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Julia package installation is not one operation. A long Pkg.add or Pkg.update command may be resolving versions and downloading files, precompiling the environment, loading a package, compiling methods on their first call, or genuinely hanging. Identify the phase first; each has a different fix.
Pkg normally precompiles packages and their dependencies after additions and updates, so apparent installation time often includes compilation. The remedies below let you separate that work, avoid repeating it, move caches to suitable storage, trace expensive code, and distinguish a slow build from a precompilation deadlock.
First, identify which phase is slow
Watch the last line printed and note exactly when the delay occurs. Do not apply storage, environment, and package-code remedies interchangeably.
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|---|---|---|
| Registry updates, version solving, or downloads take a long time | Resolution and installation | Check the project manifest, network path, and depot storage. |
Pkg.add finishes its download messages and prints “Precompiling environment” |
Automatic precompilation | Batch changes, defer automatic precompilation, or trace the precompile workload. |
The delay starts at using Package |
Package loading and initialization | Measure imports, inspect __init__(), and reduce unnecessary dependencies. |
| Loading completes, but the first call to a feature is slow | Just-in-time compilation of methods | Trace compiled methods and consider representative precompile workloads. |
| Precompilation makes no progress indefinitely | Possible open-task or IO hang | Isolate the package and dependency, then investigate initialization behavior. |
Pkg’s environment documentation explains that adding or updating packages normally triggers precompilation of the changed packages and dependencies: Pkg working with environments. A message that appears to mean “installation” therefore does not prove that downloading is the bottleneck.
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A quick observation checklist
Before changing anything, record the Julia release, the exact command, the active project directory, and the last line printed. Note whether the project has both Project.toml and Manifest.toml. Also note the depot path, whether it is local or network-mounted, and whether another Julia process is using the same depot. These details distinguish a resolver problem from a cache or filesystem problem.
Run a small, isolated reproduction when possible. Starting Julia with --startup-file=no excludes imports and commands from your personal startup file. If the same project is fast in an isolated session but slow in a normal one, the extra work is probably in startup code rather than Pkg itself. If the delay always begins after a visible “Precompiling environment” message, focus on compilation instead of repeatedly retrying downloads.
When resolution or downloading is the slow part
Use the project’s recorded environment
In a project containing Manifest.toml, instantiate the recorded versions instead of asking the resolver to construct a new environment:
julia --project=. -e 'using Pkg; Pkg.instantiate()'Pkg.instantiate() installs the environment represented by the project files. If the manifest is missing or does not describe the intended fixed state, Julia may need to resolve versions again, producing different work from a reproducible checkout. The Julia FAQ explains why preserving a manifest matters when reproducing an environment: Julia Frequently Asked Questions.
Instantiation does not mean that compilation disappears. Once the packages and artifacts are available, Pkg can still precompile the environment. Treat “download finished” and “environment ready” as separate milestones. In automation, keep the project files together and report whether the command was an initial instantiate, an update, or a package addition; those operations can exercise different resolver paths.
Separate network work from local compilation
Registry operations and artifact downloads are sensitive to the network and to where the depot is stored. If output is still showing registry or download activity, changing precompile settings will not make those transfers faster. Capture the exact command, project path, Julia version, operating system, and the point at which output stops before treating it as a compiler problem.
A slow network drive can affect both stages: it can delay artifact transfers and make the many small reads and writes used by compilation painfully slow. Move the depot only after confirming that storage is involved; a depot move cannot repair a resolver conflict or an unavailable network resource.
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Batch package changes on Julia 1.13 and later
Adding packages one at a time can trigger a precompile after each modification. For Julia 1.13 or later, Pkg documents a batching form that defers precompilation until the block finishes:
using Pkg
Pkg.precompile() do
Pkg.add("PackageA")
Pkg.add("PackageB")
Pkg.add("PackageC")
endThe package operations occur inside one transaction-like workflow, and Pkg starts the deferred precompilation after the block. This reduces repeated setup when several changes are known in advance; it does not remove the compilation required for the final environment. The version requirement and API are documented on the Pkg environments page.
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Batching is most useful when a script or setup process knows all of its additions. It is less useful when package changes are separated by long periods of interactive experimentation, because each later change can legitimately invalidate part of the previous work.
Disable automatic precompilation when you intentionally want to defer it
Set the environment variable before launching Julia:
export JULIA_PKG_PRECOMPILE_AUTO=0On Windows, set the variable with the shell’s normal environment-variable command before starting Julia. With automatic precompilation disabled, package changes return sooner because the work is postponed, not because it has disappeared. Run it explicitly when convenient:
pkg> precompileThis is useful for a scripted series of environment edits or for a session where you want to control when CPU and disk activity occur. Leaving automatic precompilation disabled indefinitely means the first later user of the environment may pay the deferred cost. In continuous integration, an explicit precompile step can make the timing visible and keep it separate from dependency editing, but it still consumes the same compilation resources.
Let background precompilation continue interactively on Julia 1.14 and later
Current Pkg documentation describes interactive controls for background precompilation beginning with Julia 1.14. Do not assume those controls exist on older Julia releases; check the version-specific environment documentation before using them: Pkg working with environments. Background execution changes when the work is performed, not how much code must be compiled. It can improve responsiveness while you continue working, but avoid terminating the process or changing the environment underneath an active precompile job.
When the package depot is slowing everything down
Recognize unsuitable depot locations
Julia’s depot contains registries, installed packages, artifacts, compiled caches, and logs. A depot inside a synchronized corporate home folder, on a slow network drive, or under a tight quota can make many small file operations expensive. The official depot guide lists these conditions as reasons to customize JULIA_DEPOT_PATH: Pkg depots.
Storage problems often look inconsistent: one package may load from a warm cache while another causes a long burst of metadata and cache-file activity. A quota can also leave enough space for downloads but not enough for compiled artifacts, so check available space rather than assuming that successful installation proves the depot is healthy.
Move the depot before Julia starts
On Unix-like systems, set the variable in the shell before launching Julia. Keep the trailing colon:
export JULIA_DEPOT_PATH="/custom/depot:"The trailing separator appends the default system depot after your custom location. Omitting it can hide bundled or system resources and may cause unnecessary standard-library precompilation. On Windows, use a semicolon as the separator, for example:
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set JULIA_DEPOT_PATH=C:customdepot;Set the environment variable before starting Julia rather than changing DEPOT_PATH after startup. A process that has already initialized its depot may have read caches and paths that will not be safely reconfigured mid-session. Confirm the effective depot in a fresh Julia process before comparing timings.
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Share a depot carefully on multi-user hosts
A shared depot on a fast local filesystem can prevent every user or job from compiling the same package independently. Since Julia 1.10, processes using the same depot coordinate through pidfile locks, according to the depot documentation. Coordination only works when the processes can access the same depot and locking semantics are reliable.
Cache reuse is not guaranteed merely because two jobs see the same directory. Environment changes, Julia version differences, and CPU-target differences can require new native-code caches. A shared depot should therefore be treated as a coordination and storage strategy, not as permission to copy arbitrary cache files between machines.
Native-code cache compatibility also matters. Julia 1.9 and later can produce caches that depend on CPU features. On a heterogeneous cluster, configure JULIA_CPU_TARGET for a compatible target rather than copying one machine’s cache indiscriminately to machines with different instruction sets. The depot guide and Julia FAQ describe these coordination and compatibility considerations: Pkg depots and Julia Frequently Asked Questions.
When loading a package or its first call is slow
Measure import-time work
Loading cost is separate from environment precompilation. Start an isolated session so your startup file does not add unrelated imports:
julia --startup-file=noThen use the import timing macro with the package:
julia> @time_imports using MyPackage@time_imports reports where package-load time is spent. The --startup-file=no option makes the observation reproducible by excluding user startup commands. Both techniques are covered in Julia’s performance guidance: Julia Performance Tips.
Measure more than one run. The first run can include cache reads and compilation, while a later run may reuse the generated cache. Record whether you are timing a fresh environment, a warm depot, or a package after source changes; otherwise two apparently contradictory timings may both be accurate.
Trace methods compiled during use
If loading is acceptable but the first call to a feature pauses, trace compilation and timing:
julia --startup-file=no --trace-compile=stderr --trace-compile-timing -e 'using MyPackage; MyPackage.some_operation()'The trace identifies methods compiled and reports their timings. It diagnoses where compilation work occurs; it does not automatically optimize those methods or guarantee a shorter run. Use a representative call, because tracing an unused code path cannot explain a delay in a different feature.
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Reduce avoidable package-load work
For an application or package you maintain, the performance guide recommends:
- Depend only on packages that are actually needed at load time.
- Use package extensions for optional interoperability so an optional integration is not imported unconditionally.
- Keep
__init__()lightweight and avoid expensive or unnecessary initialization. - Investigate invalidations, which can force methods to be recompiled after definitions change.
These are package-design changes, not user-side switches. A slow import caused by a package’s dependency graph or initialization code must be corrected in that code or avoided by choosing a different loading path. Removing a dependency simply because it is large can be unsafe; first verify whether the application actually needs the functionality it provides.
Trace methods executed during precompilation
To pass compile tracing into precompile subprocesses, set Julia’s precompile trace setting and run the package-manager command:
julia --startup-file=no
julia> Base.PRECOMPILE_TRACE_COMPILE[] = "stderr"
julia> using Pkg
pkg> precompileThe trace can reveal a particular package or method responsible for a large portion of precompilation. The official performance page also lists Tracy and SnoopCompile as profiling options when a deeper investigation is needed: Julia Performance Tips. Keep tracing for diagnosis; it adds output and can itself affect the behavior you are measuring.
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For package authors, precompile representative workloads
PrecompileTools.jl can execute representative workloads during precompilation so native code for common operations is cached ahead of first use. That can reduce time to first execution for users, while moving some computation into the precompile phase. Workloads should reflect real, common calls and remain configurable; this is a maintainer-level trade-off, not a universal end-user installation-speed switch. The performance guide discusses this approach alongside other compilation techniques: Julia Performance Tips.
Overly broad workloads can make installation and precompilation longer, so authors should choose representative paths rather than attempting to compile every possible method combination. If a package is used in several distinct ways, document which workloads are covered and continue measuring both precompile time and first-use latency.
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Distinguish a long compile from no progress
A long compile eventually emits progress or completes. A hang often shows no progress indefinitely, leaves a precompile worker alive, or reproduces at the same package every time. Do not repeatedly delete caches or interrupt production jobs without first recording the package name, dependency versions, Julia version, operating system, and exact output.
Deleting caches can force all ordinary compilation to happen again and therefore obscure the original symptom. First determine whether the process is doing CPU or IO work, waiting for another process, or repeatedly reaching the same initialization point. The goal is to produce a small, repeatable case, not merely to make one invocation disappear.
Isolate the package and dependency
The Julia developer guide describes hangs caused by open tasks or IO created during package initialization. Follow a reduction sequence:
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- Identify the package whose precompile subprocess stops making progress.
- Try precompiling that package in a minimal environment containing only its required dependencies.
- Reduce imports and included files until the smallest reproducing dependency set is known.
- Bisect dependencies by removing or re-adding them to determine which combination triggers the behavior.
- Inspect initialization code, especially
__init__(), for timers, background tasks, channels, sockets, or other persistent IO.
A package can precompile alone yet hang when loaded as a dependency if its initialization creates persistent work in that context. Maintainers should close or await such work, or prevent inappropriate initialization during precompilation. End users should report the minimal reproducer rather than patching package internals blindly. The diagnostic sequence is documented in Fixing precompilation hangs due to open tasks or IO, generated for Julia 1.13.0 on 2026-09-09.
Timers and background tasks are legitimate in a running application but can keep a precompile process alive when they are created at the wrong time. A maintainer may need to move that setup behind an explicit runtime path, close resources during teardown, or wait for a task to finish. These are code corrections; changing a user’s depot or disabling automatic precompilation only masks the symptom.
Common approaches that do not solve the underlying phase
- Repeatedly reinstalling the same package: this repeats downloads and may trigger the same precompile work without changing the cause.
- Disabling automatic precompilation and stopping there: the compilation requirement remains and will surface at the next explicit precompile or first use.
- Moving only
DEPOT_PATHinside a running session: setJULIA_DEPOT_PATHbefore launch so Julia initializes all paths consistently. - Copying compiled caches across unlike machines: CPU features, Julia versions, and environment changes can make those caches unusable.
- Calling every long compile a hang: collect output and timing first; a genuine hang requires isolation of initialization behavior.
Choose a remedy by symptom
| Remedy | Where it changes behavior | Version or host constraint | What it actually changes |
|---|---|---|---|
Batch edits with Pkg.precompile() do ... end |
User package workflow | Programmatic form requires Julia 1.13 or later | Combines several edits before one precompile; reduces repeated work. |
Set JULIA_PKG_PRECOMPILE_AUTO=0, then run pkg> precompile |
User or automation environment | Set before launching Julia; available behavior depends on the Julia/Pkg version in use | Moves compilation to an explicit later step; does not remove it. |
| Use interactive background-precompile controls | User session | Documented for Julia 1.14 and later | Lets work continue in the background; does not reduce total compilation. |
Relocate or share JULIA_DEPOT_PATH |
User or system storage configuration | Preserve the default depot separator; shared hosts need reliable locking and compatible CPU targets | Improves filesystem access and cache reuse; may avoid duplicate compilation. |
| Trace imports and precompile methods | Package or application development | Use the documented tracing flags and macros | Finds expensive methods and dependencies so code can be changed; tracing itself is diagnostic. |
| Isolate an open-task or IO hang | Package-maintainer debugging | Applies to persistent initialization behavior, not ordinary slow compilation | Finds and corrects code that prevents the precompile process from finishing. |
What to record before asking for help
A precise report is more actionable than “Julia is slow.” Capture:
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- Julia version, including whether the release is 1.13, 1.14, or another version relevant to the control you used.
- Operating system, CPU architecture, and whether the machine is local, virtualized, or a shared cluster node.
- The exact command and project directory.
- Whether
Project.tomlandManifest.tomlare present, and whether you usedPkg.instantiate(). - The depot path and whether it is synchronized, quota-limited, or on a network filesystem.
- The last output line, elapsed time, and whether the delay occurs during download, “Precompiling environment,”
using Package, or a first function call. - For a suspected hang, the package and dependency versions plus a minimal reproducer.
This information separates resolver and storage issues from package-code costs and from a true initialization deadlock, allowing the appropriate fix to be applied once instead of repeatedly clearing caches or reinstalling unrelated packages.
Frequently Asked Questions
Why does Julia precompile every time I add a package?
Pkg normally precompiles changed packages and their dependencies after additions or updates. If you make several edits separately, each edit can trigger work. On Julia 1.13 and later, batch edits with Pkg.precompile() do ... end; alternatively disable automatic precompilation and run pkg> precompile explicitly later. These options change when work occurs, not the amount of code that must eventually be compiled.
How do I speed up Julia package installation?
First determine whether the delay is resolution/download, precompilation, loading, or first-use compilation. For a fixed checkout, use Pkg.instantiate() with its manifest. For repeated edits, batch them. If the depot is on synchronized or network storage, set JULIA_DEPOT_PATH to a fast local location before launching Julia, while preserving the default depot separator.
Is a long “Precompiling environment” message a hang?
Not necessarily. Compilation can be lengthy while still making progress. Record the last output and elapsed time, then use compile tracing to identify expensive methods. Treat it as a possible hang when progress stops indefinitely or the same package always blocks; isolate that package and inspect initialization for persistent tasks or IO.
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You can set JULIA_PKG_PRECOMPILE_AUTO=0 before starting Julia, but this postpones compilation. Run pkg> precompile at a controlled time, or the first later user of the environment may pay the deferred cost.
Why is using Package slow even after precompilation?
Package loading can include dependency imports and __init__() work that is distinct from environment precompilation. Use --startup-file=no and @time_imports to locate import costs. Package maintainers can reduce unnecessary dependencies, use extensions for optional integrations, simplify initialization, and investigate invalidations.
Can I share Julia’s compiled cache between cluster nodes?
A shared depot can reduce duplicate compilation when processes use the same reliable filesystem; since Julia 1.10, shared-depot processes coordinate with pidfile locks. However, Julia version, environment changes, and CPU features affect cache compatibility. On heterogeneous machines, configure a compatible JULIA_CPU_TARGET instead of copying one node’s cache indiscriminately.
The Bottom Line
Find the phase before changing the remedy. Use a manifest and Pkg.instantiate() for reproducible environments, batch or explicitly schedule precompilation when editing dependencies, and move the depot off synchronized or slow storage. Measure imports and compile traces when package code is responsible. If there is truly no progress, reduce the environment and investigate persistent tasks or IO in initialization. Every fix has a scope: some reduce repeated work, some improve storage or cache reuse, and some only move compilation to a more convenient time.
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