## _Cluster_ de Docker Swarm Mode com Traefik e HTTPS

Você pode ter um _cluster_ de Docker Swarm Mode configurado em minutos (cerca de 20) com o Traefik controlando HTTPS (incluindo aquisição e renovação de certificados).

Utilizando o Docker Swarm Mode, você pode iniciar com um _"cluster"_ de apenas uma máquina (que pode até ser um servidor por 5 dólares / mês) e então você pode aumentar conforme a necessidade adicionando mais servidores.

namespace generator {

OpSpec OpSpec::Create(const OpDef& op_def, const ApiDef& api_def) {
  return OpSpec(op_def, api_def);
}

OpSpec::OpSpec(const OpDef& op_def, const ApiDef& api_def)
    : name_(op_def.name()),
      summary_(api_def.summary()),
      description_(api_def.description()) {
  absl::flat_hash_set<string> inferred_attrs;
  // Parse the arguments
  for (const OpDef::ArgDef& arg_def : op_def.input_arg()) {

			pods:          []*v1.Pod{testPodWithResource},
			templates:     nil,
			key:           podKey(testPodWithResource),
			expectedError: true,
		},
		{
			name:           "find-existing-claim-by-label",
			pods:           []*v1.Pod{testPodWithResource},
			key:            podKey(testPodWithResource),
			claims:         []*resourcev1alpha2.ResourceClaim{generatedTestClaim},

				if c.Name == inject.ProxyContainerName {
					out.insertContainer(p.Namespace, deployment, p.Name, c.Name)
				}
			}
		}

		out.Labels[PodKey(p.Namespace, p.Name)] = p.Labels
		out.Annotations[PodKey(p.Namespace, p.Name)] = p.Annotations
		out.Pod[PodKey(p.Namespace, p.Name)] = &pods.Items[i]
	}

	return out, nil
}

// Resources defines a tree of cluster resource names.
type Resources struct {

namespace tensorflow {
namespace generator {

ArgSpec::ArgSpec(const OpDef::ArgDef& arg_def, ArgType arg_type, int position)
    : name_(arg_def.name()),
      description_(arg_def.description()),
      arg_type_(arg_type),
      position_(position) {}

ArgSpec ArgSpec::CreateInput(const OpDef::ArgDef& arg_def, int position) {
  if (arg_def.is_ref()) {

//
// This represents the type information with OpDef::ArgDef and any type-related
// context.
class ArgType {
 public:
  ArgType() = default;
  ArgType(const ArgType& other) = default;
  static ArgType CreateInput(const OpDef::ArgDef& arg_def);
  static ArgType CreateInputRef(const OpDef::ArgDef& arg_def);
  static ArgType CreateOutput(const OpDef::ArgDef& arg_def);

struct OpInfo {
  // graph_op_def: The OpDef used by the runtime, has the names that
  //   must be used when calling NodeBuilder.
  // interface_op_def: The OpDef used in the interface in the generated
  //   code, with possibly overridden names and defaults.
  OpInfo(const OpDef& graph_op_def, const ApiDef& api_def,
         const std::vector<string>& aliases);
  OpInfo(const OpDef& graph_op_def, const ApiDef& api_def);

namespace generator {

ArgType ArgType::CreateInput(const OpDef::ArgDef& arg_def) {
  return ArgType(arg_def, kInput);
}

ArgType ArgType::CreateInputRef(const OpDef::ArgDef& arg_def) {
  return ArgType(arg_def, kInputRef);
}

ArgType ArgType::CreateOutput(const OpDef::ArgDef& arg_def) {
  return ArgType(arg_def, kOutput);
}

ArgType::ArgType(const OpDef::ArgDef& arg_def, Kind kind)

namespace generator {

// An input or output argument to an Op.
//
// Essentially, this represents an OpDef::ArgDef and its context within the Op.
class ArgSpec {
 public:
  ArgSpec() = default;
  ArgSpec(const ArgSpec& other) = default;
  static ArgSpec CreateInput(const OpDef::ArgDef& arg_def, int position);
  static ArgSpec CreateOutput(const OpDef::ArgDef& arg_def, int position);

  const string& name() const { return name_; }

#include "tensorflow/core/lib/strings/str_util.h"

namespace tensorflow {
namespace generator {

AttrSpec AttrSpec::Create(const OpDef::AttrDef& attr_def) {
  return AttrSpec(attr_def);
}

AttrSpec::AttrSpec(const OpDef::AttrDef& attr_def) {
  name_ = attr_def.name();
  description_ = attr_def.description();
  full_type_ = attr_def.type();
  default_value_ = attr_def.default_value();