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• WARN : Tried to pass invalid video frame, marking as broken : Your frame has data type int64, but we require uint8

  5 septembre 2019, par Tavo Diaz

  I am doing some Udemy AI courses and came across with one that "teaches" a bidimensional cheetah how to walk. I was doing the exercises on my computer, but it takes too much time. I decided to use Google Cloud to run the code and see the results some hours after. Nevertheless, when I run the code I get the following error " WARN : Tried to pass
  invalid video frame, marking as broken : Your frame has data type int64, but we require uint8 (i.e. RGB values from 0-255)".

  After the code is executed, I see into the folder and I don’t see any videos (just the meta info).

  Some more info (if it helps) :
  I have a 1 CPU (4g), SSD Ubuntu 16.04 LTS

  I have not tried anything yet to solve it because I don´t know what to try. Im looking for solutions on the web, but nothing I could try.

  This is the code

  import os 
  
  import numpy as np
  
  import gym
  
  from gym import wrappers
  
  import pybullet_envs
  
  
  
  
  
  class Hp():
  
      def __init__(self):
  
          self.nb_steps = 1000
  
          self.episode_lenght =   1000
  
          self.learning_rate = 0.02
  
          self.nb_directions = 32
  
          self.nb_best_directions = 32
  
          assert self.nb_best_directions <= self.nb_directions
  
          self.noise = 0.03
  
          self.seed = 1
  
          self.env_name = 'HalfCheetahBulletEnv-v0'
  
  
  
  
  
  class Normalizer():
  
      def __init__(self, nb_inputs):
  
          self.n = np.zeros(nb_inputs)
  
          self.mean = np.zeros(nb_inputs)
  
          self.mean_diff = np.zeros(nb_inputs)
  
          self.var = np.zeros(nb_inputs)
  
  
  
      def observe(self, x):
  
          self.n += 1.
  
          last_mean = self.mean.copy()
  
          self.mean += (x - self.mean) / self.n
  
          #abajo es el online numerator update
  
          self.mean_diff += (x - last_mean) * (x - self.mean)
  
          #abajo online computation de la varianza
  
          self.var = (self.mean_diff / self.n).clip(min = 1e-2)  
  
  
  
      def normalize(self, inputs):
  
          obs_mean = self.mean
  
          obs_std = np.sqrt(self.var)
  
          return (inputs - obs_mean) / obs_std
  
  
  
  class Policy():
  
      def __init__(self, input_size, output_size):
  
          self.theta = np.zeros((output_size, input_size))
  
  
  
      def evaluate(self, input, delta = None, direction = None):
  
          if direction is None:
  
              return self.theta.dot(input)
  
          elif direction == 'positive':
  
              return (self.theta + hp.noise * delta).dot(input)
  
          else:
  
              return (self.theta - hp.noise * delta).dot(input)
  
  
  
      def sample_deltas(self):
  
          return [np.random.randn(*self.theta.shape) for _ in range(hp.nb_directions)]
  
  
  
      def update (self, rollouts, sigma_r):
  
          step = np.zeros(self.theta.shape)
  
          for r_pos, r_neg, d in rollouts:
  
              step += (r_pos - r_neg) * d
  
          self.theta += hp.learning_rate / (hp.nb_best_directions * sigma_r) * step
  
  
  
  
  
  def explore(env, normalizer, policy, direction = None, delta = None):
  
      state = env.reset()
  
      done = False
  
      num_plays = 0.
  
      #abajo puede ser promedio de las rewards
  
      sum_rewards = 0
  
      while not done and num_plays < hp.episode_lenght:
  
          normalizer.observe(state)
  
          state = normalizer.normalize(state)
  
          action = policy.evaluate(state, delta, direction)
  
          state, reward, done, _ = env.step(action)
  
          reward = max(min(reward, 1), -1)
  
          #abajo sería poner un promedio
  
          sum_rewards += reward
  
          num_plays += 1
  
      return sum_rewards
  
  
  
  def train (env, policy, normalizer, hp):
  
      for step in range(hp.nb_steps):
  
          #iniciar las perturbaciones deltas y los rewards positivos/negativos
  
          deltas = policy.sample_deltas()
  
          positive_rewards = [0] * hp.nb_directions
  
          negative_rewards = [0] * hp.nb_directions
  
          #sacar las rewards en la dirección positiva
  
          for k in range(hp.nb_directions):
  
              positive_rewards[k] = explore(env, normalizer, policy, direction = 'positive', delta = deltas[k])
  
          #sacar las rewards en dirección negativo
  
          for k in range(hp.nb_directions):
  
              negative_rewards[k] = explore(env, normalizer, policy, direction = 'negative', delta = deltas[k])
  
          #sacar todas las rewards para sacar la desvest
  
          all_rewards = np.array(positive_rewards + negative_rewards)
  
          sigma_r = all_rewards.std()
  
          #acomodar los rollauts por el max (r_pos, r_neg) y seleccionar la mejor dirección
  
          scores = {k:max(r_pos, r_neg) for k, (r_pos, r_neg) in enumerate(zip(positive_rewards, negative_rewards))}
  
          order = sorted(scores.keys(), key = lambda x:scores[x])[:hp.nb_best_directions]
  
          rollouts = [(positive_rewards[k], negative_rewards[k], deltas[k]) for k in order]
  
          #actualizar policy
  
          policy.update (rollouts, sigma_r)
  
          #poner el final reward del policy luego del update
  
          reward_evaluation = explore (env, normalizer, policy)
  
          print('Paso: ', step, 'Lejania: ', reward_evaluation)
  
  
  
  def mkdir(base, name):
  
      path = os.path.join(base, name)
  
      if not os.path.exists(path):
  
          os.makedirs(path)
  
      return path
  
  work_dir = mkdir('exp', 'brs')
  
  monitor_dir = mkdir(work_dir, 'monitor')
  
  
  
  hp = Hp()
  
  np.random.seed(hp.seed)
  
  env = gym.make(hp.env_name)
  
  env = wrappers.Monitor(env, monitor_dir, force = True)
  
  nb_inputs = env.observation_space.shape[0]
  
  nb_outputs = env.action_space.shape[0]
  
  policy = Policy(nb_inputs, nb_outputs)
  
  normalizer = Normalizer(nb_inputs)
  
  train(env, policy, normalizer, hp)

• Piwik PRO is hiring a Technical Support Specialist (Remote)

  13 mars 2015, par Piwik Core Team — Jobs

  At Piwik and Piwik PRO we develop the leading open source web analytics platform, used by more than one million websites worldwide. Our vision is to build the best open alternative to Google Universal Analytics. We are growing and now looking for a Technical Support Specialist !

  What will you be doing ?

  • Supporting Piwik PRO clients on a daily basis – providing rapid responses via phone and email.
  • Participating in calls with Piwik PRO clients analyzing requirements for enterprise customers.
  • Onboarding clients – planning and coordinating the implementation with the dedicated technical team.
  • Communicating complex problems to the dedicated technical team.

  We can promise you :

  • A competitive salary.
  • The opportunity to develop your skills and gain more experience by working on exceptional projects.
  • Access to a regularly updated resource library and the opportunity to contribute to it.
  • Flexible working hours.

  Desired Skills and Experience

  You won’t make it without :

  • A solid technical background and a familiarity with the IT sector.
  • A receptive mind.
  • Great English communication skills (speaking, reading, writing, and listening).
  • Previous experience of working with corporate clients and a sixth sense for striking up relationships.
  • Appropriately mixed abilities : solid communication skills, inquisitiveness and an analytical mind (have the ability to draw conclusions based on gathered data).
  • Great self-organization skills.

  About Piwik PRO

  At Piwik and Piwik PRO we develop the leading open source web analytics platform, used by over 1.1M websites worldwide and is currently ranked the 7th most used web analytics tool in the world. Our vision is to build the best open alternative to Google Universal Analytics.

  The Piwik platform collects, stores and processes a lot of information : hundreds of millions of data points each month. We create intuitive, simple and beautiful reports that delight our users.

  Piwik PRO provides cloud hosting solutions and enterprise-level support and consultancy services. We’re in the process of expanding our US team and are currently seeking to hire a Technical Support Specialist who will focus on assisting our US clients.

  Location

  Ideally you will be located in the USA or Canada (Remote work).

  Apply online

  To apply for this position, please Apply online here. We look forward to receiving your applications !

• Piwik PRO is hiring a Technical Support Specialist (Remote)

  13 mars 2015, par Piwik Core Team — Jobs

  At Piwik and Piwik PRO we develop the leading open source web analytics platform, used by more than one million websites worldwide. Our vision is to build the best open alternative to Google Universal Analytics. We are growing and now looking for a Technical Support Specialist !

  What will you be doing ?

  • Supporting Piwik PRO clients on a daily basis – providing rapid responses via phone and email.
  • Participating in calls with Piwik PRO clients analyzing requirements for enterprise customers.
  • Onboarding clients – planning and coordinating the implementation with the dedicated technical team.
  • Communicating complex problems to the dedicated technical team.

  We can promise you :

  • A competitive salary.
  • The opportunity to develop your skills and gain more experience by working on exceptional projects.
  • Access to a regularly updated resource library and the opportunity to contribute to it.
  • Flexible working hours.

  Desired Skills and Experience

  You won’t make it without :

  • A solid technical background and a familiarity with the IT sector.
  • A receptive mind.
  • Great English communication skills (speaking, reading, writing, and listening).
  • Previous experience of working with corporate clients and a sixth sense for striking up relationships.
  • Appropriately mixed abilities : solid communication skills, inquisitiveness and an analytical mind (have the ability to draw conclusions based on gathered data).
  • Great self-organization skills.

  About Piwik PRO

  At Piwik and Piwik PRO we develop the leading open source web analytics platform, used by over 1.1M websites worldwide and is currently ranked the 7th most used web analytics tool in the world. Our vision is to build the best open alternative to Google Universal Analytics.

  The Piwik platform collects, stores and processes a lot of information : hundreds of millions of data points each month. We create intuitive, simple and beautiful reports that delight our users.

  Piwik PRO provides cloud hosting solutions and enterprise-level support and consultancy services. We’re in the process of expanding our US team and are currently seeking to hire a Technical Support Specialist who will focus on assisting our US clients.

  Location

  Ideally you will be located in the USA or Canada (Remote work).

  Apply online

  To apply for this position, please Apply online here. We look forward to receiving your applications !