Supporting passing ML framework name and version through the PjRt Cli…

…ent Create options in PyTorch/xla. (#7249)

torch_xla/__init__.py

@@ -77,6 +77,7 @@ def _setup_default_env():
     os.environ.setdefault('TPU_ML_PLATFORM', 'PyTorch/XLA')
     # This is used for ML Framework Telemetry.
     os.environ.setdefault('TPU_ML_PLATFORM_VERSION', __version__)
+    os.environ.setdefault('ENABLE_RUNTIME_UPTIME_TELEMETRY', '1')
 
     if tpu.version() == 4:
       os.environ.setdefault('TPU_MEGACORE', 'megacore_dense')

torch_xla/_internal/tpu.py

@@ -17,6 +17,7 @@
 import torch_xla.core.xla_env_vars as xenv
 import torch_xla.core.xla_model as xm
 from torch_xla.experimental import plugins
+from torch_xla.version import __version__
 
 _GCE_METADATA_ROOT_URL = 'http://metadata.google.internal/computeMetadata/v1'
 _ACCELERATOR_TYPE_TO_HOST_BOUNDS = {
@@ -347,5 +348,9 @@ def physical_chip_count(self):
   def client_create_options(self):
     return {
         'max_inflight_computations':
-            xu.getenv_as('XLA_TPU_MAX_INFLIGHT_COMPUTATIONS', int, 4)
+            xu.getenv_as('XLA_TPU_MAX_INFLIGHT_COMPUTATIONS', int, 4),
+        'ml_framework_name':
+            'PyTorch/XLA',
+        'ml_framework_version':
+            __version__
     }