Barış Deniz Sağlam bdsaglam

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

I supervise undergraduate/postgraduate/UROP projects as part of BICV. The PI, Dr Anil Bharath, has a nice set of FAQs for prospective research students, which should give you an idea of what our group specialises in. My topic of research is deep reinforcement learning, which is less focused on computer vision and more on general machine learning or even artificial intelligence. Note that I only supervise students at Imperial College London, so please do not contact me about supervision otherwise.

I expect students to be a) highly motivated and b) technically proficient.

a) Projects that I supervise revolve around cutting-edge research, and specifically deep learning. Projects can, and have in the past, relied on research released during the course of the project. Some parts of machine learning can be found in optional modules in bioengineering courses, but (modern) deep learning is currently not taught at Imperial (as far as I am aware). I usually give crash

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	import logging
	import sys
	from logging.handlers import TimedRotatingFileHandler

	FORMATTER = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
	LOG_FILE = "my_app.log"

	def get_console_handler():
	console_handler = logging.StreamHandler(sys.stdout)
	console_handler.setFormatter(FORMATTER)