Pin jaxlib to use cuda120 build with cuda-nvcc and update README note

README.md

@@ -54,7 +54,12 @@ The primary use of these Docker images is running on Pangeo Cloud deployments wi
 * Since 2020.10.16, [mamba](https://github.com/mamba-org/mamba) is installed into the base-image and conda-lock environment and is used by default to solve for a compatible environment (see #146)
 * For a simple list of packages for a given image, you can use a link like this: https://github.com/pangeo-data/pangeo-docker-images/blob/2020.10.08/pangeo-notebook/packages.txt
 * To compare changes between two images, you can use a link like this: https://github.com/pangeo-data/pangeo-docker-images/compare/2020.10.03..2020.10.08
-* Our `ml-notebook` image now contains JAX and TensorFlow with XLA enabled. Due to licensing issues, conda-forge does not have `ptxas`, but `ptxas` is needed for XLA to work correctly. Should you like to use JAX and/or TensorFlow with XLA optimization, please install `ptxas` on your own, for example, by `conda install -c nvidia cuda-nvcc`. At the time of writing (October 2022), JAX throws a compilation error if the `ptxas` version is higher than the driver version. There does not exist an easy solution for K80 GPUs, but in the case of T4 GPUs, you should install `conda install -c nvidia cuda-nvcc==11.6.*` to be safe. Alternatively for any GPU, you could set an environment variable to resolve the error caused by JAX: `XLA_FLAGS="--xla_gpu_force_compilation_parallelism=1"`. The aforementioned error will be removed (and likely turned into a warning) in a future version of JAX. See https://github.com/google/jax/issues/12776#issuecomment-1276649134
+* As of 2024.05.21, the `ml-notebook` and `pytorch-notebook` docker images contain
+  machine learning libraries built with CUDA 12. In previous versions, we have suggested
+  `ml-notebook` users to install `cuda-nvcc` manually to obtain JAX and/or TensorFlow
+  with [XLA](https://openxla.org/xla) optimization, but this workaround should no longer
+  be needed if you are using `ml-notebook` 2024.06.02 or newer that comes with
+  `cuda-nvcc` pre-installed.
 * There used to be a `pangeo/forge` image, built for use with [pangeo-forge](https://pangeo-forge.org/). It is
   no longer actively maintained or used, but you can still use the [historical tags](https://quay.io/repository/pangeo/forge?tab=tags)
-  if you wish.
+  if you wish.

ml-notebook/environment.yml

@@ -8,6 +8,7 @@ dependencies:
  - cuda-version>=12.0
  - flax>=0.8.0
  - jax
+ - jaxlib>=0.4.23=cuda120*
  - jupyterlab-nvdashboard
  - keras-cv
  - tensorflow>=2.15.0=cuda120*

tests/test_ml-notebook.py

@@ -1,7 +1,8 @@
-import pytest
 import importlib
-import sys
 import os
+import logging
+
+import pytest
 
 packages = [
     # machine learning stuff
@@ -23,10 +24,39 @@ def test_start():
         assert os.environ['PANGEO_ENV'] == 'ml-notebook'
 
 def test_jax_tf_together():
-    """ sometimes this impport fails due to sharing private symbols
-        complicated longer story, but it is better to
-        ensure they can coexist
     """
-    import tensorflow, jax
+    Sometimes this import fails due to sharing private symbols.
+    Complicated long story, but it is better to ensure they can coexist
+    """
+    import jax
+    import tensorflow
     assert int(tensorflow.__version__[0]) >= 2
     assert int(jax.__version__[0]) >= 0
+
+
+def test_jax_random_number_generator():
+    """
+    Ensure that initializing a random number generator on JaX works.
+
+    Regression test for checking that JaX and cuda-nvcc are installed and compatible on
+    GPU devices, see https://github.com/pangeo-data/pangeo-docker-images/issues/438.
+    """
+    import jax
+    import numpy as np
+    from jax import random
+
+    # Test running on CPU
+    with jax.default_device(jax.devices("cpu")[0]):
+        key = random.key(seed=42)
+        x = random.normal(key=key)
+        np.testing.assert_allclose(x, -0.18471177)
+
+    # Test running on GPU (need to run locally)
+    try:
+        gpu_device = jax.devices("gpu")[0]
+        with jax.default_device(gpu_device):
+            key = random.key(seed=24)
+            x = random.normal(key=key)
+            np.testing.assert_allclose(x, -1.168644)
+    except RuntimeError:  # Unknown backend: 'gpu' requested
+        logging.log(level=logging.INFO, msg="JAX was not tested on a GPU device")