Worker Memory Management
Worker Memory Management¶
For cluster-wide memory-management, see Managing Memory.
Workers are given a target memory limit to stay under with the
--memory-limit keyword or the
keyword argument, which sets the memory limit per worker processes launched
$ dask-worker tcp://scheduler:port --memory-limit=auto # TOTAL_MEMORY * min(1, nthreads / total_nthreads) $ dask-worker tcp://scheduler:port --memory-limit="4 GiB" # four gigabytes per worker process.
Workers use a few different heuristics to keep memory use beneath this limit:
Spilling based on managed memory¶
Every time the worker finishes a task, it estimates the size in bytes that the result
costs to keep in memory using the
sizeof function. This function defaults to
sys.getsizeof() for arbitrary objects, which uses the standard Python
__sizeof__ protocol, but also has special-cased implementations for common data
types like NumPy arrays and Pandas dataframes. The sum of the
sizeof of all data
tracked by Dask is called managed memory.
When the managed memory exceeds 60% of the memory limit (target threshold), the worker
will begin to dump the least recently used data to disk. You can control this location
$ dask-worker tcp://scheduler:port --memory-limit="4 GiB" --local-directory /scratch
That data is still available and will be read back from disk when necessary. On the diagnostic dashboard status page, disk I/O will show up in the task stream plot as orange blocks. Additionally, the memory plot in the upper left will show a section of the bar colored in grey.
Spilling based on process memory¶
The approach above can fail for a few reasons:
Custom objects may not report their memory size accurately
User functions may take up more RAM than expected
Significant amounts of data may accumulate in network I/O buffers
To address this, we periodically monitor the process memory of the
worker every 200 ms. If the system reported memory use is above 70% of the target memory
usage (spill threshold), then the worker will start dumping unused data to disk, even
sizeof recording hasn’t yet reached the normal 60% threshold. This
more aggressive spilling will continue until process memory falls below 60%.
At 80% process memory load, the worker’s thread pool will stop starting computation on additional tasks in the worker’s queue. This gives time for the write-to-disk functionality to take effect even in the face of rapidly accumulating data. Currently executing tasks continue to run. Additionally, data transfers to/from other workers are throttled to a bare minimum.
At 95% process memory load (terminate threshold), a worker’s nanny process will terminate it. Tasks will be cancelled mid-execution and rescheduled elsewhere; all unique data on the worker will be lost and will need to be recomputed. This is to avoid having our worker job being terminated by an external watchdog (like Kubernetes, YARN, Mesos, SGE, etc..). After termination, the nanny will restart the worker in a fresh state.
These values can be configured by modifying the
distributed: worker: # Fractions of worker process memory at which we take action to avoid memory # blowup. Set any of the values to False to turn off the behavior entirely. memory: target: 0.60 # fraction of managed memory where we start spilling to disk spill: 0.70 # fraction of process memory where we start spilling to disk pause: 0.80 # fraction of process memory at which we pause worker threads terminate: 0.95 # fraction of process memory at which we terminate the worker
Using the dashboard to monitor memory usage¶
The dashboard (typically available on port 8787) shows a summary of the overall memory usage on the cluster, as well as the individual usage on each worker. It provides different memory readings:
Overall memory used by the worker process (RSS), as measured by the OS
Sum of the
sizeofof all Dask data stored on the worker, excluding spilled data.
Memory usage that Dask is not directly aware of. It is estimated by subtracting managed memory from the total process memory and typically includes:
The Python interpreter code, loaded modules, and global variables
Memory temporarily used by running tasks
Dereferenced Python objects that have not been garbage-collected yet
Unused memory that the Python memory allocator did not return to libc through free yet
Unused memory that the user-space libc free function did not release to the OS yet (see memory allocators below)
- unmanaged recent
Unmanaged memory that has appeared within the last 30 seconds. This is not included in the ‘unmanaged’ memory measure above. Ideally, this memory should be for the most part a temporary spike caused by tasks’ heap use plus soon-to-be garbage collected objects.
The time it takes for unmanaged memory to transition away from its “recent” state can be tweaked through the
distributed.worker.memory.recent-to-old-timekey in the
~/.config/dask/distributed.yamlfile. If your tasks typically run for longer than 30 seconds, it’s recommended that you increase this setting accordingly.
distributed.scheduler.Scheduler.rebalance()ignore unmanaged recent memory. This behaviour can also be tweaked using the Dask config - see the methods’ documentation.
managed memory that has been spilled to disk. This is not included in the ‘managed’ measure above. This measure reports the number of bytes actually spilled to disk, which may differ from the output of
sizeofparticularly in case of compression.
The sum of managed + unmanaged + unmanaged recent is equal by definition to the process memory.
Memory not released back to the OS¶
In many cases, high unmanaged memory usage or “memory leak” warnings on workers can be misleading: a worker may not actually be using its memory for anything, but simply hasn’t returned that unused memory back to the operating system, and is hoarding it just in case it needs the memory capacity again. This is not a bug in your code, nor in Dask — it’s actually normal behavior for all processes on Linux and MacOS, and is a consequence of how the low-level memory allocator works (see below for details).
Because Dask makes decisions (spill-to-disk, pause, terminate,
rebalance()) based on the worker’s memory usage as reported by
the OS, and is unaware of how much of this memory is actually in use versus empty and
“hoarded”, it can overestimate — sometimes significantly — how much memory the process
is using and think the worker is running out of memory when in fact it isn’t.
More in detail: both the Linux and MacOS memory allocators try to avoid performing a brk kernel call every time the application calls free by implementing a user-space memory management system. Upon free, memory can remain allocated in user space and potentially reusable by the next malloc - which in turn won’t require a kernel call either. This is generally very desirable for C/C++ applications which have no memory allocator of their own, as it can drastically boost performance at the cost of a larger memory footprint. CPython however adds its own memory allocator on top, which reduces the need for this additional abstraction (with caveats).
There are steps you can take to alleviate situations where worker memory is not released back to the OS. These steps are discussed in the following sections.
Manually trim memory¶
Linux workers only
It is possible to forcefully release allocated but unutilized memory as follows:
import ctypes def trim_memory() -> int: libc = ctypes.CDLL("libc.so.6") return libc.malloc_trim(0) client.run(trim_memory)
This should be only used as a one-off debugging experiment. Watch the dashboard while
running the above code. If unmanaged worker memory (on the “Bytes stored” plot)
decreases significantly after calling
client.run(trim_memory), then move on to the
next section. Otherwise, you likely do have a memory leak.
Note that you should only run this malloc_trim if you are using the default glibc memory allocator. When using a custom allocator such as jemalloc (see below), this could cause unexpected behavior including segfaults. (If you don’t know what this means, you’re probably using the default glibc allocator and are safe to run this).
Automatically trim memory¶
Linux workers only
To aggressively and automatically trim the memory in a production environment, you
should instead set the environment variable
MALLOC_TRIM_THRESHOLD_ (note the final
underscore) to 0 or a low number; see the mallopt man page for details. Reducing
this value will increase the number of syscalls, and as a consequence may degrade
The variable must be set before starting the
If using a Nanny, the
MALLOC_TRIM_THRESHOLD_ environment variable
will automatically be set to
65536 for the worker process which the nanny is
monitoring. You can modify this behavior using the
Linux and MacOS workers
Alternatively to the above, you may experiment with the jemalloc memory allocator, as follows:
conda install jemalloc LD_PRELOAD=$CONDA_PREFIX/lib/libjemalloc.so dask-worker <...>
conda install jemalloc DYLD_INSERT_LIBRARIES=$CONDA_PREFIX/lib/libjemalloc.dylib dask-worker <...>
Alternatively on macOS, install globally with homebrew:
brew install jemalloc DYLD_INSERT_LIBRARIES=$(brew --prefix jemalloc)/lib/libjemalloc.dylib dask-worker <...>
jemalloc offers a wealth of configuration settings; please refer to its documentation.
Ignore process memory¶
If all else fails, you may want to stop Dask from using memory metrics from the OS (RSS) in its decision-making:
distributed: worker: memory: rebalance: measure: managed_in_memory spill: false pause: false terminate: false
This of course will be problematic if you have a genuine issue with unmanaged memory, e.g. memory leaks and/or suffer from heavy fragmentation.
User-defined managed memory containers¶
This feature is intended for advanced users only; the built-in container for managed memory should fit the needs of most. If you’re looking to dynamically spill CUDA device memory into host memory, you should use dask-cuda.
The design described in the sections above stores data in the worker’s RAM, with
automatic spilling to disk when the
spill thresholds are passed.
If one desires a different behaviour, a
data= parameter can be passed when
This optional parameter accepts any of the following values:
an instance of
a callable which returns a
a tuple of
callable which returns a
dict of keyword arguments to the callable
Doing so causes the Worker to ignore both the
target and the
However, if the object also supports the following duck-type API in addition to the
MutableMapping API, the
spill threshold will remain active:
- class distributed.spill.ManualEvictProto(*args, **kwargs)¶
Duck-type API that a third-party alternative to SpillBuffer must respect (in addition to MutableMapping) if it wishes to support spilling when the
distributed.worker.memory.spillthreshold is surpassed.
This is public API. At the moment of writing, Dask-CUDA implements this protocol in the ProxifyHostFile class.
- evict() int ¶
Manually evict a key/value pair from fast to slow memory. Return size of the evicted value in fast memory.
If the eviction failed for whatever reason, return -1. This method must guarantee that the key/value pair that caused the issue has been retained in fast memory and that the problem has been logged internally.
This method never raises.