# Execute in parallel non parallelizable tasks

**URL:** <https://discuss.ray.io/t/execute-in-parallel-non-parallelizable-tasks/13862>\
**Category:** Ray Core\
**Created:** [February 29, 2024, 7:44am UTC](https://discuss.ray.io/t/execute-in-parallel-non-parallelizable-tasks/13862 "2024-02-29T07:44:40Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![acosta](https://avatars.discourse-cdn.com/v4/letter/a/13edae/32.png) [@acosta](https://discuss.ray.io/u/acosta)\
**Post date:** [February 29, 2024, 7:44am UTC](https://discuss.ray.io/t/execute-in-parallel-non-parallelizable-tasks/13862/1 "2024-02-29T07:44:40Z")

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I have a data preprocessing code that consists in a list of tasks that are not parallelizable tasks.

Is it possible to run them in parallel on different workers?

I tried the following but it does not seem to work. I see only one worker active.

```auto
import ray
import pandas as pd

df = pd.csv_read('file.csv')

ray.init(num_cpus=1)

df_ref = ray.put(df)

@ray.remote
def pow2(data):
    pows = []
    for i in range(len(data)):
        pows.append(data.iloc[i, 'values']**2)
return daily_avg

@ray.remote
def pow3(data):
    pows = []
    for i in range(len(data)):
        pows.append(data.iloc[i, 'values']**3)
return daily_avg

result_pow2 = pow2.remote(df_ref)
result_pow3 = pow3.remote(df_ref)

pow2_result, pow3_result = ray.get([result_pow2, result_pow3])

df["pow2"] = pow2_result
df["pow3"] = pow3_result

print(df)

```

Note: the tasks in the example may easily be parallelized… Please, just consider the workflow.

I also would like to avoid rewriting the code to make a batching approach work as I need to quickly test different things…

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<div class="post-metadata">

**Author:** ![jjyao](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/jjyao/32/1799_2.png) [@jjyao](https://discuss.ray.io/u/jjyao)\
**Post date:** [March 17, 2024, 5:06am UTC](https://discuss.ray.io/t/execute-in-parallel-non-parallelizable-tasks/13862/2 "2024-03-17T05:06:09Z")

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> [@acosta](#):
>
> `ray.init(num_cpus=1)`

You started a cluster with only 1 cpu so only one task (requires 1 cpu) can run at a single point of time.