Upgrade RedisAI to 1.2.7

.github/workflows/run_tests.yml

@@ -23,11 +23,13 @@ jobs:
       matrix:
         os: [macos-10.15, ubuntu-20.04] # Operating systems
         compiler: [8] # GNU compiler version
-        rai: [1.2.3, 1.2.5] # Redis AI versions
+        rai: [1.2.3, 1.2.5, 1.2.7] # Redis AI versions
         py_v: [3.7, 3.8, 3.9] # Python versions
         exclude:
           - os: macos-10.15 # Do not build with Redis AI 1.2.5 on MacOS
             rai: 1.2.5
+          - py_v: 3.7 # ONNX requires python >= 3.8
+            rai: 1.2.7
 
     env:
       SMARTSIM_REDISAI: ${{ matrix.rai }}

doc/changelog.rst

@@ -23,13 +23,16 @@ This section details changes made in the development branch that have not yet be
 Description
 
 - Fix bug in colocated database entrypoint when loading PyTorch models
+- Add support for RedisAI 1.2.7, pyTorch 1.11.0, Tensorflow 2.8.0, ONNXRuntime 1.11.1
 
 Detailed Notes
 
 - Fix bug in colocated database entrypoint stemming from uninitialized variables.  This bug affects PyTorch models being loaded into the database. (PR237_)
+- The release of RedisAI 1.2.7 allows us to update support for recent versions of pyTorch, Tensorflow, and ONNX (PR234_)
+- Make installation of correct Torch backend more reliable according to instruction from pyTorch
 
 .. _PR237: https://github.com/CrayLabs/SmartSim/pull/237
-
+.. _PR234: https://github.com/CrayLabs/SmartSim/pull/234
 
 0.4.1
 -----

doc/installation.rst

@@ -59,36 +59,25 @@ Supported Versions
     to support Windows.
 
 
-
-SmartSim supports multiple machine learning libraries through
-the use of RedisAI_. The following libraries are supported.
-
-.. list-table:: Supported ML Libraries
-   :widths: 50 50 50 50
-   :header-rows: 1
-   :align: center
-
-   * - Library
-     - Versions
-     - Python Versions
-     - Built By Default
-   * - PyTorch_
-     - 1.7
-     - 3.7 - 3.9
-     - Yes
-   * - Tensorflow_ / Keras_
-     - 2.5.2
-     - 3.7 - 3.9
-     - Yes
-   * - ONNX_
-     - 1.9
-     - 3.7 - 3.9
-     - No
+Native support for various machine learning libraries and their
+versions is dictated by our dependency on RedisAI_ 1.2.7. Users
+can also select RedisAI 1.2.3 or 1.2.5 (though that also limits
+the version of the ML libraries).
+
++------------------+----------+-------------+---------------+
+| RedisAI          | PyTorch  | Tensorflow  | ONNX Runtime  |
++==================+==========+=============+===============+
+| 1.2.7 (default)  | 1.11.0   | 2.8.0       | 1.11.1        |
+| 1.2.5            | 1.9.0    | 2.6.0       | 1.9.0         |
+| 1.2.3            | 1.7.0    | 2.5.2       | 1.9.0         |
++------------------+----------+-------------+---------------+
 
 TensorFlow_ 2.0 and Keras_ are supported through graph freezing_.
 
 ScikitLearn_ and Spark_ models are supported by SmartSim as well
-through the use of the ONNX_ runtime.
+through the use of the ONNX_ runtime (which is not built by
+default due to issues with glibc on a variety of Linux
+platforms and lack of support for Mac OS X).
 
 .. _Spark: https://spark.apache.org/mllib/
 .. _Keras: https://keras.io

smartsim/_core/_cli/build.py

@@ -23,22 +23,31 @@
 
 
 def _install_torch_from_pip(versions, device="cpu", verbose=False):
+
     packages = []
     end_point = None
-    # if we are on linux cpu, use the torch without CUDA
-    if sys.platform == "linux" and device == "cpu":
-        packages.append(f"torch=={versions.TORCH}+cpu")
-        packages.append(f"torchvision=={versions.TORCHVISION}+cpu")
+
+    if sys.platform == "darwin":
+        if device == "gpu":
+            logger.warning("GPU support is not available on Mac OS X")
+        # The following is deliberately left blank as there is no
+        # alternative package available on Mac OS X
+        device_suffix = ""
+        end_point = None
+
+    # if we are on linux cpu, either CUDA or CPU must be installed
+    elif sys.platform == "linux":
         end_point = "https://download.pytorch.org/whl/torch_stable.html"
+        if device in ["gpu","cuda"] :
+            device_suffix = versions.TORCH_CUDA_SUFFIX
+        elif device == "cpu":
+            device_suffix = versions.TORCH_CPU_SUFFIX
 
-    # otherwise just use the version downloaded by pip
-    else:
-        packages.append(f"torch=={versions.TORCH}")
-        packages.append(f"torchvision=={versions.TORCHVISION}")
+    packages.append(f"torch=={versions.TORCH}{device_suffix}")
+    packages.append(f"torchvision=={versions.TORCHVISION}{device_suffix}")
 
     pip_install(packages, end_point=end_point, verbose=verbose)
 
-
 class Build:
     def __init__(self):
         parser = argparse.ArgumentParser()

smartsim/_core/_install/buildenv.py

@@ -116,6 +116,9 @@ class RedisAIVersion(Version_):
 
     2. Used to set the default values for PyTorch, TF, and ONNX
     given the SMARTSIM_REDISAI env var set by the user.
+
+    NOTE: Torch requires additional information depending on whether
+    CPU or GPU support is requested
     """
 
     defaults = {
@@ -126,6 +129,8 @@ class RedisAIVersion(Version_):
             "onnxmltools": "1.10.0",
             "scikit-learn": "1.0.2",
             "torch": "1.7.1",
+            "torch_cpu_suffix": "+cpu",
+            "torch_cuda_suffix": "+cu110",
             "torchvision": "0.8.2",
         },
         "1.2.5": {
@@ -135,8 +140,21 @@ class RedisAIVersion(Version_):
             "onnxmltools": "1.10.0",
             "scikit-learn": "1.0.2",
             "torch": "1.9.1",
+            "torch_cpu_suffix": "+cpu",
+            "torch_cuda_suffix": "+cu111",
             "torchvision": "0.10.1",
         },
+        "1.2.7": {
+            "tensorflow": "2.8.0",
+            "onnx": "1.11.0",
+            "skl2onnx": "1.11.1",
+            "onnxmltools": "1.11.1",
+            "scikit-learn": "1.1.1",
+            "torch": "1.11.0",
+            "torch_cpu_suffix": "+cpu",
+            "torch_cuda_suffix": "+cu113",
+            "torchvision": "0.12.0",
+        },
     }
     # deps are the same between the following versions
     defaults["1.2.4"] = defaults["1.2.3"]
@@ -146,7 +164,7 @@ def __init__(self, vers):
             if vers.startswith("1.2"):
                 # resolve to latest version for 1.2.x
                 # the str representation will still be 1.2.x
-                self.version = "1.2.5"
+                self.version = "1.2.7"
             else:
                 raise SetupError(
                     f"Invalid RedisAI version {vers}. Options are {self.defaults.keys()}"
@@ -194,7 +212,7 @@ class Versioner:
     REDIS_BRANCH = get_env("SMARTSIM_REDIS_BRANCH", REDIS)
 
     # RedisAI
-    REDISAI = RedisAIVersion(get_env("SMARTSIM_REDISAI", "1.2.3"))
+    REDISAI = RedisAIVersion(get_env("SMARTSIM_REDISAI", "1.2.7"))
     REDISAI_URL = get_env(
         "SMARTSIM_REDISAI_URL", "https://github.com/RedisAI/RedisAI.git/"
     )
@@ -204,6 +222,8 @@ class Versioner:
     # torch can be set by the user because we download that for them
     TORCH = Version_(get_env("SMARTSIM_TORCH", REDISAI.torch))
     TORCHVISION = Version_(get_env("SMARTSIM_TORCHVIS", REDISAI.torchvision))
+    TORCH_CPU_SUFFIX = Version_(get_env("TORCH_CPU_SUFFIX", REDISAI.torch_cpu_suffix))
+    TORCH_CUDA_SUFFIX = Version_(get_env("TORCH_CUDA_SUFFIX", REDISAI.torch_cuda_suffix))
 
     # TensorFlow and ONNX only use the defaults, but these are not built into
     # the RedisAI package and therefore the user is free to pick other versions.
@@ -240,12 +260,19 @@ def ml_extras_required(self):
         ml_extras = []
         ml_defaults = self.REDISAI.get_defaults()
 
-        # remove torch and torch vision as they will be installed
+        # remove torch-related fields as they will be installed
         # by the cli process for use in the RAI build. We don't install
         # them here as the user needs to decide between GPU/CPU. All other
-        # libraries work on both devices
-        del ml_defaults["torch"]
-        del ml_defaults["torchvision"]
+        # libraries work on both devices. The correct versions and suffixes
+        # were scraped from https://pytorch.org/get-started/previous-versions/
+        _torch_fields = [
+            "torch",
+            "torchvision",
+            "torch_cpu_suffix",
+            "torch_cuda_suffix"
+        ]
+        for field in _torch_fields:
+            ml_defaults.pop(field)
 
         for lib, vers in ml_defaults.items():
             ml_extras.append(f"{lib}=={vers}")