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Start by watching the last message Julia prints, then use the matching remedy: keep a manifest for reproducible installs, batch package changes, move a depot off slow or synchronized storage, trace expensive loading and compilation, or isolate a package whose initialization leaves tasks or I/O running.
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Identify the slow phase before changing anything
Run the same command again while watching its output. Pkg normally precompiles packages and their dependencies after add and update, so a command that looks like an installation can spend most of its time compiling an environment. The official Pkg environment documentation describes this automatic behavior and the explicit precompile operation.
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This phase shows registry activity, version resolution, downloads, unpacking, and writes into the depot. It is usually the first work after add or instantiate. A slow network, a busy package server, or a depot on a synchronized or network-mounted folder can dominate here. If output stops before any “Precompiling environment” message, investigate storage and network conditions rather than compiler behavior.
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Environment precompilation
Messages such as “Precompiling environment” indicate that package source has already been obtained and Julia is building reusable caches. The number of packages is not a reliable predictor of duration: dependency graphs, invalidations, the Julia version, CPU target, and whether a compatible cache already exists all matter. Repeated package changes can legitimately trigger this phase again.
Package loading
If the delay occurs at using SomePackage after installation has completed, measure loading separately. Loading runs top-level code, resolves dependencies, executes initialization, and may compile methods needed by that package. The performance guide recommends @time_imports to locate package-load costs; its guidance is in the development-version Julia Performance Tips.
First execution of a feature
A package can load quickly while the first call to a particular algorithm is slow. Julia then compiles methods for the concrete argument types and code paths used by that call. This is normal just-in-time compilation, not necessarily a failed installation. Repeat the same call to see whether subsequent executions avoid that one-time cost.
A genuine precompilation hang
A process that makes no progress indefinitely, especially when a package works alone but hangs as a dependency, can indicate persistent tasks or open I/O created during initialization. That is different from expensive but finite compilation. The developer guide’s diagnostic procedure is titled Fixing precompilation hangs due to open tasks or IO.
Capture a reproducible description of the delay
Before deleting caches or changing global settings, record enough context for the problem to be reproduced:
- Record the Julia release with
julia --version, the operating system, and whether the machine is local, a container, a shared workstation, or a cluster node. - From the project directory, start Julia and run
using Pkg; Pkg.status(). Note whether the project has aManifest.tomland whether the delay followsadd,update,instantiate,precompile, orusing PackageName. - Inspect the depot location with
DEPOT_PATHinside Julia. On a Unix-like shell,echo $JULIA_DEPOT_PATHshows an explicitly configured path; on Windows PowerShell, use$env:JULIA_DEPOT_PATH. - Copy the final several lines printed before the pause. “Resolving package versions,” download messages, “Precompiling environment,” and a package-load prompt point to different remedies.
- Repeat once with startup customizations excluded:
julia --startup-file=no. If the delay disappears, inspect the user startup file rather than blaming the package installation.
Do not treat a single elapsed time as a universal benchmark. Hardware, storage, cache state, dependency graph, and Julia release all change the result; the official Pkg page’s illustrative example is not an expected duration for every environment.
When resolution, downloads, or installation are the slow part
Use the project’s manifest when you need recorded versions
For a project that includes a Manifest.toml, Pkg.instantiate() installs the versions recorded for that environment. A missing manifest, or one that does not contain the intended fixed state, can require a different resolution operation. Preserve the manifest when reproducing an environment across machines; the Julia Frequently Asked Questions explains why it matters for version reproducibility.
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This distinction prevents a common misdiagnosis: deleting a manifest or starting in a different project can make Julia resolve a larger or different graph, after which precompilation also has more work to do. Activate the intended project before running installation commands:
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julia --project=. -e 'using Pkg; Pkg.instantiate()'
The command installs the active project’s recorded environment when a suitable manifest is present. It does not guarantee fast downloads if the depot or network path is slow.
Check the storage path used by the depot
Pkg stores registries, source trees, artifacts, compiled caches, and preferences in its depot. A synchronized corporate home directory, a quota-constrained filesystem, or a slow network drive can make both installation and compilation appear stuck. The official Pkg depot guide recommends customizing the depot for these situations.
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export JULIA_DEPOT_PATH="/custom/depot:"
The trailing colon is significant: it retains the default system depot after the custom depot. On Windows, use a semicolon as the separator, for example:
$env:JULIA_DEPOT_PATH="D:JuliaDepot;"
Changing DEPOT_PATH after Julia has started is too late for startup-time decisions and can produce confusing results. Configure the environment variable in the shell, job script, service definition, or launcher, then start a new Julia process. Omitting the trailing separator can hide bundled or system resources and lead to unnecessary standard-library precompilation.
When every package change triggers another long precompile
Batch changes instead of compiling after each one
On Julia 1.13 and later, Pkg documents a programmatic batching form that defers automatic precompilation until the block finishes:
julia --project=. -e 'using Pkg; Pkg.precompile() do
Pkg.add("PackageA")
Pkg.add("PackageB")
end'
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Use the same idea from a Julia session when adding or updating several packages. The point is to make one environment change set, followed by one precompile pass, rather than repeatedly paying startup and dependency-analysis costs. The exact package names and operations belong inside the block; do not batch unrelated projects together.
Temporarily defer automatic precompilation
If you are making many exploratory edits, set JULIA_PKG_PRECOMPILE_AUTO=0 before launching Julia, or use the session-level controls documented on the Pkg environment page. This postpones automatic work; it does not remove the compilation requirement. Run an explicit precompile when the environment is ready:
pkg> precompile
Deferral is useful for a setup script or a sequence of package changes, but it can make the first later using command pay the accumulated cost. Keep automatic precompilation enabled when predictable package-load latency is more important than an uninterrupted setup session.
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Current Pkg documentation describes interactive background-precompilation controls for Julia 1.14 and later. Treat these as version-specific features: do not assume the same controls exist in older Julia releases. They are useful when you want to continue working while Pkg performs cache generation, but they still consume CPU, memory, and storage bandwidth.
Make cache reuse possible on workstations and clusters
Share a depot carefully on multi-user hosts
A shared depot can avoid compiling identical package versions separately for every account. Since Julia 1.10, processes using the same depot coordinate through pidfile locks, as documented in the depot guide and the Julia FAQ. This coordination prevents several workers from unnecessarily generating the same cache at once, but it does not make incompatible environments identical.
Give each job a stable, writable depot location and allow the lock files to work. Do not place a shared depot on a filesystem with unreliable locking semantics, and do not interrupt a process that is actively writing caches unless you have a recovery plan.
Account for Julia and CPU-target compatibility
Cache reuse depends on more than package versions. Julia releases, platform, and CPU features affect native-code compatibility. Julia 1.9 and later can produce native-code caches whose compatibility depends on CPU features; heterogeneous cluster nodes may therefore need an appropriate JULIA_CPU_TARGET setting. Choose a target compatible with every machine that will consume the cache, accepting that a conservative target may sacrifice some instruction-specific performance.
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Do not copy one machine’s compiled cache indiscriminately across dissimilar CPUs or operating systems. A cache miss after moving an environment can be expected rather than evidence that Pkg is reinstalling the source incorrectly.
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Trace package-load and compilation costs
Measure imports without startup-file noise
Use a clean process to separate package behavior from personal startup code:
julia --startup-file=no -e 'using SomePackage'
To identify which imports account for loading time, use the documented macro:
julia --startup-file=no -e '@time_imports using SomePackage'
The output points to packages whose loading is expensive. It diagnoses where time is spent; it does not automatically make those packages faster. Repeat the measurement after each targeted change rather than assuming that the largest dependency is the only cause.
Trace methods compiled during a load
Julia can print methods compiled and their timings with:
julia --startup-file=no --trace-compile=stderr --trace-compile-timing -e 'using SomePackage'
This is useful when a package’s top-level code exercises a large amount of functionality during loading. Capture the output to a file if it is extensive, then group entries by package or call path. Compilation tracing identifies cost; it does not eliminate the methods that the application actually needs.
Trace methods used by environment precompilation
To pass tracing options to precompilation subprocesses, set the documented flag inside Julia and then run Pkg’s precompile operation:
julia --project=.
julia> Base.PRECOMPILE_TRACE_COMPILE[] = "stderr"
julia> using Pkg
pkg> precompile
Use this when the delay is specifically in “Precompiling environment,” not merely in the first call after using. For deeper investigations, the official performance guide also mentions Tracy and SnoopCompile. These tools help explain invalidations and compilation work; they are diagnostic instruments, not universal speed switches.
Reduce recurring package-load work
Keep dependencies to what the package needs
For package authors, remove dependencies that are not required for the core code path. Every dependency can add loading, method-definition, invalidation, and precompilation work. Optional interoperability can often be moved behind package extensions so users who do not need that integration avoid loading it.
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Make initialization cheap and safe
Review __init__() methods for expensive setup, unnecessary file or network I/O, and work that every user pays for even when a feature is unused. Initialization should establish required runtime state rather than eagerly constructing optional resources. This is both a performance concern and a precompilation concern: side effects that are appropriate after startup may be unsafe while Pkg is building a cache.
Address invalidations instead of hiding them
Changing a method or type used widely can invalidate compiled code and force downstream recompilation. Trace the invalidated paths, narrow overly broad method definitions where appropriate, and avoid changing global interfaces merely to silence a timing symptom. The performance guide’s loading and invalidation advice applies primarily to package maintainers; end users should report a reproducible case rather than editing installed package files.
Use representative precompile workloads
Package authors can use PrecompileTools.jl to run representative workloads while precompiling, caching native code for common operations and reducing time to first execution. This moves some work into the precompile phase and is configurable. It is not a universal end-user fix for slow installation, and an excessive workload can make environment precompilation longer.
Diagnose a true precompilation hang
When output stops indefinitely, first determine whether the same package hangs by itself or only through another dependency. The developer guide’s procedure is a maintainer-oriented investigation:
- Create a minimal environment containing the suspected package and as few dependencies as possible.
- Precompile the package on its own. If it succeeds, add dependencies or imports back in small groups.
- Use dependency bisection: remove roughly half of the imports, test again, and continue narrowing the set that reintroduces the hang.
- Reduce the package’s included code and initialization until the operation completes, then restore pieces incrementally.
- Inspect
__init__()for timers, background tasks, channels, sockets, file handles, or other persistent I/O. A package may precompile alone yet hang as a dependency when initialization leaves work running.
Timers and background tasks are examples of persistent work that can keep a precompile subprocess alive. Maintainers should close or wait for resources that must finish, or prevent inappropriate initialization during precompilation. End users should not apply internal source edits blindly; report the minimal reproducer, Julia version, package versions, project files, and the last output line to the package maintainers.
Choose the remedy that matches the symptom
| Symptom | Where to change it | Version or host constraint | What the remedy changes |
|---|---|---|---|
Several add or update commands each trigger precompilation |
User workflow or setup script | Programmatic batching is documented for Julia 1.13 and later | Batches changes and shifts one compilation pass to the end |
| You want installation commands to finish before compiling | Shell environment or Julia session | JULIA_PKG_PRECOMPILE_AUTO=0; run pkg> precompile later |
Defers work; does not eliminate compilation |
| Downloads and cache writes are slow on a synchronized, quota-limited, or network depot | User or administrator storage configuration | Set JULIA_DEPOT_PATH before startup; preserve the default depot separator |
Moves I/O to faster storage and can improve cache reuse |
| Multiple users compile the same environment on one host | Shared depot and job configuration | Julia 1.10 and later coordinate same-depot processes with pidfile locks | Reduces duplicate compilation while retaining lock coordination |
| Cache reuse fails across cluster nodes | Cluster administrator’s depot and CPU-target policy | Julia 1.9 and later native-code compatibility can depend on CPU features | Aligns cache generation with machines that will consume it |
using is slow or the first feature call compiles heavily |
Package design and precompile workload | Use tracing, dependency reduction, extensions, and representative workloads | Reduces unnecessary load or shifts common compilation earlier |
| Precompilation never completes and a package leaves work running | Package source and dependency diagnosis | Use the developer-guide isolation and bisection procedure | Finds and corrects persistent task or I/O behavior rather than ordinary compile cost |
What to include when asking for help
A useful issue report distinguishes “slow” from “stuck.” Include the Julia version, operating system and CPU, exact command, active project path, whether Project.toml and Manifest.toml are present, depot path, package versions, and the last output line. State whether the delay occurs during resolution, download, “Precompiling environment,” using, or the first call to a function. On a cluster, include whether the depot is shared and whether nodes have different CPU features. This information lets maintainers decide whether to inspect storage, cache compatibility, package initialization, invalidations, or a genuine hang.
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