• How can I make sure svd having same result on different machine: Here this is mentioned: export NUMEXPR_NUM_THREADS=1; export OPENBLAS_NUM_THREADS=1 - no luck, but finally: export OPENBLAS_CORETYPE=prescott - bingo!

According to OpenBLAS ussage instructions (OpenBLAS is "an optimized BLAS (Basic Linear Algebra Subprograms) library" if you have same knowleadge about it as I do), OPENBLAS_CORETYPE is environment variable which control the kernel selection. Looking at Prescott CPU description, it was launched in 2000, so is probably a safe default. Some more details about our setup:

Numpy in our setup is linked with these libraries:

ldd $( rpm -ql python3-numpy | grep '\.so$' ) | grep -v '\.so:$' | sed 's/([0-9a-zx]\+)/(...)/' | sort -u
	/lib64/ld-linux-x86-64.so.2 (...)
	libc.so.6 => /lib64/libc.so.6 (...)
	libdl.so.2 => /lib64/libdl.so.2 (...)
	libgcc_s.so.1 => /lib64/libgcc_s.so.1 (...)
	libgfortran.so.5 => /lib64/libgfortran.so.5 (...)
	libm.so.6 => /lib64/libm.so.6 (...)
	libopenblasp.so.0 => /lib64/libopenblasp.so.0 (...)
	libpthread.so.0 => /lib64/libpthread.so.0 (...)
	libpython3.7m.so.1.0 => /lib64/libpython3.7m.so.1.0 (...)
	libquadmath.so.0 => /lib64/libquadmath.so.0 (...)
	libutil.so.1 => /lib64/libutil.so.1 (...)
	linux-vdso.so.1 (...)

The code is packaged in Singularity containers and is running on Metacentrum cloud. Two machines we have hit were - the one with correct result:

Singularity> tail -n 28 /proc/cpuinfo

processor       : 15
vendor_id       : GenuineIntel
cpu family      : 6
model           : 58
model name      : Intel Xeon E3-12xx v2 (Ivy Bridge)
stepping        : 9
microcode       : 0x1
cpu MHz         : 2199.998
cache size      : 16384 KB
physical id     : 15
siblings        : 1
core id         : 0
cpu cores       : 1
apicid          : 15
initial apicid  : 15
fpu             : yes
fpu_exception   : yes
cpuid level     : 13
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx rdtscp lm constant_tsc rep_good nopl xtopology pni pclmulqdq ssse3 cx16 sse4_1 sse4_2 x2apic popcnt
+tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm kaiser fsgsbase smep erms xsaveopt arat
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf
bogomips        : 4399.99
clflush size    : 64
cache_alignment : 64
address sizes   : 40 bits physical, 48 bits virtual
power management:

Singularity> uname -a
Linux [hostname] 4.9.0-8-amd64 #1 SMP Debian 4.9.110-3+deb9u4 (2018-08-21) x86_64 x86_64 x86_64 GNU/Linux

The other host - the one with wrong results:

Singularity> tail -n 28 /proc/cpuinfo

processor       : 63
vendor_id       : GenuineIntel
cpu family      : 6
model           : 85
model name      : Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz
stepping        : 4
microcode       : 0x200004d
cpu MHz         : 2399.392
cache size      : 22528 KB
physical id     : 1
siblings        : 32
core id         : 15
cpu cores       : 16
apicid          : 63
initial apicid  : 63
fpu             : yes
fpu_exception   : yes
cpuid level     : 22
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology
+nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault
+epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt
+clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke flush_l1d
bugs            : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa itlb_multihit
bogomips        : 4201.71
clflush size    : 64
cache_alignment : 64
address sizes   : 46 bits physical, 48 bits virtual
power management:

Singularity> uname -a
Linux [hostname] 4.19.0-9-amd64 #1 SMP Debian 4.19.118-2 (2020-04-29) x86_64 x86_64 x86_64 GNU/Linux

Packages in the container are:

• python3-3.7.4-1.fc30.x86_64
• python3-numpy-1.16.4-2.fc30.x86_64

If you want to try, full test case is here:

import numpy

a = [[0.67115835,-0.74131401],[0.74131401,0.67115835]]
b = [[-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,-4.95494273,-1.77170756,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,0.64695557,-3.83073022,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,-1.91809893,2.14768601,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.99713467,-0.2208969,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936,3.96850733,4.57202936],[1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,1.87737564,4.99951546,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,3.8703295,-0.52141675,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,-2.14706037,1.84069058,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,0.15277681,-3.98429871,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674,-2.93121093,-5.91274674]]
c = numpy.matmul(a,b)
print(c)

Quick Python performance tuning cheat-sheet

Just a few commands without any context:

Profiling with cProfile

This helped me to find slowest functions, because when optimizing, I need to focus on these (best ration of work needed vs. benefits). This helped me to find function which did some unnecessary calsulations over and over again:

$ python -m cProfile -o cProfile-first_try.out ./layout-generate.py ...
$ python -m pstats cProfile-first_try.out 
Welcome to the profile statistics browser.
cProfile-first_try.out% sort
Valid sort keys (unique prefixes are accepted):
cumulative -- cumulative time
module -- file name
ncalls -- call count
pcalls -- primitive call count
file -- file name
line -- line number
name -- function name
calls -- call count
stdname -- standard name
nfl -- name/file/line
filename -- file name
cumtime -- cumulative time
time -- internal time
tottime -- internal time
cProfile-first_try.out% sort tottime
cProfile-first_try.out% stats 10
Sat Aug 12 23:19:40 2017    cProfile-first_try.out

         18508294 function calls (18501563 primitive calls) in 8.369 seconds

   Ordered by: internal time
   List reduced from 2447 to 10 due to restriction <10>

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
    27837    4.230    0.000    5.015    0.000 ./utils_matrix2layout.py:14(get_distance_matrix_2d)
    10002    1.356    0.000    1.513    0.000 ./utils_matrix2layout.py:244(get_measured_error_2d)
  5674796    0.572    0.000    0.572    0.000 /usr/lib64/python2.7/collections.py:90(__iter__)
  5340664    0.219    0.000    0.219    0.000 {math.sqrt}
  5432768    0.189    0.000    0.189    0.000 {abs}
   230401    0.183    0.000    0.183    0.000 /usr/lib64/python2.7/collections.py:71(__setitem__)
        1    0.178    0.178    0.282    0.282 ./utils_matrix2layout.py:543(count_angles_layout)
    10018    0.119    0.000    0.345    0.000 /usr/lib64/python2.7/_abcoll.py:548(update)
        1    0.102    0.102    6.749    6.749 ./utils_matrix2layout.py:393(iterate_evolution)
     1142    0.092    0.000    0.111    0.000 /usr/lib64/python2.7/site-packages/numpy/linalg/linalg.py:1299(svd)

To explain the columns, Instant User’s Manual says:

tottime

for the total time spent in the given function (and excluding time made in calls to sub-functions)

cumtime

is the cumulative time spent in this and all subfunctions (from invocation till exit). This figure is accurate even for recursive functions.

Lets compile to C with Cython

Simply performing this on a module which does most of the work gave me about 20% speedup:

# dnf install python2-Cython
$ cython utils_matrix2layout.py
$ gcc `python2-config --cflags --ldflags` -shared utils_matrix2layout.c -o utils_matrix2layout.so

There is much more to do to optimize it, but that would need additional work, so not now :-) Some helpful links:

• use python2-config to get compile and linking options
• when you want to create *.so instead of executable, you need to use -shared
• bloq with summary and nice FAQ

Total newbie guide for MicroPython on ESP8266

OK, disclaimer first. I know completely nothing about microelectronics. With that in mind:

Mine goal is to create cheep battery powered thermometer which would be reporting temperature in fixed intervals using http via mine home router WiFi network. This was occupying mine mind from this article (in czech only, executive summary: ESP8266 is old, new and better ESP32 is on the way). Till reading that article I did not knew there is cheep SoC which can connect to WiFi and you can read from external sensors attached to it (i.e. thermometer). There is lots of guides for this exact thing.

Purchasing ESP8266 ESP-01 and USB programmer

On E-Bay I have bought 3 ESP8266 Serial WIFI Wireless Transceiver Module Send Receive LWIP AP+STA for $9.44 (I have no idea on what all these letters in the name mean, but you want ESP8266 version labelled as ESP-01). And because I'm scared of wiring anything myself, I have bought ESP01 Programmer Adapter UART GPIO0 ESP-01 Adaptateur ESP8266 USB nb (to connect ESP8266 to computer via serial port emulated via USB; you do not need to install any drivers on Fedora 24). It took less than 20 days to receive all the items.

Lets use Python

I like Python, so was very surprised that there is a way how to program in it on ESP8266: MicroPython - a lean and efficient Python implementation for microcontrollers and constrained systems - especially MicroPython port for the WiFi modules based on Espressif ESP8266 chip.

To be able to build MicroPython, we need to build esp-open-sdk first.

Building toolchain: esp-open-sdk

esp-open-sdk if SDK for software development with the Espressif ESP8266 chips and at the end, it had 3.8GB on the disk and it contains some non-open-source binary blobs and I do not understand majority of the README, so I have decided to work with that as different user on my Fedora 24 system:

# dnf install autoconf gcc gcc-c++ gperf bison flex texinfo patch libtool ncurses-devel expat-devel pyserial help2man
# useradd esp
# sudo -u esp -i
$ git clone https://github.com/pfalcon/esp-open-sdk.git
$ cd esp-open-sdk/
$ make

I have taken this partly from Starting IoT development in Fedora (ESP8266), from Building and Running MicroPython on the ESP8266 (they are building in virtual machine here) and from esp8266/README.md in MicroPython git linked above.

Building MicroPython ESP8266 port

Now here we build binary image we will later upload to the chip.

$ git clone https://github.com/micropython/micropython.git
$ cd micropython/
$ git submodule update --init
$ make -C mpy-cross
$ export PATH=/home/esp/esp-open-sdk/xtensa-lx106-elf/bin:$PATH
$ cd esp8266/
$ make axtls
$ make

Flashing (uploading) MicroPython image to ESP8266

First try to talk to original firmware

Plug ESP8266 board into the UART-to-USB converter firmly and put it into USB port. Now you will see bunch of messages in journal (tail it with # journalctl -f). We need device name (that differs based on which USB port you will use). Now you can connect to serial console:

<date> <hostname> kernel: usb 3-1: new full-speed USB device number 6 using xhci_hcd
<date> <hostname> kernel: usb 3-1: New USB device found, idVendor=1a86, idProduct=7523
<date> <hostname> kernel: usb 3-1: New USB device strings: Mfr=0, Product=2, SerialNumber=0
<date> <hostname> kernel: usb 3-1: Product: USB2.0-Serial
<date> <hostname> kernel: ch341 3-1:1.0: ch341-uart converter detected
<date> <hostname> kernel: usb 3-1: ch341-uart converter now attached to ttyUSB0
<date> <hostname> mtp-probe[10120]: checking bus 3, device 6: "/sys/devices/pci0000:00/0000:00:14.0/usb3/3-1"
<date> <hostname> mtp-probe[10120]: bus: 3, device: 6 was not an MTP device
<date> <hostname> systemd-udevd[10119]: Process '/usr/bin/setfacl -m g:lirc:rw ' failed with exit code 2.

$ screen /dev/ttyUSB0 115200

This will fail because your "esp" user does not have write permissions to /dev/ttyUSB0. What I did was I have added "esp" user to "dialout" group with # usermod -a -G dialout esp, logged out, in again and verified that I have the group with $ groups command. Once done, start screen again.

You can try some AT commands - e.g. to switch to "station" mode and then listing available "access points" (your home WiFi network should be amongst them) as noted in Getting Started with ESP8266 article. Note that you need to press "Ctrl+M" (i.e. carriage return, "Enter" worked for me as well) and "Ctrl+J" (i.e. linefeed) to submit each command.

AT+GMR
AT version:0.25.0.0(Jun  5 2015 16:27:16)
SDK version:1.1.1
Ai-Thinker Technology Co. Ltd.
Jun  5 2015 23:07:20

OK
AT+CWMODE=3

OK
AT+CWLAP
+CWLAP:(3,"Internet_80",-82,"5c:f4:ab:02:da:12",1)
+CWLAP:(3,"Stonehenge",-81,"48:5b:39:38:56:56",6)
+CWLAP:(3,"krakonos",-67,"10:c3:7b:d6:b8:34",10)

OK

Once done, to terminate screen, use "Ctrl+a \".

To get some detail on current serial port setup (mostly baud rate is important), use $ stty < /dev/ttyUSB0.

Upload new firmware

First you need to start into flashing mode - to do that you need to wire GPIO 0 to GND before pushing into USB port. If your USB-to-UART converter do not have any switch or so, you need to be creative. For me, one small wire squeezed between these two pins made the trick - see the photo :-)

No erase current flash content and upload yours (took me few tries to actually upload correct file, so make sure you are uploading build/firmware-combined.bin). It all takes below minute:

$ esptool.py -p /dev/ttyUSB0 -b 115200 erase_flash
$ esptool -p /dev/ttyUSB0 --baud 115200 write_flash --flash_size=8m --verify 0 build/firmware-combined.bin

So I have Python on that tiny chip now?

Remove whatever you have used to start in flashing mode and plug it in again to start in normal mode. Again, use screen to connect. Press "Enter" and say wow, familiar >>> is here!

>>> print("Hello world")
Hello world
>>> print(sys.version)
3.4.0
>>> print(sys.implementation)
(name='micropython', version=(1, 8, 4))
>>> print(sys.platform)
esp8266

Next step would be to learn how to access wifi in MicroPython.