diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3620f419..380f055a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 #### Added
 
+- Added configuration options to specify number of workers and maximum partition size for parallel operations. [#777](https://github.com/askap-vast/vast-pipeline/pull/777)
 - Added vast_pipeline.utils.delete_run.py to enable deletion of pipeline runs using raw SQL [#775](https://github.com/askap-vast/vast-pipeline/pull/775)
 
 #### Changed
@@ -27,6 +28,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 #### List of PRs
 
+- [#777](https://github.com/askap-vast/vast-pipeline/pull/777): feat: Allow user to specify number of cores and memory size of partitions via configuration.
 - [#776](https://github.com/askap-vast/vast-pipeline/pull/776): fix: Minor memory optimisations
 - [#775](https://github.com/askap-vast/vast-pipeline/pull/775): fix, feat: Enabled deletion of pipeline runs directly using SQL rather than via django
 - [#734](https://github.com/askap-vast/vast-pipeline/pull/734): Shortened forced fits measurement names
diff --git a/docs/using/runconfig.md b/docs/using/runconfig.md
index 864f3bdc..de54bd2f 100644
--- a/docs/using/runconfig.md
+++ b/docs/using/runconfig.md
@@ -127,6 +127,21 @@ Below is an example of a default `config.yaml` file. Note that no images or othe
       # aggregate pair metrics that are stored in Source objects.
       source_aggregate_pair_metrics_min_abs_vs: 4.3
 
+    processing:
+      # Options to control use of Dask parallelism
+      # NOTE: These are advanced options and you should only change them if you know what you are doing.
+
+      # The total number of workers available to Dask ('null' means use one less than all cores)
+      num_workers: null
+
+      # The number of workers to use for disk IO operations (e.g. when reading images for forced extraction)
+      num_workers_io: 5
+
+      # The default maximum size (in MB) to allow per partition of Dask DataFrames
+      # Increasing this will create fewer partitions and will potentially increase the memory footprint
+      # of parallel tasks.
+      max_partition_mb: 15
+
     ```
 
 !!! note
diff --git a/poetry.lock b/poetry.lock
index 2c9557c2..fceb5448 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -211,13 +211,13 @@ xbr = ["base58 (>=2.1.0)", "cbor2 (>=5.2.0)", "click (>=8.1.2)", "ecdsa (>=0.16.
 
 [[package]]
 name = "automat"
-version = "24.8.0"
+version = "24.8.1"
 description = "Self-service finite-state machines for the programmer on the go."
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "Automat-24.8.0-py3-none-any.whl", hash = "sha256:0a6f26e0d52fb84afcfcbabb7d86ca6dbe0b2bb64735ad95df39c103bf8de091"},
-    {file = "automat-24.8.0.tar.gz", hash = "sha256:f55735fdae5f461176ba2c8be7438ee06e47c4028ad80d1b4d5abef96d9d5863"},
+    {file = "Automat-24.8.1-py3-none-any.whl", hash = "sha256:bf029a7bc3da1e2c24da2343e7598affaa9f10bf0ab63ff808566ce90551e02a"},
+    {file = "automat-24.8.1.tar.gz", hash = "sha256:b34227cf63f6325b8ad2399ede780675083e439b20c323d376373d8ee6306d88"},
 ]
 
 [package.dependencies]
@@ -398,89 +398,89 @@ typing-extensions = ">=3.10.0"
 
 [[package]]
 name = "certifi"
-version = "2024.7.4"
+version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
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-    {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"},
+    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
 ]
 
 [[package]]
 name = "cffi"
-version = "1.17.0"
+version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -507,101 +507,116 @@ tests = ["async-timeout", "coverage (>=4.5,<5.0)", "pytest", "pytest-asyncio", "
 
 [[package]]
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 [[package]]
@@ -747,38 +762,38 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "cryptography"
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 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
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 [package.dependencies]
@@ -791,7 +806,7 @@ nox = ["nox"]
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 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
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 test-randomorder = ["pytest-randomly"]
 
 [[package]]
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 description = "serialize all of Python"
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 [package.extras]
@@ -1246,13 +1267,13 @@ resolved_reference = "8f4307825781743755d189418a9cb9111aaf0b63"
 
 [[package]]
 name = "fsspec"
-version = "2024.6.1"
+version = "2024.10.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1468,13 +1489,13 @@ docs = ["Sphinx"]
 
 [[package]]
 name = "griffe"
-version = "1.1.0"
+version = "1.4.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1587,51 +1608,62 @@ idna = ">=2.5"
 
 [[package]]
 name = "idna"
-version = "3.7"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
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 ]
 
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
 [[package]]
 name = "importlib-metadata"
-version = "8.2.0"
+version = "8.5.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-zipp = ">=0.5"
+zipp = ">=3.20"
 
 [package.extras]
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+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=2.2)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.4.3"
+version = "6.4.5"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "incremental"
@@ -1723,39 +1755,43 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-ena
 
 [[package]]
 name = "jaraco-context"
-version = "5.3.0"
+version = "6.0.1"
 description = "Useful decorators and context managers"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jaraco.context-6.0.1-py3-none-any.whl", hash = "sha256:f797fc481b490edb305122c9181830a3a5b76d84ef6d1aef2fb9b47ab956f9e4"},
+    {file = "jaraco_context-6.0.1.tar.gz", hash = "sha256:9bae4ea555cf0b14938dc0aee7c9f32ed303aa20a3b73e7dc80111628792d1b3"},
 ]
 
 [package.dependencies]
 "backports.tarfile" = {version = "*", markers = "python_version < \"3.12\""}
 
 [package.extras]
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-testing = ["portend", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["portend", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "jaraco-functools"
-version = "4.0.2"
+version = "4.1.0"
 description = "Functools like those found in stdlib"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jaraco.functools-4.1.0-py3-none-any.whl", hash = "sha256:ad159f13428bc4acbf5541ad6dec511f91573b90fba04df61dafa2a1231cf649"},
+    {file = "jaraco_functools-4.1.0.tar.gz", hash = "sha256:70f7e0e2ae076498e212562325e805204fc092d7b4c17e0e86c959e249701a9d"},
 ]
 
 [package.dependencies]
 more-itertools = "*"
 
 [package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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+enabler = ["pytest-enabler (>=2.2)"]
+test = ["jaraco.classes", "pytest (>=6,!=8.1.*)"]
+type = ["pytest-mypy"]
 
 [[package]]
 name = "jedi"
@@ -1834,13 +1870,13 @@ files = [
 
 [[package]]
 name = "keyring"
-version = "25.3.0"
+version = "25.5.0"
 description = "Store and access your passwords safely."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1854,121 +1890,135 @@ pywin32-ctypes = {version = ">=0.2.0", markers = "sys_platform == \"win32\""}
 SecretStorage = {version = ">=3.2", markers = "sys_platform == \"linux\""}
 
 [package.extras]
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+cover = ["pytest-cov"]
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+enabler = ["pytest-enabler (>=2.2)"]
+test = ["pyfakefs", "pytest (>=6,!=8.1.*)"]
+type = ["pygobject-stubs", "pytest-mypy", "shtab", "types-pywin32"]
 
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 ]
 
 [[package]]
@@ -2242,13 +2292,13 @@ test = ["coverage", "flake8 (>=3.0)", "shtab"]
 
 [[package]]
 name = "mkdocs"
-version = "1.6.0"
+version = "1.6.1"
 description = "Project documentation with Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2273,13 +2323,13 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "1.0.1"
+version = "1.2.0"
 description = "Automatically link across pages in MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2336,13 +2386,13 @@ mkdocs = ">=1.0"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.31"
+version = "9.5.43"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2422,28 +2472,29 @@ python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.10.8"
+version = "1.10.9"
 description = "A Python handler for mkdocstrings."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 griffe = ">=0.49"
+mkdocs-autorefs = ">=1.0"
 mkdocstrings = ">=0.25"
 
 [[package]]
 name = "more-itertools"
-version = "10.4.0"
+version = "10.5.0"
 description = "More routines for operating on iterables, beyond itertools"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -2621,14 +2672,19 @@ files = [
 
 [[package]]
 name = "paginate"
-version = "0.5.6"
+version = "0.5.7"
 description = "Divides large result sets into pages for easier browsing"
 optional = false
 python-versions = "*"
 files = [
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+    {file = "paginate-0.5.7-py2.py3-none-any.whl", hash = "sha256:b885e2af73abcf01d9559fd5216b57ef722f8c42affbb63942377668e35c7591"},
+    {file = "paginate-0.5.7.tar.gz", hash = "sha256:22bd083ab41e1a8b4f3690544afb2c60c25e5c9a63a30fa2f483f6c60c8e5945"},
 ]
 
+[package.extras]
+dev = ["pytest", "tox"]
+lint = ["black"]
+
 [[package]]
 name = "pandas"
 version = "1.5.3"
@@ -2892,13 +2948,13 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "pip"
-version = "24.2"
+version = "24.3.1"
 description = "The PyPA recommended tool for installing Python packages."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pip-24.3.1-py3-none-any.whl", hash = "sha256:3790624780082365f47549d032f3770eeb2b1e8bd1f7b2e02dace1afa361b4ed"},
+    {file = "pip-24.3.1.tar.gz", hash = "sha256:ebcb60557f2aefabc2e0f918751cd24ea0d56d8ec5445fe1807f1d2109660b99"},
 ]
 
 [[package]]
@@ -2922,29 +2978,29 @@ testing = ["mock", "pytest", "pytest-rerunfailures"]
 
 [[package]]
 name = "platformdirs"
-version = "4.2.2"
+version = "4.3.6"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb"},
+    {file = "platformdirs-4.3.6.tar.gz", hash = "sha256:357fb2acbc885b0419afd3ce3ed34564c13c9b95c89360cd9563f73aa5e2b907"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
-type = ["mypy (>=1.8)"]
+docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
+type = ["mypy (>=1.11.2)"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.47"
+version = "3.0.48"
 description = "Library for building powerful interactive command lines in Python"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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-    {file = "prompt_toolkit-3.0.47.tar.gz", hash = "sha256:1e1b29cb58080b1e69f207c893a1a7bf16d127a5c30c9d17a25a5d77792e5360"},
+    {file = "prompt_toolkit-3.0.48-py3-none-any.whl", hash = "sha256:f49a827f90062e411f1ce1f854f2aedb3c23353244f8108b89283587397ac10e"},
+    {file = "prompt_toolkit-3.0.48.tar.gz", hash = "sha256:d6623ab0477a80df74e646bdbc93621143f5caf104206aa29294d53de1a03d90"},
 ]
 
 [package.dependencies]
@@ -2980,24 +3036,20 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
 
 [[package]]
 name = "psycopg2"
-version = "2.9.9"
+version = "2.9.10"
 description = "psycopg2 - Python-PostgreSQL Database Adapter"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-    {file = "psycopg2-2.9.9.tar.gz", hash = "sha256:d1454bde93fb1e224166811694d600e746430c006fbb031ea06ecc2ea41bf156"},
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 ]
 
 [[package]]
@@ -3055,24 +3107,24 @@ numpy = ">=1.16.6"
 
 [[package]]
 name = "pyasn1"
-version = "0.6.0"
+version = "0.6.1"
 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
+    {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
 ]
 
 [[package]]
 name = "pyasn1-modules"
-version = "0.4.0"
+version = "0.4.1"
 description = "A collection of ASN.1-based protocols modules"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pyasn1_modules-0.4.1-py3-none-any.whl", hash = "sha256:49bfa96b45a292b711e986f222502c1c9a5e1f4e568fc30e2574a6c7d07838fd"},
+    {file = "pyasn1_modules-0.4.1.tar.gz", hash = "sha256:c28e2dbf9c06ad61c71a075c7e0f9fd0f1b0bb2d2ad4377f240d33ac2ab60a7c"},
 ]
 
 [package.dependencies]
@@ -3266,13 +3318,13 @@ test = ["pretend", "pytest (>=3.0.1)", "pytest-rerunfailures"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.2"
+version = "3.1.4"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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+    {file = "pyparsing-3.1.4-py3-none-any.whl", hash = "sha256:a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c"},
+    {file = "pyparsing-3.1.4.tar.gz", hash = "sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032"},
 ]
 
 [package.extras]
@@ -3312,13 +3364,13 @@ postgresql = ["psycopg2"]
 
 [[package]]
 name = "pytz"
-version = "2024.1"
+version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
-    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
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+    {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
+    {file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
 ]
 
 [[package]]
@@ -3342,13 +3394,13 @@ tests = ["flake8", "pytest"]
 
 [[package]]
 name = "pyvo"
-version = "1.5.2"
+version = "1.6"
 description = "Astropy affiliated package for accessing Virtual Observatory data and services"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "pyvo-1.5.2.tar.gz", hash = "sha256:b8a24c44dace5c607b1d93afd0257d15fa109f1e865772347ea949eea01c8f71"},
+    {file = "pyvo-1.6-py3-none-any.whl", hash = "sha256:65740f565ffbe947c2a93aade9be905eaa3b0ab86660bb805e1beb3cc3ff4812"},
+    {file = "pyvo-1.6.tar.gz", hash = "sha256:db6ef423e6008eba1ca68459e7e2a91dbc34cbe1bda2f29f64ba57d8df570caa"},
 ]
 
 [package.dependencies]
@@ -3356,7 +3408,7 @@ astropy = ">=4.1"
 requests = "*"
 
 [package.extras]
-all = ["pillow"]
+all = ["defusedxml", "pillow"]
 docs = ["sphinx-astropy"]
 test = ["pytest-astropy", "pytest-doctestplus (>=0.13)", "requests-mock"]
 
@@ -3463,90 +3515,105 @@ hiredis = ["hiredis (>=0.1.3)"]
 
 [[package]]
 name = "regex"
-version = "2024.7.24"
+version = "2024.9.11"
 description = "Alternative regular expression module, to replace re."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -3656,13 +3723,13 @@ jeepney = ">=0.6"
 
 [[package]]
 name = "service-identity"
-version = "24.1.0"
+version = "24.2.0"
 description = "Service identity verification for pyOpenSSL & cryptography."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3672,7 +3739,7 @@ pyasn1 = "*"
 pyasn1-modules = "*"
 
 [package.extras]
-dev = ["pyopenssl", "service-identity[idna,mypy,tests]"]
+dev = ["coverage[toml] (>=5.0.2)", "idna", "mypy", "pyopenssl", "pytest", "types-pyopenssl"]
 docs = ["furo", "myst-parser", "pyopenssl", "sphinx", "sphinx-notfound-page"]
 idna = ["idna"]
 mypy = ["idna", "mypy", "types-pyopenssl"]
@@ -3680,19 +3747,23 @@ tests = ["coverage[toml] (>=5.0.2)", "pytest"]
 
 [[package]]
 name = "setuptools"
-version = "72.2.0"
+version = "75.3.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
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+type = ["importlib-metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.12.*)", "pytest-mypy"]
 
 [[package]]
 name = "six"
@@ -3775,55 +3846,55 @@ files = [
 
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 [package.dependencies]
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 [[package]]
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 python-versions = ">=3.8.0"
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-automat = ">=0.8.0"
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 constantly = ">=15.1"
 hyperlink = ">=17.1.1"
 idna = {version = ">=2.4", optional = true, markers = "extra == \"tls\""}
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 name = "txaio"
@@ -4087,35 +4158,35 @@ files = [
 
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+version = "6.0.12.20240917"
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 [[package]]
@@ -4131,13 +4202,13 @@ files = [
 
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+version = "2.2.3"
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 [package.extras]
@@ -4283,18 +4354,22 @@ viz = ["matplotlib", "nc-time-axis", "seaborn"]
 
 [[package]]
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-version = "3.20.0"
+version = "3.20.2"
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+cover = ["pytest-cov"]
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+enabler = ["pytest-enabler (>=2.2)"]
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+type = ["pytest-mypy"]
 
 [[package]]
 name = "zope-event"
@@ -4316,54 +4391,57 @@ test = ["zope.testrunner"]
 
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 name = "zope-interface"
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+    {file = "zope.interface-7.1.1.tar.gz", hash = "sha256:4284d664ef0ff7b709836d4de7b13d80873dc5faeffc073abdb280058bfac5e3"},
 ]
 
 [package.dependencies]
 setuptools = "*"
 
 [package.extras]
-docs = ["Sphinx", "repoze.sphinx.autointerface", "sphinx-rtd-theme"]
-test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
-testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
+docs = ["Sphinx", "furo", "repoze.sphinx.autointerface"]
+test = ["coverage[toml]", "zope.event", "zope.testing"]
+testing = ["coverage[toml]", "zope.event", "zope.testing"]
 
 [extras]
 prod = ["gevent", "gunicorn"]
@@ -4371,4 +4449,4 @@ prod = ["gevent", "gunicorn"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.0,<3.11"
-content-hash = "8153f316f4042aacea8eacc6fb2781de953a53d248905257f9aa10272272c752"
+content-hash = "1999025d7ad15dffd95030c93eb80068fd74f567ee2c023020ce7a7d1be00c09"
diff --git a/pyproject.toml b/pyproject.toml
index 05fc4f58..cb419709 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ whitenoise = "^5.2.0"
 gevent = { version = "^21.1.2", optional = true }
 gunicorn = { version = "^20.0.4", optional = true }
 forced-phot = { git = "https://github.com/askap-vast/forced_phot.git" }
-strictyaml = "^1.3.2"
+strictyaml = "^1.6.2"
 colorcet = "^2.0.6"
 matplotlib = "^3.5.0"
 holoviews = "^1.14.7"
diff --git a/vast_pipeline/config_template.yaml.j2 b/vast_pipeline/config_template.yaml.j2
index 85a93e5f..21b19ff8 100644
--- a/vast_pipeline/config_template.yaml.j2
+++ b/vast_pipeline/config_template.yaml.j2
@@ -163,3 +163,18 @@ variability:
   # Only measurement pairs where the Vs metric exceeds this value are selected for the
   # aggregate pair metrics that are stored in Source objects.
   source_aggregate_pair_metrics_min_abs_vs: {{ source_aggregate_pair_metrics_min_abs_vs }}
+
+processing:
+  # Options to control use of Dask parallelism
+  # NOTE: These are advanced options and you should only change them if you know what you are doing.
+
+  # The total number of workers available to Dask ('null' means use one less than all cores)
+  num_workers: {{ num_workers }}
+
+  # The number of workers to use for disk IO operations (e.g. when reading images for forced extraction)
+  num_workers_io: {{ num_workers_io }}
+
+  # The default maximum size (in MB) to allow per partition of Dask DataFrames
+  # Increasing this will create fewer partitions and will potentially increase the memory footprint
+  # of parallel tasks.
+  max_partition_mb: {{ max_partition_mb }}
diff --git a/vast_pipeline/pipeline/association.py b/vast_pipeline/pipeline/association.py
index d5e46cb0..06c7d6db 100644
--- a/vast_pipeline/pipeline/association.py
+++ b/vast_pipeline/pipeline/association.py
@@ -7,7 +7,6 @@
 import pandas as pd
 from typing import Tuple, Dict, List
 import dask.dataframe as dd
-from psutil import cpu_count
 
 from astropy import units as u
 from astropy.coordinates import SkyCoord
@@ -20,7 +19,7 @@
     reconstruct_associtaion_dfs
 )
 from vast_pipeline.pipeline.config import PipelineConfig
-from vast_pipeline.utils.utils import StopWatch, calculate_n_partitions
+from vast_pipeline.utils.utils import StopWatch, calculate_workers_and_partitions
 
 
 logger = logging.getLogger(__name__)
@@ -1586,11 +1585,12 @@ def parallel_association(
     # Add an increment to any new source values when using add_mode to avoid
     # getting duplicates in the result laater
     id_incr_par_assoc = max(done_source_ids) if add_mode else 0
-
-    n_cpu = cpu_count() - 1
-    logger.debug(f"Running association with {n_cpu} CPUs")
-    n_partitions = calculate_n_partitions(images_df, n_cpu)
-    
+    n_workers, n_partitions = calculate_workers_and_partitions(
+        images_df,
+        n_cpu=config['processing']['num_workers'],
+        max_partition_mb=config['processing']['max_partition_mb']
+        )
+    logger.debug(f"Running association with {n_workers} CPUs")
     # pass each skyreg_group through the normal association process.
     results = (
         dd.from_pandas(images_df.set_index('skyreg_group'), npartitions=n_partitions)
@@ -1608,7 +1608,7 @@ def parallel_association(
             id_incr_par_assoc=id_incr_par_assoc,
             parallel=True,
             meta=meta
-        ).compute(n_workers=n_cpu, scheduler='processes')
+        ).compute(n_workers=n_workers, scheduler='processes')
     )
 
     # results are the normal dataframe of results with the columns:
diff --git a/vast_pipeline/pipeline/config.py b/vast_pipeline/pipeline/config.py
index d81a7374..82f63391 100644
--- a/vast_pipeline/pipeline/config.py
+++ b/vast_pipeline/pipeline/config.py
@@ -16,7 +16,7 @@
 
 
 def make_config_template(template_path: str, **kwargs) -> str:
-    """Generate the contents of a run configuration file from on a Jinja2 template.
+    """Generate the contents of a run configuration file from a Jinja2 template.
 
     Args:
         template_path: Path to a Jinja2 template.
@@ -114,6 +114,22 @@ class PipelineConfig:
                     "source_aggregate_pair_metrics_min_abs_vs": yaml.Float(),
                 }
             ),
+            yaml.Optional("processing"): yaml.Map(
+                {
+                    yaml.Optional(
+                        "num_workers",
+                        default=settings.PIPE_RUN_CONFIG_DEFAULTS['num_workers']):
+                        yaml.NullNone() | yaml.Int() | yaml.Str(),
+                    yaml.Optional(
+                        "num_workers_io",
+                        default=settings.PIPE_RUN_CONFIG_DEFAULTS['num_workers_io']):
+                        yaml.NullNone() | yaml.Int() | yaml.Str(),
+                    yaml.Optional(
+                        "max_partition_mb",
+                        default=settings.PIPE_RUN_CONFIG_DEFAULTS['max_partition_mb']):
+                        yaml.Int()
+                }
+            )
         }
     )
     # path to default run config template
@@ -511,6 +527,13 @@ def validate(self, user: User = None):
                     if not os.path.exists(file):
                         raise PipelineConfigError(f"{file} does not exist.")
 
+        # ensure num_workers and num_workers_io are
+        # either None (from null in config yaml) or an integer
+        for param_name in ('num_workers', 'num_workers_io'):
+            param_value = self['processing'][param_name]
+            if (param_value is not None) and (type(param_value) is not int):
+                raise PipelineConfigError(f"{param_name} can only be an integer or 'null'")
+
     def check_prev_config_diff(self) -> bool:
         """
         Checks if the previous config file differs from the current config file. Used in
diff --git a/vast_pipeline/pipeline/finalise.py b/vast_pipeline/pipeline/finalise.py
index 2da077f3..b282ccc0 100644
--- a/vast_pipeline/pipeline/finalise.py
+++ b/vast_pipeline/pipeline/finalise.py
@@ -88,7 +88,9 @@ def final_operations(
     source_aggregate_pair_metrics_min_abs_vs: float,
     add_mode: bool,
     done_source_ids: List[int],
-    previous_parquets: Dict[str, str]
+    previous_parquets: Dict[str, str],
+    n_cpu: int = 0,
+    max_partition_mb: int = 15
 ) -> Tuple[int, int]:
     """
     Performs the final operations of the pipeline:
@@ -136,7 +138,9 @@ def final_operations(
     )
     log_total_memory_usage()
 
-    srcs_df = parallel_groupby(sources_df)
+    srcs_df = parallel_groupby(sources_df,
+                               n_cpu=n_cpu,
+                               max_partition_mb=max_partition_mb)
 
     mem_usage = get_df_memory_usage(srcs_df)
     logger.info('Groupby-apply time: %.2f seconds', timer.reset())
@@ -179,7 +183,10 @@ def final_operations(
     # create measurement pairs, aka 2-epoch metrics
     if calculate_pairs:
         timer.reset()
-        measurement_pairs_df = calculate_measurement_pair_metrics(sources_df)
+        measurement_pairs_df = calculate_measurement_pair_metrics(
+            sources_df,
+            n_cpu=n_cpu,
+            max_partition_mb=max_partition_mb)
         logger.info(
             'Measurement pair metrics time: %.2f seconds',
             timer.reset())
diff --git a/vast_pipeline/pipeline/forced_extraction.py b/vast_pipeline/pipeline/forced_extraction.py
index 56e7aec4..143d72e7 100644
--- a/vast_pipeline/pipeline/forced_extraction.py
+++ b/vast_pipeline/pipeline/forced_extraction.py
@@ -5,7 +5,6 @@
 import pandas as pd
 import dask.dataframe as dd
 import dask.bag as db
-from psutil import cpu_count
 from glob import glob
 
 from astropy import units as u
@@ -19,7 +18,7 @@
 from vast_pipeline.pipeline.loading import make_upload_measurements
 
 from forced_phot import ForcedPhot
-from ..utils.utils import StopWatch
+from ..utils.utils import StopWatch, calculate_workers_and_partitions
 from vast_pipeline.image.utils import open_fits
 
 
@@ -259,7 +258,7 @@ def finalise_forced_dfs(
 def parallel_extraction(
     df: pd.DataFrame, df_images: pd.DataFrame, df_sources: pd.DataFrame,
     min_sigma: float, edge_buffer: float, cluster_threshold: float,
-    allow_nan: bool, add_mode: bool, p_run_path: str
+    allow_nan: bool, add_mode: bool, p_run_path: str, n_workers: int = 5
 ) -> pd.DataFrame:
     """
     Parallelize forced extraction with Dask
@@ -289,6 +288,8 @@ def parallel_extraction(
             True when the pipeline is running in add image mode.
         p_run_path:
             The system path of the pipeline run output.
+        n_workers:
+            The desired number of workers for Dask
 
     Returns:
         Dataframe with forced extracted measurements data, columns are
@@ -374,7 +375,7 @@ def image_data_func(image_name: str) -> Dict[str, Any]:
             npartitions=len(list_meas_parquets)
         )
         .map(get_data_from_parquet, p_run_path, add_mode)
-        .compute()
+        .compute(num_workers=n_workers, scheduler="processes")
     )
     mapping = pd.DataFrame(mapping)
     # remove not used columns from images_df and merge into mapping
@@ -393,7 +394,6 @@ def image_data_func(image_name: str) -> Dict[str, Any]:
     )
     del col_to_drop
 
-    n_cpu = cpu_count() - 1
     bags = db.from_sequence(list_to_map, npartitions=len(list_to_map))
     forced_dfs = (
         bags.map(lambda x: extract_from_image(
@@ -402,7 +402,7 @@ def image_data_func(image_name: str) -> Dict[str, Any]:
             allow_nan=allow_nan,
             **x
         ))
-        .compute()
+        .compute(num_workers=n_workers, scheduler='processes')
     )
     del bags
     # create intermediates dfs combining the mapping data and the forced
@@ -497,7 +497,8 @@ def forced_extraction(
     sources_df: pd.DataFrame, cfg_err_ra: float, cfg_err_dec: float,
     p_run: Run, extr_df: pd.DataFrame, min_sigma: float, edge_buffer: float,
     cluster_threshold: float, allow_nan: bool, add_mode: bool,
-    done_images_df: pd.DataFrame, done_source_ids: List[int]
+    done_images_df: pd.DataFrame, done_source_ids: List[int],
+    n_cpu: int = 5
 ) -> Tuple[pd.DataFrame, int]:
     """
     Check and extract expected measurements, and associated them with the
@@ -533,6 +534,8 @@ def forced_extraction(
         done_source_ids:
             List of the source ids that were already present in the previous
             run (used in add image mode).
+        n_cpu:
+            The desired number of workers for Dask.
 
     Returns:
         The `sources_df` with the extracted sources added.
@@ -604,11 +607,14 @@ def forced_extraction(
             f" (from {total_to_extract} total)"
         )
 
+    # Don't care about n_partitions in this step
+    n_workers, _ = calculate_workers_and_partitions(None, n_cpu)
+
     timer.reset()
     extr_df = parallel_extraction(
         extr_df, images_df, sources_df[['source', 'image', 'flux_peak']],
         min_sigma, edge_buffer, cluster_threshold, allow_nan, add_mode,
-        p_run.path
+        p_run.path, n_workers=n_workers
     )
     logger.info(
         'Force extraction step time: %.2f seconds', timer.reset()
@@ -712,7 +718,7 @@ def forced_extraction(
         n_forced = (
             dd.read_parquet(forced_parquets, columns=['id'])
             .count()
-            .compute()
+            .compute(num_workers=n_workers, scheduler='processes')
             .values[0]
         )
     else:
diff --git a/vast_pipeline/pipeline/main.py b/vast_pipeline/pipeline/main.py
index a0425ccc..3d935099 100644
--- a/vast_pipeline/pipeline/main.py
+++ b/vast_pipeline/pipeline/main.py
@@ -266,6 +266,8 @@ def process_pipeline(self, p_run: Run) -> None:
             sources_df.loc[sources_df['forced'] == False, missing_source_cols],
             images_df,
             skyregs_df,
+            n_cpu=self.config['processing']['num_workers'],
+            max_partition_mb=self.config['processing']['max_partition_mb']
         )
 
         # STEP #4 New source analysis
@@ -274,7 +276,9 @@ def process_pipeline(self, p_run: Run) -> None:
             missing_sources_df,
             self.config["new_sources"]["min_sigma"],
             self.config["source_monitoring"]["edge_buffer_scale"],
-            p_run
+            p_run,
+            n_cpu=self.config['processing']['num_workers_io'],
+            max_partition_mb=self.config['processing']['max_partition_mb']
         )
 
         # Drop column no longer required in missing_sources_df.
@@ -299,7 +303,8 @@ def process_pipeline(self, p_run: Run) -> None:
                 self.config["source_monitoring"]["allow_nan"],
                 self.add_mode,
                 done_images_df,
-                done_source_ids
+                done_source_ids,
+                n_cpu=self.config['processing']['num_workers_io']
             )
             mem_usage = get_df_memory_usage(sources_df)
             logger.debug(f"Step 5: sources_df memory usage: {mem_usage}MB")
@@ -319,7 +324,9 @@ def process_pipeline(self, p_run: Run) -> None:
             self.config["variability"]["source_aggregate_pair_metrics_min_abs_vs"],
             self.add_mode,
             done_source_ids,
-            self.previous_parquets
+            self.previous_parquets,
+            n_cpu=self.config['processing']['num_workers'],
+            max_partition_mb=self.config['processing']['max_partition_mb']
         )
 
         log_total_memory_usage()
diff --git a/vast_pipeline/pipeline/new_sources.py b/vast_pipeline/pipeline/new_sources.py
index 14d2b3df..4ef9dd08 100644
--- a/vast_pipeline/pipeline/new_sources.py
+++ b/vast_pipeline/pipeline/new_sources.py
@@ -3,7 +3,6 @@
 import numpy as np
 import dask.dataframe as dd
 
-from psutil import cpu_count
 from astropy import units as u
 from astropy.coordinates import SkyCoord
 from astropy.wcs import WCS
@@ -13,7 +12,7 @@
 
 from vast_pipeline.models import Image, Run
 
-from vast_pipeline.utils.utils import StopWatch, calculate_n_partitions
+from vast_pipeline.utils.utils import StopWatch, calculate_workers_and_partitions
 from vast_pipeline.pipeline.utils import get_df_memory_usage
 from vast_pipeline.image.utils import open_fits
 
@@ -197,7 +196,8 @@ def get_image_rms_measurements(
 
 
 def parallel_get_rms_measurements(
-    df: pd.DataFrame, edge_buffer: float = 1.0
+    df: pd.DataFrame, edge_buffer: float = 1.0,
+    n_cpu: int = 0, max_partition_mb: int = 15
 ) -> pd.DataFrame:
     """
     Wrapper function to use 'get_image_rms_measurements'
@@ -211,6 +211,11 @@ def parallel_get_rms_measurements(
         edge_buffer:
             Multiplicative factor to be passed to the
             'get_image_rms_measurements' function.
+        n_cpu:
+            The desired number of workers for Dask
+        max_partition_mb:
+            The desired maximum size (in MB) of the partitions for Dask.
+
 
     Returns:
         The original input dataframe with the 'img_diff_true_rms' column
@@ -230,10 +235,13 @@ def parallel_get_rms_measurements(
         'img_diff_true_rms': 'f',
     }
 
-    n_cpu = cpu_count() - 1
-    logger.debug(f"Running association with {n_cpu} CPUs")
-    n_partitions = calculate_n_partitions(out, n_cpu)
-
+    n_workers, n_partitions = calculate_workers_and_partitions(
+        df,
+        n_cpu=n_cpu,
+        max_partition_mb=max_partition_mb
+    )
+    logger.debug(f"Running get_image_rms_measurements with {n_workers} CPUs")
+    
     out = (
         dd.from_pandas(out, npartitions=n_partitions)
         .groupby('img_diff_rms_path')
@@ -241,7 +249,7 @@ def parallel_get_rms_measurements(
             get_image_rms_measurements,
             edge_buffer=edge_buffer,
             meta=col_dtype
-        ).compute(num_workers=n_cpu, scheduler='processes')
+        ).compute(num_workers=n_workers, scheduler='processes')
     )
 
     # We don't need all of the RMS measurements, just the lowest. Keeping all
@@ -272,7 +280,8 @@ def parallel_get_rms_measurements(
 
 def new_sources(
     sources_df: pd.DataFrame, missing_sources_df: pd.DataFrame,
-    min_sigma: float, edge_buffer: float, p_run: Run
+    min_sigma: float, edge_buffer: float, p_run: Run, n_cpu: int = 5,
+    max_partition_mb: int = 15
 ) -> pd.DataFrame:
     """
     Processes the new sources detected to check that they are valid new
@@ -294,6 +303,10 @@ def new_sources(
             'get_image_rms_measurements' function.
         p_run:
             The pipeline run.
+        n_cpu:
+            The desired number of workers for Dask
+        max_partition_mb:
+            The desired maximum size (in MB) of the partitions for Dask.
 
     Returns:
         The original input dataframe with the 'img_diff_true_rms' column
@@ -466,7 +479,8 @@ def new_sources(
     logger.debug("Getting rms measurements...")
 
     new_sources_df = parallel_get_rms_measurements(
-        new_sources_df, edge_buffer=edge_buffer
+        new_sources_df, edge_buffer=edge_buffer,
+        n_cpu=n_cpu, max_partition_mb=max_partition_mb
     )
     logger.debug(f"Time to get rms measurements: {debug_timer.reset()}s")
 
diff --git a/vast_pipeline/pipeline/pairs.py b/vast_pipeline/pipeline/pairs.py
index a7c5eb10..8d678cc1 100644
--- a/vast_pipeline/pipeline/pairs.py
+++ b/vast_pipeline/pipeline/pairs.py
@@ -4,9 +4,8 @@
 import dask.dataframe as dd
 import numpy as np
 import pandas as pd
-from psutil import cpu_count
 
-from vast_pipeline.utils.utils import calculate_n_partitions
+from vast_pipeline.utils.utils import calculate_workers_and_partitions
 
 
 logger = logging.getLogger(__name__)
@@ -46,7 +45,8 @@ def calculate_m_metric(flux_a: float, flux_b: float) -> float:
     return 2 * ((flux_a - flux_b) / (flux_a + flux_b))
 
 
-def calculate_measurement_pair_metrics(df: pd.DataFrame) -> pd.DataFrame:
+def calculate_measurement_pair_metrics(
+        df: pd.DataFrame, n_cpu: int = 0, max_partition_mb: int = 15) -> pd.DataFrame:
     """Generate a DataFrame of measurement pairs and their 2-epoch variability metrics
     from a DataFrame of measurements. For more information on the variability metrics, see
     Section 5 of Mooley et al. (2016), DOI: 10.3847/0004-637X/818/2/105.
@@ -54,6 +54,10 @@ def calculate_measurement_pair_metrics(df: pd.DataFrame) -> pd.DataFrame:
     Args:
         df (pd.DataFrame): Input measurements. Must contain columns: id, source, flux_int,
             flux_int_err, flux_peak, flux_peak_err, has_siblings.
+        n_cpu:
+            The desired number of workers for Dask
+        max_partition_mb:
+            The desired maximum size (in MB) of the partitions for Dask.
 
     Returns:
         Measurement pairs and 2-epoch metrics. Will contain columns:
@@ -66,10 +70,14 @@ def calculate_measurement_pair_metrics(df: pd.DataFrame) -> pd.DataFrame:
             vs_peak, vs_int - variability t-statistic
             m_peak, m_int - variability modulation index
     """
-    n_cpu = cpu_count() - 1
-    logger.debug(f"Running association with {n_cpu} CPUs")
-    n_partitions = calculate_n_partitions(df.set_index('source'), n_cpu)
-
+    
+    n_workers, n_partitions = calculate_workers_and_partitions(
+        df,
+        n_cpu=n_cpu,
+        max_partition_mb=max_partition_mb
+    )
+    logger.debug(f"Running association with {n_workers} CPUs")
+    
     """Create a DataFrame containing all measurement ID combinations per source.
     Resultant DataFrame will have a MultiIndex(["source", RangeIndex]) where "source" is
     the source ID and RangeIndex is an unnamed temporary ID for each measurement pair,
@@ -94,7 +102,7 @@ def calculate_measurement_pair_metrics(df: pd.DataFrame) -> pd.DataFrame:
         .groupby("source")["id"]
         .apply(
             lambda x: pd.DataFrame(list(combinations(x, 2))), meta={0: "i", 1: "i"},)
-        .compute(num_workers=n_cpu, scheduler="processes")
+        .compute(num_workers=n_workers, scheduler="processes")
     )
 
     """Drop the RangeIndex from the MultiIndex as it isn't required and rename the columns.
diff --git a/vast_pipeline/pipeline/utils.py b/vast_pipeline/pipeline/utils.py
index 2b0409c5..fa704ae1 100644
--- a/vast_pipeline/pipeline/utils.py
+++ b/vast_pipeline/pipeline/utils.py
@@ -19,14 +19,13 @@
 from astropy.coordinates import SkyCoord, Angle
 from django.conf import settings
 from django.contrib.auth.models import User
-from psutil import cpu_count
 from itertools import chain
 
 from vast_pipeline.image.main import FitsImage, SelavyImage
 from vast_pipeline.image.utils import open_fits
 from vast_pipeline.utils.utils import (
     eq_to_cart, StopWatch, optimize_ints, optimize_floats,
-    calculate_n_partitions
+    calculate_workers_and_partitions
 )
 from vast_pipeline.models import (
     Band, Image, Run, SkyRegion
@@ -669,13 +668,15 @@ def groupby_funcs(df: pd.DataFrame) -> pd.Series:
     return pd.Series(d).fillna(value={"v_int": 0.0, "v_peak": 0.0})
 
 
-def parallel_groupby(df: pd.DataFrame) -> pd.DataFrame:
+def parallel_groupby(df: pd.DataFrame, n_cpu: int = 0, max_partition_mb: int = 15) -> pd.DataFrame:
     """
     Performs the parallel source dataframe operations to calculate the source
     metrics using Dask and returns the resulting dataframe.
 
     Args:
         df: The sources dataframe produced by the previous pipeline stages.
+        n_cpu: The desired number of workers for Dask
+        max_partition_mb: The desired maximum size (in MB) of the partitions for Dask.
 
     Returns:
         The source dataframe with the calculated metric columns.
@@ -707,10 +708,11 @@ def parallel_groupby(df: pd.DataFrame) -> pd.DataFrame:
         'eta_peak': 'f',
         'related_list': 'O'
     }
-    n_cpu = cpu_count() - 1
-    logger.debug(f"Running association with {n_cpu} CPUs")
-    n_partitions = calculate_n_partitions(df, n_cpu)
-
+    n_workers, n_partitions = calculate_workers_and_partitions(
+        df,
+        n_cpu=n_cpu,
+        max_partition_mb=max_partition_mb)
+    logger.debug(f"Running association with {n_workers} CPUs")
     out = dd.from_pandas(df.set_index('source'), npartitions=n_partitions)
     out = (
         out.groupby('source')
@@ -718,7 +720,7 @@ def parallel_groupby(df: pd.DataFrame) -> pd.DataFrame:
             groupby_funcs,
             meta=col_dtype
         )
-        .compute(num_workers=n_cpu, scheduler='processes')
+        .compute(num_workers=n_workers, scheduler='processes')
     )
 
     out['n_rel'] = out['related_list'].apply(
@@ -751,7 +753,7 @@ def calc_ave_coord(grp: pd.DataFrame) -> pd.Series:
     return pd.Series(d)
 
 
-def parallel_groupby_coord(df: pd.DataFrame) -> pd.DataFrame:
+def parallel_groupby_coord(df: pd.DataFrame, n_cpu: int = 0, max_partition_mb: int = 15) -> pd.DataFrame:
     """
     This function uses Dask to perform the average coordinate and unique image
     and epoch lists calculation. The result from the Dask compute is returned
@@ -759,6 +761,8 @@ def parallel_groupby_coord(df: pd.DataFrame) -> pd.DataFrame:
 
     Args:
         df: The sources dataframe produced by the pipeline.
+        n_cpu: The desired number of workers for Dask
+        max_partition_mb: The desired maximum size (in MB) of the partitions for Dask.
 
     Returns:
         The resulting average coordinate values and unique image and epoch
@@ -770,15 +774,17 @@ def parallel_groupby_coord(df: pd.DataFrame) -> pd.DataFrame:
         'wavg_ra': 'f',
         'wavg_dec': 'f',
     }
-    n_cpu = cpu_count() - 1
-    logger.debug(f"Running association with {n_cpu} CPUs")
-    n_partitions = calculate_n_partitions(df, n_cpu)
+    n_workers, n_partitions = calculate_workers_and_partitions(
+        df,
+        n_cpu=n_cpu,
+        max_partition_mb=max_partition_mb)
+    logger.debug(f"Running association with {n_workers} CPUs")
 
     out = dd.from_pandas(df.set_index('source'), npartitions=n_partitions)
     out = (
         out.groupby('source')
         .apply(calc_ave_coord, meta=col_dtype)
-        .compute(num_workers=n_cpu, scheduler='processes')
+        .compute(num_workers=n_workers, scheduler='processes')
     )
 
     return out
@@ -899,7 +905,8 @@ def check_primary_image(row: pd.Series) -> bool:
 
 
 def get_src_skyregion_merged_df(
-    sources_df: pd.DataFrame, images_df: pd.DataFrame, skyreg_df: pd.DataFrame
+    sources_df: pd.DataFrame, images_df: pd.DataFrame, skyreg_df: pd.DataFrame,
+    n_cpu: int = 0, max_partition_mb: int = 15
 ) -> pd.DataFrame:
     """
     Analyses the current sources_df to determine what the 'ideal coverage'
@@ -916,6 +923,10 @@ def get_src_skyregion_merged_df(
         skyreg_df:
             Contains the sky regions of the pipeline run. I.e. all
             sky region objects for the run loaded into a dataframe.
+        n_cpu:
+            The desired number of workers for Dask
+        max_partition_mb:
+            The desired maximum size (in MB) of the partitions for Dask.
 
     Returns:
         DataFrame containing missing image information (see source code for
@@ -986,7 +997,9 @@ def get_src_skyregion_merged_df(
     # calculate some metrics on sources
     # compute only some necessary metrics in the groupby
     timer = StopWatch()
-    srcs_df = parallel_groupby_coord(sources_df)
+    srcs_df = parallel_groupby_coord(sources_df,
+                                     n_cpu=n_cpu,
+                                     max_partition_mb=max_partition_mb)
     logger.debug('Groupby-apply time: %.2f seconds', timer.reset())
 
     del sources_df
diff --git a/vast_pipeline/utils/utils.py b/vast_pipeline/utils/utils.py
index 4719191b..a57dd0c7 100644
--- a/vast_pipeline/utils/utils.py
+++ b/vast_pipeline/utils/utils.py
@@ -13,6 +13,7 @@
 from astropy.coordinates import SkyCoord, Longitude, Latitude
 import numpy as np
 import pandas as pd
+from psutil import cpu_count
 
 
 logger = logging.getLogger(__name__)
@@ -421,6 +422,33 @@ def calculate_n_partitions(df, n_cpu, partition_size_mb=15):
 
     partition_size_mb = int(np.ceil(mem_usage_mb / n_partitions))
 
-    logger.debug(f"Using {n_partitions} partions of {partition_size_mb}MB")
+    logger.debug("Using %d partions of %dMB", n_partitions, partition_size_mb)
 
     return n_partitions
+
+def calculate_workers_and_partitions(df, n_cpu=None, max_partition_mb=15):
+    """
+    Return number of workers and the number of partitions for Dask
+
+    Args:
+        df: The pandas dataframe to be partitionined.
+            Don't calculate partitions if df is None
+        num_cpu_max: The maximum number of workers to allocate.
+                     The default of None means use one less than all available cores
+        max_partition_mb: The maximum partition size in MB.
+
+    Returns:
+        (num_workers, n_partitions): Calculated workers and partitions.
+    """
+    num_cpu = cpu_count() - 1
+    num_workers = num_cpu if n_cpu is None else n_cpu
+    if num_workers > num_cpu:
+        logger.debug("%d desired workers is greater than available cores. Limiting to %s.",
+                     num_workers, num_cpu)
+        num_workers = num_cpu
+    n_partitions = 0
+    if df is not None:
+        n_partitions = calculate_n_partitions(df, num_workers,
+                                              partition_size_mb=max_partition_mb)
+
+    return num_workers, n_partitions
diff --git a/webinterface/settings.py b/webinterface/settings.py
index 5e57e49a..6336dd43 100644
--- a/webinterface/settings.py
+++ b/webinterface/settings.py
@@ -336,6 +336,9 @@
     'suppress_astropy_warnings': True,
     'pair_metrics': True,
     'source_aggregate_pair_metrics_min_abs_vs': 4.3,
+    'num_workers': 'null',
+    'num_workers_io': 5,
+    'max_partition_mb': 15
 }
 
 # default max concurrent pipeline runs