我一直在尝试学习机器学习，并想跟随一门课程，但需要使用Tensorflow，我一直在尝试在我的系统上安装它。我有一台2021年14英寸16GB的Macbook Pro，搭载M1 Pro芯片，运行Ventura 13.1。我一直在参考this article并搜索如何在M1上安装Tensorflow，但都没有成功。我已经成功安装了tensorflow-macos和tensorflow-metal，但在Jupyter中运行一些示例代码时，出现了一个我不理解的错误。在Jupyter中，当我运行以下命令时：
抱歉，我只是简单地把整个错误代码转储了出来，但是你可以看到出了些问题。它似乎只运行第一个Epoch，我不确定出了什么问题。我已经按照那份指南以及tensor flow-metal的说明进行了一切操作。我已经四处查找了，但是在经过数小时的奋斗后，这就是我所获得的结果。我今天刚刚更新了我的Mac，因此Xcode命令行工具应该是最新的。非常感谢任何建议或帮助我解读错误代码。我只想学习机器学习，但如果没有这个工作，我甚至不能跟进我的课程。
我已经多次卸载和重新安装了适用于M1的Conda Miniforge。我创建并尝试了一个空环境中的步骤。我按照上面链接的指南中列出的步骤进行了操作，并进行了多次操作。最初我遇到了一些关于numpy、h5py、grcio和protobuf的问题，但是在调整版本后，我不再收到它们的错误代码，因此我不确定是否都没问题，但我没有看到任何明确的提及。我也运行了
import tensorflow as tf print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
我得到了
可用的GPU数量：1
因此，似乎我已经安装了tensorflow和metal，但是当我尝试运行其余的代码时，我会得到：
TensorFlow version: 2.11.0
Num GPUs Available: 1
Metal device set to: Apple M1 Pro
WARNING:tensorflow:AutoGraph could not transform <function normalize_img at 0x14a4cec10> and will run it as-is.
Cause: Unable to locate the source code of <function normalize_img at 0x14a4cec10>. Note that functions defined in certain environments, like the interactive Python shell, do not expose their source code. If that is the case, you should define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.experimental.do_not_convert. Original error: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
2022-12-13 13:54:33.658225: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-12-13 13:54:33.658309: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
WARNING:tensorflow:AutoGraph could not transform <function normalize_img at 0x14a4cec10> and will run it as-is.
Cause: Unable to locate the source code of <function normalize_img at 0x14a4cec10>. Note that functions defined in certain environments, like the interactive Python shell, do not expose their source code. If that is the case, you should define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.experimental.do_not_convert. Original error: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <function normalize_img at 0x14a4cec10> and will run it as-is.
Cause: Unable to locate the source code of <function normalize_img at 0x14a4cec10>. Note that functions defined in certain environments, like the interactive Python shell, do not expose their source code. If that is the case, you should define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.experimental.do_not_convert. Original error: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
Epoch 1/12
2022-12-13 13:54:34.162300: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-12-13 13:54:34.163015: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
2022-12-13 13:54:35.383325: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
2022-12-13 13:54:35.383350: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
2022-12-13 13:54:35.389028: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
2022-12-13 13:54:35.389049: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
2022-12-13 13:54:35.401250: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
2022-12-13 13:54:35.401274: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
2022-12-13 13:54:35.405004: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
2022-12-13 13:54:35.405025: W tensorflow/core/framework/op_kernel.cc:1830] OP_REQUIRES failed at xla_ops.cc:418 : NOT_FOUND: could not find registered platform with id: 0x14a345660
---------------------------------------------------------------------------
NotFoundError Traceback (most recent call last)
File <timed exec>:45
File ~/conda/envs/mlp3/lib/python3.8/site-packages/keras/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
67 filtered_tb = _process_traceback_frames(e.__traceback__)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
File ~/conda/envs/mlp3/lib/python3.8/site-packages/tensorflow/python/eager/execute.py:52, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
50 try:
51 ctx.ensure_initialized()
---> 52 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
53 inputs, attrs, num_outputs)
54 except core._NotOkStatusException as e:
55 if name is not None:
NotFoundError: Graph execution error:
Detected at node 'StatefulPartitionedCall_6' defined at (most recent call last):
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel_launcher.py", line 17, in <module>
app.launch_new_instance()
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel/kernelapp.py", line 711, in start
self.io_loop.start()
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/tornado/platform/asyncio.py", line 215, in start
self.asyncio_loop.run_forever()
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/asyncio/base_events.py", line 570, in run_forever
self._run_once()
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/asyncio/base_events.py", line 1859, in _run_once
handle._run()
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/asyncio/events.py", line 81, in _run
self._context.run(self._callback, *self._args)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 510, in dispatch_queue
await self.process_one()
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 499, in process_one
await dispatch(*args)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 406, in dispatch_shell
await result
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel/kernelbase.py", line 729, in execute_request
reply_content = await reply_content
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel/ipkernel.py", line 411, in do_execute
res = shell.run_cell(
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/ipykernel/zmqshell.py", line 531, in run_cell
return super().run_cell(*args, **kwargs)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 2940, in run_cell
result = self._run_cell(
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 2995, in _run_cell
return runner(coro)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/async_helpers.py", line 129, in _pseudo_sync_runner
coro.send(None)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3194, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3373, in run_ast_nodes
if await self.run_code(code, result, async_=asy):
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 3433, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "/var/folders/k4/vgd34_w913ndkfkmvgssqgjr0000gn/T/ipykernel_16072/1016625245.py", line 1, in <module>
get_ipython().run_cell_magic('time', '', 'import tensorflow as tf\nimport tensorflow_datasets as tfds\nprint("TensorFlow version:", tf.__version__)\nprint("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices(\'GPU\')))\ntf.config.list_physical_devices(\'GPU\')\n(ds_train, ds_test), ds_info = tfds.load(\n \'mnist\',\n split=[\'train\', \'test\'],\n shuffle_files=True,\n as_supervised=True,\n with_info=True,\n)\ndef normalize_img(image, label):\n """Normalizes images: `uint8` -> `float32`."""\n return tf.cast(image, tf.float32) / 255., label\nbatch_size = 128\nds_train = ds_train.map(\n normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)\nds_train = ds_train.cache()\nds_train = ds_train.shuffle(ds_info.splits[\'train\'].num_examples)\nds_train = ds_train.batch(batch_size)\nds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE)\nds_test = ds_test.map(\n normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)\nds_test = ds_test.batch(batch_size)\nds_test = ds_test.cache()\nds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE)\nmodel = tf.keras.models.Sequential([\n tf.keras.layers.Conv2D(32, kernel_size=(3, 3),\n activation=\'relu\'),\n tf.keras.layers.Conv2D(64, kernel_size=(3, 3),\n activation=\'relu\'),\n tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n# tf.keras.layers.Dropout(0.25),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(128, activation=\'relu\'),\n# tf.keras.layers.Dropout(0.5),\n tf.keras.layers.Dense(10, activation=\'softmax\')\n])\nmodel.compile(\n loss=\'sparse_categorical_crossentropy\',\n optimizer=tf.keras.optimizers.Adam(0.001),\n metrics=[\'accuracy\'],\n)\nmodel.fit(\n ds_train,\n epochs=12,\n validation_data=ds_test,\n)\n')
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/interactiveshell.py", line 2417, in run_cell_magic
result = fn(*args, **kwargs)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/IPython/core/magics/execution.py", line 1321, in time
out = eval(code_2, glob, local_ns)
File "<timed exec>", line 45, in <module>
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/engine/training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/engine/training.py", line 1249, in train_function
return step_function(self, iterator)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/engine/training.py", line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/engine/training.py", line 1222, in run_step
outputs = model.train_step(data)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/engine/training.py", line 1027, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 527, in minimize
self.apply_gradients(grads_and_vars)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1140, in apply_gradients
return super().apply_gradients(grads_and_vars, name=name)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 634, in apply_gradients
iteration = self._internal_apply_gradients(grads_and_vars)
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1166, in _internal_apply_gradients
return tf.__internal__.distribute.interim.maybe_merge_call(
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1216, in _distributed_apply_gradients_fn
distribution.extended.update(
File "/Users/imigh/conda/envs/mlp3/lib/python3.8/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1211, in apply_grad_to_update_var
return self._update_step_xla(grad, var, id(self._var_key(var)))
Node: 'StatefulPartitionedCall_6'
could not find registered platform with id: 0x14a345660
[[{{node StatefulPartitionedCall_6}}]] [Op:__inference_train_function_1261]
抱歉，我只是简单地把整个错误代码转储了出来，但是你可以看到出了些问题。它似乎只运行第一个Epoch，我不确定出了什么问题。我已经按照那份指南以及tensor flow-metal的说明进行了一切操作。我已经四处查找了，但是在经过数小时的奋斗后，这就是我所获得的结果。我今天刚刚更新了我的Mac，因此Xcode命令行工具应该是最新的。非常感谢任何建议或帮助我解读错误代码。我只想学习机器学习，但如果没有这个工作，我甚至不能跟进我的课程。
我已经多次卸载和重新安装了适用于M1的Conda Miniforge。我创建并尝试了一个空环境中的步骤。我按照上面链接的指南中列出的步骤进行了操作，并进行了多次操作。最初我遇到了一些关于numpy、h5py、grcio和protobuf的问题，但是在调整版本后，我不再收到它们的错误代码，因此我不确定是否都没问题，但我没有看到任何明确的提及。我也运行了
conda install -c conda-forge openblas
在查看了类似问题的人发布在StackOverflow这个页面上的内容后，我仍然遇到了这个错误。