add tfutils.collect_env

.github/ISSUE_TEMPLATE/unexpected-problems---bugs.md

@@ -57,8 +57,8 @@ Only in one of the two conditions can we help with it:
 
 ### 4. Your environment:
 
-Paste the output of this command: `python -c 'import tensorpack.tfutils as u; print(u.collect_env_info())'`
-If this command failed, tell us your version of Python/TF/tensorpack.
+Paste the output of this command: `python -m tensorpack.tfutils.collect_env`
+If this command failed, also tell us your version of Python/TF/tensorpack.
 
 Note that:
 

tensorpack/__init__.py

@@ -22,4 +22,4 @@
     from tensorpack.input_source import *
     from tensorpack.predict import *
 
-from tensorpack.compat import tfv1
+    from tensorpack.compat import tfv1

tensorpack/tfutils/collect_env.py

@@ -0,0 +1,133 @@
+
+import os
+import sys
+import psutil
+import tensorflow as tf
+import numpy as np
+from collections import defaultdict, OrderedDict
+from tabulate import tabulate
+
+import tensorpack
+from ..compat import tfv1
+from ..utils.utils import find_library_full_path as find_library
+from ..utils.nvml import NVMLContext
+from ..libinfo import __git_version__
+
+
+def parse_TF_build_info():
+    ret = OrderedDict()
+    from tensorflow.python.platform import build_info
+    try:
+        for k, v in list(build_info.build_info.items()):
+            if k == "cuda_version":
+                ret["TF built with CUDA"] = v
+            elif k == "cudnn_version":
+                ret["TF built with CUDNN"] = v
+            elif k == "cuda_compute_capabilities":
+                ret["TF compute capabilities"] = ",".join([k.replace("compute_", "") for k in v])
+        return ret
+    except AttributeError:
+        pass
+    try:
+        ret["TF built with CUDA"] = build_info.cuda_version_number
+        ret["TF built with CUDNN"] = build_info.cudnn_version_number
+    except AttributeError:
+        pass
+    return ret
+
+
+def collect_env_info():
+    """
+    Returns:
+        str - a table contains important information about the environment
+    """
+    data = []
+    data.append(("sys.platform", sys.platform))
+    data.append(("Python", sys.version.replace("\n", "")))
+    data.append(("Tensorpack", __git_version__ + " @" + os.path.dirname(tensorpack.__file__)))
+    data.append(("Numpy", np.__version__))
+
+    data.append(("TensorFlow", tfv1.VERSION + "/" + tfv1.GIT_VERSION + " @" + os.path.dirname(tf.__file__)))
+    data.append(("TF Compiler Version", tfv1.COMPILER_VERSION))
+    has_cuda = tf.test.is_built_with_cuda()
+    data.append(("TF CUDA support", has_cuda))
+
+    try:
+        from tensorflow.python.framework import test_util
+        data.append(("TF MKL support", test_util.IsMklEnabled()))
+    except Exception:
+        pass
+
+    try:
+        from tensorflow.python.framework import test_util
+        data.append(("TF XLA support", test_util.is_xla_enabled()))
+    except Exception:
+        pass
+
+    if has_cuda:
+        data.append(("Nvidia Driver", find_library("nvidia-ml")))
+        data.append(("CUDA libs", find_library("cudart")))
+        data.append(("CUDNN libs", find_library("cudnn")))
+        for k, v in parse_TF_build_info().items():
+            data.append((k, v))
+        data.append(("NCCL libs", find_library("nccl")))
+
+        # List devices with NVML
+        data.append(
+            ("CUDA_VISIBLE_DEVICES",
+             os.environ.get("CUDA_VISIBLE_DEVICES", "Unspecified")))
+        try:
+            devs = defaultdict(list)
+            with NVMLContext() as ctx:
+                for idx, dev in enumerate(ctx.devices()):
+                    devs[dev.name()].append(str(idx))
+
+            for devname, devids in devs.items():
+                data.append(
+                    ("GPU " + ",".join(devids), devname))
+        except Exception:
+            data.append(("GPU", "Not found with NVML"))
+
+    vram = psutil.virtual_memory()
+    data.append(("Free RAM", "{:.2f}/{:.2f} GB".format(vram.available / 1024**3, vram.total / 1024**3)))
+    data.append(("CPU Count", psutil.cpu_count()))
+
+    # Other important dependencies:
+    try:
+        import horovod
+        data.append(("Horovod", horovod.__version__ + " @" + os.path.dirname(horovod.__file__)))
+    except ImportError:
+        pass
+
+    try:
+        import cv2
+        data.append(("cv2", cv2.__version__))
+    except ImportError:
+        pass
+
+    import msgpack
+    data.append(("msgpack", ".".join([str(x) for x in msgpack.version])))
+
+    has_prctl = True
+    try:
+        import prctl
+        _ = prctl.set_pdeathsig  # noqa
+    except Exception:
+        has_prctl = False
+    data.append(("python-prctl", has_prctl))
+
+    return tabulate(data)
+
+
+if __name__ == '__main__':
+    print(collect_env_info())
+    print("Detecting GPUs using TensorFlow:")
+    try:
+        # available since TF 1.14
+        gpu_devices = tf.config.experimental.list_physical_devices('GPU')
+        gpu_devices = [x.name for x in gpu_devices]
+    except AttributeError:
+        from tensorflow.python.client import device_lib
+        local_device_protos = device_lib.list_local_devices()
+        gpu_devices = [x.name for x in local_device_protos if x.device_type == 'GPU']
+    print("GPUs:", ", ".join(gpu_devices))

tensorpack/tfutils/common.py

@@ -1,21 +1,12 @@
 # -*- coding: utf-8 -*-
 # File: common.py
 
-from collections import defaultdict, OrderedDict
-from six.moves import map
-from tabulate import tabulate
-import os
-import sys
-import psutil
 import tensorflow as tf
-import numpy as np
 
-import tensorpack
 from ..compat import tfv1
 from ..utils.argtools import graph_memoized
-from ..utils.utils import find_library_full_path as find_library
-from ..utils.nvml import NVMLContext
-from ..libinfo import __git_version__
+from .collect_env import collect_env_info
+
 
 __all__ = ['get_default_sess_config',
            'get_global_step_value',
@@ -163,112 +154,3 @@ def get_tf_version_tuple():
     Return TensorFlow version as a 2-element tuple (for comparison).
     """
     return tuple(map(int, tf.__version__.split('.')[:2]))
-
-
-def parse_TF_build_info():
-    ret = OrderedDict()
-    from tensorflow.python.platform import build_info
-    try:
-        for k, v in list(build_info.build_info.items()):
-            if k == "cuda_version":
-                ret["TF built with CUDA"] = v
-            elif k == "cudnn_version":
-                ret["TF built with CUDNN"] = v
-            elif k == "cuda_compute_capabilities":
-                ret["TF compute capabilities"] = ",".join([k.replace("compute_", "") for k in v])
-        return ret
-    except AttributeError:
-        pass
-    try:
-        ret["TF built with CUDA"] = build_info.cuda_version_number
-        ret["TF built with CUDNN"] = build_info.cudnn_version_number
-    except AttributeError:
-        pass
-    return ret
-
-
-def collect_env_info():
-    """
-    Returns:
-        str - a table contains important information about the environment
-    """
-    data = []
-    data.append(("sys.platform", sys.platform))
-    data.append(("Python", sys.version.replace("\n", "")))
-    data.append(("Tensorpack", __git_version__ + " @" + os.path.dirname(tensorpack.__file__)))
-    data.append(("Numpy", np.__version__))
-
-    data.append(("TensorFlow", tfv1.VERSION + "/" + tfv1.GIT_VERSION + " @" + os.path.dirname(tf.__file__)))
-    data.append(("TF Compiler Version", tfv1.COMPILER_VERSION))
-    has_cuda = tf.test.is_built_with_cuda()
-    data.append(("TF CUDA support", has_cuda))
-
-    try:
-        from tensorflow.python.framework import test_util
-        data.append(("TF MKL support", test_util.IsMklEnabled()))
-    except Exception:
-        pass
-
-    try:
-        from tensorflow.python.framework import test_util
-        data.append(("TF XLA support", test_util.is_xla_enabled()))
-    except Exception:
-        pass
-
-    if has_cuda:
-        data.append(("Nvidia Driver", find_library("nvidia-ml")))
-        data.append(("CUDA libs", find_library("cudart")))
-        data.append(("CUDNN libs", find_library("cudnn")))
-        for k, v in parse_TF_build_info().items():
-            data.append((k, v))
-        data.append(("NCCL libs", find_library("nccl")))
-
-        # List devices with NVML
-        data.append(
-            ("CUDA_VISIBLE_DEVICES",
-             os.environ.get("CUDA_VISIBLE_DEVICES", "Unspecified")))
-        try:
-            devs = defaultdict(list)
-            with NVMLContext() as ctx:
-                for idx, dev in enumerate(ctx.devices()):
-                    devs[dev.name()].append(str(idx))
-
-            for devname, devids in devs.items():
-                data.append(
-                    ("GPU " + ",".join(devids), devname))
-        except Exception:
-            data.append(("GPU", "Not found with NVML"))
-
-    vram = psutil.virtual_memory()
-    data.append(("Free RAM", "{:.2f}/{:.2f} GB".format(vram.available / 1024**3, vram.total / 1024**3)))
-    data.append(("CPU Count", psutil.cpu_count()))
-
-    # Other important dependencies:
-    try:
-        import horovod
-        data.append(("Horovod", horovod.__version__ + " @" + os.path.dirname(horovod.__file__)))
-    except ImportError:
-        pass
-
-    try:
-        import cv2
-        data.append(("cv2", cv2.__version__))
-    except ImportError:
-        pass
-
-    import msgpack
-    data.append(("msgpack", ".".join([str(x) for x in msgpack.version])))
-
-    has_prctl = True
-    try:
-        import prctl
-        _ = prctl.set_pdeathsig  # noqa
-    except Exception:
-        has_prctl = False
-    data.append(("python-prctl", has_prctl))
-
-    return tabulate(data)
-
-
-if __name__ == '__main__':
-    print(collect_env_info())