Cloudam, as an industry-leading cloud-HPC platform, has a variety of GPU instances, such as NVIDIA A100 and V100, which has become an awesome tool for Deep Learning professionals and fans.
Any DL engineers or fans who want to power your DL projects for the Kaggle competitions with massive high-performance computing resources, here's a hands-on tutorial of how you can seamlessly connect your Kaggle dataset to Cloudam.
1. Create an API key in Kaggle.
To do this, go to Kaggle.com and open your user settings page.
2. Next, scroll down to the API access section and click 'generate' to download an API key.
This will download a file called kaggle.json to your PC. You'll use this file in Cloudam to access Kaggle datasets and competitions.
4. Upload your kaggle.json file on the 'Storage' page.
You can easily upload files and folders to Cloudam Storage by either clicking a few buttons or drag & drop
5. Install the Kaggle API and move the kaggle.json file into ~/.kaggle
6. Now you can access datasets using the client.
7. Download the datasets and extract the CSV file from the zip file.
8. Now, you can successfully read the Kaggle dataset into Cloudam, and run a test with multiple GPU choices with the $30 Free Trial.
Cloudam is a one-stop cloud-HPC platform with 300+ pre-installed to deploy immediately. The system can smartly schedule compute nodes and dynamically schedule the software licenses, optimizing workflow and boosting efficiency for engineers and researchers.
Partnered with AWS, Azure, Google Cloud, Oracle Cloud, etc., Cloudam powers your R&D with massive cloud resources without queuing.
You can submit jobs with intuitive templates, SLURM, and Windows/Linux workstations. Whether you are a beginner or a professional, you can always find it handy to run and manage your jobs.
There is a $30 Free Trial for every new user. Why not register and boost your R&D NOW?