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41431d8bdd
currently providers have a `pip_package` list. Rather than make our own form of python dependency management, we should use `pyproject.toml` files in each provider declaring the dependencies in a more trackable manner. Each provider can then be installed using the already in place `module` field in the ProviderSpec, pointing to the directory the provider lives in we can then simply `uv pip install` this directory as opposed to installing the dependencies one by one Signed-off-by: Charlie Doern <cdoern@redhat.com>
69 lines
3.1 KiB
Python
69 lines
3.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
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# We provide two versions of these providers so that distributions can package the appropriate version of torch.
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# The CPU version is used for distributions that don't have GPU support -- they result in smaller container images.
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torchtune_def = dict(
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api=Api.post_training,
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module="llama_stack.providers.inline.post_training.torchtune",
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config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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],
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description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.",
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)
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def available_providers() -> list[ProviderSpec]:
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return [
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InlineProviderSpec(
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api=Api.post_training,
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provider_type="inline::torchtune-cpu",
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module="llama_stack.providers.inline.post_training.torchtune",
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config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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],
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description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework (CPU).",
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package_extras=["cpu"],
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),
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InlineProviderSpec(
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api=Api.post_training,
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provider_type="inline::torchtune-gpu",
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module="llama_stack.providers.inline.post_training.torchtune",
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config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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],
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description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework (GPU).",
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package_extras=["gpu"],
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),
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InlineProviderSpec(
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api=Api.post_training,
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provider_type="inline::huggingface-gpu",
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module="llama_stack.providers.inline.post_training.huggingface",
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config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig",
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api_dependencies=[
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Api.datasetio,
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Api.datasets,
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],
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description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
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),
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RemoteProviderSpec(
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api=Api.post_training,
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adapter_type="nvidia",
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provider_type="remote::nvidia",
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module="llama_stack.providers.remote.post_training.nvidia",
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config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig",
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description="NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.",
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),
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]
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