我从源代码构建了Tensorflow，并使用它的C API。到目前为止一切都很好，我也在使用AVX /AVX 2。我从源代码构建的Tensorflow也是使用XLA支持构建的。我现在还想激活XLA（加速线性代数），因为我希望它能再次提高推理过程中的性能/速度。
如果我现在开始跑步，我会收到以下消息：

2019-06-17 16:09:06.753737: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1541] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.

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在XLA的官方主页（https://www.tensorflow.org/xla/jit）上，我找到了关于如何在会话级别上打开jit的信息：

# Config to turn on JIT compilation
config = tf.ConfigProto()
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1

sess = tf.Session(config=config)

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这里（https://github.com/tensorflow/tensorflow/issues/13853）解释了如何在C API中设置TF_SetConfig。在使用以下Python代码的输出之前，我能够限制到一个核心：

config1 = tf.ConfigProto(device_count={'CPU':1})
serialized1 = config1.SerializeToString()
print(list(map(hex, serialized1)))

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我实现了如下：

uint8_t intra_op_parallelism_threads = maxCores; // for operations that can be parallelized internally, such as matrix multiplication 
        uint8_t inter_op_parallelism_threads = maxCores; // for operations that are independent in your TensorFlow graph because there is no directed path between them in the dataflow graph
        uint8_t config[]={0x10,intra_op_parallelism_threads,0x28,inter_op_parallelism_threads};
        TF_SetConfig(sess_opts,config,sizeof(config),status);

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因此，我认为这将有助于XLA激活：

config= tf.ConfigProto()
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
output = config.SerializeToString()
print(list(map(hex, output)))

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本次实施：

uint8_t config[]={0x52,0x4,0x1a,0x2,0x28,0x1};
        TF_SetConfig(sess_opts,config,sizeof(config),status);

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然而，XLA似乎仍然被停用。有人能帮我解决这个问题吗？或者，如果你再次在警告中获得战利品：

2019-06-17 16:09:06.753737: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1541] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.

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这是否意味着我必须在构建过程中设置XLA_FLAGS？
提前感谢！