# Train a model with autotrain on huggingface using the API
**URL:**
**Author:**  [@chsafouane](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fchsafouane)%5C
**Post date:** [May 30, 2024, 1:53pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftrain-a-model-with-autotrain-on-huggingface-using-the-api%2F89428%2F1 "2024-05-30T13:53:23Z")
Hello,
Up to now, I’ve been using the autotrain ui (space) on huggingface to train models.
I would like to know if there is a way to start a model training using autotrain powered bu HF GPUs but only using the API.
I’ve spotted a page in the documentation that talks about this but one has to start the app first: `autotrain app --port 8000 --host 127.0.0.1`
Is there a way to bypass this and directly train the model on HF ?
Thanks
---
**Author:**  [@abhishek](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fabhishek)%5C
**Post date:** [May 30, 2024, 2:11pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftrain-a-model-with-autotrain-on-huggingface-using-the-api%2F89428%2F2 "2024-05-30T14:11:15Z")
there are multiple ways.
1. you can use config files:
example config:
```auto
task: llm-sft
base_model: openai-community/gpt2
project_name: autotrain-gpt2-finetuned-guanaco
log: tensorboard
backend: local
data:
path: timdettmers/openassistant-guanaco
train_split: train
valid_split: null
chat_template: null
column_mapping:
text_column: text
params:
block_size: 1024
model_max_length: 2048
max_prompt_length: 512
epochs: 3
batch_size: 2
lr: 3e-5
padding: right
optimizer: adamw_torch
scheduler: linear
gradient_accumulation: 4
mixed_precision: fp16
hub:
username: ${HF_USERNAME}
token: ${HF_TOKEN}
push_to_hub: true
```
change backend from local to one of the spaces backends:
```auto
"spaces-a10g-large": "a10g-large",
"spaces-a10g-small": "a10g-small",
"spaces-a100-large": "a100-large",
"spaces-t4-medium": "t4-medium",
"spaces-t4-small": "t4-small",
"spaces-cpu-upgrade": "cpu-upgrade",
"spaces-cpu-basic": "cpu-basic",
"spaces-l4x1": "l4x1",
"spaces-l4x4": "l4x4",
"spaces-a10g-largex2": "a10g-largex2",
"spaces-a10g-largex4": "a10g-largex4",
```
e.g. `backend: spaces-a10g-largex2`
This will start training directly on spaces.
1. API way:
start the app: `autotrain app --port 8000 --host 127.0.0.1`
you can see api docs at 127.0.0.1/docs. api endpoint to create project is available at /api/create\_project
Note: local datasets are not supported in api mode.
please let me know if you have any questions.
---
**Author:**  [@chsafouane](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fchsafouane)%5C
**Post date:** [May 30, 2024, 2:48pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftrain-a-model-with-autotrain-on-huggingface-using-the-api%2F89428%2F3 "2024-05-30T14:48:16Z")
> [@abhishek](#):
>
> ` "spaces-a10g-large"`
It works like a charm.Thank you
I guess the next think I’d like to know is if there is a way to do it using python directly by passing a dict that includes all parameters (as I see that it parses a json containing the configuration under the hood)
---
**Author:**  [@abhishek](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fabhishek)%5C
**Post date:** [May 30, 2024, 4:03pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftrain-a-model-with-autotrain-on-huggingface-using-the-api%2F89428%2F4 "2024-05-30T16:03:40Z")
you can also use the autotrain cli, im pushing some improvements there so ill update here when its done 🙂
---
**Author:**  [@system](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fsystem)%5C
**Post date:** [May 31, 2024, 4:04am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ftrain-a-model-with-autotrain-on-huggingface-using-the-api%2F89428%2F5 "2024-05-31T04:04:33Z")
This topic was automatically closed 12 hours after the last reply. New replies are no longer allowed.