SKU: 89740800980
dog proof plant pots

dog proof plant pots Flower Pot Durable Rubber Treat Dispenser & Enrichment Large / Blue

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Description

dog proof plant pots Flower Pot Durable Rubber Treat Dispenser & Enrichment Large / BlueThe SodaPup Large Flower Pot is a durable rubber treat dispenser designed to provide mental stimulation and slow feeding benefits for dogs of all sizes. Made from FDA compliant, non toxic PUP X natural rubber in the USA, this enrichment toy combines functionality with eco friendly design. What Makes This Treat Dispenser Special? Premium PUP X Natural Rubber: FDA compliant, non toxic material that's safe for power chewers and super chewers Floats in

    The SodaPup Large Flower Pot is a durable rubber treat dispenser designed to provide mental stimulation and slow feeding benefits for dogs of all sizes. Made from FDA-compliant, non-toxic PUP-X natural rubber in the USA, this enrichment toy combines functionality with eco-friendly design.

    What Makes This Treat Dispenser Special?

    • Premium PUP-X Natural Rubber: FDA-compliant, non-toxic material that's safe for power chewers and super chewers
    • Floats in Water: Perfect for pool play, lake trips, or water-based enrichment activities
    • USA Manufacturing: Proudly made in America with sustainable, eco-friendly materials
    • Unique Flower Design: Eye-catching sunflower pot shape with treat-dispensing chambers
    • Versatile Enrichment: Works as a treat dispenser, slow feeder, chew toy, and boredom buster

    How Does the Flower Pot Enrichment Toy Work?

    Fill the flower pot's chambers with your dog's favorite treats, kibble, or wet food. As your dog works to extract the rewards, they engage in natural foraging behavior that provides mental stimulation and extends feeding time. The durable rubber construction withstands aggressive chewing while the varied textures massage gums and clean teeth.

    Benefits for Your Dog

    • Reduces anxiety and destructive behavior through mental engagement
    • Slows down fast eaters to improve digestion
    • Provides healthy outlet for chewing instincts
    • Suitable for puppies learning enrichment and senior dogs needing cognitive stimulation
    • Dishwasher safe for easy cleaning

    Product Specifications

    • Size: Large (5.3 oz)
    • Material: FDA-compliant PUP-X natural rubber
    • Colors: Green and orange flower design
    • Best For: Heavy chewers, power chewers, and dogs needing enrichment
    • Made in: USA
    • SKU: SPT-FP1-300

    Why Choose SodaPup Enrichment Toys?

    SodaPup specializes in creating premium dog enrichment products that prioritize safety, durability, and mental stimulation. Our Flower Pot treat dispenser is part of our commitment to providing fun, functional toys that enhance your dog's quality of life while supporting American manufacturing and sustainable practices.

    Perfect for: Interactive feeding, crate training, separation anxiety, mental enrichment, and keeping power chewers engaged.

    Care

      DISHWASHER SAFE: Dishwasher safe and easy to clean.

      Guarantee

        We stand by our products and offer a 30 day replacement guarantee with proof of purchase for rubber chew toys that have been destroyed beyond normal wear and tear.  While no dog toy is indestructible, this toy has been tooth tested and holds up to the vast majority of dogs.  Always supervise dog's play time and remove damaged toys.

        Size

          3.5” tall, 3.5” diameter. Weight: 5.2 oz. For dogs 30-65 lbs/15-30 kg. 

          VOLUME: holds 3/4 cup of small kibble, 8 Fluid Ounces

          Video

          Shipping Notes
          • Free Standard Shipping on $100+ Orders to the USA.
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          Exchange/Return Notes
          • We offer a 30-day return/exchange service after receiving.
          • Final sale items are not eligible for returns or exchanges.
          • To process your return/exchange, please contact us at [email protected]
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          SKU: 89740800980

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          Carol
          Bozeman, US
          ★★★★★ 5
          Need to read book
          Format: Hardcover
          The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on November 24, 2019
          W
          Walter Echo-Hawk, author of THE SEA OF GRASS.
          Bozeman, US
          ★★★★★ 5
          Native American history at its best!
          Format: Hardcover
          Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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          Reviewed in the United States on April 1, 2019
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          Verified Purchase
          Par
          Lake Worth, US
          ★★★★★ 5
          Excellent book on ML
          Format: Paperback
          This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on December 20, 2024
          R
          Verified Purchase
          Richard Hackathorn
          Battle Creek, US
          ★★★★★ 5
          Excellent Textbook for Hands-On Learning of ML
          Format: Kindle
          This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on February 26, 2022
          A
          Verified Purchase
          Amazon Customer
          Carnegie, US
          ★★★★★ 4
          Just learning it
          Format: Paperback
          Nice learning book just have to finish it
          WAS THIS REVIEW HELPFUL?YesReportShare
          Reviewed in the United States on December 10, 2025

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