Errors, Diagnostics & Troubleshooting — dask_setup Recipe

This notebook demonstrates the most common failure modes when using dask_setup and shows concrete solutions for each:

Memory limit exceeded (Task needs > memory_limit)
OOM crashes and memory spilling tuning
Workers failing to start
Bad chunk sizes (too small → overhead, too large → OOM)
Dashboard access on HPC
Resource detection failures
Diagnosing slowdowns with the Dask dashboard and cluster_report()

Setup

import os
import time
from contextlib import contextmanager

import dask.array as da
import numpy as np
import xarray as xr

from dask_setup import setup_dask_client
from dask_setup.config import DaskSetupConfig


@contextmanager
def demo_cluster(label="", **kwargs):
    """Context manager that creates and cleans up a cluster."""
    defaults = dict(fallback_on_detection_failure=True, dashboard=False)
    defaults.update(kwargs)
    client, cluster, tmp = setup_dask_client(**defaults)
    print(f"[{label}] {len(client.nthreads())} workers, tmp={tmp}")
    try:
        yield client, cluster, tmp
    finally:
        client.close()
        cluster.close()

print("Imports OK")

Imports OK

1. Memory Limit Exceeded

Symptom: distributed.worker - WARNING - Memory use is high ... Task needs > memory_limit

This happens when a single task is larger than the per-worker memory allocation.

Solutions:

Reduce chunk sizes
Reduce the number of workers (more memory per worker)
Lower reserve_mem_gb

# PROBLEM: many small workers → each has very little memory
# Fix: fewer workers so each gets more memory budget

with demo_cluster("memory-fix", workload_type="cpu", max_workers=2, reserve_mem_gb=2.0) as (client, _, _):
    # Moderate-size array — will fit comfortably with 2 workers
    x = da.random.random((3000, 3000), chunks=(500, 500))
    result = x.mean().compute()
    print(f"Result: {result:.6f}")
    print("Solution: use fewer workers and/or smaller chunks")

[setup_dask_client] Configuration summary
temp/spill dir: /tmp/dask-2948
Workers: 2 | threads/worker: 1 | processes: True
Memory: total ~15.6 GiB | usable ~13.6 GiB | per-worker ~6.8 GiB
Compression: spill=auto | comm=False
[memory-fix] 2 workers, tmp=/tmp/dask-2948

Result: 0.499975
Solution: use fewer workers and/or smaller chunks

2. Tuning Memory Spill Thresholds

Dask spills to disk when memory pressure reaches configurable thresholds. Lower memory_target to start spilling earlier and avoid OOM crashes.

# Conservative configuration for memory-constrained systems
conservative_config = DaskSetupConfig(
    workload_type="cpu",
    max_workers=2,
    reserve_mem_gb=2.0,
    memory_target=0.60,   # start spilling at 60% usage
    memory_spill=0.70,    # aggressive spill at 70%
    memory_pause=0.85,    # pause new tasks at 85%
    memory_terminate=0.95,
    spill_compression="auto",  # lz4 if installed, else snappy, else none
)

print("Conservative memory config:")
print(f"  memory_target   : {conservative_config.memory_target}")
print(f"  memory_spill    : {conservative_config.memory_spill}")
print(f"  spill_compression: {conservative_config.spill_compression}")

with demo_cluster("conservative", config=conservative_config) as (client, _, tmp):
    x = da.random.random((2000, 2000), chunks=(400, 400))
    result = x.mean().compute()
    print(f"  Completed with conservative thresholds — result={result:.4f}")

Conservative memory config:
  memory_target   : 0.6
  memory_spill    : 0.7
  spill_compression: auto

[setup_dask_client] Configuration summary
temp/spill dir: /tmp/dask-2948
Workers: 2 | threads/worker: 1 | processes: True
Memory: total ~15.6 GiB | usable ~13.6 GiB | per-worker ~6.8 GiB
Compression: spill=auto | comm=False
[conservative] 2 workers, tmp=/tmp/dask-2948

  Completed with conservative thresholds — result=0.4999

3. Bad Chunk Sizes

Chunks that are too small cause massive scheduler overhead (millions of tiny tasks). Chunks that are too large cause OOM. The target is 256–512 MiB per chunk.

# A realistic dataset: 10 years of daily 0.25° data (~14 GiB). It is lazy, so nothing
# is allocated; we only look at chunk sizes.
import dask.array as da
import pandas as pd

shape = (3650, 721, 1440)
ds = xr.Dataset(
    {"temperature": (["time", "lat", "lon"],
                     da.zeros(shape, dtype="float32", chunks=(1, 721, 1440)))},
    coords={
        "time": pd.date_range("2000-01-01", periods=shape[0], freq="D"),
        "lat": np.linspace(-90, 90, shape[1]),
        "lon": np.linspace(0, 359.75, shape[2]),
    },
)

dtype_bytes = ds.temperature.dtype.itemsize


def chunk_mib(chunks):
    import math
    return math.prod(chunks.values()) * dtype_bytes / 2**20


chunk_options = {
    "too_small": {"time": 1,    "lat": 721, "lon": 1440},  # one day per chunk
    "optimal":   {"time": 128,  "lat": 721, "lon": 1440},
    "too_large": {"time": 3650, "lat": 721, "lon": 1440},  # the whole variable
}

print(f"{'Strategy':<12}  {'Chunk MiB':>10}  {'n_chunks':>8}  {'Note'}")
print("-" * 55)
for label, chunks in chunk_options.items():
    mib = chunk_mib(chunks)
    n = ds.chunk(chunks).temperature.data.npartitions
    ok = "✅ optimal" if 256 <= mib <= 512 else ("⚠️ too small" if mib < 256 else "⚠️ too large")
    print(f"{label:<12}  {mib:>10.1f}  {n:>8}  {ok}")

Strategy       Chunk MiB  n_chunks  Note
-------------------------------------------------------
too_small            4.0      3650  ⚠️ too small
optimal            507.0        29  ✅ optimal
too_large        14456.1         1  ⚠️ too large

# Let dask_setup choose chunk sizes for you
from dask_setup import recommend_chunks

with demo_cluster("chunk-advisor", workload_type="cpu", max_workers=2,
                  reserve_mem_gb=2.0) as (client, _, _):
    suggested = recommend_chunks(ds, client, verbose=True)
    print(f"Recommended chunks: {suggested.chunks}")

[setup_dask_client] Configuration summary
temp/spill dir: /tmp/dask-2948
Workers: 2 | threads/worker: 1 | processes: True
Memory: total ~15.6 GiB | usable ~13.6 GiB | per-worker ~6.8 GiB
Compression: spill=auto | comm=False
[chunk-advisor] 2 workers, tmp=/tmp/dask-2948
📊 Xarray Chunking Recommendations
===================================
 Recommended chunks: {'time': 129}
 Estimated chunk size: 510.9 MiB
 Total chunks: 29

Current vs Recommended:
-------------------------
  lon: (1440,) → unchunked
  lat: (721,) → unchunked
  time: (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) → 129

 Usage:
   ds_chunked = ds.chunk({'time': 129})
Recommended chunks: {'time': 129}

/tmp/ipykernel_2948/1587581517.py:6: UserWarning: Dimension 'time' has very small chunks (4.0 MiB). This may create high task overhead.
  suggested = recommend_chunks(ds, client, verbose=True)
/tmp/ipykernel_2948/1587581517.py:6: UserWarning: Dimension 'lat' has very small chunks (4.0 MiB). This may create high task overhead.
  suggested = recommend_chunks(ds, client, verbose=True)
/tmp/ipykernel_2948/1587581517.py:6: UserWarning: Dimension 'lon' has very small chunks (4.0 MiB). This may create high task overhead.
  suggested = recommend_chunks(ds, client, verbose=True)

4. Workers Fail to Start

On some HPC systems, worker processes are killed at startup if they exceed scheduler resource limits. Fix by reducing max_workers or using workload_type="io" (single process).

# io topology = single process, many threads → no per-process startup cost
with demo_cluster("io-fallback", workload_type="io", reserve_mem_gb=2.0) as (client, _, _):
    workers = client.scheduler_info()["workers"]
    first_w = next(iter(workers.values()))
    print(f"Workers: {len(client.nthreads())}")
    print(f"Threads per worker: {first_w.get('nthreads', '?')}")
    print("Single-process / multi-thread topology avoids startup issues.")

[setup_dask_client] Configuration summary
temp/spill dir: /tmp/dask-2948
Workers: 1 | threads/worker: 4 | processes: False
Memory: total ~15.6 GiB | usable ~13.6 GiB | per-worker ~13.6 GiB
Compression: spill=auto | comm=False
[io-fallback] 1 workers, tmp=/tmp/dask-2948
Workers: 1
Threads per worker: 4
Single-process / multi-thread topology avoids startup issues.

5. Resource Detection Failures

dask_setup reads $PBS_JOBFS, $NCPUS, $PBS_MEM and psutil. If all fail, set fallback_on_detection_failure=True to get a safe 2-core, 8 GiB default.

NCI Gadi / PBS Pro note: $PBS_MEM on Gadi is a raw byte count with no unit suffix (e.g. "532575944704" for 496 GiB). dask_setup handles this correctly — bare integers in $PBS_MEM/$PBS_VMEM are treated as bytes, not megabytes. If you parse these variables yourself in a script, make sure to divide by 1024**3 rather than treating the value as megabytes (a common mistake that inflates memory by 1,048,576×).

print("HPC environment variables:")
for var in ["PBS_JOBID", "PBS_JOBFS", "NCPUS", "PBS_MEM",
            "SLURM_JOB_ID", "SLURM_CPUS_ON_NODE", "SLURM_MEM_PER_NODE"]:
    val = os.environ.get(var)
    if val:
        print(f"  {var}={val}")

# Safe fallback for unknown environments
with demo_cluster("safe-fallback", workload_type="cpu",
                  fallback_on_detection_failure=True) as (client, _, tmp):
    print(f"Workers: {len(client.nthreads())}  tmp={tmp}")
    print("fallback_on_detection_failure=True prevents errors on unknown systems.")

HPC environment variables:

[setup_dask_client] Configuration summary
temp/spill dir: /tmp/dask-2948
Workers: 4 | threads/worker: 1 | processes: True
Memory: total ~15.6 GiB | usable ~11.6 GiB | per-worker ~2.9 GiB
Compression: spill=auto | comm=False
[safe-fallback] 4 workers, tmp=/tmp/dask-2948
Workers: 4  tmp=/tmp/dask-2948
fallback_on_detection_failure=True prevents errors on unknown systems.

6. Dashboard on HPC — SSH Tunnel

The Dask dashboard runs on the compute node, which you can’t reach directly. With dashboard=True, setup_dask_client() prints the SSH tunnel command to run on your laptop. Set DASK_SETUP_LOGIN_HOST (e.g. gadi.nci.org.au) in your job script so the command names your login node; otherwise it is inferred from the node’s DNS domain, or shown as <login-node>.

os.environ["DASK_SETUP_LOGIN_HOST"] = "gadi.nci.org.au"  # normally set in the job script

client, cluster, tmp = setup_dask_client(
    workload_type="cpu",
    max_workers=2,
    reserve_mem_gb=2.0,
    dashboard=True,  # prints the dashboard URL and the tunnel command
    fallback_on_detection_failure=True,
)
client.close()
cluster.close()
del os.environ["DASK_SETUP_LOGIN_HOST"]

Dask Dashboard → http://127.0.0.1:45153/status

[setup_dask_client] Configuration summary
temp/spill dir: /tmp/dask-2948
Workers: 2 | threads/worker: 1 | processes: True
Memory: total ~15.6 GiB | usable ~13.6 GiB | per-worker ~6.8 GiB
Compression: spill=auto | comm=False

7. Post-Run Diagnostics with `cluster_report()`

cluster_report() collects peak memory, spill totals, and task throughput — useful for identifying bottlenecks without leaving the notebook.

from dask_setup.reporting import cluster_report

with demo_cluster("diagnostics", workload_type="cpu", max_workers=2,
                  reserve_mem_gb=2.0) as (client, _, _):
    # Run some work
    x = da.random.random((2000, 2000), chunks=(500, 500))
    _ = (x + x.T).mean().compute()

    # Collect diagnostics
    report = cluster_report(client)
    print(report.summary_line())
    print(f"Peak memory  : {report.peak_memory_gib:.3f} GiB (in memory, per worker)")
    print(f"Total spill  : {report.total_spill_gib:.3f} GiB")

[setup_dask_client] Configuration summary
temp/spill dir: /tmp/dask-2948
Workers: 2 | threads/worker: 1 | processes: True
Memory: total ~15.6 GiB | usable ~13.6 GiB | per-worker ~6.8 GiB
Compression: spill=auto | comm=False
[diagnostics] 2 workers, tmp=/tmp/dask-2948

workers=2 | mem=0.06 GiB/max
Peak memory  : 0.061 GiB (in memory, per worker)
Total spill  : 0.000 GiB

Quick Reference — Common Fixes

Symptom	Most likely cause	Quick fix
`Task needs > memory_limit`	Chunk too large for worker	Reduce chunks or use `max_workers=1`
Worker killed / KilledWorker	OOM	Lower `memory_target`, increase job RAM
Very slow performance	Too many tiny tasks	Increase chunk size to 256–512 MiB
Workers fail to start	HPC process limits	Use `workload_type="io"` or reduce `max_workers`
Spill goes to `/scratch`	`$PBS_JOBFS` not set	Add `#PBS -l jobfs=200gb` to job script
Dashboard unreachable	Behind compute node firewall	Use SSH tunnel from the startup output
`reserve_mem_gb` too large	Small machine	Leave it unset (defaults to 20% of RAM), or `profile="development"`
“Nanny failed to start”	`spill_compression` codec not installed (e.g. `lz4`)	Use `spill_compression="auto"`, or `pip install lz4`
Worker jobs submitted / `dask-jobqueue` ImportError inside a PBS job	`mode="auto"` resolves to `"pbs"` in batch jobs	Pass `mode="local"` for a single-node cluster
Warning: `workload_type='io'` with NetCDF	HDF5 reads serialise on one lock	Use `workload_type="cpu"` for NetCDF/HDF5
`total_mem_gib=520093696.0` in PBS log	`$PBS_MEM` bare int treated as MB	Fixed in v2.1 — update dask_setup
`validate_chunks` warns about huge chunks after `recommend_chunks`	Old chunk-size estimation bug (full dim × chunk)	Fixed in v2.1 — update dask_setup
`rechunk_dataset` fails: `extract_zarr_variable_encoding() missing 'zarr_format'`	rechunker/xarray incompatibility	Fixed in v2.1 — auto-fallback to `xarray.to_zarr()`

See the Troubleshooting wiki page for full details.