{{ template "minio.fullname" . }}.{{ .Release.Namespace }}.{{ .Values.clusterDomain }}

To access MinIO from localhost, run the below commands:

  1. export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "release={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}")

  2. kubectl port-forward $POD_NAME 9000 --namespace {{ .Release.Namespace }}

#include <utility>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),
      func_record_(new FunctionRecord(std::move(fdef), {}, true)) {}
GraphFunction::~GraphFunction() {}

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/c/tf_tensor.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {

using tensorflow::TF_StatusPtr;

void CompareNumericalAndManualGradients(
    Model model, AbstractContext* ctx,

#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.
class GraphFunction : public AbstractFunction {
 public:
  explicit GraphFunction(FunctionDef fdef);

#define TENSORFLOW_C_EAGER_GRADIENT_CHECKER_H_

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/unified_api_testutil.h"

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],

#ifndef TENSORFLOW_C_EAGER_GRADIENTS_INTERNAL_H_
#define TENSORFLOW_C_EAGER_GRADIENTS_INTERNAL_H_

#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
namespace internal {

// Helper functions which delegate to `AbstractOperation`, update
// the state of the ForwardOperation and call the tape as appropriate.

    }

    //
    // Infer modelVersion from namespace URI
    //
    void handleModelVersion(Model model) {
        String namespace = model.getDelegate().getNamespaceUri();
        if (model.getModelVersion() == null && namespace != null && namespace.startsWith(NAMESPACE_PREFIX)) {
            model.setModelVersion(namespace.substring(NAMESPACE_PREFIX.length()));
        }
    }

    //

      : AbstractOperation(kind) {}
  ~ImmediateExecutionOperation() override {}
};

namespace internal {
struct ImmediateExecutionOperationDeleter {
  void operator()(ImmediateExecutionOperation* p) const {
    if (p != nullptr) {
      p->Release();
    }
  }
};
}  // namespace internal

using ImmediateOpPtr =
    std::unique_ptr<ImmediateExecutionOperation,

#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_DISTRIBUTED_MANAGER_H_

#include <cstdint>
#include <string>

#include "tensorflow/core/platform/status.h"

namespace tsl {
class CoordinationServiceAgent;
}

namespace tensorflow {
class ImmediateExecutionContext;
class ServerDef;
class WorkerEnv;
class WorkerCacheInterface;

class ImmediateExecutionDistributedManager {
 public:

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_tensor.h"

namespace tensorflow {
namespace gradients {

using namespace std;

// ================== Helper functions =================

// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,