Skip to main content

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

Our team has a CRA application and we are using the following script to build locally and in bitbucket pipelines

node --max-old-space-size=8192 scripts/build.js

We are all getting this error now with our source code

FATAL ERROR: Ineffective mark-compacts near heap limit Allocation failed - JavaScript heap out of memory
 1: 0x111336665 node::Abort() (.cold.1) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 2: 0x11002f1c9 node::Abort() [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 3: 0x11002f3ae node::OOMErrorHandler(char const*, bool) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 4: 0x1101a41d0 v8::Utils::ReportOOMFailure(v8::internal::Isolate*, char const*, bool) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 5: 0x1101a4193 v8::internal::V8::FatalProcessOutOfMemory(v8::internal::Isolate*, char const*, bool) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 6: 0x1103458e5 v8::internal::Heap::FatalProcessOutOfMemory(char const*) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 7: 0x11034992d v8::internal::Heap::RecomputeLimits(v8::internal::GarbageCollector) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 8: 0x11034620d v8::internal::Heap::PerformGarbageCollection(v8::internal::GarbageCollector, v8::GCCallbackFlags) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
 9: 0x11034372d v8::internal::Heap::CollectGarbage(v8::internal::AllocationSpace, v8::internal::GarbageCollectionReason, v8::GCCallbackFlags) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
10: 0x110350b10 v8::internal::Heap::AllocateRawWithLightRetrySlowPath(int, v8::internal::AllocationType, v8::internal::AllocationOrigin, v8::internal::AllocationAlignment) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
11: 0x110350b91 v8::internal::Heap::AllocateRawWithRetryOrFailSlowPath(int, v8::internal::AllocationType, v8::internal::AllocationOrigin, v8::internal::AllocationAlignment) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
12: 0x11031dc27 v8::internal::Factory::NewFillerObject(int, bool, v8::internal::AllocationType, v8::internal::AllocationOrigin) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
13: 0x1106d574e v8::internal::Runtime_AllocateInYoungGeneration(int, unsigned long*, v8::internal::Isolate*) [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
14: 0x110a7e499 Builtins_CEntry_Return1_DontSaveFPRegs_ArgvOnStack_NoBuiltinExit [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]
15: 0x110aad486 Builtins_FastNewFunctionContextFunction [/Users/apple/.nvm/versions/node/v16.20.0/bin/node]

We disabled source maps in the webpack config and its still failing. We tried setting the following to false in the webpack.config.js file

// Source maps are resource heavy and can cause out of memory issue for large source files.
const shouldUseSourceMap = false

Using higher than 8GB is not doable with our current infrastructure building using node on bitbucket pipelines which is capped at 8GB of memory.

On a tight deadline with this blocker. Any ideas? Thanks!

Via Active questions tagged javascript - Stack Overflow https://ift.tt/6avZLAM

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...
Post a Comment
Read more

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings
Post a Comment
Read more

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...
Post a Comment
Read more