Add torchserve

recipes/torch-model-archiver/meta.yaml

@@ -0,0 +1,47 @@
+{% set torchserve_version = "0.12.0" %}
+# see https://github.com/pytorch/serve/blob/master/model-archiver/model_archiver/version.txt
+{% set model_archiver_version = "0.12.0" %}
+
+package:
+  name: torchserve-model-archiver
+  version: {{ model_archiver_version }}
+
+source:
+  url: https://github.com/pytorch/serve/archive/v{{ torchserve_version }}.tar.gz
+  sha256: d5eb31a540b6a1dea4ead5c8b122d8b7d40b06fe2fc257ba67ed5b071852be0b
+
+build:
+  noarch: python
+  script: {{ PYTHON }} -m pip install model-archiver/ -vv
+  number: 0
+  entry_points:
+    - torch-model-archiver = model_archiver.model_packaging:generate_model_archive
+
+requirements:
+  host:
+    - python {{ python_min }}
+    - pip
+    - setuptools
+  run:
+    - python >={{ python_min }}
+    - enum-compat
+
+test:
+  commands:
+    - torch-model-archiver --help
+  requires:
+    - python {{ python_min }}
+
+about:
+  home: https://github.com/pytorch/serve/blob/master/model-archiver/README.md
+  summary: Creates archives of trained neural net models that can be consumed by TorchServe for inference.
+  description: |
+    A key feature of TorchServe is the ability to package all model artifacts into a single model archive file. It is a separate command line interface (CLI), torch-model-archiver, that can take model checkpoints or model definition file with state_dict, and package them into a .mar file. This file can then be redistributed and served by anyone using TorchServe. It takes in the following model artifacts: a model checkpoint file in case of torchscript or a model definition file and a state_dict file in case of eager mode, and other optional assets that may be required to serve the model. The CLI creates a .mar file that TorchServe's server CLI uses to serve the models.
+  license: Apache-2.0
+  license_file: LICENSE
+  doc_url: https://pytorch.org/serve/
+  dev_url: https://github.com/pytorch/serve
+
+extra:
+  recipe-maintainers:
+    - tdsmith

recipes/torch-workflow-archiver/meta.yaml

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+{% set torchserve_version = "0.12.0" %}
+# see https://github.com/pytorch/serve/blob/master/workflow-archiver/workflow_archiver/version.txt
+{% set workflow_archiver_version = "0.2.15" %}
+
+package:
+  name: torchserve-workflow-archiver
+  version: {{ workflow_archiver_version }}
+
+source:
+  url: https://github.com/pytorch/serve/archive/v{{ torchserve_version }}.tar.gz
+  sha256: d5eb31a540b6a1dea4ead5c8b122d8b7d40b06fe2fc257ba67ed5b071852be0b
+
+build:
+  noarch: python
+  script: {{ PYTHON }} -m pip install workflow-archiver/ -vv
+  number: 0
+  entry_points:
+    - torch-workflow-archiver=workflow_archiver.workflow_packaging:generate_workflow_archive
+
+requirements:
+  host:
+    - python {{ python_min }}
+    - pip
+    - setuptools
+  run:
+    - python >={{ python_min }}
+
+test:
+  commands:
+    - torch-workflow-archiver --help
+  requires:
+    - python {{ python_min }}
+
+about:
+  home: https://github.com/pytorch/serve/tree/master/workflow-archiver
+  summary: Creates archives of workflows designed using trained neural net models that can be consumed by TorchServe inference.
+  description: |
+    A key feature of TorchServe is the ability to package workflow specification (.yaml) and other workflow dependency files into a single workflow archive file (.war). This file can then be redistributed and served by anyone using TorchServe.
+  license: Apache-2.0
+  license_file: LICENSE
+  doc_url: https://pytorch.org/serve/
+  dev_url: https://github.com/pytorch/serve
+
+extra:
+  recipe-maintainers:
+    - tdsmith

recipes/torchserve/meta.yaml

@@ -0,0 +1,53 @@
+{% set version = "0.12.0" %}
+
+package:
+  name: torchserve
+  version: {{ version }}
+
+source:
+  url: https://github.com/pytorch/serve/archive/v{{ version }}.tar.gz
+  sha256: d5eb31a540b6a1dea4ead5c8b122d8b7d40b06fe2fc257ba67ed5b071852be0b
+
+build:
+  noarch: python
+  script: {{ PYTHON }} -m pip install . -vv
+  number: 0
+  entry_points:
+    - torchserve = ts.model_server:start
+
+requirements:
+  build:
+    - gradle
+    # openjdk should be compatible with the gradle version selected in 
+    # https://github.com/pytorch/serve/blob/master/frontend/gradle/wrapper/gradle-wrapper.properties
+    # see https://docs.gradle.org/current/userguide/compatibility.html
+    - openjdk <20
+  host:
+    - python {{ python_min }}
+    - pip
+    - setuptools
+    - openjdk
+  run:
+    - python >={{ python_min }}
+    - pillow
+    - psutil
+
+test:
+  commands:
+    - torchserve -v
+  requires:
+    - python {{ python_min }}
+
+about:
+  home: https://pytorch.org/serve/
+  summary: 'Performant, flexible and easy to use tool for serving PyTorch models in production.'
+  description: |
+    TorchServe is a performant, flexible and easy to use tool for serving PyTorch models in production.
+  license: Apache-2.0
+  license_file: LICENSE
+  doc_url: https://pytorch.org/serve/
+  dev_url: https://github.com/pytorch/serve
+
+extra:
+  recipe-maintainers:
+    - tdsmith