title: AWS Sagemaker ValueError: Unsupported dtype object on array when using strings and dates
body: <p>I have a CSV file I'm trying to RCF on.  If I put a date or string in the CSV then I get an error like the one below.  If I limit it to just the integer and float fields the script runs fine.  Is there some way to process dates and string?  I see the taxi example from AWS and it has dates which appear the same as mine</p>
  <pre><code>eventData = pd.read_csv(data_location, delimiter=&quot;,&quot;, header=None, parse_dates=True)
  
  print('Starting RCF Training')
  # specify general training job information
  rcf = RandomCutForest(role=sagemaker.get_execution_role(),
                        instance_count=1,
                        instance_type='ml.m4.xlarge',
                        data_location=data_location,
                        output_path='s3://{}/{}/output'.format(bucket, prefix),
                        base_job_name=&quot;ad-rcf&quot;,
                        num_samples_per_tree=512,
                        num_trees=50)
  
  rcf.fit(rcf.record_set(eventData.values))
  </code></pre>
  <p>CSV Data that fails</p>
  <pre><code>392507,1613744,1/2/2020 19:11,1577238693,2469,3.30E+01,-9.67E+01
  691381,1888551,12/10/2019 9:22,1575641745,3460,2.37E+01,9.04E+01
  392507,1613744,1/2/2020 19:20,1577236815,1797,3.30E+01,-9.67E+01
  392507,1613744,1/29/2020 19:04,1577264188,1797,3.30E+01,-9.67E+01
  </code></pre>
  <p>Error output</p>
  <pre><code>---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  &lt;ipython-input-35-ba19bf5d66a2&gt; in &lt;module&gt;
  ---&gt; 21 rcf.fit(rcf.record_set(eventData.values))
       22 
       23 print('Done RCF Training')
  
  /opt/conda/lib/python3.7/site-packages/sagemaker/amazon/amazon_estimator.py in record_set(self, train, labels, channel, encrypt)
      281         logger.debug(&quot;Uploading to bucket %s and key_prefix %s&quot;, bucket, key_prefix)
      282         manifest_s3_file = upload_numpy_to_s3_shards(
  --&gt; 283             self.instance_count, s3, bucket, key_prefix, train, labels, encrypt
      284         )
      285         logger.debug(&quot;Created manifest file %s&quot;, manifest_s3_file)
  
  /opt/conda/lib/python3.7/site-packages/sagemaker/amazon/amazon_estimator.py in upload_numpy_to_s3_shards(num_shards, s3, bucket, key_prefix, array, labels, encrypt)
      443                 s3.Object(bucket, key_prefix + file).delete()
      444         finally:
  --&gt; 445             raise ex
      446 
      447 
  
  /opt/conda/lib/python3.7/site-packages/sagemaker/amazon/amazon_estimator.py in upload_numpy_to_s3_shards(num_shards, s3, bucket, key_prefix, array, labels, encrypt)
      424                     write_numpy_to_dense_tensor(file, shard, label_shards[shard_index])
      425                 else:
  --&gt; 426                     write_numpy_to_dense_tensor(file, shard)
      427                 file.seek(0)
      428                 shard_index_string = str(shard_index).zfill(len(str(len(shards))))
  
  /opt/conda/lib/python3.7/site-packages/sagemaker/amazon/common.py in write_numpy_to_dense_tensor(file, array, labels)
      154             )
      155         resolved_label_type = _resolve_type(labels.dtype)
  --&gt; 156     resolved_type = _resolve_type(array.dtype)
      157 
      158     # Write each vector in array into a Record in the file object
  
  /opt/conda/lib/python3.7/site-packages/sagemaker/amazon/common.py in _resolve_type(dtype)
      288     if dtype == np.dtype(&quot;float32&quot;):
      289         return &quot;Float32&quot;
  --&gt; 290     raise ValueError(&quot;Unsupported dtype {} on array&quot;.format(dtype))
      291 
      292 
  
  ValueError: Unsupported dtype object on array
  </code></pre>