# Error at training

**URL:** <https://support.prodi.gy/t/error-at-training/6194>\
**Category:** Uncategorized\
**Tags:** training\
**Created:** [December 20, 2022, 11:57am UTC](https://support.prodi.gy/t/error-at-training/6194 "2022-12-20T11:57:50Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![alvaro.marlo](https://avatars.discourse-cdn.com/v4/letter/a/b38774/32.png) [@alvaro.marlo](https://support.prodi.gy/u/alvaro.marlo)\
**Post date:** [December 20, 2022, 11:57am UTC](https://support.prodi.gy/t/error-at-training/6194/1 "2022-12-20T11:57:50Z")

</div>

Hi,

First of all I don't know if this is a bug related to Prodigy, but I would like to know if you can give me a hand.

When I run the training, it stops and returns this message:

```python
{route-to-my-project-env}/bin/prodigy: line 2: 22878 Killed: 9 python -m prodigy "$@"

```

Any idea what it could be due to?

Thanks,  
Álvaro

---

<div class="post-metadata">

**Author:** ![ryanwesslen](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ryanwesslen/32/2969_2.png) [@ryanwesslen](https://support.prodi.gy/u/ryanwesslen)\
**Post date:** [December 20, 2022, 4:34pm UTC](https://support.prodi.gy/t/error-at-training/6194/2 "2022-12-20T16:34:27Z")

</div>

hi @alvaro.marlo!

> [@alvaro.marlo](#):
>
> When I run the training, it stops and returns this message:
> 
> ```python
> {route-to-my-project-env}/bin/prodigy: line 2: 22878 Killed: 9 python -m prodigy "$@"
> 
> ```
> 
> Any idea what it could be due to?

"Killed" typically is indicative of memory issues. Especially since you're training, can you try to train on a smaller dataset?

> [@Can't install Prodigy: "Killed" during pip installation](https://support.prodi.gy/t/cant-install-prodigy-killed-during-pip-installation/1452):
>
> I’m trying to upgrade from Prodigy 1.6.1 to 1.7.1 in a virtual machine. I keep getting a ‘killed’ message. To try to increase disk and memory space, I uninstalled the old version, but I still can’t get the new one installed! This is the error message: (env) xxxxx@xxxxx:~$ pip install prodigy-1.7.1-cp35.cp36.cp37-cp35m.cp36m.cp37m-linux\_x86\_64.whl Processing ./prodigy-1.7.1-cp35.cp36.cp37-cp35m.cp36m.cp37m-linux\_x86\_64.whl Requirement already satisfied: ujson==1.35 in ./env/lib/python3.5/site-…

Depending on your OS, there are a few tools for Windows, [Mac](https://support.apple.com/guide/activity-monitor/view-memory-usage-actmntr1004/mac) or [Linux](https://www.linuxfoundation.org/blog/blog/classic-sysadmin-linux-101-5-commands-for-checking-memory-usage-in-linux) to track memory.

Be sure to also avoid any very long documents. That can sometimes cause memory issues when training.

Last, sometimes using the [`data-to-spacy`](https://prodi.gy/docs/recipes#data-to-spacy) recipe and its outputs to train with `spacy train` may help:

> [@Prodigy NER train recipe getting killed for no apparent reason](https://support.prodi.gy/t/prodigy-ner-train-recipe-getting-killed-for-no-apparent-reason/5714/2):
>
> It's hard to say for sure, but given that one dataset less works fine I'm indeed guessing it's a memory issue. Are you able to export the datasets to the .spacy format via [the data-to-spacy recipe](https://prodi.gy/docs/recipes#data-to-spacy)? If so, we might be able to pick it up from spaCy.