Python bindings for ggml
Python bindings for the ggml
tensor library for machine learning.
⚠️ Neither this project norggml
currently guarantee backwards-compatibility, if you are using this library in other applications I strongly recommend pinning to specific releases in yourrequirements.txt
file.
Requirements
- Python 3.8+
- C compiler (gcc, clang, msvc, etc)
You can install ggml-python
using pip
:
pip install ggml-python
This will compile ggml using cmake which requires a c compiler installed on your system.
To build ggml with specific features (ie. OpenBLAS, GPU Support, etc) you can pass specific cmake options through the cmake.args
pip install configuration setting. For example to install ggml-python with cuBLAS support you can run:
pip install --upgrade pip
pip install ggml-python --config-settings=cmake.args='-DGGML_CUDA=ON'
Option | Description | Default |
---|---|---|
GGML_CUDA |
Enable cuBLAS support | OFF |
GGML_CLBLAST |
Enable CLBlast support | OFF |
GGML_OPENBLAS |
Enable OpenBLAS support | OFF |
GGML_METAL |
Enable Metal support | OFF |
import ggml
import ctypes
# Allocate a new context with 16 MB of memory
params = ggml.ggml_init_params(mem_size=16 * 1024 * 1024, mem_buffer=None)
ctx = ggml.ggml_init(params)
# Instantiate tensors
x = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)
a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)
b = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)
# Use ggml operations to build a computational graph
x2 = ggml.ggml_mul(ctx, x, x)
f = ggml.ggml_add(ctx, ggml.ggml_mul(ctx, a, x2), b)
gf = ggml.ggml_new_graph(ctx)
ggml.ggml_build_forward_expand(gf, f)
# Set the input values
ggml.ggml_set_f32(x, 2.0)
ggml.ggml_set_f32(a, 3.0)
ggml.ggml_set_f32(b, 4.0)
# Compute the graph
ggml.ggml_graph_compute_with_ctx(ctx, gf, 1)
# Get the output value
output = ggml.ggml_get_f32_1d(f, 0)
assert output == 16.0
# Free the context
ggml.ggml_free(ctx)
If you are having trouble installing ggml-python
or activating specific features please try to install it with the --verbose
and --no-cache-dir
flags to get more information about any issues:
pip install ggml-python --verbose --no-cache-dir --force-reinstall --upgrade
This project is licensed under the terms of the MIT license.