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squidarthsquidarth
authoredOct 23, 2023
Update config reference (#706)
* Updated reference. * Support full config. * Move around the tabs.
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v0.60.0v0.7.14rc2
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‎docs/_snippets/config-params.mdx

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<ParamField body="description" type="str">
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### `model_name`
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Name of your model
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### `description`
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Describe your model for documentation purposes.
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</ParamField>
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<ParamField body="environment_variables" type="Dict[str, str]">
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### `model_class_name`
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(default: `Model`)
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The name of the class that defines your Truss model. Note that this class must implement
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at least a `predict` method.
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### `model_module_dir`
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(default: `model`)
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Folder in the Truss where to find the model class.
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### `data_dir`
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(default: `data/`)
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Folder where to place data files in your Truss. Note that you can access this within your model like so:
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```python model/model.py
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class Model:
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def __init__(self, **kwargs):
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data_dir = kwargs["data_dir"]
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...
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```
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### `environment_variables`
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<Warning>
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Do not store secret values directly in environment variables (or anywhere in the config file). See the `secrets` arg for information on properly managing secrets.
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</Warning>
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ENVIRONMENT: Staging
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DB_URL: https://my_database.example.com/
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```
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</ParamField>
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<ParamField body="model_metadata" type="Dict[str, str]">
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### `model_metadata`
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Set any additional metadata in this catch-all field. The entire contents of the config file are available to the model at runtime, so this is a good place to store any custom information that model needs. For example, scikit-learn models include a flag here that indicates whether the model supports returning probabilities alongside predictions.
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```yaml
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model_metadata:
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supports_predict_proba: true
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```
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</ParamField>
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<ParamField body="model_name" type="str">
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The model's name, for documentation purposes.
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</ParamField>
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<ParamField body="requirements" type="List[str]">
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This is also where display metdata can be stored
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### `requirements`
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List the Python dependencies that the model depends on. The requirements should be provided in the [pip requirements file format](https://pip.pypa.io/en/stable/reference/requirements-file-format/), but as a yaml list.
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We strongly recommend pinning versions in your requirements.
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- numpy==1.20.3
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- scipy==1.7.3
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```
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</ParamField>
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<ParamField body="resources" type="Dict[str, str]">
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Specify model server runtime resources such as CPU, RAM and GPU.
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### `resources`
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The `resources` section is where you specify the compute resources that your model needs. This includes CPU, memory, and GPU resources.
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If you need a GPU, you must also set `resources.use_gpu` to `true`.
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#### `resources.cpu`
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CPU resources needed, expressed as either a raw number, or "millicpus". For example, `1000m` and `1` are equivalent.
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Fractional CPU amounts can be requested using millicpus. For example, `500m` is half of a CPU core.
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#### `resources.memory`
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CPU RAM needed, expressed as a number with units. Units acceptable include "Gi" (Gibibytes), "G" (Gigabytes), "Mi" (Mebibytes), and"M" (Megabytes). For example, `1Gi` and `1024Mi` are equivalent.
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#### `resources.use_gpu`
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Whether or not a GPU is required for this model.
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#### `resources.accelerator`
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Which GPU you would like for your instance. Available Nvidia GPUs supported in Truss include:
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* T4
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* L4
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* A10G
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* V100
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* A100
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Note that if you need multiple GPUs to server your model, you can use the `:` operator to request multiple
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GPUs on your instance, eg:
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```yaml
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resources:
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cpu: "3"
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memory: 14Gi
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use_gpu: true
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accelerator: A10G
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...
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accelerator: A10G:2 # Requests 2 A10Gs
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```
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</ParamField>
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<ParamField body="secrets" type="Dict[str, str]">
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### `secrets`
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<Warning>
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This field can be used to specify the keys for such secrets and dummy default
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values. ***Never store actual secret values in the config***. Dummy default
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secrets:
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hf_access_token: "ACCESS TOKEN"
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```
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</ParamField>
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<ParamField body="system_packages" type="List[str]">
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### `system_packages`
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Specify any system packages that you would typically install using `apt` on a Debian operating system.
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```yaml
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- libsm6
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- libxext6
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```
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</ParamField>
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### `python_version`
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Which version of Python you'd like to use. Supported versions include:
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* py39
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* py310
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* py311
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### `base_image`
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The `base_image` option is used if you need to bring your own custom base image.
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Custom base images are useful if there are scripts that need to run at build time, or dependencies
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that are complicated to install. After creating a custom base image, you can specify it
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in this field.
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See [Custom Base Images](guides/base-images) for more detail on how to use these.
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#### `base_image.image`
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A path to the docker image you'd like to use, as
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an example, `nvcr.io/nvidia/nemo:23.03`.
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#### `base_image.python_executable_path`
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A path to the Python executable on the image. For instance, `/usr/bin/python`.
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Tying it together, a custom base image configuration might look
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like this:
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```yaml
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base_image:
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image: nvcr.io/nvidia/nemo:23.03
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python_executable_path: /usr/bin/python
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```
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### `runtime`
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Runtime settings for your model instance.
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#### `runtime.predict_concurrency`
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(default: `1`)
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This field governs how much concurrency can run in the predict method of your model. This is useful
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if you have a model that has support for parallelism, and you'd like to take advantage of that.
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By default, this value is set to 1, implying that `predict` can only run for one request at a time.
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This protects the GPU from being over-utilized, and is a good default for many models.
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See [How to configure concurrency](guides/concurrency) for more detail on how to set this value.
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### `external_data`
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Use `external_data` if you have data that you want to be bundled in your image at build time.
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This is useful if you have a large amount of data that you want to be available to your model.
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By including it at build-time, you reduce the cold-start time of your instance, as the data is
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already available in the image. You can use it like so:
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```yaml config.yaml
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external_data:
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- url: https://my-bucket.s3.amazonaws.com/my-data.tar.gz
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local_data_path: data/my-data.tar.gz
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name: my-data
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```
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#### `external_data.<list_item>.url`
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The URL to download data from.
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#### `external_data.<list_item>.local_data_path`
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The path on the image where the data will be downloaded to.
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#### `external_data.<list_item>.name`
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You can set a name for the data, which is useful for readability-purposes.
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Not required.
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### `build`
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The `build` section is used to define options for custom servers.
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The two main model servers we support are `TGI` an `vLLM`. These are
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highly optimized servers that are built to support specific LLMs.
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See the following examples for how to use each of these:
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* [TGI](examples/07-high-performance-tgi)
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* [vLLM](examples/08-high-performance-vllm)
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Example configuration for TGI, running Falcon-7B:
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```yaml config.yaml
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build:
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arguments:
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endpoint: generate_stream
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model_id: tiiuae/falcon-7b
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model_server: TGI
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```
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#### `build.model_server`
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Either `VLLM` for vLLM, or `TGI` for TGI.
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#### `build.arguments`
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The arguments for the model server. This includes information such as which model you intend to load, and
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which endpoin from the server you'd like to use.
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### `hf_cache`
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The `hf_cache` section is used for caching model weights at build-time. This is one of the biggest levers
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for decreasing cold start times, as downloading weights can be one of the lengthiest parts of starting a new
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model instance. Using this section ensures that model weights are cached at _build_ time.
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See the [model cache guide](guides/model-cache) for the full details on how to use this field.
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<Note>
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Despite the fact that this field is called the `hf_cache`, there are multiple backends supported, not just Hugging Face. You can
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also cache weights stored on GCS, for instance.
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</Note>
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#### `hf_cache.<list_item>.repo_id`
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The endpoint for your cloud bucket. Currently, we support Hugging Face and Google Cloud Storage.
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Example: `madebyollin/sdxl-vae-fp16-fix` for a Hugging Face repo, or `gcs://path-to-my-bucket` for
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a GCS bucket.
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#### `hf_cache.<list_item>.revision`
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Points to your revision. This is only relevant if you are pulling By default, it refers to `main`.
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#### `hf_cache.<list_item>.allow_patterns`
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Only cache files that match specified patterns. Utilize Unix shell-style wildcards to denote these patterns.
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By default, all paths are included.
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#### `hf_cache.<list_item>.ignore_patterns`
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Conversely, you can also denote file patterns to ignore, hence streamlining the caching process.
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By default, nothing is ignored.

‎docs/mint.json

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}
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],
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"tabs": [
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{
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"name": "Learn model deployment",
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"url": "learn"
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},
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{
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"name": "Reference docs",
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"url": "reference"
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},
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{
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"name": "Learn model deployment",
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"url": "learn"
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},
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{
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"name": "Contributor guides",
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"url": "contribute"

‎docs/reference/config.mdx

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```
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</Accordion>
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## Config reference
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## Example
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<Snippet file="config-params.mdx" />
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<RequestExample>
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Here's an example config file for a Truss that uses the [WizardLM](https://huggingface.co/WizardLM/WizardLM-7B-V1.0) model:
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```yaml WizardLM config
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description: An instruction-following LLM Using Evol-Instruct.
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system_packages: []
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```
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</RequestExample>
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## Full config reference
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<Snippet file="config-params.mdx" />

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