Memory leak and crash, now and 2 years ago, tfjs-node

[borodadada]

The problem has not disappeared anywhere, 2 years ago and now the same thing. Visually, through the task manager in the past there was an information leak, and the process was constantly increasing in memory, now this is not the case, everything is fine, but the result is the same, after a while everything crashes. I managed to take screenshots at the moment when it all started. The screenshots show my working environment, NOT the TEST that I posted, the test itself is as close and simplified as possible, in the future I will post information on this test.
I'm not doing anything, just taking screenshots.

There are 4 identical programs running on the computer, 4 copies, one of them begins to fail. This happens when the number of epochs is measured in millions. For test you can run only one copy. On a modern processor, the procedure usually takes 4-6 hours, on an old one more than a day.

Memory leak starts, this starts happening quickly, as can be seen in the screenshot

Process, note 3 other processes, usually from 100 to 200 megabytes in size

Full memory

After

All node js have closed

Logs, there is nothing in them, they are empty, the editor is open

For test simple code, just copy past

TEST CODE

const tf = require('@tensorflow/tfjs-node');

const size = 50
const units = 100

const letsgo = async function(){

    const model = tf.sequential();
    model.add( tf.layers.dense({ inputShape: [units], units, activation: 'linear', useBias: true }));
    model.add( tf.layers.dense({ units, activation: 'linear', useBias: true }));
    model.add( tf.layers.dense({ units, activation: 'linear', useBias: true }));
    model.compile({ optimizer: tf.train.adam(0.005, 0.9, 0.999), loss: tf.losses.absoluteDifference });

    let a = []
    let b = []
    for (let i = 0; i < size; i++) {
        let aa = []
        let bb = []
        for (let ii = 0; ii < units; ii++) {
            aa.push( Math.random() )
            bb.push( Math.random() )
        }
        a.push(aa)
        b.push(bb)
    }

    let xs = tf.tensor2d( a );
    let ys = tf.tensor2d( b );

    await model.fit(xs, ys, {
        epochs: 50000000,
        shuffle: false,
        verbose: 0,
        callbacks:{
            onTrainBegin: ()=>{
                console.log('start')
            },
            onTrainEnd: ()=>{
                console.log('done')
            },
            onEpochEnd: async (epoch, logs)=>{
                if( epoch % 100000 === 0 )
                    console.log(epoch, logs.loss)
            }
        }
    })
}

const loop = async function(){
    for (let i = 0; i < 1; i++) {
        await letsgo()
    }
}

loop()

System information

• Windows 11 x64
• node-v19.9.0-x64
• node-v20.15.0-x64
• "@tensorflow/tfjs": "^4.20.0",
• "@tensorflow/tfjs-node": "^4.20.0",

Okey, this is results from test code:

modern PC intel 13700, crash after 4.4 millions epochs

old PC intel 3770, crash after 4.4 millions epochs - windows 10 x64 + nodejs 20.10.0

I can't do the calculations because the program always crashes, and I need many more epochs than here!!! I really hope you fix this, it's a disaster that this bug hasn't been fixed for years!

[gaikwadrahul8]

Hi, @borodadada

I apologize for the delay in my response and thank you for bringing this issue to our attention and as far I know to avoid memory leak you'll have to use tf.tidy which executes the provided function fn and after it is executed, cleans up all intermediate tensors allocated by fn except those returned by fn. fn must not return a Promise (async functions not allowed). The returned result can be a complex object.

Using this method helps avoid memory leaks. In general, wrap calls to operations in tf.tidy() for automatic memory cleanup.

NOTE: Variables do not get cleaned up when inside a tidy(). If you want to dispose variables, please use tf.disposeVariables() or call dispose() directly on variables please refer tf.dispose.

You can also use tf.memory which returns memory info at the current time in the program.

Could you please give it try after adding tf.tidy and tf.dispose in your code and see memory leak is happening or not ?

If I have missed something here please let me know.

Thank you for your cooperation and patience.

[borodadada]

I don’t understand the principle of a memory leak, all the algorithm needs is to change the coefficients and then feedback and comparison of the result, it should work endlessly without a memory leak.
What you wrote has nothing to do with the crash of the program, because the code is as simple as possible and there is nothing in it except the fit function, which crashes.
Or I don't understand something.

[mightyplow]

You have to dispose the xs and ys after the fit step. Otherwise tfjs creates new tensors in every loop step. They fill up the memory with each step if you don't dispose them.

[borodadada]

using my example, can you show how it should be?
the crash process occurs when the fit function is executed, there is no loop, the data was declared once and after that the learning process started

[mightyplow]

it should be like this

let xs = tf.tensor2d( a );
let ys = tf.tensor2d( b );

await model.fit(xs, ys, {
        epochs: 50000000,
        shuffle: false,
        verbose: 0,
        callbacks:{
            onTrainBegin: ()=>{
                console.log('start')
            },
            onTrainEnd: ()=>{
                console.log('done')
            },
            onEpochEnd: async (epoch, logs)=>{
                if( epoch % 100000 === 0 )
                    console.log(epoch, logs.loss)
            }
        }
})

xs.dispose();
ys.dispose();

This way the tensors get unusable and are freed by tfjs.

By the way that doesn't mean that there isn't any other memory leak. I stumbled across your comment because I'm also hunting a memory issue. But disposing unused tensors will at least wipe out one possible reason.

[borodadada]

I am writing to you about this, that it has not reached this point
The program crashes on the - model.fit

await model.fit(xs, ys, {
        epochs: 50000000,
        shuffle: false,
        verbose: 0,
        callbacks:{
            onTrainBegin: ()=>{
                console.log('start')
            },
            onTrainEnd: ()=>{
                console.log('done')
            },
            onEpochEnd: async (epoch, logs)=>{
                if( epoch % 100000 === 0 )
                    console.log(epoch, logs.loss)
            }
        }
})

this part of the code doesn't work because it should fire after the fit function

xs.dispose();
ys.dispose();

if you have the opportunity to run the code, you will see everything for yourself

Now I’ll run the test with your amendments, I’ll write a little later

[mightyplow]

Oh sorry, my fault. I didn't recognize the amount of epochs. Then you're right and it looks like some internal problem. I'll try it out and see what happens.

[borodadada]

Thank you, I'll wait for the result, this problem is bothering me a lot

[yiv1]

same problem in Bun

2025-06-26 03:02:12.840574: W tensorflow/core/framework/op_kernel.cc:1840] OP_REQUIRES failed at cwise_ops_common.h:124 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[32,16] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator mklcpu
============================================================
Bun v1.2.17 (282dda62) Windows x64
Windows v.win10_fe
CPU: sse42 avx avx2
Args: "C:\Users\****\.bun\bin\bun.exe" "src/index.ts"
Features: Bun.stderr Bun.stdout(3) jsc transpiler_cache(9) tsconfig(2) process_dlopen
Builtins: "bun:main" "bun:sqlite" "node:assert" "node:buffer" "node:child_process" "node:crypto" "node:events" "node:fs" "node:os" "node:path" 
"node:stream" "node:url" "node:util"
Elapsed: 52508590ms | User: 54715984ms | Sys: 380281ms
RSS: 4.77GB | Peak: 4.77GB | Commit: 8.95GB | Faults: 80753124

panic(main thread): attempt to unwrap error: OutOfMemory
oh no: Bun has crashed. This indicates a bug in Bun, not your code.

There were indeed errors related to memory leaks, not all tensors were cleared.
I fixed them, it got better, but after ~5 training epochs, the crash continues.

I see through Process Explorer that Working set is stabilized and does not grow infinitely. But Private bytes is slowly growing. As far as I could find out, Private bytes shows the size of memory allocated in the swap file. And I have no idea how to fix this and how to debug.