I trained a model in using Keras on Azure databricks (notebook). I would like to be able to save this model on an .h5 or .pkl file and download it to my local machine.
When I train the model locally I use the following to save the file inside a directory called models, but obviously this path does not exist on Azure.
model.save('models/cnn_w2v.h5')
I am new to Azure so any help will be greatly appreciated
Correct me if I'm wrong, you are executing this line on your DataBricks notebook:
model.save('models/cnn_w2v.h5')
Right?
So if that's the case, your model is saved, but it is stored on the Azure instance that is running behind.
You need to upload this file to Azure Storage (just add code to the notebook that does that).
Later, you will be able to download it to your local machine.
I have found the answer to my question above here: how to download files from azure databricks file store
Files stored in /FileStore are accessible in your web browser at https://.cloud.databricks.com/files/. For example, the file you stored in /FileStore/my-stuff/my-file.txt is accessible at:
"https://.cloud.databricks.com/files/my-stuff/my-file.txt"
Note If you are on Community Edition you may need to replace https://community.cloud.databricks.com/files/my-stuff/my-file.txt with https://community.cloud.databricks.com/files/my-stuff/my-file.txt?o=######where the number after o= is the same as in your Community Edition URL.
Refer: https://docs.databricks.com/user-guide/advanced/filestore.html
Related
I have a file on my computer that I want to upload to Google Colab. I know there are numerous ways to do this, including a
from google.colab import files
uploaded = files.upload()
or just uploading manually from the file system. But I want to upload that specific file without needing to choose that file myself.
Something like:
from google.colab import files
file_path = 'path/to/the/file'
files.upload(file_path)
Is there any way to do this?
Providing a file path directly rather than clicking through the GUI for an upload requires access to your local machine's file system. However, when your run cell IPython magic commands such as %pwd in Google collab, you'll notice that the current working directory shown is that of the notebook environment - not that of your machine. The way to eschew the issue are as follows.
1. Local Runtime
Only local runtimes via Jupyter seems to enable such access to the local file system. This necessitates the installation of jupyterlab, a Jupyter server extension for using a WebSocket, and launching a local server. See this tutorial.
2. Google Drive
In case Google Drive is convenient, you can upload files into Google Drive from your local machine without clicking through a GUI.
3. Embracing the GUI
If these options seem overkill, you, unfortunately, have to stick with
from google.colab import files
uploaded = files.upload()
as you alluded to.
I am trying to create my first pipeline.
So my task is to create a pipeline with python, which purpose is to upload CSV file from a local system to AWS S3 Bucket.
After that I need to copy the data from this CSV file into PostgreSQL table. I checked AWS documentation (https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/PostgreSQL.Procedural.Importing.html#USER_PostgreSQL.S3Import ). I follow the instructions but still get an error when i try to run the code from the tutorial in my python environment.
The Questions: is it possible to achieve it with python?
Could someone share a little bit knowlege and python code how he do it?
I installed the CLI rest api and now I want to save a test file to my local desktop. This is the command I have but it throws me a syntax error:
dbfs cp dbfs:/myname/test.pptx /Users/myname/Desktop.
SyntaxError: invalid syntax
dbfs cp dbfs:/myname/test.pptx /Users/myname/Desktop
Note, I am on a Mac, so hopefully the path is correct. What am I doing wrong?
To download files from DBFS to local machine, you can checkout similar SO thread, which addressing similar issue:
Not able to copy file from DBFS to local desktop in Databricks
OR
Alternately, you can use GUI tool called DBFS Explorer to download the files on you local machine.
DBFS Explorer was created as a quick way to upload and download files to the Databricks filesystem (DBFS). This will work with both AWS and Azure instances of Databricks. You will need to create a bearer token in the web interface in order to connect.
Reference: DBFS Explorer for Databricks
Hope this helps.
I am working with Azure Cloud Jupyter Notebook but i dont know how to read my data set so i need to know how to upload my csv dataset
Here's what I found in the FAQ online:
How can I upload my data and access it in a notebook?
A file can be added to the project itself from either the web or computer, or uploaded using the File Menu inside a JupyterNotebook if you chose to save under the project/ folder. Files outside the project/ folder will not be persisted. If you have multiple files that add up to over 100mb you'll need to upload them one by one.
You can also download data using the terminal or shell commands inside of a notebook from publicly accessible web sites include GitHub, Azure blob storage, nasa.gov, etc...
I want to create VM from a disk in azure using python. So I need VHD file of the disk for this purpose.
How can I download or obtain the VHD file in azure using python. And on Azure portal, where are the VHD files listed?
I referred a video where it was told to search under storage account->containers->vhds, but I did not find anything as such. Then I found this :- blob_service.get_blob_to_path, but it also did not work for me.
Can anyone help me with this problem?
I want to obtain VHD file of the disk in azure using python.