# Embeddings

The library allow to easily compute embeddings with a specific model in the backend.

```python
from biotransformers import BioTransformers

sequences = [...]
bio_trans = BioTransformers("esm1b_t33_650M_UR50S",num_gpus=1)
embeddings = bio_trans.compute_embeddings(sequences, pool_mode=("cls","mean"), batch_size=8)
```

By default, the `pool_mode` argument contains 3 mode:

- `cls` : return the `<CLS>` token embedding in the sequence.
- `mean` : if sequence has shape (num_token, embedding_size), the num_token dimension is averaging and the embedding has shape (num_token,)
- `full` : no pooling function applied, all the embeddings for each sequence are return.

## Multi-gpu inference

If you want to make the inference on several GPUs, you have to intialize ray as below to use instantiate multiple workers.

```{tip}
batch_size corresponds to the number of sequence that you want to distribute on each GPU.
```

```python
from biotransformers import BioTransformers
import ray

ray.init()
sequences = [...]
bio_trans = BioTransformers("esm1b_t33_650M_UR50S",num_gpus=4)
embeddings = bio_trans.compute_embeddings(sequences, pool_mode=("cls","mean"), batch_size=8)
```