diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..93133c6 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,26 @@ +name: Docs + +# Build the MkDocs site and publish it to the gh-pages branch (GitHub Pages). +# Deploys only from main (i.e. on a release merge) or when run manually — never +# from develop, so published docs always match a released state. +on: + push: + branches: [main] + workflow_dispatch: + +permissions: + contents: write + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Install uv + uses: astral-sh/setup-uv@v5 + - name: Sync docs deps + run: uv sync --group docs + - name: Build and deploy + run: uv run mkdocs gh-deploy --force diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 0000000..035435b --- /dev/null +++ b/docs/api.md @@ -0,0 +1,28 @@ +# API reference + +The public Python API — what you import in your Dask jobs. + +## Capturing the source map + +::: daskgenie.track + +::: daskgenie.watch + +::: daskgenie.get_layer_map + +## Local profiling + +::: daskgenie.LocalProfiler + options: + members: + - __init__ + +## Uploading to the collector + +::: daskgenie.create_run + +::: daskgenie.upload_graph + +## Distributed + +::: daskgenie.client.register diff --git a/docs/assets/logo.svg b/docs/assets/logo.svg new file mode 100644 index 0000000..9b8795f --- /dev/null +++ b/docs/assets/logo.svg @@ -0,0 +1,22 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 0000000..786b75d --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1 @@ +--8<-- "CHANGELOG.md" diff --git a/docs/configuration.md b/docs/configuration.md new file mode 100644 index 0000000..f53823d --- /dev/null +++ b/docs/configuration.md @@ -0,0 +1,35 @@ +# Configuration + +## Environment variables + +| Variable | Component | Purpose | +| --- | --- | --- | +| `DASKGENIE_DSN` | collector | Postgres/TimescaleDB DSN; selects the Timescale backend (default in Docker). | +| `DASKGENIE_DB` | collector | SQLite path (or `:memory:`) when no DSN is set. | +| `DASKGENIE_HOST` / `DASKGENIE_PORT` | collector | Bind address (default `127.0.0.1:8765`). | +| `COLLECTOR_URL` | dashboard | Where the Next.js server proxies `/api` (e.g. `http://collector:8765`). | +| `NEXT_PUBLIC_COLLECTOR_WS` | dashboard | WebSocket base the browser connects to (default `ws://:8765`). | + +## Profiler knobs + +`register()` and `LocalProfiler` accept: + +| Argument | Default | Effect | +| --- | --- | --- | +| `deep` | `False` | Enable the memray deep-memory engine. | +| `sample_interval` | `0.2` | Seconds between RSS/status samples. | +| `flush_interval` | `0.5` | Seconds between pushes to the collector. Keep it well under how fast a worker OOMs so the killer chunk's metadata is sent before the process dies. | +| `deep_epoch_seconds` | `5.0` | memray rotation window; smaller = finer time resolution, more overhead. | + +## Storage backend + +The collector defaults to **TimescaleDB** in Docker (set by `DASKGENIE_DSN` in +`docker-compose.yml`); without a DSN it uses the self-contained SQLite store — +which is also what the test suite runs on. Point it at any Postgres/Timescale +instance with `DASKGENIE_DSN=postgresql://user:pass@host:5432/db`. + +## Prometheus + +The collector exposes `/metrics` with per-worker RSS and managed-memory gauges +plus sample/death counters, so existing Grafana setups get value without the +custom UI. diff --git a/docs/contributing.md b/docs/contributing.md new file mode 100644 index 0000000..ea38c9b --- /dev/null +++ b/docs/contributing.md @@ -0,0 +1 @@ +--8<-- "CONTRIBUTING.md" diff --git a/docs/dashboard.md b/docs/dashboard.md new file mode 100644 index 0000000..f2c6f09 --- /dev/null +++ b/docs/dashboard.md @@ -0,0 +1,44 @@ +# The dashboard + +The dashboard lists every run (with the machine that opened it) and updates live +over a WebSocket while a job runs. Open a run for these tabs: + +## Overview +Live stats (workers, tasks, deaths, peak RSS), memory-over-time, and the hottest +allocation line. + +## Timeline +A large, zoomable memory-over-time chart. **Click any point** to pin that instant +and see what was running (with source lines) and which lines were allocating in +that window. Below it, a stacked **per-layer allocation timeline**. + +Navigation on every chart: scroll to zoom the time axis, drag to pan, shift-drag +to box-zoom. + +## Workers +A live, native-Dask-style table: per-worker RSS against the memory limit, managed +memory, CPU, threads, and executing/ready counts. + +## Task stream +Task rectangles coloured by layer on a zoomable time axis — an **ALL TASKS** +global lane (all workers packed together) on top and **PER WORKER** lanes below. + +## Graph +The real connected task DAG. Small graphs use an interactive view; large ones +render the whole graph on a pan/zoom canvas (no collapsing to a blob). Hover a +node to light up its edges; click for its source line and chunk sizes. + +## Memory +The deep view (needs `deep=True`): an allocation **flamegraph** (per-worker, +click to zoom, framework frames dimmed so your code stands out), a **peak bytes +by source line** table, and **peak memory by task**. + +## Post-mortem +Each worker death: the allocation lines at the high-water mark, the suspect task, +its source line (syntax highlighted), and the chunk it was holding +(`(4000, 4000) float64 = 128 MB`). + +!!! info "OOM is a heuristic" + The scheduler doesn't tell a plugin *why* a worker left. DaskGenie flags a + *suspected* OOM only when tasks were in flight at an unexpected removal, and + never over-claims. diff --git a/docs/deep-memory.md b/docs/deep-memory.md new file mode 100644 index 0000000..f142de8 --- /dev/null +++ b/docs/deep-memory.md @@ -0,0 +1,50 @@ +# Deep memory (memray) + +Pass `deep=True` to profile *inside* the memory. On each worker DaskGenie drives +[memray](https://github.com/bloomberg/memray) **as a library** — a short capture +rotated every few seconds, read back and folded to the first line of *your* code +responsible for each high-water-mark allocation. You never see or handle a +capture file; only the aggregates are pushed. + +```python +# distributed +run_id = genie.register(client, "http://localhost:8765", deep=True) + +# local schedulers +with dg.LocalProfiler(url, source_map=smap, collection=result, deep=True) as prof: + result.compute(scheduler="threads") +``` + +Needs the `deep` extra (`pip install 'daskgenie[deep]'`). Deep mode costs roughly +1.5–2× runtime, so it is opt-in per run. + +## What you get + +- **Peak bytes by source line** — `job.py:42 build = 12.8 GB`. +- **A per-worker flamegraph** — the full call stack (root → leaf) for each + allocation, aggregated into an icicle you can zoom into; framework frames are + dimmed so your code stands out. This is the memray "tree" read, on your code. +- **Per-layer allocations over time** and **per-task peak memory**. +- On a worker death, the allocation lines at the **high-water mark** in the + post-mortem. + +## How it works + +The engine runs a background thread of **epochs**: a memray `Tracker` captures to +a throwaway temp file for a few seconds, then stops, is read back with +`FileReader`, folded to the high-water-mark bytes per user source line (and full +call stacks for the flamegraph), and the file is deleted before the next epoch +starts. Rotation keeps each capture tiny and makes attribution time-resolved. +Everything is guarded — a memray failure degrades to Tier-1 sampling and never +crashes or OOMs the job. + +## Limitations + +- **memray** is Linux/macOS + CPython only; a single tracker runs per process. +- Source-line attribution is strongest for `dask.array` and `dask.delayed`. For + `dask.dataframe` (dask-expr) and xarray the heavy work runs inside library/C + code, so per-line allocations fold to framework frames — the flamegraph still + shows the full tree. +- On a **hard OOM kill** the in-flight epoch can die before it flushes, so a + specific post-mortem's line attribution is best-effort. The **Memory** tab + remains the reliable place to see the culprit line. diff --git a/docs/examples.md b/docs/examples.md new file mode 100644 index 0000000..92583e3 --- /dev/null +++ b/docs/examples.md @@ -0,0 +1,37 @@ +# Examples + +Runnable scripts covering the breadth of Dask live in the +[`examples/`](https://github.com/polymood/DaskGenie/tree/develop/examples) +directory. + +| Script | Shows | +| --- | --- | +| `graph_source_map.py` | Source attribution only — layer → the line that built it. | +| `local_scheduler.py` | Memory + chunks + graph for a local scheduler. | +| `distributed_oom.py` | A real worker OOM and its post-mortem on `dask.distributed`. | +| `deep_oom.py` | Deep memory (memray): the source line that allocated the OOM array. | +| `big_pipeline_oom.py` | A minutes-long multi-stage pipeline, a large task graph, a real OOM. | +| `threaded_big.py` | Threaded scheduler hoarding memory instead of spilling to Zarr. | +| `threaded_crash.py` | Threaded scheduler that actually crashes (self-limiting `MemoryError`). | +| `dask_delayed.py` | `dask.delayed` custom ETL DAG on a distributed cluster. | +| `dask_dataframe.py` | `dask.dataframe` groupby + shuffle. | +| `dask_bag.py` | `dask.bag` aggregation on the processes scheduler. | +| `xarray_zarr.py` | xarray on Zarr — a chunked cube → anomaly. | +| `xarray_netcdf.py` | xarray on NetCDF (HDF5) — rolling detrend. | + +## Running them + +Start the stack once, then run an example and open the dashboard: + +```bash +docker compose up -d --build + +uv run --extra demo --extra deep python examples/deep_oom.py +uv run --extra examples --extra deep python examples/xarray_zarr.py +``` + +!!! note "Attribution across collections" + Source-line attribution is strongest for `dask.array` and `dask.delayed`. For + `dask.dataframe` (dask-expr) and xarray the heavy work runs inside library C + code, so allocations fold to framework frames — the flamegraph still shows + the full call tree, and the graph / memory views work throughout. diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..66180d2 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,40 @@ +# DaskGenie + +A memory profiler and live dashboard for Dask that ties a worker's memory back to +the **line of your code** that caused it. + +DaskGenie fuses [memray](https://github.com/bloomberg/memray)-deep allocation +tracing with Dask's task graph and streams it to a real-time dashboard. When a +worker dies from an out-of-memory error, you can see the suspect task, the chunk +it was holding, and the exact source line that allocated the array that killed +it — not just "a worker disappeared". It works on `dask.distributed` and on the +local (threaded / processes / synchronous) schedulers, across `dask.array`, +`dask.dataframe`, `dask.bag`, `dask.delayed`, and xarray on Zarr/NetCDF. + +## Features + +- **Source attribution** — every task-graph layer mapped back to the user source + line that built it, never into `dask`/`numpy`/`xarray` internals. +- **Deep memory (memray, as a library)** — per-line high-water-mark attribution + and a per-worker flamegraph / memray-style tree. Opt-in `deep=True`. +- **Worker-death post-mortem** — which chunk, and which line, killed the worker. +- **Real-time dashboard** — a Next.js app streaming over WebSocket: live workers, + a zoomable task stream, the whole task-graph DAG, memory-over-time with a + click-to-inspect spike explorer, per-layer allocations over time, and the + flamegraph. +- **Any scheduler** — distributed plugins or the local-scheduler `LocalProfiler`. +- **Team-friendly** — runs record the machine (hostname + IP) that opened them. +- **Prometheus + TimescaleDB** — `/metrics` for Grafana, TimescaleDB or SQLite. + +## Where to next + +
+ +- :material-download: **[Installation](installation.md)** — `pip install daskgenie` and the extras. +- :material-rocket-launch: **[Quick start](quickstart.md)** — bring up the stack and profile a job. +- :material-memory: **[Deep memory](deep-memory.md)** — the memray engine and flamegraph. +- :material-view-dashboard: **[The dashboard](dashboard.md)** — every tab explained. +- :material-cog: **[Configuration](configuration.md)** — environment variables and knobs. +- :material-code-braces: **[API reference](api.md)** — the public Python API. + +
diff --git a/docs/installation.md b/docs/installation.md new file mode 100644 index 0000000..55380da --- /dev/null +++ b/docs/installation.md @@ -0,0 +1,37 @@ +# Installation + +```bash +pip install daskgenie +``` + +DaskGenie requires **Python 3.11 or newer**. + +## Optional extras + +```bash +pip install 'daskgenie[deep]' # memray-backed deep memory profiling +pip install 'daskgenie[collector]' # the FastAPI collector service (server side) +pip install 'daskgenie[examples]' # xarray, zarr, pandas, ... for the examples +``` + +| Extra | Pulls in | For | +| --- | --- | --- | +| `deep` | `memray` | Line-level allocation tracing and the flamegraph. | +| `collector` | `fastapi`, `uvicorn`, `psycopg`, `websockets`, ... | Running the collector service yourself (usually you run it via Docker instead). | +| `examples` | `xarray`, `zarr`, `pandas`, `pyarrow`, `h5netcdf`, ... | The runnable example scripts. | +| `demo` | `distributed`, `numpy` | The minimal distributed demos. | + +!!! note "memray platform support" + Deep memory profiling needs memray, which is **Linux/macOS + CPython** only. + Where memray can't be imported, the profiler silently degrades to lightweight + RSS / managed-memory sampling — you still get memory-over-time and per-chunk + metadata, just not per-line attribution. + +## The dashboard stack + +The collector, TimescaleDB, and the web dashboard run as containers — you don't +pip-install them. See [Quick start](quickstart.md): + +```bash +docker compose up -d --build +``` diff --git a/docs/local-schedulers.md b/docs/local-schedulers.md new file mode 100644 index 0000000..d47dfba --- /dev/null +++ b/docs/local-schedulers.md @@ -0,0 +1,33 @@ +# Local schedulers + +The distributed worker/scheduler plugins only exist on `dask.distributed`. For +the local schedulers (`.compute(scheduler="threads"|"processes"|"synchronous")`, +the default for bare dask collections) use `LocalProfiler`, which hooks Dask's +callback API instead of installing cluster plugins. + +```python +import daskgenie as dg + +with dg.track() as source_map: + result = build_pipeline() + +with dg.LocalProfiler( + "http://localhost:8765", + run_name="threaded job", + source_map=source_map, + collection=result, + deep=True, +) as prof: + result.compute(scheduler="threads") + +# prof.run_id shows up in the dashboard like any other run +``` + +`LocalProfiler` samples process RSS over time, tags each sample with the tasks +running at that instant, records the shape/dtype/nbytes of each task's output +chunk, and — with `deep=True` — runs the same memray engine as the distributed +path. There is no worker-death attribution here: a local scheduler has no workers +to lose, so an OOM simply kills the process (the memory-over-time curve up to that +point is still captured, flushed once per second). + +See [`examples/xarray_zarr.py`](examples.md) and the other threaded examples. diff --git a/docs/quickstart.md b/docs/quickstart.md new file mode 100644 index 0000000..bde8900 --- /dev/null +++ b/docs/quickstart.md @@ -0,0 +1,55 @@ +# Quick start + +## 1. Bring up the dashboard stack + +The **collector** (API + `/metrics` + WebSocket), **TimescaleDB**, and the +**Next.js dashboard** run with Docker: + +```bash +docker compose up -d --build +# dashboard → http://localhost:3000 collector API → http://localhost:8765 +``` + +## 2. Point a job at the collector + +Each `register()` opens a **run** that appears live on the dashboard: + +```python +from distributed import Client, LocalCluster +import daskgenie as dg +import daskgenie.client as genie + +client = Client(LocalCluster(processes=True)) +run_id = genie.register(client, "http://localhost:8765", run_name="nightly ETL", deep=True) + +with dg.track() as source_map: + result = build_pipeline() # your dask work +genie.upload_graph("http://localhost:8765", run_id, source_map, collection=result) + +result.compute() +``` + +- `register()` installs the worker + scheduler plugins and returns the `run_id`. +- `dg.track()` captures the map from graph layers to the source lines that built + them. +- `upload_graph(..., collection=result)` sends the source map **and** the concrete + task graph so the dashboard can draw it. +- `deep=True` turns on the memray engine (needs the `deep` extra). + +Open [http://localhost:3000](http://localhost:3000), pick the run, and explore it +as it runs. See [The dashboard](dashboard.md) for every tab. + +## Local schedulers + +Not on `dask.distributed`? Use [`LocalProfiler`](local-schedulers.md) for the +threaded / processes / synchronous schedulers. + +## Seeing a real OOM + +```bash +docker compose up -d --build +uv run --extra demo --extra deep python examples/deep_oom.py +``` + +Then open the run's **Memory** and **Post-mortem** tabs — DaskGenie names the +source line that allocated the array that killed the worker. diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..1be5470 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,74 @@ +site_name: DaskGenie +site_description: A memory profiler and live dashboard for Dask that ties a worker's memory back to the line of your code that caused it. +site_url: https://polymood.github.io/DaskGenie/ +repo_url: https://github.com/polymood/DaskGenie +repo_name: polymood/DaskGenie +edit_uri: edit/develop/docs/ + +theme: + name: material + logo: assets/logo.svg + favicon: assets/logo.svg + icon: + repo: fontawesome/brands/github + palette: + - media: "(prefers-color-scheme: light)" + scheme: default + primary: deep orange + accent: deep orange + toggle: + icon: material/weather-night + name: Switch to dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: deep orange + accent: deep orange + toggle: + icon: material/weather-sunny + name: Switch to light mode + features: + - navigation.sections + - navigation.top + - navigation.tracking + - content.code.copy + - toc.follow + +markdown_extensions: + - admonition + - attr_list + - md_in_html + - tables + - toc: + permalink: true + - pymdownx.highlight: + anchor_linenums: true + - pymdownx.superfences + - pymdownx.inlinehilite + - pymdownx.snippets: + base_path: ["."] + check_paths: true + +plugins: + - search + - mkdocstrings: + handlers: + python: + paths: [src] + options: + show_source: false + docstring_style: google + separate_signature: true + show_root_heading: true + +nav: + - Home: index.md + - Installation: installation.md + - Quick start: quickstart.md + - The dashboard: dashboard.md + - Deep memory: deep-memory.md + - Local schedulers: local-schedulers.md + - Configuration: configuration.md + - Examples: examples.md + - API reference: api.md + - Contributing: contributing.md + - Changelog: changelog.md diff --git a/pyproject.toml b/pyproject.toml index 2483929..d099ae8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,6 +76,10 @@ dev = [ "memray>=1.11", "jinja2>=3.1", ] +docs = [ + "mkdocs-material>=9.5", + "mkdocstrings[python]>=0.26", +] [build-system] requires = ["hatchling"] diff --git a/uv.lock b/uv.lock index 002a943..e1680a6 100644 --- a/uv.lock +++ b/uv.lock @@ -88,6 +88,28 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/66/40/c53deb2cd0c9b0fb636d24d9f40924cf2e65028e6b20b10cd5c1eeb2c730/ast_serialize-0.6.0-cp39-abi3-win_arm64.whl", hash = "sha256:ccd132fe8db56f61fe743b1f644d01b8d65b83248a8da506f3132bda86d6ed5e", size = 1072965, upload-time = "2026-06-30T20:02:54.097Z" }, ] +[[package]] +name = "babel" +version = "2.18.0" +source = { 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