14 docs tagged with "systems-programming"

How build systems and CI/CD pipelines keep ML projects reproducible, tested, and safely deployable - covering Make, Bazel, DVC, MLflow, GitHub Actions, and canary deployments.

Learn why C and C++ form the foundation of every major ML framework, and how to read, write, and debug C++ code as an ML systems engineer.

Master mutexes, condition variables, atomics, lock-free programming, and thread pools - the concurrency building blocks behind every high-throughput ML data pipeline and inference server.

File descriptors, the VFS layer, O_flags, select/poll/epoll, io module internals, and zero-copy techniques.

IaC for ML infrastructure - Terraform GPU clusters on AWS/GCP/Azure, Helm charts for model serving, Pulumi Python IaC, Ansible for GPU node setup, GitOps with ArgoCD, spot instance handling, and infrastructure cost optimization.

OS primitives, sockets, file descriptors, shared memory, IPC, and writing C extensions - the full systems programming toolkit for Python engineers.

C++ basics for ML engineers, Python C extensions, Cython, Pybind11, and writing custom PyTorch operators - bridging the gap between Python ML code and high-performance native implementations.

Observability for ML systems - structured logging with structlog, distributed tracing with OpenTelemetry, Prometheus metrics for inference servers, Grafana dashboards, ML-specific alerting, and production profiling.

Processes, signals, environment, users, time, and the os/signal/resource modules - Python's interface to the POSIX operating system.

Master serialization formats for ML systems - Protocol Buffers, Apache Arrow, safetensors, Parquet, HDF5, MessagePack, and pickle - with performance benchmarks, security considerations, and schema evolution strategies.

POSIX shared memory, pipes, FIFOs, message queues, semaphores, and multiprocessing.shared_memory - Python inter-process communication.

Bash scripting for ML engineers - automating training launches, multi-node coordination, GPU monitoring, checkpoint management, parallel data downloads, and writing robust production-grade shell scripts.

BSD socket API in Python, TCP/UDP from scratch, non-blocking sockets, Unix domain sockets, and building a minimal HTTP server.

Learn how Linux system calls underpin every ML workload - from dataset loading with mmap to epoll-based inference servers, seccomp sandboxing, and io_uring async I/O.