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# Installation
Before you start, you will need to setup your environment, install the appropriate packages, and configure Accelerate. Accelerate is tested on **Python 3.8+**.
Accelerate is available on pypi and conda, as well as on GitHub. Details to install from each are below:
## pip
To install Accelerate from pypi, perform:
```bash
pip install accelerate
```
## conda
Accelerate can also be installed with conda with:
```bash
conda install -c conda-forge accelerate
```
## Source
New features are added every day that haven't been released yet. To try them out yourself, install
from the GitHub repository:
```bash
pip install git+https://github.com/huggingface/accelerate
```
If you're working on contributing to the library or wish to play with the source code and see live
results as you run the code, an editable version can be installed from a locally-cloned version of the
repository:
```bash
git clone https://github.com/huggingface/accelerate
cd accelerate
pip install -e .
```
## Configuration
After installing, you need to configure Accelerate for how the current system is setup for training.
To do so run the following and answer the questions prompted to you:
```bash
accelerate config
```
To write a barebones configuration that doesn't include options such as DeepSpeed configuration or running on TPUs, you can quickly run:
```bash
python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='fp16')"
```
Accelerate will automatically utilize the maximum number of GPUs available and set the mixed precision mode.
To check that your configuration looks fine, run:
```bash
accelerate env
```
An example output is shown below, which describes two GPUs on a single machine with no mixed precision being used:
```bash
- `Accelerate` version: 1.2.0.dev0
- Platform: Linux-6.8.0-47-generic-x86_64-with-glibc2.35
- `accelerate` bash location: /home/zach/miniconda3/envs/accelerate/bin/accelerate
- Python version: 3.10.13
- Numpy version: 1.26.4
- PyTorch version (GPU?): 2.5.1+cu124 (True)
- PyTorch XPU available: False
- PyTorch NPU available: False
- PyTorch MLU available: False
- PyTorch MUSA available: False
- System RAM: 187.91 GB
- GPU type: NVIDIA GeForce RTX 4090
- `Accelerate` default config:
- compute_environment: LOCAL_MACHINE
- distributed_type: MULTI_GPU
- mixed_precision: no
- use_cpu: False
- debug: False
- num_processes: 2
- machine_rank: 0
- num_machines: 1
- gpu_ids: all
- rdzv_backend: static
- same_network: True
- main_training_function: main
- enable_cpu_affinity: False
- downcast_bf16: no
- tpu_use_cluster: False
- tpu_use_sudo: False
- tpu_env: []
```
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