*TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

# ==============================================================================
#
# setup.python.sh: Install a specific Python version and packages for it.
# Usage: setup.python.sh <pyversion> <requirements.txt>

# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

# Set up shared custom sources
apt-get update

  return status.ok();
}

// Tests whether a URI scheme results in a filesystem that is supported.
//
// As we need these URI schemes to instantiate the test suite when
// `testing::InitGoogleTest` gets called, here we just store them to an
// internal scheme registry. See `URISchemeRegister` above.
static bool GetURIScheme(const std::string& scheme) {
  tensorflow::SchemeVector()->push_back(scheme);
  return true;

  TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3,
} TFE_ContextDevicePlacementPolicy;
// LINT.ThenChange(//tensorflow/c/eager/immediate_execution_context.h)

// Sets the default execution mode (sync/async). Note that this can be
// overridden per thread using TFE_ContextSetExecutorForThread.
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetAsync(TFE_ContextOptions*,

1.  `envs/*` are lists of variables made with bash syntax. A user must set a
    `TFCI` env param pointing to a list of `env` files.
2.  `utilities/setup.sh`, initialized by all top-level scripts, reads and sets
    values from those `TFCI` paths.
    -   `set -a` / `set -o allexport` exports the variables from `env` files so
        all scripts can use them.
    -   `utilities/setup_docker.sh` creates a container called `tf` with all

  // executor. After that, the value returned by DeviceName will be a full
  // device name such as "/job:localhost/replica:0/task:0/device:GPU:1".
  virtual const string& DeviceName() const = 0;

  // Sets the operation device name.
  //
  // The given `name` must be parseable by DeviceNameUtils::ParseFullName, and
  // the result will be used as a constraint for device placement. See the

Status AddInput(AbstractOperation*, AbstractTensorHandle*, ForwardOperation*);
Status AddInputList(AbstractOperation*,
                    absl::Span<AbstractTensorHandle* const> inputs,
                    ForwardOperation*);

// Sets the attrs in the `ForwardOperation`.
Status SetAttrString(AbstractOperation*, const char* attr_name,
                     const char* data, size_t length, ForwardOperation*);

"""
contrib = _LazyLoader("contrib", globals(), "tensorflow.contrib",
                      _CONTRIB_WARNING)
# The templated code that replaces the placeholder above sometimes
# sets the __all__ variable. If it does, we have to be sure to add
# "contrib".
if "__all__" in vars():
  vars()["__all__"].append("contrib")

from tensorflow.python.platform import flags

class TracingOperation : public AbstractOperation {
 protected:
  explicit TracingOperation(AbstractOperationKind kind)
      : AbstractOperation(kind) {}

 public:
  // Sets the name of the operation: this is an optional identifier that is
  // not intended to carry semantics and preserved/propagated without
  // guarantees.
  virtual Status SetOpName(const char* op_name) = 0;

    TFE_MonitoringSampler2* sampler);
TF_CAPI_EXPORT extern TFE_MonitoringSamplerCell* TFE_MonitoringGetCellSampler2(
    TFE_MonitoringSampler2* sampler, const char* label1, const char* label2);

// Sets whether to use TFRT
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetTfrt(TFE_ContextOptions*,
                                                     bool use_tfrt);