I got what I need. You have my thumps up

Great video, how about setting up this on a local cluster instead of AWS

Nice explanation David

Hi @saturn Cloud I am getting an error message import pandas as pd import numpy as np import boto3 from sagemaker import get_execution_role conn = boto3.client('s3') bucket = 'sagemakerkaggle' content = conn.list_objects(Bucket=bucket)['Contents'] dataset = 'montocarlo/train.csv' df = pd.read_csv(f"s3//{bucket}/{dataset}") FileNotFoundError Traceback (most recent call last) Cell In[7], line 1 ----> 1 df = pd.read_csv(f"s3//{bucket}/{dataset}") File /opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:948, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend) 935 kwds_defaults = _refine_defaults_read( 936 dialect, 937 delimiter, (...) 944 dtype_backend=dtype_backend, 945 ) 946 kwds.update(kwds_defaults) --> 948 return _read(filepath_or_buffer, kwds) File /opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:611, in _read(filepath_or_buffer, kwds) 608 _validate_names(kwds.get("names", None)) 610 # Create the parser. --> 611 parser = TextFileReader(filepath_or_buffer, **kwds) 613 if chunksize or iterator: 614 return parser File /opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1448, in TextFileReader.__init__(self, f, engine, **kwds) 1445 self.options["has_index_names"] = kwds["has_index_names"] 1447 self.handles: IOHandles | None = None -> 1448 self._engine = self._make_engine(f, self.engine) File /opt/conda/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1705, in TextFileReader._make_engine(self, f, engine) 1703 if "b" not in mode: 1704 mode += "b" -> 1705 self.handles = get_handle( 1706 f, 1707 mode, 1708 encoding=self.options.get("encoding", None), 1709 compression=self.options.get("compression", None), 1710 memory_map=self.options.get("memory_map", False), 1711 is_text=is_text, 1712 errors=self.options.get("encoding_errors", "strict"), 1713 storage_options=self.options.get("storage_options", None), 1714 ) 1715 assert self.handles is not None 1716 f = self.handles.handle File /opt/conda/lib/python3.10/site-packages/pandas/io/common.py:863, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options) 858 elif isinstance(handle, str): 859 # Check whether the filename is to be opened in binary mode. 860 # Binary mode does not support 'encoding' and 'newline'. 861 if ioargs.encoding and "b" not in ioargs.mode: 862 # Encoding --> 863 handle = open( 864 handle, 865 ioargs.mode, 866 encoding=ioargs.encoding, 867 errors=errors, 868 newline="", 869 ) 870 else: 871 # Binary mode 872 handle = open(handle, ioargs.mode) FileNotFoundError: [Errno 2] No such file or directory: 's3//sagemakerkaggle/montocarlo/train.csv'

Wow... I loved this presentation... so useful🤩 Thank you so much

Ngrok is not secure!!!

Sveiki 🙂

Anyone get any warning saying could not modify ulimit?

Excellent tutorial!

Do you have a continuation video for it?

My dear this is a brilliant intro to Shiny in terms of concepts, has opened my eyes and massively improved my understanding

fantastic video! Love your teaching style! It will be great to see a video on how to set ray on docker for local development.. probably chat with your pdf tutorial?

why did you miss the IAM roles and Permissions part ?

Well, your video just saved my day!!! Thank you

I am running mlflow server with local host inside a vm and using the same as tracking uri, but when I do start_run() I get an error of 400 or 403. How do I resolve this.

I wanted to see how you added that logo and top banner text and photos. I felt cheated to watch all the video and not see what I came for.

Excellent

Great video ❤❤❤ thanks

It's really sad that I didn't share the code or the link to your blog. We need at least the yaml file

how you used the ip address and script to bring up Jupyter was not made clear, not very helpful video

But how do you get one ? :)

We’re you able to run tensorboard for ray machine learning task?

Takes a lot of time for the cluster to be set up .

Great video, thank you very much!

Good Job!

Excellent demo! Would be great to share the code.

Great video, thanks. I did have one question; is there a way to also version control the data that the model was trained on? It is super useful to be able to track several versions of the model in production, but in my usecase I also may want to also track the dataframe that each version of the production model was trained on. Can MLFlow do this or do you know any other tools that can easily integrate with MLFlow for this?

it helped me a lot

where is the blog post

Thaks for the video! It worked for the most part and then I had an error: ParamValidationError Traceback (most recent call last) - any thoughts would be appreciated! thank you!

So good. Your enthusiasm & relatable communication made this easy to understand. Thanks for putting this out there!

How did you set the password? Very helpful video, thanks!

Very helpfull bud, thanks a lot.

I can't use Ngrok for company security policies. Was looking for a native solution.

Maravilhoso! Estava procurando alguma coisa parecida! E se meu bucket tiver milhares de pastas e arquivos? Como pego somente uma pasta para fazer análise no sagemaker?

I had Issue on Waiting for oidc-authservice pods to be ready ... running command: kubectl wait --for=condition=ready pod -l 'app in (authservice)' --timeout=240s -n istio-system error: timed out waiting for the condition on pods/authservice-0 Waiting for oidc-authservice pods to be ready ... running command: kubectl wait --for=condition=ready pod -l 'app in (authservice)' --timeout=240s -n istio-system error: timed out waiting for the condition on pods/authservice-0 Waiting for oidc-authservice pods to be ready ... running command: kubectl wait --for=condition=ready pod -l 'app in (authservice)' --timeout=240s -n istio-system error: timed out waiting for the condition on pods/authservice-0 is there any additional volumes to be attached ?

Flew over my head :(

Excellent, thanks

Video was good, why do not you share the yaml file or the blogpost to get them

Your vid has been really helpful to me, thanks

thanks so much !!! it is so clear. I have a question though how much does the bucket, with cognito service and the the UI hosting costs you per month or week? And is there any options to reduce the costs in any manners ?

Hi can you make a tutorial on how to deployu to prod already trained models?

不错不错

Thanks for the video, looking forward to more!

Do you hava something related to inference?

Very helpful. Thanks

😎 "PromoSM"

Thanks for a very intuitive demo! Where do I find the blog posts that you mentioned?

hey, could you please give some explanation for the dockerfile. thanks.

Thank you