JupyterLab TensorFlow 2.3构建失败，524

Question

我在AI中创建了一个新笔记本，它具有以下属性：

​

当我打开笔记本时，我会收到以下提示：

​

一段时间后，我收到以下错误消息：

Build failed with 524.

    If you are experiencing the build failure after installing an extension (or trying to include previously installed extension after updating JupyterLab) please check the extension repository for new installation instructions as many extensions migrated to the prebuilt extensions system which no longer requires rebuilding JupyterLab (but uses a different installation procedure, typically involving a package manager such as 'pip' or 'conda').

    If you specifically intended to install a source extension, please run 'jupyter lab build' on the server for full output.

当我在终点站运行jupyter实验室构建时，我得到：

    [LabBuildApp] WARNING | Config option `kernel_spec_manager_class` not recognized by `LabBuildApp`.
[LabBuildApp] JupyterLab 3.2.8
[LabBuildApp] Building in /opt/conda/share/jupyter/lab
[LabBuildApp] Building jupyterlab assets (production, minimized)
Build failed.
Troubleshooting: If the build failed due to an out-of-memory error, you
may be able to fix it by disabling the `dev_build` and/or `minimize` options.

If you are building via the `jupyter lab build` command, you can disable
these options like so:

jupyter lab build --dev-build=False --minimize=False

You can also disable these options for all JupyterLab builds by adding these
lines to a Jupyter config file named `jupyter_config.py`:

c.LabBuildApp.minimize = False
c.LabBuildApp.dev_build = False

If you don't already have a `jupyter_config.py` file, you can create one by
adding a blank file of that name to any of the Jupyter config directories.
The config directories can be listed by running:

jupyter --paths

Explanation:

- `dev-build`: This option controls whether a `dev` or a more streamlined
`production` build is used. This option will default to `False` (i.e., the
`production` build) for most users. However, if you have any labextensions
installed from local files, this option will instead default to `True`.
Explicitly setting `dev-build` to `False` will ensure that the `production`
build is used in all circumstances.

- `minimize`: This option controls whether your JS bundle is minified
during the Webpack build, which helps to improve JupyterLab's overall
performance. However, the minifier plugin used by Webpack is very memory
intensive, so turning it off may help the build finish successfully in
low-memory environments.

An error occurred.
RuntimeError: JupyterLab failed to build
See the log file for details:  /tmp/jupyterlab-debug-ke3s6jt2.log
(base) jupyter@lookalike-conversion-model2:~$

当我检查日志时，我得到以下错误(post底部的完全错误)：

FATAL ERROR: Ineffective mark-compacts near heap limit Allocation failed - JavaScript heap out of memory

我想现在如何修复这个错误来实现一个成功的构建？我认为这是在以后运行管道时导致问题的原因，因为一旦模型完成了培训，我就会得到这个错误：

2022-02-24T13:15:54.660529854ZERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.

任何帮助都将不胜感激！

完整日志堆栈跟踪：

[LabBuildApp] Building in /opt/conda/share/jupyter/lab
[LabBuildApp] Node v12.22.6

[LabBuildApp] Yarn configuration loaded.
[LabBuildApp] Building jupyterlab assets (production, minimized)
[LabBuildApp] > node /opt/conda/lib/python3.7/site-packages/jupyterlab/staging/yarn.js install --non-interactive
[LabBuildApp] yarn install v1.21.1
[1/5] Validating package.json...
[2/5] Resolving packages...
success Already up-to-date.
Done in 0.89s.

[LabBuildApp] > node /opt/conda/lib/python3.7/site-packages/jupyterlab/staging/yarn.js yarn-deduplicate -s fewer --fail
[LabBuildApp] yarn run v1.21.1
$ /opt/conda/share/jupyter/lab/staging/node_modules/.bin/yarn-deduplicate -s fewer --fail
Done in 1.53s.

[LabBuildApp] > node /opt/conda/lib/python3.7/site-packages/jupyterlab/staging/yarn.js run build:prod:minimize
[LabBuildApp] yarn run v1.21.1
$ webpack --config webpack.prod.minimize.config.js

<--- Last few GCs --->

[17013:0x55b0f665b100]   203434 ms: Mark-sweep 2025.5 (2051.4) -> 2024.2 (2051.4) MB, 1531.2 / 0.0 ms  (average mu = 0.079, current mu = 0.009) allocation failure scavenge might not succeed
[17013:0x55b0f665b100]   205286 ms: Mark-sweep 2028.4 (2054.3) -> 2025.7 (2052.1) MB, 1842.0 / 0.0 ms  (average mu = 0.040, current mu = 0.005) allocation failure scavenge might not succeed


<--- JS stacktrace --->

==== JS stack trace =========================================

    0: ExitFrame [pc: 0x12edd074a8d9]
    1: StubFrame [pc: 0x12edd0708ad2]
    2: StubFrame [pc: 0x12edd07bae96]
Security context: 0x18eab2cb2ec9 <JSObject>
    3: /* anonymous */(aka /* anonymous */) [0x1766605ff901] [/opt/conda/share/jupyter/lab/staging/node_modules/webpack/node_modules/webpack-sources/lib/applySourceMap.js:156] [bytecode=0x1766605fa259 offset=503](this=0x1ea94dc00451 <undefined>,0x1dcd32299611 <String[2]: e.>,0x0b62ae...

FATAL ERROR: Ineffective mark-compacts near heap limit Allocation failed - JavaScript heap out of memory
 1: 0x55b0f3faff69 node::Abort() [webpack]
 2: 0x55b0f3ee7b87 std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > node::SPrintFImpl<char const*>(char const*, char const*&&) [webpack]
 3: 0x55b0f41390b2 v8::Utils::ReportOOMFailure(v8::internal::Isolate*, char const*, bool) [webpack]
 4: 0x55b0f413938b v8::internal::V8::FatalProcessOutOfMemory(v8::internal::Isolate*, char const*, bool) [webpack]
 5: 0x55b0f42ccf96  [webpack]
 6: 0x55b0f42df8ea v8::internal::Heap::PerformGarbageCollection(v8::internal::GarbageCollector, v8::GCCallbackFlags) [webpack]
 7: 0x55b0f42e05f4 v8::internal::Heap::CollectGarbage(v8::internal::AllocationSpace, v8::internal::GarbageCollectionReason, v8::GCCallbackFlags) [webpack]
 8: 0x55b0f42e27ed v8::internal::Heap::AllocateRawWithLightRetry(int, v8::internal::AllocationType, v8::internal::AllocationOrigin, v8::internal::AllocationAlignment) [webpack]
 9: 0x55b0f42e2855 v8::internal::Heap::AllocateRawWithRetryOrFail(int, v8::internal::AllocationType, v8::internal::AllocationOrigin, v8::internal::AllocationAlignment) [webpack]
10: 0x55b0f42a8fde v8::internal::Factory::AllocateRawWithImmortalMap(int, v8::internal::AllocationType, v8::internal::Map, v8::internal::AllocationAlignment) [webpack]
11: 0x55b0f42b1770 v8::internal::Factory::NewRawOneByteString(int, v8::internal::AllocationType) [webpack]
12: 0x55b0f44f2011 v8::internal::String::SlowFlatten(v8::internal::Isolate*, v8::internal::Handle<v8::internal::ConsString>, v8::internal::AllocationType) [webpack]
13: 0x55b0f44cd0ca v8::internal::StringTable::LookupString(v8::internal::Isolate*, v8::internal::Handle<v8::internal::String>) [webpack]
14: 0x55b0f45ea563 v8::internal::Runtime_HasProperty(int, unsigned long*, v8::internal::Isolate*) [webpack]
15: 0x12edd074a8d9 
Aborted
error Command failed with exit code 134.
info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.

[LabBuildApp] JupyterLab failed to build
[LabBuildApp] Traceback (most recent call last):

[LabBuildApp]   File "/opt/conda/lib/python3.7/site-packages/jupyterlab/debuglog.py", line 48, in debug_logging
    yield

[LabBuildApp]   File "/opt/conda/lib/python3.7/site-packages/jupyterlab/labapp.py", line 176, in start
    raise e

[LabBuildApp]   File "/opt/conda/lib/python3.7/site-packages/jupyterlab/labapp.py", line 173, in start
    app_options=app_options, production = production, minimize=self.minimize)

[LabBuildApp]   File "/opt/conda/lib/python3.7/site-packages/jupyterlab/commands.py", line 483, in build
    production=production, minimize=minimize, clean_staging=clean_staging)

[LabBuildApp]   File "/opt/conda/lib/python3.7/site-packages/jupyterlab/commands.py", line 695, in build
    raise RuntimeError(msg)

[LabBuildApp] RuntimeError: JupyterLab failed to build

[LabBuildApp] Exiting application: JupyterLab

Answer 1

要回答您的问题并用作解决方法，您应该使用以下命令(如在关于此问题的错误提交上显示的那样)：

sudo -i
jupyter lab build --dev-build=False --minimize=False
jupyter labextension  list

无论如何，我已经用这个案例打开了一个问题跟踪器，您可以在这个链接上检查它，请在上面投票，让我们等待google的官方回应。

另外，如果你想要更多的细节，你可以查看我发现的类似案例的列表，这让我认为这是一个持续的问题，与网络/图像有关，它目前(部分)被关注，这就是为什么我也在谷歌上打开了一个问题跟踪器。

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• 打开笔记本会导致524 (超时)错误